ABSTRACT
While many studies have tried to measure the magnitude of smart cities, they have not highlighted the degree of smartness and the interaction between smartness and the urban economy across all regions in the country. This study highlights them by employing a new 5Ic smart city index (Information, communication, and technology, Innovation, Intelligence, Infrastructure, and Inflow) based on all US Metropolitan Statistical Areas (MSAs) by employing the Seemingly Unrelated Regression (SUR) model. This study finds that New York, NY is the highest smart MSA, followed by Los Angeles, CA and San Francisco, CA. Second, the innovation and inflow indices and the GDP positively interact with each other (innovation → GDP: 0.006 and GDP → innovation: 0.018 and inflow → GDP: 0.031 and GDP → inflow: 0.028) when other important variables are controlled. Third, the SUR model is a better model than the OLS model since some smart city indices are associated with the GDP. Therefore, governments and urban planners should develop their smart city strategies based on the magnitude of smartness and the interaction between smartness and the urban economy in their regions.
Highlights
This study highlights the degree of smartness by employing a new 5Ic smart city index (Information, communication, and technology, Innovation, Intelligence, Infrastructure, and Inflow).
This study employs the Seemingly Unrelated Regression model based on all US Metropolitan Statistical Areas.
New York is the highest smart Metropolitan Statistical Area, followed by Los Angeles and San Francisco.
The innovation and inflow indices and the Gross Domestic Product positively interact with each other.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Notes
1 In this paper, the ICT is calculated by the number of ICT industries (North American Industry Classification System (NAICS: 51)) (see e.g. Giotopoulos & Fotopoulos, Citation2010; Hacklin, Marxt, & Fahrni, Citation2009; Marinković, Nikolić, & Rakićević, Citation2018), Innovation is measured by the patents (see e.g. Holgersson, Citation2013; Kingston, Citation2001; McAleer & Slottje, Citation2005), and Intelligence is estimated by the number of people who have a graduate degree (see e.g. Landon-Murray, Citation2016; Malhotra, Kantor, & Vlahovic, Citation2018; National Research Council, Citation2013). Infrastructure is calculated by the number of internet subscriptions (see e.g. Jin, Gubbi, Marusic, & Palaniswami, Citation2014; Mohanty, Choppali, & Kougianos, Citation2016; Rao & Prasad, Citation2018; Sotres, Santana, Sánchez, Lanza, & Muñoz, Citation2017), inflow is estimated by the number of foreigners since they could be one of the representative indices for openness of regions (see e.g. Czymara, Citation2021; Kassim, Citation2014; Pischke & Velling, Citation1997), and the urban economy is estimated by the GDP (see e.g. Ding & Lichtenberg, Citation2011; García, Citation2010; Kosareva & Polidi, Citation2017). The data is from Occupation Employment Statistics (OES) from the Bureau of Labor Statistics, the United States Patent and Trademark Office, and the economic statistics in the U.S. Census Bureau.
The first ∼ fifth equations show the effect of the GDP on smart city indices. The population is a common explanatory variable for equations 1 ∼ 5. The number of people is highly related to ICT, innovation, intelligence, infrastructure, and inflow (see e.g. Chatterjee & Eliashberg, Citation1990; Glover & Simon, Citation1975; Howard, Citation2001; Jin & Cho, Citation2015; Lan, Gong, Da, & Wen, Citation2020). Each equation has its unique independent variable. For example, ICT agglomeration are mainly concentrated in the tertiary industries (NAICS 51 ∼ 92) (see e.g. Wang, Dong, & Dong, Citation2022; Zhang & Liu, Citation2015; Zhu & Lu, Citation2022). High-tech industries exert a significant impact on innovation (see e.g. Frenkel, Citation2003; Kline & Rosenberg, Citation2010; Yum, Citation2019a). Employment is one of the most important factors for the intelligent people (see e.g. Lepak & Snell, Citation2002; Simon, Citation1998; Wagner, Citation1997). The number of computers could be an important factor for accessing the infrastructure (see e.g. Chinn & Fairlie, Citation2010; Fil & Ryan, Citation2014; Ryan & Lewis, Citation2017). People tend to prefer to live with the whites in the US (see e.g. Alba, Logan, & Crowder, Citation1997; Quillian, Citation1999; South & Crowder, Citation1998). The labour and capital stock are for the Cobb-Douglas function. The Cobb-Douglas function is one of the most representative functions for estimating the productivity (see e.g. Felipe & Adams, Citation2005; Goldberger, Citation1968; Simon & Levy, Citation1963).
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