Exploring the Complexity of Location Choices of the Creative Class in Europe: Evidence from the EU Labor Force Survey 1995–2010

Abstract

This paper proposes a new idea for the current argument over Florida’s cultural policies, as location choices of the creative class is a complex process involving some basic aspects of socio-economic progress. Based on the European Labor Force Survey (EU LFE) dataset, tolerance and openness indicators which represent the quality of a “people climate” are found to be positively correlated with the creative class’s location in large regions and less so in smaller ones, where business climate-related parameters, i.e., the quality of local governments and the location of universities, have stronger positive effects on locational choices of the creative class. Moreover, graduates with non-creative jobs and creative professionals (i.e., workers who provide creative solutions during the work process such as high-tech technicians or legal and healthcare workers) are concerned more about the people climate, while creative workers with a degree and a creative core (e.g., workers who provide original ideas such as scientists, engineers and artists) are more likely to prioritize a business climate. Therefore, we argue that the promotion of a “tolerant” climate, as Florida advocates, is not a one-size-fits-all solution. Instead, policy makers should appropriately relate different preferences of creative workers to their unique strengths. This provides more insights into defining the concept of creativity beyond prioritized individual success, as well as understanding the preferences and actual needs of highly skilled workers in Europe.

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

“…This seminal framework has been especially widely applied to investigate regional innovation capacity. Compared to national innovation capacity, RIC is easier to manage and coordinate communication and reciprocity among different "actors" who play larger roles in determining its networking quality [11,13]. Numerous indicators have been utilized to measure RIC [14], such as R&D inputs, patent data, the number of new products, the number of high-tech employees, and research expenditures [15,16], since these indicators are believed to reflect innovative efforts and the efficiency of an innovation process.…”