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Heterogeneity in Rural Household Food Demand and Its Determinants in Ondo State, Nigeria: An Application of Quadratic Almost Ideal Demand System

Author

Listed:
  • Ayodele Fashogbon
  • Omobowale Oni

Abstract

This study described the food demand of rural households of Nigeria with a view to identifying its determinants and responsiveness to price and household food expenditure.The study made use of 121 rural households in Ondo state using a 3-stage random sampling technique.Demand for food groups in this study was estimated using Quadratic Almost Ideal Demand System (QUAIDS). The Wald test revealed that the QUAIDS model was significantly corrected for endogeneity and that the inclusion of demographics in the model significantly improved estimates. Food group grains and starch basket had the largest share (49%) of household total food expenditure. Grains and starch was expenditure inelastic, animal protein was a luxury, Fruits and Vegetables group was inelastic, while Fats and Oils were elastic. Own price elasticities were all negative as expected in both uncompensated and compensated price elasticity estimates. The Hicksian cross-price elasticities showed that all food groups were net substitutes. Arising from the foregoing, the study concludes that animal protein group and fat and oil group are income responsive while others are inelastic. The study further revealed that all food groups are normal food and price inelastic with the exception of fats and oil (price elastic). Price, household size, total food expenditure, and expenditure on food away-from-home were key determinants of food demand among rural households in Ondo state.Therefore policy directed at increasing both farm income and non-farm income to increase expenditure and promote food security should be given more attention. -family-"Times New Roman","serif"; mso-fareast-font-family-??;mso-fareast-theme-font-minor-fareast;mso-font-kerning- 1.0pt;mso-ansi-language-EN-US;mso-fareast-language-TR;mso-bidi-language-AR-SA'>Spread ratio (SR) values of cookies with OFs increased. Cookies with OFs were found to be larger in diameter than control cookies. Increasing of OMF and OMFP levels in the cookies led to darker appearance of the cookies than the control. In general, hardness tends to increase as the level of OMFs and OMFPs increased in the cookies. According to sensory analysis, overall acceptance of cookies were found the best at control sample. However, panelists liked all cookies with OFs moderately or slightly. Especially, the usage of 5% for OMFs and OMFPs in cookie formulation gave satisfactory results in terms of acceptability. The present study demonstrated that considerable nutritive and functional improvement could be attained by the addition of OFs to cookie formulation.

Suggested Citation

  • Ayodele Fashogbon & Omobowale Oni, 2013. "Heterogeneity in Rural Household Food Demand and Its Determinants in Ondo State, Nigeria: An Application of Quadratic Almost Ideal Demand System," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 5(2), pages 169-169, January.
    • Handle: RePEc:ibn:jasjnl:v:5:y:2013:i:2:p:169
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    References listed on IDEAS

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    1. Abdulai, Awudu & Aubert, Dominique, 2004. "Nonparametric and parametric analysis of calorie consumption in Tanzania," Food Policy, Elsevier, vol. 29(2), pages 113-129, April.
    2. James Banks & Richard Blundell & Arthur Lewbel, 1997. "Quadratic Engel Curves And Consumer Demand," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 527-539, November.
    3. Jerry Hausman, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
    4. Jacinto F. Fabiosa, 2005. "Growing Demand for Animal-Protein-Source Products in Indonesia: Trade Implications," Center for Agricultural and Rural Development (CARD) Publications 05-wp400, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    5. Jacinto F. Fabiosa, 2005. "Growing Demand for Animal-Protein-Source Products in Indonesia: Trade Implications," Food and Agricultural Policy Research Institute (FAPRI) Publications (archive only) 05-wp400, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    6. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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