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Assessing the Effect of Agriculture Sub-sectors on the Gambia’s Economic Growth Using Time Series Econometric Models
International Journal of Agricultural Economics
Volume 5, Issue 4, July 2020, Pages: 142-149
Received: Jul. 1, 2020; Accepted: Jul. 21, 2020; Published: Aug. 19, 2020
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Fatou Jobarteh, The Gambia National Bureau of Statistics, Kanifing Institutional Layout, Greater Banjul, The Gambia
Majige Selemani, Department of Research, Bank of Tanzania, Dar es Salaam, Tanzania
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The study aimed to assess the contribution of agriculture sub-sectors on the economic growth of The Gambia. The study used time series data obtained from The Gambia Bureau of Statistics for the period from 2004 to 2016. Variables included: Economic growth, Crops, Livestock, Fisheries and Forestry. Denton’s method of disaggregation was used to convert annual data into quarterly series. Auto Regressive Distributed Lag model of the co-integrating vector was re-parameterized into Error Correction Model and used to assess the effect of the study variables to the economic growth. The results reveal that crops and fisheries sub-sectors have strong positive effect on economic growth in the long-run while crops and livestock sub-sector have positive effect in the short run to economic growth in first lag. The study concludes that agriculture sub-sectors influence economic growth in The Gambia. Therefore, the study recommends that massive attention and investments be directed to the agriculture sector for more economic expansion.
Auto Regressive Distributed Lag Model, Economic Growth, Agriculture Sub-sectors
To cite this article
Fatou Jobarteh, Majige Selemani, Assessing the Effect of Agriculture Sub-sectors on the Gambia’s Economic Growth Using Time Series Econometric Models, International Journal of Agricultural Economics. Vol. 5, No. 4, 2020, pp. 142-149. doi: 10.11648/j.ijae.20200504.17
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This article 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.
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