Analyzing Income Per Capita Convergence among the Provinces of Iran Using a Dynamic Spatial Panel Data Approach

Document Type : Scientific paper

Author

Assistant Professor, Department of Economics, Islamic Azad University, Qazvin Branch, Qazvin, Iran

Abstract

In this study, the hypothesis of per capita income convergence between the provinces of the Islamic Republic of Iran during the period 2000-2018 has been tested. The convergence hypothesis has been tested in several Persian articles, even at the provincial level. But the present study differs from previous studies in two ways. First, the use of real GDP per capita without oil data as a measure of per capita income that will eliminate the bias caused by alternative variables. Second, all previous studies have used cross-sectional data estimators to estimate the beta convergence equation, the results of which will be biased. However, in this research, the dynamic spatial panel data model and the SYS-GMM estimator have been used, which will eliminate this bias. Also, two types of matrices were used to study the spatial interactions between the provinces.
The results show that, the slope of the absolute beta convergence model is negative and the hypothesis of absolute convergence is not rejected and the rate of this conditional convergence between the provinces of the country is equal to 10.5%. That is, regional inequality is declining during this period. In addition, the results show that the hypothesis of per capita income convergence is not rejected and the hypothesis of spillover effects due to spatial interactions between the provinces of the country is confirmed. Both effects of neighborhood overflow and migration are statistically significant, but the effects of migration overflow have a greater effect than the effects of overflow due to neighborhoods in the provinces. Sensitivity analysis from the three perspectives of changing the spatial interaction matrix, changing the alternative variable of real per capita income and changing the estimator shows the stability of the results and the lack of seriousness of the small sample bias for the SYS-GMM estimator.

Keywords

References

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Macroeconomics Research Letter
Volume 16, Issue 31
August 2021
Pages 105-124

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History

  • Receive Date: 10 October 2020
  • Revise Date: 30 October 2021
  • Accept Date: 03 November 2021

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