ABSTRACT
In this paper, we analyze income per capita absolute and club convergence of 383 metropolitan areas of the U.S. To our knowledge, we are the first to provide club convergence analysis of income per capita at the metropolitan area-level. Using data for 1969–2017 containing 18,767 observations, we employ a data-driven convergence model that also allows heterogeneity in the panel. We demonstrate evidence of the absence of absolute convergence and the presence of club convergence. Furthermore, we use the ordered logit model to analyze the economic, demographic, and several other factors influencing club membership. Our results reveal a diversity of growth experiences across the U.S. metropolitan areas by extensively accounting for heterogeneity using a novel transition dynamic modeling framework.
Acknowledgements
I am grateful to the editor and two anonymous referees for their valuable comments that significantly improved the quality of this paper. Any errors are my own. Contact: Imran Arif. Email: [email protected]
Disclosure statement
No potential conflict of interest was reported by the author(s).
Supplementary material
Supplemental data for this article can be accessed here.
Notes
1 e.g., Barro and Sala-I Martin (Citation1992), Mankiw, Romer, and Weil (Citation1992), Sala-I Martin (Citation1996), Higgins, Levy, and Young (Citation2006), and Young, Higgins, and Levy (Citation2008).
2 Also see Mankiw, Romer, and Weil (Citation1992), Sala-I Martin (Citation1996).
3 We are grateful to Bennett (Citation2021) for sharing their raw data and Stata codes with us.
4 See Hodrick and Prescott (Citation1997).
5 Please see the Supplementary Appendix A for the details of Phillips and Sul (Citation2007), Phillips and Sul (Citation2009) algorithm. We employ Du (Citation2017) club clustering algorithm in Stata.
6 Please see the supplementary appendix for the descriptive statistics and MSA club membership.
7 Please see the supplementary appendix for a complete list of club membership.
8 Please see Zhang, Xu, and Wang (Citation2019) and Von Lyncker and Thoennessen (Citation2017), who use the same methodology.