Food Acquisition Strategies and Poverty Transitions in Post-Conflict Mali: Evidence from Pseudo-Panel Analysis

Food Acquisition Strategies and Poverty Transitions in Post-Conflict Mali: Evidence from Pseudo-Panel Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Food Acquisition Strategies and Poverty Transitions in Post-Conflict Mali: Evidence from Pseudo-Panel Analysis Melain Modeste SENOU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8221742/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 This study examines how different household food acquisition strategies influence poverty dynamics in post-conflict Mali, using nationally representative data from the “ Enquête Modulaire Permanente auprès des Ménages (EMOP)” collected between 2013 and 2021. A pseudo-panel was constructed from repeated cross-sectional EMOP data, and multinomial logit models, survival analysis, and Cox proportional hazards models were employed to assess poverty entry, exit, and duration outcomes across various food acquisition strategies. Findings reveal high poverty rates, with 81.9% of households facing food poverty and 71.9% experiencing multidimensional poverty. Although the majority of households (71.5%) rely on market-based food strategies, these do not necessarily enhance poverty mobility. Subsistence and mixed strategies, while associated with an increased initial risk of falling into poverty, significantly improve the likelihood of sustained poverty exit over time. Gift-based strategies, used by a small share (0.9%) are linked to faster poverty escapes. Female-headed households, despite higher vulnerability to monetary poverty, exhibit stronger rates of poverty exit. The results challenge traditional livelihood diversification theories by suggesting that household strategies in fragile settings are shaped more by adaptive responses to institutional breakdown than by optimization logic. Effective post-conflict interventions should consider the varying short- and long-term impacts of food strategies and address gender-specific poverty pathways when designing social protection programs. JEL Classification: D12, I32, O12, Q18, C41, C25 food security poverty dynamics post-conflict recovery livelihood strategies survival analysis household welfare Mali vulnerability Figures Figure 1 1. Introduction Mali’s development trajectory has been deeply disrupted by cycles of political instability and armed conflict, most notably the 2012 military coup and subsequent insurgencies in the north. Although international interventions have contributed to a tenuous peace, Mali remains in a state of protracted fragility characterized by recurrent violence, displacement, and governance challenges (International Crisis Group, 2023; United Nations Development Programme [UNDP], 2022 ). These events have caused enduring “legacy effects” ((Kene et al., 2025; Justino, 2009 )), disrupting livelihoods, fragmenting markets, and weakening public infrastructure critical to food systems and welfare delivery (Food and Agriculture Organization [FAO], 2021 ). Post-conflict recovery has proven uneven across Mali’s regions. Some areas have benefited from aid and stabilization programs, while others, particularly in central and northern Mali, continue to experience exclusion from public services and economic opportunity (Bertelsmann, 2024 ). Meanwhile, households are increasingly affected by “double exposure” to conflict-related shocks and climate stressors such as irregular rainfall and desertification (Cissé et al., 2022 ; World Bank, 2023 ). These overlapping challenges have compelled households to adapt their food acquisition strategies, relying more on subsistence production, informal support networks, and hybrid mechanisms when market access is unreliable. Poverty remains widespread and multidimensional. According to the World Bank ( 2023 ), Mali's national poverty rate stood at 44.4% in 2021, with rural areas disproportionately affected. Food insecurity is acute: over 1.5 million people faced crisis or worse levels of food insecurity in 2023, even during post-harvest periods (Cadre Harmonisé, 2023 ). These structural vulnerabilities emphasize the importance of moving beyond static poverty headcounts toward dynamic measures that capture both transitions into and out of poverty and the strategies households use to navigate these changes (Baulch & Hoddinott, 2000 ; Carter & Barrett, 2006 ). Despite the salience of these issues, empirical research on the relationship between food acquisition strategies and poverty transitions in post-conflict contexts remains limited. Most studies focus on emergency aid responses during active conflict or use cross-sectional data that fail to capture dynamic shifts in household welfare. Little is known about how food acquisition strategies, whether market-dependent, subsistence-oriented, gift-reliant, or mixed, shape the likelihood and duration of poverty in fragile post-conflict environments (Rohner et al., 2021 ; Rufino et al., 2023 ). Moreover, the temporal effectiveness of such strategies remains understudied, as does their role in either reinforcing or breaking poverty traps under institutional fragility. This study addresses these gaps by applying a pseudo-panel approach to nationally representative EMOP data (2013–2021) to analyze how household food acquisition strategies affect poverty entry, exit, and persistence in post-conflict Mali. To our knowledge, this is the first study to use longitudinal pseudo-panel data to assess multi-dimensional poverty transitions linked to food strategies in Mali. Using multinomial logit models, random effects probit, and Cox proportional hazards estimation, we provide robust evidence on how different strategies shape both short- and long-term poverty outcomes. Our analysis contributes to three key debates: (1) the adaptive versus optimizing nature of livelihood responses in fragile settings, (2) the role of strategy diversification in enhancing household resilience, and (3) the need for differentiated, time-sensitive social protection policies in post-conflict recovery contexts. Ultimately, this study sheds light on how food acquisition choices reflect both survival imperatives and strategic adaptation in the face of deep structural and institutional constraints. The rest of the paper is structured as follows: Section 2 reviews the relevant literature. Section 3 describes the data and methodology; Section 4 presents the main empirical findings. The paper ended with a concluding remark. 2. Related literature The intersection of food security, livelihood strategies, and poverty dynamics in post-conflict settings has become a critical area of research in development economics and conflict studies (Muhyie et al., 2025 ; Gebrihet & Gebresilassie, 2025 , Sklar et al., 2025 ). Much of this work is anchored in livelihood diversification theory, which conceptualizes households as rational actors who reallocate resources and diversify activities to manage risk and improve welfare (Ellis, 2000 ). In post-conflict environments, however, these choices are not purely strategic or optimizing but often reflect constrained adaptation under institutional failure, environmental stress, and market fragmentation. Households in fragile contexts typically rely on a mix of food acquisition strategies, including market purchases, subsistence farming, social support (gifts and transfers), and hybrid combinations. These strategies respond to disrupted formal markets, reduced remittance flows, and declining institutional capacities. Barrett and Lentz ( 2009 ) and Maxwell et al. ( 2003 ) provide early typologies of these approaches, distinguishing market-dependent, subsistence-based, and gift-oriented strategies, while more recent empirical work finds that mixed strategies, those combining elements of multiple channels, are increasingly common in crisis-prone regions (Rufino et al., 2013; Mgomezulu et al., 2024 ). Market access plays a central role in determining the feasibility and effectiveness of different food strategies. Studies in Sub-Saharan Africa show that proximity to markets improves dietary diversity and reduces food insecurity (Adundo & Annys, 2025 ; Morrissey et al., 2024 ; Usman & Haile, 2022 ; Nandi et al., 2021 ). However, reliance on markets can also increase household exposure to price shocks, especially in post-conflict economies where inflation, supply chain disruptions, and speculation are prevalent. Iheonu and Oladipupo ( 2024 ) demonstrate that food price volatility is significantly correlated with rising poverty levels in African countries, with the poorest often hit hardest. In the Malian context, where both physical and institutional access to markets is uneven, this duality makes market dependence both a necessity and a source of vulnerability. Food aid and gift-based strategies also feature prominently in post-conflict recovery. While these strategies provide essential relief during acute crises, their long-term role in poverty alleviation is more ambiguous. Kahsay et al. ( 2021 ) show that while households in Ethiopia rely on aid for survival, aid dependency can inhibit productive investment if not paired with empowerment programs. Similarly, Khofi et al. ( 2024 ) observe that food insecurity in South Africa often reflects deeper structural exclusion rather than temporary shortfalls. Gift-based strategies, embedded in kinship networks or community solidarity, can offer temporary buffers but are constrained by social capital and may be inaccessible in fragmented post-conflict settings (Fafchamps & Gubert, 2007 ). Beyond formal and informal support, households develop a range of adaptive and coping strategies to survive in resource-scarce environments. These include both conventional methods, such as food storage and budgeting, and unorthodox practices like scavenging, illicit trade, or strategic migration (Watson et al., 2024 ). Such behaviors reflect resilience but also signal desperation and the erosion of protective institutions. Studies in Burkina Faso and Ethiopia underscore the importance of household characteristics, such as size, education, and income diversity, in shaping resilience and food security (Ouoba & Sawadogo, 2022 ; Fikire & Zegeye, 2022 ). Yet these factors operate within larger structural constraints that remain underexplored. A growing body of literature emphasizes the importance of analyzing poverty as a dynamic rather than static condition, particularly in contexts of conflict and instability. While poverty headcounts offer a snapshot, they fail to reveal the processes through which households fall into, remain in, or escape poverty. Recent studies employing synthetic or pseudo-panel methods in Tanzania, Indonesia, and West Africa (Dang & Lanjouw, 2013 ; Leyaro & Hongoli, 2024 ; Tantriana, 2024 ) show that transitions in and out of poverty are frequent but often cyclical, with many households trapped in vulnerability. These works argue that food acquisition strategies are not just reactive but potentially transformative, capable of altering household welfare trajectories when appropriately supported. Despite these contributions, gaps remain. Few studies examine the relative effectiveness of different food strategies across multiple poverty dimensions, monetary, food, and multidimensional, or evaluate how these strategies perform over time. Existing work tends to treat strategies as static categories without fully capturing their temporal dynamics or interactions with institutional recovery. Moreover, research in post-conflict contexts often prioritizes emergency response or stabilization rather than the long-term evolution of household welfare and resilience strategies. This study addresses these limitations by applying a pseudo-panel approach to EMOP data from 2013 to 2021 to examine how food acquisition strategies influence poverty entry, persistence, and exit in post-conflict Mali. By integrating multinomial logit, probit, and survival analysis methods, the study offers new insights into the dynamic role of food strategies in shaping poverty outcomes and contributes to the design of more effective, context-sensitive interventions in fragile settings. 3. Methodology and data 3.1. Conceptual Framework This study builds on the sustainable livelihoods’ framework (Scoones, 1998 ) and the theory of household risk management in post-conflict settings (Justino, 2009 ; Verwimp et al., 2019 ). In fragile environments like Mali, institutional breakdowns, damaged infrastructure, and ongoing insecurity undermine traditional market mechanisms and force households to adopt adaptive food acquisition strategies to manage risk and maintain basic welfare. These strategies serve dual purposes: short-term consumption smoothing and long-term poverty reduction (Dercon, 2002 ). We classify food acquisition strategies into four categories: market-dependent, subsistence-based, gift-oriented, and mixed strategies. This categorization builds on previous work by Maxwell et al. ( 2003 ) and Ruel et al. ( 2013 ). These strategic responses are analyzed not in isolation but in relation to household-level demographic, economic, and geographic factors that influence both food access and poverty dynamics. Poverty is conceptualized as a dynamic process, whereby households can transition into or out of poverty states over time depending on both structural factors and strategic responses (Baulch & Hoddinott, 2000 ; Carter & Barrett, 2006 ). Post-conflict contexts intensify these dynamics, raising the likelihood of poverty traps and increasing the cost of escape due to market failures, high uncertainty, and weak institutional recovery. 3.2. Data and pseudo-panel construction The empirical analysis uses data from four rounds of the Enquête Modulaire Permanente auprès des Ménages (EMOP) in Mali: 2013, 2015, 2017, 2019, and 2021. These nationally representative household surveys provide detailed information on food acquisition strategies, consumption, demographics, shocks, and living conditions. Since EMOP is a repeated cross-sectional survey without longitudinal follow-up, we construct a pseudo-panel following Deaton ( 1985 ) and Verbeek and Nijman ( 1992 ). To create cohorts, we group households based on time-invariant characteristics: Birth cohort of household head (5-year intervals), geographic region (9 administrative regions), urban/rural residence and education level of household head (none, primary, secondary and University) This approach yields 766 synthetic cohorts consistently observed across survey waves, allowing us to track average behavior and outcomes over time. A minimum cell size of 10 households per cohort-year was used to ensure statistical reliability. Table 1 summarizes the cohort construction process. Table 1 Pseudo-panel cohort construction summary Variable Used for Grouping Categories Used Rationale Birth year of HH head 5-year cohorts (e.g., 1960–64, 1965–69) Relatively stable, proxy for lifecycle Region 9 administrative regions Captures spatial variation Urban/Rural Urban, Rural Structural differences in livelihoods Education level of HH head None, Primary, Secondary+ Proxy for human capital and opportunities We conduct robustness checks by testing the stability of cohort characteristics over time and limiting our analysis to cohorts observed in all five waves to reduce potential biases from sample attrition or compositional changes. 3.3. Variable definitions and measurement We use three main poverty measures, following established multidimensional poverty literature: Monetary Poverty , Food Poverty and Multidimensional Poverty Index (MPI). Monetary Poverty is defined based on national consumption-based poverty lines, following Deaton and Zaidi ( 2002 ). A household is classified as poor if its per capita consumption falls below the national threshold. Binary indicators for poverty status are constructed for each wave and used to model transition probabilities (entry, persistence, exit). Food Poverty is defined as the inability to meet minimum caloric requirements (2,100 kcal per adult equivalent per day), using household food consumption data converted into caloric values based on FAO food composition tables (Smith & Subandoro, 2007 ). Multidimensional Poverty Index (MPI) as far as it is concerned, is constructed using the Alkire-Foster methodology (Alkire & Santos, 2014 ), incorporating deprivations in education, health, and living standards. Households are considered multidimensionally poor if they are deprived in at least 33% of weighted indicators. In addition to static poverty indicators, binary variables for poverty transition (entry/exit) and a continuous variable for poverty duration are created by tracking each cohort's status across waves. Food Acquisition Strategies are the primary explanatory variables and are classified into four mutually exclusive categories based on self-reported household behavior regarding their primary method of acquiring food: Market-dependent for food that is mainly purchased from formal or informal markets. Subsistence-based denotes food that is mainly produced by the household through agriculture or livestock. Gift-based denotes food that is primarily obtained through non-market transfers (e.g., food gifts, community sharing, remittances, or food aid) and Mixed strategies denotes no single dominant source; households combine two or more methods without clear primacy (Ruel et al., 2013 ; Maxwell et al., 2003 ) We also include the food share variable, representing the proportion of total expenditure allocated to food (Engel's law), which serves as an indicator of household food security and consumption patterns (Subramanian & Deaton, 1996 ). Control variables encompass household size and composition, including the dependency ratio to capture the burden of non-working members (Lanjouw & Ravallion, 1995 ). Household head characteristics, particularly gender, are included given their importance in agricultural and market participation decisions (Quisumbing et al., 1996). Geographic factors include residence type and regional indicators, while market access is captured through distance to market measures (Fafchamps & Hill, 2005 ). Economic shocks are incorporated through a price shock index that captures exogenous variation in food prices (Deaton, 1989 ; Friedman & Levinsohn, 2002 ). 3.4. Estimation strategy and model selection Our estimation strategy proceeds through three complementary stages that build upon each other to provide a comprehensive analysis of the relationship between food acquisition strategies and poverty transitions. The first stage involves descriptive analysis to establish the empirical patterns in our data. We construct detailed transition matrices that document poverty mobility patterns across different time periods and examine how these patterns vary by food acquisition strategy. This descriptive analysis provides the foundation for understanding the magnitude and direction of poverty transitions in post-conflict Mali. The second stage focuses on formal econometric analysis of poverty transitions using the multinomial logit framework developed by McFadden ( 1974 ). The third stage employs duration analysis to examine the length of poverty spells and how food acquisition strategies influence poverty persistence, following Jenkins ( 2005 ). Poverty transition analysis To analyze poverty transitions, we employ a multinomial logit framework that estimates the probability of different transition outcomes, following the methodology established by McFadden ( 1974 ) and applied to poverty dynamics by Cappellari & Jenkins ( 2004 ). The model allows us to examine how food acquisition strategies influence the likelihood of entering poverty, exiting poverty, or remaining in the same poverty status. The multinomial logit specification is particularly appropriate for our analysis as it accommodates the discrete nature of transition outcomes while allowing for flexible relationships between explanatory variables and different transition types (Greene, 2018 ). The model specifies the probability of transition outcome j for cohort i at time t as: $$\:P\left({Transition}_{it}=j\right)=\frac{\text{e}\text{x}\text{p}({X}_{it}{\beta\:}_{j}+{FS}_{it}{\gamma\:}_{j})}{\sum\:_{k=0}^{j}\text{e}\text{x}\text{p}({X}_{it}{\beta\:}_{k}+{FS}_{it}{\gamma\:}_{k})}$$ 1 where \(\:{X}_{it}\:\) represents a vector of control variables, \(\:{FS}_{it}\) denotes food acquisition strategies, and \(\:j\) indexes transition types (entry, exit, persistence). The coefficients \(\:{\gamma\:}_{j}\) capture the differential effects of each strategy relative to the reference category (market-dependent strategy) on transition probabilities. This specification allows us to test whether diversified or alternative food strategies provide advantages in terms of poverty mobility compared to market-dependent approaches (Train, 2009 ). Poverty status analysis For analyzing the relationship between food strategies and poverty status, we employ random effects probit models that account for the pseudo-panel structure of our data, following Cappellari & Jenkins ( 2004 ). The random effects specification is particularly suitable for our context as it allows for unobserved cohort-specific heterogeneity while maintaining efficiency in estimation. The model assumes that unobserved cohort characteristics are randomly distributed and uncorrelated with the included explanatory variables. The probit specification models the probability of being in poverty as: $$\:P\left({Poverty}_{it}=1\right)={\Phi\:}(\alpha\:+{X}_{it}\beta\:+{FS}_{it}\gamma\:+{\mu\:}_{i}+{\epsilon\:}_{it})$$ 2 where \(\:{\Phi\:}\) is the standard normal cumulative distribution function, \(\:{\mu\:}_{i}\) captures unobserved cohort-specific heterogeneity, and \(\:{\epsilon\:}_{it}\) is the idiosyncratic error term. The random effects component captures persistent unobserved differences across cohorts that might influence poverty outcomes (Wooldridge, 2010 ). This approach provides consistent estimates of the average effects of food strategies on poverty outcomes while accounting for the clustered nature of the pseudo-panel data. Duration analysis framework To examine how long households remain in poverty and how food acquisition strategies affect poverty duration, we employ Cox proportional hazards models, following Jenkins ( 2005 ). This semi-parametric approach is particularly valuable as it does not require assumptions about the underlying distribution of poverty durations while allowing for flexible modeling of covariate effects. The hazard function represents the instantaneous risk of exiting poverty at any given time, conditional on having remained in poverty up to that point. The Cox model specification is: $$\:h\left(t\backslash\:X,\:FS\right)={h}_{0}\left(t\right)\text{e}\text{x}\text{p}({X}_{it}\beta\:\:+\:{FS}_{it}\gamma\:)$$ 3 where \(\:{h}_{0}\left(t\right)\) is the baseline hazard function and the model estimates how food acquisition strategies affect the rate of poverty exit. The exponential form ensures that hazard ratios are always positive and provides a natural interpretation in terms of multiplicative effects on the baseline hazard (Cleves et al., 2016 ). We test the proportional hazards assumption using Schoenfeld residuals (Schoenfeld, 1982 ) and employ Kaplan-Meier estimators for non-parametric analysis of survivor functions (Kaplan & Meier, 1958 ). This multi-stage approach ensures that our findings are robust across different methodological approaches and provides a comprehensive understanding of the poverty-food strategy relationship. 4. Results and discussions 4.1. Descriptive results The descriptive statistics presented in Table 2 reveal several critical insights about the sample characteristics and the nature of poverty in post-conflict Mali. The poverty indicators paint a stark picture of widespread deprivation. Monetary poverty affects 41.4% of households, while food poverty reaches an alarming 81.9% of the sample, indicating that the vast majority of households struggle to meet basic nutritional needs. Most concerning is the multidimensional poverty rate of 71.9%, suggesting that households face multiple simultaneous deprivations beyond income and food security. This pattern is typical of post-conflict settings where destruction of infrastructure, displacement, and institutional breakdown create complex poverty challenges that extend beyond simple income measures. Table 2 Descriptive statistics Variable Obs Mean Std. Dev. Min Max Poverty indicators Monetary poverty 3,902 0.414 0.448 0 1 Food poverty 3,902 0.819 0.329 0 1 Multidimensional poverty 3,902 0.719 0.388 0 1 Food strategy Market dependent 3,902 0.715 0.373 0 1 Subsistence oriented 3,902 0.103 0.236 0 1 Gift oriented 3,902 0.009 0.065 0 1 Mix strategy 3,902 0.171 0.274 0 1 Instruments Distance to market 3,902 0.058 0.266 0 2 Price shock 3,902 -0.001 0.054 -0.117 0.153 Controls variables Size of the household 3,901 7.552 6.040 1 71 Dependent ratio 3,901 4.552 5.010 1 9 Residence 3,901 0.563 0.495 0 1 Region 3,901 4.193 2.134 1 9 The distribution of food acquisition strategies shows that market-dependent approaches dominate, with 71.5% of households primarily relying on market purchases for food. This suggests that despite the post-conflict context, market mechanisms remain the primary food access channel for most households. Subsistence-oriented strategies account for only 10.3% of households, while gift-dependent strategies represent just 0.9%, indicating limited reliance on traditional safety nets. Mixed strategies are employed by 17.1% of households, suggesting that a significant minority recognizes the benefits of diversification in their food acquisition approach. The household characteristics reveal important demographic patterns. The average household size of 7.6 members is substantially larger than typical household sizes in stable economies, possibly reflecting post-conflict family reunification patterns or extended family support systems. The dependency ratio of 4.6 indicates a high burden of non-productive members relative to productive adults, which likely constrains household capacity to invest in poverty-reducing activities. The fact that 56.3% of households are rural-based aligns with Mali's predominantly agricultural economy but also suggests limited access to urban economic opportunities for many households. The poverty transition matrix provides crucial insights into the dynamics of poverty mobility in post-conflict Mali (Table 2 ). The diagonal elements reveal concerning patterns of poverty persistence, with households in the highest poverty category (0.8 to 1) showing a 22.92% probability of remaining in extreme poverty. More troubling is that 73.91% of extremely poor households transition to the next highest poverty category rather than escaping poverty entirely, indicating that even when households experience some improvement, they rarely achieve substantial poverty reduction. Table 2 Transition matrix for poverty Monetary Poverty Lag_Monetary Poverty [0 to 0.4[ [0.4 to 0.6[ [0.6 to 0.8[ [0.8 to 1[ Total [0 to 0.4[ 51.30 8.70 10.57 29.43 100.00 23.82 4.04 4.91 13.67 46.43 [0.4 to 0.6[ 56.07 2.89 4.05 36.99 100.00 3.66 0.19 0.26 2.42 6.53 [0.6 to 0.8[ 58.40 1.26 4.20 36.13 100.00 5.25 0.11 0.38 3.25 8.98 [0.8 to 1] 73.91 0.99 2.18 22.92 100.00 28.12 0.38 0.83 8.72 38.05 Total 60.85 4.72 6.38 28.05 100.00 60.85 4.72 6.38 28.05 100.00 The upper-left quadrant shows that households in the lowest poverty category (0 to 0.4) have a 51.30% probability of remaining in this favorable state, suggesting some stability among the better-off households. However, the fact that 29.43% of these relatively well-off households fall into the highest poverty category indicates significant vulnerability even among those initially better positioned. This pattern reflects the volatile nature of post-conflict economies where households can experience rapid deterioration in their circumstances. The transition patterns reveal limited upward mobility, with very few households moving from higher to lower poverty categories. For instance, only 2.89% of households in the 0.4–0.6 poverty range move to the lowest poverty category. This suggests that once households fall into poverty, they face substantial barriers to escaping it, which is characteristic of poverty traps where households lack the assets or opportunities necessary for sustained improvement. The overall distribution shows that 60.85% of households remain in or transition to the lowest poverty category, while 28.05% end up in the highest poverty category. This bimodal distribution suggests a polarization of outcomes, with households either maintaining relative stability or falling into severe deprivation, with limited middle-ground positions. 4.2. Econometrics results The empirical analysis reveals that food acquisition strategies significantly influence household poverty trajectories in post-conflict Mali, though their effects are neither static nor uniform across poverty dimensions or time periods. Poverty transition analysis Table 4 displays the marginal effects from the multinomial logit estimation, revealing significant relationships between food acquisition strategies and poverty transitions that challenge conventional assumptions about household food security strategies in post-conflict settings. The results present a nuanced picture where the effectiveness of different strategies varies dramatically between poverty entry and exit processes. Table 4 Marginal effect of multinomial Logit estimation for Estimation of poverty transition (entry or exiting from poverty) (1) (2) VARIABLES Entry Poverty Exit Poverty Food strategy ( Market-dependent as reference) Subsistence-oriented -0.011*** -0.044*** (0.001) (0.006) Gift-dependent -0.001 0.018*** (0.124) (0.006) Mixed -0.021*** 0.079*** (0.002) (0.001) Household size 0.023*** 0.030*** (0.001) (0.007) Household gender -0.000 0.168 (0.000) (0.241) Dependency ratio 0.034*** -0.010*** (0.001) (0.007) Residence ( Rural as reference ) -0.012** 0.336*** (0.000) (0.016) Year ( 2013 as reference ) 2015 0.303*** -0.674*** (0.025) (0.013) 2017 0.603*** -0.774*** (0.055) (0.033) 2019 0.436*** -0.803*** (0.053) (0.039) 2021 -0.002 0.107 (0.002) (0.081) Region (Bamako as reference) Kayes 0.002* 0.475*** (0.000) (0.083) Koulikoro 0.001** 0.469*** (0.000) (0.103) Sikasso 0.000* 0.431*** (0.000) (0.075) Segou 0.000 0.413*** (0.000) (0.104) Mopti 0.000* 0.624*** (0.000) (0.068) Tombouctou 0.000 0.182 (0.000) (0.117) Gao 0.000 0.699*** (0.000) (0.079) Kidal 0.000 0.549*** (0.000) (0.079) Observations 1,378 1,378 Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 For poverty entry, the results reveal counterintuitive patterns that reflect the constrained choices households face in post-conflict Mali. Subsistence-oriented strategies show a significant negative marginal effect of -0.011, indicating that households employing these strategies are actually less likely to enter poverty compared to market-dependent households. This finding aligns with theoretical expectations that subsistence strategies serve as protective mechanisms against market volatility and price shocks, particularly relevant in post-conflict contexts as documented by Rufino et al. (2013) in East Africa. The protective effect of subsistence strategies likely reflects their role as insurance against market failures and price volatility that characterize post-conflict economies, supporting Mgomezulu et al. ( 2024 )'s findings on the resilience benefits of subsistence agriculture. Mixed strategies demonstrate the strongest protective effect against poverty entry (-0.021), validating Ellis's (2000) diversification theory and supporting empirical evidence from Ouoba and Sawadogo ( 2022 ) that households with diversified food acquisition approaches maintain better food security during crises. This significant negative effect contradicts the initial misinterpretation and instead confirms that strategic diversification serves as effective risk management, allowing households to buffer against the failure of any single food acquisition channel. The magnitude of this effect suggests that diversification provides substantial protection against poverty entry in the volatile post-conflict environment of Mali. As far as gift-dependent strategies are concerned, they show no significant effect on poverty entry (-0.001), indicating that reliance on social safety nets neither increases nor decreases poverty entry risk. This finding aligns with Fafchamps and Lund's (2003) analysis of informal insurance networks, suggesting that gift-based strategies serve more as survival mechanisms for already vulnerable households rather than preventive measures for non-poor households. For poverty exit, the patterns reveal different dynamics that highlight the varying effectiveness of strategies once households are already poor. Subsistence-oriented strategies show a significant negative effect on poverty exit (-0.044), indicating that while these strategies protect against poverty entry, they may limit pathways out of poverty once established. The negative effect suggests that subsistence strategies may trap households in low-level equilibria, aligning with asset-based poverty trap theory (Barrett and Carter, 2013 ). Mixed strategies demonstrate the strongest positive effect on poverty exit (0.079), confirming that strategic diversification becomes particularly valuable for escaping poverty. This finding validates Barrett et al.'s ( 2001 ) portfolio theory and supports evidence from post-conflict contexts where diversified approaches provide multiple pathways out of poverty. The large magnitude indicates that diversification can substantially improve poverty exit probabilities, possibly by reducing dependence on any single vulnerable strategy while maximizing opportunities across different channels. Gift-dependent strategies show a significant positive effect on poverty exit (0.018), indicating that social safety nets, while not preventing poverty entry, can effectively help poor households improve their circumstances. This asymmetric effect supports findings by Kahsay et al. ( 2021 ) that aid mechanisms, despite their limitations, can facilitate economic mobility for those already in poverty. The control variables provide important insights into demographic determinants of poverty transitions. Household size shows positive effects on both poverty entry (0.023) and exit (0.030), suggesting that larger households face higher poverty risk but also possess greater capacity for poverty escape through enhanced labor supply, consistent with theoretical expectations about household labor allocation. The dependency ratio demonstrates opposing effects, increasing poverty entry risk (0.034) while reducing exit probability (-0.010), confirming that high dependency burdens constrain household welfare improvement by limiting productive capacity. Temporal patterns reveal important insights about Mali's post-conflict recovery trajectory. The 2017- and 2019-year effects show consistently high poverty entry rates (0.603 and 0.436 respectively) and low exit rates (-0.774 and − 0.803), indicating that the immediate post-conflict period was characterized by widespread welfare deterioration. The 2021 results show stabilization with minimal poverty entry effects (-0.002) and modest recovery in exit rates (0.107), suggesting gradual improvement in household welfare dynamics as the post-conflict transition progressed. The control variables reveal important demographic effects. Household size shows positive effects on both poverty entry and exit, suggesting that larger households face higher poverty risk but also have greater capacity for poverty escape, possibly through increased labor supply. The dependency ratio shows opposing effects, increasing poverty entry risk while reducing exit probability, confirming that high dependency burdens constrain household welfare improvement. In summary, multinomial logit estimates show that subsistence-based strategies offer a protective effect against poverty entry, especially in rural areas and among low-education cohorts. These households are less likely to fall into poverty compared to those relying on market purchases, a finding consistent with earlier research on self-reliance in contexts of price volatility (Rufino et al., 2013). However, the likelihood of poverty exit is relatively low for subsistence households, suggesting these strategies provide stability rather than upward mobility. In contrast, gift-based strategies, although rare (only 0.9% of the sample), are strongly associated with higher probabilities of poverty exit and shorter poverty spells. This likely reflects the concentrated and often targeted nature of such transfers (e.g., humanitarian aid or remittances), which may be sufficient to lift households temporarily above poverty thresholds. However, their long-term sustainability remains uncertain. A key contribution of this study is the nuanced analysis of mixed strategies, which combine market, subsistence, and/or gift-based approaches. While earlier models suggest that mixed strategies increase the risk of poverty entry, closer examination reveals that this effect is time- and group-specific. Disaggregated results show that in the early post-conflict period (2013–2017), households adopting mixed strategies were often those already facing shocks and were thus more vulnerable. However, in later waves (2019–2021), mixed strategy adopters show the highest probability of sustained poverty exit and perform better on multidimensional and food poverty indicators. This pattern suggests a time-varying adaptive advantage, where households gradually learn to optimize diversified strategies in response to instability. Multidimensional Poverty Analysis Table 5 presents probit estimation results examining the impact of food acquisition strategies on different poverty dimensions, revealing that strategy effectiveness varies significantly across monetary, food, and multidimensional poverty measures. This multidimensional analysis provides crucial insights into the specific pathways through which different strategies influence household welfare. Table 5 Probit estimation of Food strategy effect on poverty (1) (3) (5) VARIABLES Monetary Poverty Food Poverty MPI Food share -0.055*** 0.081*** -0.042*** (0.007) (0.012) (0.014) Food strategy ( Market-dependent) Subsistence-oriented -1.222** -0.293*** -0.668** (0.472) (0.007) (0.281) Gift-dependent -0.172 -0.220*** -0.582*** (0.419) (0.112) (0.120) Mixed -0.192*** -0.370*** -0.684*** (0.004) (0.132) (0.170) Household size 0.033** 0.040*** 0.014 (0.007) (0.008) (0.010) Household gender 1.968*** -0.243 0.523* (0.567) (0.278) (0.300) Dependency ratio 0.142*** 0.254*** 0.083*** (0.011) (0.030) (0.028) Residence (Rural as reference) 0.436 0.565*** 0.227 (0.441) (0.113) (0.151) Constant 0.743 -2.235*** -1.019*** (0.902) (0.355) (0.390) Year Yes Yes Yes Region Yes Yes Yes lnsig2u -10.022 -3.987 0.460* (333.802) (6.396) (0.252) LR test of rho 7.6e-06 0.03 65.20 Observations 1,171 1,498 1,498 Number of cohorts 766 974 974 Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 The food share variable demonstrates differential effects across poverty types, with a significant negative effect on monetary poverty (-0.055) and multidimensional poverty (-0.042) but a positive effect on food poverty (0.081). This apparent paradox reflects the complex relationship between food expenditure patterns and welfare outcomes. The positive effect on food poverty likely indicates that households allocating larger portions of their budget to food may be those facing the most severe food access constraints, requiring higher expenditure shares to achieve basic food security. However, the negative effects on monetary and multidimensional poverty suggest that prioritizing food expenditure, while costly in the short term, contributes to broader welfare improvements, supporting Jensen and Miller's (2008) findings on the welfare effects of food budget allocation. Subsistence-oriented strategies demonstrate consistent negative effects across all poverty measures, indicating their effectiveness for poverty reduction once established. The strongest impact on monetary poverty (-1.222) suggests that successful subsistence strategies provide substantial welfare improvements through reduced market dependence and increased food security. This validates de Janvry et Sadoulet.'s (2005) argument that agricultural self-sufficiency can provide substantial welfare improvements in rural contexts, challenging market-oriented development paradigms that emphasize commercialization over food self-sufficiency. The large magnitude of the monetary poverty effect indicates that subsistence strategies can fundamentally alter household economic positions, possibly through reduced food expenditure requirements and increased food security. Gift-dependent strategies show no significant effect on monetary poverty but significant negative effects on both food poverty (-0.220) and multidimensional poverty (-0.582). This pattern indicates that social safety nets are particularly effective for addressing non-monetary dimensions of poverty, providing food security and other welfare improvements even when they don't translate into increased monetary resources. This finding supports evidence from Watson et al. ( 2024 ) that informal networks provide crucial support for food security, though their impact on broader economic outcomes may be limited. Mixed strategies demonstrate strong and consistent poverty-reducing effects across food poverty (-0.370) and multidimensional poverty (-0.684), with the largest magnitudes among all strategies. This confirms that strategic diversification can be highly effective for comprehensive poverty reduction, addressing multiple dimensions of deprivation simultaneously. The superior performance of mixed strategies validates Barrett et al.'s ( 2001 ) portfolio theory and supports Nandi et al.'s ( 2021 ) findings that diversified approaches often outperform single-strategy approaches in achieving sustained welfare improvements. The lack of significant effect on monetary poverty suggests that mixed strategies may improve welfare through non-monetary channels such as food security and access to services rather than direct income effects. Duration analysis The poverty duration analysis reveals striking differences in how long households remain in poverty depending on their food acquisition approach. Figure 1 presents survival curves showing that gift-dependent strategies are associated with the shortest poverty spells, with households employing these strategies escaping poverty most rapidly. This suggests that social safety nets, while used by relatively few households, provide highly targeted support that addresses specific household needs effectively, aligning with Fafchamps and Gubert's (2007) findings on the efficiency of informal insurance mechanisms. Market-dependent strategies show intermediate poverty duration patterns with steady but gradual poverty exits over time. This pattern suggests that while market strategies don't provide rapid poverty escape, they offer consistent pathways out of poverty for households with adequate market access and purchasing power. The gradual nature of poverty exit through market strategies likely reflects the time needed to build market relationships and accumulate resources for sustained consumption improvements, as documented by Fafchamps and Hill ( 2005 ). Subsistence-oriented strategies are associated with longer poverty durations, suggesting that while these strategies may prevent extreme deprivation and protect against poverty entry, they provide limited pathways for substantial welfare improvement. Mixed strategies show intermediate poverty duration patterns, falling between the rapid exit associated with gift strategies and the slower progress of subsistence approaches. This suggests that while diversification provides benefits over pure subsistence approaches, the effectiveness depends on the specific combination of strategies employed and the household's capacity to manage multiple approaches simultaneously. The log-rank test results (Table 5 ) provide strong statistical evidence (chi2 = 22.90, p < 0.0001) that poverty duration differs significantly across food acquisition strategies. The observed versus expected event patterns reveal important insights about strategy effectiveness that confirm the survival curve analysis. Table 6 Log-rank test for equality of survivor functions Events Food strategy Observed Expected Market-dependent 1121 1161.47 Subsistence-oriented 78 53.84 Gift-dependent 5 4.47 Mixed 305 289.21 Total 1509 1509.00 chi2(3) 22.90 Pr > chi2 0.0000 Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Market-dependent strategies show fewer poverty exits than expected (1121 observed vs. 1161.47 expected), indicating that these strategies may be less effective for poverty escape than random chance would predict. This suggests that market dependence in post-conflict Mali may expose households to volatility and price shocks that prolong poverty spells, supporting Iheonu and Oladipupo's (2024) findings on the vulnerability of market-dependent households to price volatility in Sub-Saharan Africa. Subsistence-oriented strategies show substantially more poverty exits than expected (78 observed vs. 53.84 expected), indicating that once households successfully establish subsistence systems, they are more likely to escape poverty than statistical models would predict. These findings challenges market-oriented development paradigms and supports Ellis's (2000) argument that diverse livelihood portfolios can be more effective than market specialization in unstable environments. Mixed strategies show slightly more poverty exits than expected (305 observed vs. 289.21 expected), confirming the modest benefits of diversification. The relatively small difference suggests that while mixed strategies provide some advantages, they are not dramatically superior to other approaches, possibly because the benefits of diversification are offset by the challenges of managing multiple strategies simultaneously. The Cox proportional hazards estimation (Table 7 ) reveals factors influencing the rate of poverty exit over time. Gift-dependent strategies show a strong negative coefficient (-1.391), indicating significantly lower hazard rates for poverty exit, which translates to longer average poverty durations. This apparent contradiction with the survival curve analysis reflects the heterogeneous nature of gift-based strategies, where some households may experience rapid exit while others become dependent on transfers for extended periods. Table 7 Poverty duration estimation (1) VARIABLES Coef. Food strategy ( Market-dependent) Subsistence-oriented -0.111*** (0.003) Gift-dependent -1.391*** (0.445) Mixed -0.031*** (0.011) Household size 0.054*** (0.005) Household gender 1.373*** (0.080) Dependency ratio -0.059*** (0.018) Residence (Rural as reference) -0.068 (0.047) Region Yes Wald chi2(15) 1051.94 Log pseudolikelihood -8090.2746 Observations 1,489 Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 The significant negative coefficients for subsistence-oriented (-0.111) and mixed strategies (-0.031) indicate that these strategies reduce the hazard rate for poverty exit compared to market-dependent strategies (the reference category). This means households using subsistence-oriented or mixed strategies have lower probabilities of escaping poverty at any given time, suggesting longer poverty duration. The gift-dependent strategy shows an even stronger negative effect (-1.391), indicating the lowest hazard rate for poverty exit among all strategies. Household size shows a positive coefficient (0.054), indicating that larger households have higher hazard rates for poverty exit, meaning they escape poverty faster. This likely reflects enhanced labor supply and income-generating capacity within larger households. Similarly, female-headed households demonstrate a strongly positive coefficient (1.373), indicating significantly higher hazard rates for poverty exit compared to male-headed households. This suggests female-headed households escape poverty more quickly, potentially reflecting superior resource management capabilities, access to targeted support programs, or gender-specific livelihood strategies. The rejection of proportional hazards assumptions (p = 0.0219) in Table 7 indicates time-varying effects that reflect the dynamic nature of post-conflict recovery documented by Collier and Duponchel ( 2013 ). This finding supports Justino's (2009) argument that post-conflict transitions involve multiple phases with varying opportunities and constraints, requiring analytical approaches that account for temporal heterogeneity. Table 8 Test of proportional-hazards assumption chi2 df Prob > chi2 Global test 27.95 15 0.0219 Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Vulnerability analysis The vulnerability analysis (Table 9 ) reveals extremely low vulnerability levels across the sample, with a mean vulnerability of 0.0028. This surprisingly low vulnerability may reflect effective household adaptation to post-conflict conditions rather than measurement limitations. The small difference between poor and non-poor households (0.0029 vs. 0.0028) suggests that vulnerability, as measured, captures dynamic risk exposure that affects all households similarly regardless of current poverty status. Table 9 Vulnerability analysis Variable Sample Obs Mean Std. Dev. Min Max Vulnerability Full 1,052 0.0028 0.022 0 0.508 Non-Poor 3 0.0029 0.005 0 0.008 Poor 1,049 0.0028 0.022 0 0.508 Robust standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 The low overall vulnerability levels may reflect post-conflict adaptation processes where households have developed resilience through experience with shocks and uncertainty, supporting theories of adaptive capacity development in unstable environments. The maximum vulnerability level of 0.51 indicates that while most households have developed effective coping mechanisms, a small minority still faces substantial risk exposure, suggesting the need for targeted interventions for the most vulnerable households. These relationships confirm the theoretical expectations about how market access and price volatility influence household food acquisition decisions. The large magnitude of price shock effects suggests that households are highly responsive to market conditions, rapidly adjusting their strategies in response to price changes. This responsiveness may reflect the precarious nature of household food security in post-conflict settings, where small changes in market conditions can force major strategic adjustments. 5. Concluding remarks This study set out to explore how food acquisition strategies shape poverty transitions in post-conflict Mali, using pseudo-panel data from four waves of the nationally representative EMOP survey (2013–2021). By combining multinomial logit, random effects probit, and Cox survival models, the analysis captures both the directional and temporal aspects of poverty dynamics across multiple welfare dimensions. The findings challenge prevailing assumptions in development theory that frame household behavior primarily through the lens of economic optimization and livelihood diversification. Instead, the evidence underscores a more complex reality: households in fragile contexts engage in adaptive behavior shaped less by strategic choice and more by institutional constraints and structural exclusion. The selection and effectiveness of food strategies—whether market-based, subsistence-oriented, gift-reliant, or mixed—reflect adaptive responses to volatility, access barriers, and social capital erosion rather than proactive income-maximizing decisions. In this sense, food acquisition strategies operate as informal coping infrastructures that mediate household exposure to risk and determine the duration and reversibility of poverty spells. While subsistence strategies appear to shield households from falling into poverty, they often lack the upward mobility potential necessary for sustainable recovery. Gift-based strategies, though limited in scale, demonstrate the strongest association with rapid poverty exit and shorter durations of deprivation. Mixed strategies, initially linked to higher poverty entry risks, emerge over time as the most resilient pathway, especially as households learn to balance risk exposure across multiple food access channels. These results point to a time-varying, adaptive advantage associated with hybrid food sourcing in a context of institutional fragility. The primary contribution of this study is thus not only empirical but conceptual: it reconceptualizes household food strategies as dynamic behavioral adaptations to weak institutions, volatile markets, and recurring shocks. This perspective enriches both the poverty dynamics and food security literatures by emphasizing the endogeneity of household resilience to structural fragility rather than assuming exogenous livelihood preferences. From a policy standpoint, this insight has significant implications. First, recovery efforts should go beyond promoting formal market integration or input-based agricultural subsidies. Instead, interventions must recognize and strengthen existing informal adaptive systems, including subsistence production, reciprocal food sharing, and women-led coping networks. Second, social protection policies must account for the time sensitivity of strategy effectiveness, particularly for households transitioning from subsistence to market-based modes. Targeted support to mixed-strategy adopters such as access to credit, transport, or storage, may accelerate sustained poverty exits. Finally, programs that reduce spatial and institutional barriers to food access (e.g., by improving market connectivity, reducing food price volatility, and strengthening decentralized governance) are essential to enhancing the resilience of household strategies. By focusing on the interplay between strategy effectiveness and institutional fragility, this study offers new insights for designing more context-responsive, equity-sensitive interventions in fragile and post-conflict states. Several limitations should be acknowledged. This analysis treats strategies as discrete categories without examining mixed strategy compositions or community-level factors influencing effectiveness. Future research should investigate how specific strategy combinations within mixed portfolios affect outcomes and incorporate social capital dynamics. Additionally, broader welfare analysis encompassing nutritional status, asset accumulation, and resilience measures would provide more comprehensive understanding of strategy effectiveness across multiple wellbeing dimensions. The study ultimately demonstrates that post-conflict food acquisition strategies represent adaptive responses to institutional fragility rather than optimization behavior, requiring nuanced policy approaches that recognize temporal variations in strategy effectiveness and the complex interplay between survival, recovery, and development objectives in fragile contexts. Declarations Ethics declaration not applicable Consent to participate: not applicable Informed consent : not applicable Competing interests : The authors declare no competing interests Clinical trial number not applicable. Consent to Publish declaration : not applicable Funding: There was no funding for this research. Author Contribution SMM writes all the manuscript Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Adundo LA, Annys S. Assessing Market Food Diversity of three Food Environments of Nairobi. Kenya using Spatial and Descriptive Analyses; 2025. Alkire S, Santos ME. Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index. World Dev. 2014;59:251–74. Barrett CB, Carter MR. The economics of poverty traps and persistent poverty: Empirical and policy implications. J Dev Stud. 2013;49(7):976–90. 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10:28:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4584613,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8221742/v1/02e7fff5-cc26-4ef1-b43e-5edd48799a59.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Food Acquisition Strategies and Poverty Transitions in Post-Conflict Mali: Evidence from Pseudo-Panel Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMali\u0026rsquo;s development trajectory has been deeply disrupted by cycles of political instability and armed conflict, most notably the 2012 military coup and subsequent insurgencies in the north. Although international interventions have contributed to a tenuous peace, Mali remains in a state of protracted fragility characterized by recurrent violence, displacement, and governance challenges (International Crisis Group, 2023; United Nations Development Programme [UNDP], \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These events have caused enduring \u0026ldquo;legacy effects\u0026rdquo; ((Kene et al., 2025; Justino, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)), disrupting livelihoods, fragmenting markets, and weakening public infrastructure critical to food systems and welfare delivery (Food and Agriculture Organization [FAO], \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Post-conflict recovery has proven uneven across Mali\u0026rsquo;s regions. Some areas have benefited from aid and stabilization programs, while others, particularly in central and northern Mali, continue to experience exclusion from public services and economic opportunity (Bertelsmann, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meanwhile, households are increasingly affected by \u0026ldquo;double exposure\u0026rdquo; to conflict-related shocks and climate stressors such as irregular rainfall and desertification (Ciss\u0026eacute; et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These overlapping challenges have compelled households to adapt their food acquisition strategies, relying more on subsistence production, informal support networks, and hybrid mechanisms when market access is unreliable.\u003c/p\u003e \u003cp\u003ePoverty remains widespread and multidimensional. According to the World Bank (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Mali's national poverty rate stood at 44.4% in 2021, with rural areas disproportionately affected. Food insecurity is acute: over 1.5\u0026nbsp;million people faced crisis or worse levels of food insecurity in 2023, even during post-harvest periods (Cadre Harmonis\u0026eacute;, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These structural vulnerabilities emphasize the importance of moving beyond static poverty headcounts toward dynamic measures that capture both transitions into and out of poverty and the strategies households use to navigate these changes (Baulch \u0026amp; Hoddinott, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Carter \u0026amp; Barrett, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the salience of these issues, empirical research on the relationship between food acquisition strategies and poverty transitions in post-conflict contexts remains limited. Most studies focus on emergency aid responses during active conflict or use cross-sectional data that fail to capture dynamic shifts in household welfare. Little is known about how food acquisition strategies, whether market-dependent, subsistence-oriented, gift-reliant, or mixed, shape the likelihood and duration of poverty in fragile post-conflict environments (Rohner et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rufino et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the temporal effectiveness of such strategies remains understudied, as does their role in either reinforcing or breaking poverty traps under institutional fragility.\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by applying a pseudo-panel approach to nationally representative EMOP data (2013\u0026ndash;2021) to analyze how household food acquisition strategies affect poverty entry, exit, and persistence in post-conflict Mali. To our knowledge, this is the first study to use longitudinal pseudo-panel data to assess multi-dimensional poverty transitions linked to food strategies in Mali. Using multinomial logit models, random effects probit, and Cox proportional hazards estimation, we provide robust evidence on how different strategies shape both short- and long-term poverty outcomes.\u003c/p\u003e \u003cp\u003eOur analysis contributes to three key debates: (1) the adaptive versus optimizing nature of livelihood responses in fragile settings, (2) the role of strategy diversification in enhancing household resilience, and (3) the need for differentiated, time-sensitive social protection policies in post-conflict recovery contexts. Ultimately, this study sheds light on how food acquisition choices reflect both survival imperatives and strategic adaptation in the face of deep structural and institutional constraints.\u003c/p\u003e \u003cp\u003eThe rest of the paper is structured as follows: Section 2 reviews the relevant literature. Section 3 describes the data and methodology; Section 4 presents the main empirical findings. The paper ended with a concluding remark.\u003c/p\u003e"},{"header":"2. Related literature","content":"\u003cp\u003eThe intersection of food security, livelihood strategies, and poverty dynamics in post-conflict settings has become a critical area of research in development economics and conflict studies (Muhyie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gebrihet \u0026amp; Gebresilassie, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Sklar et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Much of this work is anchored in livelihood diversification theory, which conceptualizes households as rational actors who reallocate resources and diversify activities to manage risk and improve welfare (Ellis, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). In post-conflict environments, however, these choices are not purely strategic or optimizing but often reflect constrained adaptation under institutional failure, environmental stress, and market fragmentation.\u003c/p\u003e \u003cp\u003eHouseholds in fragile contexts typically rely on a mix of food acquisition strategies, including market purchases, subsistence farming, social support (gifts and transfers), and hybrid combinations. These strategies respond to disrupted formal markets, reduced remittance flows, and declining institutional capacities. Barrett and Lentz (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Maxwell et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) provide early typologies of these approaches, distinguishing market-dependent, subsistence-based, and gift-oriented strategies, while more recent empirical work finds that mixed strategies, those combining elements of multiple channels, are increasingly common in crisis-prone regions (Rufino et al., 2013; Mgomezulu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMarket access plays a central role in determining the feasibility and effectiveness of different food strategies. Studies in Sub-Saharan Africa show that proximity to markets improves dietary diversity and reduces food insecurity (Adundo \u0026amp; Annys, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Morrissey et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Usman \u0026amp; Haile, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nandi et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, reliance on markets can also increase household exposure to price shocks, especially in post-conflict economies where inflation, supply chain disruptions, and speculation are prevalent. Iheonu and Oladipupo (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) demonstrate that food price volatility is significantly correlated with rising poverty levels in African countries, with the poorest often hit hardest. In the Malian context, where both physical and institutional access to markets is uneven, this duality makes market dependence both a necessity and a source of vulnerability.\u003c/p\u003e \u003cp\u003eFood aid and gift-based strategies also feature prominently in post-conflict recovery. While these strategies provide essential relief during acute crises, their long-term role in poverty alleviation is more ambiguous. Kahsay et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that while households in Ethiopia rely on aid for survival, aid dependency can inhibit productive investment if not paired with empowerment programs. Similarly, Khofi et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) observe that food insecurity in South Africa often reflects deeper structural exclusion rather than temporary shortfalls. Gift-based strategies, embedded in kinship networks or community solidarity, can offer temporary buffers but are constrained by social capital and may be inaccessible in fragmented post-conflict settings (Fafchamps \u0026amp; Gubert, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond formal and informal support, households develop a range of adaptive and coping strategies to survive in resource-scarce environments. These include both conventional methods, such as food storage and budgeting, and unorthodox practices like scavenging, illicit trade, or strategic migration (Watson et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such behaviors reflect resilience but also signal desperation and the erosion of protective institutions. Studies in Burkina Faso and Ethiopia underscore the importance of household characteristics, such as size, education, and income diversity, in shaping resilience and food security (Ouoba \u0026amp; Sawadogo, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fikire \u0026amp; Zegeye, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yet these factors operate within larger structural constraints that remain underexplored.\u003c/p\u003e \u003cp\u003eA growing body of literature emphasizes the importance of analyzing poverty as a dynamic rather than static condition, particularly in contexts of conflict and instability. While poverty headcounts offer a snapshot, they fail to reveal the processes through which households fall into, remain in, or escape poverty. Recent studies employing synthetic or pseudo-panel methods in Tanzania, Indonesia, and West Africa (Dang \u0026amp; Lanjouw, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Leyaro \u0026amp; Hongoli, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tantriana, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show that transitions in and out of poverty are frequent but often cyclical, with many households trapped in vulnerability. These works argue that food acquisition strategies are not just reactive but potentially transformative, capable of altering household welfare trajectories when appropriately supported.\u003c/p\u003e \u003cp\u003eDespite these contributions, gaps remain. Few studies examine the relative effectiveness of different food strategies across multiple poverty dimensions, monetary, food, and multidimensional, or evaluate how these strategies perform over time. Existing work tends to treat strategies as static categories without fully capturing their temporal dynamics or interactions with institutional recovery. Moreover, research in post-conflict contexts often prioritizes emergency response or stabilization rather than the long-term evolution of household welfare and resilience strategies.\u003c/p\u003e \u003cp\u003eThis study addresses these limitations by applying a pseudo-panel approach to EMOP data from 2013 to 2021 to examine how food acquisition strategies influence poverty entry, persistence, and exit in post-conflict Mali. By integrating multinomial logit, probit, and survival analysis methods, the study offers new insights into the dynamic role of food strategies in shaping poverty outcomes and contributes to the design of more effective, context-sensitive interventions in fragile settings.\u003c/p\u003e"},{"header":"3. Methodology and data","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Conceptual Framework\u003c/h2\u003e \u003cp\u003eThis study builds on the sustainable livelihoods\u0026rsquo; framework (Scoones, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and the theory of household risk management in post-conflict settings (Justino, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Verwimp et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In fragile environments like Mali, institutional breakdowns, damaged infrastructure, and ongoing insecurity undermine traditional market mechanisms and force households to adopt adaptive food acquisition strategies to manage risk and maintain basic welfare. These strategies serve dual purposes: short-term consumption smoothing and long-term poverty reduction (Dercon, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe classify food acquisition strategies into four categories: market-dependent, subsistence-based, gift-oriented, and mixed strategies. This categorization builds on previous work by Maxwell et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Ruel et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These strategic responses are analyzed not in isolation but in relation to household-level demographic, economic, and geographic factors that influence both food access and poverty dynamics.\u003c/p\u003e \u003cp\u003ePoverty is conceptualized as a dynamic process, whereby households can transition into or out of poverty states over time depending on both structural factors and strategic responses (Baulch \u0026amp; Hoddinott, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Carter \u0026amp; Barrett, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Post-conflict contexts intensify these dynamics, raising the likelihood of poverty traps and increasing the cost of escape due to market failures, high uncertainty, and weak institutional recovery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data and pseudo-panel construction\u003c/h2\u003e \u003cp\u003eThe empirical analysis uses data from four rounds of the \u003cem\u003eEnqu\u0026ecirc;te Modulaire Permanente aupr\u0026egrave;s des M\u0026eacute;nages (EMOP) in Mali: 2013, 2015, 2017, 2019, and 2021.\u003c/em\u003e These nationally representative household surveys provide detailed information on food acquisition strategies, consumption, demographics, shocks, and living conditions. Since EMOP is a repeated cross-sectional survey without longitudinal follow-up, we construct a pseudo-panel following Deaton (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) and Verbeek and Nijman (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo create cohorts, we group households based on time-invariant characteristics: Birth cohort of household head (5-year intervals), geographic region (9 administrative regions), urban/rural residence and education level of household head (none, primary, secondary and University)\u003c/p\u003e \u003cp\u003eThis approach yields 766 synthetic cohorts consistently observed across survey waves, allowing us to track average behavior and outcomes over time. A minimum cell size of 10 households per cohort-year was used to ensure statistical reliability. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the cohort construction process.\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\u003ePseudo-panel cohort construction summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable Used for Grouping\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories Used\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRationale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth year of HH head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-year cohorts (e.g., 1960\u0026ndash;64, 1965\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelatively stable, proxy for lifecycle\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 administrative regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaptures spatial variation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban/Rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban, Rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructural differences in livelihoods\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level of HH head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone, Primary, Secondary+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProxy for human capital and opportunities\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\u003eWe conduct robustness checks by testing the stability of cohort characteristics over time and limiting our analysis to cohorts observed in all five waves to reduce potential biases from sample attrition or compositional changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Variable definitions and measurement\u003c/h2\u003e \u003cp\u003eWe use three main poverty measures, following established multidimensional poverty literature: \u003cem\u003eMonetary Poverty\u003c/em\u003e, \u003cem\u003eFood Poverty\u003c/em\u003e and \u003cem\u003eMultidimensional Poverty Index (MPI). Monetary Poverty\u003c/em\u003e is defined based on national consumption-based poverty lines, following Deaton and Zaidi (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). A household is classified as poor if its per capita consumption falls below the national threshold. Binary indicators for poverty status are constructed for each wave and used to model transition probabilities (entry, persistence, exit). \u003cem\u003eFood Poverty\u003c/em\u003e is defined as the inability to meet minimum caloric requirements (2,100 kcal per adult equivalent per day), using household food consumption data converted into caloric values based on FAO food composition tables (Smith \u0026amp; Subandoro, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Multidimensional Poverty Index (MPI) as far as it is concerned, is constructed using the Alkire-Foster methodology (Alkire \u0026amp; Santos, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), incorporating deprivations in education, health, and living standards. Households are considered multidimensionally poor if they are deprived in at least 33% of weighted indicators.\u003c/p\u003e \u003cp\u003eIn addition to static poverty indicators, binary variables for poverty transition (entry/exit) and a continuous variable for poverty duration are created by tracking each cohort's status across waves.\u003c/p\u003e \u003cp\u003eFood Acquisition Strategies are the primary explanatory variables and are classified into four mutually exclusive categories based on self-reported household behavior regarding their \u003cem\u003eprimary\u003c/em\u003e method of acquiring food: \u003cem\u003eMarket-dependent\u003c/em\u003e for food that is mainly purchased from formal or informal markets. \u003cem\u003eSubsistence-based\u003c/em\u003e denotes food that is mainly produced by the household through agriculture or livestock. \u003cem\u003eGift-based\u003c/em\u003e denotes food that is primarily obtained through non-market transfers (e.g., food gifts, community sharing, remittances, or food aid) and \u003cem\u003eMixed strategies\u003c/em\u003e denotes no single dominant source; households combine two or more methods without clear primacy (Ruel et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Maxwell et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWe also include the food share variable, representing the proportion of total expenditure allocated to food (Engel's law), which serves as an indicator of household food security and consumption patterns (Subramanian \u0026amp; Deaton, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Control variables encompass household size and composition, including the dependency ratio to capture the burden of non-working members (Lanjouw \u0026amp; Ravallion, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Household head characteristics, particularly gender, are included given their importance in agricultural and market participation decisions (Quisumbing et al., 1996). Geographic factors include residence type and regional indicators, while market access is captured through distance to market measures (Fafchamps \u0026amp; Hill, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Economic shocks are incorporated through a price shock index that captures exogenous variation in food prices (Deaton, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Friedman \u0026amp; Levinsohn, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Estimation strategy and model selection\u003c/h2\u003e \u003cp\u003eOur estimation strategy proceeds through three complementary stages that build upon each other to provide a comprehensive analysis of the relationship between food acquisition strategies and poverty transitions. The first stage involves descriptive analysis to establish the empirical patterns in our data. We construct detailed transition matrices that document poverty mobility patterns across different time periods and examine how these patterns vary by food acquisition strategy. This descriptive analysis provides the foundation for understanding the magnitude and direction of poverty transitions in post-conflict Mali. The second stage focuses on formal econometric analysis of poverty transitions using the multinomial logit framework developed by McFadden (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). The third stage employs duration analysis to examine the length of poverty spells and how food acquisition strategies influence poverty persistence, following Jenkins (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePoverty transition analysis\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo analyze poverty transitions, we employ a multinomial logit framework that estimates the probability of different transition outcomes, following the methodology established by McFadden (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) and applied to poverty dynamics by Cappellari \u0026amp; Jenkins (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The model allows us to examine how food acquisition strategies influence the likelihood of entering poverty, exiting poverty, or remaining in the same poverty status. The multinomial logit specification is particularly appropriate for our analysis as it accommodates the discrete nature of transition outcomes while allowing for flexible relationships between explanatory variables and different transition types (Greene, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe model specifies the probability of transition outcome j for cohort i at time t as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:P\\left({Transition}_{it}=j\\right)=\\frac{\\text{e}\\text{x}\\text{p}({X}_{it}{\\beta\\:}_{j}+{FS}_{it}{\\gamma\\:}_{j})}{\\sum\\:_{k=0}^{j}\\text{e}\\text{x}\\text{p}({X}_{it}{\\beta\\:}_{k}+{FS}_{it}{\\gamma\\:}_{k})}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents a vector of control variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{FS}_{it}\\)\u003c/span\u003e\u003c/span\u003e denotes food acquisition strategies, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e indexes transition types (entry, exit, persistence). The coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{j}\\)\u003c/span\u003e\u003c/span\u003e capture the differential effects of each strategy relative to the reference category (market-dependent strategy) on transition probabilities. This specification allows us to test whether diversified or alternative food strategies provide advantages in terms of poverty mobility compared to market-dependent approaches (Train, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePoverty status analysis\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFor analyzing the relationship between food strategies and poverty status, we employ random effects probit models that account for the pseudo-panel structure of our data, following Cappellari \u0026amp; Jenkins (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The random effects specification is particularly suitable for our context as it allows for unobserved cohort-specific heterogeneity while maintaining efficiency in estimation. The model assumes that unobserved cohort characteristics are randomly distributed and uncorrelated with the included explanatory variables.\u003c/p\u003e \u003cp\u003eThe probit specification models the probability of being in poverty as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:P\\left({Poverty}_{it}=1\\right)={\\Phi\\:}(\\alpha\\:+{X}_{it}\\beta\\:+{FS}_{it}\\gamma\\:+{\\mu\\:}_{i}+{\\epsilon\\:}_{it})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Phi\\:}\\)\u003c/span\u003e\u003c/span\u003e is the standard normal cumulative distribution function, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e captures unobserved cohort-specific heterogeneity, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the idiosyncratic error term. The random effects component captures persistent unobserved differences across cohorts that might influence poverty outcomes (Wooldridge, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This approach provides consistent estimates of the average effects of food strategies on poverty outcomes while accounting for the clustered nature of the pseudo-panel data.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDuration analysis framework\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo examine how long households remain in poverty and how food acquisition strategies affect poverty duration, we employ Cox proportional hazards models, following Jenkins (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This semi-parametric approach is particularly valuable as it does not require assumptions about the underlying distribution of poverty durations while allowing for flexible modeling of covariate effects. The hazard function represents the instantaneous risk of exiting poverty at any given time, conditional on having remained in poverty up to that point.\u003c/p\u003e \u003cp\u003eThe Cox model specification is:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:h\\left(t\\backslash\\:X,\\:FS\\right)={h}_{0}\\left(t\\right)\\text{e}\\text{x}\\text{p}({X}_{it}\\beta\\:\\:+\\:{FS}_{it}\\gamma\\:)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{h}_{0}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is the baseline hazard function and the model estimates how food acquisition strategies affect the rate of poverty exit. The exponential form ensures that hazard ratios are always positive and provides a natural interpretation in terms of multiplicative effects on the baseline hazard (Cleves et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We test the proportional hazards assumption using Schoenfeld residuals (Schoenfeld, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) and employ Kaplan-Meier estimators for non-parametric analysis of survivor functions (Kaplan \u0026amp; Meier, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1958\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis multi-stage approach ensures that our findings are robust across different methodological approaches and provides a comprehensive understanding of the poverty-food strategy relationship.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and discussions","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Descriptive results\u003c/h2\u003e \u003cp\u003eThe descriptive statistics presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e reveal several critical insights about the sample characteristics and the nature of poverty in post-conflict Mali. The poverty indicators paint a stark picture of widespread deprivation. Monetary poverty affects 41.4% of households, while food poverty reaches an alarming 81.9% of the sample, indicating that the vast majority of households struggle to meet basic nutritional needs. Most concerning is the multidimensional poverty rate of 71.9%, suggesting that households face multiple simultaneous deprivations beyond income and food security. This pattern is typical of post-conflict settings where destruction of infrastructure, displacement, and institutional breakdown create complex poverty challenges that extend beyond simple income measures.\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\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonetary poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultidimensional poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFood strategy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket dependent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubsistence oriented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGift oriented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMix strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInstruments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrice shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eControls variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\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 distribution of food acquisition strategies shows that market-dependent approaches dominate, with 71.5% of households primarily relying on market purchases for food. This suggests that despite the post-conflict context, market mechanisms remain the primary food access channel for most households. Subsistence-oriented strategies account for only 10.3% of households, while gift-dependent strategies represent just 0.9%, indicating limited reliance on traditional safety nets. Mixed strategies are employed by 17.1% of households, suggesting that a significant minority recognizes the benefits of diversification in their food acquisition approach.\u003c/p\u003e \u003cp\u003eThe household characteristics reveal important demographic patterns. The average household size of 7.6 members is substantially larger than typical household sizes in stable economies, possibly reflecting post-conflict family reunification patterns or extended family support systems. The dependency ratio of 4.6 indicates a high burden of non-productive members relative to productive adults, which likely constrains household capacity to invest in poverty-reducing activities. The fact that 56.3% of households are rural-based aligns with Mali's predominantly agricultural economy but also suggests limited access to urban economic opportunities for many households.\u003c/p\u003e \u003cp\u003eThe poverty transition matrix provides crucial insights into the dynamics of poverty mobility in post-conflict Mali (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The diagonal elements reveal concerning patterns of poverty persistence, with households in the highest poverty category (0.8 to 1) showing a 22.92% probability of remaining in extreme poverty. More troubling is that 73.91% of extremely poor households transition to the next highest poverty category rather than escaping poverty entirely, indicating that even when households experience some improvement, they rarely achieve substantial poverty reduction.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTransition matrix for poverty\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMonetary Poverty\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLag_Monetary Poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[0 to 0.4[\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[0.4 to 0.6[\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[0.6 to 0.8[\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.8 to 1[\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0 to 0.4[\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e46.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.4 to 0.6[\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.6 to 0.8[\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.8 to 1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\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 upper-left quadrant shows that households in the lowest poverty category (0 to 0.4) have a 51.30% probability of remaining in this favorable state, suggesting some stability among the better-off households. However, the fact that 29.43% of these relatively well-off households fall into the highest poverty category indicates significant vulnerability even among those initially better positioned. This pattern reflects the volatile nature of post-conflict economies where households can experience rapid deterioration in their circumstances. The transition patterns reveal limited upward mobility, with very few households moving from higher to lower poverty categories. For instance, only 2.89% of households in the 0.4\u0026ndash;0.6 poverty range move to the lowest poverty category. This suggests that once households fall into poverty, they face substantial barriers to escaping it, which is characteristic of poverty traps where households lack the assets or opportunities necessary for sustained improvement. The overall distribution shows that 60.85% of households remain in or transition to the lowest poverty category, while 28.05% end up in the highest poverty category. This bimodal distribution suggests a polarization of outcomes, with households either maintaining relative stability or falling into severe deprivation, with limited middle-ground positions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Econometrics results\u003c/h2\u003e \u003cp\u003eThe empirical analysis reveals that food acquisition strategies significantly influence household poverty trajectories in post-conflict Mali, though their effects are neither static nor uniform across poverty dimensions or time periods.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePoverty transition analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the marginal effects from the multinomial logit estimation, revealing significant relationships between food acquisition strategies and poverty transitions that challenge conventional assumptions about household food security strategies in post-conflict settings. The results present a nuanced picture where the effectiveness of different strategies varies dramatically between poverty entry and exit processes.\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\u003eMarginal effect of multinomial Logit estimation for Estimation of poverty transition (entry or exiting from poverty)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntry Poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExit Poverty\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFood strategy (\u003c/b\u003e\u003cb\u003eMarket-dependent as reference)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubsistence-oriented\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.011***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.044***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGift-dependent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMixed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.021***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.079***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.023***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.241)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependency ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.010***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (\u003cem\u003eRural as reference\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.336***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear (\u003cem\u003e2013 as reference\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.303***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.674***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.603***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.774***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.033)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.436***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.803***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.081)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (Bamako as reference)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKayes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.475***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.083)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKoulikoro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.469***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.103)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSikasso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.431***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.075)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.413***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.104)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMopti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.624***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTombouctou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.699***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKidal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.549***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eRobust standard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor poverty entry, the results reveal counterintuitive patterns that reflect the constrained choices households face in post-conflict Mali. Subsistence-oriented strategies show a significant negative marginal effect of -0.011, indicating that households employing these strategies are actually less likely to enter poverty compared to market-dependent households. This finding aligns with theoretical expectations that subsistence strategies serve as protective mechanisms against market volatility and price shocks, particularly relevant in post-conflict contexts as documented by Rufino et al. (2013) in East Africa. The protective effect of subsistence strategies likely reflects their role as insurance against market failures and price volatility that characterize post-conflict economies, supporting Mgomezulu et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)'s findings on the resilience benefits of subsistence agriculture. Mixed strategies demonstrate the strongest protective effect against poverty entry (-0.021), validating Ellis's (2000) diversification theory and supporting empirical evidence from Ouoba and Sawadogo (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that households with diversified food acquisition approaches maintain better food security during crises. This significant negative effect contradicts the initial misinterpretation and instead confirms that strategic diversification serves as effective risk management, allowing households to buffer against the failure of any single food acquisition channel. The magnitude of this effect suggests that diversification provides substantial protection against poverty entry in the volatile post-conflict environment of Mali. As far as gift-dependent strategies are concerned, they show no significant effect on poverty entry (-0.001), indicating that reliance on social safety nets neither increases nor decreases poverty entry risk. This finding aligns with Fafchamps and Lund's (2003) analysis of informal insurance networks, suggesting that gift-based strategies serve more as survival mechanisms for already vulnerable households rather than preventive measures for non-poor households.\u003c/p\u003e \u003cp\u003eFor poverty exit, the patterns reveal different dynamics that highlight the varying effectiveness of strategies once households are already poor. Subsistence-oriented strategies show a significant negative effect on poverty exit (-0.044), indicating that while these strategies protect against poverty entry, they may limit pathways out of poverty once established. The negative effect suggests that subsistence strategies may trap households in low-level equilibria, aligning with asset-based poverty trap theory (Barrett and Carter, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Mixed strategies demonstrate the strongest positive effect on poverty exit (0.079), confirming that strategic diversification becomes particularly valuable for escaping poverty. This finding validates Barrett et al.'s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) portfolio theory and supports evidence from post-conflict contexts where diversified approaches provide multiple pathways out of poverty. The large magnitude indicates that diversification can substantially improve poverty exit probabilities, possibly by reducing dependence on any single vulnerable strategy while maximizing opportunities across different channels. Gift-dependent strategies show a significant positive effect on poverty exit (0.018), indicating that social safety nets, while not preventing poverty entry, can effectively help poor households improve their circumstances. This asymmetric effect supports findings by Kahsay et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that aid mechanisms, despite their limitations, can facilitate economic mobility for those already in poverty.\u003c/p\u003e \u003cp\u003eThe control variables provide important insights into demographic determinants of poverty transitions. Household size shows positive effects on both poverty entry (0.023) and exit (0.030), suggesting that larger households face higher poverty risk but also possess greater capacity for poverty escape through enhanced labor supply, consistent with theoretical expectations about household labor allocation. The dependency ratio demonstrates opposing effects, increasing poverty entry risk (0.034) while reducing exit probability (-0.010), confirming that high dependency burdens constrain household welfare improvement by limiting productive capacity.\u003c/p\u003e \u003cp\u003eTemporal patterns reveal important insights about Mali's post-conflict recovery trajectory. The 2017- and 2019-year effects show consistently high poverty entry rates (0.603 and 0.436 respectively) and low exit rates (-0.774 and \u0026minus;\u0026thinsp;0.803), indicating that the immediate post-conflict period was characterized by widespread welfare deterioration. The 2021 results show stabilization with minimal poverty entry effects (-0.002) and modest recovery in exit rates (0.107), suggesting gradual improvement in household welfare dynamics as the post-conflict transition progressed.\u003c/p\u003e \u003cp\u003eThe control variables reveal important demographic effects. Household size shows positive effects on both poverty entry and exit, suggesting that larger households face higher poverty risk but also have greater capacity for poverty escape, possibly through increased labor supply. The dependency ratio shows opposing effects, increasing poverty entry risk while reducing exit probability, confirming that high dependency burdens constrain household welfare improvement.\u003c/p\u003e \u003cp\u003eIn summary, multinomial logit estimates show that subsistence-based strategies offer a protective effect against poverty entry, especially in rural areas and among low-education cohorts. These households are less likely to fall into poverty compared to those relying on market purchases, a finding consistent with earlier research on self-reliance in contexts of price volatility (Rufino et al., 2013). However, the likelihood of poverty exit is relatively low for subsistence households, suggesting these strategies provide stability rather than upward mobility. In contrast, gift-based strategies, although rare (only 0.9% of the sample), are strongly associated with higher probabilities of poverty exit and shorter poverty spells. This likely reflects the concentrated and often targeted nature of such transfers (e.g., humanitarian aid or remittances), which may be sufficient to lift households temporarily above poverty thresholds. However, their long-term sustainability remains uncertain.\u003c/p\u003e \u003cp\u003eA key contribution of this study is the nuanced analysis of mixed strategies, which combine market, subsistence, and/or gift-based approaches. While earlier models suggest that mixed strategies increase the risk of poverty entry, closer examination reveals that this effect is time- and group-specific. Disaggregated results show that in the early post-conflict period (2013\u0026ndash;2017), households adopting mixed strategies were often those already facing shocks and were thus more vulnerable. However, in later waves (2019\u0026ndash;2021), mixed strategy adopters show the highest probability of sustained poverty exit and perform better on multidimensional and food poverty indicators. This pattern suggests a time-varying adaptive advantage, where households gradually learn to optimize diversified strategies in response to instability.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMultidimensional Poverty Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents probit estimation results examining the impact of food acquisition strategies on different poverty dimensions, revealing that strategy effectiveness varies significantly across monetary, food, and multidimensional poverty measures. This multidimensional analysis provides crucial insights into the specific pathways through which different strategies influence household welfare.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProbit estimation of Food strategy effect on poverty\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonetary Poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFood Poverty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMPI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood share\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.055***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.081***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.042***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFood strategy (\u003c/b\u003e\u003cb\u003eMarket-dependent)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubsistence-oriented\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.222**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.668**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.472)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.281)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGift-dependent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.220***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.582***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.419)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.120)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMixed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.192***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.370***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.684***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.170)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.033**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.968***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.523*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.300)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependency ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.142***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.254***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.083***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (Rural as reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.565***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.151)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.235***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.019***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.390)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnsig2u\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.460*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(333.802)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.252)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLR test of rho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6e-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of cohorts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eRobust standard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe food share variable demonstrates differential effects across poverty types, with a significant negative effect on monetary poverty (-0.055) and multidimensional poverty (-0.042) but a positive effect on food poverty (0.081). This apparent paradox reflects the complex relationship between food expenditure patterns and welfare outcomes. The positive effect on food poverty likely indicates that households allocating larger portions of their budget to food may be those facing the most severe food access constraints, requiring higher expenditure shares to achieve basic food security. However, the negative effects on monetary and multidimensional poverty suggest that prioritizing food expenditure, while costly in the short term, contributes to broader welfare improvements, supporting Jensen and Miller's (2008) findings on the welfare effects of food budget allocation.\u003c/p\u003e \u003cp\u003eSubsistence-oriented strategies demonstrate consistent negative effects across all poverty measures, indicating their effectiveness for poverty reduction once established. The strongest impact on monetary poverty (-1.222) suggests that successful subsistence strategies provide substantial welfare improvements through reduced market dependence and increased food security. This validates de Janvry et Sadoulet.'s (2005) argument that agricultural self-sufficiency can provide substantial welfare improvements in rural contexts, challenging market-oriented development paradigms that emphasize commercialization over food self-sufficiency. The large magnitude of the monetary poverty effect indicates that subsistence strategies can fundamentally alter household economic positions, possibly through reduced food expenditure requirements and increased food security.\u003c/p\u003e \u003cp\u003eGift-dependent strategies show no significant effect on monetary poverty but significant negative effects on both food poverty (-0.220) and multidimensional poverty (-0.582). This pattern indicates that social safety nets are particularly effective for addressing non-monetary dimensions of poverty, providing food security and other welfare improvements even when they don't translate into increased monetary resources. This finding supports evidence from Watson et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that informal networks provide crucial support for food security, though their impact on broader economic outcomes may be limited.\u003c/p\u003e \u003cp\u003eMixed strategies demonstrate strong and consistent poverty-reducing effects across food poverty (-0.370) and multidimensional poverty (-0.684), with the largest magnitudes among all strategies. This confirms that strategic diversification can be highly effective for comprehensive poverty reduction, addressing multiple dimensions of deprivation simultaneously. The superior performance of mixed strategies validates Barrett et al.'s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) portfolio theory and supports Nandi et al.'s (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) findings that diversified approaches often outperform single-strategy approaches in achieving sustained welfare improvements. The lack of significant effect on monetary poverty suggests that mixed strategies may improve welfare through non-monetary channels such as food security and access to services rather than direct income effects.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDuration analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe poverty duration analysis reveals striking differences in how long households remain in poverty depending on their food acquisition approach. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents survival curves showing that gift-dependent strategies are associated with the shortest poverty spells, with households employing these strategies escaping poverty most rapidly. This suggests that social safety nets, while used by relatively few households, provide highly targeted support that addresses specific household needs effectively, aligning with Fafchamps and Gubert's (2007) findings on the efficiency of informal insurance mechanisms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMarket-dependent strategies show intermediate poverty duration patterns with steady but gradual poverty exits over time. This pattern suggests that while market strategies don't provide rapid poverty escape, they offer consistent pathways out of poverty for households with adequate market access and purchasing power. The gradual nature of poverty exit through market strategies likely reflects the time needed to build market relationships and accumulate resources for sustained consumption improvements, as documented by Fafchamps and Hill (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Subsistence-oriented strategies are associated with longer poverty durations, suggesting that while these strategies may prevent extreme deprivation and protect against poverty entry, they provide limited pathways for substantial welfare improvement. Mixed strategies show intermediate poverty duration patterns, falling between the rapid exit associated with gift strategies and the slower progress of subsistence approaches. This suggests that while diversification provides benefits over pure subsistence approaches, the effectiveness depends on the specific combination of strategies employed and the household's capacity to manage multiple approaches simultaneously.\u003c/p\u003e \u003cp\u003eThe log-rank test results (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) provide strong statistical evidence (chi2\u0026thinsp;=\u0026thinsp;22.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) that poverty duration differs significantly across food acquisition strategies. The observed versus expected event patterns reveal important insights about strategy effectiveness that confirm the survival curve analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLog-rank test for equality of survivor functions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eEvents\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMarket-dependent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1161.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubsistence-oriented\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGift-dependent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMixed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e289.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1509.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003echi2(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePr\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eRobust standard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMarket-dependent strategies show fewer poverty exits than expected (1121 observed vs. 1161.47 expected), indicating that these strategies may be less effective for poverty escape than random chance would predict. This suggests that market dependence in post-conflict Mali may expose households to volatility and price shocks that prolong poverty spells, supporting Iheonu and Oladipupo's (2024) findings on the vulnerability of market-dependent households to price volatility in Sub-Saharan Africa.\u003c/p\u003e \u003cp\u003eSubsistence-oriented strategies show substantially more poverty exits than expected (78 observed vs. 53.84 expected), indicating that once households successfully establish subsistence systems, they are more likely to escape poverty than statistical models would predict. These findings challenges market-oriented development paradigms and supports Ellis's (2000) argument that diverse livelihood portfolios can be more effective than market specialization in unstable environments. Mixed strategies show slightly more poverty exits than expected (305 observed vs. 289.21 expected), confirming the modest benefits of diversification. The relatively small difference suggests that while mixed strategies provide some advantages, they are not dramatically superior to other approaches, possibly because the benefits of diversification are offset by the challenges of managing multiple strategies simultaneously.\u003c/p\u003e \u003cp\u003eThe Cox proportional hazards estimation (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) reveals factors influencing the rate of poverty exit over time. Gift-dependent strategies show a strong negative coefficient (-1.391), indicating significantly lower hazard rates for poverty exit, which translates to longer average poverty durations. This apparent contradiction with the survival curve analysis reflects the heterogeneous nature of gift-based strategies, where some households may experience rapid exit while others become dependent on transfers for extended periods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePoverty duration estimation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFood strategy (\u003cem\u003eMarket-dependent)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSubsistence-oriented\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.111***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGift-dependent\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.391***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.445)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMixed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.031***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.373***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.080)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependency ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.059***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (Rural as reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.047)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald chi2(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1051.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog pseudolikelihood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8090.2746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eRobust standard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe significant negative coefficients for subsistence-oriented (-0.111) and mixed strategies (-0.031) indicate that these strategies reduce the hazard rate for poverty exit compared to market-dependent strategies (the reference category). This means households using subsistence-oriented or mixed strategies have lower probabilities of escaping poverty at any given time, suggesting longer poverty duration. The gift-dependent strategy shows an even stronger negative effect (-1.391), indicating the lowest hazard rate for poverty exit among all strategies.\u003c/p\u003e \u003cp\u003eHousehold size shows a positive coefficient (0.054), indicating that larger households have higher hazard rates for poverty exit, meaning they escape poverty faster. This likely reflects enhanced labor supply and income-generating capacity within larger households. Similarly, female-headed households demonstrate a strongly positive coefficient (1.373), indicating significantly higher hazard rates for poverty exit compared to male-headed households. This suggests female-headed households escape poverty more quickly, potentially reflecting superior resource management capabilities, access to targeted support programs, or gender-specific livelihood strategies.\u003c/p\u003e \u003cp\u003eThe rejection of proportional hazards assumptions (p\u0026thinsp;=\u0026thinsp;0.0219) in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicates time-varying effects that reflect the dynamic nature of post-conflict recovery documented by Collier and Duponchel (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This finding supports Justino's (2009) argument that post-conflict transitions involve multiple phases with varying opportunities and constraints, requiring analytical approaches that account for temporal heterogeneity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTest of proportional-hazards assumption\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003echi2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eRobust standard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eVulnerability analysis\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe vulnerability analysis (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) reveals extremely low vulnerability levels across the sample, with a mean vulnerability of 0.0028. This surprisingly low vulnerability may reflect effective household adaptation to post-conflict conditions rather than measurement limitations. The small difference between poor and non-poor households (0.0029 vs. 0.0028) suggests that vulnerability, as measured, captures dynamic risk exposure that affects all households similarly regardless of current poverty status.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVulnerability analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVulnerability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.508\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\u003eRobust standard errors in parentheses\u003c/p\u003e \u003cp\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e \u003cp\u003eThe low overall vulnerability levels may reflect post-conflict adaptation processes where households have developed resilience through experience with shocks and uncertainty, supporting theories of adaptive capacity development in unstable environments. The maximum vulnerability level of 0.51 indicates that while most households have developed effective coping mechanisms, a small minority still faces substantial risk exposure, suggesting the need for targeted interventions for the most vulnerable households.\u003c/p\u003e \u003cp\u003eThese relationships confirm the theoretical expectations about how market access and price volatility influence household food acquisition decisions. The large magnitude of price shock effects suggests that households are highly responsive to market conditions, rapidly adjusting their strategies in response to price changes. This responsiveness may reflect the precarious nature of household food security in post-conflict settings, where small changes in market conditions can force major strategic adjustments.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Concluding remarks","content":"\u003cp\u003eThis study set out to explore how food acquisition strategies shape poverty transitions in post-conflict Mali, using pseudo-panel data from four waves of the nationally representative EMOP survey (2013\u0026ndash;2021). By combining multinomial logit, random effects probit, and Cox survival models, the analysis captures both the directional and temporal aspects of poverty dynamics across multiple welfare dimensions. The findings challenge prevailing assumptions in development theory that frame household behavior primarily through the lens of economic optimization and livelihood diversification. Instead, the evidence underscores a more complex reality: households in fragile contexts engage in adaptive behavior shaped less by strategic choice and more by institutional constraints and structural exclusion. The selection and effectiveness of food strategies\u0026mdash;whether market-based, subsistence-oriented, gift-reliant, or mixed\u0026mdash;reflect adaptive responses to volatility, access barriers, and social capital erosion rather than proactive income-maximizing decisions. In this sense, food acquisition strategies operate as informal coping infrastructures that mediate household exposure to risk and determine the duration and reversibility of poverty spells. While subsistence strategies appear to shield households from falling into poverty, they often lack the upward mobility potential necessary for sustainable recovery. Gift-based strategies, though limited in scale, demonstrate the strongest association with rapid poverty exit and shorter durations of deprivation. Mixed strategies, initially linked to higher poverty entry risks, emerge over time as the most resilient pathway, especially as households learn to balance risk exposure across multiple food access channels. These results point to a time-varying, adaptive advantage associated with hybrid food sourcing in a context of institutional fragility.\u003c/p\u003e \u003cp\u003eThe primary contribution of this study is thus not only empirical but conceptual: it reconceptualizes household food strategies as dynamic behavioral adaptations to weak institutions, volatile markets, and recurring shocks. This perspective enriches both the poverty dynamics and food security literatures by emphasizing the endogeneity of household resilience to structural fragility rather than assuming exogenous livelihood preferences. From a policy standpoint, this insight has significant implications. First, recovery efforts should go beyond promoting formal market integration or input-based agricultural subsidies. Instead, interventions must recognize and strengthen existing informal adaptive systems, including subsistence production, reciprocal food sharing, and women-led coping networks. Second, social protection policies must account for the time sensitivity of strategy effectiveness, particularly for households transitioning from subsistence to market-based modes. Targeted support to mixed-strategy adopters such as access to credit, transport, or storage, may accelerate sustained poverty exits. Finally, programs that reduce spatial and institutional barriers to food access (e.g., by improving market connectivity, reducing food price volatility, and strengthening decentralized governance) are essential to enhancing the resilience of household strategies. By focusing on the interplay between strategy effectiveness and institutional fragility, this study offers new insights for designing more context-responsive, equity-sensitive interventions in fragile and post-conflict states.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. This analysis treats strategies as discrete categories without examining mixed strategy compositions or community-level factors influencing effectiveness. Future research should investigate how specific strategy combinations within mixed portfolios affect outcomes and incorporate social capital dynamics. Additionally, broader welfare analysis encompassing nutritional status, asset accumulation, and resilience measures would provide more comprehensive understanding of strategy effectiveness across multiple wellbeing dimensions. The study ultimately demonstrates that post-conflict food acquisition strategies represent adaptive responses to institutional fragility rather than optimization behavior, requiring nuanced policy approaches that recognize temporal variations in strategy effectiveness and the complex interplay between survival, recovery, and development objectives in fragile contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics declaration\u003c/h2\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\u003e \u003cb\u003eInformed consent\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003enot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e \u003cb\u003eCompeting interests\u003c/b\u003e:\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish declaration\u003c/b\u003e: not applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThere was no funding for this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSMM writes all the manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdundo LA, Annys S. Assessing Market Food Diversity of three Food Environments of Nairobi. Kenya using Spatial and Descriptive Analyses; 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlkire S, Santos ME. Measuring acute poverty in the developing world: Robustness and scope of the multidimensional poverty index. World Dev. 2014;59:251\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett CB, Carter MR. The economics of poverty traps and persistent poverty: Empirical and policy implications. J Dev Stud. 2013;49(7):976\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett CB, Lentz EC. Food insecurity. In: Blume L, Durlauf S, editors. The New Palgrave Dictionary of Economics Online. Palgrave Macmillan; 2009.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrett CB, Reardon T, Webb P. 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Mali Economic Update: Navigating Recovery Amid Fragility. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldbank.org/en/country/mali/publication/mali-economic-update-2023\u003c/span\u003e\u003cspan address=\"https://www.worldbank.org/en/country/mali/publication/mali-economic-update-2023\" targettype=\"URL\" 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":false,"hideJournal":true,"highlight":"","institution":"","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, poverty dynamics, post-conflict recovery, livelihood strategies, survival analysis, household welfare, Mali, vulnerability","lastPublishedDoi":"10.21203/rs.3.rs-8221742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8221742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines how different household food acquisition strategies influence poverty dynamics in post-conflict Mali, using nationally representative data from the “\u003cem\u003eEnquête Modulaire Permanente auprès des Ménages (EMOP)”\u003c/em\u003e collected between 2013 and 2021. A pseudo-panel was constructed from repeated cross-sectional EMOP data, and multinomial logit models, survival analysis, and Cox proportional hazards models were employed to assess poverty entry, exit, and duration outcomes across various food acquisition strategies. Findings reveal high poverty rates, with 81.9% of households facing food poverty and 71.9% experiencing multidimensional poverty. Although the majority of households (71.5%) rely on market-based food strategies, these do not necessarily enhance poverty mobility. Subsistence and mixed strategies, while associated with an increased initial risk of falling into poverty, significantly improve the likelihood of sustained poverty exit over time. Gift-based strategies, used by a small share (0.9%) are linked to faster poverty escapes. Female-headed households, despite higher vulnerability to monetary poverty, exhibit stronger rates of poverty exit. The results challenge traditional livelihood diversification theories by suggesting that household strategies in fragile settings are shaped more by adaptive responses to institutional breakdown than by optimization logic. Effective post-conflict interventions should consider the varying short- and long-term impacts of food strategies and address gender-specific poverty pathways when designing social protection programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification:\u003c/strong\u003e D12, I32, O12, Q18, C41, C25\u003c/p\u003e","manuscriptTitle":"Food Acquisition Strategies and Poverty Transitions in Post-Conflict Mali: Evidence from Pseudo-Panel Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 18:49:54","doi":"10.21203/rs.3.rs-8221742/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":"f00bb68c-0832-4046-a10e-2dd2439953d8","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T10:25:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 18:49:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8221742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8221742","identity":"rs-8221742","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}