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Food SecurityThe Science, Sociology and Economicsof Food Production and Access to Food ISSN 1876-4517Volume 4Number 4 Food Sec. (2012) 4:619-632DOI 10.1007/s12571-012-0206-z

Food insecurity in urban poor householdsin Mumbai, India

Nilesh Chatterjee, Genevie Fernandes &Mike Hernandez

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ORIGINAL PAPER

Food insecurity in urban poor households in Mumbai, India

Nilesh Chatterjee & Genevie Fernandes & Mike Hernandez

Received: 19 August 2011 /Accepted: 9 July 2012 /Published online: 7 August 2012# Springer Science+Business Media B.V. & International Society for Plant Pathology 2012

Abstract India ranks 66th of 88 countries in the GlobalHunger Index and has a quarter of the world’s hungry. Foodsecurity status of 377 million inhabitants of India’s urbanareas, of which one-fourth live in extreme poverty, is poorlydocumented. The purpose of this study was to determine (a)the extent of food insecurity among households in urbanslums, (b) to quantitatively assess their subjective experien-ces related to food insecurity and (c) to identify sub-groupsamong the urban poor that are vulnerable to food insecurity.A cross-sectional, interviewer-administered survey of adultfemale respondents from 283 households, selected usingtwo-stage cluster sampling, was conducted in slums acrossthree municipal wards in the city of Mumbai. Food insecu-rity, as measured by the Household Food Insecurity AccessScale (HFIAS), was found in a large number of householdsin the urban slums of Mumbai; 59.7 % (n0169) householdswere categorized as severely food-insecure, 16.6 % (n047)as mildly to moderately food-insecure, and 23.7 % (n067)as food-secure. Further analysis revealed that severe foodinsecurity was significantly associated with lower monthlyhousehold income and other socioeconomic status measures

such as lower household monthly per capita income, lowerrank in the standard of living index (SLI) and less monthlyper capita expense on food items. Households where thewoman was the primary income-earner and contributed thelargest share to the monthly household income, and wasolder, less educated, with less media use or access weremore likely to experience severe food insecurity. Althoughcorrective steps at the household level such as livelihoodsecurity schemes and income generation programs are nec-essary, they will not be sufficient to eliminate this problem;state intervention is required in order to assure food securityfor the urban poor. The Government of India has drafted aFood Security Bill; but the criteria for determining whichhouseholds are vulnerable and deserving are still beingdebated. The findings of this study highlight the urgencyof corrective action and also provide pointers for the iden-tification of vulnerable or priority sub-groups. Food securitypolicies and programs have to be implemented immediatelyand effectively in order to ensure that subsidies and fooditems are allocated to the households of the vulnerable urbanpoor.

Keywords Household food insecurity . Urban poor . Urbanslums . Vulnerable households . HFIAS . Mumbai . India

Introduction

India, touted as a consistently growing, strong emergingeconomy over the last decade, remains a poor performerwith respect to hunger and malnutrition. India is ranked 66thout of 88 countries in the Global Hunger Index of theInternational Food Policy Research Institute (von Grebmeret al. 2009). Although, it is the world’s second largestproducer of rice and wheat (FAO 2009), India has about aquarter of the world’s hungry – 230 million people –

N. ChatterjeeJohns Hopkins University Center for Communication Programs(JHUCCP),New Delhi, India

G. FernandesKalyani Media Group,Mumbai, India

M. HernandezUniversity of Texas School of Public Health,Houston, TX, USA

N. Chatterjee (*)9 Rekha (2nd floor), Pestom Sagar Road No. 4, Chembur,Mumbai 400089, Indiae-mail: nileshchatterjee@gmail.com

Food Sec. (2012) 4:619–632DOI 10.1007/s12571-012-0206-z

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according to the UN World Food Programme (FAO 2008).A survey of the nutritional status of 109,093 children under5 years of age conducted across 112 districts of India in2010–2011 (covering nearly 20 per cent of Indian childrenacross nine states), showed that rates of child malnutritionare still unacceptably high; 42 % of children are under-weight and 58 % are stunted (Naandi Foundation 2011).

Hunger – defined both as lack of access to food and asactual malnutrition – is seen as the potential consequence ofa root factor – food insecurity, which implies a limitedability to obtain adequate food (Anderson 1990). The con-cept of household food insecurity is all encompassing, cap-turing not only hunger and malnutrition but alsohouseholders’ worries and uncertainties about food. Theseinclude perceptions of problems with the quality and quan-tity of food available, its accessibility and acceptability,experiences of hunger and the kind of work done to generateincome to buy food (Carlson et al. 1999). There is now agrowing political and scientific interest in food security. Theobjective is to direct scarce resources where they can do thegreatest good and therefore actions have to be guided byreliable information and evidence as to who is food inse-cure, where, when and why. This requires improved mea-surement of food insecurity and its causes and greaterattention to the key institutional and policy lessons alreadylearned (Barrett 2010).

India’s urban areas are often neglected in Indian hungerstudies. Malnutrition levels of urban, poor children in slums(54 %) are the worst amongst all urban groups and evenhigher than children in rural areas (51 %). Nutritional prob-lems such as protein energy malnutrition (PEM), anemiaand vitamin A deficiency continue to persist among thesechildren (Ghosh and Shah 2004; UHRC 2008). Food secu-rity worsened between 1998 and 2006 in the more urbanizedstates of Maharashtra, Andhra Pradesh and Karnataka; inMaharashtra, around 24 % of the population had a caloricintake of less than the norm of 1,890 Kcal per day, which isa direct measure of inadequate intake of food (MSSRF andWFP 2010). Even in the affluent states within India, thepercentage of underweight children younger than 3 yearshas risen in the last decade (Chatterjee 2007). In Mumbai,the capital of Maharashtra and the commercial capital ofIndia, more than half the population lives in slums, andnearly 46 % of children are stunted, 36 % are underweightand 45 % of women suffer from anemia (Gupta et al. 2009).

About 377 million of India’s 1.2 billion population live inurban areas (Office of the Registrar and Census Commis-sioner of India 2011a, b). Urbanization in India has in-creased from 27.81 % in the 2001 Census to 31.16 % inthe 2011 Census, while the proportion of rural populationhas declined from 72.19 % to 68.84 % (Office of theRegistrar General and Census Commissioner of India2011b). The urban population in India is expected to

increase to more than 550 million by 2030 (Gupta et al.2009). Roughly a fifth (21 %: 79 million) of the country’stotal urban population is reported to be living in extremepoverty (Press Information Bureau 2010); and around 93million people (25 %) live in urban slums in unhygienicconditions with inadequate sanitary and drinking water fa-cilities (Ministry of Housing and Urban Poverty Alleviation2010). The majority of the urban slum population has lim-ited or no access to public services (Agarwal and Taneja2005) and face problems with the public distribution systemfor food (SMS Foundation 2011). Access to public servicesbecomes even more difficult for those living in non-notifiedslums (slums that have not been notified or recognizedunder any act). With urban migration expected to increasein the years ahead, the problem of malnutrition in urbanslums will become even more acute unless special effortsare initiated to address the food security problems of theurban poor.

The purpose of this study, conducted in the city of Mum-bai, India, was (a) to determine the extent of food insecurityamong households in urban slums, (b) to quantitativelyassess their subjective experiences related to food insecurityusing a cross-culturally validated food security instrument;and (c) to identify sub-groups among the urban poor that arevulnerable to food insecurity.

Methods

A cross-sectional, interviewer-administered survey was con-ducted with adult female respondents of 283 householdsinside urban slums, located in three municipal wards ofnorthern Mumbai, to assess the prevalence and extent offood insecurity by using a pre-tested questionnaire. Sam-pling and data collection activities were conducted in themonths of January to March 2010. Trained female inter-viewers administered the pre-tested survey instrument inlocal languages to adult respondents living in urban slumhouseholds during daytime hours between 9 am and 5 pm.

Study setting

The Brihanmumbai Municipal Corporation (BMC) has di-vided Mumbai, the commercial capital of India with apopulation of 15 million residents, into 24 municipal wardsfor administrative purposes (MCGM 2010), with a wardoffice, designated ward officer and various municipal ser-vice departments. Wards are defined geographical entities,comprised mainly of families that reside in flats/apartmentsin buildings/apartment blocks or poor families that live inadjoining slums (and in some cases on the street). For thisstudy, researchers surveyed slums in three wards - R(South), P (North) and H (East) – stretching along the

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northwest part of Mumbai. Urban slums in Mumbai areorganized into “bastis” or nagars (large conglomerations ofshanty houses). Each basti is made up of chawls; and achawl has anywhere between 100 and 150 shanty houses(small dwellings that are usually 10 feet×10 feet or less andmay or may not have finished walls and roofs).

Subjects: sample and sampling

The 30×7 cluster sampling approach formed the samplingmethod for this study (Frerichs and Shaheen 2001; Bennettet al. 1991). Based on a two-stage sampling process, the firststage saw probability-proportionate-to-size (PPS) sampling of30 clusters; and then 7–10 households per cluster at the secondstage. Two-stage cluster surveys are often preferred over one-stage, simple random sample surveys because all eligiblepersons in the population do not have to be identified andlisted prior to selection (Frerichs and Shaheen 2001). Typical-ly, such surveys have 210–300 subjects, are completed in ashort period of time and calculate prevalence within +/− 10percentage points (Frerichs and Shaheen 2001).

Chawls in urban slum bastis were the clusters for thisstudy. A list of all possible chawls (blocks/clusters) from theslums in the study area of three municipal wards viz. R(South), P (North) and H (East) was made, and from thiscomprehensive list 30 chawls – the primary sampling units–were selected. As the number of clusters/chawls to be sur-veyed in each ward were selected using probability-proportionate-to-size (PPS) method - for every 5 chawlsfrom P (North) 3 chawls from H (East) and 1 chawl fromR-South ward were selected randomly until a total of 30chawls was reached. In the second stage, researchers went toeach of the 30 chawls in which they interviewed adultfemale respondents in 7 to 10 households per chawl startingwith a house at a centrally defined point and from that indexhousehold capturing every 7th house to the right of theinterviewer until she had completed 7 to 10 interviews inthat chawl. In case of non-response, call-backs were notimplemented; the interviewer simply proceeded to the nexthousehold on the right. Refusal rate which included bothlocked doors (no one available in household to answer) andactual refusal to answer, despite an adult member of thehouse being present, was less than 5 % of the actual samplestudied. Most female respondents in slum households werewilling to talk about their food-related problems.

Survey instrument

The survey instrument captured four main modules (or seg-ments) in this study: Respondent and Family (Household)Description, Household Socioeconomic Status (Standard ofLiving), Household Monthly Expenditures and HouseholdFood Insecurity Access Scale (HFIAS).

a. Respondent and Family (Household) Description:This module consisted of questions related to the respon-

dent and her family (and household) such as age, maritalstatus, number of family members living in the household(family size), years of education, employment status, per-sonal income and household income. In order to gauge theemployment status of the respondent and family members,respondents were asked to respond with only one option foreach family member to the question: “How would youdescribe your current work situation?” with possible re-sponse options being unemployed, retired, student, home-duties, part-time work, fulltime work and self-employed;those reported as working part-time, full-time or self-employed were classified as working or employed whileall others were classified as non-working or unemployed.The total numbers of currently unemployed family memberswas divided by the total number of currently employedfamily members to arrive at the currently unemployed:employed ratio.

Data on personal income and household income weregathered by asking two questions: “What is your approxi-mate monthly personal income?” and “What is the approx-imate monthly income in your household?” There arelimitations to data derived from these questions due to recallbias and the tendency to provide socially desirableresponses. In many cases, the expectation of benefits froma related social welfare or food program leads to under-reporting of actual income. However, in order to compen-sate for any limitations of income data, additional data weregathered to assess and classify the socioeconomic status ofthe household. Housing characteristics and household assetsdata were collected in order to assess household wealth andcompute a standard of living index using measures that aresimilar to those of large-scale national surveys. The house-hold assets data coupled with data gathered on monthlyhousehold expenses helped to verify and triangulateincome-related information with expenses for each householdand also enabled comparison of findings from this study withnational or regional surveys. All income and expenditure datawere classified into three categories: ≤33rd percentile; >33rdand ≤67th percentile; >67th percentile.b. Household Socioeconomic Status (Standard of Living):

Household socioeconomic status was assessed using theDistrict Level Household Survey – Standard of Living Index(SLI) (IIPS 2006). SLI is computed by summing the scores,based on relative significance of presence or ownership ofhousehold characteristics or assets - drinking water, type oftoilet, type of materials used for the house, availability ofelectricity, source of fuel for cooking and ownership ofvarious household assets such as television, fan, mobilephone, landline, bed, chair, cupboard, blender/mixer, vehi-cle, CD player, sewing machine and refrigerator. SLI scoreswere classified into three categories: Low SLI (≤33rd

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percentile); Medium SLI (>33rd and ≤67th percentile); HighSLI (>67th percentile).c. Household Monthly Per Capita Expenditure (MPCE):

An adapted version of the household consumer ex-penditure schedule used by the National Sample Survey(NSS) (2009–10) (NSSO 2011) was used to measureMPCE including food expenses. The expenses sheetadministered in this study had two parts - food expensesand expenses on other non-food household items.Groups of expenditure items included — different typesof food items, fuel, travel and conveyance, water, elec-tricity, rent and non-food items such as clothing, foot-wear, toiletries or personal items, education and medicalcare. In addition to NSS items, this study also includedan item called loan repayment. The reference period ofrecall was generally the last 30 days, except for lessfrequent purchases, in which case the reference periodwas 1 year, and the annual expense for these items wasdivided by 12 to arrive at a monthly average. The totalexpenditure incurred by households on domestic con-sumption during a month was divided by family sizeto calculate the total household monthly per capita ex-penditure (MPCE), food-related MPCE, and MPCE onnon-food items.d. Household Food Insecurity Access Scale (HFIAS):

The food security module for this study was adaptedfrom the Household Food Insecurity Access Scale(HFIAS) developed by the Food and Nutrition TechnicalAssistance (FANTA) project and validated across differ-ent cultures and countries (Coates et al. 2007; Coates etal. 2006).The instrument consisted of nine yes/no ques-tions (shown below) that represent a generally increasinglevel of severity of food insecurity. All of the severity(yes/no) questions ask whether the respondent or otherhousehold members either felt a certain way or per-formed a particular behavior over the previous 4 weeks.Each severity question is followed by a frequency-of-occurrence question, which asks the respondent howoften the severity condition happened ranging from Rare-ly (01), Sometimes (02), and Often (03).

These nine indicators also provide summary informa-tion on the prevalence of household experience of eachof the three domains reflected in the HFIAS–Anxiety(Worry) and Uncertainty about food, Inadequate Qualityof Food, and Poor or Insufficient Quantity of FoodIntake. The questionnaire had 1 of 9 items to representthe Anxiety domain; 2 items to capture the domain ofInadequate Quality of Food; and 6 items to assess thedomain of Insufficient Quantity or Intake of Food com-pared to three items for Inadequate Quality and fiveitems for Insufficient Quantity, respectively, in theHFIAS Indicator Guide (Coates et al. 2007). The last

3 items (7, 8, 9) in the list below represent the severeconditions of food insecurity and are the same as in theHFIAS.

Domain A) Anxiety and uncertainty about the household foodsupply:

1- Did you worry that your household would not have enough food?

Domain B) Inadequate Quality (includes variety and preferencesof type of food):

2- Did you eat the same foods daily because you did not have money tobuy other foods?

3- Did you or anyone in the household have to eat any type of food thatyou did not want (undesirable food) because there was no money tobuy the foods you preferred?

Domain C) Less Quantity or Insufficient Food Intake:

4- Did you ever eat less than you felt you should because you did nothave enough money to buy food?

5- Have you or any other adult in your household cut the size of yourmeals because you did not have enough money to buy food?

6- Did you skip some of your daily meals because you did not haveenough money for food?

7- The food you had did not last, and you didn’t have enough money tobuy more? (Severe condition – ran out of food)

8- Were you ever hungry and you did not eat a meal because you didnot have enough money to buy food? (Severe condition – hungry)

9- Did you or another adult in your household ever not eat for a wholeday because you did not have enough money to buy food? (Severecondition – going whole day without eating)

Response options to the nine severity items in the HFIASwere coded 1 for yes responses and 0 for “no” responses.Then the Household Food Insecurity Access Scale (HFIAS)Score was computed as a continuous measure of the degreeof food insecurity in the household by summing the codesfor each frequency-of-occurrence question, following eachof the nine severity items, such that Never 0 0; Rarely 0 1,Sometimes 0 2, and Often 0 3 (Coates et al. 2007). Themaximum score for households in this sample was 27(households reporting “yes” to all nine severity items andall nine frequency-of-occurrence questions as “often” codedwith response code of 3); and the minimum score was 0(households that responded “no” to all nine severity itemsand frequency-of-occurrence questions were skipped by theinterviewer). The higher the score, the more food insecuritywas reportedly experienced by the household.

The continuous score of HFIAS was then classified intothree categories of Household Food Insecurity Access Prev-alence (HFIAP) based on the categorization scheme recom-mended by the HFIAS Indicator Guide (Coates et al. 2007).The HFIAP categorizes households progressively into fourlevels of household food insecurity: food secure; mildlyfood insecure, moderately food insecure, and severely foodinsecure households. The food secure household was onethat reported no or never to all nine items on the HFIAS, orrarely experienced worry. The mildly food insecure

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household worried about not having enough food some-times or often, and/or ate a monotonous diet (same foods)and/or some foods considered undesirable, but only rarely;but did not consume insufficient quantity of food. Themoderately food insecure household sacrificed quality morefrequently by eating a monotonous diet or undesirable foodssometimes or often, and/or consumed insufficient quantityby eating less, reducing size of meals or number of meals,rarely or sometimes. The severely food insecure householdcut the size of meals, or number of meals often, and/orexperienced any of the three most severe conditions (run-ning out of food, going hungry, or going a whole daywithout eating). In this study, the Household Food Insecu-rity Access Prevalence (HFIAP) status variable was furtherrecoded into three levels of household food insecurity foranalysis purposes. Mildly and moderately food insecurehouseholds were combined into one category. Thus, therewere three types of households in data analysis: food secure;mildly to moderately food insecure, and severely food inse-cure households.

Cognitive testing and field testing of the survey tool

To identify understanding, cognitive and/or perception prob-lems, each of the survey instrument’s sections was discussedwith urban slum residents (not in the study area) duringseveral preliminary meetings. These meetings provided valu-able contributions to the instrument’s adaptation and makingthe instrument easier to understand and answer. Discussionswith test respondents using the original instrument showed itwas necessary to keep the questionnaire easy to understandand include simple response options such as “Yes/No”responses because respondents found such items easy to un-derstand and answer. During the pretesting, respondentsexpressed concerns with one of the items on the originalHFIAS-FANTA (Coates et al. 2007) – “In the past 4 weeks,were you or any household member not able to eat the kinds offoods you preferred because of a lack of resources?” First,there was lack of consensus on preferred foods given thediversity of religions, languages, customs, and geographicorigins of people living in Mumbai’s slums. Second, respond-ents said there were differences of opinion within the familyon preferred foods; what was preferred by an adult member,maybe a traditional food, was not always preferred by thechildren who were raised in the city. Living in the city hadaltered food preferences of some family members. Third,given their low incomes, the women said that, in general, theywere unable to procure and cook the preferred foods of familymembers. Respondents felt that this could lead to difficultiesin understanding the question when the questionnaire wasadministered in a community setting. As an alternative, par-ticipants found the item that asks respondents whether theyhave been eating less, to have more resonance, especially

among women respondents, and also capture one dimensionof their food-insecurity reality better. Question wording andacceptability for all other items in the questionnaire were alsodiscussed and based on affirmation by the pre-test group, thestudy proceeded to instrument finalization, interviewer train-ing and data collection.

Data analysis

Data quality checks were conducted during data collection.Univariate descriptive statistics were generated to summarizethe characteristics of the entire sample and each of the seg-ments of the questionnaire. Frequencies and percentages werereported in cases of categorical variables, and means andstandard deviations reported for continuous variables. Themedian value and range (minimum and maximum values)were also reported. Bivariate analysis was conducted to deter-mine which respondent and household characteristics andmonthly expenditure data were related to Household FoodInsecurity Access Prevalence (HFIAP). The means for age;number of family members; years of education; media use/access score; household income, household income per capitaper month, personal income of female respondent as propor-tion of monthly household income, standard of living index(SLI) score; unemployed to employed ratio; householdMPCE(monthly per capita expenditure) and food related MPCE tonon-food MPCE ratio were compared across the three cate-gories of HFIAP (Household Food Insecurity Access Preva-lence) – Food-Secure households, Mild and Moderate-Food-Insecure and Severely Food-Insecure households using Anal-ysis of Variance (ANOVA). Furthermore, pair-wise compar-isons were conducted, using the Bonferroni correction, toidentify which two groups were significantly different fromone another. Association of HFIAP with variables measuredusing dichotomous or nominal responses such as maritalstatus (married versus non-married), religion (Hindu, Muslim,Buddhist, Christian) and current employment status of respon-dent (working or not working) were examined using cross-tabs, and statistical significance was assessed using the Chi-square test. Multivariate regression analyses, both unadjustedand adjusted, were used to examine the independent effect ofrespondent and household (socioeconomic status) character-istics, which were significant in the bivariate analysis, onHFIAS score. Data entry and analysis was conducted usingSPSS version 10.0 for Windows (SPSS Inc.).

Results

Respondent and family characteristics

All 283 respondents interviewed were female and 230(81.3 %) were married. The mean age was 36.71 years and

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the median 35 years; 198 respondents (70.0 %) were Hindus,44 (15.5 %) were Buddhists, 27 (9.5 %) were Christian, and14 (5.0 %) were Muslim. Both mean and median numbers offamily members in households were 5.00 and the mean num-ber of children and adolescents in households was 1.77 (me-dian 2). Educational levels of respondents was generally low;32.8 % had completed education to below 10th standard and30.1 % of respondents had no formal education at all; meanyears of education was 6.09. About 42 % of respondentsreported using more than two sources of media in the past4 weeks among the four sources of television, newspaper,radio and internet, with 78 % of respondents using televisionas a media source. Just over half of respondents (56.5 %) wasemployed and the ratio of unemployed to employed membersin the families was 2:1 or more in just less than half (44.9 %)of households. Approximately two-fifths (39.6 %) of house-holds had a low score (≤33rd percentile) on the standard ofliving index (SLI: Table 1).

Of the 283 respondents, 39.2 % reported a monthlyhousehold income of Indian Rupees (INR) 4,000 or less.The mean monthly household income was INR 5508.48(US$100.62; EUR 78.92) and the median INR 5000. Themean personal income of the respondents per month wasINR 882.51 and the median personal income was INR 500;with 43.6 % of the respondents (n0123) reporting no per-sonal income. Almost one in three (32.9 %) of respondentscontributed more than 24 % of their personal income to thetotal household monthly income (Table 1).

Monthly household expenditure and expenditures on foodand non-food items

The average Monthly Per Capita Expenditure (MPCE) forthe sampled households was INR 1324 (US$30). Around32.9 % households had MPCE less than INR 911; 34.2 % ofhouseholds had MPCE between INR 912 and INR 1394;32.9 % households had MPCE above or equal to INR 1395.The average total monthly food expense of the householdsstudied was INR 2927 (EUR 42; US$53); and the averagemonthly per capita expense on food (MPCE-Food) was INR593 (SD: 239; US$11). Average MPCE on nonfood items(MPCE-Non Food Items) was INR 731 (SD: 652: Table 1).The average per person per day expense on food in thesampled households was INR 19.77 per day (EUR 0.28per day/US$0.36); median INR 18.74 and Mode INR 12(EUR 0.17 or US$0.21). As expected, low socioeconomicgroups spent less on every major food item as compared tomedium and high socioeconomic groups.

Household food insecurity

The Household Food Insecurity Access Scale (HFIAS)scores for households ranged from a minimum of 0 to

Table 1 Socio demographic characteristics of respondents (N0283)

Characteristics No. %

Age in years (Mean 36.71; SD 10.84; Median 35; Min 18, Max 70)

≤30 years 95 33.6

31–40 years 100 35.3

≥41 years 88 31.1

Religion

Hindu 198 70.0

Buddhist 44 15.5

Christian 27 9.5

Islam 14 5.0

Marital Status

Married 230 81.3

Single/Widowed/Divorced 53 18.7

Number of family members in household (Mean 5.08; SD 1.70;Median 5; Min 2, Max 12)

≤4 114 40.2

5–6 121 42.8

≥7 members 48 17.0

Number of children & adolescents (≤ 17 years) in family (Mean1.77; SD 1.16;Median 2; Min 0, Max 6)

≤1 110 38.8

2 110 38.9

≥3 63 22.3

Education of Respondent (Years Mean 6.09; SD 4.42; Median 7;Min 0, Max 16)

No School 85 30.1

Below 5th std 31 10.9

Below 10th std 93 32.8

Completed 10th std 54 19.1

Completed 12th std/Bachelor’s degree 20 7.1

Media Access (Mean score 1.27; SD 0.71; Median 1; Min 0, Max 2)

Did not use/access any media 44 15.5

Used 1 type 120 42.5

Used 2 or more media 119 42.0

Employment Status of Respondent

Employed 160 56.5

Unemployed 123 43.5

Currently unemployed members/employed members ratio (Mean 1.91;SD 1.15; Median 1; Min 1, Max 4)

≤1:1 156 55.1

2:1 42 14.8

3:1 39 13.8

≥4:1 46 16.3

SLI (Standard of Living Index) Score (Mean 12.01; SD 3.43;Median 12; Min 0, Max 19)

Low (≤33rd percentile) 112 39.6

Middle (>33rd and ≤67th percentile) 104 36.7

High(>67th percentile) 67 23.7

Household Income per month (INR) (Mean 5508.48; SD 3076.93;Median 5000; Min 500, Max 25000)

≤Rs 4000 111 39.2

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maximum of 27 with a mean score of 10.92 (SD 9.20) andmedian score of 10. The findings for each of the 9 items in theHFIAS and the three domains of food insecurity - Worry orUncertainty about food; Inadequate Quality of Food; andInsufficient Quantity of Food- represented in the HFIAS areshown in Table 2.

1) Worry or Uncertainty: Although 66.1 % (187 of thetotal 283 households surveyed) responded affirmatively(yes) to the severity item asking if they were everworried that their household would not have enoughfood; only 41.7 % households (118) reported that theyworried “often” when the corresponding frequency ofoccurrence question was asked.

2) Inadequate Quality of Food: Again 66.1 % ofhouseholds responded affirmatively to the yes/no

item that they ever ate the same foods daily and60.8 % said they ate undesirable foods because theydid not have resources to buy other foods. Whenaffirmative responses to these two items were com-bined, 200 households (70.7 %) reported consuminginadequate quality of food.

3) Insufficient Quantity of Food: One in two house-holds (147 of 283; 51.9 %) reported that an adult inthe household had to reduce/cut size of their mealsbecause they did not have enough money to buyfood; 49.1 % had to eat less than they felt theyshould; and 32.2 % skipped some daily meals.Slightly more than one in two households (159 of283 households; 56.2 %) ran out of food; 36.1 %were hungry and did not eat a meal for lack ofresources; and in 66 households (23.3 %), at leastone adult went without food for a whole day be-cause of lack of money to buy food. When theaffirmative responses for the 6 items in the domainof insufficient quantity or food intake were com-bined, it was found that 183 households (64.7 %)had experienced at least one of the severity condi-tions related to insufficient quantity of food.

Categorizing the continuous HFIAS score into thethree Household Food Insecurity Access Prevalence(HFIAP) groups (based on the FANTA Indicator Guide)showed that 59.7 % (169) of the households wereseverely food insecure; 16.6 % (47) were mildly tomoderately food insecure and 23.7 % (67) were foodsecure, that is they had answered no to all 9 items orreported anxiety rarely in the HFIAS instrument (seeTable 2). Thus, combining the severe and mild to mod-erate groups demonstrated that 76.3 % householdsreported some form of food insecurity ranging frommild to severe.

Bivariate and multivariate analysis

As shown in Table 3, severe food insecurity was in-versely associated with all income and socioeconomicstatus (SES) measures of the household such as Month-ly Household Income, Household Monthly Per CapitaIncome, Standard of Living Index Score, and MonthlyPer Capita Expenditure (MPCE) on food (p-value<0.001); the lower the SES the more severe the foodinsecurity. Households, where the contribution of per-sonal income of female respondents as a proportion ofmonthly household income was higher (P<0.001), werealso found to be more severely food-insecure. In addition,households with older female respondents (P<0.05), respond-ents with fewer years of education (P<0.001), followingBuddhist or Christian religion (P<0.001), and single/

Table 1 (continued)

Characteristics No. %

>Rs 4000 and ≤6000 95 33.5

>Rs 6000 77 27.3

Household Monthly Per Capita Income (MPCI in INR) (Mean1138.67; SD 602.72; Median 1000; Min 83, Max 3333)

≤800 95 33.6

>800 and ≤1250 109 38.5

>1250 79 27.9

Respondents’ Personal Income per month (Indian Rupees-INR) (Mean882.51; SD 1294.61; Median 500; Min 0, Max 12000)

No Personal income 123 43.6

≤Rs 1500 105 37.1

>Rs 1500 55 19.3

Percent of women’s income as a share of total household monthlyincome (Mean 20.25; SD 28.51; Median 7.14; Min 0, Max 100)

≤0 (≤33rd percentile) 123 43.5

≥1 and ≤23 (>33rd and ≤67th percentile) 67 23.6

≥24 (>67th percentile) 93 32.9

Total Monthly Per Capita Expenditure (MPCE in INR) (Mean 1323.66;SD 792.73; Median 1085; Min 315, Max 6925)

≤911 93 32.9

>911 and ≤1394 97 34.2

≥1395 93 32.9

Monthly Per Capita Expenditure on Food (MPCE-Food) (INR)(Mean 593.12;SD 238.61; Median 562; Min 124, Max 1794)

≤458 93 32.9

>458 and ≤659 97 34.2

≥660 93 32.9

Monthly Per Capita Expenditure on Non-Food items (MPCE-NonFood Items) (INR) (Mean 730.55; SD 652.61; Median 525;Min 20, Max 5743)

≤392 93 32.9

>392 and ≤739 97 34.2

≥740 93 32.9

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widowed/divorced (p00.054) were more likely to be severelyfood-insecure. The pair-wise comparison of the means of thecontinuous indicators above for food-secure households and

mild to moderate food-insecure households was not statisti-cally significant. However, as shown in Table 3, pair-wisecomparison ofmeans between food-secure group and severely

Table 2 (I) Frequency of occurrence of nine items on Household Food Insecurity Access Scale (HFIAS); (II) Prevalence of Household FoodInsecurity Domains; and (III) Household Food Insecurity Access Prevalence (HFIAP) Categories (n0283)

I. HFIAS ITEMS Frequency of occurrence: n (%)

Never Rarely Sometimes Often

DOMAIN: Anxiety and uncertainty about the household food supply:

1- Did you worry that yourhousehold would not haveenough food?

96 (33.9 %) 14 (4.9 %) 55 (19.4 %) 118 (41.7 %)

DOMAIN: Insufficient Quality (includes variety and preferences of the type of food):

2- Did you eat the same foodsdaily because you did not havemoney to buy other foods?

96 (33.9 %) 17 (6.0 %) 41 (14.5 %) 129 (45.6 %)

3- Did you or anyone in thehousehold have to eat any typeof food that you did not want(undesirable food) because there was no money?

111 (39.2 %) 9 (3.2 %) 63 (22.3 %) 100 (35.3 %)

DOMAIN: Insufficient food intake or quantity of food:

4- Did you ever eat less than youfelt you should because youdid not have enough money tobuy food?

144 (50.9 %) 8 (2.8 %) 69 (24.4 %) 62 (21.9 %)

5- Have you or any other adult inyour household cut the size ofyour meals because you did nothave enough money to buyfood?

136 (48.1 %) 10 (3.5 %) 60 (21.2 %) 77 (27.2 %)

6- Did you skip some of yourdaily meals because you didnot have enough money forfood?

192 (67.8 %) 6 (2.1 %) 37 (13.1 %) 48 (17.0 %)

7- The food you had did not last,and you didn’t have enoughmoney to buy more? (Severe:Run out of food)

124 (43.8 %) 18 (6.4 %) 66 (23.3 %) 75 (26.5 %)

8- Were you ever hungry and you did not eat a meal because youdid not have enough money to buy food? (Severe condition: Hungry)

181 (64.0 %) 6 (2.1 %) 46 (16.3 %) 50 (17.7 %)

9- Did you or another adult inyour household ever not eat forwhole day because you did nothave enough money to buyfood? (Severe condition: Didnot eat for whole day)

217 (76.7 %) 9 (3.2 %) 28 (9.9 %) 29 (10.2 %)

II. Household Food Insecurity Access-related Domains No to all items indomain [n (%)]

Yes to at leastone item [n (%)]

Anxiety (Worry) or Uncertainty about food 96 (33.9 %) 187 (66.1 %)

Inadequate quality 83 (29.3 %) 200 (70.7 %)

Insufficient quantity or food intake 100 (35.3 %) 183 (64.7)

III. Household Food Insecurity Access Prevalence (HFIAP) Categories N (%)

Severely Food Insecure Households 169 (59.7 %)

Mild to Moderately Food Insecure Households 47 (16.6 %)

Food Secure Households 67 (23.7 %)

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food-insecure group was highly statistically significant in thecases of all the above indicators.

The unadjusted multiple regression analysis indicated thatage and all the income and socio-economic measures had asignificant relationship with the continuous variable, HFIASScore. Marital status and religion, with the exception of thecomparison betweenBuddhist andHindu, were not significantlyrelated to the food insecurity score. All the socio-economicstatus variables were strongly correlated with each other imped-ing their use in amultiple linear regressionmodel. Therefore, thefinal analysis assessed the effect of each socio-economic factorwhile adjusting for age- the only socio-demographic character-istic significantly related to HFIAS Score but not significantlycorrelated with economic indicators. In multiple linear regres-sion models adjusted for age, socio-economic status measurespredicting household food insecurity scores were ranked byimportance based on R2 (i.e. the percent of variation in HFIASScore explained by a predictor) and included the following: (a)Monthly per capita household income (P<0.001; R2019.5 %);(b) Standard of living index (P<0.001; R2018.1 %); (c)Household income per month in Indian Rupees (P<0.001;R2018.1 %); (d) Monthly per capita expense on food(P<0.001; R2015.7 %); and (e) Percent of personal incomeof female respondents to household income (P<0.001;R2013.8 %).

Discussion

The purpose of this study was to assess the extent of foodinsecurity among households in urban slums, to assess thesubjective experiences of food insecurity in differentdomains using the cross-culturally validated HFIAS instru-ment and identify sub-groups among the urban poor that arevulnerable to food insecurity. This study found that nearlythree out of five households (59.7 %) had experiencedsevere food insecurity in Mumbai’s slums; and if the house-holds that experienced mild to moderate food insecuritywere combined with the severely insecure households, thennearly two out of every three urban poor households wouldhave reported experiencing some form of food insecurity. Inone of every two households (51.9 %), at least one adultreported cutting the size of meals and in nearly one out offour households (23.3 %), an adult had not eaten for a wholeday due to lack of resources to buy food. These findingsunderscore the seriousness of the problem of food insecuri-ty, potential food shortfalls and hunger among the urbanpoor and calls for urgent action on food security and nutri-tion programs.

These findings are consistent and correlated with otherstudies and reports. For instance, a household food insecu-rity study conducted in the urban slums of New Delhi foundthat almost two-thirds of the households (65.8 %) in the

study sample could not afford to eat a balanced meal andhad to compromise on the quality of food due to lack ofmoney; 51 % reported that the food they bought did not lastand they did not have the resources to buy more; and 14.7 %faced a condition wherein one or more family memberswere hungry and did not eat as they could not afford food(Agarwal et al. 2009). These findings can also be correlatedwith data from a survey of the nutritional status of 109,093children under 5 years of age conducted across 112 districtsof India in 2010–2011 (covering nearly 20 per cent of Indianchildren across 9 states), which showed that rates of childmalnutrition are still unacceptably high; 42 % of childrenare underweight and 58 % are stunted (Naandi Foundation2011). Another event occurred in India in the year precedingthe study that can help explain the findings of this study.Food price inflation across India, based on the wholesaleprice index (WPI) for food items and food products, entereddouble digits in April 2009 and touched the 20 % level inDecember 2009 (Government of India 2010; Times NewsNetwork 2009a, b), just prior to the start of data collection inJanuary 2010. At this rate of inflation, consumers wereforced to spend about 20 % more on food compared to theprevious year to maintain the same consumption level andthe difficulty in obtaining food supplies was probably top-most on respondents’ minds. With nearly 2 in 5 householdsearning less than Rs. 4000 (<US$100) and 1 in 3 earningbetween Rs. 4,000 and Rs. 6,000 per month, the rise in foodprices without concomitant rise in income meant automaticincrease in food insecurity among the urban poor. House-holds, most probably, managed food insecurity by drastical-ly reducing the quantity and quality of food consumed as isevident in the results.

Earlier studies on food insecurity had noted an orderli-ness or pattern to the food insecurity response by house-holds. When livelihoods and incomes are constrained,households first experience worry about where they wouldobtain sufficient food, then attempt different strategies toaugment food supply. If the problem persists, then house-holds compromise the quality of food consumed and finallyreduce the quantity of the food consumed by adults (Radi-mer et al. 1990; Radimer et al. 1992). However, our study inMumbai did not find an ordered, progressive pattern ofresponses to the three domains of HFIAS. For instance, Item1 in the Anxiety Domain of HFIAS deals with worry aboutfood, and is considered the least severe indicator of foodinsecurity. However, it did not receive the largest share ofaffirmative responses as expected. Instead responses toitems in the domain of inadequate quality (eating foods thatare undesirable) received more affirmative responses. Also,there was not much difference in the percentage of affirma-tive responses between the domain of anxiety (66.1 %) anddomain of insufficient quantity (64.7 %). This lack of or-derliness in responses to the three domains of HFIAS has

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also been reported by studies conducted in other countrieswhere food insecurity is an everyday reality. Studies inBangladesh have revealed that more households answeredaffirmatively to items on consumption of lower-quality food(55.3 %) than worrying about their food situation (36.3 %;Coates et al. 2006). Similarly, in a Tanzanian sample, 54 %worried about food while 67.9 % compromised on qualityby eating a limited variety of foods and 53 % ate fewermeals in a day (Knueppell et al. 2009).

A probable explanation is that uncertainty about householdfood supplies (food insecurity) has become a common or every-day occurrence among the urban poor and therefore people donot worry about it anymore. This is supported by the fact thatunder-nutrition is very prevalent among urban poor children andfood inflation has been a constant factor in India for the pastcouple of years leading to sharp increases in cost of living.Furthermore, there is the concept of fate or destiny in the Indian

cultural context. Sowhat peopleworry aboutmay be determinedmore by the social and cultural conditions of their lives. Thenotion of leaving things to fate may also lead to a perceived lackof control over one’s life in general, including food supplies ofthe household; and as one cannot control it, one need not worryabout it. These findings with respect to the domains emphasizethe need for further research with HFIAS items to understandhow the urban poor in India experience and manage foodsecurity. If the experience of food insecurity has become aneveryday reality, almost a way of life, for the urban poor, thenthey think of the struggle for food as a norm. In that case theHFIAS instrument needs to be modified appropriately to reflectthis new reality of urban poor lives. The testing of HFIAS willalso help the understanding and explanation of some otherfindings with respect to items within the domain of insufficientquantity on the scale. In response to items in the domain ofinsufficient quantity or food intake (including occurrences that

Table 3 Food Insecurity (HFIAP) by specific respondent and household characteristics: Factors associated with higher risk of food insecurity

Indicators Household Food Insecurity Access Prevalence (HFIAP) p-value£

Food Secure (n067) Mild/ModeratelyFood Insecure (n047)

Severely FoodInsecure (n0169)

Age (in years)

Mean ± SD 34.09±10.92a 35.79±8.86 38.00±11.15a 0.036

Religion (row%)

Hindu 53 (26.8) 35 (17.7) 110 (55.5) <0.001Islam 2 (14.3) 8 (57.1) 4 (28.6)

Buddhist 6 (13.6) 2 (4.5) 36 (81.8)

Christian 6 (22.2) 2 (7.4) 19 (70.4)

Marital Status (row%)

Married 54 (23.5) 44 (19.1) 132 (57.4) 0.054Single 13 (24.5) 3 (5.7) 37 (69.8)

Years of school completed

Mean ± SD 7.97±3.88a 6.83±4.26 5.14±4.41a <0.001

Household income/month, in Indian Rupees (INR)

Mean ± SD 6900±3656a 5987±2576 4824±2741a <0.001

Monthly Per Capita Household Income, (INR)

Mean ± SD 1419±671a 1203±477 1009±566a <0.001

Proportion/Percent of Personal Income of female respondents to Household Income (monthly)

Mean ± SD 13.97±21.18a 10.28±15.36b 25.50±32.43a,b <0.001

Standard of Living Index (SLI) Score

Mean ± SD 13.87±2.95a 12.68±3.51b 11.08±3.26a,b <0.001

Media Access (Use) Score

Mean ± SD 1.87±0.87a 1.53±0.11b 1.16±0.06a,b <0.001

Monthly Per Capita Expense (MPCE) on Food (INR)

Mean ± SD 659.46±195.03a 648.72±291.86b 551.35±230.07a,b 0.001

* Food Secure; Mild and Moderately Food Insecure combined; and Severely Food Insecure – classified and computed according to FANTA-HFIASIndicator Guide (Coates et al. 2007)£Means were compared across groups using a one-way ANOVA- Superscripts indicate that pair-wise comparisons were statistically significant using a Bonferroni correction

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happened rarely, sometimes, and often), 56.2 % of respondentssaid the household ran out of food and they did not have moneyto buymore; 49.1% said they had to eat less; and 51.9% cut thesize ofmeals. However, only 32.2 % said they skipped mealsor had fewer meals in a day. These findings, althoughinclusive of occurrences that happened rarely or sometimes,may suggest that households are managing the problem byeating meals that are smaller in size rather than skippingone of the daily meals. The majority of the sampled house-holds reported that they ate on three occasions each day –breakfast, lunch and dinner; however, tea, even consumedby itself, was also considered as a meal or eating occasion.It is also possible that one member, especially the woman, issacrificing the quantity of food she consumes. Thus, infuture research we have to measure the meals, mealtimesand diets of each member in the urban poor household. Amore detailed study of HFIAS items, the actual behaviorsassociated with these items, along with diet and nutritionstudies for each family member is required in order toexplain and understand better the phenomenon of foodinsecurity among the urban poor.

There are some other limitations and strengths of the datacollection and sampling methods followed in this study. Thesurvey method relies on self-reports and recall leading to abias in findings. Respondents often match self-reports tosocial expectations; and in some cases the respondentexpected some monetary help and therefore may have pro-vided a more dismal picture. Also, most often food insecu-rity is seasonal and irregular — associated with temporaryunemployment, episodes of ill health, or other adverseevents (in this case a sharp increase in food prices in2009) — and people may anticipate such possibilities androutinely engage in precautionary behaviors to try to miti-gate their risk. Hence perception-based survey measureshave been consistently found to document food insecurityrates several times higher than related hunger or insufficientintake measures (National Research Council 2005). However,the benefit of this data-collection method is that it also allowshouseholds to share their experiences and expands the conceptof food insecurity. Other strengths of the current study werethe use of a food insecurity instrument designed to be used in across-cultural setting, multiple ways and measures to assesssocioeconomic status, including household assets, housingcharacteristics and monthly expenditures, and the use of atried and tested method to select the sample.

The EPI ‘30×7’ cluster survey method was developed tomeet the needs of health managers for reliable estimates ofvaccine coverage. It has since been used for thousands ofsurveys, with or without adaptation. The method is stan-dardized, quick to implement, and approximately self-weighting (and therefore simple to analyze), but it hasimportant limitations. Firstly, clusters were selected withprobability proportional to size (pps) according to the most

recent data, but these data regarding population, populationdensity, and numbers of chawls/households can be inaccu-rate, particularly with respect to fast-growing urban areassuch as Mumbai which has high levels of in-country migra-tion. This may mean that such areas, which may have thepoorest indicators, will often be under-represented in thesample and the overall estimate will be biased. Secondly,the method does not select households from a samplingframe, but instructs the interviewer to follow a procedurein the field, resulting in a cluster of households being se-lected within the community. This procedure is open toconscious or unconscious bias of the interviewer and doesnot lead to a sample selected with known probability. Thisstudy tried to compensate for this limitation by traininginterviewers to follow the strategy of moving to the rightand covering every seventh household and then having asupervisor monitor the interviewers continuously. However,this exercise may still not cover all the households in thearea and may miss out on some crucial households orpopulation groups. Thirdly, in case of non-response, onesimply goes on to select the next household, leading to biasif non-responders differ systematically from those who doparticipate (Milligan et al. 2004). As this study was conductedin only three wards in Mumbai, generalization of results toother urban areas in India may also have limitations.

The study also aimed to find who or which householdswere more vulnerable to food insecurity. The findings em-phatically demonstrate that low socioeconomic status(assessed with different measures such as household in-come, per capita income, standard of living index) is astrong predictor of severe food insecurity. In this study,households in the severely ‘food insecure’ household cate-gory earned nearly a third less (P<.01) every month than the‘food secure’ households, while their expenses on food wasonly 15 % less than the food-secure group. Similar findingsassociating food insecurity and income have been reportedin other food insecurity related studies as well (Agarwal etal. 2009; Tingay et al. 2003). In a nutritional survey ofchildren under 5 years of age conducted across 112 districtsin India in 2010–2011 mentioned above, prevalence of childmalnutrition was significantly higher among children fromlow-income families (Naandi Foundation 2011). It is nowdocumented that low income is a definite factor that createsvulnerability to food insecurity, but the majority of house-holds in our population of interest - urban poor - are fromthe low-income bracket. Therefore, this study examinedother factors in order to characterize and help in the identi-fication of vulnerable sub-groups. This study created a var-iable using the existing income variables and found thathouseholds where the proportion of women’s income con-tribution to the overall monthly household income washigher were more likely to report severe food insecurity.Additionally, in food-insecure households, the woman was

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less educated, older, with less media use or access and morelikely to be not married. The nutrition status survey ofchildren from 112 districts in India also found that rates ofchild underweight and stunting were significantly higheramong mothers with low levels of education (Naandi Foun-dation 2011). Thus, urban poor households that are mostvulnerable seem to be female-headed or female-maintained(Buvinić and Gupta 1997), where the woman contributes amajor share of the household income; she is typically lesseducated, older and is in a low-income job. Similar findingshave been reported by studies in other countries (Hakeem etal. 2003; Tarasuk 2001).

The present study also found that, compared to otherhouseholds, a significantly higher number of severelyfood-insecure households had borrowed money in thepast year (54 %; P<.05) and were behind in paymentsfor at least one of the three critical monthly utilities ofwater, electricity and gas (63 %; P<.001), and althoughnot statistically significant, spent more every month forrepayment of loans compared to the other households.Not only is the severely food-insecure household in afinancial bind, it also suffers from lack of social sup-port. A significantly large number (67 %; P<.01) ofseverely food-insecure households did not have peoplethey could turn to for help in case they needed moneyin an emergency. Thus, the vulnerable household seem-ingly has deficiencies in all forms of household capital– financial capital, human capital and social capital.Urban slum households vulnerable to food-insecurityseem to be caught in the vicious cycle of poverty, foodinsecurity, malnutrition, and increased morbidity; thusreducing the capacity for work and productivity inadults, which in turn affects their income-earning poten-tial and the socio-economic status of the household.Although some steps at the household level, especiallylivelihood security, are necessary, they will not be suf-ficient for households to get out of this vicious cycle.What is necessary and could be sufficient are actions atthe level of the government in the form of appropriatepolicy and effective implementation of programs, or elsethe food-insecure urban poor groups are in danger ofbecoming chronically hungry, malnourished, andunproductive.

With inflation rates peaking in 2009–10, food secu-rity finally received national attention. After extensivedeliberations and consultations, a Draft National FoodSecurity Bill was released on 21 January 2011 (NationalAdvisory Council 2011). It was introduced in the IndianParliament on December 22, 2011 by the Minister forConsumer Affairs, Food and Public Distribution and iscurrently being examined by a parliamentary panel.What some of the urban poor respondents shared withus during the surveys was the necessity of either

increasing incomes or controlling food prices. However,the government clearly did not want to tamper withmarket forces and decided to offer subsidies to the poor.The Food Security Bill seeks to provide food and nu-tritional security by providing specific entitlements tonearly 75 % of the rural population and 50 % of theurban population. Of these, at least 46 % of rural and28 % of urban population will possibly be classified as“priority” households entitling them to 7 kilograms (kg)food grains per person per month at Indian Rupees(INR) 3/kg for rice, INR 2/kg for wheat, and INR 1/kg for coarse grains. The rest are classified as “general”households and are entitled to 3 kg food grains perperson per month at 50 % of minimum support price(MSP) offered to farmers by government during pro-curement. Initial estimates suggest that the expensesgenerated by the bill could be upwards of IndianRupees 1 lakh crore (INR 1 trillion; US$18 billion).Given the economic and political ramifications, the billhas generated a huge debate. Furthermore, the criteriafor classifying households into “priority” and “general”categories have not yet been determined. The on-goingdebate is that a strong mechanism is required to identifythe right beneficiaries to ensure that the right house-holds are able to avail themselves of the subsidies aswell as to avoid deserving households from being ex-cluded (National Advisory Council 2011; Press Informa-tion Bureau 2011a, b). The findings from this paperprovide more evidence for the urgency of institutingfood security measures for the urban poor in India;and also provide useful and usable information for re-solving the debate on identification of vulnerable house-holds to address the issue of food security among theurban poor.

Acknowledgments We are grateful to all the respondents who tooktime from work and their hardships to share their experiences with us;the interviewers; Rev. George Daniel and the field-staff of BombayUrban Industrial League for Development (BUILD) who helped intimely conduct and completion of this study. We also wish to acknowl-edge the support of Bread for the World for supporting this research.

Conflict of interest The authors declare that they have no conflict ofinterest.

Disclaimer The opinions, findings, and conclusions or recommen-dations expressed herein are those of the authors and do not necessarilyreflect the views of the institutions the authors are affiliated with.

Authorship statement NC conceptualized and designed the studyand study tools. NC also supervised data collection, data entry and datamanagement and wrote the paper. GF participated in study design,tested study tools, carried out and supervised data collection, con-ducted and supervised data entry, and participated in writing of thepaper. MH conceptualized the paper and analysis plan, conducted the

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data analysis, and wrote related sections of the manuscript. All authorsprovided inputs in writing, reviewing and editing the entire manuscript.

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Nilesh Chatterjee is the Head ofResearch and Evaluation Divisionof Johns Hopkins University Cen-ter for Communication Programs(JHUCCP), NewDelhi, India. Hehas a doctorate in Behavioral Sci-ence (Public Health) fromUniver-sity of Texas School of PublicHealth; Master’s degree in Ap-plied (Medical) Sociology fromUniversity ofMaryland BaltimoreCounty; and a medical degreefrom Bombay University. He hasworked in public health, commu-nication, behaviour and lifestyle

change at individual patient-level and state- and country-wide programs.He has conducted various research projects and published articles onevaluation research, community trials, surveys, focus groups and otherqualitative research in the USA, India, Mexico and Bangladesh. In India,he has conducted evaluations of various public health, education, andpoverty alleviation projects. He has worked in various capacities - Assis-tant professor at Texas A&MUniversity; Adjunct faculty at University of

Texas School of Public Health and Texas Woman’s University; ProjectManager and Program Evaluation and Design Consultant; Investigator onprojects funded by Centers for Disease Control (CDC), National Centeron Minority Health and Health Disparities, USA, CONACYT in Mexicoand international non-profits. He has also worked in education manage-ment and as a management consultant in India.

Genevie Fernandes has a Bach-elor’s degree in Psychology(Honors) & Anthropology fromSt Xavier’s College and Master’sdegree in Sociology from Univer-sity of Mumbai. She has beenworking extensively in the do-main of Public Health and Live-lihoods with Mumbai’s urbanpoor. Genevie has conducted twocity-wide studies, using bothquantitative and qualitative meth-ods; a food security study for aninternational not-for-profit orga-nization and a Communication

Needs Assessment (CNA) in HIV/AIDS for a government agency. Inthe livelihood domain, she has recruited, trained and placed urban-pooryouths in entry level jobs in the Hospitality and Retail Sector and helpeddevelop an “Employability Learning – Employment Exchange” (EXEL)Center in a rural district. At present, Genevie is the Monitoring &Evaluation Consultant with Mumbai District AIDS Control Society(MDACS).

Mike Hernandez has a Mastersdegree in Mathematics from Tex-as A&M University (CorpusChristi campus) and a Masters inBiostatistics from the Universityof Texas School of Public Health,where he is currently completinghis doctoral studies in biostatis-tics. Mike is employed at the Uni-versity of Texas MD AndersonCancer Center as a statistical ana-lyst in the Department of Biosta-tistics. Mike has taught statisticsin various universities and col-leges in Texas and authored vari-

ous publications and conference presentations. He is also co-author of astatistics textbook titled - Biostatistics: A Guide to Design, Analysis andDiscovery 2nd ed.

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