Exploring the Impact of Cash-Based Interventions on Alleviating Food Insecurity Among Displaced Populations Amidst COVID-19: A Case Study of Cox Bazar, Bangladesh.

Exploring the Impact of Cash-Based Interventions on Alleviating Food Insecurity Among Displaced Populations Amidst COVID-19: A Case Study of Cox Bazar, Bangladesh. | 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 Exploring the Impact of Cash-Based Interventions on Alleviating Food Insecurity Among Displaced Populations Amidst COVID-19: A Case Study of Cox Bazar, Bangladesh. Meshack Achore, Thelma Abu, Pindar Yawulda Mbaya, Eugene Osei Yeboah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5363128/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Food insecurity is a significant issue impacting millions of people compelled to abandon their homes due to conflict, natural disasters, or economic difficulties. Cash incentives have become a critical mitigating tool in alleviating the food insecurity challenges of these displaced populations amid COVID-19. This paper examines the determinants of food insecurity and how cash incentives alleviate food insecurity among displaced populations. The study used the 2020–2021 high-frequency survey data to achieve this objective. The high-frequency survey continues the 2019 Cox Bazar Panel Survey baseline, representative research of Rohingya displaced after 2017 in host communities in Bangladesh's Cox's Bazar district. Receiving cash transfers (incentives) is significantly associated with lower odds of experiencing food insecurity (OR = 0.19, CI = 0.91-0.380). Female participants have higher odds (OR = 3.44, CI = 1.41-5.36) of experiencing food insecurity than their male counterparts. Being a recipient of assistance from UN agencies was associated with slightly lower odds (OR = 0.88, CI = 0.69-0.97) of experiencing food insecurity. To achieve sustainable food security, we must integrate cash incentives into a comprehensive social support system that includes employment initiatives, affordable housing, healthcare, and education. This holistic strategy can address the underlying reasons why people face food insecurity, giving them the tools and chances to become stable in the long term and less reliant on short-term monetary incentives. Food insecurity displaced populations COVID-19 Bangladesh Cox Bazar Introduction Food insecurity, defined as the lack of access to safe and nutritious food for average human growth and development and an active and healthy life (FAO, 2019), is a significant public health concern. Food insecurity can be acute or chronic. Acute food insecurity occurs suddenly and can be severe, threatening lives and livelihoods. Chronic food insecurity is when people can't consistently get enough food to meet their energy needs over time (www.fsinplatform.org). Given its negative impacts, most governments and development organizations, such as the World Bank and the United Nations, have made several commitments to address food insecurity. For instance, the United Nations Sustainable Development Goal 2 is set to create a world free of hunger by 2030. Despite these global efforts, current estimates indicate that 45 million children under 5 were malnourished, with 148 million suffering from stunted growth and wasting (SDG Report, 2023). Pandemics such as COVID-19, armed conflict, extreme poverty, and natural disasters worsen food insecurity (UN, 2023). In 2023, the World Food Program reported that over 333 million people faced severe food insecurity, implying those individuals or households experienced uncertainty about the source of their next meal (FAO, 2023). This is an increase of 200 million from the pre-COVID-19 pandemic figures, with the most impacted population being the forcibly displaced (WFP, 2023). COVID-19 has exacerbated issues of food insecurity among the forcibly displaced. Aside from the health hazards the COVID-19 virus poses, lockdowns to stop its spread have impacted the livelihoods of locals, refugees, and migrants. Particularly relating to their capacity to supplement relief from humanitarian agencies such as UNHCR. As a result, these populations now experience higher levels of food insecurity. A recent study by the World Food Programme indicates that 86% of Rohingya refugees were highly vulnerable to poverty and hunger by the end of 2020, up from 70% in 2019. In addition, a studyreported that 23% of Bangladeshi and 18% of Rohingya adolescents reported feeling hungrier during the pandemic. There are also gender dynamics, with girls (22%) being more likely to report hunger than boys (14%) in the camps (Guglielmi et al., 2020). 1.2 Cash-Based Strategies for Alleviating Food Insecurity Cash-based interventions (CBIs), a system designed to systematically distribute cash or vouchers to the poor, vulnerable, and populations displaced by emergencies, can be a cost-effective and efficient way of meeting basic food requirements ; they significantly boost individuals' and households' nutritional needs by reducing poverty, increasing food security, and improving access to health facilities (Grijalva-Eternod et al., 2018). CBIs, particularly for nutrition outcomes, offer enormous potential to alleviate food insecurity's unfairness and vulnerability. They can help vulnerable individuals in various circumstances, from humanitarian aid to long-term resilience and poverty-alleviation programs, as well as through current social protection measures (Grijalva-Eternod et al., 2018). Cash-based intervention programs for food assistance can potentially expand local markets and build economic security for refugees and host nations while meeting these communities' nutritional and basic needs (Lash et al ., 2023). A survey by Bhatia et al. (2018) asked the Rohingya household respondents what they would do if they received cash assistance; 51.75% said they would spend it on food, 32% on shelter, and 24.6% on clothing. CBIs aim to improve the beneficiaries' ability to acquire food and other needs, and they often have additional multi-sectoral objectives, such as enabling livelihood investments, that can improve health outcomes (Sircar & Friedman, 2018). Research by Aizer et al . in 2016 suggested that targeted cash transfers can positively affect things like health and education in the short and medium term, increasing the effectiveness of poverty reduction programs. Although CBIs have become an instrument of humanitarian action to improve access to food and nutrition during emergencies , research evidence of the efficacy of these CBIs in alleviating food insecurity, particularly among forcibly displaced populations, is limited. The current study thus aims to explore the impact of cash transfers on food insecurity, using the forcibly displaced population in Cox Bazar as a case study . The availability of such evidence would help improve nutritional outcomes and identify activities that cash-related programs and projects can employ to enhance their impact. In addition, the evidence can help policymakers and humanitarian organizations such as the United Nations High Commission for Refugees (UNHCR) effectively respond to questions relating to the effectiveness of their cash-based interventions. 1.3 Context This study focuses on forcibly displaced populations in Cox’s Bazar in Bangladesh. Although Bangladesh has made significant strides in developing social and economic services, food insecurity remains a big challenge. Reports indicate Bangladesh ranks 88th out of 117 nations for extreme hunger (Global Hunger Index, 2019). The country has been quite successful in using a variety of strategies to relieve the burden of extreme hunger. Despite a significant increase in food accessibility, one-third of the population still lives in poverty in Bangladesh, indicating that there is still a significant amount of food insecurity in the country (Roy et al. 2019). Generally, 36% of the population faces mild to chronic food insecurity (Bangladesh IPC Chronic Food Insecurity Report, 2022). The number of food insecure people, particularly in Cox Bazar, has increased dramatically given the influx of displaced population into the city. The ongoing violence, discrimination, and persecution in Myanmar have forced many families to flee their homes to Bangladesh, leaving their livelihoods behind and making them more vulnerable to food insecurity and malnutrition. The Rohingya people are among the most marginalized groups globally. Despite living in Myanmar for generations, they have not been recognized as citizens since 1982 (Milton et al., 2017; UNHCR, 2023). In 2017, over 750,000 Rohingya people had to flee to Cox’s Bazar, Bangladesh, for safety (Tay et al ., 2019). Now, more than 960,000 Rohingya people live in Bangladesh, with a majority residing in Cox Bazar’s region (UNHCR, 2023). Bangladesh is not part of the 1951 Refugee Convention, but it allowed the Rohingya people to stay within its borders (Bhatia, 2018). The World Food Program (WFP) reports that food-insecure people are primarily below the poverty line, mainly displaced populations, due to monetary variables such as unemployment and rising food prices. Methods 3.1 Data source The current study examines the impact of cash-based interventions on food insecurity among forcibly displaced populations in Cox’s Bazar during the COVID-19 outbreak.The study used the 2020–2021 high-frequency survey data to achieve this objective. The high-frequency survey continues the 2019 Cox's Bazar Panel Survey baseline, a representative study of Rohingya displaced after 2017 in host communities in Bangladesh's Cox's Bazar district. We designed high-frequency phone tracking (HFT) surveys to maintain contact with baseline respondents and collect timely data on crucial welfare indicators such as employment, necessities, and education. Three rounds of the HFT were completed between 2020 and 2021 to create welfare updates on the host and Rohingya population living in Cox's Bazar, Bangladesh, particularly considering the COVID-19 pandemic. The tracking surveys gathered data on three main welfare dimensions: employment, access to necessities, and school-age children's educational status. The survey collected data on several variables, including food access, employment, education, and access to necessities of life. Data availability and ethical considerations The data were stored in the publicly accessible repository of the UNCHR/World Bank (https://microdata.unhcr.org/index.php/catalog/822). To access the data, individuals must complete a registration process exclusively provided for legitimate research. Consent forms were administered following the principles of human subject protection at the household and individual levels. 1.2 Analyse s We performed the statistical analyses using SPSS software version 24. The analyses involved creating several indexes, including a food insecurity index. We summarise categorical variables using their corresponding frequencies and percentages. On the other hand, we summarise continuous variables using means and standard deviation. We used literature and binary logistics regression to identify potential variables associated with food insecurity. We selected variables for the multiple logistic regression models with P-values less than or equal to 0.2 to control the possible effect of confounders. Model 1 examined the association between cash transfers and food insecurity. Model 2 examined the impact of cash transfers on food insecurity using logistic regression after adjusting for all household factors associated with food insecurity. We establish the significance of the relationships using a 95% CI. Results Descriptive and bivariate analysis of the association between cash incentives and food insecurity Table 1 below presents a descriptive and bivariate analysis of the association between cash incentives and food insecurity. Food insecurity was prevalent among most participants (86.7%). A notable portion of the participants (19.4%) received cash incentives. Approximately one-third (35.9%) were unemployed. A significant percentage of the individuals had no job. (41.3%) lacked formal education. Over a quarter of the participants (27.4%) reported increased income over the years, and the majority (60.8%) received government assistance. Only a tiny percentage (5.4%) received aid from UN agencies, while 35.6% of the participants sought assistance from other non-governmental organizations. On average, participants were 31.9 years old, and the typical household size was 3.5 people. In examining bivariate associations, compared to their counterparts, participants who received cash incentives had reduced odds of experiencing food insecurity (OR = 0.14, CI = 0.08-0.32). Conversely, the married showed increased odds (OR = 1.34, CI = 1.10-3.42) of experiencing food insecurity compared to their never-married counterparts. Female participants displayed significantly higher odds (OR = 3.38, CI = 1.23-6.02) of experiencing food insecurity. Unemployment correlates with increased odds (OR = 3.12, CI = 2.42-4.73) of experiencing food insecurity compared to the employed. Similarly, participants experiencing a decline in income over the years showed a higher likelihood (OR = 1.30, CI = 1.01-5.07) of reporting food insecurity. Also, assistance from UN agencies is associated with substantially lower odds (OR = 0.23, CI = 0.12-0.51) of food insecurity. Lastly, compared to their counterparts, larger household sizes are linked to increased odds of reporting food insecurity (OR = 1.76, CI = 1.23-5.07), compared with participants in smaller households. Multivariate analysis of the association between cash incentives and food insecurity Table 2 below presents results from analyzing the multivariate association between cash incentives and food insecurity. Like the bivariate analysis, receiving cash transfers (incentives) is significantly associated with lower odds of experiencing food insecurity (OR = 0.19, CI = 0.91-0.380). Female participants have higher odds (OR = 3.44, CI = 1.41-5.36) of experiencing food insecurity than their male counterparts. Unemployment was strongly associated with increased odds (OR = 3.45, CI = 2.67-6.13) of being food insecure, and participants currently in school are less likely to experience food insecurity (OR = 0.67, CI = 0.14-0.95). Experiencing a decrease in income over the years was associated with higher odds (OR = 1.46, CI = 1.39-5.44) of food insecurity while being a recipient of assistance from UN agencies was associated with slightly lower odds (OR = 0.88, CI = 0.69-0.97) of experiencing food insecurity. Lastly, larger household size was linked to increased odds of food insecurity (OR = 1.89, CI = 1.35-4.47). Discussion This paper examined the relationship between cash transfer incentives and food insecurity among displaced populations in Cox Bazar, Bangladesh. Given its perceived impacts and the preferences of people living in vulnerable situations, CT has become a typical humanitarian response. The current study found that the unconditional cash transfer incentive implemented among displaced people in Cox Bazar is significantly associated with lower levels of food insecurity (OR = 0.19, CI = 0.91-0.380). CT influences purchasing power and is often the primary household food source, especially in limited income-generating settings. Our findings are like those of other authors. For instance, Falb et al. (2020), in their research on the International Rescue Committee's unconditional cash assistance program on food security among displaced people in northern Raqqa Governorate, found a significant decrease in food insecurity over time. Similarly, in their review, Van Daalen et al. (2022) report that CTs are positively associated with food security in humanitarian settings. Beyond these humanitarian settings, among populations living in other forms of vulnerability, Saldivar-Frausto et al. (2021) established the program to reduce food insecurity in their research on the conditional cash transfers program among low-income households in Mexico between 2012 and 2016. Our study revealed that the source of the CT is relevant to experiences of food insecurity. Households that received assistance (CT) from the UN and other humanitarian agencies had slightly lower odds of experiencing food insecurity. However, the government's aid to households did not show statistical significance. The probable explanation for this difference is intentionality and consistency. Host nation governments hardly provide cash incentives to displaced populations, and in instances where they do, it is primarily fluid, resulting in less impact on food insecurity. Humanitarian organizations, including the World Food Programme, the United Nations Children’s Fund (UNICEF), the UN High Commissioner for Refugees (UNHCR), and the International Rescue Committee (IRC), on the other hand, are intentional and consistent in making cash provisions to displaced populations, resulting in a reduction in food insecurity over time (Van Daalen et al., 2022). Conversely, Makkar et al. (2022), in exploring the relationship between food insecurity and cash transfers in India pre- and post-COVID-19 pandemic, recorded an increase in food insecurity during the COVID-19 pandemic. However, households receiving the government’s cash transfers were 25% less likely to experience food insecurity than non-beneficiaries. Doocy et al. (2020) compared the different food-related transfer modalities in their research on the nutritional status of pregnant and lactating mothers in crisis in Somalia. Like the current study, the non-assistance group recorded the lowest meal frequency, with an average of 2.0 meals daily and 10.2% consuming one meal or less daily. Meal frequency at the end line was significantly greater among mixed transfer recipients. Generally, aid plays a significant role in addressing food security in areas experiencing extreme hardship. However, frequency and consistency are vital in achieving optimal food security among displaced populations. Various studies have established the precarity of displaced women. In emergencies, women have been known to seek and require cash. From our study, women had higher odds of experiencing food insecurity than their male counterparts. According to research by Woodman (2019), compared to their male counterparts, more than half of the displaced Rohingya women and female household heads reported selling aid items for cash. These situations further influence the call for prioritization of CT programs specifically focusing on women in humanitarian areas. Women are also more likely to prioritize purchasing household needs, including food, and CT elevates the voice of women. We observe women's empowerment as an outcome and a channel through which CT impacts food security. The Child Grant Programme (CGP) showcases the link between CT and women's increased participation in decision-making. The CGP also reduced women's severe poverty headcount rate by 5.4 percentage points after 24 months (Burchi, 2018; Bonilla et al., 2017). Note that CT does not automatically ensure women's empowerment. If the implementation process fails to assess and integrate the risks adequately, CT can potentially endanger women (Woodman 2019). Integrating cultural context and intersectional identities into CT implementation is essential, as it creates the potential to mainstream positive context and gender-specific outcomes. Households living below the poverty line and possessing minimal to no assets are generally associated with food insecurity. Our study found that households that experienced decreased income over the years were associated with higher odds of food insecurity. Similarly, being unemployed was strongly associated with increased odds of food insecurity. MacPherson and Sterck (2021), in their research among refugees in Kenya, identified that cash transfers and involvement in agriculture positively correlated with dietary diversity and food security. In our study, household size is a critical predictor of dependency ratio and a key driver of experiencing food insecurity. We found that larger household size was associated with food insecurity. Between 2012 and 2016, Saldivar-Frausto et al. (202) observed a more significant decrease in food insecurity among households without children than households with children as part of the CCT program. They attributed this experience to the fact that households without children may experience food and income security, and the perception of food insecurity may decrease significantly. Limitations One of this study's strengths is its larger sample size. However, we recommend interpreting the results cautiously due to the following limitations: First, equitable representation across all socio-demographic categories, such as age, gender, nationality, and area, was impossible in our study. Secondly, we collected the data using a convenience sampling method, which was susceptible to selection bias. The nature of the study had an impact on participant recruitment. For instance, several participants declined to participate in this study because they lacked legal immigration status. Furthermore, the study may have excluded certain demographics from our target populations, including those with physical or mental disabilities, injured individuals, and the elderly. Last, this is a cross-sectional analysis, and we are thus unable to establish causality. Conclusion and recommendations Cash incentives have demonstrated efficacy in mitigating food insecurity by offering direct financial assistance to individuals and families in need. Cash incentives enhance individuals' purchasing power, enabling access to nutritious food and alleviating hunger and malnutrition. This method provides flexibility, allowing the recipients to select options that align with their dietary requirements, enhancing food security and overall well-being. Although cash incentives can substantially alleviate immediate food insecurity, they represent more than an isolated solution. We must also tackle structural issues such as unemployment, poverty, and insufficient access to nutritious food. Consequently, cash incentives should be regarded as an element of a comprehensive strategy to address food insecurity, integrated with other enduring interventions such as employment generation, education, and healthcare accessibility. Cash incentives should be accompanied by educational programs instructing recipients on budgeting and healthier food selections. This method guarantees that the funds are utilized to satisfy immediate nutritional requirements and promote healthier dietary practices that can enhance long-term wellness and mitigate health complications from inadequate nutrition. In addition, cash incentive programs ought to prioritize at-risk populations, including low-income families, single-parent households, and elderly individuals, who are more susceptible to food insecurity. Furthermore, programs should encourage cash incentives in local markets to boost local economies, assist farmers, and guarantee access to fresh, nutritious food options within communities. Finally, we must integrate cash incentives into a comprehensive social support system that includes employment initiatives, affordable housing, healthcare, and education to achieve sustainable food security. This holistic strategy can address the underlying reasons why people face food insecurity, giving them the tools and chances to become stable in the long term and less reliant on short-term monetary incentives. Our findings underscore the need for all relevant stakeholders, especially the displaced individuals, to be involved in designing and implementing cash transfer programs. Declarations Ethical Approval Not applicable The study was based on publicly available secondary data from the UNHCR/World Bank repository. Thus, this study did not require ethical approval. Consent to Participate Not applicable Consent for Publication Not applicable Funding No external funding was received for this research. The study was self-funded by the authors. 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World Food Programme (WFP). UN World Food Programme. https://www.wfp.org/campaign/global-food-crisis, Accessed 19 th May 2024 Van Daalen, K. R., Dada, S., James, R., Ashworth, H. C., Khorsand, P., Lim, J., & Blanchet, K. (2022). Impact of conditional and unconditional cash transfers on health outcomes and use of health services in humanitarian settings: a mixed-methods systematic review. British Medical Journal Global Health , 7 (1), e007902. WHO. (2020). WHO Coronavirus Disease (COVID-19) Dashboard, September 2020. https://covid19.who.int, Accessed 18 th May 2024 Woodman, D. (2019). What women want: Developing Gender-Inclusive Cash Transfer Programming. Foreign Policy , 71. World Food Program. (2022). Hunger is on the rise . Unprecedented levels of food insecurity require urgent action to prevent famine. https://gho.unocha.org/trends/hunger-rise-unprecedented-levels-food-insecurity-require-urgent-action-prevent-famine. Zabir, A. A., Mahmud, A., Islam M. A., & et al . (2020). COVID-19 and food supply in Bangladesh: A review. South Asian Journal Social Studies and Economics , 10(1), 15–23. Tables Table 1 should be placed on page 8 after the descriptive and bivariate analysis in the results section Table 1: Descriptive and bivariate analysis of the association between cash incentives and food insecurity. Variables Descriptive statistics Bivariate analysis Discrete variables: Frequency (%) OR (95%CI) Food Food secure 248(13.3%) ……. Food insecure 1589(86.7%) …….. Cash Transfers No cash incentive 1404 (80.6) 1.00(1.00-1.00) Cash incentive 339 (19.4) 0.14(0.08-0.32) ** Marital Status Never married 2540 (74.0) 1.00(1.00,1.00) Married 584 (17.0) 1.34(1.10-3.42) ** Widowed 250 (7.3) 1.21(0.71-3.62) Divorced 19 (.6) 1.55(0.64-3.24) Separated 41 (1.2) 1.42(0.44,4.11) Sex Male 1531 (44.5%) 1.00(1.00-1.00) Female 1906 (55.5%) 3.38(1.23-6.02) *** Employment Employed 2203 (64.1) 1.00(1.00-1.00) Unemployed 1232 (35.9) 3.12(2.42-4.73) *** Education No formal education 1417 (41.3) 1.00(1.00-1.00) Yes, but I stopped 1809 (52.7) .44(0.51-3.23) Yes, currently attending 208 (6.1) 0.61(0.11-6.61] Increase in income over the years As usual/same 52 (9.1) 1.00(1.00-1.00) Increased 157 (27.4) 0.47[0.22-1.16] Decreased 365 (63.6) 1.30[1.01-5.07] ** Received assistance from the government No 195 (39.2) 1.00(1.00-1.00) Yes 302 (60.8) 1.25(0.61-2.12) Received assistance from the UN No 470 (94.6) 1.00(1.00-1.00) Yes 27 (5.4) 1.01(0.66,1.44) Received assistance from Other NGO No 320 (64.4) 1.00(1.00-1.00) Yes 177 (35.6) 0.23(0.12-,0.51) ** First Assistance No 164 (33) 1.00(1.00-1.00) Yes 333 (67.0) 1.53 (0.89-2.51) Continuous variables: Mean (SD) Age 31.9 (12.9) 1.29[0.63,2.63] Household size 3.5 (2.1) 1.76[1.23-5.07] ** Table 2 should be placed on page 9 after the multivariate analysis description in the result section Table 2: Multivariate analysis of the association between cash incentives and food insecurity Variables Model 1: Bivariate Model 2: Multivariate Discrete variables: cOR(95%CI) aOR(95%CI) Cash Transfers No cash incentive 1.00(1.00-1.00) 1.00(1.00-1.00) Cash incentive 0.14(0.08-0.32) ** 0.19(0.91-0.380) ** Marital Status Married 1.00(1.00,1.00) Never married 1.24(0.86-3.42) Widowed 1.31(0.94-1.90) Divorced 1.47(0.79-2.89) Separated 1.52(0.98-3.84) Sex Male 1.00(1.00-1.00) Female 3.44(1.41-5.36) *** Employment Employed 1.00(1.00-1.00) Unemployed 3.45(2.67-6.13) ** Education No formal education 1.00(1.00-1.00) Yes, but I stopped .44(0.51-3.23) Yes, currently attending 0.67(0.14-0.95) ** Increase in income over the years As usual/same 1.00(1.00-1.00) Increased 0.58(0.34-1.45) Decreased 1.46(1.39-5.44) ** Received assistance from the government No 1.00(1.00-1.00) Yes 1.39(0.77-2.42) Received assistance from the UN No 1.00(1.00-1.00) Yes 0.88(0.69-0.97) *** Received assistance from other NGO No 1.00(1.00-1.00) Yes 0.67(0.12-,1.02) First Assistance No 1.00(1.00-1.00) Yes 1.35(0.60-2.57) Continuous variables: Age 1.11(0.68-1.97) Household size 1.89(1.35-4.47) ** N AIC 1765.90 BIC 1964.10 Additional Declarations No competing interests reported. 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Acute food insecurity occurs suddenly and can be severe, threatening lives and livelihoods. Chronic food insecurity is when people can't consistently get enough food to meet their energy needs over time (www.fsinplatform.org). Given its negative impacts, most governments and development organizations, such as the World Bank and the United Nations, have made several commitments to address food insecurity. For instance, the United Nations Sustainable Development Goal 2 is set to create a world free of hunger by 2030. Despite these global efforts, current estimates indicate that 45 million children under 5 were malnourished, with 148 million suffering from stunted growth and wasting (SDG Report, 2023). Pandemics such as COVID-19, armed conflict, extreme poverty, and natural disasters worsen food insecurity (UN, 2023). \u0026nbsp;In 2023, the World Food Program reported that over 333 million people faced severe food insecurity, implying those individuals or households experienced uncertainty about the source of their next meal (FAO, 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis is an increase of 200 million from the pre-COVID-19 pandemic figures,\u0026nbsp;with the most impacted population being the forcibly displaced (WFP, 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCOVID-19 has exacerbated issues of\u0026nbsp;food\u0026nbsp;insecurity among the forcibly displaced. \u0026nbsp;Aside from the health hazards the COVID-19 virus poses, lockdowns to stop its spread have impacted the livelihoods of locals, refugees, and migrants. Particularly relating to their capacity to supplement relief from humanitarian agencies such as UNHCR. As a result, these populations now experience higher levels of food insecurity. A recent study by the World Food Programme indicates that 86% of Rohingya refugees were highly vulnerable to poverty and hunger by the end of 2020, up from 70% in 2019. \u0026nbsp;In\u0026nbsp;addition, a studyreported that 23% of Bangladeshi and 18%\u0026nbsp;of Rohingya adolescents reported feeling hungrier during the pandemic. There are also gender dynamics,\u0026nbsp;with girls (22%) being more likely to report hunger than boys (14%) in the\u0026nbsp;camps (Guglielmi\u0026nbsp;et al., 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Cash-Based Strategies for Alleviating Food Insecurity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCash-based interventions (CBIs), a system designed to systematically distribute cash or vouchers to the poor, vulnerable, and populations displaced by emergencies, can be a cost-effective and efficient way of meeting basic food requirements\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003ethey significantly boost individuals' and households' nutritional needs by reducing poverty, increasing food security, and improving access to health facilities (Grijalva-Eternod et al., 2018). CBIs, particularly for nutrition outcomes, offer enormous potential to alleviate food insecurity's unfairness and vulnerability. They can help vulnerable individuals in various circumstances, from humanitarian aid to long-term resilience and poverty-alleviation programs, as well as through current social protection measures (Grijalva-Eternod et al., 2018). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCash-based intervention programs for food assistance can potentially expand local markets and build economic security for refugees and host nations while meeting these communities' nutritional and basic needs (Lash \u003cem\u003eet al\u003c/em\u003e., 2023). A\u0026nbsp;survey\u0026nbsp;by Bhatia et al. (2018) asked the Rohingya household respondents what they would do if they\u0026nbsp;received\u0026nbsp;cash assistance; 51.75% said they would spend it on food, 32% on shelter, and 24.6% on clothing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCBIs aim to improve the beneficiaries' ability to acquire food and other\u0026nbsp;needs,\u0026nbsp;and they often have additional multi-sectoral objectives, such as enabling livelihood investments,\u0026nbsp;that\u0026nbsp;can improve health outcomes (Sircar \u0026amp; Friedman, 2018). Research by Aizer \u003cem\u003eet al\u003c/em\u003e. in 2016 suggested that targeted cash transfers can\u0026nbsp;positively affect\u0026nbsp;things like health and education in the short and medium term,\u0026nbsp;increasing the effectiveness of poverty reduction programs.\u0026nbsp;Although CBIs have become an instrument of humanitarian action to improve access to food and nutrition during emergencies\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eresearch evidence of the efficacy of these CBIs in alleviating food insecurity, particularly among forcibly displaced populations, is limited. The current study thus aims to explore the impact of cash transfers on food insecurity, using the forcibly displaced population in Cox Bazar as a case study\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe availability of such evidence would help improve nutritional outcomes and identify activities that cash-related programs and projects can employ to enhance their impact. In addition, the evidence can help policymakers and humanitarian organizations such as the United Nations High Commission for Refugees (UNHCR) effectively respond to questions relating to the effectiveness of their cash-based interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Context\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study\u0026nbsp;focuses\u0026nbsp;on forcibly displaced populations in Cox’s Bazar in Bangladesh. \u0026nbsp;Although Bangladesh has made significant strides in developing social and economic services, food insecurity remains a big challenge. Reports indicate Bangladesh ranks 88th out of 117 nations for\u0026nbsp;extreme hunger (Global Hunger Index, 2019). \u0026nbsp;The country\u0026nbsp;has been quite successful in using a variety of strategies to relieve the burden of extreme hunger. Despite a significant increase in food accessibility, one-third of the population still lives in poverty in Bangladesh, indicating that there is still a significant amount of food insecurity in the country (Roy et al. 2019). Generally, 36% of the\u0026nbsp;population faces\u0026nbsp;mild to chronic food insecurity (Bangladesh IPC Chronic Food Insecurity Report, 2022). The number of food\u0026nbsp;insecure people, particularly in Cox\u0026nbsp;Bazar,\u0026nbsp;has increased dramatically given the influx of displaced population into the city. The ongoing violence, discrimination,\u0026nbsp;and persecution in Myanmar have forced many families to flee their homes to\u0026nbsp;Bangladesh,\u0026nbsp;leaving their livelihoods behind and making them more vulnerable to food insecurity and malnutrition. The Rohingya people are among the most\u0026nbsp;marginalized\u0026nbsp;groups globally. Despite living in Myanmar for generations, they have not been recognized as citizens since 1982 (Milton \u003cem\u003eet al.,\u003c/em\u003e 2017; UNHCR, 2023). In 2017, over 750,000 Rohingya people had to flee to Cox’s Bazar, Bangladesh, for safety (Tay\u0026nbsp;\u003cem\u003eet al\u003c/em\u003e., 2019). Now, more than 960,000 Rohingya people live in Bangladesh, with a majority residing in Cox Bazar’s region (UNHCR, 2023). Bangladesh is not part of the 1951 Refugee Convention, but it allowed the Rohingya people to stay within its borders (Bhatia, 2018). The World Food Program (WFP) reports that\u0026nbsp;food-insecure people\u0026nbsp;are\u0026nbsp;primarily\u0026nbsp;below the poverty line,\u0026nbsp;mainly displaced populations,\u0026nbsp;due to monetary variables such as unemployment and rising food prices.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e3.1 Data source\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe current study examines the impact of cash-based interventions on food insecurity among forcibly displaced populations in Cox\u0026rsquo;s Bazar during the COVID-19 outbreak.The study used the 2020\u0026ndash;2021 high-frequency survey data to achieve this objective. The high-frequency survey continues the 2019 Cox\u0026apos;s Bazar Panel Survey baseline, a representative study of Rohingya displaced after 2017 in host communities in Bangladesh\u0026apos;s Cox\u0026apos;s Bazar district. We designed high-frequency phone tracking (HFT) surveys to maintain contact with baseline respondents and collect timely data on crucial welfare indicators such as employment, necessities, and education. Three rounds of the HFT were completed between 2020 and 2021 to create welfare updates on the host and Rohingya population living in Cox\u0026apos;s Bazar, Bangladesh, particularly considering the COVID-19 pandemic. The tracking surveys gathered data on three main welfare dimensions: employment, access to necessities, and school-age children\u0026apos;s educational status. The survey collected data on several variables, including food access, employment, education, and access to necessities of life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability and ethical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were stored in the publicly accessible repository of the UNCHR/World Bank (https://microdata.unhcr.org/index.php/catalog/822). To access the data, individuals must complete a registration process exclusively provided for legitimate research. Consent forms were administered following the principles of human subject protection at the household and individual levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2\u0026nbsp;\u003cem\u003eAnalyse\u003c/em\u003es\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed the statistical analyses using SPSS software version 24. The analyses involved creating several indexes, including a food insecurity index. We summarise categorical variables using their corresponding frequencies and percentages. On the other hand, we summarise continuous variables using means and standard deviation. We used literature and binary logistics regression to identify potential variables associated with food insecurity. We selected variables for the multiple logistic regression models with P-values less than or equal to 0.2 to control the possible effect of confounders. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel 1 examined the association between cash transfers and food insecurity. Model 2 examined\u0026nbsp;the\u0026nbsp;impact\u0026nbsp;of cash transfers on food insecurity using logistic regression after adjusting for all household factors associated with food insecurity.\u0026nbsp;We establish the significance of the relationships using a 95% CI.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive and bivariate analysis of the association between cash incentives and food insecurity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 below presents a descriptive and bivariate analysis of the association between cash incentives and food insecurity. Food insecurity was prevalent among most participants (86.7%). A notable portion of the participants (19.4%) received cash incentives. Approximately one-third (35.9%) were unemployed. A significant percentage of the individuals had no job. (41.3%) lacked formal education. Over a quarter of the participants (27.4%) reported increased income over the years, and the majority (60.8%) received government assistance. Only a tiny percentage (5.4%) received aid from UN agencies, while 35.6% of the participants sought assistance from other non-governmental organizations. On average, participants were 31.9 years old, and the typical household size was 3.5 people.\u003c/p\u003e\n\u003cp\u003eIn examining bivariate associations, compared to their counterparts, participants who received cash incentives had reduced odds of experiencing food insecurity (OR = 0.14, CI = 0.08-0.32). Conversely, the married showed increased odds (OR = 1.34, CI = 1.10-3.42) of experiencing food insecurity compared to their never-married counterparts. Female participants displayed significantly higher odds (OR = 3.38, CI = 1.23-6.02) of experiencing food insecurity. Unemployment correlates with increased odds (OR = 3.12, CI = 2.42-4.73) of experiencing food insecurity compared to the employed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, participants experiencing a decline in income over the years showed a higher likelihood (OR = 1.30, CI = 1.01-5.07) of reporting food insecurity. Also, assistance from UN agencies is associated with substantially lower odds (OR = 0.23, CI = 0.12-0.51) of food insecurity. Lastly, compared to their counterparts, larger household sizes are linked to increased odds of reporting food insecurity (OR = 1.76, CI = 1.23-5.07), compared with participants in smaller households.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate analysis of the association between cash incentives and food insecurity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 below presents results from analyzing the multivariate association between cash incentives and food insecurity. Like the bivariate analysis, receiving cash transfers (incentives) is significantly associated with lower odds of experiencing food insecurity (OR = 0.19, CI = 0.91-0.380). \u0026nbsp;Female participants have higher odds (OR = 3.44, CI = 1.41-5.36) of experiencing food insecurity than their male counterparts. Unemployment was strongly associated with increased odds (OR = 3.45, CI = 2.67-6.13) of being food insecure, and participants currently in school are less likely to experience food insecurity (OR = 0.67, CI = 0.14-0.95). Experiencing a decrease in income over the years was associated with higher odds (OR = 1.46, CI = 1.39-5.44) of food insecurity while being a recipient of assistance from UN agencies was associated with slightly lower odds (OR = 0.88, CI = 0.69-0.97) of experiencing food insecurity. Lastly, larger household size was linked to increased odds of food insecurity (OR = 1.89, CI = 1.35-4.47).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper examined the relationship between cash transfer incentives and food insecurity among displaced populations in Cox Bazar, Bangladesh. Given its perceived impacts and the preferences of people living in vulnerable situations, CT has become a typical humanitarian response. \u0026nbsp;The current study found that the unconditional cash transfer incentive implemented among displaced people in Cox Bazar is significantly associated with lower levels of food insecurity (OR = 0.19, CI = 0.91-0.380). CT influences purchasing power and is often the primary household food source, especially in limited income-generating settings. Our findings are like those of other authors. For instance, Falb et al. (2020), in their research on the International Rescue Committee's unconditional cash assistance program on food security among displaced people in northern Raqqa Governorate, found a significant decrease in food insecurity over time. \u0026nbsp;Similarly, in their review, Van Daalen et al. (2022) report that CTs are positively associated with food security in humanitarian settings. Beyond these humanitarian settings, among populations living in other forms of vulnerability, Saldivar-Frausto et al. (2021) established the program to reduce food insecurity in their research on the conditional cash transfers program among low-income households in Mexico between 2012 and 2016.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study revealed that the source of the CT is relevant to experiences of food insecurity. Households that received assistance (CT) from the UN and other humanitarian agencies had slightly lower odds of experiencing food insecurity. However, the government's aid to households did not show statistical significance. The probable explanation for this difference is intentionality and consistency. Host nation governments hardly provide cash incentives to displaced populations, and in instances where they do, it is primarily fluid, resulting in less impact on food insecurity. Humanitarian organizations, including the World Food Programme, the United Nations Children’s Fund (UNICEF), the UN High Commissioner for Refugees (UNHCR), and the International Rescue Committee (IRC), on the other hand, are intentional and consistent in making cash provisions to displaced populations, resulting in a reduction in food insecurity over time (Van Daalen et al., 2022).\u003c/p\u003e\n\u003cp\u003eConversely, Makkar et al. (2022), in exploring the relationship between food insecurity and cash transfers in India pre- and post-COVID-19 pandemic, recorded an increase in food insecurity during the COVID-19 pandemic. However, households receiving the government’s cash transfers were 25% less likely to experience food insecurity than non-beneficiaries. Doocy et al. (2020) compared the different food-related transfer modalities in their research on the nutritional status of pregnant and lactating mothers in crisis in Somalia. Like the current study, the non-assistance group recorded the lowest meal frequency, with an average of 2.0 meals daily and 10.2% consuming one meal or less daily. \u0026nbsp;Meal frequency at the end line was significantly greater among mixed transfer recipients. Generally, aid plays a significant role in addressing food security in areas experiencing extreme hardship. However, frequency and consistency are vital in achieving optimal food security among displaced populations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVarious studies have established the precarity of displaced women. In emergencies, women have been known to seek and require cash. From our study, women had higher odds of experiencing food insecurity than their male counterparts. According to research by Woodman (2019), compared to their male counterparts, more than half of the displaced Rohingya women and female household heads reported selling aid items for cash. These situations further influence the call for prioritization of CT programs specifically focusing on women in humanitarian areas. Women are also more likely to prioritize purchasing household needs, including food, and CT elevates the voice of women. We observe women's empowerment as an outcome and a channel through which CT impacts food security. The Child Grant Programme (CGP) showcases the link between CT and women's increased participation in decision-making. The CGP also reduced women's severe poverty headcount rate by 5.4 percentage points after 24 months (Burchi, 2018; Bonilla et al., 2017).\u003c/p\u003e\n\u003cp\u003eNote that CT does not automatically ensure women's empowerment. If the implementation process fails to assess and integrate the risks adequately, CT can potentially endanger women (Woodman 2019). Integrating cultural context and intersectional identities into CT implementation is essential, as it creates the potential to mainstream positive context and gender-specific outcomes.\u003c/p\u003e\n\u003cp\u003eHouseholds living below the poverty line and possessing minimal to no assets are generally associated with food insecurity. Our study found that households that experienced decreased income over the years were associated with higher odds of food insecurity. Similarly, being unemployed was strongly associated with increased odds of food insecurity. MacPherson and Sterck (2021), in their research among refugees in Kenya, identified that cash transfers and involvement in agriculture positively correlated with dietary diversity and food security. In our study, household size is a critical predictor of dependency ratio and a key driver of experiencing food insecurity. We found that larger household size was associated with food insecurity. \u0026nbsp;Between 2012 and 2016, Saldivar-Frausto et al. (202) observed a more significant decrease in food insecurity among households without children than households with children as part of the CCT program. \u0026nbsp;They attributed this experience to the fact that households without children may experience food and income security, and the perception of food insecurity may decrease significantly.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of this study's strengths is its larger sample size. However, we recommend interpreting the results cautiously due to the following limitations: First, equitable representation across all socio-demographic categories, such as age, gender, nationality, and area, was impossible in our study. Secondly, we collected the data using a convenience sampling method, which was susceptible to selection bias. The nature of the study had an impact on participant recruitment. For instance, several participants declined to participate in this study because they lacked legal immigration status. Furthermore, the study may have excluded certain demographics from our target populations, including those with physical or mental disabilities, injured individuals, and the elderly. Last, this is a cross-sectional analysis, and we are thus unable to establish causality.\u003c/p\u003e"},{"header":"Conclusion and recommendations","content":"\u003cp\u003eCash incentives have demonstrated efficacy in mitigating food insecurity by offering direct financial assistance to individuals and families in need. Cash incentives enhance individuals' purchasing power, enabling access to nutritious food and alleviating hunger and malnutrition. This method provides flexibility, allowing the recipients to select options that align with their dietary requirements, enhancing food security and overall well-being. Although cash incentives can substantially alleviate immediate food insecurity, they represent more than an isolated solution. We must also tackle structural issues such as unemployment, poverty, and insufficient access to nutritious food. Consequently, cash incentives should be regarded as an element of a comprehensive strategy to address food insecurity, integrated with other enduring interventions such as employment generation, education, and healthcare accessibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCash incentives should be accompanied by educational programs instructing recipients on budgeting and healthier food selections. This method guarantees that the funds are utilized to satisfy immediate nutritional requirements and promote healthier dietary practices that can enhance long-term wellness and mitigate health complications from inadequate nutrition. In addition, cash incentive programs ought to prioritize at-risk populations, including low-income families, single-parent households, and elderly individuals, who are more susceptible to food insecurity. Furthermore, programs should encourage cash incentives in local markets to boost local economies, assist farmers, and guarantee access to fresh, nutritious food options within communities. Finally, we must integrate cash incentives into a comprehensive social support system that includes employment initiatives, affordable housing, healthcare, and education to achieve sustainable food security.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis holistic strategy can address the underlying reasons why people face food insecurity, giving them the tools and chances to become stable in the long term and less reliant on short-term monetary incentives. Our findings underscore the need for all relevant stakeholders, especially the displaced individuals, to be involved in designing and implementing cash transfer programs.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eThe study was based on publicly available secondary data from the UNHCR/World Bank repository. \u0026nbsp;Thus, this study did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this research. The study was self-funded by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest in relation to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e2022 Annual report on cash assistance. UNHCR. https://www.unhcr.org/media/2022-annual-report-cash-assistance, Accessed on 26\u003csup\u003eth\u003c/sup\u003e May 2024.\u003c/li\u003e\n \u003cli\u003eAizer A., Eli S., Ferrie J., \u0026amp; Lleras-Muney, A. (2016). The Long-Run Impact of Cash Transfers to Poor Families. \u003cem\u003eAmerican Econnomic Review\u003c/em\u003e, 106(4):935-971. https://doi.org/10.1257/aer.20140529.\u003c/li\u003e\n \u003cli\u003eBhatia, A., Mahmud, A., Fuller, A., Shin, R., Rahman, A., Shatil, T., Sultana, M., Morshed, K. A. M., Leaning, J., \u0026amp; Balsari, S. 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R., Swaminathan, S., Travasso, S. M., John, A. T., Webb, P., \u0026amp; Thomas, T. (2022). Role of cash transfers in mitigating food insecurity in India during the COVID-19 pandemic: a longitudinal study in the Bihar state. \u003cem\u003eBritish Medical Journal open\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(6), e060624.\u003c/li\u003e\n \u003cli\u003eMilton, A. H., Rahman, M., Hussain, S., Jindal, C., Choudhury, S., Akter, S., \u0026amp; et al. (2017). Trapped in statelessness: Rohingya refugees in Bangladesh. \u003cem\u003eInternational Journal of Environmental Research Public Health,\u003c/em\u003e 14(8), 942.\u003c/li\u003e\n \u003cli\u003ePaslakis, G., Dimitropoulos, G., Katzman D. K. (2021). 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Aligning programs and policies to support food security and public health goals in the United States. \u003cem\u003eAnnual Review of Public Health,\u0026nbsp;\u003c/em\u003e40 319\u0026ndash;337.\u003c/li\u003e\n \u003cli\u003eSircar, N. R., \u0026amp; Friedman, E. A. (2018). Financial security and public health: How basic income \u0026amp; cash transfers can promote health. \u003cem\u003eGlobal Public Health\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(12), 1878\u0026ndash;1888. https://doi.org/10.1080/17441692.2018.1460383.\u003c/li\u003e\n \u003cli\u003eTay, A. K., Riley, A., Islam, R., Welton-Mitchell, C., Duchesne, B., Waters, V., \u0026amp; et al. (2019). The culture, mental health and psychosocial wellbeing of Rohingya refugees: A systematic review. \u003cem\u003eEpidemiology Psychiatric Sciences\u003c/em\u003e, 28(5), 489\u0026ndash;94.\u003c/li\u003e\n \u003cli\u003eThe state of food security and Nutrition in the world 2023. (n.d.-b). https://www.fao.org/3/CC3017EN/online/CC3017EN.html, Accessed 16\u003csup\u003eth\u003c/sup\u003e May 2024\u003c/li\u003e\n \u003cli\u003eTiwari, S., Daidone, S., Ruvalcaba, M. A., Prifti, E., Handa, S., Davis, B., \u0026amp; Seidenfeld, D. (2016). Impact of cash transfer programs on food security and nutrition in sub-Saharan Africa: A cross-country analysis. \u003cem\u003eGlobal Food Security\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 72-83.\u003c/li\u003e\n \u003cli\u003eUNHCR. (2022). \u003cem\u003eCash-Based Interventions\u003c/em\u003e. https://emergency.unhcr.org/entry/257802/cash-based-interventions-cbis, Accessed 14\u003csup\u003eth\u003c/sup\u003e May 2024\u003c/li\u003e\n \u003cli\u003eUnited Nations. \u003cem\u003e\u0026nbsp;SDG indicators\u003c/em\u003e. United Nations. https://unstats.un.org/sdgs/report/2023/, Accessed 3\u003csup\u003erd\u003c/sup\u003e May 2024\u003c/li\u003e\n \u003cli\u003eUnited Nations. \u003cem\u003eGoal 2: Zero Hunger - United Nations Sustainable Development\u003c/em\u003e. United Nations. https://www.un.org/sustainabledevelopment/hunger/, Accessed 3\u003csup\u003erd\u003c/sup\u003e May 2024\u003c/li\u003e\n \u003cli\u003eUnited Nations. World Food Programme (WFP). UN World Food Programme. https://www.wfp.org/campaign/global-food-crisis, Accessed 19\u003csup\u003eth\u003c/sup\u003e May 2024\u003c/li\u003e\n \u003cli\u003eVan Daalen, K. R., Dada, S., James, R., Ashworth, H. C., Khorsand, P., Lim, J., \u0026amp; Blanchet, K. (2022). Impact of conditional and unconditional cash transfers on health outcomes and use of health services in humanitarian settings: a mixed-methods systematic review. \u003cem\u003eBritish Medical Journal Global Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), e007902.\u003c/li\u003e\n \u003cli\u003eWHO. (2020). WHO Coronavirus Disease (COVID-19) Dashboard, September 2020. https://covid19.who.int, Accessed 18\u003csup\u003eth\u003c/sup\u003e May 2024\u003c/li\u003e\n \u003cli\u003eWoodman, D. (2019). What women want: Developing Gender-Inclusive Cash Transfer Programming. \u003cem\u003e\u0026nbsp;Foreign Policy\u003c/em\u003e, 71.\u003c/li\u003e\n \u003cli\u003eWorld Food Program. (2022). \u003cem\u003eHunger is on the rise\u003c/em\u003e. Unprecedented levels of food insecurity require urgent action to prevent famine. https://gho.unocha.org/trends/hunger-rise-unprecedented-levels-food-insecurity-require-urgent-action-prevent-famine.\u003c/li\u003e\n \u003cli\u003eZabir, A. A., Mahmud, A., Islam M. A., \u0026amp; \u003cem\u003eet al\u003c/em\u003e. (2020). COVID-19 and food supply in Bangladesh: A review. \u003cem\u003eSouth Asian Journal Social Studies and Economics\u003c/em\u003e, 10(1), 15\u0026ndash;23.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1 should be placed on page 8 after the descriptive and bivariate analysis in the results section\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1: Descriptive and bivariate analysis of the association between cash incentives and food insecurity.\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 44.7115%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBivariate analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrete variables:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eFood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eFood secure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e248(13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u0026hellip;\u0026hellip;.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eFood insecure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e1589(86.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u0026hellip;\u0026hellip;..\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eCash Transfers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eNo cash incentive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e1404 (80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eCash incentive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e339 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e0.14(0.08-0.32) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e2540 (74.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e584 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.34(1.10-3.42) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e250 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.21(0.71-3.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e19 (.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.55(0.64-3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e41 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.42(0.44,4.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e1531 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e1906 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e3.38(1.23-6.02) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e2203 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e1232 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e3.12(2.42-4.73) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e1417 (41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eYes, but I stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e1809 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e.44(0.51-3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eYes, currently attending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e208 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e0.61(0.11-6.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eIncrease in income over the years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eAs usual/same\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e52 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eIncreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e157 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e0.47[0.22-1.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eDecreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e365 (63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.30[1.01-5.07] **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eReceived assistance from the government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e195 (39.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e302 (60.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.25(0.61-2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eReceived assistance from the UN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e470 (94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e27 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.01(0.66,1.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eReceived assistance from Other NGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e320 (64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e177 (35.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e0.23(0.12-,0.51) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eFirst Assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e164 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e333 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.53 (0.89-2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous variables:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e31.9 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.29[0.63,2.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eHousehold size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.6795%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.0321%;\"\u003e\n \u003cp\u003e3.5 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.9551%;\"\u003e\n \u003cp\u003e1.76[1.23-5.07] **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2 should be placed on page 9 after the multivariate analysis description in the result section\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Multivariate analysis of the association between cash incentives and food insecurity\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"617\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 55.2674%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eModel 1: Bivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003eModel 2: Multivariate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrete variables:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecOR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eaOR(95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eCash Transfers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eNo cash incentive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eCash incentive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e0.14(0.08-0.32) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e0.19(0.91-0.380) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.24(0.86-3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.31(0.94-1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.47(0.79-2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.52(0.98-3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e3.44(1.41-5.36) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e3.45(2.67-6.13) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eYes, but I stopped\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e.44(0.51-3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eYes, currently attending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e0.67(0.14-0.95) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eIncrease in income over the years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eAs usual/same\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eIncreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e0.58(0.34-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eDecreased\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.46(1.39-5.44) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eReceived assistance from the government\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.39(0.77-2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eReceived assistance from the UN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e0.88(0.69-0.97) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eReceived assistance from other NGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e0.67(0.12-,1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eFirst Assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.00(1.00-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.35(0.60-2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContinuous variables:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.11(0.68-1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eHousehold size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1.89(1.35-4.47) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1765.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.3323%;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9352%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.7974%;\"\u003e\n \u003cp\u003e1964.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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 insecurity, displaced populations, COVID-19, Bangladesh, Cox Bazar","lastPublishedDoi":"10.21203/rs.3.rs-5363128/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5363128/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFood insecurity is a significant issue impacting millions of people compelled to abandon their homes due to conflict, natural disasters, or economic difficulties. Cash incentives have become a critical mitigating tool in alleviating the food insecurity challenges of these displaced populations amid COVID-19. This paper examines the determinants of food insecurity and how cash incentives alleviate food insecurity among displaced populations. The study used the 2020–2021 high-frequency survey data to achieve this objective. The high-frequency survey continues the 2019 Cox Bazar Panel Survey baseline, representative research of Rohingya displaced after 2017 in host communities in Bangladesh's Cox's Bazar district. Receiving cash transfers (incentives) is significantly associated with lower odds of experiencing food insecurity (OR = 0.19, CI = 0.91-0.380). Female participants have higher odds (OR = 3.44, CI = 1.41-5.36) of experiencing food insecurity than their male counterparts. Being a recipient of assistance from UN agencies was associated with slightly lower odds (OR = 0.88, CI = 0.69-0.97) of experiencing food insecurity. To achieve sustainable food security, we must integrate cash incentives into a comprehensive social support system that includes employment initiatives, affordable housing, healthcare, and education. This holistic strategy can address the underlying reasons why people face food insecurity, giving them the tools and chances to become stable in the long term and less reliant on short-term monetary incentives.\u003c/p\u003e","manuscriptTitle":"Exploring the Impact of Cash-Based Interventions on Alleviating Food Insecurity Among Displaced Populations Amidst COVID-19: A Case Study of Cox Bazar, Bangladesh.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-18 07:27:21","doi":"10.21203/rs.3.rs-5363128/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":"48aa6ce4-eca5-4396-844e-7d620fb2a08b","owner":[],"postedDate":"November 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-12T11:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-18 07:27:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5363128","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5363128","identity":"rs-5363128","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}