Distinct household coping profiles explain geographic disparities in food security across Philippine fisheries management areas

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Guiñares This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7430169/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 Food security in the Philippines is marked by significant geographic disparities, yet the underlying household behavioral patterns that drive these differences are poorly understood. This study addresses this gap by analyzing survey data from 90,886 households from 2018 to 2021 across twelve Fisheries Management Areas, employing a quantitative modeling approach to identify the drivers of food insecurity and characterize underlying coping profiles. The analysis identified significant spatial hotspots of vulnerability (particularly FMAs 5, 8, and 4) and resilience (FMA 6). To explain these disparities, Latent Class Analysis revealed four distinct coping profiles: "Resilient" (48.2%), "Community Borrowers" (13.6%), "Loan Seekers" (10.8%), and a highly vulnerable "High-Stress" group (27.5%), highlighting a widespread reliance on informal debt. A significant trend of improving food security was observed, with insecurity scores decreasing by 1.20 points in 2021 compared to 2018, coinciding with a sharp increase in the proportion of households classified as "Resilient." The findings suggest that pandemic-era social safety nets had a significant buffering effect, improving overall resilience despite underlying economic precarity. This evidence advocates for a dual policy approach: geographically targeted interventions for vulnerability hotspots and broader programs to enhance financial inclusion and build long-term household resilience against national crises. Future research should explore the distinct vulnerabilities of fishing versus non-fishing households and explore the community-level drivers within hotspots to develop more targeted policy interventions. Food security Coping strategies Latent class analysis Philippines Fisheries Management Areas (FMAs) Vulnerability Geographic disparities Figures Figure 1 Figure 2 Figure 3 1 Introduction Food security remains a critical and multifaceted challenge in the Philippines (Abasolo & Montefrio, 2025 ; Ortega-Espaldon & Medina, 2024 ; Taniushkina et al., 2024 ; Darwis et al., 2024 ; Cordero, 2024 ), a nation whose archipelagic geography, vulnerability to climate events, and uneven agricultural infrastructure (Cordero, 2024 ; Lacsa, 2024 ; dela Luna & Talavera, 2022 ) and socioeconomic inequalities (San Juan & Agustin, 2019 ; Ulep et al., 2023 ) create persistent obstacles to ensuring its people have consistent access to sufficient, safe, and nutritious food (FAO, 2020; Leroy et al., 2015 ; Yusriadi, 2025 ). While national surveys indicate that a significant portion of the population experiences food insecurity, recent findings from the 2023 National Nutrition Survey reveal that 31.4% of Filipino households face moderate to severe food insecurity, with 2.7% experiencing severe food insecurity (Cordero, 2024 ; Patalen et al., 2020 ; DOST-FNRI, 2021 )—a situation exacerbated by global shocks like the COVID-19 pandemic (Darwis et al., 2024 ; Reyes et al., 2022 )—this burden is not evenly distributed. Pronounced geographic disparities exist, with rural and coastal settings (Andriesse, 2022 ; Martinez, 2020 ; Andriesse, 2017 ), particularly in hotspot regions like the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM) and Eastern Visayas, consistently facing higher rates of insecurity linked to poverty, limited livelihoods, and environmental fragility (Pantolla & Atibagos-Nacion, 2023 ; Abuza & Lischin, 2020 ). Critical to this challenge is the fisheries sector, which provides over 50% of the nation's animal protein (Lowe et al., 2022 ; Talbot et al., 2024 ; Muallil et al., 2024 ) and employs approximately 1.6 million people (Andriesse et al., 2022 ; Maritime Fairtrade, 2023 ; Lamarca, 2018 ). However, this vital resource is under severe threat. Internal pressures, including overfishing and habitat degradation, have contributed to a significant decline in fish stocks over the past decade (Muallil et al., 2014 ; Hunnam, 2021 ; Willette et al., 2011 ). These pressures are compounded by external threats, most notably Illegal, Unreported, and Unregulated (IUU) fishing (Pomeroy et al., 2016 ; Oloko et al., 2025 ; DA-BFAR, 2024 ; USAID & Bureau of Fisheries and Aquatic Resources, 2021) and geopolitical conflict in the West Philippine Sea (Pomeroy et al., 2016 ; Arceo et al., 2024; DA-BFAR, 2024 ), which curtail access to traditional fishing grounds. In response, the Philippines adopted Fisheries Administrative Order No. 263 (FAO 263) in 2019. This order established an ecosystem-approach to fisheries management (EAFM) by delineating the country's 2.2 million km² exclusive economic zone into twelve Fisheries Management Areas (FMAs) (Fabinyi, 2024 ). This spatial framework, informed by biophysical characteristics, fish stock distributions, and administrative boundaries, provides a crucial platform for coordinated policy, science-based stock assessments, and targeted management interventions (Fagbinyi, 2024; Oceana, n.d). It specifically aims to address the limitations of a decentralized governance structure, where municipal waters (extending up to 15 km from the coastline) fall under local jurisdiction, often leading to fragmented management of shared resources that transcend political boundaries (Oceana Philippines, 2025 ). Despite this policy, coastal households continue to face significant hardship and employ a diverse range of coping strategies to navigate food shortages (Fabinyi & Barclay, 2022 ; DOST-FNRI, 2021 ), from less severe, debt-based mechanisms to more extreme, asset-depleting measures like selling fishing gear (Gebregziabher et al., 2025 ; Mozumder et al., 2023 ; Gibson et al., 2021 ; Zongo et al., 2023 ; Protacio et al., 2025 ; Golloso-Gubat et al., 2024 ). While the geographic disparities in food security are well-documented, a deeper understanding of the underlying household-level behaviors that characterize this vulnerability within the FMA framework is needed. Specifically, a gap exists in understanding how distinct, multifaceted profiles of coping strategies are distributed across the FMAs, particularly in the context of both climate and geopolitical pressures. It remains unclear if the observed geographic differences in food security can be explained by a different prevalence of these distinct behavioral profiles. Therefore, this study aims to: (1) identify the key geographic (FMA) and temporal (Year) predictors of household food insecurity; (2) identify and characterize distinct, latent profiles of household coping behaviors using Latent Class Analysis; and (3) determine how these behavioral profiles are distributed across the FMAs, thereby providing a deeper, behavioral explanation for the geographic disparities in food security in the coastal Philippines. 2 Materials and Methods 2.1 Data Source and Sampling Design This study utilizes secondary data from the Expanded National Nutrition Survey (ENNS), a nationally representative, cross-sectional survey conducted by the Department of Science and Technology-Food and Nutrition Research Institute (DOST-FNRI) of the Philippines. The analysis includes pooled data from the 2018, 2019, and 2021 survey rounds. The 2020 round was suspended due to operational challenges posed by the COVID-19 pandemic. The final sample for this analysis consists of over 90,000 households. The Expanded National Nutrition Survey (ENNS) employs a stratified multi-stage sampling design to ensure representativeness at the national, regional, and provincial/Highly Urbanized City (HUC) levels. This design is based on the 2013 Master Sample for household surveys developed by the Philippine Statistics Authority (PSA), as adopted and validated by the Food and Nutrition Research Institute (FNRI) for the ENNS rolling survey from 2018 to 2020. The household location data, originally coded by province, were aggregated into broader ecological and administrative regions for analysis. Following the framework of the Philippines' Fisheries Administrative Order No. 263, each unique location code was clustered into one of the twelve designated Fisheries Management Areas (FMAs). This spatial clustering allows for an ecosystem-based analysis of the data, aligning the household survey responses with the relevant management zones for Philippine marine resources. The ENNS employs a stratified multi-stage sampling design to ensure representativeness at national, regional, and provincial/Highly Urbanized City (HUC) levels, based on the Master Sample for household surveys developed by the PSA. Given this complex survey design, all statistical analyses presented herein were weighted using the survey weights provided in the ENNS dataset to produce unbiased, nationally representative estimates and account for clustering and stratification effects. Ethical clearance for the ENNS was obtained by the DOST-FNRI. 2.2 Variables and Measures 2.2.1 Food Security Outcome Variables Household food insecurity was measured using the full nine-item module of the Household Food Insecurity Access Scale (HFIAS), an internationally validated tool for assessing food access (Marques et al., 2015 ; Kabalo et al., 2019 ; Coates et al., 2007 ). The nine questions assess the frequency of occurrence of anxiety over food supply, insufficient quality (e.g., limited variety), and insufficient quantity of food intake over the past 30 days. The specific items include whether a household worried about food, was unable to eat preferred foods, ate a limited variety, ate unwanted foods, ate smaller meals, ate fewer meals, had no food of any kind, went to sleep hungry, or went a whole day and night without eating. From these items, two primary outcome variables were constructed: HFIAS Score (Quantitative): A continuous score was calculated by summing the numeric frequency-of-occurrence responses (0="never", 1="rarely", 2="sometimes", 3="often") to the nine questions. This results in a quantitative scale from 0 (most food secure) to 27 (most food insecure), which served as the dependent variable in the linear regression analysis. HFIAS Category (Qualitative): Households were classified into four ordinal categories based on their responses: 'Food Secure', 'Mildly Food Insecure', 'Moderately Food Insecure', and 'Severely Food Insecure', following standard HFIAS guidelines. 2.2.2 Predictor Variables Fisheries Management Area (FMA) (Qualitative): The primary geographic predictor was the Fisheries Management Area. Households were assigned to one of the 12 FMAs based on their province/HUC code (provhuc), according to the classification established by Fisheries Administrative Order 263. It should be noted that this serves as a geographic proxy, and may include non-coastal or non-fisheries-dependent households within a given province, a potential source of misclassification bias. Survey Year (Qualitative): The survey year (2018, 2019, or 2021) was included as a categorical predictor to assess temporal trends. 2.2.3 Household Coping Strategies (Qualitative) Household coping behaviors were measured using a series of binary (Yes/No) questions. For the Latent Class Analysis, seven key coping strategy variables were selected to represent a range of financial, social, and food-based behaviors while ensuring model stability. The selected strategies were: borrowing food from neighbors/relatives (stratg10), purchasing food on credit (stratg11), restricting adult consumption for children (stratg12), obtaining loans from relatives/friends (stratg15), obtaining loans from non-relatives (stratg16), selling assets (stratg17), and having a household member migrate for work (stratg18). 2.3 Statistical Analysis All statistical analyses were conducted using R (Version R-4.5.1), incorporating the survey package to account for the complex sampling design and the poLCA package for Latent Class Analysis. Missing data for the selected variables was minimal (< 5%) and was handled by listwise deletion. 2.3.1 Descriptive Statistics Survey-weighted means, standard deviations (SD), and medians were calculated for the quantitative HFIAS score. Survey-weighted frequencies and percentages were calculated for all qualitative variables. 2.3.2 Linear Regression To identify the geographic and temporal predictors of food insecurity severity, a survey-weighted generalized linear model was fitted using the svyglm function from the survey package. The continuous HFIAS score served as the dependent variable. Model assumptions, including linearity and homoscedasticity, were assessed through visual inspection of residual plots. 2.3.3 Latent Class Analysis (LCA) To identify unobserved profiles of households based on their shared patterns of coping strategies, a Latent Class Analysis was performed using the poLCA package in R, which applies finite mixture modeling to categorical data and estimates class membership probabilities based on conditional response patterns (Linzer & Lewis, 2011 ; Nylund-Gibson et al., 2022; Oberski, 2016 ). Models with two to five latent classes were fitted. The optimal number of classes was selected by identifying the model with the lowest Bayesian Information Criterion (BIC), while also considering the Akaike Information Criterion (AIC), theoretical interpretability, and class separation as indicated by entropy values (> 0.80 indicating good classification). Models with two to five classes were tested, and the 4-class model was selected as the optimal solution based on its strong theoretical interpretability and its superior model fit, as indicated by the Bayesian Information Criterion (BIC = 464543.7). 2.3.4 Latent Class Regression To link the identified behavioral profiles to geography and time, a two-step analytical approach was used. First, each household was assigned to its most likely latent class based on modal posterior probabilities from the selected 4-class LCA model. Second, a survey-weighted multinomial logistic regression was fitted, with the four-level class membership as the categorical outcome and FMA and survey year as predictors. The 'Resilient / Low Copers' class was set as the reference category. We acknowledge that this common two-step approach can potentially underestimate standard errors compared to an integrated model. 3 Results 3.1 What is the overall state of household food insecurity? The analysis reveals a statistically significant geographic disparity in household vulnerability across the Philippine FMAs, confirmed by both the prevalence of food insecurity (p < 0.001) and the intensity of coping strategies (p = 0.026). This study identifies FMAs 5, 8, and 4 as critical hotspots of vulnerability, where households consistently report the most severe levels of food insecurity and a heightened reliance on coping behaviors. FMA 5, in particular, exhibited the highest mean Coping Strategies Index (CSI) score of 14.1, indicating more frequent and severe food-related compromises, while FMA 6 emerged as the most resilient area with the lowest CSI score of 10.3 (see Table 1 ). Despite these geographic disparities in outcomes, the behavioral responses across FMAs were consistent—characterized by a universal dependence on debt and social networks, including borrowing from relatives and purchasing food on credit. This dual finding suggests that while policy interventions must be geographically targeted to the most vulnerable FMAs, broader economic support is needed across all regions to address the underlying financial fragility that renders households susceptible to food insecurity. Strengthening financial inclusion, stabilizing access to credit, and promoting savings mechanisms are essential complements to food-focused interventions. Table 1 Household food insecurity and coping strategies by fisheries management area a,b p-value from Chi-squared test for association between FMA and food insecurity status Characteristic Overall FMA 1 FMA 2 FMA 3 FMA 4 FMA 5 FMA 6 FMA 7 FMA 8 FMA 9 FMA 10 FMA 11 FMA 12 p-value HFIAS Score, Mean (SD) 6 (5.3) 3.6 (4.4) 4.6 (4.7) 4.8 (5.2) 4.9 (4.9) 5.3 (5.3) 2.8 (4.2) 4.8 (5.2) 5.1 (4.9) 4.5 (4.7) 4.4 (5.2) 4.4 (5.0) 3.5 (4.5) Coping Strategies Index, Mean (SD) 11.2 (11.0) 12.3 (11.5) 12.8 (12.4) 12.9 (12.2) 14.1 (13.1) 10.3 (11.8) 12.7 (12.4) 13.5 (12.2) 12.4 (11.5) 12.1 (12.4) 12.0 (11.9) 11.0 (11.5) 0.026 [b] Food Secure 407 (0.4%) 69 (42.6%) 33 (21.3%) 60 (39.7%) 86 (39.6%) 20 (23.5%) 50 (27.2%) 25 (42.4%) 14 (29.8%) 26 (25.2%) 9 (17.3%) 8 (20.0%) 7 (26.9%) Mildly Food Insecure 443 (0.5%) 45 (27.8%) 63 (40.6%) 63 (41.7%) 56 (25.8%) 41 (48.2%) 67 (36.4%) 9 (15.3%) 20 (42.6%) 25 (24.3%) 24 (46.2%) 19 (47.5%) 11 (42.3%) Moderately Food Insecure 385 (0.4%) 44 (27.2%) 53 (34.2%) 25 (16.6%) 69 (31.8%) 19 (22.4%) 60 (32.6%) 20 (33.9%) 12 (25.5%) 47 (45.6%) 17 (32.7%) 13 (32.5%) 6 (23.1%) Severely Food Insecure 46 (0.1%) 4 (2.5%) 6 (3.9%) 3 (2.0%) 6 (2.8%) 5 (5.9%) 7 (3.8%) 5 (8.5%) 1 (2.1%) 5 (4.9%) 2 (3.8%) 2 (7.7%) < 0.001 [a] Coping Strategies, n (%) Borrow food 32,767 (20.1%) 5,057 (20.3%) 3,931 (20.9%) 2,713 (21.5%) 4,427 (21.1%) 1,691 (21.6%) 5,246 (18.2%) 2,067 (19.6%) 947 (19.3%) 3,320 (19.6%) 888 (18.2%) 1,333 (20.4%) 1,147 (20.7%) Purchase food on credit 38,373 (23.5%) 5,680 (22.8%) 4,662 (24.8%) 2,882 (22.8%) 5,135 (24.5%) 1,809 (23.2%) 6,243 (21.6%) 2,441 (23.1%) 1,203 (24.5%) 4,289 (25.3%) 1,176 (24.0%) 1,548 (23.7%) 1,305 (23.5%) Restrict adult consumption 16,036 (9.8%) 2,234 (9.0%) 1,848 (9.8%) 1,416 (11.2%) 1,884 (9.0%) 749 (9.6%) 2,896 (10.0%) 1,180 (11.2%) 450 (9.2%) 1,747 (10.3%) 525 (10.7%) 600 (9.2%) 507 (9.1%) Loan from relatives/friend 38,368 (23.5%) 5,918 (23.7%) 4,425 (23.5%) 2,880 (22.8%) 4,791 (22.9%) 1,824 (23.3%) 7,190 (24.9%) 2,352 (22.3%) 1,116 (22.8%) 3,881 (22.9%) 1,160 (23.7%) 1,551 (23.7%) 1,280 (23.1%) Loan from non-relatives 26,710 (16.3%) 4,078 (16.3%) 2,873 (15.3%) 1,943 (15.4%) 3,301 (15.8%) 1,226 (15.7%) 5,080 (17.6%) 1,830 (17.3%) 827 (16.9%) 2,677 (15.8%) 867 (17.7%) 1,194 (18.2%) 814 (14.7%) Sold assets 4,186 (2.6%) 556 (2.2%) 366 (1.9%) 398 (3.1%) 602 (2.9%) 221 (2.8%) 986 (3.4%) 235 (2.2%) 83 (1.7%) 335 (2.0%) 124 (2.5%) 155 (2.4%) 125 (2.3%) Member migrated for work 6,935 (4.2%) 1,424 (5.7%) 698 (3.7%) 411 (3.3%) 798 (3.8%) 293 (3.8%) 1,214 (4.2%) 465 (4.4%) 276 (5.6%) 675 (4.0%) 152 (3.1%) 163 (2.5%) 366 (6.6%) Across the full sample, 48.2% of households experienced some degree of food insecurity, while 51.8% were classified as food secure. Latent class analysis revealed four distinct coping profiles, with 25.8% of households falling into the most vulnerable "High-Stress" category—characterized by frequent and severe coping behaviors. Financial strategies dominated the response landscape: 23.5% of households reported borrowing from relatives or friends, and an equal proportion purchased food on credit. In contrast, more extreme measures—such as selling productive assets—were employed by only 2.6% of households, suggesting that while financial fragility is widespread, asset depletion remains a last resort. These patterns underscore the centrality of informal financial networks in buffering food insecurity, while also highlighting the limits of household resilience under prolonged economic stress. To visualize the interrelationships among household coping behaviors, a co-occurrence network analysis was conducted (Fig. 1 ). The resulting network reveals a tightly interconnected core centered on informal debt and social borrowing, with 'Loan from relatives/friends,' 'Purchase food on credit,' 'Borrow food,' and 'Loan from non-relatives' forming the dominant cluster. This financial nexus functions as a gateway to more severe coping strategies, showing strong linkages to 'Restrict adult consumption'—a transitional behavior that bridges routine financial stress with deeper forms of deprivation. Beyond this core, more extreme responses such as 'Sold assets' and 'Member migrated for work' occupy the network’s periphery, suggesting a clear escalation pathway in household coping responses. These findings highlight not only the structural centrality of informal financial mechanisms but also the progressive nature of food insecurity, where initial reliance on social capital may eventually give way to irreversible livelihood compromises. 3.2 Do geographic location and time predict food insecurity? To identify the key drivers of household food insecurity, a linear regression model was used to assess the effects of geographic location (FMA) and survey year on HFIAS scores (see Table 2 ). Both predictors were found to be statistically significant, indicating that food insecurity levels vary meaningfully across space and time. The model’s intercept of 4.14 represents the expected HFIAS score for the baseline category: households located in FMA 1 during the 2018 survey year. Coefficients for other FMAs and years reflect deviations from this baseline, with higher scores indicating greater food insecurity. Table 2 Linear regression model of geographic (fma) and temporal (year) predictors of household food insecurity Term Estimate Std.error Statistic p.value (Intercept) 4.14 0.043 96.3 0 FMAFMA 10 0.712 0.0968 7.35 1.97E-13 FMAFMA 11 0.56 0.0863 6.49 8.76E-11 FMAFMA 12 -0.000184 0.0914 -0.00202 0.998389531 FMAFMA 2 1.04 0.0638 16.4 3.31E-60 FMAFMA 3 1.31 0.0722 18.1 2.35E-73 FMAFMA 4 1.32 0.0624 21.1 9.78E-99 FMAFMA 5 1.65 0.087 18.9 7.33E-80 FMAFMA 6 -0.677 0.0505 -13.4 6.83E-41 FMAFMA 7 1.41 0.0789 17.8 9.01E-71 FMAFMA 8 1.07 0.105 10.2 2.98E-24 FMAFMA 9 0.761 0.0634 12 3.84E-33 enns_year2019 -0.281 0.0452 -6.21 5.31E-10 enns_year2021 -1.2 0.0352 -34.2 1.92E-254 The results quantify the significant disparities in food security across the fisheries management areas. A clear set of regions emerged as vulnerability hotspots with significantly higher food insecurity compared to the baseline. FMA 5 was the most insecure, with a predicted HFIAS score 1.65 points higher than FMA 1 (p < 0.001). Other highly vulnerable regions included FMA 7 (+ 1.41 points), FMA 4 (+ 1.32 points), and FMA 3 (+ 1.31 points). In stark contrast, FMA 6 was the only region that was significantly more food-secure, with a predicted score 0.68 points lower than the baseline (p < 0.001). Notably, FMA 12 was statistically indistinguishable from FMA 1 (p = 0.998), indicating a similar level of food security. A significant temporal trend was also identified. Compared to the 2018 baseline, HFIAS scores were significantly lower in both 2019 (β = -0.28, p < 0.001) and, more dramatically, in 2021 (β = -1.20, p < 0.001). This counterintuitive but powerful trend of improving food security, with the most substantial improvement occurring during the height of the COVID-19 pandemic, suggests that external factors—such as the widespread implementation of government social amelioration programs (ayuda)—may have successfully buffered households against the economic shocks of the period. The predicted trends in HFIAS scores for each FMA across the three years are visualized in Fig. 2 . 3.3 Are there distinct profiles of household coping behavior? To understand the underlying patterns of coping behaviors, a Latent Class Analysis (LCA) was performed on the household data. The analysis identified a 4-class model as the best statistical fit, revealing distinct and meaningful profiles of how households manage food insecurity. The largest identified class was termed "Resilient / Low Copers," representing 48.2% of the sample, who use almost no coping strategies. The second-largest group was the "High-Stress / Diversified Copers" (27.5%), who employ a wide range of severe strategies. Two smaller, distinct profiles were also identified: "Community Borrowers" (13.6%), who rely on local credit and food sharing, and "Loan Seekers" (10.8%), who primarily pursue cash loans. The detailed probability profiles defining each of these classes are presented in Table 3 . Table 3 Item-response probabilities for the 4-class model of household coping strategies Coping Strategy Class 1: Community Borrowers (13.6%) Class 2: Resilient / Low Copers (48.2%) Class 3: Loan Seekers (10.8%) Class 4: High-Stress / Diversified Copers (27.5%) Probability (%) of using strategy Borrow food from neighbors/relatives 64.70% 0.30% 15.90% 92.60% Purchase food on credit 74.10% 1.50% 42.40% 98.00% Restrict adult consumption for children 27.10% 0.80% 19.00% 42.10% Loan from relatives/friend 53.00% 1.30% 74.00% 96.30% Loan from non-relatives/friend 10.80% 0.00% 60.40% 78.00% Sold assets 2.80% 0.20% 8.50% 11.70% Member migrated for work 10.50% 0.80% 13.80% 15.80% Note: Model fit statistics for the selected 4-class model: N = 90,886; Log-likelihood = -232,094.9; AIC = 464,251.8; BIC = 464,543.7. Probabilities are the conditional item response probabilities (Pr(2)) for each class. Class population shares are estimated from the model. The geographic distribution of these behavioral profiles provides a powerful explanation for the regional disparities identified in the regression analysis. The concentration of the most vulnerable profile, Class 4 ("High-Stress Copers"), aligns perfectly with the previously identified vulnerability hotspots. FMA 5 has the highest proportion of this class, with 35.1% of its households falling into this high-stress category, followed closely by FMA 8 (33.3%) and FMA 4 (32.4%). This finding demonstrates that the high food insecurity scores in these regions are driven by a large segment of the population being in a state of severe coping. The distribution of the most stable profile, Class 2 ("Resilient / Low Copers"), confirms the locations of the most food-secure areas. FMA 6 stands out again as the most resilient, with 60.3% of its households belonging to this resilient class, followed by FMA 1 (54.1%) and FMA 12 (53.3%). In these regions, a clear majority of the population is stable and does not need to engage in regular coping behaviors. This segmentation moves beyond simply identifying that certain regions are more vulnerable and begins to explain the underlying behavioral patterns that define that vulnerability. 3.4 How are these behavioral profiles distributed geographically? A latent class regression was used to identify the predictors of coping profile membership, with the "Resilient" profile as the baseline (see Table 4 for full model results). Both geography (FMA) and survey year were significant predictors. For instance, households in FMA 11 had 24% higher odds of belonging to the "High-Stress" profile compared to the baseline FMA 1 (p = 0.002). A critical temporal trend also emerged; the odds of a household being in the "High-Stress" profile were 55% higher in 2021 relative to the 2018 baseline (OR = 1.55, p < 0.001). However, the model-predicted probabilities, which provide a more direct interpretation of these trends, are visualized in Fig. 3 . This figure clearly shows that the absolute probability of belonging to the "Resilient" profile increased significantly across most FMAs in 2021, indicating an overall improvement in household resilience. Table 4 Distribution of latent coping profiles by fisheries management area y.level term estimate std.error statistic p.value conf.low conf.high 1 (Intercept) 4.278122 0.030798 47.19451 0 4.027518 4.544318 1 FMAFMA 10 0.893974 0.067383 -1.66329 0.096254 0.783372 1.020192 1 FMAFMA 11 1.081136 0.063965 1.21962 0.222609 0.953748 1.22554 1 FMAFMA 12 0.857587 0.062643 -2.45248 0.014187 0.758501 0.969618 1 FMAFMA 2 0.602734 0.044395 -11.4039 4.00E-30 0.552506 0.65753 1 FMAFMA 3 0.862543 0.052353 -2.8245 0.004736 0.778428 0.955748 1 FMAFMA 4 0.570519 0.043432 -12.9215 3.41E-38 0.523963 0.621212 1 FMAFMA 5 0.605868 0.058924 -8.50399 1.83E-17 0.539786 0.680041 1 FMAFMA 6 1.577614 0.038299 11.90416 1.13E-32 1.463527 1.700594 1 FMAFMA 7 0.662231 0.056379 -7.31024 2.67E-13 0.592952 0.739603 1 FMAFMA 8 0.617185 0.06933 -6.96075 3.38E-12 0.538768 0.707015 1 FMAFMA 9 0.619878 0.042652 -11.2123 3.55E-29 0.570165 0.673925 1 enns_year2019 0.888498 0.03028 -3.90427 9.45E-05 0.837301 0.942825 1 enns_year2021 1.317346 0.025278 10.90354 1.11E-27 1.25367 1.384256 3 (Intercept) 0.999502 0.039188 -0.0127 0.989866 0.925607 1.079297 3 FMAFMA 10 1.159737 0.08236 1.799332 0.071966 0.986858 1.362901 3 FMAFMA 11 1.159991 0.079793 1.859953 0.062892 0.992052 1.35636 3 FMAFMA 12 0.718981 0.084503 -3.90425 9.45E-05 0.60924 0.848489 3 FMAFMA 2 0.77607 0.056891 -4.4561 8.35E-06 0.694184 0.867614 3 FMAFMA 3 0.779621 0.068907 -3.61283 0.000303 0.68113 0.892353 3 FMAFMA 4 0.716903 0.056103 -5.9322 2.99E-09 0.642252 0.800231 3 FMAFMA 5 0.708352 0.076938 -4.4817 7.41E-06 0.609199 0.823643 3 FMAFMA 6 1.537255 0.047599 9.033774 1.66E-19 1.400327 1.687572 3 FMAFMA 7 0.970981 0.070143 -0.41983 0.67461 0.846262 1.114081 3 FMAFMA 8 0.878533 0.085906 -1.50748 0.131689 0.742395 1.039637 3 FMAFMA 9 0.797962 0.054391 -4.14952 3.33E-05 0.717273 0.887727 3 enns_year2019 1.044097 0.037765 1.142668 0.253176 0.969606 1.124311 3 enns_year2021 1.065631 0.031992 1.986945 0.046929 1.000864 1.13459 4 (Intercept) 1.745966 0.034237 16.27818 1.41E-59 1.632652 1.867145 4 FMAFMA 10 0.818495 0.075654 -2.64743 0.008111 0.705699 0.94932 4 FMAFMA 11 1.243731 0.069592 3.134199 0.001723 1.085149 1.425488 4 FMAFMA 12 0.720086 0.0713 -4.60564 4.11E-06 0.626172 0.828086 4 FMAFMA 2 0.999909 0.047413 -0.00191 0.998476 0.911177 1.097283 4 FMAFMA 3 1.166315 0.056068 2.743966 0.00607 1.044938 1.301791 4 FMAFMA 4 0.994609 0.046303 -0.11674 0.907063 0.908321 1.089094 4 FMAFMA 5 0.935982 0.062836 -1.05289 0.292392 0.827525 1.058653 4 FMAFMA 6 0.789569 0.043577 -5.42188 5.90E-08 0.724932 0.859969 4 FMAFMA 7 1.027204 0.059815 0.448729 0.653627 0.913571 1.154971 4 FMAFMA 8 0.895349 0.074947 -1.47493 0.14023 0.773032 1.03702 4 FMAFMA 9 0.726388 0.047212 -6.77103 1.28E-11 0.662189 0.796811 4 enns_year2019 1.09795 0.033241 2.811142 0.004937 1.028698 1.171864 4 enns_year2021 1.549132 0.027843 15.72 1.10E-55 1.466859 1.63602 5 (Intercept) 0.597556 0.044431 -11.5888 4.69E-31 0.54772 0.651927 5 FMAFMA 10 0.554989 0.110066 -5.34958 8.82E-08 0.447297 0.688609 5 FMAFMA 11 0.711151 0.100121 -3.40457 0.000663 0.584438 0.865337 5 FMAFMA 12 0.832739 0.090296 -2.02705 0.042658 0.697668 0.993961 5 FMAFMA 2 0.659314 0.064836 -6.42474 1.32E-10 0.580636 0.748653 5 FMAFMA 3 0.948672 0.072979 -0.72202 0.470285 0.822235 1.094552 5 FMAFMA 4 0.813443 0.060825 -3.39467 0.000687 0.722026 0.916434 5 FMAFMA 5 0.814506 0.083221 -2.4654 0.013686 0.691921 0.958809 5 FMAFMA 6 0.747483 0.05665 -5.13755 2.78E-07 0.66893 0.835261 5 FMAFMA 7 0.926041 0.076977 -0.99817 0.318196 0.796356 1.076846 5 FMAFMA 8 0.762266 0.102534 -2.6475 0.008109 0.623489 0.931931 5 FMAFMA 9 0.456647 0.067929 -11.5391 8.38E-31 0.399723 0.521678 5 enns_year2019 1.100756 0.046515 2.063774 0.039039 1.00484 1.205826 5 enns_year2021 1.536549 0.037763 11.37446 5.60E-30 1.426928 1.654591 4 Discussion This study provides a multi-faceted assessment of household food insecurity across the twelve FMAs of the Philippines. Our analyses revealed three primary findings: significant and persistent geographic disparities in food security; a significant improvement in food security in 2021; and four distinct behavioral profiles that provide a deeper explanation for these outcomes. 4.1 Geographic hotspots of vulnerability require targeted policy interventions The identification of clear geographic disparities is a critical finding for both marine and social policy. The consistent ranking of FMAs 5, 7, 4, and 8 as having the highest levels of food insecurity and the largest concentrations of "High-Stress" households underscores the limitations of a "one-size-fits-all" national food security strategy. This spatial heterogeneity reflects deep-rooted structural inequalities—such as uneven access to markets, infrastructure, and social protection—that shape household vulnerability across regions. These findings align with previous research documenting significant regional disparities in food insecurity across the Philippines, driven by social determinants including income, education, and geographic isolation (Durano et al., 2024 ; Ulep, 2021 ). This heightened vulnerability in specific coastal zones reflects the critical dependence of local economies on small-scale fisheries. In many coastal municipalities, artisanal fishing is not only a primary source of food and income but also a cultural and ecological cornerstone. However, climate variability, declining fish stocks, and habitat degradation have increasingly undermined the resilience of these communities. Recent studies show that small-scale fishers in regions such as the Davao Gulf and Zamboanga Peninsula face compounding risks—from eroding coastlines and illegal commercial fishing to reduced catch per unit effort and limited access to credit and alternative livelihoods (Macusi et al., 2021 ; Macusi & Macusi, 2025 ). These stressors exacerbate food insecurity and argue for place-based, fisheries-sensitive social protection strategies, which are often susceptible to both ecological pressures and resource-use conflicts (Nguyen et al., 2023 ; Andrews et al., 2021 ). Instead, these findings provide an evidence base for spatially targeted interventions. Resources for food assistance, alternative livelihood programs, and social safety nets should be prioritized for these identified high-risk FMAs to address the persistent, underlying drivers of vulnerability. 4.2 Widespread improvement in 2021 suggests the effectiveness of pandemic-era social safety nets Perhaps the most striking finding is the dramatic and widespread improvement in food security observed in 2021. This trend is particularly notable given the unprecedented challenges posed by the COVID-19 pandemic, which severely disrupted the Philippine food system—especially in urban centers—by fracturing supply chains, constraining mobility, and undermining household access to affordable and nutritious food. Despite these shocks, targeted government interventions, adaptive coping strategies among households, and the gradual reopening of economic activities appear to have contributed to a rebound in food availability and access. (Huang et al., 2025 ; Durano et al., 2024 ). It is within this context of acute stress that the subsequent improvement in food security must be understood. Both the linear regression and the latent class regression confirm this trend. While this study cannot establish direct causality, this counterintuitive finding strongly suggests that the large-scale government social amelioration programs (e.g., ayuda ) implemented during the pandemic were effective in buffering households against food crises. While our study provides indirect, macro-level evidence, this result aligns with household-level analyses of the Philippine Social Amelioration Program (SAP), which found that emergency cash transfers significantly improved household welfare and consumption during lockdown periods (Vannavanit & Takahashi, 2025 ; Brooks et al., 2022 ; Strupat et al., 2025 ; Islam et al., 2024 ). This result carries significant policy implications, underscoring the importance of broad, rapid, and well-targeted social safety net interventions in shielding vulnerable populations from the worst effects of widespread socioeconomic shocks. In contexts of acute disruption—such as pandemics, climate-induced disasters, or economic downturns—such programs not only buffer immediate welfare losses but also help preserve long-term human capital and social cohesion. The Philippine experience with SAP demonstrates that timely cash transfers, when delivered at scale, can mitigate food insecurity and stabilize household consumption, even in the absence of perfect targeting or infrastructure. Future policy design should prioritize institutional readiness, digital delivery systems, and inclusive targeting mechanisms to ensure that emergency support reaches those most at risk—quickly and equitably. 4.3 Behavioral profiles reveal widespread economic precarity beyond food insecurity The Latent Class Analysis provided a deeper insight into household behavior, revealing distinct coping profiles that range from reliance on social capital—such as borrowing food or accessing community support—to more extreme, asset-depleting behaviors like selling fishing gear or reducing food intake. These behavioral patterns align with established frameworks that categorize coping strategies along a continuum from reversible “stress” responses to irreversible “crisis” responses. This typology, often used in food security and humanitarian contexts, helps distinguish between short-term adaptive behaviors and long-term erosive strategies that compromise future resilience. The empirical identification of these latent classes not only validates the heterogeneity of household responses but also underscores the need for tailored policy interventions that reflect varying degrees of vulnerability and adaptive capacity (Ricarte et al., 2022 ; Hung et al., 2024 ; Xiangbo et al., 2024; Opoku et al., 2025; Nylund-Gibson & Choi, 2018 ). The identification of distinct household coping profiles helps illuminate the persistent puzzle of food insecurity in the Philippines; wherein national-level statistics often obscure the heterogeneity of household-level experiences (Kiunisala et al., 2025 ; Yohannes et al., 2023 ). While debt-based coping mechanisms—such as borrowing money or purchasing food on credit—are well-documented responses to economic shocks (Iskander et al., 2025 ; Mazenda et al., 2024; Hangoma et al., 2024 ; Giovanis et al., 2024; Siddik et al., 2025 ; Quilloy et al., 2016 ), our Latent Class Analysis reveals that these behaviors are concentrated within specific subgroups rather than uniformly distributed across all food-insecure households. Notably, the emergence of “Community Borrowers” and “Loan Seekers” as dominant profiles among non-resilient households underscores a broader economic precarity that transcends mere food access. These findings suggest that policy interventions must extend beyond food availability to address structural constraints in financial inclusion, access to affordable and stable credit, and mechanisms for household savings. Such multidimensional support is essential to enhance both short-term coping capacity and long-term resilience, but our analysis reveals this is a distinct profile and not a universal response among all stressed households. The discovery that a large portion of non-resilient households fall into the "Community Borrowers" and "Loan Seekers" profiles reveals a widespread economic precarity that extends beyond just food access. This suggests that policy should not only focus on food availability but also on financial inclusion, access to stable credit, and building household savings. This finding aligns with broader patterns in the Philippines, where borrowing is deeply embedded in household financial behavior, yet access to stable, affordable credit remains limited. Community-led loan initiatives have shown promise in mitigating exploitative lending practices, but their reach is still modest (Hill & Kozup, 2007 ; Berger & Udell, 1998 ; Mull, 2016 ; Dimitrova-Grajzl, 2024). These insights suggest that food security policy must evolve to address underlying financial fragilities—through inclusive credit systems, savings mobilization, and social protection mechanisms that reduce reliance on erosive coping strategies. 4.4 Limitations and future research A key limitation of this study is the absence of household-level demographic and socioeconomic data. Therefore, we could not run a model to identify traditional predictors like education or household size. The analysis was consequently focused on the predictive effects of geography and time. Future research should aim to integrate these datasets with socioeconomic surveys to build more comprehensive models. Furthermore, acquiring a shapefile of the FMAs would allow for more advanced spatial analyses like Geographically Weighted Regression to explore if the impact of the 2021 improvement varied geographically. 5 Conclusion This study offers a granular assessment of food security across the Philippine Fisheries Management Areas (FMAs), identifying critical geographic hotspots and revealing an unexpected trend of pandemic-era improvement in certain regions. Through Latent Class Analysis, it also uncovers distinct behavioral profiles that shape household vulnerability—ranging from reliance on social networks to debt-based coping strategies. These findings underscore the complexity of food insecurity and highlight the limitations of aggregate national statistics in capturing localized realities. By illuminating both spatial and behavioral dimensions of resilience, the study provides an evidence base for designing more targeted, context-sensitive policies that address not only food availability but also the structural drivers of economic precarity in coastal communities. < Declarations Clinical trial number not applicable. Competing interests The author has no competing interests to declare that are relevant to the content of this article. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Funding No funds, grants, or other support was received. Acknowledgements The author thanks the Department of Science and Technology-Food and Nutrition Research Institute (DOST-FNRI) for providing the Expanded National Nutrition Survey (ENNS) dataset. This data is publicly available upon request from the eNutrition website (enutrition.fnri.dost.gov.ph) in accordance with the Philippine Freedom of Information (FOI) Law. 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Translational Issues Psychol Sci 4(4):440–461. https://doi.org/10.1037/tps0000176 Nylund-Gibson K, Garber AC, Singh J, Witkow MR, Nishina A, Bellmore A (2023) The Utility of Latent Class Analysis to Understand Heterogeneity in Youth Coping Strategies: A Methodological Introduction. Behav Disorders 48(2):106–120. https://doi.org/10.1177/01987429211067214 Oberski D (2016) Mixture Models: Latent Profile and Latent Class Analysis. In J. Robertson & M. Kaptein (Eds.), Modern Statistical Methods for HCI . Springer. https://doi.org/10.1007/978-3-319-26633-6_12 Oceana Philippines. (n.d.). Fisheries Management Area (FMA) briefing document. Retrieved August 21 (2025) from https://ph.oceana.org/wp-content/uploads/sites/16/oceana_-_fisheries_management_area_briefer.pdf Oceana Philippines (2025) Primer on Municipal Waters and Local Governments’ Jurisdiction Over Them . Retrieved August 21, 2025, from https://ph.oceana.org/wp-content/uploads/sites/16/2025/02/IUUF_PRIMERonMUNICIPALWATERS_AtinAngKinse_05Feb2025_FAforPRINTING-1.pdf Oloko A, Dahmouni I, Le Billon P et al (2025) Gender dynamics, climate change threats and illegal, unreported, and unregulated fishing. Discover Sustain 6:494. https://doi.org/10.1007/s43621-025-01227-4 Opoku Mensah S, Jacobs B, Cunningham R (2025) The role of intersectionality in shaping adaptive capacity of smallholder farmers in the Talensi district of Ghana. Climate Dev 1–16. https://doi.org/10.1080/17565529.2025.2453569 Ortega-Espaldon MV, Medina CD (2024) Climate change and food security in the Philippines: Impacts, adaptation, and climate change action. In K. B. Berse, J. M. Pulhin, & A. G. M. La Viña (Eds.), Climate Emergency in the Philippines (pp. 67–84). Springer. https://doi.org/10.1007/978-981-99-7804-5_5 Pantolla H, Atibagos-Nacion N (2023) Spatial analysis of poverty incidence and road networks in Eastern Visayas Region, Philippines. Philippine Journal of Science, 152 (4), 1267–1283. Retrieved August 21, 2025, from https://philjournalsci.dost.gov.ph/spatial-analysis-of-poverty-incidence-and-road-networks-in-eastern-visayas-region-philippines/ Patalen CF, Ikeda N, Angeles-Agdeppa I, Vargas MB, Nishi N, Duante CA, Capanzana MV (2020) Data resource profile: The Philippine National Nutrition Survey (NNS). Int J Epidemiol 49(3):742–743f. https://doi.org/10.1093/ije/dyaa045 Philippine Statistics Authority (PSA) (2013) 2013 master sample design . Philippine Statistics Authority. Retrieved August 21, 2025, from https://psada.psa.gov.ph/2013-master-sample-design Pomeroy R, Parks J, Mrakovcich KL, LaMonica C (2016) Drivers and impacts of fisheries scarcity, competition, and conflict on maritime security. Mar Policy 67:94–104. https://doi.org/10.1016/j.marpol.2016.01.005 Protacio KIT, Talavera MTM, Bustos AR, Marasigan SB (2025) Determinants of food insecurity among municipal fishing households during the COVID-19 pandemic under Alert Level 1 in Kawit, Cavite, Philippines. Philippine J Fisheries 32(1):135–148. https://doi.org/10.31398/tpjf/32.1.2024-0005 Quilloy K, Sumalde Z, Foronda C, Rola A, Rola A (2016) Household Coping Strategies For Food Security During Extreme Events . UPLB Center for Strategic Planning and Policy Studies. https://serp-p.pids.gov.ph/publication/public/view?slug=household-coping-strategies-for-food-security-during-extreme-events Raza M, Abu Hatab A (2025) Assessment of vulnerability and resilience of smallholder farming households to flood risks: Insights from the Southern Punjab region of Pakistan. Int J Disaster Risk Reduct 126:105600. https://doi.org/10.1016/j.ijdrr.2025.105600 Reyes CM, Ortiz MKP, Arboneda AA, Vargas ARP (2022) The Philippines’ response to the COVID-19 pandemic: Learning from experience and emerging stronger to future shocks . Philippine Institute for Development Studies. Retrieved August 21, 2025, from https://pidswebs.pids.gov.ph/CDN/document/pidsbk2022-covid19.pdf Ricarte PP, Vista AB, Rañola RF Jr., Briones ND (2022) Enhancing adaptive capacity to flooding of households: Evidence from lakeshore communities of Pila, Laguna, Philippines. Clim Disaster Dev J 5(1):23–34. https://doi.org/10.18783/cddj.v005.i01.a03 San Juan DM, Agustin PJC (2019) Poverty, inequality, and development in the Philippines: Official statistics and selected life stories. Eur J Sustainable Dev 8(1):290–299. https://doi.org/10.14207/ejsd.2019.v8n1p290 Siddik MNA, Miah MF, Hosen ME (2025) Insight into households' financial hardship and coping strategies during COVID-19 pandemic: A study on char dwellers of Bangladesh. Asian J Econ Bank 6(2). https://doi.org/10.1108/AJEB-06-2023-0056 Strupat C, Nshakira-Rukundo E, Reichert A (2025) The Impact of Shock-Responsive Social Cash Transfers: Evidence from an Aggregate Shock in Kenya. J Dev Stud 1–25. https://doi.org/10.1080/00220388.2025.2504425 Talbot E, Jontila JBS, Gonzales BJ, Dolorosa RG, Jose ED, Sajorne R, Sailley S, Kay S, Queirós AM (2024) Incorporating climate-readiness into fisheries management strategies. Sci Total Environ 918:170684. https://doi.org/10.1016/j.scitotenv.2024.170684 Taniushkina D, Lukashevich A, Shevchenko V, Vorobev S, Pyanova L, Nechaeva T (2024) Case study on climate change effects and food security in Southeast Asia. Sci Rep 14:16150. https://doi.org/10.1038/s41598-024-65140-y Ulep VGT (2021) Breaking the curse: Addressing chronic malnutrition in the Philippines using a health system lens (PIDS Discussion Paper Series No. 2021-41). Philippine Institute for Development Studies. https://doi.org/10.62986/dp2021.41 Ulep VGT, Uy J, Casas LDD, Capanzana MV, Nkoroi A, Galera RG Jr., Carpio ME, Tan F (2023) The determinants of socioeconomic inequality and the trajectory of child stunting in the Philippines (PIDS Discussion Paper Series No. 2023-04). Philippine Institute for Development Studies. Retrieved August 21, 2025, from https://pidswebs.pids.gov.ph/CDN/document/pidsdps2304.pdf USAID, & Bureau of Fisheries and Aquatic Resources (2021) Counting the cost of illegal, unreported and unregulated fishing in the Philippines [Executive Summary]. Retrieved August 21, 2025, from https://www.bfar.da.gov.ph/wp-content/uploads/2021/05/CountingtheCostExecSummaryFINAL.pdf Vannavanit W, Takahashi K (2025) Effects of an unconditional cash transfer program during the pandemic: Empirical evidence from Thailand. Int J Disaster Risk Reduct 121:105409. https://doi.org/10.1016/j.ijdrr.2025.105409 Willette DA, Bognot EDC, Mutia MTM, Santos MD, National Fisheries Research and Development Institute (2011) Biology and ecology of sardines in the Philippines: A review (Technical Paper Series, Vol. 13, No. 1). Bureau of Fisheries and Aquatic Resources;. Retrieved August 21, 2025, from https://nfrdi.da.gov.ph/tpjf/etc/Willette%20et%20al.%20Sardines%20Review.pdf Xu X, Xu C, Li C, Fu C, Zhou Y (2024) Assessing and Comparing Smallholders' Vulnerability to Climate Change Among Countries in the Pan-Third Pole Region. J Resour Ecol 15(4):1015–1026. https://doi.org/10.5814/j.issn.1674-764x.2024.04.021 Yohannes G, Wolka E, Bati T et al (2023) Household food insecurity and coping strategies among rural households in Kedida Gamela District, Kembata-Tembaro zone, Southern Ethiopia: mixed-methods concurrent triangulation design. BMC Nutr 9:4. https://doi.org/10.1186/s40795-022-00663-z Yusriadi Y (2025) The impact of free nutritious meal programs on food security: A systematic review. J Indonesian Scholars Social Res 5(1):92–97. https://doi.org/10.59065/jissr.v5i1.177 Zongo B, Combary OS, Zare A, Samake A, Gado AD, Keïta K, Toé P, Dogot T (2023) Food Security Status and Coping Strategies of Households in Inland Fisheries: Evidence from Fishermen from Niger River Basin. Asian J Agricultural Ext Econ Sociol 41(11):44–57. https://doi.org/10.9734/ajaees/2023/v41i112260 Additional Declarations The authors declare no competing interests. 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Guiñares","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYLCCBDDJfABEMjYwMLARq4UtgQQtEMBjQJwW8/bj1yQe7rDL5+c/8026gMFGdsMB3mMP8GmROZNTJpF4Jtly5ozcbdIzGNKMNxzgSzfAp0VCgidNIrGN2cDgBu82aR6Gw4kbDvCYSRChpd7A/vyZZ0At/4nRwn4MqOWwgQFDDhtQywEitPDkMFskth03kLiRZmw9wyDZeOZhvjT8WtiPP7z5s63agL//8MPbBRV2sn3He4/h1QKLDjBgZgCxmXnwa2BgYH+ApAViCCEto2AUjIJRMMIAAOSwQoSYz+UOAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-9232-7152","institution":"University of the Philippines Open University","correspondingAuthor":true,"prefix":"","firstName":"Recamar","middleName":"C.","lastName":"Guiñares","suffix":""}],"badges":[],"createdAt":"2025-08-22 03:05:59","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-7430169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7430169/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89787759,"identity":"86e9d903-9b05-45ff-b2be-2d3bd4170913","added_by":"auto","created_at":"2025-08-25 04:44:49","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":363086,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence network of household coping strategies. Each node represents a specific coping strategy, with the size of the node proportional to its overall prevalence. The edges (lines) connecting the nodes indicate that the strategies were used by the same households, with the thickness of the edge proportional to the frequency of their co-occurrence\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7430169/v1/f97b559b794c78264962220c.jpeg"},{"id":89788026,"identity":"59517107-c331-47c3-932d-e66c607867f0","added_by":"auto","created_at":"2025-08-25 04:52:49","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":362210,"visible":true,"origin":"","legend":"\u003cp\u003eModel-predicted mean HFIAS scores by fisheries management area and survey year\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7430169/v1/96b3bf47c3cf0f7181616f9f.jpeg"},{"id":89786906,"identity":"da9bc971-e6e9-44cd-bdb6-82044e39c03f","added_by":"auto","created_at":"2025-08-25 04:36:49","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":382130,"visible":true,"origin":"","legend":"\u003cp\u003eModel-predicted probabilities of latent class membership by fisheries management area and survey year\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7430169/v1/a0a45c8b835dc648d6e2f964.jpeg"},{"id":89788545,"identity":"b539075c-c1ee-4365-8b45-e737b5e7b9b8","added_by":"auto","created_at":"2025-08-25 05:00:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2609506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7430169/v1/76fc0752-af95-4fb2-b19a-ca4827afc9f9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDistinct household coping profiles explain geographic disparities in food security across Philippine fisheries management areas\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFood security remains a critical and multifaceted challenge in the Philippines (Abasolo \u0026amp; Montefrio, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ortega-Espaldon \u0026amp; Medina, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Taniushkina et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Darwis et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cordero, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), a nation whose archipelagic geography, vulnerability to climate events, and uneven agricultural infrastructure (Cordero, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lacsa, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; dela Luna \u0026amp; Talavera, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and socioeconomic inequalities (San Juan \u0026amp; Agustin, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ulep et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) create persistent obstacles to ensuring its people have consistent access to sufficient, safe, and nutritious food (FAO, 2020; Leroy et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yusriadi, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While national surveys indicate that a significant portion of the population experiences food insecurity, recent findings from the 2023 National Nutrition Survey reveal that 31.4% of Filipino households face moderate to severe food insecurity, with 2.7% experiencing severe food insecurity (Cordero, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Patalen et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; DOST-FNRI, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u0026mdash;a situation exacerbated by global shocks like the COVID-19 pandemic (Darwis et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Reyes et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u0026mdash;this burden is not evenly distributed. Pronounced geographic disparities exist, with rural and coastal settings (Andriesse, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Martinez, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Andriesse, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), particularly in hotspot regions like the Bangsamoro Autonomous Region in Muslim Mindanao (BARMM) and Eastern Visayas, consistently facing higher rates of insecurity linked to poverty, limited livelihoods, and environmental fragility (Pantolla \u0026amp; Atibagos-Nacion, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Abuza \u0026amp; Lischin, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCritical to this challenge is the fisheries sector, which provides over 50% of the nation's animal protein (Lowe et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Talbot et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Muallil et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and employs approximately 1.6\u0026nbsp;million people (Andriesse et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maritime Fairtrade, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lamarca, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, this vital resource is under severe threat. Internal pressures, including overfishing and habitat degradation, have contributed to a significant decline in fish stocks over the past decade (Muallil et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hunnam, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Willette et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These pressures are compounded by external threats, most notably Illegal, Unreported, and Unregulated (IUU) fishing (Pomeroy et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oloko et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; DA-BFAR, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; USAID \u0026amp; Bureau of Fisheries and Aquatic Resources, 2021) and geopolitical conflict in the West Philippine Sea (Pomeroy et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Arceo et al., 2024; DA-BFAR, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which curtail access to traditional fishing grounds. In response, the Philippines adopted Fisheries Administrative Order No. 263 (FAO 263) in 2019. This order established an ecosystem-approach to fisheries management (EAFM) by delineating the country's 2.2\u0026nbsp;million km\u0026sup2; exclusive economic zone into twelve Fisheries Management Areas (FMAs) (Fabinyi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This spatial framework, informed by biophysical characteristics, fish stock distributions, and administrative boundaries, provides a crucial platform for coordinated policy, science-based stock assessments, and targeted management interventions (Fagbinyi, 2024; Oceana, n.d). It specifically aims to address the limitations of a decentralized governance structure, where municipal waters (extending up to 15 km from the coastline) fall under local jurisdiction, often leading to fragmented management of shared resources that transcend political boundaries (Oceana Philippines, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite this policy, coastal households continue to face significant hardship and employ a diverse range of coping strategies to navigate food shortages (Fabinyi \u0026amp; Barclay, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; DOST-FNRI, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), from less severe, debt-based mechanisms to more extreme, asset-depleting measures like selling fishing gear (Gebregziabher et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mozumder et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gibson et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zongo et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Protacio et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Golloso-Gubat et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile the geographic disparities in food security are well-documented, a deeper understanding of the underlying household-level behaviors that characterize this vulnerability within the FMA framework is needed. Specifically, a gap exists in understanding how distinct, multifaceted profiles of coping strategies are distributed across the FMAs, particularly in the context of both climate and geopolitical pressures. It remains unclear if the observed geographic differences in food security can be explained by a different prevalence of these distinct behavioral profiles. Therefore, this study aims to: (1) identify the key geographic (FMA) and temporal (Year) predictors of household food insecurity; (2) identify and characterize distinct, latent profiles of household coping behaviors using Latent Class Analysis; and (3) determine how these behavioral profiles are distributed across the FMAs, thereby providing a deeper, behavioral explanation for the geographic disparities in food security in the coastal Philippines.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Source and Sampling Design\u003c/h2\u003e\u003cp\u003eThis study utilizes secondary data from the Expanded National Nutrition Survey (ENNS), a nationally representative, cross-sectional survey conducted by the Department of Science and Technology-Food and Nutrition Research Institute (DOST-FNRI) of the Philippines. The analysis includes pooled data from the 2018, 2019, and 2021 survey rounds. The 2020 round was suspended due to operational challenges posed by the COVID-19 pandemic. The final sample for this analysis consists of over 90,000 households.\u003c/p\u003e\u003cp\u003eThe Expanded National Nutrition Survey (ENNS) employs a stratified multi-stage sampling design to ensure representativeness at the national, regional, and provincial/Highly Urbanized City (HUC) levels. This design is based on the 2013 Master Sample for household surveys developed by the Philippine Statistics Authority (PSA), as adopted and validated by the Food and Nutrition Research Institute (FNRI) for the ENNS rolling survey from 2018 to 2020.\u003c/p\u003e\u003cp\u003eThe household location data, originally coded by province, were aggregated into broader ecological and administrative regions for analysis. Following the framework of the Philippines' Fisheries Administrative Order No. 263, each unique location code was clustered into one of the twelve designated Fisheries Management Areas (FMAs). This spatial clustering allows for an ecosystem-based analysis of the data, aligning the household survey responses with the relevant management zones for Philippine marine resources.\u003c/p\u003e\u003cp\u003eThe ENNS employs a stratified multi-stage sampling design to ensure representativeness at national, regional, and provincial/Highly Urbanized City (HUC) levels, based on the Master Sample for household surveys developed by the PSA. Given this complex survey design, all statistical analyses presented herein were weighted using the survey weights provided in the ENNS dataset to produce unbiased, nationally representative estimates and account for clustering and stratification effects. Ethical clearance for the ENNS was obtained by the DOST-FNRI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Variables and Measures\u003c/h2\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 Food Security Outcome Variables\u003c/h2\u003e\u003cp\u003eHousehold food insecurity was measured using the full nine-item module of the Household Food Insecurity Access Scale (HFIAS), an internationally validated tool for assessing food access (Marques et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kabalo et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Coates et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The nine questions assess the frequency of occurrence of anxiety over food supply, insufficient quality (e.g., limited variety), and insufficient quantity of food intake over the past 30 days. The specific items include whether a household worried about food, was unable to eat preferred foods, ate a limited variety, ate unwanted foods, ate smaller meals, ate fewer meals, had no food of any kind, went to sleep hungry, or went a whole day and night without eating. From these items, two primary outcome variables were constructed:\u003c/p\u003e\u003cp\u003eHFIAS Score (Quantitative): A continuous score was calculated by summing the numeric frequency-of-occurrence responses (0=\"never\", 1=\"rarely\", 2=\"sometimes\", 3=\"often\") to the nine questions. This results in a quantitative scale from 0 (most food secure) to 27 (most food insecure), which served as the dependent variable in the linear regression analysis.\u003c/p\u003e\u003cp\u003eHFIAS Category (Qualitative): Households were classified into four ordinal categories based on their responses: 'Food Secure', 'Mildly Food Insecure', 'Moderately Food Insecure', and 'Severely Food Insecure', following standard HFIAS guidelines.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Predictor Variables\u003c/h2\u003e\u003cp\u003eFisheries Management Area (FMA) (Qualitative): The primary geographic predictor was the Fisheries Management Area. Households were assigned to one of the 12 FMAs based on their province/HUC code (provhuc), according to the classification established by Fisheries Administrative Order 263. It should be noted that this serves as a geographic proxy, and may include non-coastal or non-fisheries-dependent households within a given province, a potential source of misclassification bias.\u003c/p\u003e\u003cp\u003eSurvey Year (Qualitative): The survey year (2018, 2019, or 2021) was included as a categorical predictor to assess temporal trends.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.2.3 Household Coping Strategies (Qualitative)\u003c/h2\u003e\u003cp\u003eHousehold coping behaviors were measured using a series of binary (Yes/No) questions. For the Latent Class Analysis, seven key coping strategy variables were selected to represent a range of financial, social, and food-based behaviors while ensuring model stability. The selected strategies were: borrowing food from neighbors/relatives (stratg10), purchasing food on credit (stratg11), restricting adult consumption for children (stratg12), obtaining loans from relatives/friends (stratg15), obtaining loans from non-relatives (stratg16), selling assets (stratg17), and having a household member migrate for work (stratg18).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using R (Version R-4.5.1), incorporating the survey package to account for the complex sampling design and the poLCA package for Latent Class Analysis. Missing data for the selected variables was minimal (\u0026lt;\u0026thinsp;5%) and was handled by listwise deletion.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Descriptive Statistics\u003c/h2\u003e\u003cp\u003eSurvey-weighted means, standard deviations (SD), and medians were calculated for the quantitative HFIAS score. Survey-weighted frequencies and percentages were calculated for all qualitative variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Linear Regression\u003c/h2\u003e\u003cp\u003eTo identify the geographic and temporal predictors of food insecurity severity, a survey-weighted generalized linear model was fitted using the svyglm function from the survey package. The continuous HFIAS score served as the dependent variable. Model assumptions, including linearity and homoscedasticity, were assessed through visual inspection of residual plots.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3 Latent Class Analysis (LCA)\u003c/h2\u003e\u003cp\u003eTo identify unobserved profiles of households based on their shared patterns of coping strategies, a Latent Class Analysis was performed using the poLCA package in R, which applies finite mixture modeling to categorical data and estimates class membership probabilities based on conditional response patterns (Linzer \u0026amp; Lewis, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nylund-Gibson et al., 2022; Oberski, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Models with two to five latent classes were fitted. The optimal number of classes was selected by identifying the model with the lowest Bayesian Information Criterion (BIC), while also considering the Akaike Information Criterion (AIC), theoretical interpretability, and class separation as indicated by entropy values (\u0026gt;\u0026thinsp;0.80 indicating good classification). Models with two to five classes were tested, and the 4-class model was selected as the optimal solution based on its strong theoretical interpretability and its superior model fit, as indicated by the Bayesian Information Criterion (BIC\u0026thinsp;=\u0026thinsp;464543.7).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4 Latent Class Regression\u003c/h2\u003e\u003cp\u003eTo link the identified behavioral profiles to geography and time, a two-step analytical approach was used. First, each household was assigned to its most likely latent class based on modal posterior probabilities from the selected 4-class LCA model. Second, a survey-weighted multinomial logistic regression was fitted, with the four-level class membership as the categorical outcome and FMA and survey year as predictors. The 'Resilient / Low Copers' class was set as the reference category. We acknowledge that this common two-step approach can potentially underestimate standard errors compared to an integrated model.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1 What is the overall state of household food insecurity?\u003c/h2\u003e\u003cp\u003eThe analysis reveals a statistically significant geographic disparity in household vulnerability across the Philippine FMAs, confirmed by both the prevalence of food insecurity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the intensity of coping strategies (p\u0026thinsp;=\u0026thinsp;0.026). This study identifies FMAs 5, 8, and 4 as critical hotspots of vulnerability, where households consistently report the most severe levels of food insecurity and a heightened reliance on coping behaviors. FMA 5, in particular, exhibited the highest mean Coping Strategies Index (CSI) score of 14.1, indicating more frequent and severe food-related compromises, while FMA 6 emerged as the most resilient area with the lowest CSI score of 10.3 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Despite these geographic disparities in outcomes, the behavioral responses across FMAs were consistent\u0026mdash;characterized by a universal dependence on debt and social networks, including borrowing from relatives and purchasing food on credit. This dual finding suggests that while policy interventions must be geographically targeted to the most vulnerable FMAs, broader economic support is needed across all regions to address the underlying financial fragility that renders households susceptible to food insecurity. Strengthening financial inclusion, stabilizing access to credit, and promoting savings mechanisms are essential complements to food-focused interventions.\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\u003eHousehold food insecurity and coping strategies by fisheries management area \u003csup\u003ea,b\u003c/sup\u003e p-value from Chi-squared test for association between FMA and food insecurity status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFMA 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFMA 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFMA 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFMA 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eFMA 5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFMA 6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFMA 7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eFMA 8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eFMA 9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eFMA 10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eFMA 11\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eFMA 12\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\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\u003eHFIAS Score, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.6 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.6 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.8 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.9 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.3 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.8 (4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.8 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.1 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4.5 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e4.4 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e4.4 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e3.5 (4.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoping Strategies Index, Mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.2 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.3 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.8 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.9 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.1 (13.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.3 (11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.7 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e13.5 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e12.4 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e12.1 (12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e12.0 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e11.0 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.026 [b]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood Secure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e407 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69 (42.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86 (39.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e50 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e25 (42.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e14 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e26 (25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e9 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e8 (20.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e7 (26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMildly Food Insecure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e443 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63 (40.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63 (41.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41 (48.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e67 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e9 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e20 (42.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e25 (24.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e24 (46.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e19 (47.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e11 (42.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerately Food Insecure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53 (34.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25 (16.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e69 (31.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e20 (33.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e12 (25.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e47 (45.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e17 (32.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e13 (32.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e6 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Food Insecure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46 (0.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6 (3.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5 (5.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e5 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e2 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e2 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001 [a]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoping Strategies, n (%)\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorrow food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32,767 (20.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,057 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3,931 (20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,713 (21.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4,427 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,691 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5,246 (18.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2,067 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e947 (19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3,320 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e888 (18.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1,333 (20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1,147 (20.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase food on credit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38,373 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,680 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4,662 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,882 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5,135 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,809 (23.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6,243 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2,441 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1,203 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4,289 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1,176 (24.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1,548 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1,305 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRestrict adult consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16,036 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2,234 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,848 (9.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,416 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1,884 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e749 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2,896 (10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1,180 (11.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e450 (9.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1,747 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e525 (10.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e600 (9.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e507 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoan from relatives/friend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38,368 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,918 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4,425 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2,880 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4,791 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,824 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7,190 (24.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2,352 (22.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1,116 (22.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3,881 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e1,160 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1,551 (23.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1,280 (23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoan from non-relatives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26,710 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,078 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,873 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,943 (15.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3,301 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1,226 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5,080 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1,830 (17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e827 (16.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e2,677 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e867 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e1,194 (18.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e814 (14.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSold assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,186 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e556 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e366 (1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e398 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e602 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e221 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e986 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e235 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e83 (1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e335 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e124 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e155 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e125 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMember migrated for work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,935 (4.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,424 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e698 (3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e411 (3.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e798 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e293 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1,214 (4.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e465 (4.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e276 (5.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e675 (4.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e152 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e163 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e366 (6.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross the full sample, 48.2% of households experienced some degree of food insecurity, while 51.8% were classified as food secure. Latent class analysis revealed four distinct coping profiles, with 25.8% of households falling into the most vulnerable \"High-Stress\" category\u0026mdash;characterized by frequent and severe coping behaviors. Financial strategies dominated the response landscape: 23.5% of households reported borrowing from relatives or friends, and an equal proportion purchased food on credit. In contrast, more extreme measures\u0026mdash;such as selling productive assets\u0026mdash;were employed by only 2.6% of households, suggesting that while financial fragility is widespread, asset depletion remains a last resort. These patterns underscore the centrality of informal financial networks in buffering food insecurity, while also highlighting the limits of household resilience under prolonged economic stress.\u003c/p\u003e\u003cp\u003eTo visualize the interrelationships among household coping behaviors, a co-occurrence network analysis was conducted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The resulting network reveals a tightly interconnected core centered on informal debt and social borrowing, with 'Loan from relatives/friends,' 'Purchase food on credit,' 'Borrow food,' and 'Loan from non-relatives' forming the dominant cluster. This financial nexus functions as a gateway to more severe coping strategies, showing strong linkages to 'Restrict adult consumption'\u0026mdash;a transitional behavior that bridges routine financial stress with deeper forms of deprivation. Beyond this core, more extreme responses such as 'Sold assets' and 'Member migrated for work' occupy the network\u0026rsquo;s periphery, suggesting a clear escalation pathway in household coping responses. These findings highlight not only the structural centrality of informal financial mechanisms but also the progressive nature of food insecurity, where initial reliance on social capital may eventually give way to irreversible livelihood compromises.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Do geographic location and time predict food insecurity?\u003c/h2\u003e\u003cp\u003eTo identify the key drivers of household food insecurity, a linear regression model was used to assess the effects of geographic location (FMA) and survey year on HFIAS scores (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both predictors were found to be statistically significant, indicating that food insecurity levels vary meaningfully across space and time. The model\u0026rsquo;s intercept of 4.14 represents the expected HFIAS score for the baseline category: households located in FMA 1 during the 2018 survey year. Coefficients for other FMAs and years reflect deviations from this baseline, with higher scores indicating greater food insecurity.\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\u003eLinear regression model of geographic (fma) and temporal (year) predictors of household food insecurity\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=\"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=\"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\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd.error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStatistic\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\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.97E-13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.76E-11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.000184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.998389531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0638\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.31E-60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.35E-73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.78E-99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.33E-80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.83E-41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 7\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\u003e0.0789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.01E-71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.98E-24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFMAFMA 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.84E-33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eenns_year2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.31E-10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eenns_year2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-34.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.92E-254\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 results quantify the significant disparities in food security across the fisheries management areas. A clear set of regions emerged as vulnerability hotspots with significantly higher food insecurity compared to the baseline. FMA 5 was the most insecure, with a predicted HFIAS score 1.65 points higher than FMA 1 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other highly vulnerable regions included FMA 7 (+\u0026thinsp;1.41 points), FMA 4 (+\u0026thinsp;1.32 points), and FMA 3 (+\u0026thinsp;1.31 points). In stark contrast, FMA 6 was the only region that was significantly more food-secure, with a predicted score 0.68 points lower than the baseline (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, FMA 12 was statistically indistinguishable from FMA 1 (p\u0026thinsp;=\u0026thinsp;0.998), indicating a similar level of food security.\u003c/p\u003e\u003cp\u003eA significant temporal trend was also identified. Compared to the 2018 baseline, HFIAS scores were significantly lower in both 2019 (β = -0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and, more dramatically, in 2021 (β = -1.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This counterintuitive but powerful trend of improving food security, with the most substantial improvement occurring during the height of the COVID-19 pandemic, suggests that external factors\u0026mdash;such as the widespread implementation of government social amelioration programs (ayuda)\u0026mdash;may have successfully buffered households against the economic shocks of the period. The predicted trends in HFIAS scores for each FMA across the three years are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Are there distinct profiles of household coping behavior?\u003c/h2\u003e\u003cp\u003eTo understand the underlying patterns of coping behaviors, a Latent Class Analysis (LCA) was performed on the household data. The analysis identified a 4-class model as the best statistical fit, revealing distinct and meaningful profiles of how households manage food insecurity. The largest identified class was termed \"Resilient / Low Copers,\" representing 48.2% of the sample, who use almost no coping strategies. The second-largest group was the \"High-Stress / Diversified Copers\" (27.5%), who employ a wide range of severe strategies. Two smaller, distinct profiles were also identified: \"Community Borrowers\" (13.6%), who rely on local credit and food sharing, and \"Loan Seekers\" (10.8%), who primarily pursue cash loans. The detailed probability profiles defining each of these classes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eItem-response probabilities for the 4-class model of household coping strategies\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoping Strategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass 1: Community Borrowers (13.6%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClass 2: Resilient / Low Copers (48.2%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClass 3: Loan Seekers (10.8%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClass 4: High-Stress / Diversified Copers (27.5%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProbability (%) of using strategy\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBorrow food from neighbors/relatives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e92.60%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePurchase food on credit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e98.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRestrict adult consumption for children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42.10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoan from relatives/friend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e96.30%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLoan from non-relatives/friend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e78.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSold assets\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMember migrated for work\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.80%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Model fit statistics for the selected 4-class model: N\u0026thinsp;=\u0026thinsp;90,886; Log-likelihood = -232,094.9; AIC\u0026thinsp;=\u0026thinsp;464,251.8; BIC\u0026thinsp;=\u0026thinsp;464,543.7. Probabilities are the conditional item response probabilities (Pr(2)) for each class. Class population shares are estimated from the model.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe geographic distribution of these behavioral profiles provides a powerful explanation for the regional disparities identified in the regression analysis. The concentration of the most vulnerable profile, Class 4 (\"High-Stress Copers\"), aligns perfectly with the previously identified vulnerability hotspots. FMA 5 has the highest proportion of this class, with 35.1% of its households falling into this high-stress category, followed closely by FMA 8 (33.3%) and FMA 4 (32.4%). This finding demonstrates that the high food insecurity scores in these regions are driven by a large segment of the population being in a state of severe coping.\u003c/p\u003e\u003cp\u003eThe distribution of the most stable profile, Class 2 (\"Resilient / Low Copers\"), confirms the locations of the most food-secure areas. FMA 6 stands out again as the most resilient, with 60.3% of its households belonging to this resilient class, followed by FMA 1 (54.1%) and FMA 12 (53.3%). In these regions, a clear majority of the population is stable and does not need to engage in regular coping behaviors. This segmentation moves beyond simply identifying that certain regions are more vulnerable and begins to explain the underlying behavioral patterns that define that vulnerability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4 How are these behavioral profiles distributed geographically?\u003c/h2\u003e\u003cp\u003eA latent class regression was used to identify the predictors of coping profile membership, with the \"Resilient\" profile as the baseline (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for full model results). Both geography (FMA) and survey year were significant predictors. For instance, households in FMA 11 had 24% higher odds of belonging to the \"High-Stress\" profile compared to the baseline FMA 1 (p\u0026thinsp;=\u0026thinsp;0.002). A critical temporal trend also emerged; the odds of a household being in the \"High-Stress\" profile were 55% higher in 2021 relative to the 2018 baseline (OR\u0026thinsp;=\u0026thinsp;1.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the model-predicted probabilities, which provide a more direct interpretation of these trends, are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This figure clearly shows that the absolute probability of belonging to the \"Resilient\" profile increased significantly across most FMAs in 2021, indicating an overall improvement in household resilience.\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\u003eDistribution of latent coping profiles by fisheries management area\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ey.level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eterm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eestimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003estd.error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003estatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep.value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003econf.low\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003econf.high\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.278122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.030798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47.19451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.027518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.544318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.893974\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.067383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.66329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.096254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.783372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.020192\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.081136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.063965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.21962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.222609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.953748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.22554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.857587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.062643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.45248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.014187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.758501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.969618\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.602734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.044395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-11.4039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.00E-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.552506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.65753\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.862543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.8245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.778428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.955748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.570519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043432\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-12.9215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.41E-38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.523963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.621212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.605868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.058924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-8.50399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.83E-17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.539786\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.680041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.577614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.90416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.13E-32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.463527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.700594\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.662231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-7.31024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.67E-13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.592952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.739603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.617185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.96075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.38E-12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.538768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.707015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.619878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.042652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-11.2123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.55E-29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.570165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.673925\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenns_year2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.888498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.90427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.45E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.837301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.942825\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenns_year2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.317346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.025278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.90354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.11E-27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.25367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.384256\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.999502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.0127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.989866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.925607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.079297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.159737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.799332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.071966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.986858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.362901\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.159991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.079793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.859953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.062892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.992052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.35636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.718981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.084503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.90425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.45E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.60924\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.848489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.77607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.4561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.35E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.694184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.867614\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.779621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.068907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.61283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.68113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.892353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.716903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.9322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.99E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.642252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.800231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.708352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.076938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.4817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.41E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.609199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.823643\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.537255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.047599\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.033774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.66E-19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.400327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.687572\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.970981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.070143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.41983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.67461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.846262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.114081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.878533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.085906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.50748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.131689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.742395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.039637\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.797962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.054391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.14952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.33E-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.717273\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.887727\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenns_year2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.044097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.037765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.142668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.253176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.969606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.124311\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenns_year2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.065631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.031992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.986945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.046929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.13459\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.745966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.27818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.41E-59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.632652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.867145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.818495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.075654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.64743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.705699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.94932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.243731\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.069592\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.134199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.085149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.425488\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.720086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-4.60564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.11E-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.626172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.828086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.999909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.047413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.00191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.998476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.911177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.097283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.166315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.743966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.044938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.301791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.994609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.11674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.907063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.908321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.089094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.935982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.062836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.05289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.292392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.827525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.058653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.789569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.043577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.42188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.90E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.724932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.859969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.027204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.059815\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.448729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.653627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.913571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.154971\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.895349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.074947\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-1.47493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.773032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.03702\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.726388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.047212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.77103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.28E-11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.662189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.796811\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenns_year2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.09795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.033241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.811142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.028698\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.171864\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenns_year2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.549132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.027843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.10E-55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.466859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.63602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Intercept)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.597556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.044431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-11.5888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.69E-31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.54772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.651927\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.554989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.110066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.34958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.82E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.447297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.688609\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.711151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.100121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.40457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.584438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.865337\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.832739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.090296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.02705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.042658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.697668\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.993961\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.659314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.064836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-6.42474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.32E-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.580636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.748653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.948672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.072979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.72202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.470285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.822235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.094552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.813443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.060825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-3.39467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.722026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.916434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.814506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.083221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.4654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.013686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.691921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.958809\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.747483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-5.13755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.78E-07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.66893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.835261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.926041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.076977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.99817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.318196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.796356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.076846\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.762266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.102534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.6475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.008109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.623489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.931931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMAFMA 9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.456647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.067929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-11.5391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.38E-31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.399723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.521678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenns_year2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.100756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.046515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.063774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.039039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.00484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.205826\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eenns_year2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.536549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.037763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.37446\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.60E-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.426928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.654591\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\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study provides a multi-faceted assessment of household food insecurity across the twelve FMAs of the Philippines. Our analyses revealed three primary findings: significant and persistent geographic disparities in food security; a significant improvement in food security in 2021; and four distinct behavioral profiles that provide a deeper explanation for these outcomes.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Geographic hotspots of vulnerability require targeted policy interventions\u003c/h2\u003e\u003cp\u003eThe identification of clear geographic disparities is a critical finding for both marine and social policy. The consistent ranking of FMAs 5, 7, 4, and 8 as having the highest levels of food insecurity and the largest concentrations of \"High-Stress\" households underscores the limitations of a \"one-size-fits-all\" national food security strategy. This spatial heterogeneity reflects deep-rooted structural inequalities\u0026mdash;such as uneven access to markets, infrastructure, and social protection\u0026mdash;that shape household vulnerability across regions. These findings align with previous research documenting significant regional disparities in food insecurity across the Philippines, driven by social determinants including income, education, and geographic isolation (Durano et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ulep, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This heightened vulnerability in specific coastal zones reflects the critical dependence of local economies on small-scale fisheries. In many coastal municipalities, artisanal fishing is not only a primary source of food and income but also a cultural and ecological cornerstone. However, climate variability, declining fish stocks, and habitat degradation have increasingly undermined the resilience of these communities. Recent studies show that small-scale fishers in regions such as the Davao Gulf and Zamboanga Peninsula face compounding risks\u0026mdash;from eroding coastlines and illegal commercial fishing to reduced catch per unit effort and limited access to credit and alternative livelihoods (Macusi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Macusi \u0026amp; Macusi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These stressors exacerbate food insecurity and argue for place-based, fisheries-sensitive social protection strategies, which are often susceptible to both ecological pressures and resource-use conflicts (Nguyen et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Andrews et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Instead, these findings provide an evidence base for spatially targeted interventions. Resources for food assistance, alternative livelihood programs, and social safety nets should be prioritized for these identified high-risk FMAs to address the persistent, underlying drivers of vulnerability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Widespread improvement in 2021 suggests the effectiveness of pandemic-era social safety nets\u003c/h2\u003e\u003cp\u003ePerhaps the most striking finding is the dramatic and widespread improvement in food security observed in 2021. This trend is particularly notable given the unprecedented challenges posed by the COVID-19 pandemic, which severely disrupted the Philippine food system\u0026mdash;especially in urban centers\u0026mdash;by fracturing supply chains, constraining mobility, and undermining household access to affordable and nutritious food. Despite these shocks, targeted government interventions, adaptive coping strategies among households, and the gradual reopening of economic activities appear to have contributed to a rebound in food availability and access. (Huang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Durano et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is within this context of acute stress that the subsequent improvement in food security must be understood. Both the linear regression and the latent class regression confirm this trend. While this study cannot establish direct causality, this counterintuitive finding strongly suggests that the large-scale government social amelioration programs (e.g., \u003cem\u003eayuda\u003c/em\u003e) implemented during the pandemic were effective in buffering households against food crises. While our study provides indirect, macro-level evidence, this result aligns with household-level analyses of the Philippine Social Amelioration Program (SAP), which found that emergency cash transfers significantly improved household welfare and consumption during lockdown periods (Vannavanit \u0026amp; Takahashi, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Brooks et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Strupat et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Islam et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This result carries significant policy implications, underscoring the importance of broad, rapid, and well-targeted social safety net interventions in shielding vulnerable populations from the worst effects of widespread socioeconomic shocks. In contexts of acute disruption\u0026mdash;such as pandemics, climate-induced disasters, or economic downturns\u0026mdash;such programs not only buffer immediate welfare losses but also help preserve long-term human capital and social cohesion. The Philippine experience with SAP demonstrates that timely cash transfers, when delivered at scale, can mitigate food insecurity and stabilize household consumption, even in the absence of perfect targeting or infrastructure. Future policy design should prioritize institutional readiness, digital delivery systems, and inclusive targeting mechanisms to ensure that emergency support reaches those most at risk\u0026mdash;quickly and equitably.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Behavioral profiles reveal widespread economic precarity beyond food insecurity\u003c/h2\u003e\u003cp\u003eThe Latent Class Analysis provided a deeper insight into household behavior, revealing distinct coping profiles that range from reliance on social capital\u0026mdash;such as borrowing food or accessing community support\u0026mdash;to more extreme, asset-depleting behaviors like selling fishing gear or reducing food intake. These behavioral patterns align with established frameworks that categorize coping strategies along a continuum from reversible \u0026ldquo;stress\u0026rdquo; responses to irreversible \u0026ldquo;crisis\u0026rdquo; responses. This typology, often used in food security and humanitarian contexts, helps distinguish between short-term adaptive behaviors and long-term erosive strategies that compromise future resilience. The empirical identification of these latent classes not only validates the heterogeneity of household responses but also underscores the need for tailored policy interventions that reflect varying degrees of vulnerability and adaptive capacity (Ricarte et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hung et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xiangbo et al., 2024; Opoku et al., 2025; Nylund-Gibson \u0026amp; Choi, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The identification of distinct household coping profiles helps illuminate the persistent puzzle of food insecurity in the Philippines; wherein national-level statistics often obscure the heterogeneity of household-level experiences (Kiunisala et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Yohannes et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While debt-based coping mechanisms\u0026mdash;such as borrowing money or purchasing food on credit\u0026mdash;are well-documented responses to economic shocks (Iskander et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mazenda et al., 2024; Hangoma et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Giovanis et al., 2024; Siddik et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Quilloy et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), our Latent Class Analysis reveals that these behaviors are concentrated within specific subgroups rather than uniformly distributed across all food-insecure households. Notably, the emergence of \u0026ldquo;Community Borrowers\u0026rdquo; and \u0026ldquo;Loan Seekers\u0026rdquo; as dominant profiles among non-resilient households underscores a broader economic precarity that transcends mere food access. These findings suggest that policy interventions must extend beyond food availability to address structural constraints in financial inclusion, access to affordable and stable credit, and mechanisms for household savings. Such multidimensional support is essential to enhance both short-term coping capacity and long-term resilience, but our analysis reveals this is a distinct profile and not a universal response among all stressed households. The discovery that a large portion of non-resilient households fall into the \"Community Borrowers\" and \"Loan Seekers\" profiles reveals a widespread economic precarity that extends beyond just food access. This suggests that policy should not only focus on food availability but also on financial inclusion, access to stable credit, and building household savings. This finding aligns with broader patterns in the Philippines, where borrowing is deeply embedded in household financial behavior, yet access to stable, affordable credit remains limited. Community-led loan initiatives have shown promise in mitigating exploitative lending practices, but their reach is still modest (Hill \u0026amp; Kozup, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Berger \u0026amp; Udell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Mull, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dimitrova-Grajzl, 2024). These insights suggest that food security policy must evolve to address underlying financial fragilities\u0026mdash;through inclusive credit systems, savings mobilization, and social protection mechanisms that reduce reliance on erosive coping strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Limitations and future research\u003c/h2\u003e\u003cp\u003eA key limitation of this study is the absence of household-level demographic and socioeconomic data. Therefore, we could not run a model to identify traditional predictors like education or household size. The analysis was consequently focused on the predictive effects of geography and time. Future research should aim to integrate these datasets with socioeconomic surveys to build more comprehensive models. Furthermore, acquiring a shapefile of the FMAs would allow for more advanced spatial analyses like Geographically Weighted Regression to explore if the impact of the 2021 improvement varied geographically.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study offers a granular assessment of food security across the Philippine Fisheries Management Areas (FMAs), identifying critical geographic hotspots and revealing an unexpected trend of pandemic-era improvement in certain regions. Through Latent Class Analysis, it also uncovers distinct behavioral profiles that shape household vulnerability\u0026mdash;ranging from reliance on social networks to debt-based coping strategies. These findings underscore the complexity of food insecurity and highlight the limitations of aggregate national statistics in capturing localized realities. By illuminating both spatial and behavioral dimensions of resilience, the study provides an evidence base for designing more targeted, context-sensitive policies that address not only food availability but also the structural drivers of economic precarity in coastal communities.\u003c/p\u003e\u003c"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cp\u003eThe author has no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funds, grants, or other support was received.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThe author thanks the Department of Science and Technology-Food and Nutrition Research Institute (DOST-FNRI) for providing the Expanded National Nutrition Survey (ENNS) dataset. This data is publicly available upon request from the eNutrition website (enutrition.fnri.dost.gov.ph) in accordance with the Philippine Freedom of Information (FOI) Law. The views expressed herein are solely those of the author and not of the DOST-FNRI.\u003c/p\u003e\u003ch2\u003eData and code availability:\u003c/h2\u003e\u003cp\u003eSupporting data and code for this study may be requested from the DOST-FNRI.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbasolo AO, Montefrio MJF (2025) When one crisis comes after another: Successive shocks, food insecurity, and coastal precarity in the Philippines. 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Asian J Agricultural Ext Econ Sociol 41(11):44\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.9734/ajaees/2023/v41i112260\u003c/span\u003e\u003cspan address=\"10.9734/ajaees/2023/v41i112260\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of the Philippines Open 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 security, Coping strategies, Latent class analysis, Philippines, Fisheries Management Areas (FMAs), Vulnerability, Geographic disparities","lastPublishedDoi":"10.21203/rs.3.rs-7430169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7430169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFood security in the Philippines is marked by significant geographic disparities, yet the underlying household behavioral patterns that drive these differences are poorly understood. This study addresses this gap by analyzing survey data from 90,886 households from 2018 to 2021 across twelve Fisheries Management Areas, employing a quantitative modeling approach to identify the drivers of food insecurity and characterize underlying coping profiles. The analysis identified significant spatial hotspots of vulnerability (particularly FMAs 5, 8, and 4) and resilience (FMA 6). To explain these disparities, Latent Class Analysis revealed four distinct coping profiles: \"Resilient\" (48.2%), \"Community Borrowers\" (13.6%), \"Loan Seekers\" (10.8%), and a highly vulnerable \"High-Stress\" group (27.5%), highlighting a widespread reliance on informal debt. A significant trend of improving food security was observed, with insecurity scores decreasing by 1.20 points in 2021 compared to 2018, coinciding with a sharp increase in the proportion of households classified as \"Resilient.\" The findings suggest that pandemic-era social safety nets had a significant buffering effect, improving overall resilience despite underlying economic precarity. This evidence advocates for a dual policy approach: geographically targeted interventions for vulnerability hotspots and broader programs to enhance financial inclusion and build long-term household resilience against national crises. Future research should explore the distinct vulnerabilities of fishing versus non-fishing households and explore the community-level drivers within hotspots to develop more targeted policy interventions.\u003c/p\u003e","manuscriptTitle":"Distinct household coping profiles explain geographic disparities in food security across Philippine fisheries management areas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 04:36:44","doi":"10.21203/rs.3.rs-7430169/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":"47da3e01-6d2d-44be-9395-4d6db959c930","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T04:36:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 04:36:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7430169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7430169","identity":"rs-7430169","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}