Relationship between social economic agglomeration and labor productivity of core cities in Northeast China

Zhang, Peng; Yang, Qingshan; Zhang, Xiaochen

doi:10.1007/s11769-012-0522-4

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…One might then infer worker socialness could also be a key to enhancing a city's per worker GDP. A previous study attempted to quantify the relationship between social economic agglomeration (similar to occupational distributions from the OES data set [24]) and economic activity; they found a limited relationship between the growth rate of population density and growth rate of labor productivity, supporting our hypothesis that it is not necessarily population density that increases productivity, rather our findings support our claim that it is an increased density of face-to-face interactions that raises labor productivity [45].…”

Section: Worker Socialness and Economic Productivitysupporting

confidence: 84%

Innovations and Economic Output Scale with Social Interactions in the Workforce

2021

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The COVID-19 pandemic of 2020 fundamentally changed the way we interact with and engage in commerce. Social distancing and stay-at-home orders leave businesses and cities wondering how future economic activity moves forward. The reduction in face-to-face interactions creates an impetus to understand how social interactivity influences economic efficiency and rates of innovation. Here, we create a measure of the degree to which a workforce engages in social interactions, analyzing its relationships to economic innovation and efficiency. We do this by decomposing U.S. occupations into individual work activities, determining which of those activities are associated with face-to-face interactions. We then re-aggregate the labor forces of U.S. metropolitan statistical areas (MSA) into a metric of urban social interactiveness. Using a novel measure of urbanized area, we then calculate each MSA’s density of social work activities. We find that our metric of urban socialness is positively correlated with a city’s per worker patent production. Furthermore, we use our set of social work activities to reaggregate the workforces of U.S. industries into a metric of industry social interactivness, finding that this measure scales superlinearly with an industry’s per worker GDP. Together, the results suggest that social interaction among workers is an important driver of both a city’s rate of invention and an industry’s economic efficiency. Finally, we briefly highlight analogies between cities and stars and discuss their potential to guide further research, vis-à-vis the density of social interactions “igniting” a city or industry.

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Section: Worker Socialness and Economic Productivitysupporting

confidence: 84%

Innovations and Economic Output Scale with Social Interactions in the Workforce

2021

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“…In recent years, scholars have done many empirical researches in terms of manufacturing agglomeration patterns, location evolution, and the influence mechanism, including the research of the relationship between industrial structure, enterprise development and urban spatial expansion (Yang et al, 2004;Fan et al, 2009;Liu and Li, 2009;Qi et al, 2010); the analysis of different types of spatial pattern and the manufacturing location change from the view of capital, scale and element density (Meng and Shi, 2003;Wen, 2004;Zhang and He, 2007;Wu and Ning, 2010); the research of the relationship between manufacturing development and urban space reconstruction in micro view (Lu and Chen, 2009;Rijkers et al, 2010;Yuan et al, 2012a), the manufacturing agglomeration effects on production (Zhu and Ding, 2006;Zhang et al, 2012) or provincial scale manufacturing location evolutions (Qiu et al, 2013); and the discussion about the influence mechanism of industrial location change (Qi et al, 2006;Ng and Tuan, 2006;Chu and Liang, 2007;Ge, 2008). Due to the limited data access, there is less study on provincial manufacturing agglomeration features (Pan et al, 2011), but it is more significant to carry out the provincial level research considering administration and regional coordination.…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal pattern evolution of manufacturing geographical agglomeration and influencing factors of old industrial base: A case of Jilin Province, China

2014

Chin. Geogr. Sci.

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The primary object of this paper is to examine the spatial-temporal pattern evolution of manufacturing geographical agglomeration of the old industrial base. Industrial spatial agglomeration index and concentration ratio are used in this paper. Multiple linear regression models are also applied to try to explore the internal driving mechanisms on manufacturing geographical agglomeration. The results show that: 1) the manufacturing agglomeration degree of Jilin Province is increasing gradually. The spatial polarization structure is visible; and the central region is the agglomeration area, in addition, the manufacturing industries of Changchun Proper present a trend of dispersion; 2) the structure of manufacturing industries has changed, and the concentration ratio of labor-intensive manufacturing industry is declining, while the proportions of technology-intensive and capital-intensive manufacturing industry are relatively rising; 3) marketing level, location accessibility, labor resources, capital, science and technology innovation capability, scale economy and the level of globalization affect manufacturing agglomeration with different degree. There are significant differences of the effects about employment, technology, the quality of residents and the export-oriented market on the industrial concentration ratio; 4) in the future, the impact of policy and institution, export-oriented market and quality of resident on manufacturing geographical agglomeration pattern will be more profound.

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“…Beijing 798 Art Zone, Shanghai Zhangjiang Hi-Tech Park) are rapidly growing, while in the interior provinces the abilities to attract or retain the creative industries are limited. The spatial structure of agglomeration economy may lead to variations in productivity improvement and regional growth (Krugman, 1991;Rosenthal and Strange 2001;Zhang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Creative industries agglomeration, regional innovation and productivity growth in China

Jiang

Guo

et al. 2013

Chin. Geogr. Sci.

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This paper falls into the broad area of economic geography and economics of creativity, and it presents an alternative approach to explain why total factor productivity (TFP) growth is different across China′s regions. It establishes an empirical model to estimate the spatial agglomeration effects of creative industries on regional TFP growth, using China′s provincial panel data during the period of 2003 to 2010. We found that the creative industries agglomeration (CIA) has significant and positive impact on regional TFP growth. The result also implies that the CIA can facilitate regional TFP growth through promoting regional innovation instead of improving regional efficiency. Therefore, we argue that policy makers should take some measures to retain and establish more creative zones.

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Relationship between social economic agglomeration and labor productivity of core cities in Northeast China

Cited by 12 publications

References 7 publications

Innovations and Economic Output Scale with Social Interactions in the Workforce

Innovations and Economic Output Scale with Social Interactions in the Workforce

Spatial-temporal pattern evolution of manufacturing geographical agglomeration and influencing factors of old industrial base: A case of Jilin Province, China

Creative industries agglomeration, regional innovation and productivity growth in China

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