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Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling

Over the last decades, the Per Capita Personal Income (PCPI) variable wasa commonmeasure of the effectiveness of economic development policy. Therefore, this paper is an attempt to investigate the determinants of personal income by usingspatial panel data models for 48 U.S. states during the period from 2009 to 2017. We utilize the three following models: spatial autoregressive (SAR)model, Spatial Error (SEM)Model, and Spatial Autoregressive Combined (SAC)model,with individual(or spatial)fixedeffects according to three different known methods for constructing spatial weights matrices: binary contiguity, inverse distance, and Gaussian transformation spatial weights matrix. Additionally, we pay attention for direct and indirect effects estimates of the explanatory variables for SAR, SEM, and SAC models. The second objective of this paper is to show how to select the appropriate model to fit our data.The results indicate that the three used spatial weights matrices provide the same result based on goodness of fit criteria, and the SAC model is the most appropriate model among the models presented. However, the SACmodelwith spatial weights matrix based on inverse distance is better compared to other used models. Also, the results indicate that percentage of individuals with graduate or professional degree, real Gross Domestic Product (GDP)per capita,and number of nonfarm jobs have a positive impact on the PCPI, while the percentage of individuals without degree or bachelor’s degree have a negative impact on the PCPI. 
Goodness of Fit Criteria, Gross Domestic Product, Labor Force, Maximum Likelihood, Spatial Autoregressive Combined Model, Spatial Error Model, Spatial Weights Matrix