Urban Total Factor Productivity: Does Urban Spatial Structure Matter in China?

Abstract

With the rapid development of urbanization in China, urban spatial form has increasingly gained research attention. In this study, the DEA (data envelopment analysis)-Malmquist index model and a panel data model are used to examine the relationship between the urban spatial form and total factor productivity (TFP) of 30 provincial cities in China. Our method of measuring urban spatial form is different from the current entropy method, but we use remote sensing GIS (Geographic Information System) technology to measure the relevant data on urban compactness and urban elongation. The average values of urban compactness and urban elongation first rise, then fall, and then rise again, and there are alternate situations of urban compact development and urban sprawl and expansion. Furthermore, there is a significant positive correlation between urban compactness and TFP. Therefore, cities with high urban compactness can promote TFP. In addition, there is a significant negative correlation between urban extension rate and TFP, indicating that an increase in urban elongation has a restraining effect on TFP. Finally, the average TFP of each city shows a fluctuating trend of rising first and then declining, which is determined mainly by technological change and efficiency change. These results are expected to provide a scientific basis for the development of urban agglomerations in China.

Growth Trends and Heterogeneity of Total Factor Productivity in Nine Pan-PRD Provinces in China

“…The annual growth rate of TC in the nine Pan-PRD provinces is 6.0%, and the annual growth rate of TEC is 0.6% (only Hainan and Guangxi provinces have a negative annual growth rate of TEC), indicating that TC is the main driver of TFP growth in the nine provinces. This is similar to the findings of Yu et al [28] Guo [29], and Li et al [30]. Among them, the growth rate of TC in Hainan and, Guangxi provinces far exceeds the growth rate of TEC especially significantly, indicating that the TEC of the two provinces, especially the scale efficiency, needs to be improved urgently.…”

COVID-19: Evaluation of Fever Clinic and Fever Sentinel Configuration—A Case Study of Harbin, China

“…In the process of the rapid urbanization in China, the spatial structure of cities has mostly shown a compact pattern of outward expansion [45]. In contrast, the centers of older urban areas have a longer history of development and an obvious accumulation effect, gathering more high-quality medical-service facilities and tertiary-industry service facilities, such as for education and commerce, in the cities [46].…”

The Impact of Environmental Conditions on Urban Eco-Sustainable Total Factor Productivity: A Case Study of 21 Cities in Guangdong Province, China

“…The panel data model can be divided into the fixed effect model and random effect model. Taking the individual dimension as an example, when α i and X it are correlated (i.e., corr(α i , X it ) 0), the model is a fixed effect model; otherwise, it is a random effect model [39]. In other words, given i, if α i is a certain value, it is a fixed effect model (different individuals have different characteristics, and each individual has a special nominal value).…”