Are Female Rice Farmers Less Productive than Male Farmers? Micro-evidence from Ghana

Abstract

Gendered rice productivity gaps continue to be a major challenge to achieving food self-sufficiency and food security in sub-Saharan Africa. This study uses data of 900 rice plot managers from three regions in Northern Ghana. The Oaxaca–Blinder mean and quantile-based decomposition procedure were employed in each region separately to highlight the sources of gender differences in rice productivity. The results show that female plot managers are not disadvantaged in rice production. The results suggest that female plot managers produce 18% more rice output than male plot managers in the Upper East region, while there is no significant gender difference in the Northern and Upper West regions. Again, rice productivity differences among female and male plot managers within regions are positively influenced by age, marriage status, asset value, family labor, herbicide use, and farmer-based organization memberships. On the other hand, rice productivity differences are negatively affected by poor access to extension, farm size, household expenditure, and hired labor. Moreover, by applying an Oaxaca–Blinder decomposition approach, apart from understanding factors driving gender productivity gaps within regions, we are also able to estimate the likely benefits that each region could gain from bridging the gender gaps in rice production. It can be concluded that by accounting for regional heterogeneity there is an average gender gap in rice productivity in Northern Ghana. The gender differentials across the rice-producing regions of Ghana suggest that policies aimed at improving rice productivity from a gender perspective should consider spatial factors as well.

Resumé

Les écarts de productivité rizicole entre les sexes continuent d’être un défi majeur pour atteindre l’autosuffisance alimentaire et la sécurité alimentaire en Afrique subsaharienne. Cette étude utilise les données de 900 exploitant.e.s de parcelles de riz dans trois régions du nord du Ghana. La méthode de décomposition basée sur la moyenne et les quantiles d’Oaxaca-Blinder a été utilisée dans chaque région séparément pour mettre en évidence les sources des différences entre les sexes dans la productivité rizicole. Les résultats montrent que les femmes qui exploitent des parcelles ne sont pas désavantagées dans la production de riz. Les résultats suggèrent que les femmes exploitantes de parcelles produisent 18% de riz de plus que les hommes dans la région du Haut-Est, alors qu’il n’y a pas de différence significative entre les sexes dans les régions du Nord et du Haut-Ouest. Encore une fois, les différences de productivité rizicole parmi les exploitant.e.s de parcelles, qu’ils soient femmes et hommes, au sein des régions sont positivement influencées par l’âge, le statut matrimonial, la valeur des actifs, le travail familial, l’utilisation d’herbicides et l’appartenance à des organisations d’agriculteurs. D’un autre côté, les différences de productivité rizicole sont négativement impactées par un accès limité à la vulgarisation, la taille des exploitations, les dépenses des ménages et la main-d’œuvre salariée. De plus, en appliquant la méthode de décomposition d’Oaxaca-Blinder, nous sommes non seulement en mesure de comprendre les facteurs à l’origine des écarts de productivité entre les sexes au sein des régions, mais également d’estimer les avantages probables que chaque région pourrait tirer en comblant les écarts entre les sexes dans la production de riz. On peut en conclure qu’en tenant compte de l’hétérogénéité régionale, il existe un écart moyen entre les sexes dans la productivité rizicole dans le nord du Ghana. Les différences entre les sexes dans les régions productrices de riz du Ghana suggèrent que les politiques qui cherchent à améliorer la productivité du riz en prenant en compte le prisme du genre devraient également tenir compte les facteurs liés à l’espace.

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References

  1. Abro, Z.A., B. Debela Legesse, and M. Kassie. 2019. The joint impact of improved maize seeds on productivity and efficiency: Implications for policy. Göttingen: African Association of Agricultural Economists.

    Google Scholar 

  2. Adegbite, O.O., and C.L. Machethe. 2020. Bridging the financial inclusion gender gap in smallholder agriculture in Nigeria: An untapped potential for sustainable development. World Development 127: 104755. https://doi.org/10.1016/j.worlddev.2019.104755.

    Article  Google Scholar 

  3. AGRA. 2016. Africa agriculture status report 2016: Progress towards towards agricultural transformation. Nariobi: AGRA.

    Google Scholar 

  4. Aguilar, A., E. Carranza, M. Goldstein, et al. 2015. Decomposition of gender differentials in agricultural productivity in Ethiopia. Agricultural Economics (United Kingdom) 46: 311–334. https://doi.org/10.1111/agec.12167.

    Article  Google Scholar 

  5. Ali, D., D. Bowen, K. Deininger, and M. Duponchel. 2016. Investigating the gender gap in agricultural productivity: Evidence from Uganda. World Development 87: 152–170. https://doi.org/10.1016/j.worlddev.2016.06.006.

    Article  Google Scholar 

  6. Ali, D.A., and K. Deininger. 2015. Is there a farm size-productivity relationship in African agriculture? Evidence from Rwanda. Land Economics 91: 317–343. https://doi.org/10.3368/le.91.2.317.

    Article  Google Scholar 

  7. Backiny-Yetna, P., and K. McGee. 2015. Gender Differentials and Agricultural Productivity in Niger. Policy Research Working Paper No 7199.

  8. Bjornlund, H., A. Zuo, S.A. Wheeler, et al. 2019. The dynamics of the relationship between household decision-making and farm household income in small-scale irrigation schemes in southern Africa. Agricultural Water Management 213: 135–145. https://doi.org/10.1016/j.agwat.2018.10.002.

    Article  Google Scholar 

  9. Blinder, A.S. 1973. Wage discrimination: Reduced Form And Structural Estimates. The Journal of Human Resources 8: 436–455.

    Article  Google Scholar 

  10. Burke, W.J., E. Frossard, S. Kabwe, and T.S. Jayne. 2019. Understanding fertilizer adoption and effectiveness on maize in Zambia. Food Policy 86: 101721. https://doi.org/10.1016/j.foodpol.2019.05.004.

    Article  Google Scholar 

  11. Carroll, C.L., C.A. Carter, R.E. Goodhue, et al. 2017. Crop disease and agricultural productivity. Schlenker: Agricultural Productivity and Producer Behavior.

    Book  Google Scholar 

  12. Cowell, F.A., and E. Flachaire. 2007. Income distribution and inequality measurement: The problem of extreme values. Journal of Econometrics 141: 1044–1072. https://doi.org/10.1016/j.jeconom.2007.01.001.

    Article  Google Scholar 

  13. Croppenstedt, A., M. Goldstein, and N. Rosas. 2013. Gender and agriculture: Inefficiencies, segregation, and low productivity traps. World Bank Research Observer 28: 79–109. https://doi.org/10.1093/wbro/lks024.

    Article  Google Scholar 

  14. de la O Campos, A.P., K.A. Covarrubias, and P.A. Patron. 2016. How does the choice of the gender indicator affect the analysis of gender differences in agricultural productivity? Evidence from Uganda. World Development 77: 17–33. https://doi.org/10.1016/j.worlddev.2015.08.008.

    Article  Google Scholar 

  15. Donkor, E., E. Owusu-Sekyere, V. Owusu, et al. 2016. Impact of agricultural extension service on adoption of chemical fertilizer: Implications for rice productivity and development in Ghana. NJAS - Wageningen Journal of Life Sciences 79: 41–49. https://doi.org/10.1016/j.njas.2016.10.002.

    Article  Google Scholar 

  16. Doss, C.R. 2018. Women and agricultural productivity: Reframing the Issues. Development Policy Review 36: 35–50. https://doi.org/10.1111/dpr.12243.

    Article  Google Scholar 

  17. FAO. 2011. Guidelines for measuring household and individual dietary diversity. Rome: FAO.

    Google Scholar 

  18. FAO. 2016. Meeting our goals: FAO’s programme for gender equality in agriculture and rural development. Rome: FAO.

    Google Scholar 

  19. Firpo, S., N. Fortin, and T. Lemieux. 2009. Unconditional quantile regressions. Econometrica 77: 953–973. https://doi.org/10.3982/ecta6822.

    Article  Google Scholar 

  20. Firpo, S.P., N.M. Fortin, and T. Lemieux. 2018. Decomposing wage distributions using recentered influence function regressions. Econometrics 6: 1–40. https://doi.org/10.3390/econometrics6020028.

    Article  Google Scholar 

  21. Fortin, N., T. Lemieux, and S. Firpo. 2011. Decomposition methods in economics. Amsterdam: Elsevier Inc.

    Google Scholar 

  22. Gaba, S., E. Gabriel, J. Chadœuf, et al. 2016. Herbicides do not ensure for higher wheat yield, but eliminate rare plant species. Scientific Reports 6: 1–10. https://doi.org/10.1038/srep30112.

    Article  Google Scholar 

  23. Gebre, G.G., H. Isoda, D.B. Rahut, et al. 2019. Gender differences in agricultural productivity: Evidence from maize farm households in southern Ethiopia. GeoJournal. https://doi.org/10.1007/s10708-019-10098-y.

    Article  Google Scholar 

  24. Gichuki, C.N., J. Han, and T. Njagi. 2020. The impact of household wealth on adoption and compliance to GLOBAL GAP production standards: Evidence from Smallholder farmers in Kenya. Agriculture 10: 50. https://doi.org/10.3390/agriculture10020050.

    Article  Google Scholar 

  25. Jann, B. 2008. The Blinder–Oaxaca decomposition for linear regression models. The Stata Journal 8: 453–479. https://doi.org/10.1177/1536867X0800800401.

    Article  Google Scholar 

  26. Kihara, J., L.D. Tamene, P. Massawe, and M. Bekunda. 2015. Agronomic survey to assess crop yield, controlling factors and management implications: A case-study of Babati in northern Tanzania. Nutrient Cycling in Agroecosystems 102: 5–16. https://doi.org/10.1007/s10705-014-9648-3.

    Article  Google Scholar 

  27. Kilic, T., P. Winters, and C. Carletto. 2015. Gender and agriculture in sub-Saharan Africa: Introduction to the special issue. Agricultural Economics (United Kingdom) 46: 281–284. https://doi.org/10.1111/agec.12165.

    Article  Google Scholar 

  28. Koirala, K.H., A. Mishra, and S. Mohanty. 2016. Impact of land ownership on productivity and efficiency of rice farmers: The case of the Philippines. Land Use Policy 50: 371–378. https://doi.org/10.1016/j.landusepol.2015.10.001.

    Article  Google Scholar 

  29. Mabe, F.N., S.A. Donkoh, and S. Al-Hassan. 2019. Technology adoption typology and rice yield differentials in Ghana: Principal component analysis approach. African Journal of Science, Technology, Innovation and Development 11: 555–567. https://doi.org/10.1080/20421338.2018.1551849.

    Article  Google Scholar 

  30. Manosathiyadevan, M., V. Bhuvaneshwari, and R. Latha. 2017. Impact of insects and pests in loss of crop production: A review. In Sustainable agriculture towards food security, ed. A. Dhanarajan, 57–67. Singapore: Springer.

    Google Scholar 

  31. Mugisha, J., C. Sebatta, K. Mausch, et al. 2019. Bridging the gap: Decomposing sources of gender yield gaps in Uganda groundnut production. Gender, Technology and Development 23: 19–35. https://doi.org/10.1080/09718524.2019.1621597.

    Article  Google Scholar 

  32. Mukasa, A.N., A.O. Salami, S. Kayizzi-mugerwa, and C. John. 2015. Gender productivity differentials among smallholder farmers in Africa : A cross-country comparison. African Development Research group Working paper series 231 45.

  33. Oaxaca, R. 1973. Male–female wage differentials in urban labor markets. International Economic Review 14: 693–709.

    Article  Google Scholar 

  34. Ortega, D.L., A.S. Bro, D.C. Clay, et al. 2019. Cooperative membership and coffee productivity in Rwanda’s specialty coffee sector. Food Security 11: 967–979. https://doi.org/10.1007/s12571-019-00952-9.

    Article  Google Scholar 

  35. Oseni, G., P. Corral, M. Goldstein, and P. Winters. 2015. Explaining gender differentials in agricultural production in Nigeria. Agricultural Economics 46: 285–310. https://doi.org/10.1111/agec.12166.

    Article  Google Scholar 

  36. Patra, S., P. Mishra, S.C. Mahapatra, and S.K. Mithun. 2016. Modelling impacts of chemical fertilizer on agricultural production: A case study on Hooghly district, West Bengal, India. Modeling Earth Systems and Environment 2: 1–11. https://doi.org/10.1007/s40808-016-0223-6.

    Article  Google Scholar 

  37. Peterman, A., A. Quisumbing, J. Behrman, and E. Nkonya. 2011. Understanding the complexities surrounding gender differences in agricultural productivity in Nigeria and Uganda. Journal of Development Studies 47: 1482–1509. https://doi.org/10.1080/00220388.2010.536222.

    Article  Google Scholar 

  38. Ragasa, C., and A. Chapoto. 2017. Limits to green revolution in rice in Africa: The case of Ghana. Land Use Policy 66: 304–321. https://doi.org/10.1016/J.LANDUSEPOL.2017.04.052.

    Article  Google Scholar 

  39. Rios-Avila, F. 2020. Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition. Stata Journal 20: 51–94. https://doi.org/10.1177/1536867X20909690.

    Article  Google Scholar 

  40. Slavchevska, V. 2015. Gender differences in agricultural productivity: The case of Tanzania. Agricultural Economics (United Kingdom) 46: 335–355. https://doi.org/10.1111/agec.12168.

    Article  Google Scholar 

  41. Taylor, J. 2020. Grain and feed annual.

  42. Teklewold, H., M. Kassie, and B. Shiferaw. 2013. Adoption of multiple sustainable agricultural practices in rural Ethiopia. Journal of Agricultural Economics 64: 597–623. https://doi.org/10.1111/1477-9552.12011.

    Article  Google Scholar 

  43. Wainaina, P., S. Tongruksawattana, and M. Qaim. 2016. Tradeoffs and complementarities in the adoption of improved seeds, fertilizer, and natural resource management technologies in Kenya. Agricultural Economics (United Kingdom) 47: 351–362. https://doi.org/10.1111/agec.12235.

    Article  Google Scholar 

  44. Wassie, S.B., G.T. Abate, and T. Bernard. 2019. Revisiting farm size-productivity relationship: New empirical evidence from Ethiopia. Agrekon 58: 180–199. https://doi.org/10.1080/03031853.2019.1586554.

    Article  Google Scholar 

  45. World Bank. 2012. World Development Report 2012: Gender equality and Development. Washington DC: World Bank.

    Book  Google Scholar 

  46. World Bank Group. 2017. Ghana: Agriculture sector policy note: Transforming Agriculture for Economic Growth, Job Creation and Food Security. pp. 1–60.

  47. Zant, W. 2019. If smallholder farmers have access to the world market: the case of tobacco marketing in Malawi. European Review of Agricultural Economics. https://doi.org/10.1093/ERAE/JBZ039.

    Article  Google Scholar 

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Correspondence to Kwabena Nyarko Addai.

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Appendix

Appendix

See Tables 7, 8, 9, 10.

Table 7 Detailed regional oaxaca decomposition of the gender differential in rice productivity
Table 8 Northern regional decomposition of gender differential in rice productivity at selected points of the rice productivity distribution
Table 9 Upper East regional decomposition of gender differential in rice productivity at selected points of the rice productivity distribution
Table 10 Upper West regional decomposition of gender differential in rice productivity at selected points of the rice productivity distribution

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Addai, K.N., Lu, W. & Temoso, O. Are Female Rice Farmers Less Productive than Male Farmers? Micro-evidence from Ghana. Eur J Dev Res (2021). https://doi.org/10.1057/s41287-020-00342-4

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Keywords

  • Mean decomposition
  • Rice productivity
  • Gender gap
  • Northern Ghana
  • Quantile decomposition

JEL classification

  • O1
  • O55
  • D1