The Impact of the Spatial Agglomeration of Producer Services on Urban Productivity

doi:10.5814/j.issn.1674-764x.2023.02.012

Abstract

Abstract:

The spatial cluster effect of productive service industry agglomeration and the urban productivity level in 286 prefecture level cities in China during 2008-2018 were analyzed by using the Moran index and the Lisa cluster diagram. The results show that the spatial correlation between productive service industry agglomeration and urban productivity is high, as the high-value areas of producer services agglomeration are generally the high-value agglomeration areas of China’s urban productivity level, while the low-value areas of producer services agglomeration are generally the low-value agglomeration areas of China’s urban productivity level. Furthermore, the spatial econometric model was used to test the effect of producer services agglomeration on urban productivity in China. The results show that producer services agglomeration can effectively improve urban productivity in China, but its impact on the productivity in different cities is quite variable. The producer services specialization agglomeration on urban productivity in eastern China is more obvious, while the positive effect of the diversification agglomeration of producer services on urban productivity is more obvious in the West, the Central and the Northeast, but the promotion of eastern cities is less apparent.

Key words: productive service industry agglomeration, urban productivity, spatial measurement

ZHOU You. The Impact of the Spatial Agglomeration of Producer Services on Urban Productivity[J]. Journal of Resources and Ecology, 2023, 14(2): 344-356.

Figures/Tables 9

References 27

[1]	Andersson M. 2006. Co-location of manufacturing and producer service:A simultaneous equation approach. Working paper series in economics and institutions of innovation. London, UK: Routledge Press.
[2]	Anselin L. 1988. Spatial econometrics: Methods and models. Dordrecht, Netherlands: Kluwer Academic Publishers: 32-45.
[3]	Chen R. 2021. Study on the impact of producer services agglomeration and city size on urban productivity. Enterprise Economics, (8): 138-146. (in Chinese)
[4]	Chen S Y, Zeng L, Li W H. 2017. Local government competition, land transfer and urban productivity—Empirical evidence from 281 prefecture level cities in China. Financial Science, (7): 108-121. (in Chinese)
[5]	Chen X F, Zhou J J. 2020. Producer services agglomeration, spatial spillover and urban green total factor productivity—Empirical evidence from urban agglomeration in the Yangtze River Delta. Economic Longitude and Latitude, (4): 89-98. (in Chinese)
[6]	Combes P P. 2000. Economic structure and local growth: France, 1984-1993. Journal of Urban Economics, 47: 329-355.
[7]	Elhorst J. 2010. Applied spatial econometrics: Raising the bar. Spatial Economic Analysis, 5(1): 9-28.
[8]	Ezcurra R, Pascual M,Rapun. 2006. Regional specialization in the European Union. Regional Studies, 40(6): 601-616.
[9]	Glaeser E, Kalial H D, et al. 1992. Growth in cities. Journal of Political Economy, 100(6): 1127-1152.
[10]	Hou X S, Zhang Z Y, et al. 2014. Research on the growth effect and action path of China’s economic structure. World Economy, (5): 88-111. (in Chinese)
[11]	Hu B, Yu Z R. 2021. Research on the heterogeneous impact of local fiscal pressure on urban productivity: An analysis based on the background of new urbanization. Financial Research, 47(6): 139-153. (in Chinese)
[12]	Jacobs J. 1969. The economy of cities. New York, USA: Vintage Press: 45-62.
[13]	James P L, Kelly P R. 2009. Introduction to spatial econometrics. Boca Raton, USA: CRC Press: 513-514.
[14]	Ke S Z, Zhao Y. 2014. Industrial structure, city size and urban productivity in China. Economic Research, 4: 76-88. (in Chinese)
[15]	Li J K, Zhang T B. 2018. Study on the hierarchical impact and asymmetric effect of FDI in China’s urban productivity growth. Industrial Economic Research, 95(4): 18-29. (in Chinese)
[16]	Li R R. 2019. Producer services agglomeration and urban productivity—Differentiated learning effect, selection effect and classification effect. Chanjing Review, 10(1): 22-37. (in Chinese)
[17]	Liu P L, Song Z. 2007. Performance of China’s service and manufacturing industry enterprises: An empirical analysis based on China’s first economic census. Economic Research, (1): 47-58. (in Chinese)
[18]	Liu X W. 2019. Service industry agglomeration, urban innovation and urban productivity. Journal of Shanxi University of Finance and Economics, 41(7): 62-76. (in Chinese)
[19]	LV K B, Ren Z C. 2017. New demographic dividend, development of producer services and urban productivity—An empirical study based on urban agglomeration in the Yangtze River Delta. Nanjing Social Sciences, (1): 31-38. (in Chinese)
[20]	Marshall A. 1961. Principle of economics: An introductory volume. London, UK: Macmillan Press: 89-105.
[21]	Xi P H, Liang R B, Xie Z F, et al. 2017. Fiscal pressure, overcapacity and supply side Reform. Economic Research, (9): 88-104. (in Chinese)
[22]	Xu Q Y. 2010. An empirical study on the interactive relationship between industry and service industry in Shanghai based on VAR model. Shanghai Economic Research, (2): 90-97. (in Chinese)
[23]	Zhang C. 2018. Population density, human capital and urban productivity stratification—An empirical study based on Chinese urban data. Journal of Humanities, (2): 43-52. (in Chinese)
[24]	Zhang J, Wu G Y, Zhang J P. 2004. Estimation of China’s inter provincial physical capital stock: 1952-2000. Economic Research, (10): 35-44. (in Chinese)
[25]	Zhang S Q, Zhang P. 2017. The impact of innovation factor agglomeration and public expenditure on urban productivity—An empirical study based on the spatial panel of Beijing-Tianjin-Hebei-Mongolia. East China Economic Management, 31(11): 65-70. (in Chinese)
[26]	Zhang S Y, Wang C X, Ren Y J. 2019. Spatial spillover effect of producer services agglomeration on green total factor productivity. Soft Science, (11): 11-15, 21. (in Chinese)
[27]	Zhou Y, Tan G R. 2016. Research on the impact of producer services agglomeration on urban land intensive use from a spatial perspective. China Land Science, 30(10): 37-46. (in Chinese)

Year	Professional agglomeration of producer services (SP)			Diversified agglomeration of producer services (DV)			Urban productivity (CP)
Year	Moran	Value of Z	Value of P	Moran	Value of Z	Value of P	Moran	Value of Z	Value of P
2008	0.2397	2.7339	0.0280	0.3011	7.2405	0.0000	0.3934	8.9295	0.0000
2009	0.2786	3.1120	0.0060	0.3003	6.9101	0.0000	0.3875	8.1123	0.0000
2010	0.2817	3.2152	0.0059	0.2951	6.8716	0.0000	0.3648	8.3356	0.0000
2011	0.2919	3.3315	0.0028	0.3021	7.0123	0.0000	0.4064	8.1159	0.0000
2012	0.3012	3.6452	0.0021	0.3617	7.9803	0.0000	0.3983	5.0906	0.0892
2013	0.2498	2.8877	0.0168	0.2740	5.6689	0.0000	0.3896	7.9953	0.0000
2014	0.2325	2.4723	0.0310	0.2509	5.1562	0.0000	0.3778	7.8650	0.0000
2015	0.2401	2.7661	0.0179	0.2778	6.1101	0.0000	0.0988	8.1167	0.0000
2016	0.2652	3.0010	0.0067	0.2910	6.2391	0.0000	0.3994	7.6875	0.0000
2017	0.2633	2.9716	0.0071	0.3061	6.3744	0.0000	0.3769	7.3892	0.0000
2018	0.2382	2.5413	0.0307	0.2879	6.2305	0.0000	0.3790	8.0503	0.0000

Year	Professional agglomeration of producer services (SP)			Diversified agglomeration of producer services (DV)			Urban productivity (CP)
Year	Moran	Value of Z	Value of P	Moran	Value of Z	Value of P	Moran	Value of Z	Value of P
2008	0.2397	2.7339	0.0280	0.3011	7.2405	0.0000	0.3934	8.9295	0.0000
2009	0.2786	3.1120	0.0060	0.3003	6.9101	0.0000	0.3875	8.1123	0.0000
2010	0.2817	3.2152	0.0059	0.2951	6.8716	0.0000	0.3648	8.3356	0.0000
2011	0.2919	3.3315	0.0028	0.3021	7.0123	0.0000	0.4064	8.1159	0.0000
2012	0.3012	3.6452	0.0021	0.3617	7.9803	0.0000	0.3983	5.0906	0.0892
2013	0.2498	2.8877	0.0168	0.2740	5.6689	0.0000	0.3896	7.9953	0.0000
2014	0.2325	2.4723	0.0310	0.2509	5.1562	0.0000	0.3778	7.8650	0.0000
2015	0.2401	2.7661	0.0179	0.2778	6.1101	0.0000	0.0988	8.1167	0.0000
2016	0.2652	3.0010	0.0067	0.2910	6.2391	0.0000	0.3994	7.6875	0.0000
2017	0.2633	2.9716	0.0071	0.3061	6.3744	0.0000	0.3769	7.3892	0.0000
2018	0.2382	2.5413	0.0307	0.2879	6.2305	0.0000	0.3790	8.0503	0.0000

Variable	Mean	Standard deviation	Minimum	Maximum	Number of samples
CP (Urban productivity)	0.3472	0.2512	0.0356	0.8911	3146
SP (Professional agglomeration of producer services)	0.4811	0.2113	0.1210	1.7932	3146
DV (Diversified agglomeration of producer services)	0.9112	0.2210	0.3733	1.9024	3146
K (Per capita capital investment, ×10⁴ yuan)	2.5732	7.4047	0.6333	11.0007	3146
L (Labor input, ×10⁴ people)	103.5178	1812.4915	9.7900	2115.7700	3146
MS (Market scale, yuan)	8.9940	0.6581	6.8223	11.6972	3146
TRI (Investment in scientific and technological innovation, yuan)	3.6400	11341.6000	0.0643	201.5000	3146
FDI (Foreign direct investment, yuan)	800.2943	43221.3100	100.1142	3000.2855	3146
HC (Human capital)	0.1142	0.1281	0.0120	0.3341	3146
TC (Traffic conditions, m²)	7.1156	4.6531	0.6122	62.4785	3146

Variable	Mean	Standard deviation	Minimum	Maximum	Number of samples
CP (Urban productivity)	0.3472	0.2512	0.0356	0.8911	3146
SP (Professional agglomeration of producer services)	0.4811	0.2113	0.1210	1.7932	3146
DV (Diversified agglomeration of producer services)	0.9112	0.2210	0.3733	1.9024	3146
K (Per capita capital investment, ×10⁴ yuan)	2.5732	7.4047	0.6333	11.0007	3146
L (Labor input, ×10⁴ people)	103.5178	1812.4915	9.7900	2115.7700	3146
MS (Market scale, yuan)	8.9940	0.6581	6.8223	11.6972	3146
TRI (Investment in scientific and technological innovation, yuan)	3.6400	11341.6000	0.0643	201.5000	3146
FDI (Foreign direct investment, yuan)	800.2943	43221.3100	100.1142	3000.2855	3146
HC (Human capital)	0.1142	0.1281	0.0120	0.3341	3146
TC (Traffic conditions, m²)	7.1156	4.6531	0.6122	62.4785	3146

Variable	Fixed effect model of SDM	No fixed effect model of SDM
Constant	-0.0381**
lnSP	0.0843*** (2.4102)	0.1312** (1.9715)
lnDV	0.0281** (3.3281)	0.0648*** (2.4127)
lnK	0.2641 (0.7789）	0.2081*** (4.4569）
lnL	0.4411*** (11.7983）	0.4215*** (3.7563）
lnMS	0.0742*** (6.2673)	0.2156*** (3.3168)
lnTRI	0.1039*** (2.7523)	0.1235** (2.4353)
lnFDI	-0.1068 (-1.5564)	-0.1218 (-0.4877)
lnHC	0.2560* (1.8743)	0.1785** (2.1376)
lnTC	0.1649** (2.4494)	0.1563*** (3.2923)
ρ	0.2103*** (4.3567)	0.1988*** (3.1456)
η₁	0.0804*** (9.5648)	0.0695*** (9.3405)
η₂	0.0383*** (7.3317)	0.0306*** (6.2294)
R²	0.6066	0.5291
Adjusted R²	0.5992	0.5826
LogL	1358.1784	1531.0959