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Gender differences in banana productivity in Tanzania

   June 22, 2025  Leave a Comment on Gender differences in banana productivity in Tanzania

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Abstract

Banana is one of the key crops produced by farmers in Tanzania. The productivity of bananas among smallholder farmers is very low. One of the core reasons for this lower agricultural productivity in Tanzania is gender inequality in production. This study aimed to establish gender productivity differences in banana production in Tanzania. The study used panel data and a correlated random effects (CRE) model to determine these differences. It finds a 19% difference in banana productivity in favour of male managers, highlighting their (plot managers’) characteristics, input use, and banana plot characteristics. The area of banana cultivation by zones, namely Lake, Northern, Southern Highlands, and Eastern zones, was found to increase banana productivity. The use of organic fertiliser and receiving government extension services have a positive influence on banana productivity. On the other hand, being a female manager, an increase in banana plot area, and an increase in the usage of pesticides have a detrimental effect on banana productivity. The findings of this study suggest the need for proper banana plot management, farmer training on skills such as the timing and amount of inputs that can be applied to banana plots sustainably, and the availability of extension services to all plot managers, regardless of their gender. Additionally, the study advocates for the sustainable use of pesticides by adopting good agricultural practices like Integrated Pest Management (IPM) and using appropriate planting materials that are disease-resistant.

Authors

  • Hildo Ladislaus Mrema (Institute of Accountancy Arusha, Tanzania)
  • Razack Bakari Lokina (University of Dar es Salaam, Tanzania)
  • Onesmo Selejio (University of Dar es Salaam, Tanzania)

References

  1. Ackerberg, D. A., Caves, K., & Frazer, G. (2015). Identification properties of recent production function estimators. Econometrica, 83(6), 2411–2451. https://doi.org/10.3982/ECTA13408
  2. Agarwal, B. (2012). Food security, productivity, and gender inequality. Working Papers, Article id:7566. https://ideas.repec.org//p/ess/wpaper/id7566.html
  3. Akresh, R. (2005). Understanding Pareto inefficient intrahousehold allocations. SSRN Electronic Journal, 1–32. https://doi.org/10.2139/ssrn.866885
  4. Baltagi, B. H. (2005). Econometric analysis of panel data. John Wiley & Sons.
  5. Baltagi, B. H. (2023). The two-way Mundlak estimator. Econometric Reviews, 42(2), 240–246. https://doi.org/10.1080/07474938.2023.2178139
  6. Barrett, C. B., Bellemare, M. F., & Hou, J. Y. (2010). Reconsidering conventional explanations of the inverse productivity–size relationship. World Development, 38(1), 88–97. https://doi.org/10.1016/j.worlddev.2009.06.002
  7. Battese, G. E., Malik, S. J., & Broca, S. (1993). Production functions for wheat farmers in selected districts of Pakistan: An application of a stochastic frontier production function with time-varying inefficiency effects. The Pakistan Development Review, 32(3), 233.
  8. Battese, G. E. (1997). A note on the estimation of Cobb-Douglas production functions when some explanatory variables have zero values. Journal of Agricultural Economics, 48(1–3), 250–252. https://doi.org/10.1111/j.1477-9552.1997.tb01149.x
  9. Battese, G. E., Malik, S. J., & Gill, M. A. (1996). An investigation of technical inefficiencies of production of wheat farmers in four districts of Pakistan. Journal of Agricultural Economics, 47(1–4), pp. 37–49. https://doi.org/10.1111/j.1477-9552.1996.tb00670.x
  10. Becker, G. S. (1965). A theory of the allocation of time. The Economic Journal, 75(299), 493. https://doi.org/10.2307/2228949
  11. Bezu, S., & Villanger, E. (2019). Crop market participation among smallholder farmers in Tanzania. https://open.cmi.no/cmi-xmlui/handle/11250/2643306
  12. Browning, M., & Chiappori, P. A. (1998). Efficient intra-household allocations: A general characterisation and empirical tests. Econometrica, 66(6), 1241. https://doi.org/10.2307/2999616
  13. Calberto, G., Staver, C., & Siles, P. (2015). An assessment of global banana production and suitability under climate change scenarios. In A. Elbehri (Ed.), Climate change and food systems: Global assessments and implications for food security and trade. FAO.
  14. Cen, Y., Guo, L., Liu, M., Gu, X., Li, C., & Jiang, G. (2020). Using organic fertilisers to increase crop yield, economic growth, and soil quality in temperate farmland. PeerJ, 8, e9668. https://doi.org/10.7717/peerj.9668
  15. Chiappori, P.-A. (1992). Collective labour supply and welfare. Journal of Political Economy, 100(3), 437–467. https://doi.org/10.1086/261825
  16. Chabi, M. C., Dassou, A. G., Dossou-Aminon, I., Ogouchoro, D., Aman, B. O., & Dansi, A. (2018). Banana and plantain production systems in Benin: Ethnobotanical investigation, varietal diversity, pests, and implications for better production. Journal of Ethnobiology and Ethnomedicine, 14(1), 78. https://doi.org/10.1186/s13002-018-0280-1
  17. Croppenstedt, A., Goldstein, M., & Rosas, N. (2013). Gender and agriculture: Inefficiencies, segregation, and low productivity traps. The World Bank Research Observer, 28(1), 79–109. https://doi.org/10.1093/wbro/lks024
  18. Danso-Abbeam, G., Ehiakpor, D. S., & Aidoo, R. (2018). Agricultural extension and its effects on farm productivity and income: Insight from Northern Ghana. Agriculture & Food Security, 7(1), 74. https://doi.org/10.1186/s40066-018-0225-x
  19. Doss, C. (2015). Debunking the myth of female labour in African agriculture. https://www.ifpri.org/blog/debunking-myth-female-labor-african-agriculture
  20. Doss, C., Kovarik, C., Peterman, A., Quisumbing, A., & van den Bold, M. (2015). Gender inequalities in ownership and control of land in Africa: Myth and reality. Agricultural Economics, 46(3), 403–434. https://doi.org/10.1111/agec.12171
  21. FAO. (1984). The role of women in food production and food security in Africa: Report of the Government Consultation held in Harare, Zimbabwe, 10-13 July 1984. Food and Agriculture Organization of the United Nations.
  22. FAO. (1995). Women, agriculture and rural development: A synthesis report of the Africa region. Rome (Italy) FAO. http://www.fao.org/docrep/X0250E/X0250E00.htm
  23. FAO (Ed.). (2011). Women in agriculture: Closing the gender gap for development. FAO.
  24. FAO. (2012). Banana Production Systems at risk: Effectively responding to banana wilt disease in the Great Lakes Region. FAO.
  25. FAOSTAT. (2020). Food and Agricultural Organisation of the United Nations. FAOSTAT statistical database. http://www.fao.org/faostat/en/#data/QC
  26. Ferreira, G. F. C., Fuentes, P. A. G., & Ferreira, J. P. C. (2018). The successes and shortcoming of Costa Rica exports diversification policies. Food and Agriculture Organization of the United Nations
  27. Gebre, G. G., Isoda, H., Rahut, D. B., Amekawa, Y., & Nomura, H. (2021). Gender differences in agricultural productivity: Evidence from maize farm households in southern Ethiopia. GeoJournal, 86(2), 843–864. https://doi.org/10.1007/s10708-019-10098-y
  28. Halaby, C. N. (2003). Panel models for the analysis of change and growth in life course studies (pp. 503-527). Springer US. https://doi.org/10.1007/978-0-306-48247-2_23
  29. Hausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251. https://doi.org/10.2307/1913827
  30. Helfand, S. M., & Taylor, M. P. H. (2021). The inverse relationship between farm size and productivity: Refocusing the debate. Food Policy, 99, 101977. https://doi.org/10.1016/j.foodpol.2020.101977
  31. Karl, A. T., Yang, Y., & Lohr, S. L. (2013). A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects. Journal of Educational and Behavioral Statistics, 38(6), 577–603.
  32. Karamura, D. A., Karamura, E., & Tinzaara, W. (2012). Banana cultivar names, synonyms and their usage in Eastern Africa. Bioversity Kampala
  33. Kilic, T., Palacios-López, A., & Goldstein, M. (2015). Caught in a Productivity Trap: A Distributional Perspective on Gender Differences in Malawian Agriculture. World Development, 70, 416–463. https://doi.org/10.1016/j.worlddev.2014.06.017
  34. Kilimo Trust. (2012). Banana value chain(s) in East Africa: Consumption, productivity and challenges. Kilimo Trust.
  35. Kilimo Trust. (2015). Building blocks of trade in food. Kilimo Trust Biennial Report: July 2012 – June 2014.
  36. Lokina, R., Nerman, M., & Sandefur, J. (2011). Poverty and productivity: Small-scale farming in Tanzania, 1991–2007 (Report No. 57).
  37. Lucas, S. S., & Jomanga, K. E. (2021). The Status of Banana Production in Tanzania; a Review of Threats and Opportunities. International Journal of Current Science Research and Review, 04(10), 1260-1275. https://doi.org/10.47191/ijcsrr/V4-i10-10
  38. Marimo, P., Caron, C., Van den Bergh, I., Crichton, R., Weltzien, E., Ortiz, R., & Tumuhimbise, R. (2020). Gender and trait preferences for banana cultivation and use in Sub-Saharan Africa: A literature review. Economic Botany, 74(2), 226–241. https://doi.org/10.1007/s12231-020-09496-y
  39. Marschak, J., & Andrews, W. H. (1944). Random simultaneous equations and the theory of production. Econometrica, 12(3/4), 143. https://doi.org/10.2307/1905432
  40. Mgbenka, R. N., Mbah, E. N., & Ezeano, C. I. (2016). A review of smallholder farming in Nigeria: Need for transformation. International Journal of Agricultural Extension and Rural Development Studies, 3(2), 43-54.
  41. Mukasa, A. N., & Salami, A. O. (2015). Gender productivity differentials among smallholder farmers in Africa: A cross-country comparison. African Development Bank.
  42. Mundlak, Y. (1978). On the Pooling of Time Series and Cross Section Data. Econometrica, 46(1), 69. https://doi.org/10.2307/1913646
  43. Muyanga, M., & Jayne, T. S. (2019). Revisiting the farm size?productivity relationship based on a relatively wide range of farm sizes: Evidence from Kenya. American Journal of Agricultural Economics, 101(4), 1140–1163. https://doi.org/10.1093/ajae/aaz003
  44. OEC. (2020). Bananas in Tanzania. The Observatory of Economic Complexity. https://oec.world/en/profile/bilateral-product/bananas/reporter/tza
  45. O’Sullivan, M., Arathi, R., Raka, B., Kajal, G., & Margaux, V. (2014). Levelling the field: Improving opportunities for women farmers in Africa. World Bank Group.
  46. Otten, S. (2020). Gender-specific personality traits and their effects on the gender wage gap: A correlated random effects approach using SOEP data. SOEPpapers on Multidisciplinary Panel Data Research, Art. 1078. https://ideas.repec.org//p/diw/diwsop/diw_sp1078.html
  47. Palacios-Lopez, A., Christiaensen, L., & Kilic, T. (2017). How much of the labour in African agriculture is provided by women? Food Policy, 67, 52–63. https://doi.org/10.1016/j.foodpol.2016.09.017
  48. Peterman, A., Quisumbing, A., Behrman, J., & Nkonya, E. (2011). Understanding the complexities surrounding gender differences in agricultural productivity in Nigeria and Uganda. Journal of Development Studies, 47(10), 1482–1509. https://doi.org/10.1080/00220388.2010.536222
  49. Quisumbing, A. R. (1995). Gender differences in agricultural productivity: A survey of empirical evidence. Working or Discussion Paper, 75. http://dx.doi.org/10.22004/ag.econ.42675
  50. Schunck, R. (2013). Within and between estimates in random-effects models: Advantages and drawbacks of correlated random effects and hybrid models. The Stata Journal, 13(1), 65–76. https://doi.org/10.1177/1536867X1301300105
  51. Schunck, R., & Perales, F. (2017). Within- and between-cluster effects in generalised linear mixed models: A discussion of approaches and the Xthybrid command. The Stata Journal, 17(1), 89–115. https://doi.org/10.1177/1536867X1701700106
  52. Sipen, P., Chubo, J. K., King, P. J. H., Huat, O. K., & Davey, M. R. (2011). Genetic improvement of banana using conventional and in vitro technologies. Journal of Crop Improvement, 25(6), 697–727. https://doi.org/10.1080/15427528.2011.603406
  53. Sheahan, M., Barrett, C. B., & Goldvale, C. (2016). The unintended consequences of agricultural input intensification: Human health implications of agro-chemical use in sub-Saharan Africa. Abidjan, Côte d’Ivoire: African Development Bank.
  54. Sheahan, M., & Barrett, C. B. (2014). Understanding the Agricultural input landscape in Sub-Saharan Africa: Recent plot, household, and community-level evidence. The World Bank. https://doi.org/10.1596/1813-9450-7014
  55. Shell Foundation. (2023). East Africa fruits: Promoting banana farming in Tanzania. https://shellfoundation.org/app/uploads/2023/03/EA-Fruits-blog.pdf
  56. Slavchevska, V. (2015). Gender differences in agricultural productivity: The case of Tanzania. Agricultural Economics, 46(3), 335–355. https://doi.org/10.1111/agec.12168
  57. Udry, C. (1996). Gender, agricultural production, and the theory of the household. Journal of Political Economy, 104(5), 1010–1046. https://doi.org/10.1086/262050
  58. Udry, C., Hoddinott, J., Alderman, H., & Haddad, L. (1995). Gender differentials in farm productivity: Implications for household efficiency and agricultural policy. Food Policy, 20(5), 407–423. https://doi.org/10.1016/0306-9192(95)00035-D
  59. UNECA. (1972). Women: The neglected human resource for African development. Canadian Journal of African Studies 6(2), 359. https://doi.org/10.2307/484209
  60. Wooldridge, J. M. (2010). Econometric analysis of cross-section and panel data. MIT Press.
  61. World Bank. (2022). Tanzania economic update 17 final Report: Empowering women – expanding access to assets and economic opportunities. World Bank Group.
  62. Yao, W., & Hamori, S. (2019). The long-run relationship between farm size and productivity: A re-examination based on Chinese household aggregate panel data. China Agricultural Economic Review, 11(2), 373–386. https://doi.org/10.1108/CAER-05-2017-0103
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