Insight into households’ financial hardship and coping strategies during COVID-19 pandemic: a study on char dwellers of Bangladesh

Purpose The COVID-19 pandemic has harmed the socioeconomic well-being of millions of low-income people across the globe. The study is to examine the financial strategies adopted by Char dwellers in Bangladesh to deal with the global pandemic’s shock. Design/methodology/approach A quantitative survey was conducted for data collection, and respondents were chosen through a multistage sampling technique. Binary logistic model, probit model and multinomial logit models and Bayesian multinomial logistic models have been conducted to achieve the objectives. The objective of incorporating Bayesian models is to evaluate the probabilistic certainty and robustness of estimates in the presence of uncertainty and sparse data. Findings Findings from the binary logit model, probit model, Bayesian logit model and Bayesian probit model show that income, income instability, number of dependents in a family, shrinking work opportunities, financial contributions to family, savings and a comparison of household expenses to pre-pandemic expenses influence the households’ daily financial management strategies. The multinomial logit model and Bayesian multinomial logit model results, on the other hand, show that education, savings, employment status and major sources of expenditure influenced the choice of financial strategies mostly. Practical implications This research offers policy recommendations that might alleviate the financial challenges of extremely poor people posed by global crises such as the COVID-19 pandemic. Policymakers can use the results of this study to improve social safety nets, diversify livelihoods and provide char dwellers with banking services. Community leaders and organizations can learn from char dwellers’ coping strategies to improve financial hardship. By sharing lessons learned and best practices during the COVID-19 epidemic, stakeholders may better help vulnerable communities like char dwellers. Originality/value To the best of the authors’ knowledge, this is the first study, in Bangladesh to investigate how financially disadvantaged people living in the char region have dealt with the COVID-19 pandemic and thus this paper presents new background information on strategies adopted by poor to cope up with COVID-19 pandemic using both frequentist and Bayesian modeling approaches. A methodological breakthrough, the incorporation of Bayesian analysis strengthens and probabilistically illuminates studies of socio-economic crises. This research clarified about vulnerable groups’ crisis response, financial situations and adaptive behaviors.

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Publication Details

Journal
Asian Journal of Economics and Banking
Published
2025-06-19
DOI
https://doi.org/10.1108/ajeb-06-2023-0056
Citations
2
Primary Topic
COVID-19 Pandemic Impacts
Type
article
Field-Weighted Citation Impact
6.26
Controls
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article

Insight into households’ financial hardship and coping strategies during COVID-19 pandemic: a study on char dwellers of Bangladesh

Md. Emran Hosen, Md. Nur Alam Siddik, Md. Firoze Miah
2 citations
Asian Journal of Economics and Banking
COVID-19 Pandemic Impacts
6.26
article

Insight into households’ financial hardship and coping strategies during COVID-19 pandemic: a study on char dwellers of Bangladesh

Md. Emran Hosen, Md. Nur Alam Siddik, Md. Firoze Miah
article en
2 citations

Abstract

Purpose The COVID-19 pandemic has harmed the socioeconomic well-being of millions of low-income people across the globe. The study is to examine the financial strategies adopted by Char dwellers in Bangladesh to deal with the global pandemic’s shock. Design/methodology/approach A quantitative survey was conducted for data collection, and respondents were chosen through a multistage sampling technique. Binary logistic model, probit model and multinomial logit models and Bayesian multinomial logistic models have been conducted to achieve the objectives. The objective of incorporating Bayesian models is to evaluate the probabilistic certainty and robustness of estimates in the presence of uncertainty and sparse data. Findings Findings from the binary logit model, probit model, Bayesian logit model and Bayesian probit model show that income, income instability, number of dependents in a family, shrinking work opportunities, financial contributions to family, savings and a comparison of household expenses to pre-pandemic expenses influence the households’ daily financial management strategies. The multinomial logit model and Bayesian multinomial logit model results, on the other hand, show that education, savings, employment status and major sources of expenditure influenced the choice of financial strategies mostly. Practical implications This research offers policy recommendations that might alleviate the financial challenges of extremely poor people posed by global crises such as the COVID-19 pandemic. Policymakers can use the results of this study to improve social safety nets, diversify livelihoods and provide char dwellers with banking services. Community leaders and organizations can learn from char dwellers’ coping strategies to improve financial hardship. By sharing lessons learned and best practices during the COVID-19 epidemic, stakeholders may better help vulnerable communities like char dwellers. Originality/value To the best of the authors’ knowledge, this is the first study, in Bangladesh to investigate how financially disadvantaged people living in the char region have dealt with the COVID-19 pandemic and thus this paper presents new background information on strategies adopted by poor to cope up with COVID-19 pandemic using both frequentist and Bayesian modeling approaches. A methodological breakthrough, the incorporation of Bayesian analysis strengthens and probabilistically illuminates studies of socio-economic crises. This research clarified about vulnerable groups’ crisis response, financial situations and adaptive behaviors.

Asian Journal of Economics and BankingVol. 9(2)
Begum Rokeya University (BD)
No poverty
Openalex Percentile: Top 4%
COVID-19 Pandemic Impacts
6.26
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