FDI and industrial development in a mega-city region: a modelling study on the Pearl River Delta

Figures & data

Figure 1. The Greater PRD composed of 15 urban areas (this map is based on the standard map with review number GS (2020) 4619 from the Map Technical Review Centre of the Ministry of Natural Resources of China).

Figure 2. The evolution of industry structure and FDI inflows in the PRD.

Data source: Guangdong Statistical Yearbook (2022) and Guangdong Statistical Bulletin (2022).

Figure 3. The impact mechanism of FDI on industrial structure.

Figure 4. The impact mechanism of the industrial structure on FDI.

Table 1. Definition of variables.

Type	Variable	Definition
Endogenous variables	FDI	Actual use of FDI ($100 million)
	ISR	Industrial structure rationalisation Refer to formula (1)
	ISU	The upgrading of industrial structure Refer to formula (2)
Exogenous variables	URB	Proportion of urban population to total population (%)
	UGDP	Gross Urban Product (in 100 million Yuan)
	PAT	Total number of regional patent applications
	OPEN	Ratio of total urban import and export value over urban GDP (%)
	WAGE	Average wage of employees in a city (yuan)
	EDU	Number of graduates with higher education (per 10,000 people)
	GOV	General government expenditure (in 100 million yuan)
	ROAD	Urban road mileage per capita (km)
	FAI	Fixed asset investment (per 100 million yuan)

Table 2. Statistical description of the variables.

Variable	Mean	Std.dev.	Min	Max	Obs
FDI	13.73	16.89	0.28	82.03	210
ISR	0.157	0.129	0.005	0.5524	210
ISU	10.64	5.40	2.24	27.44	210
URB	65.77	21.14	32.47	100	210
UGDP	4,111	5,645	596	26,927	210
PAT	17,169	31,885.85	124	228,609	210
OPEN	0.12	0.112	0.02	0.470	210
WAGE	44,034	20,284.16	13,250	110,304	210
EDU	15,103	15,716.16	1181	78,031	210
GOV	404.01	669.39	20.05	4,594	210
ROAD	21.45	16.53	1.3	64.6	210
FAI	1,127.71	1,180.38	82.25	6,208	210

Table 3. LLC test for panel data.

Variable	LLC
ln_FDI	−5.674*
ln_ISR	−7.213***
ln_ISU	−9.298***
ln_URB	−9.963***
ln_UGDP	−10.030***
ln_PAT	−5,246**
ln_OPEN	−5.392**
ln_WAGE	−5.484***
ln_EDU	−6.704***
ln_GOV	−5.476**
ln_ROAD	−5.479**
ln_FAI	−5.851**

Note: *,**,***significant at the level of 10%, 5% and 1%, respectively.

Table 4. SEM model results for ISR, ISU and FDI.

Variable	ISR equation	ISU equation	FDI equation
ln_FDI	0.0350**	0.8669***
			64.998***
			1.532***
ln_URB	−0.351***	0.6866	−7.683***
ln_PAT	0.0052	−0.3619
ln_UGDP	0.107***	5.7442***	17.676***
ln_OPEN	0.0479***	0.4436	4.28***
ln_FAI	0.0973***	2.5908***
ln_WAGE			0.6066
ln_EDU			0.911***
ln_ROAD			3.208**
ln_GOV			2.636**
Constant	1.4357***	−56.609***	184.21***

Note: *, **and***Significant at the level of 10%, 5% and 1%, respectively.

Table 5. SDM model results.

Variable	ISR model	ISU model	FDI model
ln_FDI	0.0249*	1.8929***
ln_ISR			0.7568*
ln_ISU			0.0814***
ln_URB	0.2077	1.3219	−0.1622
ln_PAT	0.0327***	1.8374***
ln_UGDP	0.3447***	5.230***	0.2658*
ln_OPEN	0.0203	3.003***	0.1326
ln_FAI	0.0142	0.7199
ln_WAGE			−0.6699**
ln_EDU			−0.0315
ln_ROAD			−0.6641**
ln_GOV			−0.0082
W*ln_FDI	0.0182*	−0.4823*
W*ln_ISR			2.2255***
W*ln_ISU			0.0095*
W*ln_URB	−0.1307	1.3873**	−0.8713
W*ln_PAT	−0.0159	−0.550*
W*ln_UGDP	−0.3021***	−3.843*	0.9523
W*ln_OPEN	0.1515***	2.6176**	0.9376***
W*ln_FAI	−0.0404	−1.5326*
W*ln_WAGE			−1.8549***
W*ln_EDU			0.0651
W*ln_ROAD			0.6581*
W*ln_GOV			0.6789**
$\mathit{\rho }$	−0.1233	0.2944***
θ			0.3091***
R-sq	0.5654	0.8617	0.8136
Fixed Effect	Yes	Yes	Yes
#Obs	210	210	210

Note: *,**,***Significant at the 10%, 5%, and 1% level, respectively.

Table 6. Total effects decomposition for the ISR and ISU models.

	ISR model			ISU model
Variable	Direct	Indirect	Total	Direct	Indirect	Total
ln_FDI	0.022**	0.018*	0.040**	1.912***	0.103	2.015***
ln_URB	0.207*	−0.157	0.050	2.466	1.213**	3.679*
ln_PAT	0.031***	0.0099	0.041***	2.264***	−0.372	1.892***
ln_UGDP	0.354***	−0.3212***	0.033	4.971***	−3.215	1.756
ln_OPEN	0.014	0.138***	0.152***	3.276***	2.608***	5.884***
ln_FAI	0.016	−0.038	−0.022	0.630	−1.692	−1.062

Note: *,**,***Significant atnthe 10%, 5% and 1% level, respectively.

Table 7. Decomposition of the total effect of FDI model.

Variable	Direct	Indirect	Total
ln_ISR	1.002**	3.361***	4.364***
ln_ISH	0.084***	0.046*	0.130***
ln_URB	−0.165	−1.045	−1.215
ln_WAGE	−0.872***	−2.811***	−3.684***
ln_EDU	−0.027	0.067	0.040
ln_UGDP	0.387	1.381	1.768**
ln_ROAD	0.634***	−0.605	0.029
ln_GOV	0.647*	0.354**	1.001**
ln_OPEN	0.639**	0.429**	1.190***

Note: *,**,***Significant at the 10%, 5% and 1% level, respectively.

Table 8. SDM regression results with an inverse distance weight matrix and first-order lagged variables.

	Inverse distance weight matrix			First-order lagged variable
Variable	ISR model	ISU model	FDI model	ISR model	ISU model	FDI model
ln_FDI	0.0252**	1.8887***		0.0167*	1.7645***
ln_ISR			0.4778*			0.5828*
ln_ISU			0.0897***			0.0831***
ln_URB	0.2975*	1.8242	−0.4732	0.2159	2.8497	−0.0998
ln_PAT	0.0321***	−0.2950		0.0359***	−0.4813*
ln_UGDP	0.2652***	1.6972***	0.4351	0.3676***	6.0785***	0.3551*
ln_OPEN	−0.0471	−3.0349	−0.220	−0.0165	−2.7358	−0.1715
ln_FAI	0.0077	0.2729		0.0177	0.5230
ln_WAGE			−1.0201***			−0.8692***
ln_EDU			−0.0581			−0.0317
ln_ROAD			−0.7945***			−0.5734***
ln_GOV			−0.0105			−0.0548
W*ln_FDI	0.0330	−0.8605		0.0299	−0.6393
W*ln_ISR			2.0471**			2.162**
W*ln_ISU			0.1406*			0.0095*
W*ln_URB	0.5100	0.7773**	−2.2089	−0.1157	17.7829***	−1.4590
W*ln_PAT	−0.0118	2.6019***		−0.0109	1.6909***
W*ln_UGDP	−0.4390***	−1.1623*	0.9447	−0.3022***	−3.743*	0.8257
W*ln_OPEN	−0.1034**	−1.0469**	0.5734	−0.1626***	−3.3048***	0.4733
W*ln_FAI	−0.0404	−0.5999*		−0.0550	−1.8099**
W*ln_WAGE			−1.6899***			−1.6850***
W*ln_EDU			0.0665			0.0885
W*ln_ROAD			1.0165*			0.6216*
W*ln_GOV			0.7619**			0.6492*
$\mathit{\rho }$	−0.4954	0.2119*		−0.1192	0.2329***
θ			0.2117**			0.3687***
R-sq	0.5123	0.9113	0.6180	0.6178	0.9042	0.6174
Fixed Effect	Yes	Yes	Yes	Yes	Yes	Yes
#Obs	210	210	210	195	195	195

Note: *,**,***Significant at the 10%, 5% and 1% level, respectively.