Modern service industry agglomeration and its influencing factors: spatial interaction in Chinese cities

Figures & data

Figure 1. Spatial distribution of the Yangtze River Delta urban agglomeration.

Source: Made by author.

Table 1. Spatial Gini coefficient of modern service industry in Yangtze River Delta city group.

Year	TP	IT	FN	RE	LB	ST	ED	CE
2008	0.02044	0.01474	0.00666	0.02989	0.03450	0.04774	0.00419	0.00608
2009	0.02357	0.01484	0.00680	0.02613	0.03091	0.05850	0.00411	0.00559
2010	0.02522	0.01469	0.00944	0.01708	0.03008	0.05276	0.00435	0.00636
2011	0.02179	0.01437	0.00772	0.02195	0.02356	0.01238	0.00571	0.00457
2012	0.01337	0.01758	0.00550	0.01358	0.01532	0.01552	0.00773	0.00516
2013	0.05382	0.01159	0.01078	0.02215	0.06958	0.02266	0.00258	0.00931
2014	0.03171	0.02818	0.00845	0.02500	0.09359	0.02242	0.00315	0.00874
2015	0.03320	0.02887	0.00705	0.02164	0.09114	0.02388	0.00398	0.00829
2016	0.02292	0.03003	0.00851	0.02057	0.07894	0.02330	0.00425	0.00983
2017	0.02390	0.03199	0.00729	0.02014	0.08012	0.02522	0.00394	0.01236
2018	0.02331	0.03799	0.00652	0.01586	0.07622	0.02858	0.00376	0.00865
Average	0.026659	0.022261	0.00770	0.02127	0.05672	0.03027	0.00434	0.00772
Ranking	3	4	7	5	1	2	8	6

Data Source: Statistical Yearbook of Chinese Cities (2009 ∼ 2019).

Source: Author’s calculation.

Figure 2. Spatial distribution of modern service workers in the Yangtze River Delta urban agglomeration (2008, 2012, 2016, 2018).

Source: Made by author.

Figure 3. Spatial agglomeration and diffusion of modern service industries.

Source: Made by author.

Table 2. Results of Lagrange multiplier test.

	t-Statistic	Probability
LM test no spatial lag	36.3474	0.000
Robust LM test no spatial lag	6.3424	0.012
LM test no spatial error	30.2976	0.000
Robust LM test no spatial error	0.2926	0.589
R^2	0.9821
Conclusion	Since SLM is more significant than SEM, SLM is selected.

Source: Author’s calculation.

Table 3. Result of Hausman test for spatial lag model.

Statistic	Degrees of freedom	Probability
6.5533	8	0.5855

Source: Author’s calculation.

Table 4. Result of spatial lag model estimation of random effects.

Variable	Coefficient	t-stat	z-probability
$\mathrm{}\mathit{Intercept}$	−0.3362	−0.4595	0.6459
$LN\_\mathit{GDP}$	0.4803***	13.7146	0.0000
$\mathrm{}LN\_\mathit{NMP}$	−0.1019***	−2.7256	0.0064
$\mathrm{}LN\_\mathit{NSU}$	0.1839***	3.8777	0.0001
$\mathrm{}L{N}_{\_}PSI$	−0.2123**	−2.3709	0.0177
$\mathrm{}LN\_HM$	−0.1191***	−3.1006	0.0019
$\mathrm{}LN\_RP$	0.8235***	10.9721	0.0000
$\mathrm{}LN\_\mathit{AFC}$	0.0020	0.1459	0.8840
$\mathrm{}W\mathrm{*}\mathit{dep}.\mathit{var}.$	0.1510***	3.1760	0.0015
$\mathrm{}\mathit{teta}$	0.1221***	6.4421	0.0000
$R^2=0.9811$		$Ad\_R^2=0.8726$
$\mathit{Nobs}=451$		${\sigma }^2=0.0201$

t-statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Source: Author’s calculation.

Table 5. Result of random effects decomposition of spatial lag model.

Variable	Direct	Indirect	Total
$\mathit{Intercept}$	−0.3696 (−0.4891)	−0.0883 (−0.5423)	−0.4578 (−0.5037)
$LN\_\mathit{PGDP}$	0.4841*** (14.2587)	0.0833*** (2.8942)	0.5674*** (14.3028)
$LN\_\mathit{NMP}$	−0.1037** (−2.6530)	−0.0187* (−1.6973)	−0.1223** (−2.5452)
$LN\_\mathit{NSU}$	0.1873*** (3.8909)	0.0330** (2.0721)	0.2203*** (3.6931)
$L{N}_{\_}PSI$	−0.2125** (−2.3398)	−0.0359* (−1.8340)	−0.2483** (−2.3570)
$LN\_HM$	−0.1207*** (−3.0562)	−0.0214* (−1.8284)	−0.1421*** (−2.9313)
$LN\_RP$	0.8296*** (11.1169)	0.1437** (2.6746)	0.9733*** (9.9028)
$LN\_\mathit{AFC}$	0.0022 (0.1539)	0.0004 (0.1460)	0.0026 (0.1540)

t-statistics are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Source: Author’s calculation.

Table 6. Result of robustness test.

Variable	SLM model	Direct	Indirect	Total
$LN\_\mathit{PGDP}$	0.4363*** (9.4641)	0.4418*** (9.4382)	0.1312*** (3.7732)	0.5731*** (9.1583)
LN_NIU	−0.09167*** (−2.641)	−0.0912** (−2.5819)	−0.0278* (−1.9647)	−0.1190** (−2.4954)
$LN\_TS$	0.0672*** (3.0866）	0.0689*** (3.1789)	0.0205** (2.3673)	0.0894*** (3.1176)
$LN\_HM$	−0.1109** (−2.4688)	−0.1150** (−2.6784)	−0.0348** (−2.0774)	−0.1498** (−2.6106)
$LN\_RP$	0.6718*** (6.5158)	0.6804*** (6.4045)	0.2026*** (3.3572)	0.8830*** (6.1559)
$R^2=0.9848$			$\mathit{Corr}\_\mathit{squared}\mathrm{}$= 0.7045
$\mathit{Nobs}=410$			${\sigma }^2=0.0179$

t-statistics are in parentheses, and *, **, and ***are significance tests at the 10%, 5%, and 1% levels, respectively.

Source: Author’s calculation.