Understanding Contributions of the Creative Class to Sustainable Economic Growth in China

Abstract

By investigating the direct effect of the ‘3T’s (the creative class; tolerance; technology) and their interactive effects on GDP per capita, based on the data collected from 279 cities over the period of 2002–2014, the aim of this study is to explore the practical value of the creative class theory to the sustainable economic development in China. Using econometric estimations; the results suggest that agglomeration of the creative class; improving tolerant milieu and increasing technology investment have positive explanatory powers in understanding the disparities in GDP per capita between different cities in China. However, the complementary effects on GDP per capita are only observed between the creative class and technology or technology and tolerance, while there is an interference effect between the creative class and tolerance. These findings suggest that Florida’s advocacy for generating creative competitiveness across cities and regions by building up selected amenities may be arguable, but that the creative class is substantially contributing to regional economic growth. However, the creative class may have a unique mix with other innovative elements in different contexts. Therefore, instead of extensively focusing on a ‘one-size-fits-all’ solution that praises ‘cultural consumption’, the present study suggests a ‘three-phrase theory’; which has quite generic and flexible policy focuses on different development stages.

Regional Creative Class Agglomeration and Its Impact on Regional Economic Growth: Based on China’s Empirical Data

“…Put another way, provided that a city can attract creative talents, it can promote the economic growth of the region (Jacobs & Jane, 1985). Zhao et al (2020) research illustrates that the agglomeration of creative class, the continuous improvement of environmental tolerance and the continuous increase of technology investment have a good explanation for understanding the per capita GDP difference of 279 cities in China.…”

“…The idea of "creative class" put forward by Florida (2002) has been questioned by many scholars (Glaeser, 2004;peck, 2005;Markusen & Schrock, 2006;Bontje & Musterd, 2009). However, with the advent of the era of creative economy, more and more scholars affirm this proposition and mainly discuss the creative class from the following three aspects: first, some scholars focus on the influencing factors of the agglomeration of the creative class (Ling & Dale, 2011;You & Bie, 2017;Batabyal & Yoo, 2019) and formation mechanism (Zhao et al, 2020); The second is the analysis of the preference of creative class, such as the preference of residential location (Lawton et al, 2013) and the problem of leisure preference (Holm, 2014); the third is the effect brought by the agglomeration of creative class, such as the agglomeration of creative class promotes the regional economic growth (such as Stolarick & Currid-Halkett, 2013;Florida, 2014;Boschma et al 2009;Tiruneh, 2014;Batabyal & Beladi, 2017) can promote local innovation scale (e.g., Aneta and Valerij, 2015) and urban level innovation (Rodríguez-Pose, & Lee, 2020). It has a direct and indirect effect on regional labor productivity (for example, Florida et al, 2008), promotes regional employment growth (Boschma & Fritsch, 2007), and has an impact on regional unemployment rate (Stolarick & Currid-Halkett, 2013) and local innovation policy (Batabyal & Yoo, 2017).…”

Understanding the Complexity of Regional Innovation Capacity Dynamics in China: From the Perspective of Hidden Markov Model

“…With a hidden nature, RIC is difficult to directly measure or observe. Therefore, traditional econometric models commonly use proxy variables including R&D capital, R&D personnel, or patent quantity [3,55,56] as a replacement. These approaches, to a certain extent, allow scholars to capture RIC characteristics that are only based on a static and simplex model structure and cannot further reveal the internal dynamic process among innovation-driven factors.…”

“…RIC can be defined as the level of productivity or state of an innovation system, and it is determined by the efficiency of knowledge production and diffusion, as well as a learning environment where innovation-driven factors including universities, research institutes, and enterprises are highly intertwined [2]. The improvement of RIC is a powerful driving force and an important way to realize sustainable economic development [3]. RIC directly determines the performance of regional technological innovation [4].…”

Understanding the Effectiveness of Higher Education System: Evidences from Market Outcomes of Early University Graduates in Seven European Countries

“…Modern human capital theory [12,13] provides fundamental support for investigating how education as one of the greatest important ingredients of human capital affects sustainable economic growth [14] and increase productivity, human capital has been measured in various forms [15], such as the degree of education [16], entrepreneurship [17], individual and collective human capital [18]. Besides, another strand of investigation focuses on the role of occupational groups in economic development.…”