Cognitive Bandwidth and Food Safety Evaluating the Impact of Poverty on Investment Decisions in Nigeria's Fisheries Sector
Main Article Content
Abstract
This study investigates the cognitive barrier imposed by poverty on food safety investments among artisanal fishers in Nigeria, a critical yet neglected dimension of public health risk. Moving beyond conventional economic models, we apply Scarcity Theory to test the hypothesis that poverty imposes a cognitive tax that undermines the agency to act on safety knowledge. Using a sequential explanatory mixed-methods design, we surveyed 772 artisanal fishers across three coastal states, complemented by in-depth qualitative narratives. Key findings reveal a tiered behavioural response, anchored by a decisive cognitive liberation threshold at a monthly income of ₦80,000–₦100,000, where willingness-to-pay for safe food increases steeply. Below this threshold, mental bandwidth is monopolized by survival, rendering safety investment cognitively inaccessible. Counterintuitively, a high Cognitive Bandwidth Tax positively influenced WTP (Scaled OR=3.32), indicating that scarcity focuses attention on catastrophic, salient risks like family illness. Furthermore, the hierarchy of decision determinants shifts across income tiers: income acts as a foundational enabler, after which education and health risk beliefs become dominant drivers. We conclude that persistent food safety risks are not primarily a knowledge gap but a cognitive deficit induced by scarcity. Consequently, we recommend a fundamental policy pivot from knowledge-first to bandwidth-first strategies. For the majority below the liberation threshold, interventions must prioritize economic resilience through cash transfers and livelihood support to free cognitive space, before layering on behaviourally-designed safety education for those in transition.
Article Details
Copyright (c) 2026 Edamisan Stephen Ikuemonisan (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
Edamisan Stephen Ikuemonisan, Department of Agricultural Economics, Adekunle Ajasin University, Akungba Akoko P.M.B 001, Ondo State, Nigeria.
Dr. Edamisan Stephen Ikuemonisan is a distinguished Nigerian agricultural economist, researcher, and academic, recognized for his significant contributions to the fields of agribusiness economics, value chain analysis, and sustainable food systems.
He holds a PhD in Agricultural Economics and currently serves as a Senior Lecturer in the Department of Agricultural Economics and Farm Management at Adekunle Ajasin University, Akungba Akoko (AAUA), Nigeria. His research focuses on improving the economic resilience, productivity, and sustainability of smallholder farmers and agri-food enterprises in Nigeria and across West Africa, with particular expertise in staple crop value chains such as cassava, maize, and rice.
A prolific scholar, Dr. Ikuemonisan has authored and co-authored numerous peer-reviewed articles in respected national and international journals. His work often bridges rigorous economic analysis with practical policy implications, addressing critical issues like market access, food security, profitability, and the impact of climate-smart agricultural practices. He is deeply committed to mentoring the next generation of agricultural economists and development practitioners through his teaching and supervision of postgraduate students.
Dr. Ikuemonisan is also actively engaged in policy dialogue and agricultural development projects. He frequently contributes as a resource person and technical advisor to committees and initiatives aimed at strengthening Nigeria's agricultural sector and informal food economy. Known for his empirical, data-driven approach, he strives to generate evidence that supports both entrepreneurial decision-making and informed public policy for sustainable agricultural transformation.
His research embodies a pragmatic vision: to demonstrate that ecological sustainability and economic profitability in agriculture are not only compatible but are essential foundations for equitable growth and food security in Africa.
Andam, K. S., Edeh, H., Oboh, V., Pauw, K., & Thurlow, J. (2020). Impacts of COVID-19 on food systems and poverty in Nigeria. Advances in Food Security and Sustainability, 5, 145–173. https://doi.org/10.1016/bs.af2s.2020.09.002 DOI: https://doi.org/10.1016/bs.af2s.2020.09.002
Baer, T., & Schnall, S. (2021). Quantifying the cost of decision fatigue: Suboptimal risk decisions in finance. Royal Society Open Science, 8(5), 201059. https://doi.org/10.1098/rsos.201059 DOI: https://doi.org/10.1098/rsos.201059
Chen, P. J., & Antonelli, M. (2020). Conceptual models of food choice: Influential factors related to foods, individual differences, and society. Foods, 9(12), 1898. DOI: https://doi.org/10.3390/foods9121898
Cochran, W. G. (1963). Sampling techniques (2nd ed.). John Wiley & Sons.
de Almeida, L., Strabelli, D. G., & Matsushita, R. Y. (2024). Payday cycles in cognitive function and economic behavior: Evidence from a field experiment. Journal of Economic Behavior & Organization, 217, 398–413. https://doi.org/10.1016/j.jebo.2023.11.011 DOI: https://doi.org/10.1016/j.jebo.2023.01.015
de Bruijn, E. J., & Antonides, G. (2021). Poverty and economic decision making: A review of scarcity theory. Theory and Decision, 92(1), 5–37. https://doi.org/10.1007/s11238-021-09838-9 DOI: https://doi.org/10.1007/s11238-021-09802-7
Durão, S., Visser, M. E., Figueira, A. L., & Oliveira, J. M. (2020). Effects of cash transfers on child health and development in low- and middle-income countries: A systematic review. Social Science & Medicine, 265, 113301. https://doi.org/10.1016/j.socscimed.2020.113301 DOI: https://doi.org/10.1016/j.socscimed.2020.113301
Flores, N. E., & Carson, R. T. (1997). The relationship between the income elasticities of demand and willingness to pay. Journal of Environmental Economics and Management, 33(3), 287–295. https://doi.org/10.1006/jeem.1997.0998 DOI: https://doi.org/10.1006/jeem.1997.0998
Garg, R., Sahu, P., & Bala, M. M. (2024). Sample size determination for cross-sectional studies in public health: A practical guide. Journal of Public Health Research, 13(1), 22799036241226832. https://doi.org/10.1177/22799036241226832
Grace, D. (2015). Food safety in low and middle income countries. International Journal of Environmental Research and Public Health, 12(9), 10490–10507. https://doi.org/10.3390/ijerph120910490 DOI: https://doi.org/10.3390/ijerph120910490
Grace, D. (2023). Burden of foodborne disease in low-income and middle-income countries and opportunities for scaling food safety interventions. Food Security, 15, 1475–1488. https://doi.org/10.1007/s12571-023-01391-3 DOI: https://doi.org/10.1007/s12571-023-01391-3
Habiyaremye, N., Mburu, J., & Mose, L. O. (2023). Consumers' willingness to pay for aflatoxin-safe milk in Kenya: A contingent valuation approach. Food Control, 145, 109432.
Ivankova, N. V., Creswell, J. W., & Stick, S. L. (2006). Using mixed-methods sequential explanatory design: From theory to practice. Field Methods, 18(1), 3–20. https://doi.org/10.1177/1525822X05282260 DOI: https://doi.org/10.1177/1525822X05282260
Karanja, A., Njoroge, S. M., & Mburu, J. (2022). Consumer risk-resilience and food choice in informal markets: Evidence from urban Kenya. Food Policy, 112, 102361. https://doi.org/10.1016/j.foodpol.2022.102361 DOI: https://doi.org/10.1016/j.foodpol.2022.102361
Kavanaugh, M., Hamshaw, K., & Ritz, J. (2021). Measuring health risk perceptions related to food: Development and validation of a multidimensional scale. Food Quality and Preference, 92, 104227. https://doi.org/10.1016/j.foodqual.2021.104227
Kim, J. (2017). Income thresholds and consumer behavior: Evidence from a field experiment. Journal of Economic Psychology, 63, 52–63. https://doi.org/10.1016/j.joep.2017.10.001 DOI: https://doi.org/10.1016/j.joep.2017.10.001
Kim, J., & Kim, S. (2024). Nonlinear relationships in environmental valuation: A fractional polynomial approach. Journal of Environmental Management, 351, 119856. https://doi.org/10.1016/j.jenvman.2023.119856 DOI: https://doi.org/10.1016/j.jenvman.2023.119856
Kumar, N. K. (2024). F-Test and analysis of variance (ANOVA) in economics. Mikailalsys Journal of Mathematics and Statistics, 2(3), 102-113.. https://doi.org/10.58578/mjms.v2i3.3449 DOI: https://doi.org/10.58578/mjms.v2i3.3449
Kwoba, E., Sirma, A. J., & Thomas, L. F. (2023). Food safety interventions in informal markets in low- and middle-income countries: A systematic review. Food Control, 153, 109946. https://doi.org/10.1016/j.foodcont.2023.109946 DOI: https://doi.org/10.1016/j.foodcont.2023.109946
Liguori, J., Trübswasser, U., Pradelles, R., & Le Port, A. (2022). Consumer food safety concerns and practices in low- and middle-income countries: A systematic review. Food Control, 136, 108872. https://doi.org/10.1016/j.foodcont.2022.108872 DOI: https://doi.org/10.1016/j.gfs.2021.100606
Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2020). Scarcity and cognitive function around payday: A pre-registered study. Journal of Public Economics, 183, 104143. https://doi.org/10.1016/j.jpubeco.2020.104143 DOI: https://doi.org/10.1016/j.jpubeco.2020.104143
Mullainathan, S., & Shafir, E. (2013). Scarcity: Why having too little means so much. Henry Holt and Company.
Naznin, P. S., Katrawath, D., Sabu, R. M., & Shankar, A. R. (2023, April). Determination of factors affecting the willingness to pay elicited by the contingent valuation payment card method using structural equation modelling. In International Conference on Sustainable Infrastructure: Innovation, Opportunities and Challenges (pp. 45–56). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-97-4852-5_4
Ogwu, M. C., Izah, S. C., Ntuli, N. R., & Odubo, T. C. (2024). Food security complexities in the global south. In Food safety and quality in the global south (pp. 3-33). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-97-2428-4_1
Seyoum, A., Desta, A., & Mengistu, S. (2024). Aquaculture practices and food safety risks in East Africa: A review of antimicrobial use and water quality. Aquaculture Reports, 33, 101887. https://doi.org/10.1016/j.aqrep.2024.101887
Tyllianakis, E., & Skuras, D. (2016). The income elasticity of willingness-to-pay (WTP) revisited: A meta-analysis of studies for restoring good ecological status (GES) of water bodies under the Water Framework Directive (WFD). Journal of Environmental Management, 182, 531–541. https://doi.org/10.1016/j.jenvman.2016.08.012 DOI: https://doi.org/10.1016/j.jenvman.2016.08.012
van der Veer, A., Madern, T., & van Lenthe, F. J. (2024). Tunneling, cognitive load and time orientation and their relations with dietary behavior of people experiencing financial scarcity – an AI-assisted scoping review elaborating on scarcity theory. International Journal of Behavioral Nutrition and Physical Activity, 21(1), 26. https://doi.org/10.1186/s12966-024-01576-9 DOI: https://doi.org/10.1186/s12966-024-01576-9
Waage, J., Grace, D., & Fèvre, E. M. (2022). Changing food systems and infectious disease risks in low-income and middle-income countries. The Lancet Planetary Health, 6(9), e760–e768. https://doi.org/10.1016/S2542-5196(22)00116-4 DOI: https://doi.org/10.1016/S2542-5196(22)00116-4