Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling
Abstract
Over the last decades, the Per Capita Personal Income (PCPI) variable was a common measure of the effectiveness of economic development policy. Therefore, this paper is an attempt to investigate the determinants of personal income by using spatial 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) fixe defects 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 SAC model with 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.
How to cite this article: Youssef AH, Abonazel MR, Shalaby OA. Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling. J Adv Res Appl Math Stat 2020; 5(1&2): 1-13.
DOI: https://doi.org/10.24321/2455.7021.202001
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41. Studenmund AH. Using Econometrics: A Practical Guide. 7th Edition, Pearson 2016.
42. Tran TQ. Nonfarm Employment and Household Income among Ethnic Minorities in Vietnam. Economic Research-Ekonomska Istraživanja 2015; 28(1): 703-716.
43. Vega SH, Elhorst JP. On Spatial Econometric Models, Spillover Effects, and W. In 53rd ERSA Congress, Palermo, Italy, 2013.
44. Youssef AH, and Abonazel MR. Alternative GMM Estimators for First-Order Autoregressive Panel Model: An Improving Efficiency Approach. Communications in Statistics-Simulation and Computation 2017; 46(4): 3112-3128.
45. Youssef AH, Abonazel MR, Ahmed EG. Estimating the Number of Patents in the World Using Count Panel Data Models. Asian Journal of Probability and Statistics 2020; 6(4): 24-33.
2. Abonazel MR. Different Estimators for Stochastic Parameter Panel Data Models with Serially Correlated Errors. Journal of Statistics Applications and Probability 2018; 7(3): 423-434.
3. Abonazel MR. Generalized Estimators of Stationary Random-Coefficients Panel Data Models: Asymptotic and Small Sample Properties. Revstat Statistical Journal 2019; 17(4): 493–521.
4. Abonazel MR, Abd-Elftah AI. Forecasting Egyptian GDP Using ARIMA Models. Reports on Economics and Finance 2019; 5(1): 35 – 47.
5. Abonazel M, Rabie A. The Impact of Using Robust Estimations in Regression Models: An Application on the Egyptian Economy. Journal of Advanced Research
in Applied Mathematics and Statistics 2019; 4(2): 8-16.
6. Akgüç M. The Effects of Different Stages of Education on Income across Countries. Working paper, Toulouse of Economics TSE, 2011: 1-28.
7. Anselin L, Hudak S. Spatial Econometrics in Practice: A Review of Software Options. Regional Science and Urban Economics 1992; 22(3): 509-536.
8. Anselin L. Spatial Econometrics: Methods and Models. Springer Science and Business Media, 1988.
9. Anselin L, Bera AK. Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics, in Handbook of Applied Economic
Statistics, New York and NY, 1998: 237-289.
10. Anselin L, Bera AK, Florax R et al. Simple Diagnostic Tests for Spatial Dependence. Regional Science and Urban Economics 1996; 26(1): 77-104.
11. Anselin L, Gallo J, Jayet JH. Spatial Panel Econometrics. In The Econometrics of Panel Data. Springer, Berlin, Heidelberg, 2008.
12. Baltagi B. Econometric Analysis of Panel Data. John Wiley & Sons, 2008.
13. Barufi AM, Haddad E, Paez A. Infant Mortality in Brazil, 1980-2000: A Spatial Panel Data Analysis. BMC Public Health 2012; 12(1): 181.
14. Bennett P. Examining the Effects of Education on Financial Savings Behavior, 2018.
15. Blanden J, Gregg P. Family Income and Educational Attainment: A Review of Approaches and Evidence for Britain. Oxford Review of Economic Policy 2004; 20(2): 245-263.
16. Brosio G. Jimenez JP, Ruelas I. Looking at the Nexus between Personal Income Distribution and Regional GDP Inequality in Decentralized Systems. Documento Presentado en las Quintas Jornadas Iberoamericanas de Financiacion Local, Santiago de Compostela, 2016.
17. Brueckner M, Lederman D. Inequality and GDP Per Capita: The Role of Initial Income, World Bank, 2017.
18. Burridge P. On the Cliff]Ord Test for Spatial Correlation. Journal of the Royal Statistical Society: Series B (Methodological) 1980; 42(1): 107-108.
19. Card D. The Causal Effect of Education on Earnings, Handbook of Labor Economics, 1999; 3: 1801-1863.
20. Chang S, Gupta R, Miller SM. Causality between Per Capita Real GDP and Income Inequality in the US: Evidence from a Wavelet Analysis. Social Indicators Research 2018; 135(1): 269-289.
21. De Janvry A, Sadoulet E, Zhu N. The Role of Non-farm Incomes in Reducing Rural Poverty and Inequality in China, 2005.
22. Dube J, Legros D. Spatial Econometrics Using Microdata. John Wiley & Sons, 2014.
23. Elhorst JP. Specification and Estimation of Spatial Panel Data Models. International Regional Science Review 2003; 26(3): 244-268.
24. Elhorst JP. Spatial Panel Data Models. Handbook of Applied Spatial Analysis. Springer, 2009.
25. Elhorst JP. Applied Spatial Econometrics: Raising the Bar. Spatial Economic Analysis 2010, 5(1): 9-28.
26. Elhorst JP. Spatial Econometrics: from Cross-Sectional Data to Spatial Panels. Heidelberg: Springer, 2014.
27. Franzese RJ, Hays JC. Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data. Political
Analysis 2007; 15: 140-164.
28. Herrera M, Mur J, Ruiz M. A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix. Entropy 2019; 21(2): 160.
29. Hicks MJ, Devaraj S, Faulk D et al. The Causes of State Differences in Per Capita Income: How Does Indiana Fare. Ball State University Center for Business and
Economic Research, 2013.
30. Kalogirou S, Hatzichristos T. A Spatial Modelling Framework for Income Estimation. Spatial Economic Analysis 2007; 2(3): 297-316.
31. Kapoor M, Kelejian HH, Prucha IR. Panel Data Models with Spatially Correlated Error Components. Journal of Econometrics 2007; 140(1): 97-130.
32. Kopczewska K, Kud.a J, Walczyk K. Strategy of Spatial Panel Estimation: Spatial Spillovers between Taxation and Economic Growth. Applied Spatial Analysis and
Policy 2017; 10(1): 77-102.
33. Lee LF, Yu J. Some Recent Developments in Spatial Panel Data Models. Regional Science and Urban Economics 2010a; 40(5): 255-271.
34. Lee LF, Yu J. Estimation of Spatial Autoregressive Panel Data Models with Fixed Effects. Journal of Econometrics 2010b; 154(2): 165-185.
35. LeSage J, Pace RK. Introduction to Spatial Econometrics. Boca Raton: CRC Press, Taylor and Francis Group, 2009.
36. Lottmann F. Explaining Regional Unemployment Differences in Germany: A Spatial Panel Data Analysis. No. 2012-026. SFB 649 Discussion Paper, 2012.
37. Millo G, Piras G. splm: Spatial Panel Data Models in R. Journal of Statistical Software 2012; 47(1): 1-38.
38. Mollers J, Buchenrieder G. Effects of Rural Non-farm Employment on Household Welfare and Income Distribution of Small Farms in Croatia. Quarterly Journal of International Agriculture 2011; 50(892-2016-65199): 217-235.
39. Paul RK. Multicollinearity: Causes, Effects and Remedies. M. Sc. Thesis, Indian Agricultural Statistics Research Institute, New Delhi, India, 2006.
40. Purwaningsih T, Ghosh A, Chumairoh C. Spatial Data Modeling in Disposable Income per Capita in China Using Nationwide Spatial Autoregressive (SAR). International Journal of Advances in Intelligent Informatics 2017; 3(2): 98-106.
41. Studenmund AH. Using Econometrics: A Practical Guide. 7th Edition, Pearson 2016.
42. Tran TQ. Nonfarm Employment and Household Income among Ethnic Minorities in Vietnam. Economic Research-Ekonomska Istraživanja 2015; 28(1): 703-716.
43. Vega SH, Elhorst JP. On Spatial Econometric Models, Spillover Effects, and W. In 53rd ERSA Congress, Palermo, Italy, 2013.
44. Youssef AH, and Abonazel MR. Alternative GMM Estimators for First-Order Autoregressive Panel Model: An Improving Efficiency Approach. Communications in Statistics-Simulation and Computation 2017; 46(4): 3112-3128.
45. Youssef AH, Abonazel MR, Ahmed EG. Estimating the Number of Patents in the World Using Count Panel Data Models. Asian Journal of Probability and Statistics 2020; 6(4): 24-33.
Published
2020-07-23
How to Cite
YOUSSEF, Ahmed H; ABONAZEL, Mohamed R; SHALABY, Ohood A.
Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling.
Journal of Advanced Research in Applied Mathematics and Statistics, [S.l.], v. 5, n. 1&2, p. 1-13, july 2020.
ISSN 2455-7021.
Available at: <http://thejournalshouse.com/index.php/Journal-Maths-Stats/article/view/12>. Date accessed: 22 dec. 2024.
Section
Research Article
