Spatial Regression of the Gross County Product of Kenya on Induced Latent Variables
Keywords:
Gross county product, spatial dependence, thematic maps, Latent variablesAbstract
Because of a very shallow study carried out to measure regional economic progress in Kenya, we were prompted to investigate on the role of geographical analysis in economic development. The induction of the Gross County Product (GCP) in 2013 had brought about a new viewpoint of assessing the economic growth pattern of Kenya from a single value of the Gross Domestic Product (GDP) to a disaggregate measure that was inclusive of the contributive efforts from each county. Investigating the spatial dependence of this GCP on latent variables solved the error of model misspecification and proved the spill-over effect of the Kenyan economy at the county levels. The Local Indicator of Spatial Association (LISA) (Moran I test) revealed spatial clustering and the Lagrange Multiplier (LM) Test together with the spatial Hausman test suggested an error model fit. Meanwhile, the likelihood ratio test considered a restricted spatial model more suitable than the nested model. Not only was the economic pattern monitored but also a correct version of the 6 economic blocs of Kenya was developed by use of thematic maps where the counties were geographically classified according to the spatial implication.
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