Food Production Modelling Using Fixed Effect Panel Data for Nigeria and Other 14 West African Countries (1990-2013)
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 4, July 2016, Pages: 208-218
Received: May 22, 2016;
Accepted: May 31, 2016;
Published: Jul. 8, 2016
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Olatunji Taofik Arowolo, Department of Mathematics and Statistics, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria
Matthew Iwada Ekum, Department of Mathematics and Statistics, Lagos State Polytechnic, Ikorodu, Lagos, Nigeria
In this research, the fixed effect panel data predictive model was employed to formulate panel regression models of food production of 15 selected Economic Community of West African States (ECOWAS) using four (4) World Development Indicators (WDI) as explanatory variables. Data were collected from 1990 to 2013. The four WDI are Food imports (% of merchandise imports), Agricultural land (% of land area), Fertilizer consumption (kilograms per hectare of arable land) and Inflation (consumer prices annual %). The fixed effect with cross-sectional seemingly unrelated regression (SUR) static panel data method was employed. The result of the analysis shows that agricultural land and fertilizer consumption have significant positive effect on the food production index of ECOWAS countries, while food imports and rate of inflation have significant negative effect on food production index of the ECOWAS countries. It is seen that 98.8% of the variation in food production among ECOWAS countries can be explained by the variations in food imports, agricultural land, fertilizer consumption and inflation. We therefore recommend that ECOWAS countries should increase agricultural land and fertilizer consumption and reduce food imports and rate of inflation in order to boost their food production level and have excess to export.
Olatunji Taofik Arowolo,
Matthew Iwada Ekum,
Food Production Modelling Using Fixed Effect Panel Data for Nigeria and Other 14 West African Countries (1990-2013), American Journal of Theoretical and Applied Statistics.
Vol. 5, No. 4,
2016, pp. 208-218.
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