Spatial Regression of the Gross County Product of Kenya on Induced Latent Variables


  • Samson Tsuma Egerton University, 39, Gilgil 20116 , Kenya
  • Prof. Ali Salim Department of Mathematics, Egerton University, 536, Nakuru, 20115, Kenya
  • Dr. George Matiri Department of Mathematics, Egerton University, 536, Nakuru, 20115, Kenya
  • Dr. Justin Obwoge Department of Mathematics, Egerton University, 536, Nakuru, 20115, Kenya


Gross county product, spatial dependence, thematic maps, Latent variables


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.


Akaike, H. (1974). A new look at the statistical model identification. IEEE transactions on automatic control, 19(6), 716-723.

Anselin, L., & Arribas-Bel, D. (2013). Spatial fixed effects and spatial dependence in asingle cross?section. Papers in Regional Science, 92(1), 3-17.

Anselin, L., & Florax, R. J. G. M. (1995). Small sample properties of tests for spatial dependence in regression models: some further results. In L. Anselin, & R. J. G. M. Florax (Eds.), New directions in spatial econometrics (pp. 21-74). Springer.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16, 74-94.

Bai, A., Hira, S., & Deshpande, P. S. (2015). An application of factor analysis in the evaluation of country economic rank. Procedia Computer Science, 54, 311-317.

Bai, C. E., Ma, H., & Pan, W. (2012). Spatial spillover and regional economic growth in China. China Economic Review, 23(4), 982-990.

Balash, V., Balash, O., Faizliev, A., & Chistopolskaya, E. (2020). Economic growth patterns: Spatial econometric analysis for Russian regions. Information, 11(6), 289.

Burkey, M. L. (2018). Spatial econometrics and GIS YouTube playlist. Region 5(3), R13- R18.

Cronbach, L.J.(1951).Coefficient alpha and the internal structure of tests. psychometrika, 16(3), 297-334.

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography: perspectives on spatial data analysis. Sage.

George, D., & Mallery, P. (2003). SPSS for Windows Sep by Step: A Simple Guide and Reference, Boston, MA: Allyn & Bacon. Obtenido


Karim, A., Faturohman, A., Suhartono, S., Prastyo, D. D., & Manfaat, B. (2017). Regression models for spatial data: An example from Gross Domestic Regional Bruto in Central Java. Jurnal ekonomi pembangunan: Kajian masalah ekonomi dan pembangunan, 18(2), 213-224.

King, G. (1989). Unifying political methodology: The likelihood theory of statistical inference. Cambridge university press.

LeSage, J. P., & Pace, R. K. (2009). Spatial econometric models. In Handbook of applied spatial analysis: Software tools, methods and applications (pp. 355-376). Springer Berlin Heidelberg.

McMillen, D. P. (2012). Perspectives on spatial econometrics: linear smoothing with structured models. Journal of Regional Science, 52(2), 192-209.

Moran, P.A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17-23.

Nunnally, J. C., & Bernstein, IH (1994). The assessment of reliability. Psychometric theory . 3 (1), 248-292.

Okwi, P. O., Ndeng'e, G., Kristjanson, P., Arunga, M., Notenbaert, A., Omolo, A. & Owuor, J. (2007). Spatial determinants of poverty in rural Kenya. Proceedings of the National Academy of Sciences, 104(43), 16769-16774.

Oud, J. H., & Folmer, H. (2008). A structural equation approach to models with spatial dependence. Geographical analysis, 40(2), 152-166.

Páez, A., Le Gallo, J., Buliung, R. N., & Dall’Erba, S. (2010). Progress in spatial analysis: introduction. In Progress in Spatial Analysis (pp. 1-13). Springer Berlin Heidelberg.

Ramirez, M.T., & Loboguerrero, A.M. (2002). Spatial dependence and economic growth: evidence from a panel of countries. Borradores de economia working paper, 206.

Revelle, W. (2016). How to: Use the psych package for factor analysis and data reduction. Evanston, IL: Northwestern university, Department of psychology.

Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461–464.

Syed, A. A. S. G., & Shaikh, F. M. (2013). Effects of macroeconomic variables on gross domestic product (GDP) in Pakistan. Procedia economics and finance, 5, 703-711.

Szendi, D. (2016) Spatial beta convergence analysis of the real GDP per capita across Germany and Hungary. Doktornduszok Fóruma Szekciókiadván. ResearchGate.

Xiangyu, G., Zhou, Z., Zhou, Y., Ye, X., & Liu, S. (2018). A spatial panel data analysis of economic growth, urbanization, and NOx emissions in China. International journal of environmental research and public health, 15(4), 725.

Yoo, D. (2016, June). Mapping and economic development: Spatial information matters. In Journal of economic history (Vol. 76, No. 2, pp. 640-641). Cambridge university press.




How to Cite

Tsuma, S., Prof. Ali Salim, Dr. George Matiri, & Dr. Justin Obwoge. (2023). Spatial Regression of the Gross County Product of Kenya on Induced Latent Variables. International Journal of Sciences: Basic and Applied Research (IJSBAR), 70(1), 1–46. Retrieved from