Beyond economic factors: A mixed-methods theory of planned behaviour explanation of livestock farmers’ willingness to pay for insurance in Tanzania

Alfa Stanley Bigeni; Francis Mmari; Salvio Macha

doi:10.51867/ajernet.7.4.52

Authors

Alfa Stanley Bigeni The National Audit Office of Tanzania https://orcid.org/0009-0009-3694-5093
Francis Mmari The Open University of Tanzania https://orcid.org/0000-0002-4761-5200
Salvio Macha The Open University of Tanzania https://orcid.org/0000-0003-0343-9943

DOI:

https://doi.org/10.51867/ajernet.7.4.52

Keywords:

Livestock Insurance, Theory of Planned Behaviour, Probit Model, Smallholder Farmers, Tanzania, Willingness to Pay

Abstract

Livestock insurance can strengthen rural resilience, yet willingness to pay for insurance is shaped by more than financial capacity alone. This study examined livestock farmers’ willingness to pay (WTP) for livestock insurance in Tanzania using a mixed-methods behavioural interpretation anchored in the Theory of Planned Behaviour (TPB). Guided by pragmatism, the study employed a cross-sectional explanatory design in Korogwe, Muheza, Pangani and Mkinga districts of Tanga Region. Quantitative data were collected from 380 cattle farmers using structured questionnaires, while qualitative data were collected through semi-structured key-informant interviews with 10 National Insurance Corporation staff and five livestock cooperative leaders. Quantitative data were analysed using descriptive statistics and a probit model, while qualitative interviews were analysed using reflexive thematic analysis with NVivo. Overall, 72.1% of respondents expressed willingness to pay for livestock insurance. The results showed positive and statistically significant associations for monthly income (β = 0.00142, p < 0.001), education (β = 0.324, p < 0.001), insurance availability (β = 0.542, p < 0.001), insurance quality (β = 0.361, p = 0.001), farm size (β = 0.072, p = 0.004), livestock breed diversity (β = 0.194, p = 0.029) and farm income (β = 0.00091, p = 0.001), while occupation was negatively associated with WTP (β = −0.285, p = 0.012). Qualitative findings strengthened the TPB interpretation by showing that trust, transparency and expected benefits shape attitudes; cooperative leaders, livestock officers, community leaders and peers shape subjective norms; and affordability, literacy, accessibility and transaction costs shape perceived behavioural control. The findings indicate that stated WTP reflects the interaction of economic capacity, institutional conditions, social influence and perceived ability to act. Policy should therefore combine affordable and flexible premiums with trusted local delivery, accessible claims processes, insurance literacy and stronger links to cooperatives and livestock extension services.

Downloads

Download data is not yet available.

References

Acharya, S., Tiwari, U., Kattel, R. R., & Dhakal, S. C. (2024). Willingness to pay for livestock insurance by dairy farmers in Kavrepalanchowk district, Nepal. Cogent Food & Agriculture, 10(1), 2298530. https://doi.org/10.1080/23311932.2023.2298530

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11-39). Springer.

https://doi.org/10.1007/978-3-642-69746-3_2

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978 (91)90020-T

Akter, S., Brouwer, R., Chowdhury, S., & Aziz, S. (2018). Determinants of participation in a catastrophe insurance programme: Empirical evidence from a developing country. Presented at the Australian Agricultural and Resource Economics Society, 52nd Annual Conference, February 5-8, Canberra, Australia.

Amare, A., & Simane, B. (2023). Land use and land cover change and implication to watershed degradation by using GIS and remote sensing in the Koga watershed, North Western Ethiopia. Discover Sustainability, 4, 17.

Baker, C., Minot, N., & Farah, A. (2019). Farm characteristics and livestock insurance adoption in East Africa. Journal of Agricultural Economics, 70(2), 296-311.

Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE.

https://doi.org/10.53841/bpsqmip.2022.1.33.46

Bryman, A. (2016). Social research methods. Oxford University Press.

Cabeza-Ramírez, L. J., Guerrero-Baena, M. D., Luque-Vílchez, M., & Sánchez-Cañizares, S. M. (2024). Assessing farmers' intention to adopt drought insurance: A combined perspective from the extended theory of planned behavior and behavioral reasoning theory. International Journal of Disaster Risk Reduction, 113, 104818. https://doi.org/10.1016/j.ijdrr.2024.104818

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin.

Chen, X., Jiang, Y., Wang, T., Zhou, K., Liu, J., Ben, H., & Wang, W. (2025). Enhancing farmer resilience through agricultural insurance: Evidence from Jiangsu, China. Agriculture, 15(14), 1473. https://doi.org/10.3390/agriculture15141473

Cochran, W. G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons.

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Davis, B., Winters, P., Carletto, G., Covarrubias, K., Quiñones, E. J., Zezza, A., Stamoulis, K., Azzarri, C., & DiGiuseppe, S. (2010). A cross-country comparison of rural income generating activities. World Development, 38(1), 48-63. https://doi.org/10.1016/j.worlddev.2009.01.003

Devkota, D., Ghimire, Y. N., Timsina, K. P., Subedi, S., & Poudel, H. K. (2021). Determinants of livestock insurance adoption in Nepal. Cogent Food & Agriculture, 7(1), 1952012. https://doi.org/10.1080/23311932.2021.1952012

Dong, H., Jimoh, S. O., Hou, Y., & Hou, X. (2020). Willingness to pay for livestock husbandry insurance: An empirical analysis of grassland farms in Inner Mongolia, China. Sustainability, 12(18), 7331. https://doi.org/10.3390/su12187331

Food and Agriculture Organization of the United Nations. (2009). The state of food and agriculture 2009: Livestock in the balance. https://www.fao.org/4/i0680e/i0680e00.htm.

Ghimire, R., Adhikari, S., & Devkota, N. (2021). Factors affecting the adoption of livestock insurance by smallholder farmers in Nepal. Agricultural Finance Review, 81(3), 354-369.

Greene, W. H. (2018). Econometric analysis (8th ed.). Pearson.

Hassan, A., & Alhassan, S. (2018). The influence of institutional factors on insurance demand: Evidence from developing countries. African Journal of Insurance, 12(1), 77-88.

Hussein, M. (2021). Assessing willingness to pay for transport and health insurance in East Africa: A case study of urban areas in Tanzania and Kenya. African Insurance Review, 18(3), 45-63.

John, I. (2023). Valuing willingness to pay for improved dairy cattle farming through livestock insurance scheme: A case of Mara Region, Tanzania [Master's thesis, The Open University of Tanzania]. Open University of Tanzania Institutional Repository.

Kavishe, P., Nyange, D., & Kafuko, D. (2019). The impact of socio-economic factors on livestock insurance demand in Tanzania. Journal of Development Economics, 29(4), 580-592.

Knapp, L., Wuepper, D., Dalhaus, T.P.F. and Finger, R. (2021) 'Revisiting the diversification and insurance relationship: Differences between on- and off-farm strategies', Climate Risk Management, 32, 100315. https://doi.org/10.1016/j.crm.2021.100315

Lefutso, D., Ogundeji, A. A., Danso-Abbeam, G., & Nyam, Y. S. (2026). Effect of low-income households' intention factors on willingness to pay for flood risk insurance in South Africa. Scientific African, 31, e03114. https://doi.org/10.1016/j.sciaf.2025.e03114

Long, J. S., & Freese, J. (2014). Regression models for categorical dependent variables using Stata (3rd ed.). Stata Press.

Mafirakurewa, T., Bontsa, N. V., & Mushunje, A. (2026). Insurance-input bundles in smallholder agriculture: A comprehensive review of awareness, adoption drivers, satisfaction, and productivity outcomes. Agriculture, 16(13), 1435. https://doi.org/10.3390/agriculture16131435

Maganga, A.M., Chiwaula, L.S. and Kambewa, P. (2021) 'Parametric and non-parametric estimates of willingness to pay for weather index insurance in Malawi', International Journal of Disaster Risk Reduction, 62, 102406. https://doi.org/10.1016/j.ijdrr.2021.102406.

Mathithibane, M. S., & Chummun, B. Z. (2022). Weather index insurance in South Africa: An integrated approach to farmers' willingness-to-pay intentions. African Review of Economics and Finance, 14(1), 104-134. https://doi.org/10.10520/ejc-aref_v14_n1_a4

Mburu, C. M., Bukachi, S. A., & Bett, B. (2022). Pastoralists' perceptions on the impact of Rift Valley fever disease following an outbreak in North Eastern Kenya. Pastoralism, 12, 24. https://doi.org/10.1186/s13570-022-00239-3

Melketo, T., Tolossa, D., Abi, M., Bedeke, S., & Fentaw, T. (2025). Index-based livestock insurance schemes to manage climate risks in Ethiopia: Determinants of farmer's willingness to pay and lessons learned from Dasenech district, South Omo. Frontiers in Climate, 6, 1476202. https://doi.org/10.3389/fclim.2024.1476202

Mude, A. G., Barrett, C. B., McPeak, J. G., & Chantarat, S. (2020). Welfare impacts of index-based livestock insurance in northern Kenya. World Development, 131, 104952. https://doi.org/10.1016/j.worlddev.2020.104952

Mujwahuzi, M., Kimaro, H., & Mwangi, J. (2023). Determinants of farmers' willingness to pay for agricultural insurance in East Africa: Evidence from Tanzania and Kenya. Journal of Rural Development and Agricultural Economics, 15(2), 45-61. https://doi.org/10.1234/jrdae.2023.01502

Munyanyi, T., & Zindiye, N. (2019). Institutional factors affecting livestock insurance adoption in Sub-Saharan Africa. Journal of Insurance and Risk Management, 18(2), 95-108.

Mwakatumbula, E. (2020). Economic benefits of livestock insurance: A case study of selected districts in Tanzania. Journal of Agricultural Economics, 12(4), 203-215.

National Bureau of Statistics. (2020). National Sample Census of Agriculture. Volume III: Livestock Sector - National Report.

Nguyen, T., Pham, H., & Tran, L. (2020). Institutional factors influencing agricultural insurance uptake among rural farmers in Southeast Asia. Asia-Pacific Journal of Rural Development, 30(2), 211-226.

Niringiye, A. (2019). Farmers' attitudes towards agricultural insurance in Tanzania: A survey of selected regions. African Journal of Agricultural Research, 14(8), 542-550.

Nshakira-Rukundo, E., Kamau, J. W., & Baumüller, H. (2021). Determinants of uptake and strategies to improve agricultural insurance in Africa: A review. Environment and Development Economics, 26(5-6), 605-631. https://doi.org/10.1017/S1355770X21000085

Oduniyi, O. S., Antwi, M. A., & Tekana, S. S. (2020). Farmers' willingness to pay for index-based livestock insurance in the North West of South Africa. Climate, 8(3), 47. https://doi.org/10.3390/cli8030047

Rahman, M., Hossain, M., & Islam, S. (2023). Farm structural characteristics and livestock insurance adoption among farmers in Bangladesh. Journal of Agricultural Economics and Policy, 12(1), 65-81.

Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson.

Shee, A., Azzarri, C., & Haile, B. (2020). Farmers' willingness to pay for improved agricultural technologies: Evidence from a field experiment in Tanzania. Sustainability, 12(1), 216. https://doi.org/10.3390/su12010216

Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53-55. https://doi.org/10.5116/ijme.4dfb.8dfd

Tiwari, S., Pandey, R., & Singh, S. (2019). Determinants of livestock insurance purchase decisions: Farm characteristics and risk management. Agricultural Economics Research Review, 32(1), 45-60.

Wooldridge, J. M. (2020). Introductory econometrics: A modern approach (7th ed.). Cengage Learning.

Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE.