Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022
This paper examines the current state of food insecurity in Bangladesh and its socio-economic drivers using data from the latest Household Income and Expenditure Survey (HIES 2022). Unlike previous studies that relied on less precise measures of food insecurity, such as food expenditure, diversity,...
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| Language: | English |
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Elsevier
2025-01-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024176128 |
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| author | Faria Rauf Ria Md. Muhitul Alam Md. Azad Uddin Mohaimen Mansur Md. Israt Rayhan |
| author_facet | Faria Rauf Ria Md. Muhitul Alam Md. Azad Uddin Mohaimen Mansur Md. Israt Rayhan |
| author_sort | Faria Rauf Ria |
| collection | DOAJ |
| description | This paper examines the current state of food insecurity in Bangladesh and its socio-economic drivers using data from the latest Household Income and Expenditure Survey (HIES 2022). Unlike previous studies that relied on less precise measures of food insecurity, such as food expenditure, diversity, and calorie intake, this study employs the internationally recognized Food Insecurity Experience Scale (FIES) and Rasch model-based thresholds to classify households as food secure or insecure. Multilevel logistic regression is used to identify significant predictors of moderate and severe food insecurity, considering the hierarchical structure of the data, with households nested within geographical clusters. Key factors found to be significantly associated with food security include the wealth index, land ownership, education of the household head, family size, remittance income and exposure to shocks. A classification tree, a popular machine learning method, is also applied to explore important interactions among these determinants. The tree analysis confirms the importance of several regression-based predictors and identifies households at the highest risk of food insecurity through variable interactions. Factors such as poverty, lack of land ownership, low education levels, and high dependency ratios collectively increase a household's vulnerability to moderate food insecurity to around 51% while the national prevalence is 19%. District-level maps of food insecurity prevalence reveal significant regional disparities, underscoring the need for targeted, district-specific interventions to effectively combat food insecurity. More broadly, policies promoting education and family planning, training in better shock management, and facilitating remittance flows through simplified processes may contribute to addressing the food insecurity challenge. |
| format | Article |
| id | doaj-art-0cd24c90a04c4ef9bb0ff4d96499cd3d |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Heliyon |
| spelling | doaj-art-0cd24c90a04c4ef9bb0ff4d96499cd3d2025-01-17T04:51:41ZengElsevierHeliyon2405-84402025-01-01111e41581Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022Faria Rauf Ria0Md. Muhitul Alam1Md. Azad Uddin2Mohaimen Mansur3Md. Israt Rayhan4Institute of Statistical Research and Training, University of Dhaka, BangladeshInstitute of Statistical Research and Training, University of Dhaka, BangladeshBangladesh Bank, BangladeshInstitute of Statistical Research and Training, University of Dhaka, Bangladesh; Corresponding author.Institute of Statistical Research and Training, University of Dhaka, BangladeshThis paper examines the current state of food insecurity in Bangladesh and its socio-economic drivers using data from the latest Household Income and Expenditure Survey (HIES 2022). Unlike previous studies that relied on less precise measures of food insecurity, such as food expenditure, diversity, and calorie intake, this study employs the internationally recognized Food Insecurity Experience Scale (FIES) and Rasch model-based thresholds to classify households as food secure or insecure. Multilevel logistic regression is used to identify significant predictors of moderate and severe food insecurity, considering the hierarchical structure of the data, with households nested within geographical clusters. Key factors found to be significantly associated with food security include the wealth index, land ownership, education of the household head, family size, remittance income and exposure to shocks. A classification tree, a popular machine learning method, is also applied to explore important interactions among these determinants. The tree analysis confirms the importance of several regression-based predictors and identifies households at the highest risk of food insecurity through variable interactions. Factors such as poverty, lack of land ownership, low education levels, and high dependency ratios collectively increase a household's vulnerability to moderate food insecurity to around 51% while the national prevalence is 19%. District-level maps of food insecurity prevalence reveal significant regional disparities, underscoring the need for targeted, district-specific interventions to effectively combat food insecurity. More broadly, policies promoting education and family planning, training in better shock management, and facilitating remittance flows through simplified processes may contribute to addressing the food insecurity challenge.http://www.sciencedirect.com/science/article/pii/S2405844024176128Food insecurityFood insecurity experience scaleRasch modelMultilevel logistic regressionClassification treeVariable importance |
| spellingShingle | Faria Rauf Ria Md. Muhitul Alam Md. Azad Uddin Mohaimen Mansur Md. Israt Rayhan Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022 Heliyon Food insecurity Food insecurity experience scale Rasch model Multilevel logistic regression Classification tree Variable importance |
| title | Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022 |
| title_full | Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022 |
| title_fullStr | Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022 |
| title_full_unstemmed | Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022 |
| title_short | Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022 |
| title_sort | experience based food insecurity in bangladesh evidence from household income and expenditure survey 2022 |
| topic | Food insecurity Food insecurity experience scale Rasch model Multilevel logistic regression Classification tree Variable importance |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024176128 |
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