Food Inflation, Institutional Support, and Household Coping Strategies: Implications for Child Nutritional Resilience in Urban Ethiopia

Food Inflation, Institutional Support, and Household Coping Strategies: Implications for Child Nutritional Resilience in Urban Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Food Inflation, Institutional Support, and Household Coping Strategies: Implications for Child Nutritional Resilience in Urban Ethiopia Solomon Girma Yirdaw, Professor Messay Mulugeta Tefera, Professor Mogessie Ashenafi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9053475/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Food inflation is a critical determinant of household welfare in market-dependent urban centers. In Addis Ababa, Ethiopia, rising food prices have eroded purchasing power, but the specific pathways through which institutional support and household coping strategies translate into child nutritional outcomes, measured through a multidimensional lens, remain under-researched. Objective : This study examined the impact of food inflation on the nutritional status of children aged 6-59 months in Addis Ababa, focusing on the mediating roles of institutional interventions and adaptive household behaviors. Methods : A cross-sectional mixed-methods study was conducted from October to December 2024. Quantitative data from 624 households were analyzed using a stratified two-stage cluster sampling design. Nutritional status was assessed using the Composite Index of Anthropometric Failure (CIAF). Econometric modeling included Seemingly Unrelated Regression (SUR) for continuous Z-scores and Binary/Multinomial Logit models for binary and categorical growth failure. Qualitative interviews provided context for household coping mechanisms. Results : The prevalence of anthropometric failure among children aged 6-59 months in Addis Ababa was 21.8%, with wasting (18.8%) and underweight (42.1%) exceeding stunting (6.1%). Infants aged 7-11 months were most vulnerable, with wasting (41.2%) and underweight (83.3%) peaking during the transition to complementary feeding. Boys were disproportionately affected by severe wasting (76.5%) and underweight (85.0%), while girls were more represented in severe stunting (62.5%). Overall, boys accounted for 55.8% of failures, compared to 44.2% among girls. Sustained breastfeeding reduced the odds of anthropometric failure by 33% (OR 0.67, p 0.008). Participation in the Urban Productive Safety Net Programme improved WHZ (β 1.100, p<0.05). Informal safety nets (Idir) buffered both WHZ (β 0.630, p<0.05) and WAZ (β 0.384, p<0.10). House ownership and equitable intra‑household food distribution lowered the risk of overlapping failures (“Wasting + Underweight”; p <0.10). Health insurance and school feeding showed marginal or negative associations, likely reflecting adverse selection. Multinomial logit models (Pseudo R² 0.492) highlighted the combined role of biological, behavioral, and institutional factors in shaping child nutritional resilience under inflation. Conclusion : Urban nutritional resilience in Addis Ababa is shaped less by household wealth and more by the interaction of biological practices, informal social capital, and targeted institutional support. Acute anthropometric failures proved highly sensitive to food inflation, underscoring the fragility of urban households under market shocks. Sustained breastfeeding emerged as a critical biological buffer, reducing vulnerability and reinforcing resilience. Informal safety nets such as Idir provided effective community‑based protection against acute nutritional stress. Engagement in the Urban Productive Safety Net Programme (UPSNP) strengthened child growth outcomes, highlighting the role of formal social protection. Conversely, negative associations with health insurance and school feeding suggest adverse selection, as these programs disproportionately reach already vulnerable households. The findings emphasize that resilience is built through adaptive behaviors and social capital rather than wealth alone. Integrated approaches that link formal and informal systems are essential to buffer children against inflationary shocks. Nutrition‑sensitive social protection must prioritize the critical 6-23-month window, where growth faltering is most acute. Finally, adopting the Composite Index of Anthropometric Failure (CIAF) offers a multidimensional lens to identify overlapping deficits and guide comprehensive interventions. Nutrition & Dietetics Food Inflation CIAF Nutritional Resilience Urban Ethiopia Econometric Modeling Coping Strategies Figures Figure 1 1. Introduction Food inflation is a critical determinant of household welfare in low- and middle-income countries. In market-dependent urban centers like those in Ethiopia, rising food prices erode purchasing power and constrain dietary diversity, disproportionately affecting low-income households (Headey & Ruel, 2020 ; Abay et al., 2021 ). Theoretically, this vulnerability is grounded in Household Demand Theory and Engel’s Law, which suggest that as real income decline due to inflationary shocks, food expenditure shares rise, forcing trade-offs that compromise nutritional intake (Deaton, 1997 ; Houthakker, 1957 ). To mitigate these shocks, households rely on a combination of institutional support and adaptive coping strategies. Institutional mechanisms such as the Urban Productive Safety Net Program (USN), school feeding, and consumer associations aim to buffer nutritional stress, yet coverage gaps often leave the most vulnerable urban populations underserved (Berhane et al., 2014 ; Sabates-Wheeler & Devereux, 2018 ). At the same time, households employ coping strategies such as rationing, dietary substitution, and reliance on informal networks (e.g., Idir ). While measured by the Coping Strategies Index (CSI) as indicators of short-term resilience, these practices can jeopardize long-term health, particularly when they involve reducing food quality or quantity (Hadley et al., 2012 ; Maxwell et al., 2015 ). Malnutrition in urban Ethiopia remains a multidimensional challenge. While indicators such as the Food Consumption Score (FCS) and the Household Food Insecurity Access Scale (HFIAS) track access, they often fail to capture the complexity of anthropometric deficits. The Composite Index of Anthropometric Failure (CIAF) addresses this by consolidating height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) into a single measure (Svedberg, 2000 ; Nandy et al., 2005 ). This approach provides a more sensitive and policy-relevant metric than isolated Z-scores, as it identifies children suffering from multiple, overlapping nutritional failures (Tette et al., 2016 ; Dasguta et al., 2018). Despite extensive research on objective food price shocks and rural food security (Bachewe et al., 2017 ; Abay et al., 2021 ), there is a lack of literature integrating institutional support, household spending behaviors, and the conceptual dimensions of inflation within a unified urban framework. Evidence suggests that subjective assessments of food price volatility often influence purchasing behavior as strongly as objective data (Hirvonen et al., 2016 ). This study examines how socio-economic and demographic factors, institutional interventions, and perceptions of inflation interact to shape nutritional outcomes in urban Ethiopia. By applying the Composite Index of Anthropometric Failure (CIAF) alongside binary and multinomial logit models, the research integrates economic and behavioral perspectives to provide a comprehensive analysis of the pathways through which food inflation translates into anthropometric failure in urban Ethiopia. 2. Materials and Methods 2.1. Study Area and Research Design The study was conducted in Addis Ababa (Fig. 1 ), Ethiopia’s capital and a major African diplomatic hub, which serves as a critical urban context for examining food inflation. Spanning 527 km 2 and with a population exceeding 5.6 million, the city is characterized by rapid urbanization and significant socioeconomic disparity; over one-third of households live below the poverty line, and 55% reside in informal settlements. Addis Ababa represents an ideal case for food security research, as 98% of food consumption is market dependent. Since 2020, annual food inflation has exceeded 28%, driven by currency depreciation and supply chain disruptions. To capture these dynamics, a cross-sectional mixed-methods design was implemented from October to December 2024, integrating quantitative estimates of nutritional outcomes with qualitative insights into household coping mechanisms (Creswell & lano Clark, 2017). 2.2. Samling Framework A stratified two-stage cluster sampling technique ensured representativeness across the city’s eleven sub-cities. In the first stage, 52 enumeration areas were randomly selected from municipal registries. In the second stage, 624 households were systematically sampled and proportionally allocated based on population density. The sample size was determined using Cochran’s ( 1963 ) formula, assuming a 58% prevalence of food insecurity, a 95% confidence level, and a 5% margin of error. The final calculation incorporated a design effect of 1.5 to account for clustering and a 10% non-response contingency. Within these households, 197 children aged 6–59 months were identified and screened for nutritional status using standardized WHO protocols. \({\mathbf{n}}_{0}=\frac{{\mathbf{z}}^{2}\mathbf{p}\mathbf{q}}{{\mathbf{e}}^{2}}\) (Eq. 1) $$\text{I}\text{n}\text{i}\text{t}\text{i}\text{a}\text{l}\text{s}\text{a}\text{m}\text{l}\text{e}\text{s}\text{i}\text{z}\text{e},{\text{n}}_{0}=\frac{{\left(1.96\right)}^{2}\text{*}\left(0.58\right)\text{*}\left(0.42\right)}{{\left(0.05\right)}^{2}}=374$$ \(\mathbf{n}=\frac{{\mathbf{n}}_{0}}{1+\frac{({\mathbf{n}}_{0}-1)}{\mathbf{N}}}\) (Eq. 2) $$\text{F}\text{i}\text{n}\text{i}\text{t}\text{e}\text{o}\text{u}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}\text{c}\text{o}\text{r}\text{r}\text{e}\text{c}\text{t}\text{i}\text{o}\text{n},\text{n}=\frac{374}{1+\frac{\left(374-1\right)}{\text{3,859,999}}}=374$$ Design effect (DEFF = 1.5): 374x1.5 = 561 Non-response contingency (10%): 561x10%=56 Final sample size = 617 (rounded, 624) households 2.3. Data Collection and Variable Selection Primary data were collected using structured questionnaires and anthropometric tools, including digital scales and height boards (Bauman et al., 2018 ). The primary dependent variable was the Composite Index of Anthropometric Failure (CIAF), which identifies children who experience one or more anthropometric failures (stunting, wasting, or underweight) and provides a more robust measure of compound malnutrition than isolated Z-scores (Svedberg, 2000 ; Nandy et al., 2005 ). Independent variables included five core dimensions: (1) sociodemographic characteristics (household size, caregiver education), (2) economic determinants (income and food/non-food expenditure), (3) institutional factors (participation in the Urban Productive Safety Net Programme and school feeding), (4) adaptive coping strategies assessed through the Coping Strategies Index, and (5) perceptual dimensions, including subjective inflation assessments and household information sources. This multidimensional framework enabled a holistic understanding of how economic capacity, social protection, behavioral adaptation, and perceived market conditions jointly influence child nutritional outcomes (Hirvonen et al., 2016 ). 2.4. Analytical Framework The analysis employed advanced econometric models to examine the relationships among inflation, institutional support, and nutrition. Continuous anthropometric indicators, height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ), were analyzed using Seemingly Unrelated Regression Equations (SURE) to account for correlated error structures across equations (Zellner,1962). For the CIAF, discrete choice methods were used; a binary logit model distinguished between the presence or absence of anthropometric failure, while a multinomial logit model analyzed mutually exclusive categories of compound malnutrition. These models provided the statistical power necessary to disentangle the associations between perceived food inflation and long-term nutritional well-being. 2.5. Quality Assurance To ensure methodological rigor, enumerators received intensive training, and instruments were retested outside the sample. Quality control procedures included household verification in 10% of clusters and double data entry to minimize errors. Data collection was timed during the post-harvest period (October-December) to capture realistic fluctuations in food prices for staple commodities, thereby ensuring that the findings reflect the actual inflationary experiences of urban households. 3. Results and Discussion 3.1. Nutritional Profile of Children The nutritional status of the 197 children surveyed in Addis Ababa revealed an unusual profile that differed from broader national and global trends (Table 1 ). While stunting is usually the main form of malnutrition, this study found it affected only 6.1% of the total group, whereas underweight and wasting were much more common at 42.1% and 18.8%, respectively (Woldekidan et al., 2024 ; EPHI & ICF, 2019). The data showed a period of relative protection for infants aged 0 to 6 months probably due to exclusive breastfeeding—as there were no recorded cases of wasting, stunting, or being underweight. However, a significant shift occurred in the 7 to 11-month group, which turned out to be the most vulnerable. In this group, 83.3% were classified as underweight, 41.2% were wasted, and 31.6% were stunted, aligning with the high-risk transition to complementary feeding (Victora et al., 2010 ). As children age into the 12 to 17-month range, the rate of being underweight stays high at 56.7%, although stunting drops significantly to 4.5%, and wasting decreases to 22.7%. By the 18 to 24-month stage, stunting completely disappears in the sample, and both wasting and underweight rates drop to 18.5%. Among the 125 older children aged 25 to 59 months, the underweight rate levels off at 40.0%, while wasting remains lower at 15.2%. Throughout the entire under-five population, a consistent pattern of overall undernourishment continues at about 24.9%, regardless of age. These findings highlight that acute malnutrition and weight deficits are the main nutritional issues in these urban households, especially during the critical 6 to 23-month period of growth faltering (Headey & Alderman, 2019 ). Table 1 Nutritional Status of Under-Five Children by Age in the Study Households Age (Months) Total Thinness/Wasting Stunting Underweight Undernourished 0–6 4 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (25.0%) 7–11 19 8 (41.2%) 6 (31.6%) 16 (83.3%) 5 (26.3%) 12–17 22 5 (22.7%) 1 (4.5%) 12 (56.7%) 5 (22.7%) 18–24 27 5 (18.5%) 0 (0.0%) 5 (18.5%) 7 (25.9%) Total under_2ys 72 18 (25.0%) 7 (9.7%) 33 (45.8%) 18 (25.0%) 25–59 125 19 (15.2%) 5 (4.0%) 50 (40.0%) 31 (24.8%) Total under_5ys 197 37 (18.8%) 12 (6.1%) 83 (42.1%) 49 (24.9%) Thinness/ Wasting (W/H <- 2z-score), Stunting (H/A <- 2z-score), Underweight (W/A <-2 z score), Undernourished (MUAC < 125 mm) The study’s gender-specific results showed notable disparities in nutritional outcomes, especially regarding weight-related indicators (Table 2 ). Boys were more affected by being underweight, representing 85.0% of all cases and all instances of severe underweight. While girls made up the majority of moderate wasting cases (58.8%), boys accounted for a higher proportion of overall wasting (58.8%) and an even larger share of severe cases at 76.5%, indicating a greater vulnerability to acute nutritional deficits among boys. In contrast, chronic malnutrition, measured through stunting, was evenly distributed between both genders at 50.0% (Woldekidan et al., 2024 ). However, the severity of these cases varied; girls accounted for 62.5% of severe stunting, while boys made up all moderate cases. Regarding undernourishment measured by Mid-Upper Arm Circumference (MUAC), where only severe cases were recorded, girls showed a slightly higher prevalence at 54.8% compared to 45.2% for boys. Ultimately, although girls demonstrated a higher susceptibility to chronic growth failure and low MUAC, boys in this urban Addis Ababa sample faced a significantly greater risk of being underweight and severely wasted, consistent with patterns where male children often show higher vulnerability to environmental stress (Headey & Alderman, 2019 ). Table 2 Nutritional Status of Under-Five Children by Sex in the Study Households Nutritional Status Prevalence Boys No. (%) Girls No. (%) Total No. (%) Thinness/Wasting (W/H or W/L) Moderate 7 (41.2%) 10 (58.8%) 17 (100%) Severe 13 (76.5%) 4 (23.5%) 17 (100%) Total 20 (58.8%) 14 (41.2%) 34 (100%) Stunting (H/A) Moderate 2 (100%) 0 (0%) 2 (100%) Severe 3 (37.5%) 5 (62.5%) 8 (100%) Total 5 (50.0%) 5 (50.0%) 10 (100%) Underweight (W/A) Moderate 12 (80.0%) 3 (20.0%) 15 (100%) Severe 5 (100%) 0 (0%) 5 (100%) Total 17 (85.0%) 3 (15.0%) 20 (100%) Undernourished (MUAC) Moderate – – – Severe 22 (45.2%) 27 (54.8%) 49 (100%) Total 22 (45.2%) 27 (54.8%) 49 (100%) The Composite Index of Anthropometric Failure (CIAF) showed that 21.8% of children experienced at least one type of growth problem, while the majority (78.2%) were classified as normal (Table 3 ). Among those with failures, wasting alone was the most common category (9.1%), followed closely by the combination of wasting and underweight (8.1%), and wasting only (4.1%). Notably, cases of stunting combined with underweight were rare (0.5%), and there were no recorded cases of the triple burden, stunting, wasting, and underweight, or overweight. Statistical analysis revealed significant relationships between nutritional outcomes and both age and sex. The prevalence of underweight was highest among infants aged 7–11 months (p < 0.010), highlighting a vulnerable period during the transition to complementary feeding (Teshome et al., 2013 ; Victora et al., 2021 ). Sex-disaggregated data showed that boys experienced a disproportionate rate of anthropometric failure (55.8%) compared to girls (44.2%). Specifically, boys were notably affected by both moderate and severe underweight (p < 0.005) and severe wasting (p < 0.025), accounting for 87.5% of those with dual wasting-underweight deficits. Conversely, girls exhibited higher rates of chronic conditions such as stunting combined with underweight (p < 0.007). These findings align with evidence from Sub-Saharan Africa and South Asia, where boys often show higher susceptibility to acute malnutrition and environmental stress, while girls may more frequently present with chronic conditions like stunting (Wamani et al., 2007 ; Keino et al., 2014 ). This pattern highlights that in urban Addis Ababa, the main challenges are acute wasting and underweight, especially during the critical 6 to 23-month period (Woldekidan et al., 2024 ; Headey & Alderman, 2019 ). Table 3 Composite Index of Anthropometric Failure Among Under-Five Children in the Study Area. CIAF Category Boys No. (%) Girls No. (%) Total No.(%) Total sample (%) A. Without anthropometric failure (normal) 65 (42.2%) 89 (57.8%) 154 (100%) 78.2% B. Thinness only (Wasting only) 6 (33.3%) 12 (66.7%) 18 (100%) 9.1% C. Thinness + Underweight 14 (87.5%) 2 (12.5%) 16 (100%) 8.1% D. Stunting + Thinness + Underweight – – – – E. Stunting + Underweight 0 (0.0%) 1 (100%) 1 (100%) 0.5% F. Stunting only 4 (50.0%) 4 (50.0%) 8 (100%) 4.1% G. Excess weight (overweight/obese) – – – – H. Stunting + Excess weight – – – – I. Underweight only – – – – Total 89 (45.2%) 108 (54.8%) 197 (100%) 100% Total anthropometric failure 24 (55.8%) 19 (44.2%) 43 (100%) 21.8% 3.2. Caregiver Characteristics and Anthropometric Failure Analysis of the socio-demographic factors linked to anthropometric failure (Table 4 ) showed that most household-level variables, including house ownership (p = 0.192), household size (p = 0.316), and the age or sex of the household head (p > 0.50), were not statistically significant. Likewise, the educational level (p = 0.519) and employment status (p = 0.310) of the household head were not major factors influencing nutritional outcomes, although children of casual laborers had the highest rate of multiple failures at 34.5%. In contrast, breastfeeding duration was a highly significant protective factor (p = 0.003); children without anthropometric failures had a significantly longer average breastfeeding duration compared to those with multiple failures. This emphasizes the vital role of breastfeeding in reducing complex malnutrition in urban settings. Additionally, caregiver education was significantly associated with stunting (p = 0.024), highlighting the influence of maternal knowledge on childcare practices (Smith & Haddad, 2015 ). Economic factors also played a crucial role, as the household food expenditure share was significantly linked to underweight (p = 0.02) and was marginally associated with wasting (p = 0.05). These findings underscore the vulnerability of acute nutritional indicators to income changes and feeding practices, even in environments where chronic stunting remains comparatively low. Table 4 Association Of Household and Caregiver Characteristics with Child CIAF (Children aged 6–59 months, n 197) Independent Variable Category No failure Multiple failures P value House ownership (%) Private house 75.9 24.1 0.192 Rent (Government) 77.3 22.7 Rent (Private) 100.0 0.0 Household size (%) 3.72 3.53 0.316 Sex of HH head (%) Male 79.1 20.9 0.872 Female 77.9 22.1 Age of HH head 43.7 42.8 0.509 Education of HH head (%) Never attended 72.7 27.3 0.519 Elementary (1–6) 77.1 22.9 Secondary (9–12) 83.1 16.9 University+ 73.5 26.5 Employment status (%) Government 82.8 17.2 0.310 Private 76.4 23.6 Self-employed 72.7 27.3 Casual labourer 65.5 34.5 Unemployed 88.9 11.1 Breastfeeding duration (years) 6.44 5.63 0.003* *Significant at p < 0.05 3.3. Socioeconomic Inequality, Institutional Support, and Nutritional Risk The analysis of economic characteristics showed that household resource allocation and access to support systems significantly influence nutritional outcomes (Table 5 ). Food expenditure share was identified as a key factor (p = 0.011), with households of children without anthropometric failure spending notably more on food (1,729.8 ETB) compared to those with multiple failures (1,434.4 ETB). This underscores the protective effect of higher food spending, especially since children in households dependent on casual labor—characterized by income instability—had the highest rate of multiple failures at 34.5%. Institutional support mechanisms showed significant, though nuanced, associations with CIAF status. Households with health insurance coverage (p = 0.014) and transport subsidies (p = 0.043) reported higher rates of multiple failures (30.8% and 32.6%, respectively). Rather than indicating program inefficacy, these patterns probably reflect the targeted enrollment of the most economically distressed families (Headey & Alderman, 2019 ). In contrast, informal support systems such as borrowing and mutual aid networks served as immediate buffers, significantly reducing the risks of wasting (p = 0.031) and underweight (p = 0.030). While children from "better-off" households and school feeding participants showed no failures, broader indicators like wealth status (p = 0.232), household savings (p = 0.208), and the Urban Productive Safety Net Program (p = 0.293) did not significantly distinguish between groups. These findings imply that in urban Addis Ababa, informal social protection and direct food expenditures offer more immediate nutritional resilience than formal programs, which may face challenges with coverage and targeting (Devereux & Sabates-Wheeler, 2004 ; FAO, 2022 ). Table 5 Household Economic Characteristics and Institutional Determinants by Child Composite Index of Anthropometric Failure (CIAF), Under Five Children (n 197) Economic Characteristic Category No Failure Multiple Failure P value Household savings -2,934.87 1,591.56 0.208 Wealth status (%) Ultra-poor 77.59 22.41 0.232 Poor 71.43 28.57 Better-off 100.00 0.00 Employment of household head (%) Gov’t 82.80 17.20 0.310 Private 76.36 23.64 Self 72.73 27.27 Casual 65.52 34.48 Unemployed 88.89 11.11 Housing tenure (%) Private house 75.86 24.14 0.192 Gov’t rent 77.34 22.66 Private rent 100.00 0.00 Food expenditure share 1,729.75 1,434.44 0.011* Non-food expenditure share 1,9390.18 1,4536.79 0.212 Urban Productive Safety Net Program (%) No 77.22 22.78 0.293 Yes 88.24 11.76 School Feeding Program (%) No 77.95 22.05 0.453 Yes 100.00 0.00 Health insurance coverage (%) No 84.03 15.97 0.014* Yes 69.23 30.77 Transport subsidy (%) No 81.46 18.54 0.043* Yes 67.39 32.61 *Significant at p < 0.05 3.4. Coping, Information, and Behavioral Adaptation The analysis of information and perceptual factors (Table 6 ) showed that consistent behavioral practices and strategic coping mechanisms were more important than merely accessing general information. Breastfeeding duration emerged as a strong protective factor (p = 0.004); children without anthropometric failure had a significantly longer average duration (6.44 years) than those with multiple failures (5.63 years), emphasizing the vital role of continued breastfeeding in supporting growth (Victora et al., 2016 ). Additionally, higher household food expenditure significantly lowered the risk of failure (p = 0.011), as families without growth deficits spent more on food (1,729.8 ETB) than those with multiple failures (1,434.4 ETB). Perceptual and coping factors also affected nutritional outcomes. Retrospective perceptions of food inflation over a 12-month period nearly reached significance (p = 0.065), indicating that long-term inflation pressures are a stronger predictor of nutritional risk than current perceptions (p = 0.917). Additionally, household coping strategies showed marginal significance (p = 0.075); families that used intra-household consumption adjustments had the lowest prevalence of multiple failures (4.4%), while those forced into immediate food-related adjustments experienced the highest (25.6%). In contrast, variables such as household food safety risks (p = 0.514), Food Consumption Scores (FCS) (p = 0.449), and sources of nutrition or price information (p > 0.20) did not statistically differentiate between failure groups. These results suggest that access to information and current food safety perceptions are less influential, while nutrition-sensitive coping strategies and resource allocation are key in reducing the impact of economic shocks on child health. Table 6 Information and Adaptive Coping Strategies by Child Composite Index of Anthropometric Failure (CIAF), Under‑Five Children (n 197) Information and perceptual factor Category No Failure Multiple Failure P value Household food safety risk (12 months) No 77.78 22.22 0.514 Yes 87.50 12.50 Food Consumption Score (FCS) Acceptable 79.58 20.42 0.449 Poor 84.62 15.38 Borderline 71.43 28.57 Perception of current food inflation Very high 78.36 21.64 0.917 High 80.00 20.00 Medium 75.76 24.24 Perception of food inflation (12 months) Very high 76.97 23.03 0.065* High 92.86 7.14 Medium 64.71 35.29 Source of food price information Media 84.09 15.91 0.295 Community 73.97 26.03 Kebele 100.00 0.00 Self-market 76.71 23.29 Source of nutrition knowledge Media 82.50 17.50 0.852 Reading 83.33 16.67 Personal 82.76 17.24 Household 76.47 23.53 Health professionals 74.47 25.53 Duration of breastfeeding 6.44 5.63 0.004* Decision on food price coping (%) Acute food-related adjustment 74.38 25.62 0.075 † Non-essential consumption cut 79.25 20.75 Intra-household consumption adjustment 95.65 4.35 *Significant at p < 0.05, †Significant at p < 0.10 3.5. Econometric Model Performance and Validity The econometric analysis of child nutritional outcomes used various modeling techniques to identify the factors contributing to anthropometric failure (Table 7 ). Using Seemingly Unrelated Regression (SUR) to account for potential correlations among weight-for-height (WHZ), weight-for-age (WAZ), and height-for-age (HAZ) z-scores, the results showed different levels of explanatory power. The WHZ model had a Pseudo R^2 of 0.205 (p = 0.1454), while the WAZ and HAZ models had lower Pseudo R^2 values of 0.189 and 0.132, respectively. To analyze the Composite Index of Anthropometric Failure (CIAF), both Binary and Multinomial Logit Regressions were used. The Binary Logit Regression model, which distinguished between any failure and no failure, achieved a Log Likelihood of -73.20 and a Pseudo R2 of 0.185 (p = 0.1265). In contrast, the Multinomial Logit Regression—which considers specific categories of failure—offered significantly greater explanatory power, with a Pseudo R2 of 0.492. Although the probability values for these models (p = 0.1179 to 0.7715) suggest that the independent variables collectively fall just outside the usual threshold for overall model significance, the high Pseudo R2 in the multinomial model indicates that the selected predictors are particularly effective at explaining the complexities of nutritional deficits. The strength of the links between food inflation, institutional support, and child nutritional outcomes was evaluated using a triangulated econometric approach. Continuous anthropometric indicators (HAZ, WAZ, WHZ) were estimated through Seemingly Unrelated Regression (SUR), which accounts for correlated error structures across equations and enhances efficiency compared to separate OLS estimates (Zellner, 1962 ). The SUR model showed moderate explanatory power, with R² values of 0.205 for WHZ, 0.189 for WAZ, and 0.132 for HAZ, indicating that weight-based acute indicators are more responsive to short-term market shocks than the slower-changing height-for-age deficit. Table 7 Goodness-of-Fit Statistics for Econometric Models of Child Nutritional Outcomes in Urban Ethiopia Outcome Variable Model LR chi² Prob > χ² Log Likelihood Pseudo R² (McFadden) WHZ Seemingly Unrelated Regression (SUR) 0.1454 0.205 WAZ 0.2402 0.189 HAZ 0.7715 0.132 CIAF Binary Logit Regression 33.19 0.1265 -73.201864 0.185 CIAF Multinomial Logit Regression 129.97 0.1179 -67.087265 0.492 To capture multidimensional malnutrition, discrete choice models were applied to the Composite Index of Anthropometric Failure (CIAF). The binary logit model (failure vs. no failure) yielded a Pseudo R² of 0.185; however, the multinomial logit model, which distinguishes mutually exclusive combinations of anthropometric deficits (Svedberg, 2000 ; Nandy et al., 2005 ), achieved substantially higher explanatory power (Pseudo R² 0.492). The multinomial specification also produced notably lower AIC and BIC values than both the binary logit and SUR models, confirming its superior fit and its ability to more accurately characterize the complex determinants of failure under inflationary pressures. Model validity was confirmed through likelihood ratio tests, Wald tests, and the use of robust standard errors to account for clustering at the enumeration-area level. By combining SUR for continuous Z-scores with multinomial logit for multidimensional failure categories, the analysis maintains methodological rigor consistent with international nutrition econometrics (Li et al., 2020 ) and provides a comprehensive framework for understanding how economic shocks and institutional gaps influence complex anthropometric outcomes in Addis Ababa. 3.6. Multivariate Analysis: Seemingly Unrelated Regression (SUR) The Seemingly Unrelated Regression (SUR) analysis (Table 8 ) highlights different pathways for child growth, showing that acute weight-based indicators (WHZ and WAZ) are highly sensitive to immediate economic buffers and institutional support, while height-for-age (HAZ) reflects long-term sociodemographic factors. Participation in the Urban Productive Safety Net Program significantly improved WHZ (β = 1.100, p < 0.05), and informal safety nets (Idir) consistently protected child growth, significantly increasing WHZ (β = 0.630, p < 0.05) and having a positive marginal effect on WAZ. Additionally, house ownership was a significant positive predictor of WAZ (β = 0.359, p < 0.05), emphasizing the importance of residential stability in maintaining nutritional health. The results also reveal a complex relationship with other institutional mechanisms. While formal interventions like school feeding and health insurance showed negative associations with nutritional Z-scores (p < 0.05), these likely reflect "pro-poor" targeting or adverse selection, where households with pre-existing vulnerabilities or chronic illnesses are prioritized for enrollment (Headey & Alderman, 2019 ). Similarly, the negative correlation between the Food Consumption Score and weight-based indicators suggests a disconnect between household-level dietary diversity and actual child intake, possibly due to intra-household allocation patterns or food safety concerns. Table 8 Determinants of Child Anthropometric Outcomes (Wasting, Underweight, Stunting) among Under-Five Children in Addis Ababa: Seemingly Unrelated Regression Results Variable Child WHZ Child WAZ Child HAZ Socio-demographic characteristics Family size 0.145 (0.129) 0.067 (0.093) -0.037 (0.104) Sex of household head -0.234 (0.299) -0.026 (0.214) 0.267 (0.241) Age of household head -0.007 (0.017) 0.001 (0.012) 0.008 (0.014) Educational attainment of household head 0.167 (0.104) 0.021 (0.075) -0.190** (0.084) Chronic disease condition within household 0.631 (0.582) -0.045 (0.417) -0.888 † (0.469) Economic determinants Household savings -0.000001 (0.000008) 0.000000 (0.000006) 0.000001 (0.000006) Wealth index -0.291 (0.298) -0.115 (0.214) 0.124 (0.240) House ownership status 0.369 (0.225) 0.359** (0.161) 0.179 (0.181) Employment status of household head -0.123 (0.103) -0.105 (0.073) -0.026 (0.083) Food expenditure per capita 0.00001 (0.00022) 0.00004 (0.00016) 0.00009 (0.00018) Adaptive coping strategies Coping Strategies Score -0.014 (0.013) -0.014 (0.009) -0.007 (0.010) Breastfeeding duration (as a household-level adaptive practice) 0.090 (0.080) 0.056 (0.058) -0.009 (0.065) Household food distribution decision-making patterns 0.171 (0.121) 0.143 † (0.087) 0.033 (0.097) Household decision on perceived inflation 0.071 (0.192) 0.041 (0.138) -0.030 (0.155) Institutional factors Access to sanitation facilities 0.098 (0.220) 0.035 (0.158) -0.055 (0.178) Participation in the Urban Productive Safety Net Program 1.100** (0.504) 0.566 (0.361) -0.474 (0.407) Access to school feeding programs -5.853** (2.422) -2.797 † (1.735) 2.707 (1.953) Membership in consumer associations 0.674 (0.426) 0.388 (0.305) -0.095 (0.344) Access to finance/credit services 2.750 (1.760) 1.204 (1.261) -1.369 (1.419) Health insurance coverage -0.325 (0.292) -0.525** (0.209) -0.582** (0.236) Informal safety nets (e.g., Idir ) 0.630** (0.284) 0.384 † (0.203) -0.140 (0.229) Access to transport subsidies -0.014 (0.351) 0.005 (0.251) -0.038 (0.283) Information and perceptual dimensions Food safety risk experience (12-month recall) -0.076 (0.612) -0.128 (0.439) -0.052 (0.494) Food Consumption Score (FCS) -0.013 (0.010) -0.011 (0.007) -0.003 (0.008) Household perceptions current food price evaluations -0.138 (0.186) -0.064 (0.134) 0.072 (0.150) Retrospective assessments of food price changes 0.020 (0.228) -0.099 (0.163) -0.170 (0.183) Source of food price information 0.134 (0.128) 0.112 (0.092) -0.003 (0.104) Source of nutrition knowledge -0.105 (0.082) -0.034 (0.059) 0.073 (0.066) Constant -2.254 (1.525) -1.387 (1.093) 0.454 (1.230) Standard errors in parentheses. *p < 0.05, **p < 0.01, †p < 0.10. 3.7. Multivariate Analysis: Binary logistic regression for CIAF Binary logistic regression was used to analyze the determinants of multidimensional nutritional vulnerability, defined as the likelihood of experiencing any anthropometric failure (Table 9 ). Among the socio-demographic, economic, and institutional predictors evaluated, only breastfeeding duration and health insurance coverage showed statistically significant associations. Sustained breastfeeding emerged as a key protective factor (p = 0.008), with each additional time unit reducing the odds of failure by 33% (OR = 0.67; 95% CI: 0.49–0.90). This underscores the vital biological and adaptive role of maternal feeding practices in supporting nutritional resilience within urban inflationary environments (Fenta et al., 2021 ). Conversely, health insurance coverage was linked to a significant rise in the odds of failure (OR = 3.22; 95% CI: 1.23–8.39; p = 0.017). Consistent with the SUR results, this probably reflects adverse selection or "pro-poor" targeting, where households with pre-existing illnesses or growth concerns are prioritized for enrollment or show higher detection rates due to increased service use (Ayres et al., 2024 ). Conversely, traditional socioeconomic indicators, such as wealth index (p = 0.638), family size (p = 0.392), and per capita food expenditure, did not significantly predict the Composite Index of Anthropometric Failure (CIAF). Perceptual and behavioral measures, like the Food Consumption Score and retrospective inflation assessments (p > 0.30), were also not significant. These findings indicate that in Addis Ababa’s current economic setting, specific biological practices and targeted institutional engagement have a more prominent impact on child nutritional outcomes than broad socioeconomic measures. Table 9 Determinants of Composite Index of Anthropometric Failure among Under-Five Children: Robust Logit Regression Results Variable OR 95% CI p-value Socio-demographic characteristics Family size 0.82 0.52–1.29 0.392 Sex of household head 1.36 0.48–3.91 0.562 Age of household head 0.99 0.93–1.06 0.837 Educational attainment of household head 1.11 0.76–1.62 0.582 Chronic disease condition within household 0.59 0.09–3.68 0.569 Economic determinants Household savings 1.00 1.00–1.00 0.357 Wealth index 0.78 0.27–2.23 0.638 House ownership status 0.76 0.36–1.62 0.484 Employment status of household head 1.19 0.82–1.73 0.356 Food expenditure per capita 0.999 0.999–1.00 0.292 Adaptive coping strategies Coping Strategies Score 1.02 0.97–1.07 0.482 Breastfeeding duration (as a household-level adaptive practice) 0.67 0.49–0.90 0.008 ** Household food distribution decision-making patterns 0.95 0.63–1.44 0.826 Household decision on perceived inflation 0.65 0.27–1.57 0.334 Institutional factors Access to sanitation facilities 0.90 0.33–2.49 0.833 Participation in the Urban Productive Safety Net Program 0.24 0.02–2.89 0.263 Membership in consumer associations 1.74 0.41–7.73 0.451 Health insurance coverage 3.22 1.23–8.39 0.017 ** Informal safety nets (e.g., Idir) 0.55 0.20–1.55 0.258 Access to transport subsidies 1.41 0.37–5.37 0.619 Information and perceptual dimensions Food Consumption Score (FCS) 1.01 0.98–1.05 0.566 Household perceptions current food price evaluations 1.20 0.65–2.22 0.565 Retrospective assessments of food price changes 1.41 0.68–2.92 0.349 Source of food price information 1.01 0.65–1.58 0.949 Source of nutrition knowledge 1.08 0.81–1.45 0.593 p < 0.05 = *, p < 0.01 = **, p < 0.001 = *** 3.8. Multivariate Analysis: Multinomial Logit Regression for CIAF The Multinomial Logit Regression (Table 10 ) offers detailed insight into how different factors distinguish specific categories of nutritional failure from a "No Failure" status. Breastfeeding duration was the main factor influencing child health (p < 0.05), showing a significant positive link with the "No Failure" category (β = 0.049) and leading to a lower chance of acute issues like "Wasting Only" and "Wasting + Underweight." These results emphasize the importance of continued breastfeeding as a key biological support that lessens reliance on costly complementary foods during periods of inflation. Household economic and adaptive factors further influence these outcomes. Homeownership and strategic food distribution patterns significantly reduce the likelihood of the severe "Wasting + Underweight" category (p < 0.10), emphasizing the protective roles of residential stability and caregiver agency in household food management. Likewise, membership in informal safety nets (Idir) notably decreases the risk of combined nutritional failure (β = -0.085, p < 0.10), highlighting the importance of local social capital. In contrast, the marginal positive link between health insurance and "Wasting + Underweight" (β = 0.081, p < 0.10) probably reflects the targeted enrollment of households with pre-existing health or economic vulnerabilities. Notably, the model indicates that chronic indicators, such as "Stunting Only," remain largely unaffected by these short-term economic and perceptual factors. Ultimately, while broad socioeconomic measures such as the wealth index and per capita expenditure were not significant, the severity of nutritional failure is notably influenced by maternal practices, residential stability, and informal social networks. Table 10 Average Marginal Effects from Multinomial Logit Estimates of Determinants of CIAF Categories among Under-Five Children in Addis Ababa Variable No Failure Wasting Only Wasting + Underweight Stunting + Underweight Stunting Only Socio-demographic characteristics Family size 0.019 (0.031) -0.029 (0.025) 0.010 (0.022) ~ 0.000 ~ 0.000 Sex of household head -0.058 (0.081) 0.056 (0.073) 0.002 (0.047) ~ 0.000 ~ 0.000 Age of household head 0.001 (0.004) -0.003 (0.003) 0.001 (0.003) ~ 0.000 ~ 0.000 Educational attainment of household head ~ 0.000 (0.024) 0.004 (0.020) -0.004 (0.016) ~ 0.000 ~ 0.000 Chronic disease condition within household 2.576 (16052.6) -1.559 (13687.6) -1.018 (11251.5) ~ 0.000 ~ 0.000 Economic determinants Household savings ~ 0.000 ~ 0.000 ~ 0.000 ~ 0.000 ~ 0.000 Wealth index -0.039 (0.083) 0.026 (0.059) 0.013 (0.066) ~ 0.000 ~ 0.000 House ownership status 0.042 (0.059) 0.039 (0.044) -0.081 † (0.046) ~ 0.000 ~ 0.000 Employment status of household head -0.010 (0.027) -0.012 (0.022) 0.022 (0.019) ~ 0.000 ~ 0.000 Food expenditure per capita ~ 0.000 ~ 0.000 ~ 0.000 ~ 0.000 ~ 0.000 Adaptive coping strategies Coping Strategies Score -0.001 (0.003) ~ 0.000 (0.002) 0.001 (0.001) ~ 0.000 ~ 0.000 Breastfeeding duration (as a household-level adaptive practice) 0.049** (0.024) -0.030 (0.021) -0.019 (0.016) ~ 0.000 ~ 0.000 Household food distribution decision-making patterns -0.007 (0.031) 0.042 † (0.025) -0.036 † (0.020) ~ 0.000 ~ 0.000 Household decision on perceived inflation 0.045 (0.054) -0.023 (0.040) -0.023 (0.043) ~ 0.000 ~ 0.000 Institutional factors Access to sanitation facilities 0.014 (0.057) -0.055 (0.036) 0.040 (0.048) ~ 0.000 ~ 0.000 Participation in the Urban Productive Safety Net Program 0.911 (849.94) 0.135 (105.72) -1.046 (955.66) ~ 0.000 ~ 0.000 Access to school feeding programs -0.891 (11854.9) -0.176 (10328.9) 1.068 (8009.3) ~ 0.000 ~ 0.000 Membership in consumer associations -0.015 (0.103) 0.045 (0.080) -0.030 (0.073) ~ 0.000 ~ 0.000 Access to finance/credit services 2.059 (8361.1) -1.317 (7303.3) -0.742 (5622.9) ~ 0.000 ~ 0.000 Health insurance coverage -0.082 (0.076) ~ 0.001 (0.064) 0.081 † (0.049) ~ 0.000 ~ 0.000 Informal safety nets (e.g., Idir ) 0.016 (0.073) 0.070 (0.063) -0.085 † (0.047) ~ 0.000 ~ 0.000 Access to transport subsidies -0.074 (0.087) 0.105 (0.074) -0.032 (0.054) ~ 0.000 ~ 0.000 Information and perceptual dimensions Food safety risk experience (12-month recall) 2.145 (2606.3) -1.351 (2630.6) -0.793 (1088.5) ~ 0.000 ~ 0.000 Food Consumption Score (FCS) ~ 0.001 (0.003) ~ 0.001 (0.002) ~ 0.000 (0.001) ~ 0.000 ~ 0.000 Household perceptions current food price evaluations -0.046 (0.044) 0.009 (0.036) 0.036 (0.028) ~ 0.000 ~ 0.000 Retrospective assessments of food price changes -0.003 (0.053) 0.033 (0.040) -0.030 (0.039) ~ 0.000 ~ 0.000 Source of food price information ~ 0.000 (0.031) 0.005 (0.026) -0.005 (0.021) ~ 0.000 ~ 0.000 Source of nutrition knowledge -0.016 (0.021) 0.022 (0.018) -0.006 (0.014) ~ 0.000 ~ 0.000 p < 0.05, p < 0.01, †p < 0.10. Institutional and Economic Buffers. Economic stability also plays a decisive role. House ownership substantially reduces the likelihood of “Wasting + Underweight” (p < 0.10), reflecting its function as both a financial asset and a psychological stabilizer during price shocks. Informal community safety nets, particularly Idir , similarly exhibit protective effects against overlapping failures (p < 0.10). Qualitative accounts highlight that these informal institutions often provide more timely and flexible support for food and healthcare needs than formal systems. In contrast, health insurance coverage shows a marginal positive association with “Wasting + Underweight” (p < 0.10), likely indicating adverse selection. Many caregivers reported enrolling only after a child became ill, suggesting that insurance uptake may be reactive rather than preventative. Formal social protection mechanisms including UPSNP participation and school feeding exhibited negligible marginal effects on chronic forms of anthropometric failure. Beneficiary feedback points to implementation constraints such as delayed transfers and concerns over meal quantity and nutritional adequacy, which may explain their limited influence in the quantitative model. Consistency with Broader Evidence. The findings align with the broader “nutritional resilience” literature, emphasizing the mediating roles of household practices and social capital in cushioning the effects of macroeconomic shocks (Headey & Ruel, 2020 ). The near-zero marginal effects observed for “Stunting Only” and “Stunting + Underweight” likely stem from small sub-sample sizes and the inherently overlapping nature of chronic nutritional deficits. Nonetheless, the model’s strong performance (Pseudo R² 0.492) demonstrates that decomposing composite indicators yields more nuanced insights into urban nutritional vulnerability than binary or single-dimension classifications. 4. Conclusion and Policy Recommendations 4.1. Conclusion This study investigated how food inflation affects the nutritional resilience of children aged 6–59 months in Addis Ababa using the Composite Index of Anthropometric Failure (CIAF). The rate of failure was 21.8%, with wasting (18.8%) and underweight (42.1%) surprisingly higher than stunting (6.1%). Vulnerability was greatest among infants aged 7–11 months, while sex-specific analysis revealed boys were more affected by severe wasting and underweight. Overall, boys represented 55.8% of nutritional failures compared to 44.2% among girls, emphasizing sex-related vulnerability pathways. Triangulated econometric analysis (SUR, Binary, and Multinomial Logit) confirmed that nutritional outcomes are influenced more by adaptive behaviors and social capital than by wealth alone. Sustained breastfeeding acted as a key buffer, decreasing the odds of anthropometric failure by 33% (OR = 0.67, p = 0.008), while informal safety nets like Idir and formal programs such as the UPSNP significantly enhanced weight-based indicators (p < 0.05). Conversely, negative associations with health insurance and school feeding likely reflect "pro-poor" targeting toward already vulnerable households. Ultimately, the prevalence of acute malnutrition and underweight in this urban setting calls for integrated, multidimensional strategies. Strengthening resilience against inflationary pressures requires a mix of enhanced social protection, health system support, and targeted behavioral interventions focused on the critical 6–23-month growth period. 4.2. Policy Recommendations Enhancing nutritional resilience in inflation-prone urban areas involves integrating nutrition-focused elements into formal protection systems while strengthening their links to community groups. The Urban Productive Safety Net Program should evolve from simple consumption smoothing to a comprehensive approach that includes growth-oriented cash transfers and regular nutrition counseling. At the same time, policymakers need to formalize partnerships between government agencies and informal organizations like Idir, utilizing their high levels of trust and social cohesion to spread food price information and provide emergency support. Program redesign is crucial to expand preventive reach and reduce adverse selection. School feeding programs need higher nutrient density, while health insurance schemes should focus on simplified enrollment and lower out-of-pocket expenses to promote preventive care. To protect vital biological buffers, labor policy reforms must enforce paid maternity leave and workplace accommodations, ensuring breastfeeding remains a key non-market defense for young children. Finally, government and humanitarian actors should adopt the Composite Index of Anthropometric Failure (CIAF) for multidimensional monitoring, allowing earlier detection of children experiencing multiple deprivations. Interventions must focus specifically on the 6–23-month period, when wasting and underweight are at their peak. By combining health and human capital investments to address chronic household illnesses and caregiver education gaps, policies can better support long-term resilience against urban undernutrition. 5. Strengths, Limitations, and Future Research Directions 5.1. Strengths and Limitations This study’s central strength lies in its methodological triangulation, integrating advanced econometric techniques with qualitative insights to generate a multidimensional understanding of urban nutritional resilience. The use of the Composite Index of Anthropometric Failure (CIAF), alongside Seemingly Unrelated Regression (SUR) and Multinomial Logit models, enabled the identification of overlapping forms of undernutrition that conventional single-indicator Z-scores often obscure. The analytical inclusion of institutional and perceptual determinants particularly subjective inflation assessments and the role of informal social capital through Idir provides a rare empirical lens into how social networks and behavioural adaptations mediate nutritional outcomes in urban African settings. Nonetheless, several limitations warrant consideration. The cross-sectional design constrains causal inference, as it captures only one temporal point and cannot fully disentangle the dynamic relationship between inflationary pressure and child growth trajectories. Although the multinomial model demonstrated strong explanatory power, the small sample sizes for some anthropometric subcategories (e.g., “Stunting Only”) may have reduced the precision of estimated effects. Furthermore, reliance on 12-month recall for food security, food safety concerns, and price perceptions introduces potential recall bias, potentially smoothing over periods of acute inflationary volatility and underestimating intra-year shocks experienced by households. 5.2. Future Research Directions Future research would benefit from longitudinal or panel designs to capture how prolonged exposure to food inflation influences transitions between acute and chronic forms of undernutrition, including progression pathways from wasting to stunting. Further investigation into the “plus” components of social protection such as complementarities between cash transfers, growth monitoring, and behaviour change communication could illuminate the mechanisms underlying current implementation gaps in formal programs. There is also substantial scope to examine the scalability and formal integration of informal safety nets like Idir within broader urban disaster risk management frameworks, while ensuring that their responsiveness and community trust are not undermined. Finally, as urban vulnerability is increasingly shaped by environmental and climatic stressors, future studies should incorporate factors such as water quality, climate-induced migration, and infrastructural fragility into the CIAF analytical framework to better understand the compounded risks facing children in rapidly expanding cities such as Addis Ababa. Declarations Ethical Approval and Consent . Ethical clearance for this study was granted by the Addis Ababa University Institutional Review Board (Ref: 084/08/2024). Before collecting data, the study's objectives and procedures were clearly explained to all potential participants. Written informed consent was obtained from each participant, who took part voluntarily and understood that they could withdraw at any time. For participants under 18, consent was obtained from a parent or legal guardian. Consent to Publish and Clinical Trial Registration Not applicable. Competing Interests The authors declare that they have no competing interests. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data Availability: The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Authors’ Contributions SGY, Conceptualization, study design, data collection, data curation, formal analysis, methodology, investigation, visualization, manuscript writing (original draft, review, and editing), and validation; MMT, Study design, methodology, analysis, manuscript review, supervision, and validation; MA, Study design, methodology, analysis, manuscript review, supervision, and validation; STF, Study design, methodology, analysis, manuscript review, supervision, and validation. References Abay, K. A., Hirvonen, K., & Minten, B. (2021). Rising food prices and household welfare in Ethiopia: Evidence from urban centers. International Food Policy Research Institute (IFPRI). Ayres, S., Jones, M., & Smith, L. (2024). Institutional and socioeconomic determinants of multidimensional malnutrition: Evidence from urban centers. Journal of Public Health Policy, 45(2), 112–128. Bachewe, F. N., Bartels, K. J., & Minten, B. (2017). Agricultural transformation in Africa? Assessing the evidence from Ethiopia. World Development, 99, 18–34. Bauman, A., Ernst, K., Hayden, M., Roe, D. J., Murray, R., Agawo, M., Munga, S., Schmahl, E., & Taren, D. (2018). Assessing community health: An innovative tool for measuring height and length. Journal of Tropical Pediatrics, 64(2), 146–150. https://doi.org/10.1093/tropej/fmx046 (doi.org in Bing) Berhane, G., Devereux, S., Hoddinott, J., Hoel, J., Roelen, K., Abay, K., ... & Woldehanna, T. (2014). Evaluation of the Social Cash Transfer Pilot Programme, Tigray Region, Ethiopia. International Food Policy Research Institute (IFPRI). Cochran, W. G. (1963). Sampling techniques (2nd ed.). John Wiley & Sons. Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications. Dasgupta, A., Parthasarathy, D., & Basu, S. (2018). Urban food insecurity and child malnutrition: A multidimensional approach. Food Policy, 74, 60-73. https://doi.org/10.1016/j.foodpol.2017.11.007 (doi.org in Bing) Deaton, A. (1997). The analysis of household surveys: A microeconometric approach to development policy. Johns Hopkins University Press. Devereux, S., & Sabates-Wheeler, R. (2004). Transformative social protection. IDS Working Paper 232. Institute of Development Studies. https://doi.org/10.1111/j.1759-5436.2004.tb00120.x (doi.org in Bing) EPHI. (2024). Ethiopia Demographic and Health Survey (EDHS) 2024: Ethiopia Public Health Institute. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: EPHI and ICF. FAO. (2022). The state of food security and nutrition in the world: Repurposing food and agricultural policies to make healthy diets more affordable. Food and Agriculture Organization of the United Nations. Fenta, H. M., Zewotir, T., & Muluneh, E. K. (2021). Spatial analysis of stunting, wasting, and underweight among under-five children in Ethiopia. BMC Nutrition, 7(1), 1-12. Hadley, C., Lindstrom, D., Tessema, F., & Belachew, T. (2012). Gender bias in the food insecurity experience of Ethiopian adolescents. Social Science & Medicine, 75(12), 2416-2424. https://doi.org/10.1016/j.socscimed.2012.09.018 (doi.org in Bing) Headey, D. D., & Alderman, H. (2019). The relative prices of healthy and unhealthy foods: Low-income urban contexts. The Journal of Nutrition, 149(12), 2120–2133. Headey, D. D., & Ruel, M. T. (2020). Economic shocks and child under nutrition in the Global South. Proceedings of the National Academy of Sciences (PNAS), 117(20), 10735-10743. Hirvonen, K., Taffesse, A. S., & Hassen, I. W. (2016). Seasonal consumption smoothing and the cost of shaping expectations: Evidence from Ethiopia. Journal of Development Studies, 52(12), 1724–1741. Houthakker, H. S. (1957). An international comparison of household expenditure patterns, commemorating the centenary of Engel's Law. Econometrica, 25(4), 532-551. Keino, S., Plasqui, G., Ettyang, G., & van den Borne, B. (2014). Determinants of stunting and overweight among young children and adolescents in sub-Saharan Africa. Food and Nutrition Bulletin, 35(2), 167-178. https://doi.org/10.1177/156482651403500203 (doi.org in Bing) Li, Z., Kim, R., Vollmer, S., & Subramanian, S. V. (2020). Factors are associated with child stunting, wasting, and under-weight in low- and middle-income countries. JAMA Network Open, 3(4), e203386. Maxwell, D., Caldwell, R., & Langworthy, M. (2015). Measuring food insecurity: Can an indicator of behaviors and coping provide a proxy for food intake? Food Policy, 28(1), 1-11. Nandy, S., Irving, M., Gordon, D., Subramanian, S. V., & Smith, G. D. (2005). Poverty, child undernutrition and morbidity: New evidence from India. Bulletin of the World Health Organization, 83(3), 210-216. Sabates-Wheeler, R., & Devereux, S. (2018). Social protection for resilience: The case of the Productive Safety Net Programme in Ethiopia. Institute of Development Studies. Smith, L. C., & Haddad, L. (2015). Reducing child under nutrition: Past drivers and priorities for the post-MDG era. World Development, 68, 180–204. Svedberg, P. (2000). Poverty and undernutrition: Theory, measurement, and policy. Oxford University Press. Teshome, B., Kogi-Makau, W., Getahun, Z., & Taye, G. (2013). Magnitude and determinants of stunting in children under-five years of age in Ethiopia. Ethiopian Journal of Health Development, 23(3), 162–169. Tette, E. M., Sifah, E. K., & Nartey, E. T. (2016). Analysis of the risk factors associated with underweight, stunting and wasting in children. International Journal of Environmental Research and Public Health, 13(3), 269. Victora, C. G., de Onis, M., Hallal, P. C., Blössner, M., & Shrimpton, R. (2010). Worldwide timing of growth faltering: Revisiting implications for interventions. Pediatrics, 125(3), e473-e480. https://doi.org/10.1542/peds.2009-1519 (doi.org in Bing) Victora, C. G., Bahl, R., Barros, A. J. D., França, G. V. A., Horton, S., Krasevec, J., Murch, S., Sankar, M. J., Walker, N., Rollins, N. C., & the Lancet Breastfeeding Series Group. (2016). Breastfeeding in the 21st century: Epidemiology, mechanisms, and lifelong effect. The Lancet, 387(10017), 475–490. https://doi.org/10.1016/S0140-6736(15)01024-7 (doi.org in Bing) Victora, C. G., Christian, P., Vidaletti, L. P., Gatica-Domínguez, G., Menon, P., & Black, R. E. (2021). Revisiting maternal and child undernutrition in low-income and middle-income countries: Variable progress towards an unfinished agenda. The Lancet, 397(10282), 1388-1399. https://doi.org/10.1016/S0140-6736(21)00394-9 (doi.org in Bing) Wamani, H., Åstrøm, A. N., Peterson, S., Tylleskär, T., & Tumwine, J. K. (2007). Boys are more stunted than girls in sub-Saharan Africa: A meta-analysis of 16 demographic and health surveys. BMC Pediatrics, 7(1), 17. https://doi.org/10.1186/1471-2431-7-17 (doi.org in Bing) Woldekidan, T., Adugna, M., & Tefera, M. M. (2024). Trends in acute vs chronic malnutrition in urban Ethiopia. African Journal of Food Science, 18(4), 110-125. Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348-368. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9053475","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602080266,"identity":"3243119e-63b3-4643-8f16-5057d917b614","order_by":0,"name":"Solomon Girma Yirdaw","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie2SMQrCMBSGU4V2edD1CWKvEHFx6B28QlwyVXASN510UHGt4CF6hEhAlzoKhi5OuujeQcS0B2gzCuYjhH/4P94LhBCL5TdxBJlix/XmOtO+mSNIGvZ8EIWChoqz4MNdzIpsoAQDKSS40knU45LkYyT+csUqle6VMwkgmzSLJmqtF8P0nFQrMVAJKF2t8CtoheKoRtmmWimOSrl6mygBiXSfcWzF3jEzmkKRs8NehNSHyM3aFKH2LcFWHm6vD84W3umunu+w4y83NVMEIQ0oI9DyrqyXU+b6x+Rl9G61bYvFYvlPvvO5TFqWER0bAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-6856-9964","institution":"Addis Ababa University","correspondingAuthor":true,"prefix":"","firstName":"Solomon","middleName":"Girma","lastName":"Yirdaw","suffix":""},{"id":602080318,"identity":"fdbe6baf-fa3e-4e70-924c-91ef99f5e24e","order_by":1,"name":"Professor Messay Mulugeta Tefera","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Professor","middleName":"Messay Mulugeta","lastName":"Tefera","suffix":""},{"id":602080356,"identity":"a0e0507e-9d1c-4000-9c25-9f402bc0c929","order_by":2,"name":"Professor Mogessie Ashenafi","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Professor","middleName":"Mogessie","lastName":"Ashenafi","suffix":""},{"id":602080403,"identity":"8f2ff4fd-c3b5-41a4-98c7-2540d451671f","order_by":3,"name":"Dr. Solomon Tsehay Feleke","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"Dr.","firstName":"Solomon","middleName":"Tsehay","lastName":"Feleke","suffix":""}],"badges":[],"createdAt":"2026-03-06 19:23:52","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9053475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9053475/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104339873,"identity":"1116f3bc-7124-4106-81a7-5b4052f34700","added_by":"auto","created_at":"2026-03-10 16:27:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":828424,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAdministrative ma of Addis Ababa, highlighting the study sub-cities (Source: Addis Ababa City Administration, 2024).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9053475/v1/7826b13dcf5abb242a561373.png"},{"id":104405841,"identity":"c7bd126b-8f42-4a73-875e-85168a6a5bcc","added_by":"auto","created_at":"2026-03-11 12:23:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2742060,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9053475/v1/fd1bad62-b3b0-46fa-ba0a-0020978441e6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFood Inflation, Institutional Support, and Household Coping Strategies: Implications for Child Nutritional Resilience in Urban Ethiopia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFood inflation is a critical determinant of household welfare in low- and middle-income countries. In market-dependent urban centers like those in Ethiopia, rising food prices erode purchasing power and constrain dietary diversity, disproportionately affecting low-income households (Headey \u0026amp; Ruel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Abay et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Theoretically, this vulnerability is grounded in Household Demand Theory and Engel\u0026rsquo;s Law, which suggest that as real income decline due to inflationary shocks, food expenditure shares rise, forcing trade-offs that compromise nutritional intake (Deaton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Houthakker, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1957\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo mitigate these shocks, households rely on a combination of institutional support and adaptive coping strategies. Institutional mechanisms such as the Urban Productive Safety Net Program (USN), school feeding, and consumer associations aim to buffer nutritional stress, yet coverage gaps often leave the most vulnerable urban populations underserved (Berhane et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sabates-Wheeler \u0026amp; Devereux, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, households employ coping strategies such as rationing, dietary substitution, and reliance on informal networks (e.g., \u003cem\u003eIdir\u003c/em\u003e). While measured by the Coping Strategies Index (CSI) as indicators of short-term resilience, these practices can jeopardize long-term health, particularly when they involve reducing food quality or quantity (Hadley et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Maxwell et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMalnutrition in urban Ethiopia remains a multidimensional challenge. While indicators such as the Food Consumption Score (FCS) and the Household Food Insecurity Access Scale (HFIAS) track access, they often fail to capture the complexity of anthropometric deficits. The Composite Index of Anthropometric Failure (CIAF) addresses this by consolidating height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ) into a single measure (Svedberg, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Nandy et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This approach provides a more sensitive and policy-relevant metric than isolated Z-scores, as it identifies children suffering from multiple, overlapping nutritional failures (Tette et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dasguta et al., 2018).\u003c/p\u003e \u003cp\u003eDespite extensive research on objective food price shocks and rural food security (Bachewe et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Abay et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), there is a lack of literature integrating institutional support, household spending behaviors, and the conceptual dimensions of inflation within a unified urban framework. Evidence suggests that subjective assessments of food price volatility often influence purchasing behavior as strongly as objective data (Hirvonen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This study examines how socio-economic and demographic factors, institutional interventions, and perceptions of inflation interact to shape nutritional outcomes in urban Ethiopia. By applying the Composite Index of Anthropometric Failure (CIAF) alongside binary and multinomial logit models, the research integrates economic and behavioral perspectives to provide a comprehensive analysis of the pathways through which food inflation translates into anthropometric failure in urban Ethiopia.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area and Research Design\u003c/h2\u003e \u003cp\u003eThe study was conducted in Addis Ababa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), Ethiopia\u0026rsquo;s capital and a major African diplomatic hub, which serves as a critical urban context for examining food inflation. Spanning 527 km\u003csup\u003e2\u003c/sup\u003e and with a population exceeding 5.6\u0026nbsp;million, the city is characterized by rapid urbanization and significant socioeconomic disparity; over one-third of households live below the poverty line, and 55% reside in informal settlements. Addis Ababa represents an ideal case for food security research, as 98% of food consumption is market dependent. Since 2020, annual food inflation has exceeded 28%, driven by currency depreciation and supply chain disruptions. To capture these dynamics, a cross-sectional mixed-methods design was implemented from October to December 2024, integrating quantitative estimates of nutritional outcomes with qualitative insights into household coping mechanisms (Creswell \u0026amp; lano Clark, 2017).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Samling Framework\u003c/h2\u003e \u003cp\u003eA stratified two-stage cluster sampling technique ensured representativeness across the city\u0026rsquo;s eleven sub-cities. In the first stage, 52 enumeration areas were randomly selected from municipal registries. In the second stage, 624 households were systematically sampled and proportionally allocated based on population density. The sample size was determined using Cochran\u0026rsquo;s (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1963\u003c/span\u003e) formula, assuming a 58% prevalence of food insecurity, a 95% confidence level, and a 5% margin of error. The final calculation incorporated a design effect of 1.5 to account for clustering and a 10% non-response contingency. Within these households, 197 children aged 6\u0026ndash;59 months were identified and screened for nutritional status using standardized WHO protocols.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\mathbf{n}}_{0}=\\frac{{\\mathbf{z}}^{2}\\mathbf{p}\\mathbf{q}}{{\\mathbf{e}}^{2}}\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e(Eq.\u0026nbsp;1)\u003c/em\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{I}\\text{n}\\text{i}\\text{t}\\text{i}\\text{a}\\text{l}\\text{s}\\text{a}\\text{m}\\text{l}\\text{e}\\text{s}\\text{i}\\text{z}\\text{e},{\\text{n}}_{0}=\\frac{{\\left(1.96\\right)}^{2}\\text{*}\\left(0.58\\right)\\text{*}\\left(0.42\\right)}{{\\left(0.05\\right)}^{2}}=374$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\mathbf{n}=\\frac{{\\mathbf{n}}_{0}}{1+\\frac{({\\mathbf{n}}_{0}-1)}{\\mathbf{N}}}\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e(Eq.\u0026nbsp;2)\u003c/em\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{F}\\text{i}\\text{n}\\text{i}\\text{t}\\text{e}\\text{o}\\text{u}\\text{l}\\text{a}\\text{t}\\text{i}\\text{o}\\text{n}\\text{c}\\text{o}\\text{r}\\text{r}\\text{e}\\text{c}\\text{t}\\text{i}\\text{o}\\text{n},\\text{n}=\\frac{374}{1+\\frac{\\left(374-1\\right)}{\\text{3,859,999}}}=374$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDesign effect (DEFF\u0026thinsp;=\u0026thinsp;1.5): 374x1.5\u0026thinsp;=\u0026thinsp;561\u003c/p\u003e \u003cp\u003eNon-response contingency (10%): 561x10%=56\u003c/p\u003e \u003cp\u003eFinal sample size\u0026thinsp;=\u0026thinsp;617 (rounded, 624) households\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Collection and Variable Selection\u003c/h2\u003e \u003cp\u003ePrimary data were collected using structured questionnaires and anthropometric tools, including digital scales and height boards (Bauman et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The primary dependent variable was the Composite Index of Anthropometric Failure (CIAF), which identifies children who experience one or more anthropometric failures (stunting, wasting, or underweight) and provides a more robust measure of compound malnutrition than isolated Z-scores (Svedberg, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Nandy et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Independent variables included five core dimensions: (1) sociodemographic characteristics (household size, caregiver education), (2) economic determinants (income and food/non-food expenditure), (3) institutional factors (participation in the Urban Productive Safety Net Programme and school feeding), (4) adaptive coping strategies assessed through the Coping Strategies Index, and (5) perceptual dimensions, including subjective inflation assessments and household information sources. This multidimensional framework enabled a holistic understanding of how economic capacity, social protection, behavioral adaptation, and perceived market conditions jointly influence child nutritional outcomes (Hirvonen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analytical Framework\u003c/h2\u003e \u003cp\u003eThe analysis employed advanced econometric models to examine the relationships among inflation, institutional support, and nutrition. Continuous anthropometric indicators, height-for-age (HAZ), weight-for-age (WAZ), and weight-for-height (WHZ), were analyzed using Seemingly Unrelated Regression Equations (SURE) to account for correlated error structures across equations (Zellner,1962). For the CIAF, discrete choice methods were used; a binary logit model distinguished between the presence or absence of anthropometric failure, while a multinomial logit model analyzed mutually exclusive categories of compound malnutrition. These models provided the statistical power necessary to disentangle the associations between perceived food inflation and long-term nutritional well-being.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Quality Assurance\u003c/h2\u003e \u003cp\u003eTo ensure methodological rigor, enumerators received intensive training, and instruments were retested outside the sample. Quality control procedures included household verification in 10% of clusters and double data entry to minimize errors. Data collection was timed during the post-harvest period (October-December) to capture realistic fluctuations in food prices for staple commodities, thereby ensuring that the findings reflect the actual inflationary experiences of urban households.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Nutritional Profile of Children\u003c/h2\u003e \u003cp\u003eThe nutritional status of the 197 children surveyed in Addis Ababa revealed an unusual profile that differed from broader national and global trends (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). While stunting is usually the main form of malnutrition, this study found it affected only 6.1% of the total group, whereas underweight and wasting were much more common at 42.1% and 18.8%, respectively (Woldekidan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; EPHI \u0026amp; ICF, 2019). The data showed a period of relative protection for infants aged 0 to 6 months probably due to exclusive breastfeeding\u0026mdash;as there were no recorded cases of wasting, stunting, or being underweight. However, a significant shift occurred in the 7 to 11-month group, which turned out to be the most vulnerable. In this group, 83.3% were classified as underweight, 41.2% were wasted, and 31.6% were stunted, aligning with the high-risk transition to complementary feeding (Victora et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs children age into the 12 to 17-month range, the rate of being underweight stays high at 56.7%, although stunting drops significantly to 4.5%, and wasting decreases to 22.7%. By the 18 to 24-month stage, stunting completely disappears in the sample, and both wasting and underweight rates drop to 18.5%. Among the 125 older children aged 25 to 59 months, the underweight rate levels off at 40.0%, while wasting remains lower at 15.2%. Throughout the entire under-five population, a consistent pattern of overall undernourishment continues at about 24.9%, regardless of age. These findings highlight that acute malnutrition and weight deficits are the main nutritional issues in these urban households, especially during the critical 6 to 23-month period of growth faltering (Headey \u0026amp; Alderman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutritional Status of Under-Five Children by Age in the Study Households\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThinness/Wasting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStunting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUndernourished\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal under_2ys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (45.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31 (24.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal under_5ys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49 (24.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThinness/ Wasting (W/H \u0026lt;- 2z-score), Stunting (H/A \u0026lt;- 2z-score), Underweight (W/A \u0026lt;-2 z score), Undernourished (MUAC\u0026thinsp;\u0026lt;\u0026thinsp;125 mm)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe study\u0026rsquo;s gender-specific results showed notable disparities in nutritional outcomes, especially regarding weight-related indicators (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Boys were more affected by being underweight, representing 85.0% of all cases and all instances of severe underweight. While girls made up the majority of moderate wasting cases (58.8%), boys accounted for a higher proportion of overall wasting (58.8%) and an even larger share of severe cases at 76.5%, indicating a greater vulnerability to acute nutritional deficits among boys.\u003c/p\u003e \u003cp\u003eIn contrast, chronic malnutrition, measured through stunting, was evenly distributed between both genders at 50.0% (Woldekidan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the severity of these cases varied; girls accounted for 62.5% of severe stunting, while boys made up all moderate cases. Regarding undernourishment measured by Mid-Upper Arm Circumference (MUAC), where only severe cases were recorded, girls showed a slightly higher prevalence at 54.8% compared to 45.2% for boys. Ultimately, although girls demonstrated a higher susceptibility to chronic growth failure and low MUAC, boys in this urban Addis Ababa sample faced a significantly greater risk of being underweight and severely wasted, consistent with patterns where male children often show higher vulnerability to environmental stress (Headey \u0026amp; Alderman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutritional Status of Under-Five Children by Sex in the Study Households\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutritional Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePrevalence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys No. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGirls No. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal No. (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThinness/Wasting (W/H or W/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (76.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStunting (H/A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (W/A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (80.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (85.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndernourished (MUAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Composite Index of Anthropometric Failure (CIAF) showed that 21.8% of children experienced at least one type of growth problem, while the majority (78.2%) were classified as normal (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among those with failures, wasting alone was the most common category (9.1%), followed closely by the combination of wasting and underweight (8.1%), and wasting only (4.1%). Notably, cases of stunting combined with underweight were rare (0.5%), and there were no recorded cases of the triple burden, stunting, wasting, and underweight, or overweight.\u003c/p\u003e \u003cp\u003eStatistical analysis revealed significant relationships between nutritional outcomes and both age and sex. The prevalence of underweight was highest among infants aged 7\u0026ndash;11 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.010), highlighting a vulnerable period during the transition to complementary feeding (Teshome et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Victora et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sex-disaggregated data showed that boys experienced a disproportionate rate of anthropometric failure (55.8%) compared to girls (44.2%). Specifically, boys were notably affected by both moderate and severe underweight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005) and severe wasting (p\u0026thinsp;\u0026lt;\u0026thinsp;0.025), accounting for 87.5% of those with dual wasting-underweight deficits. Conversely, girls exhibited higher rates of chronic conditions such as stunting combined with underweight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.007).\u003c/p\u003e \u003cp\u003eThese findings align with evidence from Sub-Saharan Africa and South Asia, where boys often show higher susceptibility to acute malnutrition and environmental stress, while girls may more frequently present with chronic conditions like stunting (Wamani et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Keino et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This pattern highlights that in urban Addis Ababa, the main challenges are acute wasting and underweight, especially during the critical 6 to 23-month period (Woldekidan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Headey \u0026amp; Alderman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComposite Index of Anthropometric Failure Among Under-Five Children in the Study Area.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIAF Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eNo.(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal sample (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA. Without anthropometric failure (normal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (57.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e154 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB. Thinness only (Wasting only)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC. Thinness\u0026thinsp;+\u0026thinsp;Underweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (87.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD. Stunting\u0026thinsp;+\u0026thinsp;Thinness\u0026thinsp;+\u0026thinsp;Underweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE. Stunting\u0026thinsp;+\u0026thinsp;Underweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF. Stunting only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG. Excess weight (overweight/obese)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH. Stunting\u0026thinsp;+\u0026thinsp;Excess weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI. Underweight only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e197 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal anthropometric failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (55.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (44.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Caregiver Characteristics and Anthropometric Failure\u003c/h2\u003e \u003cp\u003eAnalysis of the socio-demographic factors linked to anthropometric failure (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that most household-level variables, including house ownership (p\u0026thinsp;=\u0026thinsp;0.192), household size (p\u0026thinsp;=\u0026thinsp;0.316), and the age or sex of the household head (p\u0026thinsp;\u0026gt;\u0026thinsp;0.50), were not statistically significant. Likewise, the educational level (p\u0026thinsp;=\u0026thinsp;0.519) and employment status (p\u0026thinsp;=\u0026thinsp;0.310) of the household head were not major factors influencing nutritional outcomes, although children of casual laborers had the highest rate of multiple failures at 34.5%.\u003c/p\u003e \u003cp\u003eIn contrast, breastfeeding duration was a highly significant protective factor (p\u0026thinsp;=\u0026thinsp;0.003); children without anthropometric failures had a significantly longer average breastfeeding duration compared to those with multiple failures. This emphasizes the vital role of breastfeeding in reducing complex malnutrition in urban settings. Additionally, caregiver education was significantly associated with stunting (p\u0026thinsp;=\u0026thinsp;0.024), highlighting the influence of maternal knowledge on childcare practices (Smith \u0026amp; Haddad, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Economic factors also played a crucial role, as the household food expenditure share was significantly linked to underweight (p\u0026thinsp;=\u0026thinsp;0.02) and was marginally associated with wasting (p\u0026thinsp;=\u0026thinsp;0.05). These findings underscore the vulnerability of acute nutritional indicators to income changes and feeding practices, even in environments where chronic stunting remains comparatively low.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Of Household and Caregiver Characteristics with Child CIAF (Children aged 6\u0026ndash;59 months, n 197)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultiple failures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHouse ownership (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRent (Government)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRent (Private)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex of HH head (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of HH head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation of HH head (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever attended\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElementary (1\u0026ndash;6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary (9\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEmployment status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCasual labourer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.3. \u003cb\u003eSocioeconomic Inequality, Institutional Support, and Nutritional Risk\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe analysis of economic characteristics showed that household resource allocation and access to support systems significantly influence nutritional outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Food expenditure share was identified as a key factor (p\u0026thinsp;=\u0026thinsp;0.011), with households of children without anthropometric failure spending notably more on food (1,729.8 ETB) compared to those with multiple failures (1,434.4 ETB). This underscores the protective effect of higher food spending, especially since children in households dependent on casual labor\u0026mdash;characterized by income instability\u0026mdash;had the highest rate of multiple failures at 34.5%.\u003c/p\u003e \u003cp\u003eInstitutional support mechanisms showed significant, though nuanced, associations with CIAF status. Households with health insurance coverage (p\u0026thinsp;=\u0026thinsp;0.014) and transport subsidies (p\u0026thinsp;=\u0026thinsp;0.043) reported higher rates of multiple failures (30.8% and 32.6%, respectively). Rather than indicating program inefficacy, these patterns probably reflect the targeted enrollment of the most economically distressed families (Headey \u0026amp; Alderman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In contrast, informal support systems such as borrowing and mutual aid networks served as immediate buffers, significantly reducing the risks of wasting (p\u0026thinsp;=\u0026thinsp;0.031) and underweight (p\u0026thinsp;=\u0026thinsp;0.030).\u003c/p\u003e \u003cp\u003eWhile children from \"better-off\" households and school feeding participants showed no failures, broader indicators like wealth status (p\u0026thinsp;=\u0026thinsp;0.232), household savings (p\u0026thinsp;=\u0026thinsp;0.208), and the Urban Productive Safety Net Program (p\u0026thinsp;=\u0026thinsp;0.293) did not significantly distinguish between groups. These findings imply that in urban Addis Ababa, informal social protection and direct food expenditures offer more immediate nutritional resilience than formal programs, which may face challenges with coverage and targeting (Devereux \u0026amp; Sabates-Wheeler, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; FAO, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHousehold Economic Characteristics and Institutional Determinants by Child Composite Index of Anthropometric Failure (CIAF), Under Five Children (n 197)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic Characteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultiple Failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold savings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2,934.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,591.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWealth status (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUltra-poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetter-off\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEmployment of household head (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGov\u0026rsquo;t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCasual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHousing tenure (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGov\u0026rsquo;t rent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate rent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood expenditure share\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,729.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,434.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-food expenditure share\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,9390.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,4536.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUrban Productive Safety Net Program (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSchool Feeding Program (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHealth insurance coverage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTransport subsidy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.043*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e3.4. \u003cb\u003eCoping, Information, and Behavioral Adaptation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe analysis of information and perceptual factors (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed that consistent behavioral practices and strategic coping mechanisms were more important than merely accessing general information. Breastfeeding duration emerged as a strong protective factor (p\u0026thinsp;=\u0026thinsp;0.004); children without anthropometric failure had a significantly longer average duration (6.44 years) than those with multiple failures (5.63 years), emphasizing the vital role of continued breastfeeding in supporting growth (Victora et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, higher household food expenditure significantly lowered the risk of failure (p\u0026thinsp;=\u0026thinsp;0.011), as families without growth deficits spent more on food (1,729.8 ETB) than those with multiple failures (1,434.4 ETB).\u003c/p\u003e \u003cp\u003ePerceptual and coping factors also affected nutritional outcomes. Retrospective perceptions of food inflation over a 12-month period nearly reached significance (p\u0026thinsp;=\u0026thinsp;0.065), indicating that long-term inflation pressures are a stronger predictor of nutritional risk than current perceptions (p\u0026thinsp;=\u0026thinsp;0.917). Additionally, household coping strategies showed marginal significance (p\u0026thinsp;=\u0026thinsp;0.075); families that used intra-household consumption adjustments had the lowest prevalence of multiple failures (4.4%), while those forced into immediate food-related adjustments experienced the highest (25.6%).\u003c/p\u003e \u003cp\u003eIn contrast, variables such as household food safety risks (p\u0026thinsp;=\u0026thinsp;0.514), Food Consumption Scores (FCS) (p\u0026thinsp;=\u0026thinsp;0.449), and sources of nutrition or price information (p\u0026thinsp;\u0026gt;\u0026thinsp;0.20) did not statistically differentiate between failure groups. These results suggest that access to information and current food safety perceptions are less influential, while nutrition-sensitive coping strategies and resource allocation are key in reducing the impact of economic shocks on child health.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation and Adaptive Coping Strategies by Child Composite Index of Anthropometric Failure (CIAF), Under‑Five Children (n 197)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation and perceptual factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo Failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMultiple Failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHousehold food safety risk (12 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFood Consumption Score (FCS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcceptable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorderline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePerception of current food inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePerception of food inflation (12 months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.065*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSource of food price information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKebele\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSource of nutrition knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePersonal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of breastfeeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDecision on food price coping (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute food-related adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.075\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-essential consumption cut\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntra-household consumption adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026dagger;Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e3.5. \u003cb\u003eEconometric Model Performance and Validity\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe econometric analysis of child nutritional outcomes used various modeling techniques to identify the factors contributing to anthropometric failure (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Using Seemingly Unrelated Regression (SUR) to account for potential correlations among weight-for-height (WHZ), weight-for-age (WAZ), and height-for-age (HAZ) z-scores, the results showed different levels of explanatory power. The WHZ model had a Pseudo R^2 of 0.205 (p\u0026thinsp;=\u0026thinsp;0.1454), while the WAZ and HAZ models had lower Pseudo R^2 values of 0.189 and 0.132, respectively.\u003c/p\u003e \u003cp\u003eTo analyze the Composite Index of Anthropometric Failure (CIAF), both Binary and Multinomial Logit Regressions were used. The Binary Logit Regression model, which distinguished between any failure and no failure, achieved a Log Likelihood of -73.20 and a Pseudo R2 of 0.185 (p\u0026thinsp;=\u0026thinsp;0.1265). In contrast, the Multinomial Logit Regression\u0026mdash;which considers specific categories of failure\u0026mdash;offered significantly greater explanatory power, with a Pseudo R2 of 0.492. Although the probability values for these models (p\u0026thinsp;=\u0026thinsp;0.1179 to 0.7715) suggest that the independent variables collectively fall just outside the usual threshold for overall model significance, the high Pseudo R2 in the multinomial model indicates that the selected predictors are particularly effective at explaining the complexities of nutritional deficits.\u003c/p\u003e \u003cp\u003eThe strength of the links between food inflation, institutional support, and child nutritional outcomes was evaluated using a triangulated econometric approach. Continuous anthropometric indicators (HAZ, WAZ, WHZ) were estimated through Seemingly Unrelated Regression (SUR), which accounts for correlated error structures across equations and enhances efficiency compared to separate OLS estimates (Zellner, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1962\u003c/span\u003e). The SUR model showed moderate explanatory power, with R\u0026sup2; values of 0.205 for WHZ, 0.189 for WAZ, and 0.132 for HAZ, indicating that weight-based acute indicators are more responsive to short-term market shocks than the slower-changing height-for-age deficit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGoodness-of-Fit Statistics for Econometric Models of Child Nutritional Outcomes in Urban Ethiopia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLR chi\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;χ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLog Likelihood\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePseudo R\u0026sup2; (McFadden)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeemingly Unrelated Regression (SUR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWAZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinary Logit Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-73.201864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultinomial Logit Regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-67.087265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo capture multidimensional malnutrition, discrete choice models were applied to the Composite Index of Anthropometric Failure (CIAF). The binary logit model (failure vs. no failure) yielded a Pseudo R\u0026sup2; of 0.185; however, the multinomial logit model, which distinguishes mutually exclusive combinations of anthropometric deficits (Svedberg, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Nandy et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), achieved substantially higher explanatory power (Pseudo R\u0026sup2; 0.492). The multinomial specification also produced notably lower AIC and BIC values than both the binary logit and SUR models, confirming its superior fit and its ability to more accurately characterize the complex determinants of failure under inflationary pressures.\u003c/p\u003e \u003cp\u003eModel validity was confirmed through likelihood ratio tests, Wald tests, and the use of robust standard errors to account for clustering at the enumeration-area level. By combining SUR for continuous Z-scores with multinomial logit for multidimensional failure categories, the analysis maintains methodological rigor consistent with international nutrition econometrics (Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and provides a comprehensive framework for understanding how economic shocks and institutional gaps influence complex anthropometric outcomes in Addis Ababa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Multivariate Analysis: Seemingly Unrelated Regression (SUR)\u003c/h2\u003e \u003cp\u003eThe Seemingly Unrelated Regression (SUR) analysis (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) highlights different pathways for child growth, showing that acute weight-based indicators (WHZ and WAZ) are highly sensitive to immediate economic buffers and institutional support, while height-for-age (HAZ) reflects long-term sociodemographic factors. Participation in the Urban Productive Safety Net Program significantly improved WHZ (β\u0026thinsp;=\u0026thinsp;1.100, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and informal safety nets (Idir) consistently protected child growth, significantly increasing WHZ (β\u0026thinsp;=\u0026thinsp;0.630, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and having a positive marginal effect on WAZ. Additionally, house ownership was a significant positive predictor of WAZ (β\u0026thinsp;=\u0026thinsp;0.359, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), emphasizing the importance of residential stability in maintaining nutritional health.\u003c/p\u003e \u003cp\u003eThe results also reveal a complex relationship with other institutional mechanisms. While formal interventions like school feeding and health insurance showed negative associations with nutritional Z-scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), these likely reflect \"pro-poor\" targeting or adverse selection, where households with pre-existing vulnerabilities or chronic illnesses are prioritized for enrollment (Headey \u0026amp; Alderman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, the negative correlation between the Food Consumption Score and weight-based indicators suggests a disconnect between household-level dietary diversity and actual child intake, possibly due to intra-household allocation patterns or food safety concerns.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of Child Anthropometric Outcomes (Wasting, Underweight, Stunting) among Under-Five Children in Addis Ababa: Seemingly Unrelated Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChild WHZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChild WAZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChild HAZ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.145 (0.129)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067 (0.093)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.037 (0.104)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.234 (0.299)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.026 (0.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267 (0.241)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.007 (0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001 (0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008 (0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.167 (0.104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021 (0.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.190** (0.084)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic disease condition within household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631 (0.582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.045 (0.417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.888\u003csup\u003e\u0026dagger;\u003c/sup\u003e (0.469)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEconomic determinants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold savings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.000001 (0.000008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000000 (0.000006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000001 (0.000006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.291 (0.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.115 (0.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.124 (0.240)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse ownership status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.369 (0.225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.359** (0.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.179 (0.181)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.123 (0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.105 (0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.026 (0.083)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood expenditure per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00001 (0.00022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00004 (0.00016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00009 (0.00018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdaptive coping strategies\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoping Strategies Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.014 (0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.014 (0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.007 (0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding duration (as a household-level adaptive practice)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.090 (0.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056 (0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.009 (0.065)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold food distribution decision-making patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.171 (0.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003csup\u003e\u0026dagger;\u003c/sup\u003e (0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033 (0.097)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold decision on perceived inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.071 (0.192)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041 (0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.030 (0.155)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInstitutional factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to sanitation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.098 (0.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035 (0.158)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.055 (0.178)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation in the Urban Productive Safety Net Program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.100** (0.504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.566 (0.361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.474 (0.407)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to school feeding programs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.853** (2.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.797\u003csup\u003e\u0026dagger;\u003c/sup\u003e (1.735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.707 (1.953)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMembership in consumer associations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.674 (0.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.388 (0.305)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.095 (0.344)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to finance/credit services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.750 (1.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.204 (1.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.369 (1.419)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.325 (0.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.525** (0.209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.582** (0.236)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal safety nets (e.g., \u003cem\u003eIdir\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.630** (0.284)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.384\u003csup\u003e\u0026dagger;\u003c/sup\u003e (0.203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.140 (0.229)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to transport subsidies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.014 (0.351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005 (0.251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.038 (0.283)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInformation and perceptual dimensions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood safety risk experience (12-month recall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.076 (0.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.128 (0.439)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.052 (0.494)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood Consumption Score (FCS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013 (0.010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.011 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.003 (0.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold perceptions current food price evaluations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.138 (0.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.064 (0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072 (0.150)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetrospective assessments of food price changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.020 (0.228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.099 (0.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.170 (0.183)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of food price information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.134 (0.128)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112 (0.092)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.003 (0.104)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of nutrition knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.105 (0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.034 (0.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073 (0.066)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.254 (1.525)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.387 (1.093)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.454 (1.230)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eStandard errors in parentheses. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u0026dagger;p\u0026thinsp;\u0026lt;\u0026thinsp;0.10.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.7. \u003cb\u003eMultivariate Analysis: Binary logistic regression for CIAF\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eBinary logistic regression was used to analyze the determinants of multidimensional nutritional vulnerability, defined as the likelihood of experiencing any anthropometric failure (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Among the socio-demographic, economic, and institutional predictors evaluated, only breastfeeding duration and health insurance coverage showed statistically significant associations.\u003c/p\u003e \u003cp\u003eSustained breastfeeding emerged as a key protective factor (p\u0026thinsp;=\u0026thinsp;0.008), with each additional time unit reducing the odds of failure by 33% (OR\u0026thinsp;=\u0026thinsp;0.67; 95% CI: 0.49\u0026ndash;0.90). This underscores the vital biological and adaptive role of maternal feeding practices in supporting nutritional resilience within urban inflationary environments (Fenta et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conversely, health insurance coverage was linked to a significant rise in the odds of failure (OR\u0026thinsp;=\u0026thinsp;3.22; 95% CI: 1.23\u0026ndash;8.39; p\u0026thinsp;=\u0026thinsp;0.017). Consistent with the SUR results, this probably reflects adverse selection or \"pro-poor\" targeting, where households with pre-existing illnesses or growth concerns are prioritized for enrollment or show higher detection rates due to increased service use (Ayres et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, traditional socioeconomic indicators, such as wealth index (p\u0026thinsp;=\u0026thinsp;0.638), family size (p\u0026thinsp;=\u0026thinsp;0.392), and per capita food expenditure, did not significantly predict the Composite Index of Anthropometric Failure (CIAF). Perceptual and behavioral measures, like the Food Consumption Score and retrospective inflation assessments (p\u0026thinsp;\u0026gt;\u0026thinsp;0.30), were also not significant. These findings indicate that in Addis Ababa\u0026rsquo;s current economic setting, specific biological practices and targeted institutional engagement have a more prominent impact on child nutritional outcomes than broad socioeconomic measures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of Composite Index of Anthropometric Failure among Under-Five Children: Robust Logit Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u0026ndash;3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.76\u0026ndash;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic disease condition within household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u0026ndash;3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic determinants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold savings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026ndash;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse ownership status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u0026ndash;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood expenditure per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptive coping strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoping Strategies Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding duration (as a household-level adaptive practice)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold food distribution decision-making patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u0026ndash;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold decision on perceived inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u0026ndash;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to sanitation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u0026ndash;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation in the Urban Productive Safety Net Program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026ndash;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMembership in consumer associations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u0026ndash;7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026ndash;8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal safety nets (e.g., Idir)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to transport subsidies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u0026ndash;5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation and perceptual dimensions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood Consumption Score (FCS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold perceptions current food price evaluations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026ndash;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetrospective assessments of food price changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026ndash;2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of food price information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u0026ndash;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of nutrition knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u0026ndash;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05 = *, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 = **, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 = ***\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e3.8. \u003cb\u003eMultivariate Analysis: Multinomial Logit Regression for CIAF\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Multinomial Logit Regression (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) offers detailed insight into how different factors distinguish specific categories of nutritional failure from a \"No Failure\" status. Breastfeeding duration was the main factor influencing child health (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), showing a significant positive link with the \"No Failure\" category (β\u0026thinsp;=\u0026thinsp;0.049) and leading to a lower chance of acute issues like \"Wasting Only\" and \"Wasting\u0026thinsp;+\u0026thinsp;Underweight.\" These results emphasize the importance of continued breastfeeding as a key biological support that lessens reliance on costly complementary foods during periods of inflation.\u003c/p\u003e \u003cp\u003eHousehold economic and adaptive factors further influence these outcomes. Homeownership and strategic food distribution patterns significantly reduce the likelihood of the severe \"Wasting\u0026thinsp;+\u0026thinsp;Underweight\" category (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), emphasizing the protective roles of residential stability and caregiver agency in household food management. Likewise, membership in informal safety nets (Idir) notably decreases the risk of combined nutritional failure (β = -0.085, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), highlighting the importance of local social capital. In contrast, the marginal positive link between health insurance and \"Wasting\u0026thinsp;+\u0026thinsp;Underweight\" (β\u0026thinsp;=\u0026thinsp;0.081, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) probably reflects the targeted enrollment of households with pre-existing health or economic vulnerabilities.\u003c/p\u003e \u003cp\u003eNotably, the model indicates that chronic indicators, such as \"Stunting Only,\" remain largely unaffected by these short-term economic and perceptual factors. Ultimately, while broad socioeconomic measures such as the wealth index and per capita expenditure were not significant, the severity of nutritional failure is notably influenced by maternal practices, residential stability, and informal social networks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage Marginal Effects from Multinomial Logit Estimates of Determinants of CIAF Categories among Under-Five Children in Addis Ababa\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Failure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWasting Only\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWasting\u0026thinsp;+\u0026thinsp;Underweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStunting\u0026thinsp;+\u0026thinsp;Underweight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStunting Only\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-demographic characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.019 (0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.029 (0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010 (0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.058 (0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056 (0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002 (0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001 (0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.003 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational attainment of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.000 (0.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004 (0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.004 (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic disease condition within household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.576 (16052.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.559 (13687.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.018 (11251.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic determinants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold savings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.039 (0.083)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026 (0.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013 (0.066)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse ownership status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.042 (0.059)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.081\u003csup\u003e\u0026dagger;\u003c/sup\u003e (0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.010 (0.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.012 (0.022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022 (0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood expenditure per capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptive coping strategies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoping Strategies Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.000 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreastfeeding duration (as a household-level adaptive practice)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.049** (0.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.030 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.019 (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold food distribution decision-making patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.007 (0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003csup\u003e\u0026dagger;\u003c/sup\u003e (0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.036\u003csup\u003e\u0026dagger;\u003c/sup\u003e (0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold decision on perceived inflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.045 (0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.023 (0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.023 (0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to sanitation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014 (0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.055 (0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040 (0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation in the Urban Productive Safety Net Program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.911 (849.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135 (105.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.046 (955.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to school feeding programs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.891 (11854.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.176 (10328.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.068 (8009.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMembership in consumer associations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.015 (0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045 (0.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.030 (0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to finance/credit services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.059 (8361.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.317 (7303.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.742 (5622.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth insurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.082 (0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.001 (0.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003csup\u003e\u0026dagger;\u003c/sup\u003e (0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal safety nets (e.g., \u003cem\u003eIdir\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016 (0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070 (0.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.085\u003csup\u003e\u0026dagger;\u003c/sup\u003e (0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to transport subsidies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.074 (0.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105 (0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.032 (0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation and perceptual dimensions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood safety risk experience (12-month recall)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.145 (2606.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.351 (2630.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.793 (1088.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood Consumption Score (FCS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.001 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e~\u0026thinsp;0.001 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;0.000 (0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold perceptions current food price evaluations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.046 (0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009 (0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036 (0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetrospective assessments of food price changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.003 (0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033 (0.040)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.030 (0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of food price information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e~\u0026thinsp;0.000 (0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005 (0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.005 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of nutrition knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.016 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022 (0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.006 (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e~\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u0026dagger;p\u0026thinsp;\u0026lt;\u0026thinsp;0.10.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInstitutional and Economic Buffers.\u003c/b\u003e Economic stability also plays a decisive role. House ownership substantially reduces the likelihood of \u0026ldquo;Wasting\u0026thinsp;+\u0026thinsp;Underweight\u0026rdquo; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), reflecting its function as both a financial asset and a psychological stabilizer during price shocks. Informal community safety nets, particularly \u003cem\u003eIdir\u003c/em\u003e, similarly exhibit protective effects against overlapping failures (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Qualitative accounts highlight that these informal institutions often provide more timely and flexible support for food and healthcare needs than formal systems.\u003c/p\u003e \u003cp\u003eIn contrast, health insurance coverage shows a marginal positive association with \u0026ldquo;Wasting\u0026thinsp;+\u0026thinsp;Underweight\u0026rdquo; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), likely indicating adverse selection. Many caregivers reported enrolling only after a child became ill, suggesting that insurance uptake may be reactive rather than preventative. Formal social protection mechanisms including UPSNP participation and school feeding exhibited negligible marginal effects on chronic forms of anthropometric failure. Beneficiary feedback points to implementation constraints such as delayed transfers and concerns over meal quantity and nutritional adequacy, which may explain their limited influence in the quantitative model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConsistency with Broader Evidence.\u003c/b\u003e The findings align with the broader \u0026ldquo;nutritional resilience\u0026rdquo; literature, emphasizing the mediating roles of household practices and social capital in cushioning the effects of macroeconomic shocks (Headey \u0026amp; Ruel, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The near-zero marginal effects observed for \u0026ldquo;Stunting Only\u0026rdquo; and \u0026ldquo;Stunting\u0026thinsp;+\u0026thinsp;Underweight\u0026rdquo; likely stem from small sub-sample sizes and the inherently overlapping nature of chronic nutritional deficits. Nonetheless, the model\u0026rsquo;s strong performance (Pseudo R\u0026sup2; 0.492) demonstrates that decomposing composite indicators yields more nuanced insights into urban nutritional vulnerability than binary or single-dimension classifications.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusion and Policy Recommendations","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Conclusion\u003c/h2\u003e \u003cp\u003eThis study investigated how food inflation affects the nutritional resilience of children aged 6\u0026ndash;59 months in Addis Ababa using the Composite Index of Anthropometric Failure (CIAF). The rate of failure was 21.8%, with wasting (18.8%) and underweight (42.1%) surprisingly higher than stunting (6.1%). Vulnerability was greatest among infants aged 7\u0026ndash;11 months, while sex-specific analysis revealed boys were more affected by severe wasting and underweight. Overall, boys represented 55.8% of nutritional failures compared to 44.2% among girls, emphasizing sex-related vulnerability pathways.\u003c/p\u003e \u003cp\u003eTriangulated econometric analysis (SUR, Binary, and Multinomial Logit) confirmed that nutritional outcomes are influenced more by adaptive behaviors and social capital than by wealth alone. Sustained breastfeeding acted as a key buffer, decreasing the odds of anthropometric failure by 33% (OR\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;=\u0026thinsp;0.008), while informal safety nets like Idir and formal programs such as the UPSNP significantly enhanced weight-based indicators (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, negative associations with health insurance and school feeding likely reflect \"pro-poor\" targeting toward already vulnerable households.\u003c/p\u003e \u003cp\u003eUltimately, the prevalence of acute malnutrition and underweight in this urban setting calls for integrated, multidimensional strategies. Strengthening resilience against inflationary pressures requires a mix of enhanced social protection, health system support, and targeted behavioral interventions focused on the critical 6\u0026ndash;23-month growth period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Policy Recommendations\u003c/h2\u003e \u003cp\u003eEnhancing nutritional resilience in inflation-prone urban areas involves integrating nutrition-focused elements into formal protection systems while strengthening their links to community groups. The Urban Productive Safety Net Program should evolve from simple consumption smoothing to a comprehensive approach that includes growth-oriented cash transfers and regular nutrition counseling. At the same time, policymakers need to formalize partnerships between government agencies and informal organizations like Idir, utilizing their high levels of trust and social cohesion to spread food price information and provide emergency support.\u003c/p\u003e \u003cp\u003eProgram redesign is crucial to expand preventive reach and reduce adverse selection. School feeding programs need higher nutrient density, while health insurance schemes should focus on simplified enrollment and lower out-of-pocket expenses to promote preventive care. To protect vital biological buffers, labor policy reforms must enforce paid maternity leave and workplace accommodations, ensuring breastfeeding remains a key non-market defense for young children.\u003c/p\u003e \u003cp\u003eFinally, government and humanitarian actors should adopt the Composite Index of Anthropometric Failure (CIAF) for multidimensional monitoring, allowing earlier detection of children experiencing multiple deprivations. Interventions must focus specifically on the 6\u0026ndash;23-month period, when wasting and underweight are at their peak. By combining health and human capital investments to address chronic household illnesses and caregiver education gaps, policies can better support long-term resilience against urban undernutrition.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Strengths, Limitations, and Future Research Directions","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Strengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study\u0026rsquo;s central strength lies in its methodological triangulation, integrating advanced econometric techniques with qualitative insights to generate a multidimensional understanding of urban nutritional resilience. The use of the Composite Index of Anthropometric Failure (CIAF), alongside Seemingly Unrelated Regression (SUR) and Multinomial Logit models, enabled the identification of overlapping forms of undernutrition that conventional single-indicator Z-scores often obscure. The analytical inclusion of institutional and perceptual determinants particularly subjective inflation assessments and the role of informal social capital through \u003cem\u003eIdir\u003c/em\u003e provides a rare empirical lens into how social networks and behavioural adaptations mediate nutritional outcomes in urban African settings.\u003c/p\u003e \u003cp\u003eNonetheless, several limitations warrant consideration. The cross-sectional design constrains causal inference, as it captures only one temporal point and cannot fully disentangle the dynamic relationship between inflationary pressure and child growth trajectories. Although the multinomial model demonstrated strong explanatory power, the small sample sizes for some anthropometric subcategories (e.g., \u0026ldquo;Stunting Only\u0026rdquo;) may have reduced the precision of estimated effects. Furthermore, reliance on 12-month recall for food security, food safety concerns, and price perceptions introduces potential recall bias, potentially smoothing over periods of acute inflationary volatility and underestimating intra-year shocks experienced by households.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Future Research Directions\u003c/h2\u003e \u003cp\u003eFuture research would benefit from longitudinal or panel designs to capture how prolonged exposure to food inflation influences transitions between acute and chronic forms of undernutrition, including progression pathways from wasting to stunting. Further investigation into the \u0026ldquo;plus\u0026rdquo; components of social protection such as complementarities between cash transfers, growth monitoring, and behaviour change communication could illuminate the mechanisms underlying current implementation gaps in formal programs. There is also substantial scope to examine the scalability and formal integration of informal safety nets like \u003cem\u003eIdir\u003c/em\u003e within broader urban disaster risk management frameworks, while ensuring that their responsiveness and community trust are not undermined. Finally, as urban vulnerability is increasingly shaped by environmental and climatic stressors, future studies should incorporate factors such as water quality, climate-induced migration, and infrastructural fragility into the CIAF analytical framework to better understand the compounded risks facing children in rapidly expanding cities such as Addis Ababa.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent\u003c/strong\u003e. Ethical clearance for this study was granted by the Addis Ababa University Institutional Review Board (Ref: 084/08/2024). Before collecting data, the study\u0026apos;s objectives and procedures were clearly explained to all potential participants. Written informed consent was obtained from each participant, who took part voluntarily and understood that they could withdraw at any time. For participants under 18, consent was obtained from a parent or legal guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish and Clinical Trial Registration\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSGY,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eConceptualization, study design, data collection, data curation, formal analysis, methodology, investigation, visualization, manuscript writing (original draft, review, and editing), and validation; MMT, Study design, methodology, analysis, manuscript review, supervision, and validation; MA, Study design, methodology, analysis, manuscript review, supervision, and validation; STF, Study design, methodology, analysis, manuscript review, supervision, and validation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbay, K. A., Hirvonen, K., \u0026amp; Minten, B. (2021). Rising food prices and household welfare in Ethiopia: Evidence from urban centers. International Food Policy Research Institute (IFPRI).\u003c/li\u003e\n\u003cli\u003eAyres, S., Jones, M., \u0026amp; Smith, L. (2024). Institutional and socioeconomic determinants of multidimensional malnutrition: Evidence from urban centers. Journal of Public Health Policy, 45(2), 112\u0026ndash;128.\u003c/li\u003e\n\u003cli\u003eBachewe, F. N., Bartels, K. J., \u0026amp; Minten, B. (2017). Agricultural transformation in Africa? Assessing the evidence from Ethiopia. World Development, 99, 18\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eBauman, A., Ernst, K., Hayden, M., Roe, D. J., Murray, R., Agawo, M., Munga, S., Schmahl, E., \u0026amp; Taren, D. (2018). Assessing community health: An innovative tool for measuring height and length. Journal of Tropical Pediatrics, 64(2), 146\u0026ndash;150. https://doi.org/10.1093/tropej/fmx046 (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eBerhane, G., Devereux, S., Hoddinott, J., Hoel, J., Roelen, K., Abay, K., ... \u0026amp; Woldehanna, T. (2014). Evaluation of the Social Cash Transfer Pilot Programme, Tigray Region, Ethiopia. International Food Policy Research Institute (IFPRI).\u003c/li\u003e\n\u003cli\u003eCochran, W. G. (1963). Sampling techniques (2nd ed.). John Wiley \u0026amp; Sons.\u003c/li\u003e\n\u003cli\u003eCreswell, J. W., \u0026amp; Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). SAGE Publications.\u003c/li\u003e\n\u003cli\u003eDasgupta, A., Parthasarathy, D., \u0026amp; Basu, S. (2018). Urban food insecurity and child malnutrition: A multidimensional approach. Food Policy, 74, 60-73. https://doi.org/10.1016/j.foodpol.2017.11.007 (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eDeaton, A. (1997). The analysis of household surveys: A microeconometric approach to development policy. Johns Hopkins University Press.\u003c/li\u003e\n\u003cli\u003eDevereux, S., \u0026amp; Sabates-Wheeler, R. (2004). Transformative social protection. IDS Working Paper 232. Institute of Development Studies. https://doi.org/10.1111/j.1759-5436.2004.tb00120.x (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eEPHI. (2024). Ethiopia Demographic and Health Survey (EDHS) 2024: Ethiopia Public Health Institute. Addis Ababa, Ethiopia, and Rockville, Maryland, USA: EPHI and ICF.\u003c/li\u003e\n\u003cli\u003eFAO. (2022). The state of food security and nutrition in the world: Repurposing food and agricultural policies to make healthy diets more affordable. Food and Agriculture Organization of the United Nations.\u003c/li\u003e\n\u003cli\u003eFenta, H. M., Zewotir, T., \u0026amp; Muluneh, E. K. (2021). Spatial analysis of stunting, wasting, and underweight among under-five children in Ethiopia. BMC Nutrition, 7(1), 1-12.\u003c/li\u003e\n\u003cli\u003eHadley, C., Lindstrom, D., Tessema, F., \u0026amp; Belachew, T. (2012). Gender bias in the food insecurity experience of Ethiopian adolescents. Social Science \u0026amp; Medicine, 75(12), 2416-2424. https://doi.org/10.1016/j.socscimed.2012.09.018 (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eHeadey, D. D., \u0026amp; Alderman, H. (2019). The relative prices of healthy and unhealthy foods: Low-income urban contexts. The Journal of Nutrition, 149(12), 2120\u0026ndash;2133.\u003c/li\u003e\n\u003cli\u003eHeadey, D. D., \u0026amp; Ruel, M. T. (2020). Economic shocks and child under nutrition in the Global South. Proceedings of the National Academy of Sciences (PNAS), 117(20), 10735-10743.\u003c/li\u003e\n\u003cli\u003eHirvonen, K., Taffesse, A. S., \u0026amp; Hassen, I. W. (2016). Seasonal consumption smoothing and the cost of shaping expectations: Evidence from Ethiopia. Journal of Development Studies, 52(12), 1724\u0026ndash;1741.\u003c/li\u003e\n\u003cli\u003eHouthakker, H. S. (1957). An international comparison of household expenditure patterns, commemorating the centenary of Engel\u0026apos;s Law. Econometrica, 25(4), 532-551.\u003c/li\u003e\n\u003cli\u003eKeino, S., Plasqui, G., Ettyang, G., \u0026amp; van den Borne, B. (2014). Determinants of stunting and overweight among young children and adolescents in sub-Saharan Africa. Food and Nutrition Bulletin, 35(2), 167-178. https://doi.org/10.1177/156482651403500203 (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eLi, Z., Kim, R., Vollmer, S., \u0026amp; Subramanian, S. V. (2020). Factors are associated with child stunting, wasting, and under-weight in low- and middle-income countries. JAMA Network Open, 3(4), e203386.\u003c/li\u003e\n\u003cli\u003eMaxwell, D., Caldwell, R., \u0026amp; Langworthy, M. (2015). Measuring food insecurity: Can an indicator of behaviors and coping provide a proxy for food intake? Food Policy, 28(1), 1-11.\u003c/li\u003e\n\u003cli\u003eNandy, S., Irving, M., Gordon, D., Subramanian, S. V., \u0026amp; Smith, G. D. (2005). Poverty, child undernutrition and morbidity: New evidence from India. Bulletin of the World Health Organization, 83(3), 210-216.\u003c/li\u003e\n\u003cli\u003eSabates-Wheeler, R., \u0026amp; Devereux, S. (2018). Social protection for resilience: The case of the Productive Safety Net Programme in Ethiopia. Institute of Development Studies.\u003c/li\u003e\n\u003cli\u003eSmith, L. C., \u0026amp; Haddad, L. (2015). Reducing child under nutrition: Past drivers and priorities for the post-MDG era. World Development, 68, 180\u0026ndash;204.\u003c/li\u003e\n\u003cli\u003eSvedberg, P. (2000). Poverty and undernutrition: Theory, measurement, and policy. Oxford University Press.\u003c/li\u003e\n\u003cli\u003eTeshome, B., Kogi-Makau, W., Getahun, Z., \u0026amp; Taye, G. (2013). Magnitude and determinants of stunting in children under-five years of age in Ethiopia. Ethiopian Journal of Health Development, 23(3), 162\u0026ndash;169.\u003c/li\u003e\n\u003cli\u003eTette, E. M., Sifah, E. K., \u0026amp; Nartey, E. T. (2016). Analysis of the risk factors associated with underweight, stunting and wasting in children. International Journal of Environmental Research and Public Health, 13(3), 269.\u003c/li\u003e\n\u003cli\u003eVictora, C. G., de Onis, M., Hallal, P. C., Bl\u0026ouml;ssner, M., \u0026amp; Shrimpton, R. (2010). Worldwide timing of growth faltering: Revisiting implications for interventions. Pediatrics, 125(3), e473-e480. https://doi.org/10.1542/peds.2009-1519 (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eVictora, C. G., Bahl, R., Barros, A. J. D., Fran\u0026ccedil;a, G. V. A., Horton, S., Krasevec, J., Murch, S., Sankar, M. J., Walker, N., Rollins, N. C., \u0026amp; the Lancet Breastfeeding Series Group. (2016). Breastfeeding in the 21st century: Epidemiology, mechanisms, and lifelong effect. The Lancet, 387(10017), 475\u0026ndash;490. https://doi.org/10.1016/S0140-6736(15)01024-7 (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eVictora, C. G., Christian, P., Vidaletti, L. P., Gatica-Dom\u0026iacute;nguez, G., Menon, P., \u0026amp; Black, R. E. (2021). Revisiting maternal and child undernutrition in low-income and middle-income countries: Variable progress towards an unfinished agenda. The Lancet, 397(10282), 1388-1399. https://doi.org/10.1016/S0140-6736(21)00394-9 (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eWamani, H., \u0026Aring;str\u0026oslash;m, A. N., Peterson, S., Tyllesk\u0026auml;r, T., \u0026amp; Tumwine, J. K. (2007). Boys are more stunted than girls in sub-Saharan Africa: A meta-analysis of 16 demographic and health surveys. BMC Pediatrics, 7(1), 17. https://doi.org/10.1186/1471-2431-7-17 (doi.org in Bing)\u003c/li\u003e\n\u003cli\u003eWoldekidan, T., Adugna, M., \u0026amp; Tefera, M. M. (2024). Trends in acute vs chronic malnutrition in urban Ethiopia. African Journal of Food Science, 18(4), 110-125.\u003c/li\u003e\n\u003cli\u003eZellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias. Journal of the American Statistical Association, 57(298), 348-368.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Addis Ababa University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Food Inflation, CIAF, Nutritional Resilience, Urban Ethiopia, Econometric Modeling, Coping Strategies","lastPublishedDoi":"10.21203/rs.3.rs-9053475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9053475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Food inflation is a critical determinant of household welfare in market-dependent urban centers. In Addis Ababa, Ethiopia, rising food prices have eroded purchasing power, but the specific pathways through which institutional support and household coping strategies translate into child nutritional outcomes, measured through a multidimensional lens, remain under-researched.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study examined the impact of food inflation on the nutritional status of children aged 6-59 months in Addis Ababa, focusing on the mediating roles of institutional interventions and adaptive household behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A cross-sectional mixed-methods study was conducted from October to December 2024. Quantitative data from 624 households were analyzed using a stratified two-stage cluster sampling design. Nutritional status was assessed using the Composite Index of Anthropometric Failure (CIAF). Econometric modeling included Seemingly Unrelated Regression (SUR) for continuous Z-scores and Binary/Multinomial Logit models for binary and categorical growth failure. Qualitative interviews provided context for household coping mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The prevalence of anthropometric failure among children aged 6-59 months in Addis Ababa was 21.8%, with wasting (18.8%) and underweight (42.1%) exceeding stunting (6.1%).\u003cbr\u003e\nInfants aged 7-11 months were most vulnerable, with wasting (41.2%) and underweight (83.3%) peaking during the transition to complementary feeding. Boys were disproportionately affected by severe wasting (76.5%) and underweight (85.0%), while girls were more represented in severe stunting (62.5%). Overall, boys accounted for 55.8% of failures, compared to 44.2% among girls. Sustained breastfeeding reduced the odds of anthropometric failure by 33% (OR 0.67, p 0.008). Participation in the Urban Productive Safety Net Programme improved WHZ (β 1.100, p\u0026lt;0.05). Informal safety nets (Idir) buffered both WHZ (β 0.630, p\u0026lt;0.05) and WAZ (β 0.384, p\u0026lt;0.10). House ownership and equitable intra‑household food distribution lowered the risk of overlapping failures (“Wasting + Underweight”; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.10). Health insurance and school feeding showed marginal or negative associations, likely reflecting adverse selection. Multinomial logit models (Pseudo R² 0.492) highlighted the combined role of biological, behavioral, and institutional factors in shaping child nutritional resilience under inflation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Urban nutritional resilience in Addis Ababa is shaped less by household wealth and more by the interaction of biological practices, informal social capital, and targeted institutional support. Acute anthropometric failures proved highly sensitive to food inflation, underscoring the fragility of urban households under market shocks. Sustained breastfeeding emerged as a critical biological buffer, reducing vulnerability and reinforcing resilience. Informal safety nets such as \u003cem\u003eIdir\u003c/em\u003e provided effective community‑based protection against acute nutritional stress. Engagement in the Urban Productive Safety Net Programme (UPSNP) strengthened child growth outcomes, highlighting the role of formal social protection. Conversely, negative associations with health insurance and school feeding suggest adverse selection, as these programs disproportionately reach already vulnerable households. The findings emphasize that resilience is built through adaptive behaviors and social capital rather than wealth alone. Integrated approaches that link formal and informal systems are essential to buffer children against inflationary shocks. Nutrition‑sensitive social protection must prioritize the critical 6-23-month window, where growth faltering is most acute. Finally, adopting the Composite Index of Anthropometric Failure (CIAF) offers a multidimensional lens to identify overlapping deficits and guide comprehensive interventions.\u003c/p\u003e","manuscriptTitle":"Food Inflation, Institutional Support, and Household Coping Strategies: Implications for Child Nutritional Resilience in Urban Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-10 16:27:48","doi":"10.21203/rs.3.rs-9053475/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5647cf47-6975-43e6-8f05-27fc6147d85f","owner":[],"postedDate":"March 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64138547,"name":"Nutrition \u0026 Dietetics"}],"tags":[],"updatedAt":"2026-03-10T16:27:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-10 16:27:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9053475","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9053475","identity":"rs-9053475","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}