Credit Utilization and Poverty Status of Women Paddy Rice Farmers in Southwest, Nigeria

Abstract

The problem of poverty and non-availability of credit are part of the major challenge affecting Nigeria agriculture. The study examines credit utilization and poverty status of women paddy rice farmers in southwest Nigeria. Primary data were used to sample 300 women paddy rice farmers through multistage sampling procedure from the study area. Foster-Greer-Thorbecke (FGT) poverty measure and Endogenous Switching Regression Model (ESRM) were used to analyse the data. The study established that more than half (58%) of the households were credit constrained, while others were non-credit constrained (42%). 64.16% of the credit constrained respondents were poor while 35.84% were not poor; on the other hand 46.46% of the credit non-constrained respondents were poor while 53.54% were not poor in the area. Endogenous Switching Regression Model estimates that number of years spent in cooperative society, Educational status, saving culture were factors reducing the possibility of being credit constrained. Household size increases the probability of being credit constrained. The ESRM result also showed that, number of years spent in cooperative society, Educational status, household size and saving habit were positively related to increase in rice productivity of the respondents. Credit institutions, government and non-governmental should provide credit for women to boost their rice farming production.

Introduction

Importance of Credit in Agriculture

Credit is essential in agricultural production, particularly for enabling farmers to overcome financial constraints, adopt new technologies, and improve productivity. It boosts household income, employment, and rural development. However, access to credit remains a significant challenge—especially for rural women farmers, who often face poverty, overwork, limited access to land, training, and modern tools.

Challenges in the Rice Sector

• Local rice production in Africa, including Nigeria, lags behind consumption, leading to heavy reliance on rice imports.

• Nigeria spends over N356 billion annually on rice importation.

• Increased consumption hasn't been matched by local production due to various constraints, including poor support for small-scale farmers, many of whom are women.

Credit Access and Women Farmers

• Women are disadvantaged in accessing credit compared to men.

• Empowering women paddy rice farmers through better credit access can increase productivity, reduce unemployment, and cut down import dependence.

• 58% of surveyed women farmers were credit constrained, meaning they either had their loan requests rejected or were unwilling to apply due to fear or other limitations.

Study Area and Methodology

• Conducted in Southwest Nigeria (Ogun and Ekiti states).

• A total of 300 women paddy rice farmers were selected through multi-stage sampling.

• Used questionnaires, interviews, and focus group discussions to collect data.

• Formal credit institutions were assessed, and farmers were categorized into credit-constrained and non-constrained groups.

• Poverty line was defined using per capita expenditure (2/3 of the mean).

• Endogenous Switching Regression Model (ESRM) was employed to evaluate the impact of credit constraint on productivity.

Key Findings

• Credit Constraint Status:

  • 58% credit constrained, 42% non-constrained.

  • Majority of constrained farmers had no saving culture (78%).

  • Only 23.1% of constrained farmers belonged to cooperative societies, compared to 67.7% of non-constrained.

• Land Ownership:

  • Most farmers, both constrained and non-constrained, inherited land, often leading to land fragmentation and small-scale farming.

• Loan Outcomes:

  • 15.7% received full credit requested.

  • 36% were partially granted.

  • 21.6% were outright denied.

  • 26.7% did not apply.

• Reasons for Not Accessing Credit:

  • Fear of debt, lack of collateral, and limited financial literacy.

Poverty and Credit Impact

• Poverty was assessed using the Foster–Greer–Thorbecke (FGT) method.

• Households below 2/3 of the mean per capita expenditure were considered poor.

• The ESRM showed that credit constraints significantly reduce productivity.

• If constraints were eliminated, productivity could increase by up to 31.6% (in line with a similar study in China).

Conclusion

The study examined credit utilization and poverty status among the women paddy rice farmers in southwest Nigeria. The study established that more than half (58%) of the households is credit constrained, living in poverty while others are not credit constrained (42%). For the credit constrained respondents, 64.16% of the respondents were classified as poor while 35.84% as non-poor; on the other hand 46.46% of the credit non-constrained respondents were poor while 53.54% were non-poor in the area. This implies that majority of the credit constrained group were poor. The analysis of socioeconomic characteristics of the farmers showed that the largest percentages (88.4% and 82.7%) of the credit constrained and credit non-constrained respondents are still in their active productive age. Also, it was discovered that rice farming is profitable in the study area. The result of the first stage of the ESRM that is the Probit model shows that educational level, numbers of years spent in cooperative, household size and saving culture are the major factors influencing credit constraint condition in the study area. Again, the results of the second stage of the ESRM showed that, numbers of years spent in cooperative, educational level, household size, saving culture and extension contacts are statistically significant in explaining the variations in rice productivity among the farmers who are credit constrained, while educational level, farm size, household size, saving culture and extension contacts are statistically significant in explaining the variation in rice productivity among the farmers who are not credit constrained. Also, the result showed that farmers who are not credit constrained had higher productivity levels (7.67kg//hectare) than those who are credit constrained (6.51 kg//hectare).

References

[1] Abdulai N. A. (2016). Impact of conservation agriculture technology on household welfare in Zambia. University of Kiel, Johanna-Mestorf-Str. 5, 24118, Kiel, Germany. Agricultural Economics Journal 47 (2016) 1–13 [2] Agbaeze, E. K. and Onwuka, I. O. (2014). Impact of Micro-Credit on Poverty Alleviation In Nigeria, the Case of Enugu East Local Council. International Journal of Business and Management Review,2(1):27–51. AgriculturalEconomics, 39: 295-308 Agriculture.” Agricultural Economics 39: 295-308. [3] African Development Bank (ADB) (2014): Gender, Poverty and Environmental Indicators AfricanCountries.2ndeditionAfricanDevelopmentReport,Nigeria. Website:http:/www.africandevelopmentreport.com.ng/gender2012. [4] Akinbode, S.O. (2013). Access to Credit: Implication for Sustainable Rice Production inNigeria.Journalof Sustainable Development in Africa, 15(7): 45 - 60. [5] Alene, A. and Manyong V. 2007. “The Effects of Education on Agricultural Productivity under Traditional and Improved Technology in Northern Nigeria: An Endogenous Switching Regression Analysis.” Empirical Economics 32: 141-159. [6] Amaza, P.S. and Maurice, D.C. (2005). Identification of Factors that Influence Technical Efficiency in Rice-Based Production Systems in Nigeria. Sub-Saharan Africa International Journal, 7(3): 7 - 9. [7] Anyanwu, C. (2013). Structural Adjustment Programmes Financial Deregulation and Financial Deepening in Sub-Saharan African Countries: The Nigerian Case. Nigerian Journal, 5(10): 200 – 208. [8] Central Bank of Nigeria (CBN) (2017). Gross Domestic Product 2009 at Current Prices. Retrieved from www.cenbank.org/documents on the 7th of May 2011 at 23:54. [9] Awotide B.A, Abdoulaye T, Alene A and Manyong V. M (2015) Impact of Access to Credit on Agricultural Productivity: Evidence from Smallholder Cassava Farmers in Nigeria. A Contributed paper Prepared for Oral Presentation at the International Conference of Agricultural Economists (ICAE) Milan, Italy. [10] Dong, F. L, J. and Featherstone, A. M. (2012). Effects of credit constraints on household productivity in rural China. Agricultural Finance Review, Vol. 72 No. 3. [11] Eadgerwood, J. (2017). Sustainable Banking for the Poor Project in South Asia, World Bank Economic Review. [12] Etonihu, I.K. (2010). Farmers’ Accessibility to Agricultural Credit for Crop Production inDoma Local Government Area of Nasarawa State, Nigeria. International Journal of Development Research, 4: 61-65. [13] Fatunbi, O. (2013). Enhancing Smallholder Farmers Income and Food Security through Agricultural Research and Development in West Africa: Impact of the IAR4D in theKKM PLS. Invited paper presented at the 4th InternationalConference of the African Association of Agricultural Economists, September 22 -25,2013, Hammamet, Tunisia. [14] Fengxia Dong, Jing Lu, Allen M. Featherstone,(2012). \"Effects of credit constraints on household productivity in rural China\", Agricultural Finance Journal, Vol. 72 Issue: 3, pp.402-415. [15] Honohan, P. and Beck, T. (2007). Making Finance work for Africa. World Bank, Washington DC, USA World Bank Economic Review, 4(3): 235 – 250.http://www.fao.org/africa/news/detail-news/en/c/263354. [16] Igbalajobi, O., Fatuase, A.I. and Ajibefun, I. (2013). Determinants of Poverty Incidence among Rural Farmers in Ondo State, Nigeria. American Journal of Rural Development, 1(5): 131-137. [17] Kuwornu .J, Ohene-Ntow,I. and Brempong.S. (2012). Agricultural Credit Allocation and Constraint Analyses of Selected Maize Farmers in Ghana. British Journal of Economics, Management and Trade, 2(4): 353-374 [18] Lokshin, M. and Sajaia Z. 2004. “Maximum Likelihood Estimation of Endogenous Switching Regression Models.” The Stata Journal 4(3): 282-289. [19] Mabuza, M.L., Taeb, M. and Endo, M. (2013). Impact of Food Aid on Small holder. Agricultural Development in Swaziland. African Journal of Agriculture, 8 (2): 151-169 [20] Maddala, G.S. (1983). Limited dependent and qualitative variables in Econometrics. Cambridge University Press, Cambridge, U.K. [21] Melkamu, M. and Richard, K. B. (2015). Poverty Situation Among Small-Scale Apple Producers: Case of Chencha District in Ethiopia. Journal Of International Academic Research For Multidisciplinary, 3(2): 121 – 120. [22] nited Nations (1995). The Copenhagen Declaration and Programme of Action, World Summit for Social Development, 6-12 March 1995, New York, United Nations. [23] Oluwatayo, I.B, (2009). “Explaining inequality and welfare status of households in rural Nigeria: Evidence from Ekiti State,” In Humanity and Social Science Journal 3 (1): 70-80, 2008. [24] Omotoso, F.O and Daramola A.G (2005):Institutional And Non-Institutional Credit Supply Services to Fisher Women in Coastal Fishing Communities of South Western Nigeria. International Journal of Development Research, 9(9): 105-112. [25] Onu, D.O., Obike, K.C., Ebe, F.E, and Okpara, B.O. (2013). Empirical Assessment of the Trend in Rice Production and Imports in Nigeria (1980 – 2013). Journal of Agricultural Economics and Development, 2(7): 296-300. [26] Oparinde L. O. Fish Output and Food Security under Risk Management Strategies among Women Aquaculture Farmers in Ondo State, Nigeria Agris on-line Papers in Economics and Informatics Volume XI, Number 1, [27] Osagie, C., 2014. 2015 rice importation ban: Disregard US report, FG urged. Available from http://www.thisdaylive.com/articles/2015-rice-importation-ban-disregard-us-report-fgurged/168731/ [Accessed 17/05/14]. [28] Rahji, M.A. and M.O. Adewumi, 2008. Market supply response and demand for local rice in Nigeria: Implications for self-sufficiency policy. Journal of Central European Agriculture, 9(3): 567-574. [29] Sabo, E. (2015). Access of Women Farmers to Credits for Agricultural Production: A Case of Three Local Government Areas in Taraba State, Nigeria. Applied Science Reports, 11(1): 33 – 39 [30] Tilahun. D. Z. (2015): Access to Credit and the Impact of Credit constraints on Agricultural Productivity in Ethiopia: Evidence from Selected Zones of Rural Amhara. Working Paper No. 07-004, Department of Economics Addis Ababa University. [31] United Nations (2010). The Millennium Development Goals Report, New York, United Nations.University Press, Cambridge, U.K. [32] World Bank (2015). World Bank Forecasts Global Poverty to Fall Below 10% for First Time; Major Hurdles Remain in Goal to End Poverty by 2030\". www.worldbank.org. Retrieved 6 October 2015

Copyright

Copyright © 2025 Alamuoye Lydia Funmilayo, Amos Timothy Taye, Oseni Joseph Olufemi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.