Abstract

Exploring the relationship between finance and economic growth is a key direction of financial economics. However, most of the literature starts from the aggregate perspective and uses the GDP or per capita GDP as the explained variable to study the role of finance. Such a perspective ignores the heterogeneity of financial activities with respect to geographical distribution and makes it difficult to distinguish the roles of factor input and efficiency improvement. Because of this, this article introduces a “density” perspective on new economic geography and the measurement of the efficiency of the transition of development economics into financial economics. This article uses the stochastic frontier analysis (SFA) method to measure the technical efficiency (TE) of 272 cities in China from 2005 to 2018 and then, based on “forward-looking” and “backward-looking” methods, measures the impact of financial density on urban technical efficiency. This study found that overall, before the financial crisis in 2008, the contribution of financial density to technical efficiency showed a downward trend, and in the regional and provincial dimensions, the distribution of financial density’s contribution to technical efficiency was generally in line with that of backward regions, with less regularity in developed regions. In the urban dimension, the contribution rate of financial density to resource-based cities with slow technological progress or advanced cities with rich financial resources is not very prominent and may even play a negative role; however, cities that are at a medium level of development, rich in population resources, have convenient transportation, and have a certain industrial foundation can greatly promote the improvement of technical efficiency. Therefore, it may be possible to optimize the marginal contribution of urban financial density to the technical efficiency of Chinese cities by encouraging the flow of financial resources and activities from cities with small marginal effects to those with large marginal effects.

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