Comparative analysis of household food security and its determinants among Productive Safety Net Program (PSNP) beneficiary, graduated, and non-beneficiary in Northwestern Ethiopia

Merkeb, Yednekachew; Yasunobu, Kumi; Elias, Asres; Endalew, Birara Article Comparative analysis of household food security and its determinants among Productive Safety Net Program (PSNP) beneficiary, graduated, and non-beneficiary in Northwestern Ethiopia Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Merkeb, Yednekachew; Yasunobu, Kumi; Elias, Asres; Endalew, Birara (2024) : Comparative analysis of household food security and its determinants among Productive Safety Net Program (PSNP) beneficiary, graduated, and non-beneficiary in Northwestern Ethiopia, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-15, https://doi.org/10.1080/23322039.2024.2344269 This Version is available at: https://hdl.handle.net/10419/321476 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Comparative analysis of household food security and its determinants among Productive Safety Net Program (PSNP)beneficiary, graduated, and non-beneficiary in Northwestern Ethiopia Yednekachew Merkeb, Kumi Yasunobu, Asres Elias & Birara Endalew To cite this article: Yednekachew Merkeb, Kumi Yasunobu, Asres Elias & Birara Endalew (2024) Comparative analysis of household food security and its determinants among Productive Safety Net Program (PSNP)beneficiary, graduated, and non-beneficiary in Northwestern Ethiopia, Cogent Economics & Finance, 12:1, 2344269, DOI: 10.1080/23322039.2024.2344269 To link to this article: https://doi.org/10.1080/23322039.2024.2344269 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group View supplementary material Published online: 30 Apr 2024. Submit your article to this journal Article views: 1427 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 GENERAL & APPLIED ECONOMICS | RESEARCH ARTICLE Comparative analysis of household food security and its determinants among Productive Safety Net Program (PSNP) beneficiary, graduated, and non-beneficiary in Northwestern Ethiopia Yednekachew Merkeb a,b , Kumi Yasunobu c , Asres Elias c and Birara Endalew a,d a United Graduate School of Agricultural Sciences, Tottori University, Tottori, Japan; b Institute of Disaster Risk Management and Food Security Studies, Bahir Dar University, Bahir Dar, Ethiopia; c Faculty of Agriculture, Tottori University, Tottori, Japan; d College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar, Ethiopia ABSTRACT This study aims to compare household food security and its determinants among PSNP beneficiary, graduated, and non-beneficiary. Data was collected from 396 sample households using a structured questionnaire and key informant interview. Binary Probit regression was used to analyse the determinants of household food security. Household food security was measured using Food Insecurity Experience Scale (FIES) and Household Hunger Scale (HHS). The study found significant differences in household food security among beneficiary, graduated and non-beneficiary both in FIES and HHS. The mean raw scores of FIES and HHS for graduated households were lower than both beneficiary and non-beneficiary households. Graduated households had the highest percentage of food secure households (67.4%), followed by non-beneficiary households (61.5%) and beneficiary households (34.3%). The binary probit model showed the number of clinic visits by household head was the only factor that negatively associated with all the three groups. The number of years benefited from PSNP had a negative influence on both beneficiary and graduated households’food security. Whereas livestock had a positive effect on the food security of both graduated and non-beneficiary households, unlike dependency ratio. Livelihood zone, drought, and credit were only associated with beneficiary household food security, while crop diversification determined only graduated households’food security. Hence, the findings suggest that policymakers and practitioners should focus on improving access to health care, limit the duration of PSNP participation, promote crop diversification, and provide proper credit use training to enhance household food security. IMPACT STATEMENT Effective food security interventions play a significant role in addressing chronic food insecurity. In Ethiopia, Productive Safety Net Program (PSNP) has been implemented to provide predictable and reliable support to chronically food insecure households. Hence, this study compared the household food security and its determinants among PSNP beneficiary, graduated, and non-beneficiary. The findings showed that the household food security status of PSNP beneficiary, graduated, and non-beneficiary were significantly different. Graduated households had better household food security status than both beneficiary and non-beneficiary households. Moreover, the factors that determine the household food security status also vary among PSNP beneficiary, graduated, and non-beneficiary. The number of years benefited from PSNP had a negative effect on both beneficiary and graduated households’food security. Comparing graduated households to current beneficiaries and non-beneficiaries provides insights on the long-term effects of PSNP. This study helps policymakers and practitioners to make changes on PSNP and design effective food security intervention considering the differences in food security status and determinants among PSNP beneficiary, graduated and non-beneficiary. ARTICLE HISTORY Received 31 August 2023 Revised 13 February 2024 Accepted 14 April 2024 KEYWORDS Food insecurity experience scale; food security; probit model; Productive Safety Net Program; Ethiopia REVIEWING EDITOR Robert Read, University of Lancaster, UK SUBJECTS Development studies; Rural development; Economics and development; Population & development; Sustainable development; Economics CONTACT Yednekachew Merkeb [email protected] The United Graduate School of Agricultural Sciences, Tottori University, 4-101 Koyama-Minami, Tottori 680-8553, Japan ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2344269 https://doi.org/10.1080/23322039.2024.2344269 1. Introduction Food insecurity is an urgent issue that impacts the well-being and livelihoods of millions of people worldwide. Achieving food security is a key UN Sustainable Development Goal (SDG), emphasizing the need for sustainable access to nutritious food for all. Ethiopia, among other developing nations, has been fighting with chronic and widespread food insecurity, which has affected a significant portion of its population and large geographic areas. With a population of around 123 million people in 2022, Ethiopia ranked as the second most populous country in Africa (UNDP, 2022). Poverty and food insecurity persist despite the country’s rapid economic growth in recent years. In 2020, about 56% of the population was estimated to be food insecure moderately or severely, which showed about 7 million increase compared to 2016 (FAO et al., 2021). Recurrent droughts and other shocks have left many people with chronic food insecurity and humanitarian aid dependency. Humanitarian assistance often fails to lift households out of the poverty trap, even when it helps save lives (B en e et al., 2012). Food security interventions before 2002 were dependent on emergency food aid and had limitations in terms of sustainability and long-term impacts. To provide predictable and reliable support to vulnerable households in chronically food insecure areas, the Productive Safety Net Program (PSNP) was launched in 2005 (Ministry of Agriculture and Rural Development (MOARD), 2010). Ethiopia’s PSNP is one of SubSaharan Africa’s largest social protection programs, with about 8 million beneficiaries, which accounts for around 10% of the population and covers more than 300 chronic food insecure districts (Desalegn & Ali, 2018). PSNP was successful in saving lives and improving food security but failed to build resilience. Most households have not been lifted out of poverty by the program (Sabates-Wheeler et al., 2021). Evidence from previous studies on the impacts of PSNP in Ethiopia are mixed and different. Different studies have demonstrated the positive impact of PSNP on improving household food security (Berhane et al., 2015; Gilligan et al., 2009; Hailu & Amare, 2022; Tadesse & Gebremedhin Zeleke, 2022); children’s nutritional status (Debela et al., 2015; Porter & Goyal, 2016); and asset accumulation (Borga & D’Ambrosio, 2021; Welteji et al., 2017). In contrast to these findings, other studies have not found significant effect of participation in the PSNP on household food security (Bahru et al., 2020; Berlie, 2014; Gebrehiwot & Castilla, 2019); child nutritional status (Bahru et al., 2020; Berhane et al., 2017; Gebrehiwot & Castilla, 2019); asset accumulation (Gilligan et al., 2009); livestock holdings (Andersson et al., 2011); agricultural input use and technology adoption (Bahru & Zeller, 2022; Hoddinott et al., 2012); and non-farm income (Weldegebriel & Prowse, 2013). Most existing studies have focused solely on beneficiary households, neglecting the examination of graduated households. Moreover, most impact evaluations done on PSNP only made comparisons among current beneficiary and non-beneficiary households, whereas evidence on graduated households current food security status was not investigated. The study by Devereux & Ulrichs (2015) and SabatesWheeler et al. (2021) found that there was premature graduation in PSNP which resulted due to the political emphasis on high graduation numbers as the program success. Consequently, households were often graduated out of the program based on quotas rather than actual poverty alleviation. In addition, Sabates-Wheeler et al. (2012) also indicated that many graduated households complained that they graduated early and had no confidence not needing the PSNP benefit in the future. However, the studies done on graduated households are qualitative studies on stakeholders and graduates’perceptions on PSNP graduation which did not address their household food security status and associated factors. Moreover, it is not well known whether graduated household have better food security status than both beneficiaries and non-beneficiaries or not. Hence, this study was conducted to answer the following research questions: Is there a significant difference in household food security among PSNP beneficiaries, graduates, and non-beneficiaries? Are the determinants of household food security different among PSNP beneficiaries, graduates and non-beneficiaries? Therefore, this study aimed to compare the extent and determinants of household food security among PSNP current beneficiaries, graduates, and nonbeneficiary households. This study contributes to the existing literature on food security interventions, specifically PSNP in Ethiopia by exploring the differences in food security outcomes and determinants among beneficiary, graduated, and non-beneficiary households. Comparing graduated households to current beneficiaries and non-beneficiaries provides insights on the PSNP long-term effects. This study helps policymakers and practitioners to make informed decisions on program modifications such as on 2 Y. MERKEB ET AL. targeting, graduation, and amount of transfer; and resource allocation to target interventions effectively that address potential disparities among different groups. 2. Materials and methods 2.1. Description of study area The study was conducted in Enebesie Sar Medir district, Amhara region, Northwestern Ethiopia (Figure 1). The district is located between 10390Nto11 60N latitude and 38150Eto38 330E longitude, and its altitude varies from 950 to 3660 meters above sea level. It is one of the 64 food insecure and PSNP targeted districts in Amhara region where the PSNP has been implemented since the inception of the program in 2005. In the district, there are 37 kebeles, of which 25 kebeles are chronically food insecure and PSNP targeted. The total population of the district is about 188,533. The annual rainfall ranges from 900 to 1200 mm, and the mean monthly temperature varies from 10 to 22C. Haricot bean, sorghum, teff, wheat, bean, maize, and peas are the major crops in the district (Enebise Sar Medir District Agriculture Office (ESMDAO), 2022). The district shares three different livelihood zones, which are Abay Beshilo River Basin (ABB) (16 kebeles), Southwest ‘Woina Dega’Wheat (SWW) (12 kebeles) and Central Highland Barely and Potato (CBP) (5 kebeles). The major shocks which affect crop and livestock production are repeated occurrence of drought, crop pests and livestock diseases (Ministry of Agriculture and Rural Development (MOARD) & USAID, 2009). 2.2. Sampling procedure and sample size Multistage sampling was used to select representative sample households, taking into account possible sources of heterogeneity. In the first stage, Enebesie Sar Medir district was purposively selected based on the condition of food insecurity and the presence of PSNP intervention since the program’s inception in 2005. In the second stage, the PSNP targeted kebeles were stratified based on the existing livelihood zone (LZ) classification (Abay Beshilo River Basin, Southwest ‘woina dega’Wheat, and Central Highland Barely and Potato) to capture their heterogeneity, which are delineated taking into account population density, rainfall patterns, altitude, market dynamics, and dominant livelihood activities (Ministry of Agriculture and Rural Development (MOARD) & USAID, 2009). For instance, the Abay Beshilo River basin LZ is characterized by lowland geography, less than 900 mm annual rainfall, sparse population, crops like sorghum and teff, Figure 1. Location map of the study area. COGENT ECONOMICS & FINANCE 3 market challenges, and erratic rainfall. The Southwest ‘woina dega’Wheat LZ features midland agroecology, cultivating teff, wheat, and maize, with 900 to 1200 mm annual rainfall and good market access. The Central Highland Barley and Potato LZ distinguished by its highland agroecology, dense population, barley and potato cultivation, 1200 to 1400 mm annual rainfall, and limited market access. In the third stage, a total of five kebeles (villages) were randomly selected to represent the livelihood zones in the district, taking into account the proportionality of each stratum. In the fourth stage, the sample beneficiary, graduates and non-beneficiary households were selected using a sampling frame of each PSNP target kebeles (villages). The selection for beneficiaries and graduated households is implemented based on the PSNP Program Implementation Manual (Ministry of Agriculture (MOA), 2014). The PSNP program has a task force called the Community Food Security Task Force (CFSTF), which is responsible for identifying the beneficiaries and graduate households. The basic PSNP beneficiary eligibility criteria is being chronically food insecure household who have faced continuous food shortages (3 months of food gap or more per year) in the last 3 years. Due to the limited quota for each kebele (village), all eligible households do not participate in the program. Therefore, the CFSTF made eligible households’wealth ranking based on the household asset status such as land holding, quality of land, livestock holding, food stock, labor availability and income from agricultural and non-agricultural activities. The poorest households are selected as beneficiaries considering the quota allocated for each kebele. Then, the PSNP beneficiaries graduate when they meet the criteria of achieving household food self-sufficiency without external support. In the district, the benchmark for graduation is reaching an asset value of more than 9,000 Birr per capita. Non-beneficiary households are those households which neither participated nor graduated from PSNP. The lists and respective numbers of beneficiaries, graduates, and non-beneficiaries in each kebele were obtained from the Enebesie Sar Medir District Agriculture Office which was used as a sampling frame for this study. The sample beneficiary, graduate and non-beneficiary households were selected in consultation with the Enebesie Sar Medir District Agriculture Office and PSNP experts. Finally, Probability Proportional to Size (PPS) followed by simple random sampling procedure was used to draw 396 representative sample households from each stratum and group. The sample size was determined using the Yamane sampling formula (Yamane, 1967) as follows: n¼N 1þNe ðÞ 2¼17992 1þ17992 0:05 ðÞ 2¼396 where n is total sample size, N is the total number of households in PSNP kebeles (17992) and e is the margin of error (5%). 2.3. Methods of data collection Structured questionnaire and key informants’interviews (KII) were used to collect primary data. The questionnaire was prepared and encoded into Kobo toolbox which is a data collection software. Five enumerators and one supervisor were trained about the questionnaire content and how to use the Kobo toolbox. Pre-test was carried out in non-sampled households to check the consistency and relevance of the contents of the questionnaire. The questionnaire was revised based on the pre-test feedback. The actual data collection was conducted using Kobo collect mobile application from January to February 2023. During the data collection, the collected data were checked daily on the Kobo toolbox online server. The questionnaire covers a wide range of variables which includes sociodemographic characteristics, agriculture and non-agriculture related issues, food security, shocks, PSNP related issues, and access to infrastructure. Secondary data collected by reviewing various articles, books, government policy and strategy documents and reports. 2.4. Method of data analysis Descriptive statistics, crosstabulation and ANOVA used to analyse the characteristics of households and compare the extent of explanatory variables among beneficiary, graduated and non-beneficiary households. Household food security was measured using Food Insecurity Experience Scale (FIES) and 4 Y. MERKEB ET AL. Household Hunger Score (HHS). FIES is an experience-based metric of household food security developed by FAO (Wambogo et al., 2018). HHS is the most suitable measure for monitoring and evaluating the impact of anti-hunger policies and programmes in areas with chronic food insecurity, including those funded by a particular donor across various countries and cultures (Ballard et al., 2011). 2.4.1. Theoretical model The Agricultural Household Utility Model developed by Singh et al. (1986) was used to compare household food security status and its determinants in developing countries. It is the most widely used model to analyse rural household decision making and determinants of household food security (Feleke et al., 2005; Yovo and Gnedeka, 2023). The model assumes that households are both producers and consumers. Households seek to maximize their utility (in this case, household food security (HFS)) subject to various constraints that include PSNP participation (P), wealth (W), landholding (L), shocks (S) and other household characteristics (Z i ) and written as follows. HFS ¼FP,W,L,S,Z i ðÞ Given these constraints, households make rational decisions to achieve their HFS through their participation in the PSNP intervention. Households’consumption choices would vary among PSNP beneficiaries, graduates, and non-beneficiaries depending on their resources and constraints. According to the PSNP asset-based targeting criteria, the poorest households are selected as beneficiaries. Non-beneficiaries are better off than beneficiaries in terms of household assets and income from agricultural and nonagricultural activities. Hence, beneficiaries are expected to have lower household food security status than non-beneficiaries. PSNP provides support to beneficiaries until they graduate to improve their household assets and income to achieve household food security after graduation. Graduated households are therefore expected to achieve the same or better household food security status (HFS) than non-beneficiaries. Non-beneficiaries are used as a reference group to compare the household food security status of beneficiaries and graduated households. Therefore, it is particularly important to understand how households in each group (beneficiary, graduated and non-beneficiary) achieve their HFS based on their consumption choices and constraints. Hence, this study analysed HFS status and associated factors for each group to provide concrete information for policy makers to design interventions that target HFS of each group. 2.4.2. Econometric model To analyse determinants of household food security in beneficiary, graduated and non-beneficiary households, binary probit model was used. The dependent variable was household food security which was measured using FIES. Following the FIES result, the severely and moderately food insecure categories were recoded as food insecure, while the other was recoded as food secure due to insufficiency of the observations for regression analysis in the severely and moderately food insecure categories. Therefore, the HFS of the beneficiary, graduate and non-beneficiary households was measured as a dummy variable (1 if food secure and 0 if food insecure). As a result, this study used binary probit model to answer the research question: are the determinants of household food security different among PSNP beneficiaries, graduates, and non-beneficiaries? Therefore, a total of three binary probit models (one model for each group) were run to identify statistically significant factors affecting HFS of beneficiaries, graduates, and non-beneficiaries. The mathematical model of the binary probit model is specified as follows. Y i¼Xibþliwhere Yi¼1if Y i>0 0ifY i0  Where: Y i and Y idenote HFS of beneficiaries, graduates, and non-beneficiaries and latent variable, X i are explanatory variables that affect HFS of each group and liis the error term. COGENT ECONOMICS & FINANCE 5 First, bivariate analysis was performed at 25% significance level (Bendel & Afifi, 1977) to select explanatory variables for the multivariable analysis of the binary probit model. Household head sex, education, farm type, use of improved seed, and number of trainings were found insignificant in bivariate analysis of the three groups. After estimating the binary probit model, the Hosmer-Lemeshow goodness of fit test was conducted to check the model’s fitness for each group. The test result confirmed that the model is well fitted to explain the relationship between the dependent and explanatory variables. Marginal effects were predicted for each explanatory variable which were used to interpret statistically significant variables. 2.4.3. Description and hypothesis of variables This study was used composite explanatory variables such wealth status, dependency ratio, and livestock size (Table 1). The wealth status of sample households was determined using wealth index developed by World Food Programme (WFP) (2009). The wealth index was computed using principal component analysis. Then, the wealth index was categorized into quintiles, which are assigned as poorest (1st quantile), poor (2nd quantile), medium (3rd quantile), rich (4th quantile) and richest (5th quantile). Due to insufficient number of observations in each category, this study categorized household wealth status as poor (by merging the poorest and poor), and rich (merging the middle, rich, and richest). Similarly, previous studies also categorized quantiles into rich and poor (Workie & Tesfaw, 2021). The wealth index is not an absolute measure of wealth, rather it is a relative measure. Dependency ratio is calculated by dividing the number of non-working age (<15&>64) members of the household with working age (1564) members. The number of livestock owned by the household was converted into tropical livestock unit (TLU) using the conversion factors of Storck et al. (1991). 3. Results and discussion 3.1. Descriptive characteristics of sample households The mean household size, land size and livestock (TLU) are highest in graduated households whereas crop diversification, amount of chemical fertilizers, are highest in non-beneficiary households. The mean number of livestock owned by graduated households is 2.85 TLU, while the non-beneficiary and beneficiary households had 2.63 and 1.2 TLU, respectively. The F-test result showed that the mean difference across beneficiary, graduated and non-beneficiary households in household size, land size, livestock and crop diversification were significant at less than 1% probability level. Graduated households had highest mean household size in adult equivalence (3.95), followed by non-beneficiaries (3.55) and beneficiaries (2.93). The average number of years graduated households benefited from PSNP were 6.42 which is higher than beneficiary households (4.95) (Table 2). Table 1. Description and hypothesis of explanatory variables. Variables Description and measurement Hypothesis Dependent Variable Household food security 1 ¼Food secure; 0 ¼Food insecure Independent variables Livelihood zone 0¼Abay Beshilo; 1 ¼Southwest 2 ¼Central highland þ Household head Marital status 1¼married; 0¼single þ Credit use Household received credit (1¼yes; 0¼no) þ Wealth Status 1¼rich; 0¼poor þ Drought 1¼yes; 0¼no – Household size Number of family members in adult equivalence – Land size Cultivated land size in hectare þ Dependency ratio Ratio of non-working age (<15&>64) to working age (15-64) members – Amount of Fertilizer Amount of chemical fertilizer used in kg þ Crop diversification Number of crop varieties produced by a household þ Livestock ownership Total livestock owned by the household in TLU þ Number of Years benefited from PSNP Number of Years benefited from PSNP þ Number of clinic visits Number of clinic visits by household head – Source: Own survey data (2023). 6 Y. MERKEB ET AL. The chi-square test result demonstrated that marital status and credit usage had statistically significant association with the three groups at a probability level less than 1% (Table 3). Majority of the sample respondents were married household heads in the graduated (75%) and non-beneficiary (66.2%). Graduated households had highest percentage of credit use (34.8%). Whereas 24.6% of beneficiaries and 17.7% of non-beneficiaries were received credit. There is no significant difference in experiencing drought among beneficiary, graduate and non-beneficiary. 3.2. Food security status and PSNP participation The F-test result showed that the mean difference in raw scores of FIES and HHS between beneficiary, graduate, and non-beneficiary households was statistically significant at the 1% level of significance (Table 4). It should be noted that higher mean raw scores indicate higher food insecurity status. The mean raw scores of FIES and HHS in beneficiary households were 4.45 and 1.17 respectively. Beneficiary households had the highest raw scores as compared with graduated and non-beneficiary households. Non-beneficiary households mean raw scores were 2.86 in FIES and 0.92 in HHS which were higher than graduated but lower than beneficiaries. Consistent with these results, studies done by Hailu & Amare (2022) and Sabates-Wheeler et al. (2021) found that the food gap in beneficiaries were higher than non-beneficiaries. As indicated in Table 4, using FIES graduated households have the highest percentage of food-secure households at 67.4%, followed by non-beneficiary households at 61.5% and beneficiary households at 34.3%. Similar results were found using HHS with the highest proportion of households with no hunger Table 2. Summary statistics for continuous variables by PSNP participation. Continuous variables Beneficiary Graduated Non-beneficiary F-valueMean SD Mean SD Mean SD Household size 2.93 1.43 3.95 1.40 3.55 1.28 18.67 Dependency ratio 0.46 0.50 0.33 0.41 0.46 0.51 3.16 Land size 0.63 0.41 0.87 0.44 0.79 0.59 8.79 Livestock 1.20 1.41 2.85 1.48 2.63 2.01 32.15 Crop diversification 2.53 1.17 2.85 1.02 2.92 0.92 5.17 Amount of chemical fertilizer 107.57 101.85 150.76 120.59 160.17 130.55 7.48 Number of Years benefited 4.95 2.10 6.42 3.73 NA NA 15.10 Number of clinic visits 1.27 2.12 1.48 2.14 0.82 2.15 3.29 , indicate significant at <1% and 5% probability levels, respectively; NA - not applicable. Source: Own survey data (2023). Table 3. Descriptive results for categorical variables by PSNP participation. Variables Categories Beneficiary Graduated Non-beneficiary X 2 -valueN % Freq % Freq % Marital status Single 29 21.6 14 10.6 28 21.5 52.785 Married 52 38.8 99 75 86 66.2 Divorced 51 38.1 16 12.1 16 12.3 Credit use Yes 33 24.6 46 34.8 23 17.7 10.216 Wealth status Rich 83 61.9 68 51.5 87 66.9 6.770 Drought Yes 102 76.1 97 73.5 102 78.5 0.891 Livelihood zone Abay basin 53 39.6 55 41.7 59 45.4 10.242 Southwest wheat 58 43.3 49 37.1 61 46.9 Central highland 23 17.2 28 21.2 10 7.7 , indicate significant at <1% and 5% probability levels, respectively. Source: Own survey data (2023). Table 4. Comparison in food security status among beneficiary, graduated and non-beneficiary households using FIES and HHS. Variables Beneficiary Graduated Non-beneficiary F-valueDescription Mean(SD) Mean(SD) Mean(SD) FIES raw score Count of items with yes (0-8) 4.45(3.05) 2.27(2.53) 2.86(3.46) 18.434 HHS raw score Count of items with yes (0-6) 1.17(1.34) 0.40(0.84) 0.92(1.38) 13.99 Categories %(N) %(N) %(N) X 2 -value FIES Food secure 34.3% (46) 67.4% (89) 61.5% (80) 54.009 Moderately Food insecure 29.1(39) 25.8% (34) 10% (13) Severely Food insecure 36.6% (49) 6.8% (9) 28.5% (37) HHS No hunger 62.7% (84) 90.9% (120) 72.3% (94) 29.474 Moderate &Severe hunger 37.3% (50) 9.1% (12) 27.7% (36) , indicates significant at <1% and 5% probability levels, respectively. COGENT ECONOMICS & FINANCE 7 Kuma, B., & Gata, G. (2023). Factors affecting food price inflation in Ethiopia: An autoregressive distributed lag approach. Journal of Agriculture and Food Research,12, 100548. https://doi.org/10.1016/j.jafr.2023.100548 Mekonnen, A., Tessema, A., Ganewo, Z., & Haile, A. (2021). 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