Vol 4 No 1 (2018): IJRDO - Journal of Business Management


Koech Ronald
Ndayitwayeko Willy-Marcel
Karani Charles
Published January 31, 2018
  • Cobweb model,
  • farmgate price,
  • lagged supply
How to Cite
Ronald, K., Willy-Marcel, N., & Charles, K. (2018). MODELING OF SUPPLY AND DEMAND FOR MAIZE IN KILIFI DISTRICT, KENYA: A COBWEB MODEL APPROACH. IJRDO - Journal of Business Management (ISSN: 2455-6661), 4(1), 79-93. Retrieved from https://ijrdo.org/index.php/bm/article/view/1840


The study aimed at assessing the determinants of demand and supply of maize in Kilifi, Kenya
using the cobweb model for analysis. Results showedthat variations in production of maize were
explained by the prices of cassava, income per capita of consumers and time trend. Price of
cassava (maize substitute) werestrongly significant (p<0.01) and had expected negative sign.
This implied that if the price of cassava increased, farmers would shift from maize production to
cassava production. In the model, an increase of price of a bag of cassava by 1 US$ would
decrease the production of maize by 0.95 per cent in the long-run. Income per capita of
consumers was significant (p<0.1) though with unexpected sign. The study established that there
is need for intensification of maize production due to its importance through provision of the
prerequisite incentives such as extension and inputs.


Download data is not yet available.


1. Government of Kenya (GoK). Kilifi District Development Plan 2008-2012. Government Printer, Nairobi; 2009.
2. Waithaka, J. H. G. Assessment of the Situation and Development Prospects for the Cashew Nut Sector, Kenya. Nairobi, Kenya; 2002.
3. National Environment Management Authority (NEMA). State of the Coast Report: Towards Integrated Management of Coastal and Marine Resources in Kenya. National Environmental Management Authority (Nema), Nairobi. 2009, pp88.
4. Varian, H. Microeconomics Analysis. 3rd Ed. W.W. Norton & Company, New York; 1992.
5. Hommes, C. H. Dynamics of the cobweb model with adaptive expectations and nonlinear supply and demand. Journal of Economic Behaviour and Organization1993; 24:315-335
6. Kaldor, N. Market Imperfections and Excess Capacity. In Kaldor (1960): 62-80.
7. Ezekiel, M.‘The cobweb theorem’, Quarterly Journal of Economics, 1938; Vol 52.
8. Ndayitwayeko, W. M., M.O. Odhiambo, M. Nyangweso and M. Korir. Determinants of Beef Meat Supply in Burundi: A Vector Error Correction Model Approach Applied to structural Nerlov Paradign. The 8th AFMA Congress 2009. Moi University Press.
9. Nerlove, M. Adaptive expectations and cobweb phenomena, Quarterly Journal of Economics 1958; 72, 227-240
10. Artstein, Z. Irregular cobweb dynamics, Economic Letters 1983; 11: 15-17
11. Jensen, R. V and R. Urban. Chaotic price behaviour in a nonlinear cobweb model, Economic Letters 1984; 15: 235-240.
12. Lichtenberg, A. J and A. Ujihara. Application of nonlinear mapping theory to commodity price fluctuations, Journal of Economic Dynamics and Control 1989; 13: 225-246.
13. Day, R. H and A. Hanson. Cobweb chaos, in T.K. Paul and J.K. Sengupta, Economic models, estimation and socio-economic systems. Essays in honor of Karl A. Fox, North-Holland, Amsterdam; 1991.
14. Wang, S. and L. Xu. The Analysis of the Vegetables’ Price Fluctuation with Cobweb Model Lingling. Interdisciplinary Journal Of Contemporary Research In Business,2012; 4(8).
15. Quesada, A. Variations on the Cobweb Model. JEL A22, C61, C62, D40; 2003
16. Kaldor, N. A Classifactory Note on the Determinateness of Static Equilibrium. In Kaldor (1960): 13-33; 1934.
17. Chiang,A.C. Fundamental Methods of Mathematical Economics. Mcgraw-Hill International Editions. Third Edition; 1984
18. Mukras, M. S. Intermediate Mathematical Economics. Reata Printers Ltd. Nairobi; 2004.
19. Dowling, E. T. Theory and Problems of Introduction to Mathematical Economics. 2ndEd. Mcgraw-Hill. Inc, New York; 1980.
20. FoodandAgricultural Organization(FAO), n.d. Kenya’s maize statistics. Accessed on 3rd July 2013 from .
21. World Bank,n.d. Kenya. Accessed on 5th July 2013 from < http://www.worldbank.org/data>.