Impact of Constraints and Credit On the Probability of Participation: Evidence from Fish Producers in Nigeria
محورهای موضوعی : Extension and EconomicJob Nmadu 1 , Bukola Oluwatobi Oyediran 2 , Halima Sallawu 3
1 - Department of Agricultural Economics and Farm Management, Federal University of Technology Minna, Nigeria
2 - Department of Agricultural Economics and Farm Management, Federal University of Technology Minna, Nigeria
3 - Department of Agricultural Economics and Farm Management, Federal University of Technology Minna, Nigeria
کلید واژه: Garrett ranking, latent variable, directional relationship, difference-in-difference, fish value chain,
چکیده مقاله :
The study involved 643 fish value chain actors in Niger and Kebbi States in Nigeria from whom data were collected between April 2022 and February 2023 via structured questionnaire and analysed using Garrett ranking, Structural Equation Modelling (SEM) and regression. These two states have access to Rivers Niger, Shiroro and Kaduna and their various tributaries. From the results obtained, 48 variables out of the 65 described by the actors were considered a constraint based on the mean and five latent factors were determined and the values retrieved for further analysis. The latent variables exhibited positive bi-directional relationship between one another which is an indication that the factors are not isolated occurrences. From the propensity score matching (PSM) and regression, a number of policy variables were obtained which may call for further investigation but needs to be adequately addressed. Particularly, the tendency of low probability of participation in the face of low educational acquisition. There is also a very strong indication that the actors are conducting their businesses with low capital which has further devalue the level of participation. Ultimately, doing business with adequate capital can increase participation by up to 15% and as such, can increase outputs, income, profits and enhance livelihoods.
The study involved 643 fish value chain actors in Niger and Kebbi States in Nigeria from whom data were collected between April 2022 and February 2023 via structured questionnaire and analysed using Garrett ranking, Structural Equation Modelling (SEM) and regression. These two states have access to Rivers Niger, Shiroro and Kaduna and their various tributaries. From the results obtained, 48 variables out of the 65 described by the actors were considered a constraint based on the mean and five latent factors were determined and the values retrieved for further analysis. The latent variables exhibited positive bi-directional relationship between one another which is an indication that the factors are not isolated occurrences. From the propensity score matching (PSM) and regression, a number of policy variables were obtained which may call for further investigation but needs to be adequately addressed. Particularly, the tendency of low probability of participation in the face of low educational acquisition. There is also a very strong indication that the actors are conducting their businesses with low capital which has further devalue the level of participation. Ultimately, doing business with adequate capital can increase participation by up to 15% and as such, can increase outputs, income, profits and enhance livelihoods.
1. Ajayi, G. (2023). Analysis of Women’s Participation in Processing Cassava. International Journal of Agricultural Science Research& Technology, 11(4):223-229.
2. Anonymous. (2024). Overview of Agricultural Insurance Options for Nigerian Farmers and Traders, retrieved from https://agenpo.com/ on April 24, 2024.
3. Apata, T. G. (2011). Effects of Global Climate Change on Nigerian Agriculture: An Empirical Analysis. CBN Journal of Applied Statistics, 2(1):31-50.
4. Bhavani, G., Ravinder, V and Srinivasulu, M. (2021). Constraint analysis of quality seed production in Telangana using Garrett's ranking technique. Journal of Community Mobilization and Sustainable Development. 16(2): 560-564.
5. Ebiloma, G., R. Olatunji, T. Matthias, J. Nmadu, E. Olorunsanya, K. Baba, A. Jirgi, H. Tsado, S. Liverpool-Tasie, and T. Reardon. (2018). The rapid transformation of the fish value chain in Nigeria: evidence from Niger State. Feed the Future Innovation Lab for Food Security Policy Research Paper 112. East Lansing: Michigan State University.
6. Epskamp S (2022). _semPlot: Path Diagrams and Visual Analysis of Various SEM Packages' Output_. R package version 1.1.6,
7. Faleke, S. A., Nwabeze, G. O. and Buhari, H. A (2023). Characteristics of Capture and Culture Fishery Production in Kainji Lake Basin, Nigeria. Journal of Agricultural Extension Vol.27 (3).35-40
8. Fox J, Nie Z, Byrnes J (2022). _sem: Structural Equation Models_. R package version 3.1-15,
9. Garrett EH, Woodworth RS (1969). Statistics in psychology and education. Vakils, Feffer and Simons Pvt. Ltd., Bombay, Pp. 329
10. Ho D, Imai K, King G, Stuart E (2011). “MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.” _Journal of Statistical Software_, *42*(8), 1-28. doi:10.18637/jss.v042.i08.
11. Jalali, S. A., M. N. Ahmadabadi, B. Bavarsad & S. M. A. Zavardehi. (2023). Evaluating and Presenting a Model of Competitiveness of Agricultural Products in Khuzestan Using Theme Method. International Journal of Agricultural Science Research& Technology, 11(4): 201-212.
12. Jirgi, A. J. (2013). Technical efficiency and risk preferences of cropping systems in Kebbi State, Nigeria. Unpublished PhD Dissertation, Department of Agricultural Economics, University of Free State, Bloemfontein, South Africa.
13. Joshi, K.D., Upadhyay, S., Chaudhary, P., Shrestha, S., Bhattarai, K. and Tripathi, B.P. (2020) The Rice Processing Industry in Nepal: Constraints and Opportunities. Agricultural Sciences, 11, 1060-1080.
14. Obasi, E. U. & R. L. Adeoye, (2022). Empirical study of capture and aquaculture fish production in Nigeria. International Journal of Fisheries and Aquatic Studies; 10(4): 61-65.
15. Mufato, T. L. (2021). Determinants of smallholder farmers’ participation in pond fish production in Dara and Wonsho Districts, Sidama Zone, Southern Ethiopia. Agriculture, Forestry and Fisheries, 10(4), 132-139.
16. NAMDA (Niger State Agricultural Mechanization and Development Authority), (2013). National Farmers Data Base.
17. NPC (National Population Commission), (2006). Population data in Nigeria. Retrieved on 20th February, 2017 from http://www.population.gov.ng/population-data-in-Nigeria.
18. R Core Team. (2023). R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria.
19. Revelle, W. (2023). _psych: Procedures for Psychological, Psychometric, and Personality Research_. Northwestern University, Evanston, Illinois. R package version 2.3.6,
20. Subasinghe R, Siriwardena SN, Byrd K, Chan CY, Dizyee K, Shikuku K, Tran N, Adegoke A, Adeleke M, Anastasiou K, Beveridge M, Bogard J, Chu L, Fregene BT, Ene-Obong H, Cheong KC, Nukpezah J, Olagunju O, Powell A, Steensma J, Williams G, Shelley C and Phillips M. (2021). Nigeria fish futures. Aquaculture in Nigeria: Increasing Income, Diversifying Diets and Empowering Women. Report of the scoping study. Penang, Malaysia: WorldFish. Program Report: 2021-16.
21. Twumasi, M. A., Jiang, Y., Danquah, F. O., Chandio, A. A. & Asiamah, B. K. (2020). Determinants of credit constraints of artisanal fishermen in Ghana. Ciencia Rural, Santa Maria, 50(3), 1-10.
22. Vable, A. M., M. V. Kiang, M. M. Glymour, J. Rigdon, E. F. Drabo & S. Basu, (2019). Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects. Am J Epidemiol, 88(7):1345–1354. DOI: 10.1093/aje/kwz093
23. Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
24. Wan, F, G. A. Colditz & S. Sutcliffe, (2021). Matched Versus Unmatched Analysis of Matched Case-Control Studies. Am J Epidemiol. 190(9):1859–1866. https://doi.org/10.1093/aje/kwab056.
25. World Bank (2024). Unemployment, total (% of total labor force) accessed from https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS on April 23, 2024.
26. Yoshida K, Bartel A (2022). _tableone: Create 'Table 1' to Describe Baseline Characteristics with or without Propensity Score Weights_. R package version 0.13.2, https://CRAN.R-project.org/package=tableone