sample thesis proposal on urban safety net in ethiopia

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ETHIOPIAN CIVIL SERVICE UNIVERSITY

2020, Masters Thesis

This thesis has assessed the role of urban productive safety net program on urban poverty reduction to strengthen socialization, the case Addis Ketema sub-city .Cross-sectional research design and mixed approach are employed to gather data. Semi-structured interviews, focus group discussions, observation and household survey are used to collect data. 372 household samples are drawn randomly from woredas of the sub city. Thematic, descriptive statistics and narrative model are used for analyzing the data.

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The Productive SafetyNet Program (PSNP) is a major political ingenuity by governments and donors that makes millions of chronically food-deficient rural populations safer and more predictable from ongoing food relief. So, we will move mainly to cash-based social protection. This study aims at examining major features of Productive Safety Net Program and its impact on the livelihood of rural households in Doba District, Ethiopia. A multi-stage sampling technique was used to draw 220 sample respondents from PSNP beneficiaries and non-beneficiaries. Household survey, focus group discussions and key informant interviews were employed to collect quantitative and qualitative data. Data were analyzed using Propensity Score Matching (PSM) and binary logistic regression model. Results revealed that PSNP increased total annual income by 39% and calorie intake by 8.4% of the PSNP participant households compared to non-participant households. PSNP has positive and statistically significant effe...

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This study examines the impact of Urban Productive Safety Net Program (UPSNP) on beneficiaries' livelihood and food security in Worda 4, Llideta sub-city, Addis Ababa, Ethiopia. The study focuses on livelihood activity improvements/jobs created for beneficiaries, improvement in their food security status, and how UPSNP contributed to the beneficiaries' fight against Cvid-19. A cross-sectional research design and mixed research method of quantitative and qualitative data collection instruments are used: household survey and key informant interviews. A total of 185 sampled household heads were selected through proportionate probability sampling method from public work and direct support beneficiaries and seven respondents were selected purposively for key informant interviews. Though safety net programs are relatively new to urban areas and the complexity of urban poverty makes it difficult to pull the urban poor out of poverty in such a short period with this limited amount of transfer, UPSNP has brought positive improvements to the recipient households' livelihood and food security through monthly cash transfer and livelihood improvement. The result showed that 62.8% of the beneficiaries had got livelihood improvement services including saving experiences and different vocational trainings before graduating from UPSNP. In terms of their food security status 23%, 51%, and 19.5% of the recipients were respectively severely food insecure, moderately food insecure, and mildly food insecure. However, the program has managed to bring positive improvement to children's food consumption and adults using the cash transfer for food. The result also showed that UPSNP impacts are more positive for Public work beneficiaries than direct support beneficiaries. Finally, 88.1% of the respondents had confronted the socio-economic crisis created by covid-19 and the contribution of UPSNP to their struggle to overcome this socio-economic crisis was not helpful to 87% of beneficiaries. It is recommended that the amount of cash transfer should be adjusted based on the current market price of food. Food Security and Urban Job Creation Agency should work together with enterprise development and job creation agency to successfully support beneficiaries in becoming successful in new business activities.

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The paper is addressing the scenario of urban poor in Addis Ketema (Woreda-07) of Addis Ababa city and common issue of the study area is inadequate of infrastructure facilities. The area of the Addis Ketema is 9863.9 hectare with a population of 28099 (CSA, 2013). Tool and techniques such as household survey, Focus Group Discussion (FGD) and field observation were adapted to investigate on the sectors of socioeconomic , water supply, sewerage, drainage and solid waste management, and education. The following objectives were carried out-(1) to study the settlement pattern and socioeconomic profile, (2) to study the physical facilities (water supply, sewerage, drainage, and solid waste management) and (3) to study the education system and performance. The result of this paper had reveals that Addis Ketema is pocket where poor people lives and per capita income is 50 Dollar approximately. Maximum numbers of people are running informal economic activities and suffering from inadequate municipal services.

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See More Documents Like This

Effects of Urban Productive Safety Net Program in Addis Ababa City Administration

The case of kolfe keranio subcity, master's thesis, 2021, 114 pages, grade: 3.5, andualem melaku (author).

Abstract or Introduction

The aim of this study is to assess the effects of urban productive safety net program on the livelihood of the ultrapoor in Addis Ababa City Administration, with particular focus on Kolfe Keranio Sub-City. Specifically, the study focuses on the socio-economic status of the graduate-households before and after joining UPSNP and, challenges facing the graduate-households in the process of attaining sustainable IGA's. Furthermore, on opportunities gained benefiting from UPSNP and to assess attitudes of the communities towards the graduates/program. Within these, mixed approaches were employed to gather data. Moreover, a stratified sampling method was administered in order to maintain equal chances to women and men. To this end, a cross-sectional survey was employed to collect data from 179 graduate-household heads. Structured interviews, focus group discussions, key informant interviews as well as observation of the researcher were also adopted in order to gather qualitative data. The finding of the research reveals that UPSNP has not brought a significant change on the livelihood of the graduate-households. No convincing difference was observed among households before and after joining UPSNP, although, very few households confirmed they have seen positive effects. The investigation has also ascertained the existence of so many challenges affecting the process of IGA's from the households side and government side as well as nation-wide obstacles such as inflation/food price rise/ and house rent increment, since the majority of the graduate-households dwell in rental houses. The interpretation also confirmed the presence of special opportunities for the graduate-households being beneficiary of the program. And, almost all the communities have positive attitude towards the graduate-household and the program. Furthermore, the researcher recommended that special credit access should be facilitated to support and boosts the IGA's. Eventually, work places should be allotted before grant, problem of housing and inflation should be taken careful attention to solve and additional components other than the support component should be facilitated.

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Title: Effects of Urban Productive Safety Net Program in Addis Ababa City Administration

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Open Access

Peer-reviewed

Research Article

Food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia

Contributed equally to this work with: Atimen Derso, Metadel Adane

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft

Affiliation Department of Environmental Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

Affiliation Department of Public Health, Saint Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Atimen Derso,
Hailemichael Bizuneh,
Awoke Keleb,
Ayechew Ademas,
Metadel Adane

Published: September 27, 2021
https://doi.org/10.1371/journal.pone.0256634
Peer Review
Reader Comments

Measuring household food insecurity in specific geographic areas provides vital information that enables appropriate and effective intervention measures to be taken. To that end, this study aimed to assess the prevalence of food insecurity and associated factors among Urban Productive Safety Net Program (UPSNP) beneficiary households in Addis Ababa, Ethiopia’s capital city.

A community-based cross-sectional study was conducted among 624 UPSNP beneficiary households in nine districts of Addis Ababa from June to July 2019. A multi-stage sampling method was used; study participants were selected using a simple random sampling technique after establishing the proportionally allocated sample size for 9 districts. Data were collected by trained personnel using a pretested, structured questionnaire. The outcome variable was food insecurity as measured by Household Food Insecurity Access Scale (HFIAS), a tool developed by the Food and Nutrition Technical Assistance Scale (FANTA) and validated for developing countries, including Ethiopia. A binary (crude odds ratio [COR]) and multivariable (adjusted odds ratio [AOR]) logistic regression analysis were employed at 95% CI (confidence interval). From the bivariate analysis, factors having a p -value<0.25 were included in the multivariable analysis. From the multivariable analysis, any variable at p -value < 0.05 at 95% CI was declared significantly associated with household food insecurity. Model fitness was also checked using the Hosmer-Lemeshow test with p -value>0.05.

The prevalence of household food insecurity was 77.1% [95%CI:73.8–80.7] during the month prior to the survey. Illiteracy of household head [AOR: 2.56; 95%CI:1.08–6.07], family size of 4 or more [AOR: 1.87, 95%CI:1.08–3.23], high dependency ratio [AOR: 3.95; 95%CI:1.31–11.90], household lack of access to credit [AOR:2.85; 95%CI:1.25–6.49], low household income [AOR: 4.72; 95%CI:2.32–9.60] and medium household income [AOR: 9.78; 95%CI:4.29–22.35] were significantly associated with household food insecurity.

We found that three in four of Addis Ababa’s UPSNP beneficiary households were food-insecure. Implementation of measures to improve household income, minimize the dependency ratio of households, and arrange access to credit services are paramount ways to tackle food insecurity problems in Addis Ababa.

Citation: Derso A, Bizuneh H, Keleb A, Ademas A, Adane M (2021) Food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia. PLoS ONE 16(9): e0256634. https://doi.org/10.1371/journal.pone.0256634

Editor: Abid Hussain, International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, NEPAL

Received: September 24, 2020; Accepted: August 11, 2021; Published: September 27, 2021

Copyright: © 2021 Derso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: Funding for this study was provided by Saint Paul’s Hospital Millennium Medical College. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AOR, Adjusted odds ratio; COR, Crude odds ratio; FANTA, Food and Nutrition Technical scale; HFIAS, Household food insecurity access scale; UPSNP, Urban productive safety net program; USD, United States Dollar; CI, Confidence Interval; EDHS, Ethiopian Demographic and Health Survey; UNICEF, United Nations International Children’s Emergency

Introduction

Household food insecurity exists when a family does not have adequate physical, social and economic access to sufficient, safe and nutritious food to meet the dietary needs and food preferences of its members for an active and healthy life [ 1 , 2 ]. It is one of the underlying causes of all forms of malnutrition, including inadequate quantity, poor quality, and continuity of diet [ 3 , 4 ] that persists as a major challenge around the world.

Lack of access to sufficient and nutritious food is a significant challenge to achieving international nutritional targets for children. For instance, according to the 2018 Global Nutrition Report, only half (51%) of children aged 6 to 23 months around the world get the recommended minimum number of daily meals, and only one in five children (16%) eat a minimally acceptable diet [ 5 ]. From a 2019 report by the United Nations International Children’s Emergency Fund (UNICEF) on the state of the world’s children, by 2018 the proportion of children between 6 and 23 months of age eating a diverse diet is only one in three. However, for the world’s poorest children, the ratio falls to only one in five [ 6 ].

The prevalence of poverty and the urban population in Africa are growing rapidly [ 7 ] Both of these factors are increasing the challenge of meeting food security needs of the urban population. Members of this poor urban population experience under-nutrition, micronutrient deficiencies, and being overweight and obese at the same time, increasing the double burden of malnutrition [ 8 ]. Currently, one in three undernourished children live in an urban setting, a proportion that will increase over the next decade in response to globalization, migration, population growth, income inequality and climate change across low- and middle-income countries [ 9 – 11 ].

In Africa, food insecurity is a major public health problem that appears to be increasing in almost all regions, causing 52.5% of the population to be exposed to moderate or severe food insecurity. In 2018, among populations of Sub-Sahara and Eastern Africa, the magnitude of food insecurity was 57.7% and 62.7% respectively.

In eastern Africa, as a consequence of food insecurity, nearly one-third of the population (30.8%) was undernourished due to a lack of sufficient calories [ 3 ].

Food insecurity is manifested in the reduction of food consumption and a lack of access to food variety and diversity [ 12 , 13 ], affecting per capita food consumption. Inadequate access to food causes inequality of food distribution among family members, especially a reduction to infants and children [ 14 ], as well as anxiety and stress among women in the household [ 15 ]. Ultimately, a lack of access to food affects the physical and mental development of an individual and the economic productivity of a population and a country [ 3 , 16 ].

According to a 2018 global report on food crises, the severity of food insecurity in Ethiopia increased proportionally between 2016 and 2017 despite a decrease in absolute numbers; around 8.5 million people lack food security as a result of population displacement, political instability, armed clashes, persistent drought and an increase in food prices [ 17 ]. Food insecurity has an indirect significant contribution to under-nutrition that ultimately affects the economic growth of a country. According to the African Union (AU) 2014 report on the cost of hunger in Africa, under-nutrition in Ethiopia costs 16.5% of gross domestic product (GDP) and causes 28% of under-five child mortality, 15.8% of students repeating a grade, and nearly 13 billion Ethiopian birr lost in productivity in manual activities [ 18 ].

From the 2016 Ethiopian Demographic Health Survey (EDHS) report, only 45% of children aged 6 to 23 months had gotten the recommended minimum number of meals, and only 7% of them had eaten a minimally acceptable diet in the previous five years [ 19 ]. Great efforts have been made by Ethiopian social protection programs. Evidence shows that a rural safety net program and cash and food transfers to households has improved food security and dietary diversity of low-income rural households; the program has now been expanded to Ethiopia’s urban settings [ 20 ].

The UPSNP is a pilot project launched in 2017 by the Ethiopian government and aid organizations to help urban poor food insecure households to improve nutritional status and address the underlying causes of food insecurity by providing them with food and cash [ 16 ]. Addis Ababa is one of the pilot project areas. However, the status of food insecurity among urban beneficiary households has not been assessed since starting the program. This study was developed to address this gap in knowledge which will help programmers to evaluate and revisit the approaches of the program in an urban setting.

Therefore, this study aims to gather area-specific evidence to assess the status of food insecurity and associated factors among UPSNP beneficiary households in Ethiopia’s capital city, Addis Ababa. A key goal of this study is to contribute to the scaling up of the UPSNP, thereby helping to change the food security status of Addis Ababa’s urban poor.

Methods and materials

Study design, period and study area.

A community-based cross-sectional study was conducted from June to July 2019 in Addis Ababa, Ethiopia. This study covered 9 districts (called woredas in Amharic, the local language) found in three sub-cities of Addis Ababa (Arada, Lideta and Yeka). However, in all, the UPSNP was implemented in 90 selected districts (of 116 existing districts) across all 10 sub-cities. About 78,543 households have been participating in UPSNP since 2017 [ 21 ]. According to the projection of the 2007 Ethiopian population census, the total population of the city in 2017 was 4,567,857 with an estimated 913,572 households [ 22 ].

The national nutrition policy of Ethiopia addresses food insecurity through preventive approaches via nutrition-sensitive interventions that address underlying causes such as poverty [ 23 ]. The government of Ethiopia, the World Food Program (WFP) and development partners have worked together to improve food security, and to stabilize assets covering 10 million Ethiopian people through the UPSNP [ 21 ].

The UPSNP targets urban poor households (those earning below 2 United States Dollars [USD] per day) with nutritionally vulnerable members in the identified pilot cities including Addis Ababa and some regional towns with interventions that improve their living and food security levels [ 21 ].

Source population, inclusion and exclusion criteria

All UPSNP beneficiary households in Addis Ababa were the source population of the study. The study population was randomly selected UPSNP beneficiary households in the systematically selected districts. From the study population, household respondents who were unable to communicate for various reasons such as illness were excluded. Registered and active UPSNP beneficiary households in the selected districts at the time of data collection were included.

Sample size determination and sampling procedure

sample thesis proposal on urban safety net in ethiopia

then P (population value) = p(sample value) ± z SE P

We used multi-stage sampling in two stages. During the first stage, out of Addis Ababa’s 10 sub-cities, 30% were selected (the 3 sub-cities Arada, Lideta and Yeka) by probability proportional to size sampling. Then, in the second stage, from the selected sub-cities, 30% of the 30 active districts implementing the program were again selected by probability proportional to size sampling, which is 9 districts; and then sample size was proportionally allocated to the 9 districts. Finally, using their payment code list already available in the respective districts sampling unit (household) was selected using systematic sampling techniques. From the selected households, the study participants were selected based on their experience of household food status according to FANTA guidelines [ 25 ].

Outcome variable measurement

This study’s outcome variable was household food insecurity status [“insecure household = 1” or “secure household = 0”] during the month previous to the survey. We used a standard tool HFIAS that was able to provide urban-level data on the program, a tool developed by FANTA and validated for developing countries, including Ethiopia. However, the indicator of the program is not able to quantify the level of food insecurity, rather it only measures the time a household lives with the program and withdraws from the program. HFIAS has three domains: anxiety and uncertainty domain, insufficient quality domain, and insufficient food intake (quantity) and its physical consequences domain. The prevalence or proportion of the three domains were evaluated based on the number of households experiencing one or more behaviours in each specific domain relative to the total number of household responding affirmatively to behaviour questions in that specific domain [ 25 ].

HFIAS has a set of nine closed-ended questions categorized as “yes” or “no”, which represent occurrence; and under each “yes” category 3 option of occurrence frequency [rarely = 1, sometimes = 2, and often = 3]. The status of household food insecurity was estimated into two categories [food insecure and food secure] using the prevalence of insecure household food access. Then, household food insecurity status was classified as mild, moderate or severe based on the frequency of the occurrence of each item in Table 1 .

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https://doi.org/10.1371/journal.pone.0256634.t001

A food-secure household was declared when household score [item one = ‘0’ or ‘1’ and items two to nine = ‘0’].

A food-insecure household was declared when household score [item one = ‘2’ or ‘3’ and/or item two to nine = ‘0’or ‘1’ or ‘2’ or ‘3’].

Mildly food-insecure household [(item one = ‘2’ or ‘3’ or item two = ‘1,’ ‘2,’ or ‘3’ or item three = ‘1’ or item four = ‘1’) and item five to nine = ‘0’].

Moderately food-insecure household [(item three = ‘2’ or ‘3’ or item four = ‘2’ or ‘3’ or item five = ‘1’ or ‘2’ or item six = ‘1’ or ‘2’) and item seven to nine = ‘0’] and

Severely food-insecure household score [item five = ‘3’ or item six = ‘3’ or item seven = ‘1,’ ‘2,’ or ‘3’ or item eight = ‘1,’ ‘2,’ or ‘3’ or item nine = ‘1,’ ‘2,’or ‘3’] [ 25 ] ( Table 1 ).

When there was a response of yes, there was an occurrence frequency measurement with three options, ‘Rarely = happened once or twice’, ‘Sometimes = happened three to ten times’ and ‘Often = happened more than ten times’ during the month previous to the survey [ 25 ].

Operational definitions

Urban productive safety net program (upsnp)..

A social protection system that is being implemented in the major urban centres of Ethiopia targeting chronic food insecure households and providing cash or food for the beneficiaries (households) either “for work”, “for free” or “a combination of both” on a regular basis for a five-year period [ 26 ].

Household Food Insecurity Access Scale is a set of questions related to the experience of food access that appeared to distinguish food-secure from food-insecure households across different cultural contexts, and is used to estimate the prevalence of household food insecurity and its category [ 27 ].

Food insecurity.

Food insecurity is a state that exists when any people do not have adequate physical, social or economic access to sufficient, safe and nutritious food that meets the dietary needs and food preferences for an active and healthy life [ 28 ].

Recall period.

The respondent was asked an occurrence question about whether the condition had happened in the past four weeks (30 days) [ 25 ].

Dependency ratio.

It was computed from the ratio of dependent members (age group less than 18 and greater than 64 years) to productive household members (age group 18–64 years) and finally sorted into three groups (tertiles) of low, medium and high.

Household income.

It was computed into tertiles (three groups) as low (375–1500 birr), medium (1560–2000 birr) or high (2100–4500 birr) from scores of average household monthly income from any sources.

Data collection, quality and management

A pre-tested structured questionnaire was used to collect data on socio-demographic variables and household food insecurity. The socio-demographic part was adapted from published literature and the EDHS [ 29 ]. A household food insecurity HFIAS questionnaire was adopted from the FANTA, a validated tool for food insecurity study in developing countries, including Ethiopia [ 25 ]. The questionnaire was first prepared in English and then translated to Amharic and back to English by language experts to ensure consistency. Data collectors were 4 MPH holder/Public Health Nutrition professionals; 2 supervisors were MPH holders in Public Health Nutrition.

Since Addis Ababa’s UPSNP agency does not have a central database containing a list of beneficiary households and their characteristics, households were selected based on multi-stage probability proportional to size sampling design from 10 sub-cities having different sizes of beneficiary households; that design was able to weight size difference and reduce selection bias. The quality of the data was ensured through adequate training of data collectors and supervisors on the objective of the study and the overall approaches of the study. The outcome variable was measured by an HFIAS indicator that is validated, highly reliable and a culturally sound tool able to control bias and reveal the true prevalence or change in food insecurity level in this study target area.

A pre-test was conducted on 5% of the sample size in a nearby area outside of the selected study areas. Data were collected by face-to-face interviews with mothers or heads of households. The completeness of questionnaires was checked daily before data entry. The completeness of data was checked manually, and after editing and cleaning, data entry was done by using EpiData version 3.1. After checking the consistency, data were exported to Statistical Package for Social Science (SPSS) version 25.0 for analysis.

Data analysis: Method of estimation and model equation

Prevalence of household food insecurity and level of food insecurity status (mild, moderate and severe) were computed from nine questions of the HFIAS ( Table 1 ). To estimate the probability of the two outcome variables, the proportion of households having the characteristics of food insecurity (P) or not having the characteristics of food insecurity (1-P) computed. We also computed frequency distribution, categorization for continuous and re-categorization for categorical variables.

To determine the independent effect of predictor variables, we conducted a logistic regression model (logit transformation) written as Eqs 1 and 2 .

Where x 1, . . . ., x n is the predictor variables and ‘P’ is the proportion of food insecure household.

To find out the factors associated with food insecurity, data were analysed using binary logistic regression model at 95%CI. From the bivariate analysis, factors having a p -value of less than 0.25 were included in the multivariable analysis. By adjusting the confounders, from the multivariable logistic regression analysis, any variable at p -value <0.05 with 95% CI was declared a factor significantly associated with household food insecurity. Multicollinearity was checked among independent variables using standard error; a standard error of more than 2 indicated multicollinearity (maximum SE = 0.56). Model fitness was also checked using the Hosmer-Lemeshow test with p -value >0.05 ( p = 0.858).

Ethics approval and consent to participate.

Ethical clearance was obtained from the Ethical Review Board of Saint Paul’s Hospital Millennium Medical College, Addis Ababa. Before the data collection started, an official request for permission to proceed was granted by the Addis Ababa City UPSNP administration office. Before each interview, data collectors explained the purpose of the study, gave assurance of confidentiality, addressed any other ethical issues and obtained informed verbal consent from each household respondent.

Socio-demographic and economic characteristics of the study participants

Of 624 participants, 607 participated in this study (97.3% response rate). The mean age of household head and mean family size were 44.66±13.16 and 4.04±1.75, respectively. Nearly two-thirds 382 (62.9%) of households were headed by a female. One-fourth 154 (25.4%) of household heads were illiterate. Eighty-three (13.7%) households were found in the high dependency ratio group. Just more than half 317 (52.2%) of study participants were married ( Table 2 ). Three hundred seventy-nine (62.4%) households’ only source of income was from the UPSNP safety net. The mean household monthly income was 65.13±1.18 USD The proportion of household income spent on food and food-related items was more than 75% for 431 (71%) of households ( Table 3 ).

https://doi.org/10.1371/journal.pone.0256634.t002

https://doi.org/10.1371/journal.pone.0256634.t003

The occurrence of Household Food Insecurity Access Scale conditions.

The proportion of households experiencing worry about not having enough food was 528 (87%). The majority of the households 456 (75%) replied affirmatively to having been unable to eat their preferred food in the 4 weeks before the interview due to a lack of resources ( Fig 1 ). The number of households that had eaten a limited variety of foods in the 4 weeks before the interview due to lack of resources was 462 (76.1%). Households that had eaten smaller amounts at a meal or fewer meals than preferred in the 4 weeks before the interview numbered 426 (70.2%) and 383 (63.1%), respectively. Moreover, the number of households with an affirmative response to the severe conditions of going to sleep hungry or going a whole day and night without food were 219 (36.1%) and 39 (6.4%), respectively ( Table 4 ).

https://doi.org/10.1371/journal.pone.0256634.g001

https://doi.org/10.1371/journal.pone.0256634.t004

The occurrence of household food insecurity domain

The nine occurrence questions were grouped into three domains according to similarities of their characteristics. the proportion of households falling into the anxiety domain was 87% (528), insufficient quality of food domain 76.3% (463) and insufficient quantity of food and physical consequences domain 72.5% (440) ( Fig 2 ).

https://doi.org/10.1371/journal.pone.0256634.g002

Prevalence of household food insecurity

The overall prevalence of household food insecurity was 77.1% (468) with 95% CI፡ 73.8–80.7. Four hundred sixty-eight (77.1%) households had experienced some extent of food insecurity in the four weeks before the survey; mildly food insecure 4 (0.7%), moderately food insecure 129 (21.3%) and severely food insecure 335 (55.2%), while 139 (22.9%) households were food secure ( Fig 3 ).

https://doi.org/10.1371/journal.pone.0256634.g003

Factors associated with household food insecurity.

Based on the result of multivariable analysis, households headed by an uneducated person was 2.56 times (AOR: 2.56; 95%CI: 1.08–6.07) more likely to be food insecure than households headed by individuals who had an education above secondary level, and the odds of food insecurity among households headed by a person with completed secondary-level education were 3.22 times greater (AOR: 3.22; 95%CI: 1.54–6.75) than among households headed by someone who had completed above a secondary level of education ( Table 5 ).

https://doi.org/10.1371/journal.pone.0256634.t005

Households with 4 or more family members were nearly twice (AOR: 1.87; 95%CI: 1.08–3.23) as likely to suffer from food insecurity as households having fewer than 4 family members. Likewise, households with a high dependency ratio were almost 4 times (AOR: 3.95; 95%CI: 1.31–11.90) as food insecure as households with a low dependency ratio. Households with no access to credit services were nearly three times [AOR: 2.85; 95%CI:1.25–6.49] more likely to develop food insecurity than households that had access to credit services ( Table 5 ).

The odds of food insecurity among those with a low household income were nearly 5 times (AOR: 4.72; 95%CI: 2.32–9.60) higher than those with high household income; moreover, households with a medium income were 10 times (AOR: 9.78; 95%CI: 4.29–22.35) more likely to experience food insecurity than household with a high income ( Table 5 ).

A HFIAS method was carried out using a cross-sectional study to assess the prevalence of household food insecurity among UPSNP beneficiary households in Addis Ababa;. We found that the prevalence of food insecurity was 77.1% and factors significantly associated with food insecurity were education level, dependency ratio, access to credit service and household income.

The prevalence of food insecurity (77.1%) found in this study was consistent with a previous study in Addis Ababa (75.0%) [ 30 ], East Badawacho District, South Ethiopia (75.8%) [ 31 ] and Sekela District, Western Ethiopia (74.1%) [ 32 ], but higher than found in several other studies in Addis Ababa (58.16%) [ 24 ], Wolaita Soda (37.6%) [ 33 ], West and East Gojjam (55.3%) [ 34 ]. The higher prevalence in our study might be attributable to our study being conducted among poor households that were UPSNP beneficiaries, whereas the households in other studies were from different parts of Ethiopia that were not safety net beneficiaries. In Addis Ababa, being poor is key to eligibility for the UPSNP safety net beneficiary program. The variations of these results indicate that area-specific food insecurity surveys are very important to visualize the differences in people’s real situations.

Furthermore, the higher degree of household food insecurity found in this study relative to a national prevalence of food insecurity survey (35.0%) [ 35 ] might be due to our very small study area compared to the large coverage of the Ethiopian studies, which allows our findings to give a better insight about food insecurity status in the studied areas.

Our study revealed that 70.2% of households ate smaller amounts at meals than preferred, 63.1% of households also missed some meals during the studied period and 36.1% of the households also reported they went to bed hungry during the survey period. These results were higher than those of a study conducted in East Badawacho, South Ethiopia [ 31 ] that found 62.3% and 10.7% of households were eating a smaller amount of food and going to sleep at night hungry, respectively. The increment of households experiencing food insecurity conditions might be due to householders’ low purchasing power to access the available food and also the high inflation of food prices at the time of this survey, forcing householders to reduce the variety and amount of food they consumed, especially low-income urban households that spent a large proportion of their income to purchase food.

Our findings of the households falling in anxiety and uncertainties domain (87.0%), insufficient food quantity domain (76.3%), and insufficient food intake and its physical consequence domain (72.5%) were higher than the findings in Wolaita Sodo Town [ 33 ] that reported the percentage of households falling in these domains were 37.3%, 37.6% and 33.3%, respectively; and in West Abaya District 38.1%, 38.1%, and 34.5%, respectively [ 36 ]. These discrepancies may be attributed to the possibility that vulnerability to food insecurity among the urban poor is higher than among semi-urban and rural households. It can be also explained by the difference that semi-urban and rural households can access food from their garden production; in contrast, urban poor households are dependent on purchasing from the market food supply.

This study’s findings that a household head’s educational status of none or primary school completed as significantly associated with household food insecurity is consistent with other studies [ 12 , 36 , 37 ]. This might be because when the householder’s level of education increases, there is access to better job opportunities and boosting of labour productivity that ultimately increases household income and provides an asset to safeguard the access to and utilization of food.

In this study, having a large number of household members increased the likelihood of food insecurity, a finding similar to that of studies conducted in Addis Ababa, South Africa, and Latin America [ 2 , 24 , 38 , 39 ]. In contrast to our study, studies in Kenya and other rural parts of north Ethiopia found that households with a large family size were more like to be food secure as compared to those with a small family size [ 40 , 41 ]. This discrepancy may be due to the fact that as the household size becomes larger, the number of economically active members may also increase; and the increased participation in income-generating opportunities leads to more food security than for households with lower family size.

Households with a high dependency ratio have a greater chance of being food insecure than those with a low dependency ratio. This might be because the dependent members are less likely to participate in productive work and their full dependence on others to purchase food and other commodities increases the burden on the household. Our findings were similar to those of several studies in Africa [ 33 , 38 , 40 ].

We also found that lack access to credit services was one of the factors associated factors of food insecurity as also reported by other studies in Ethiopia [ 24 , 37 ]. This may be due to households having access to credit participating in diversified income-generating opportunities that are able to boost the financial power of the household. Access to credit services also helps households to cope with the food shortage situation by stabilizing the food purchasing powers of the household. We also found that households in low- and medium-income categories were more likely to experience food insecurity than those who had high household income, a finding consistent with previous studies conducted globally, including a study in Ethiopia [ 2 , 12 , 33 , 38 ]. The low household income can induce food insecurity; as a vicious cycle, food insecurity also reduces household productivity.

Limitation of the study and future research directions

The estimation of prevalence of household food insecurity relied on a one-month recall of occurrence; recall and social desirability bias could increase or decrease its reported prevalence. However, to overcome such limitations, we used HFIAS, a validated, highly reliable and culturally sound tool that was able to minimize bias and reveal a relatively good estimate of the prevalence. An attempt also was made to minimize the possibility of bias by explaining the main objectives of the study and by applying appropriate data collection procedures and interview techniques through the use of trained data collectors and supervisors.

Furthermore, the absence of baseline data for food insecurity levels before the UPSNP began in Addis Ababa limits our ability to compare the levels of food insecurity in our study with those before the program began. In the future, the question of whether the UPSNP safety net program creates an attitude of dependency on aid will be useful to investigate in order to provide possible help designing mechanisms to end the need for beneficiaries to receive the aid for so long.

Future researchers are also encouraged to investigate the consequences of the UPSNP such as conflict between beneficiaries and non-beneficiaries, food price inflation, drought, capability of the program beneficiaries, and internal population movements and more. It is also recommended that a study be conducted on the cause and effect relationship between the UPSNP and poverty to ensure that urban food security is addressed at the country level over time.

Implications of the study for practice and/or policy

The rural safety net program is an older project in Ethiopia, whereas the urban safety net program UPSNP is a new program scaled up from the rural experience and started as a pilot in 2017 in urban Ethiopia. One of the strengths of this study was that it investigated the food insecurity status of beneficiary households within the pilot project area in Addis Ababa. The findings will have practical application in helping to design a sustainable urban safety net program that improves the livelihoods of current beneficiaries and others in the future. To make a change on the level of food insecurity, this study shows that implementers should give due attention to variables such as household income levels, number of dependent members of beneficiary households, and household access to credit.

Furthermore, the evidence in this study may help programmers and policymakers to give priority to solving the challenges of the predictors for household food insecurity it revealed. Considering the prevalence of food insecurity and associated factors among UPSNP beneficiary households in Addis Ababa, the findings may help the city’s UPSNP agency in collaboration with governmental and non-governmental organizations to further scale up programs that will help households help themselves in a sustainable manner. Further, the findings regarding beneficiaries of this urban safety net program may guide its expansion to other urban areas of Ethiopia, thereby reducing national food insecurity levels, and making progress toward the UN Sustainable Development Goal to end all forms of malnutrition.

Conclusions

This study showed that three in four households were experiencing food insecurity and that an appropriate multi-dimensional approach including such interventions as improving educational status, creating employment opportunities or the opening of small business enterprises through credit service support may help to diversify income sources and to reduce the dependency ratio. It is recommended that the UPSNA do an impact evaluation of the current safety net program and consider increasing the amount of the per-person monthly payment, which in turn would increase the purchasing power of urban poor households. It is also highly recommended that the urban safety net program give special focus on the creation of sustainable income opportunities for users to improve their means of self-support.

Supporting information

S1 file. english version of the questionnaire for the survey of food insecurity status and determinants among urban productive safety net program beneficiary households in addis ababa, ethiopia..

https://doi.org/10.1371/journal.pone.0256634.s001

S2 File. Amharic version of the questionnaire for the survey of food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia.

https://doi.org/10.1371/journal.pone.0256634.s002

S1 Dataset. Data set for the survey of food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia.

https://doi.org/10.1371/journal.pone.0256634.s003

Acknowledgments

We acknowledge Addis Ababa City Urban Productive Safety Net Program Agency for giving us relevant information for this study. We also highly appreciate the study area’s district administrators for their support by providing necessary information. We thank data collectors, supervisors and household members for their voluntary participation in the study. Last but not least, we also thank Lisa Penttila for language editing of the manuscript.

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Food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia

Atimen derso.

1 Department of Environmental Health, College of Medicine and Health Sciences, Wollo University, Dessie, Ethiopia

Hailemichael Bizuneh

2 Department of Public Health, Saint Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia

Awoke Keleb

Ayechew ademas, metadel adane, associated data.

All relevant data are within the manuscript and its Supporting Information files.

Introduction

In eastern Africa, as a consequence of food insecurity, nearly one-third of the population (30.8%) was undernourished due to a lack of sufficient calories [ 3 ].

Methods and materials

Study design, period and study area.

Source population, inclusion and exclusion criteria

Sample size determination and sampling procedure

To estimate sample size, we considered the assumptions of sample estimator, previous prevalence (proportion of food insecurity), sample variability and limit of uncertainty (95% confidence interval). Here, we assumed the UPSNP beneficiary population is approximately normally distributed then standard error of population value ‘p’ is S E p = P ( 1 − P ) / n ,

then P (population value) = p(sample value) ± z SE P

± Z α/2 * SE P = variability of population value (W),

Finally, the sample size was determined using a single proportion formula:

with the assumption of the prevalence ( p ) of household food insecurity at 58.16%, taken from a previous study in Addis Ababa, Ethiopia [ 24 ], with a 95% confidence interval (CI) (Z a /2 = 1.96) and ( w ) marginal error (5%). A 1.5 design effect was also considered, and then 10% contingency for non-response rate was included. Finally, we obtained a final sample size of 624 households.

Outcome variable measurement

Source:[ 25 ].

A food-secure household was declared when household score [item one = ‘0’ or ‘1’ and items two to nine = ‘0’].

A food-insecure household was declared when household score [item one = ‘2’ or ‘3’ and/or item two to nine = ‘0’or ‘1’ or ‘2’ or ‘3’].

Mildly food-insecure household [(item one = ‘2’ or ‘3’ or item two = ‘1,’ ‘2,’ or ‘3’ or item three = ‘1’ or item four = ‘1’) and item five to nine = ‘0’].

Operational definitions

Urban productive safety net program (upsnp).

Food insecurity

Recall period

The respondent was asked an occurrence question about whether the condition had happened in the past four weeks (30 days) [ 25 ].

Dependency ratio

Household income

It was computed into tertiles (three groups) as low (375–1500 birr), medium (1560–2000 birr) or high (2100–4500 birr) from scores of average household monthly income from any sources.

Data collection, quality and management

Data analysis: Method of estimation and model equation

To determine the independent effect of predictor variables, we conducted a logistic regression model (logit transformation) written as Eqs 1 and 2 .

Where x 1, . . . ., x n is the predictor variables and ‘P’ is the proportion of food insecure household.

Ethics approval and consent to participate

Socio-demographic and economic characteristics of the study participants

*Mean family size (persons) = 4.04±1.75

*History of chronic medical problems included one or more of diabetes, hypertension, HIV/AIDS, TB (tuberculosis bacillus), mental disorder or other chronic health problems.

The occurrence of Household Food Insecurity Access Scale conditions

An external file that holds a picture, illustration, etc.
Object name is pone.0256634.g001.jpg

The occurrence of household food insecurity domain

An external file that holds a picture, illustration, etc.
Object name is pone.0256634.g002.jpg

Prevalence of household food insecurity

An external file that holds a picture, illustration, etc.
Object name is pone.0256634.g003.jpg

Factors associated with household food insecurity

*Variables from the bivariate analysis that had P -value<0.25 were adjusted for multivariable analysis: age of household head; marital status of household head; sex of household head; educational status of household head; household family size; dependency ratio; history of medical problem; main sources of household food; gender of food purchaser; proportion of income spent on food; ownership/rental of house; sources of household income; average monthly household income; access to credit services and access to free health services.

Limitation of the study and future research directions

Implications of the study for practice and/or policy

Conclusions

Supporting information

Acknowledgments.

Abbreviations

Funding statement.

Funding for this study was provided by Saint Paul’s Hospital Millennium Medical College. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

PLoS One. 2021; 16(9): e0256634.

Decision Letter 0

16 Nov 2020

PONE-D-20-30124

Food Insecurity Status and Determinants among Urban Productive Safety Net Program Beneficiary Households in Ethiopia

Dear Dr. Adane (PhD),

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Reviewer #1: The paper presents useful information on food security. A number of comments address the need to more clearly source and date data presented, especially when using those data to support claims that the food insecurity situation is worsening. The absence of baseline data for food insecurity levels in 2017, when the program began, limits the ability to directly compare the levels of food security reported in the survey results with those before the program began. As such the findings report one month’s helpful. A table that clearly shows the three levels of food insecurity and the number used for the dichotomous variable used in the analysis would be useful. It is possible that some of this is in Table 4 which I could not access.

Dr. Cheryl Christensen

Reviewer #2: The study addresses a very important topic i.e. understanding food insecurity in urban Ethiopia using a household survey data. It presents an analysis of the food insecurity status or urban households in Ethiopia and examines its determinants. The study finds that the key determinants of food insecurity include education, family size, access to credit and income.

But the study does not address significant problems in sample selection, estimation, and presentation of results. The following comments could provide some recommendations, if followed could improve the chances of publication in the future.

- Study notes that a sample of 624 households were selected from the 78,543 households from the Urban Productive Safety Net Program (PSNP) Beneficiary Households using a “single proportion formula”. It is not clear why this sample size is chosen. Please provide a solid framework to justify your sample choice. It would also be good to provide a summary statistic of the sample households in comparison to the entire data set since the PSNP data is already available.

- It would be good to state from the outset what the key objectives of the study are and the value added of the study. The study goes on describing food insecurity in the study area without providing the key objectives and contribution of this particular study in relation to existing literature. Identifying the contribution of this particular study would help readers follow the logic in the paper.

o Is it that we don’t have good evidence in urban areas?

o Is it the food insecurity measure used that makes this study unique and contributing to existing studies in the literature?

- A concise and clear description of the food insecurity measure used could improve the study since that could be one of the key contributions. You mention that you use a “tool that has been developed by Food and Nutrition Technical Assistance (FANTA). Here, provide why this measure is preferred to other measures of food insecurity. The PSNP itself may already have other indicators that do not require collecting additional data. How does this measure compare to this measure?

- The section “Data Quality Assurance” and “Data Management and Statistical Analysis” could be replaced by a methods section that clearly specifics the following

o Data: Sample selection, choice of indicators (including food insecurity measure), description of study area (urban Addis Ababa for context)

o Estimation: method of estimation (you indicated logistic), justify choice of method, benchmark model (equation), definition of variables (already in previous section)

Though the study addresses an important topic, it requires significant revision to be considered for publication. If the author addresses the issues indicated above and provides greater clarity in the write up, the chances of publication will be improved either in this journal or elsewhere.

Reviewer #3: Comments to the authors

This study is very important in terms of developing policies regarding tackling household food insecurity from developing countries perspective. However, this study has been nicely presented, need some focus on the following points,

1. Introduction is too long to read. Some information are surplus like adult and child malnutrition etc. Please provide precious information to make the introduction short.

2. This study is lacking the findings of previous studies to assess the appropriate gap. Please add the proper theoretical framework.

3. I don’t think the section “operational definition” section is adding much in this study. Better to add the study variable list.

4. The “Methodology” section is too long. Data collection tools and procedure and data quality assurance could be readjusted.

5. “Socio-demographic Characteristics of the Study Participants” and “Socioeconomic Characteristics of Study Participants” do not add much significant information in this study. So, need to reorganize in to one para and highlight only key findings.

6. “The occurrence of household food insecurity domain” this section is unclear. How it measured.

7. Why did not you compare the results between UPSNP safety net beneficiary and other available samples.

8. There are numerous studies in many developing countries regarding this issue. What is the difference between current study and previous study except study population?

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Reviewer #1: Yes: Dr. Cheryl Christensen

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Submitted filename: ccb_Manusccript Review_Food Insecurity Status and Determinants among Urban Productive Safety Net Program Beneficiary Households in Ethiopia.docx

Submitted filename: Comments to the authors.docx

Author response to Decision Letter 0

Date: March 08, 2021

Manuscript ID: PONE-D-20-30124

Food Insecurity Status and Determinants among Urban Productive Safety Net Program Beneficiary Households in Addis Ababa, Ethiopia

Corresponding authors: Metadel Adane (PhD)

Dear Dr. Yacob Zereyesus, Ph.D., Academic Editor

Dear Reviewers,

Thank you all for your letter dated 16 Jan 2020 with a decision of revision required. We were pleased to know that our manuscript was considered potentially acceptable for publication in PLoS ONE, subject to adequate revision as requested by the reviewers, academic editor and the journal. Based on the instructions provided in your letter, we uploaded the file of the rebuttal letter; the marked up copy of the revised manuscript highlighting the changes made in the original submitted version and the clean copy of the revised manuscript.

We have revised the manuscript by modifying the abstract, introduction, methods, results, discussion and other sections, based on the comments made by the reviewers and using the journal guidelines. Accordingly, we have marked in red color all the changes made during the revision process. Appended to this letter is our point-by-point response (rebuttal letter) to the comments made by the reviewers.

We agree with almost all the comments/questions raised by the reviewers and provided justification for disagreeing with some of them. We would like to take this opportunity to express our thanks to the reviewers for their valuable comments and to thank you for allowing us to resubmit a revision of the manuscript.

I hope that the revised manuscript is accepted for publication in PLoS ONE.

Sincerely yours,

Metadel Adane (PhD)

Rebuttal letter

Response to the Journal Requirements

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response: We thank you for your key comments and we revised the manuscript accordingly POLS ONE manuscript preparation templates including file naming (Please see the revised version).

2. Please include additional information regarding the survey or questionnaire used in the study and ensure that you have provided sufficient details that others could replicate the analyses. For

instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information., or include a citation if it has been published previously.

Response: We provided the survey tool or questionnaire as supportive information in both English and Amharic (original language) version labeled as S1 and S2, respectively (Please see on the revised version).

Response: Thank you for this key comment. We used adjusted analysis during multivariable logistic regression analysis and please see the revised version in page 14 from lines 321 to 326.

Response: Thank you again for your valuable comment. Our study is purely cross-sectional survey which is not a type of cluster study. First proportional to size allocation of samples were performed to account sample size allocation in a representative manner. Thus, using survey weights is not used in our study due to the study design and sampling methods type. Cluster weights are common for a study that has a cluster type.

5. We suggest you thoroughly copyedit your manuscript for language usage, spelling, and grammar. If you do not know anyone who can help you do this, you may wish to consider employing a professional scientific editing service.

Response: Many thanks for this comment and we addressed the issue of language usage, spelling and grammar carefully by getting support from the experience language editor as you can see in our acknowledgment section in page 22 from lines 553 to 534.

6. Please provide the name of the colleague or the details of the professional service that edited your manuscript

Response: Lisa Penttila, who is from Canada is edited our manuscript.

7. Provide a copy of your manuscript showing your changes by either highlighting

them or using track changes (uploaded as a *supporting information*

Response: We revised the manuscript by highlighting the change and labeled as * ‘manuscript showing your changes

8. Provide a clean copy of the edited manuscript (uploaded as the new *manuscript* file)

Response: We prepared clean copy of the revised manuscript file, labeled as the new*manuscript* file

Response to reviewers

Reviewer # 1

Comment on the paper

Reviewer # 1-The paper presents useful information on food security.

Response: Thank you for the positive remark on our paper and we really appreciate your scientific judgment. Regarding to other comments, we improved the manuscripts by considering all your concerns. We found that all comments are very useful and thank you again.

Reviewer # 1-A number of comments address the need to more clearly source and date data presented, especially when using those data to support claims that the food insecurity situation is worsening. The absence of baseline data for food insecurity levels in 2017, when the program began, limits the ability to directly compare the levels of food security reported in the survey results with those before the program began. As such the findings report one month’s helpful.

Response: Thank you very much for recognizing our study importance and you clearly pointed out the challenges of our study was lack of baseline data in 2017, where one month’s of our study is still very useful (Please see the revised version in the introduction section).

Reviewer # 1- A table that clearly shows the three levels of food insecurity and the number used for the dichotomous variable used in the analysis would be useful. It is possible that some of this is in Table 4 which I could not access.

Response: We updated Table 4 to show the level of food insecurity and dichotomous variable (Please see the revised version table 4).

Reviewer# 2

Comment on the Paper

But the study does not address significant problems in sample selection, estimation, and presentation of results.

Response: Thank you for recognizing of our study addresses important issues about the urban food insecurity. We appreciate your positive reflection in our study. We accepted all your concerns and have addressed them accordingly. We found that the paper substantially improved because of your pertinent comments and thank you very much for your scientific input (please see the revised version to see all the updates regarding sample selection, estimation, presentation of results and other concerns).

Question 1: Study notes that a sample of 624 households were selected from the 78,543 households from the Urban Productive Safety Net Program (PSNP) Beneficiary Households using a “single proportion formula”. It is not clear why this sample size is chosen. Please provide a solid framework to justify your sample choice.

Response: We explained the sample size calculation process as follows.

To estimate sample size, we considered the assumptions of sample estimator, previous prevalence (proportion of food insecurity), sample variability and limit of uncertainty (95% confidence interval). Here we assumed the UPSNP beneficiary population is approximately normally distribute then standard error of population value ‘p’ is 〖SE〗_p=√( P(1-P)/n) ,

then P(population value) = p(sample value) � z SEP

� Zα/2 * SEP = variability of population value (W),

W = Zα/2 *SEP = z √( P(1-P)/n)

Finally sample size was determined using a single proportion formula [n= (〖[ (z〗^(2 ) α⁄2)*(p(1-p)])/w^2 ] with the assumption of the prevalence (p) of household food insecurity was 58.16%, taken from a previous study in Addis Ababa, Ethiopia [22], 95% confidence interval (CI) (Za/2 = 1.96) and (w) marginal error (5%). A 1.5 design effect was also considered, and then 10% contingency for non-response rate included. Finally, we obtained a final sample size of 624 households (Please see the revised version in sample size determination in page 8 to 9).

Question 2: It would also be good to provide a summary statistic of the sample households in comparison to the entire data set since the PSNP data is already available.

Response: thank you, we introduced the sample summary statistics in the first paragraph of result section only for our sampled households because the detailed entire data set of PSNP beneficiary households in city is not available at central level rather only absolute number of beneficiary households are there at woreda level (please see the revised version). As reviewer #1 also noted and understood the situation, the absence of baseline data for food insecurity levels during the urban safety net program began in Addis Ababa limits us to compare the levels of food insecurity reported our study with those before the program began, which compromise to evaluate the change of food insecurity status due to the existing safety net program contribution (We noted this as one of the study the limitation in page 20 from lines 499to 503).

Question 3: It would be good to state from the outset what the key objectives of the study are and the value added of the study. The study goes on describing food insecurity in the study area without providing the key objectives and contribution of this particular study in relation to existing literature. Identifying the contribution of this particular study would help readers follow the logic in the paper.

-Is it that we don’t have good evidence in urban areas?

-Is it the food insecurity measure used that makes this study unique and contributing to existing studies in the literature?

- A concise and clear description of the food insecurity measure used could improve the study since that could be one of the key contributions.

Response: Dear reviewer, we thank you for these key comments and we agreed with all your ideas and the manuscript is already updated accordingly and please see revised version in the last two paragraphs of introduction section in page 6.

Question 4: You mention that you use a “tool that has been developed by Food and Nutrition Technical Assistance (FANTA). Here, provide why this measure is preferred to other measures of food insecurity. The PSNP itself may already have other indicators that do not require collecting additional data. How does this measure compare to this measure?

Response: You point is reasonable. Thank you. We made the revision according to your comment with the justification of why FANTA tool was used and please see the revised version in the outcome variable measurement in page 9 and 10.

Question 5: The section “Data Quality Assurance” and “Data Management and Statistical Analysis” could be replaced by a methods section that clearly specifics the following

- Data: Sample selection, choice of indicators (including food insecurity measure), description of study area (urban Addis Ababa for context)

- Estimation: method of estimation (you indicated logistic), justify choice of method, benchmark model (equation), definition of variables (already in previous section)

Response: We updated these sections as per your comment. Please see the revised version in page 13 to 14. Thank you.

Reviewer #3

Reviewer #3: Comments to the authors

Response: We appreciate your positive reflection about the importance of our study and thank you for that. All your comments are addressed herewith.

Question 1. Introduction is too long to read. Some information are surplus like adult and child malnutrition etc. Please provide precious information to make the introduction short.

Response: We agree with your comment. However, as you know food insecurity mainly affect children, which exposed for malnutrition for under-five children. To show the magnitude of the problem, we give emphasis about children. Furthermore, we also give background information about adult since food insecurity in urban areas also the main concern for adults. We tried to minimize the introduction, but still we feel that it is too much despite this does not have a problem for our study (See the revised version of the introduction).

Question 2. This study is lacking the findings of previous studies to assess the appropriate gap. Please add the proper theoretical framework.

Response: We updated the introduction by including previous studies similar with our objectives to make strong the theoretical framework (See the updated version of the introduction)

Question 3. I don’t think the section “operational definition” section is adding much in this study. Better to add the study variable list.

Response: Operational definitions are very important to give information about measurement of variables (both outcome and independent variables) and feel that keeping it very nice for readers. Most of the key variables are listed within the operational definitions, we did not include some of the variables like socio-demographic since it is not as such complex to measure.

Question 4. The “Methodology” section is too long. Data collection tools and procedure and data quality assurance could be readjusted.

Response: We merged as suggested.

Question 5. “Socio-demographic Characteristics of the Study Participants” and “Socioeconomic Characteristics of Study Participants” do not add much significant information in this study. So, need to reorganize in to one para and highlight only key findings.

Response: We accept your comment, to make key hints for the readers, we write one paragraph about socio-demographic and economic characteristics of the study participants as you suggested (See in page 14 and 15).

Question 6. “The occurrence of household food insecurity domain” this section is unclear. How it measured.

Response: To make it clear, we elaborate how the occurrence of household food insecurity domain was measured in outcome variable measurement section in page 9 to 11. Hoping that now it. Thank you very much.

Question 7. Why did not you compare the results between UPSNP safety net beneficiary and other available samples?

Response: We believe that this question is valid and we appreciate your observation. However, the absence of baseline data for food insecurity levels during the urban safety net program began in Addis Ababa limits us to compare the levels of food insecurity found our study with those before the program began, which compromise to evaluate the change of food insecurity status due to the existing safety net program contribution (We noted this as one of the study the limitation in page 20).

Question 8. There are numerous studies in many developing countries regarding this issue. What is the difference between current study and previous study except study population?

Response: Dear reviewer, although there are many studies which has been conducted so far, ours is different regarding setting and its objectives that addressed, which is helpful in local context in Addis Ababa. The main objective of our study was to assess the level of food insecurity that could be reduced by transfer of cash to households at urban level. Urban poor’s are dependent on purchasing of food and related items so that transfer of cash at urban level has been designed to improve household income and ultimately improve level food security to the long term and response of emergency. So assessing status of food insecurity in pilot project area of UPSNP generated vital information that will helps the implementers to as an input evaluate the progress and give spotlight to readjust in sustainable manner and scale up the program, which all makes our study different from the other study regarding food insecurity. We explained this under the sub-themes of Implications of the study for practice and/or policy (Please see the revised version in page 21 to 22).

We would like to thank the reviewers and editors for evaluating our manuscript. We have tried to address all the concerns in a proper way and believe that our paper has been improved considerably. We would be happy to make further corrections if necessary and look forward to hearing from you all soon.

Metadel Adane (PhD in Water and Public Health)

Submitted filename: Response to reviewers.docx

Decision Letter 1

12 Aug 2021

PONE-D-20-30124R1

Dear Dr. Adane,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Abid Hussain

Additional Editor Comments (optional):

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

2. Is the manuscript technically sound, and do the data support the conclusions?

3. Has the statistical analysis been performed appropriately and rigorously?

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6. Review Comments to the Author

Reviewer #1: (No Response)

7. PLOS authors have the option to publish the peer review history of their article ( what does this mean? ). If published, this will include your full peer review and any attached files.

Reviewer #1: Yes: Cheryl Christensen

Acceptance letter

17 Sep 2021

Dear Dr. Adane:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact gro.solp@sserpeno .

If we can help with anything else, please email us at gro.solp@enosolp .

Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

Dr. Abid Hussain

20.500.12592/2c8w4x

Ethiopia - Urban Productive Safety Net and Jobs Project

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Food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia

Introduction

Methods and materials

Source population, inclusion and exclusion criteria

Sample size determination and sampling procedure

Outcome variable measurement

Operational definitions

Food insecurity.

Recall period.

Dependency ratio.

Household income.

Data collection, quality and management

Data analysis: Method of estimation and model equation

Ethics approval and consent to participate.

Socio-demographic and economic characteristics of the study participants

The occurrence of Household Food Insecurity Access Scale conditions.

The occurrence of household food insecurity domain

Prevalence of household food insecurity

Factors associated with household food insecurity.

Limitation of the study and future research directions

Implications of the study for practice and/or policy

Conclusions

Supporting information

S2 File. Amharic version of the questionnaire for the survey of food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia.

S1 Dataset. Data set for the survey of food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia.

Acknowledgments

Food insecurity status and determinants among Urban Productive Safety Net Program beneficiary households in Addis Ababa, Ethiopia

Hailemichael Bizuneh

Awoke Keleb

Introduction

Methods and materials

Source population, inclusion and exclusion criteria

Sample size determination and sampling procedure

Outcome variable measurement

Operational definitions

Food insecurity

Recall period

Dependency ratio

Household income

Data collection, quality and management

Data analysis: Method of estimation and model equation

Ethics approval and consent to participate

Socio-demographic and economic characteristics of the study participants

The occurrence of Household Food Insecurity Access Scale conditions

The occurrence of household food insecurity domain

Prevalence of household food insecurity

Factors associated with household food insecurity

Limitation of the study and future research directions

Implications of the study for practice and/or policy

Conclusions

Supporting information

Abbreviations

Data Availability

Decision Letter 0

Author response to Decision Letter 0

Decision Letter 1