Are characteristics of metropolis matters for structural transformation of provinces: A spatial approach in the case of Vietnam

Figures & data

Figure 1. Conceptual framework and hypotheses.

Table 1. Descriptive statistics of variables

Variables	Description	Mean	SD	Min	Max
Independent variables
Inc	Average monthly income per capita in the metropolis (million VND)	4.718	0.984	2.843	6.177
UDEm	The underemployment rate in the metropolis (%)	0.950	0.917	0.200	3.800
Land	The rate of land area used for non-agricultural production and business (%)	3.200	1.220	0.878	5.262
Inter	The rate of internet subscribers (%)	36.319	22.054	9.055	67.813
Pop	Metropolis population (million people)	4.452	2.493	0.569	7.650
GPMan	The gross product of the manufacturing sector (trillion VND)	86.069	57.586	11.764	181.001
Dependent variable
SnA	Non-agricultural sector share (%)	71.650	10.177	45.020	93.140

Source: Authors’ calculation using data from GSO (2015–2018).

Figure 2. (a) Geographic locations of the study areas and metropolises (left panel). (b) Quantile map of non-agriculture share in study areas of Vietnam 2018 (right panel).

Figure 3. Moran Scatter plot of non-agriculture share in provinces of Vietnam 2018.

Table 2. Moran’s test of the spatial autocorrelation of non-agriculture share in provinces of Vietnam

Year	2015	2016	2017	2018
Moran’s I	0.3919	0.3734	0.3914	0.3850
Standard deviate	4.6862	4.4698	4.6795	4.6042
Variance	0.0076	0.0076	0.0076	0.0076
P-value	1.39E-06	3.91E-06	1.44E-06	2.07E-06

Table 3. Lagrange multiplier and Robust tests

Test	Null hypothesis	Value of Test	P-Value
Lagrange multiplier tests (LM)
LM-Lag	No spatial autocorrelation (λ = 0) assuming no random effects (${\sigma }_{\mu }^2$=0)	11.944	0.0005
LM-Error	No random effects (${\sigma }_{\mu }^2$=0) assuming no spatial correlation (λ = 0)	14.642	0.0001
Robust Lagrange multiplier tests (RLM)
RLM-Lag	No spatial autocorrelation (λ = 0) assuming the possible existence of random effects (${\sigma }_{\mu }^2$≥0)	0.3383	0.5608
RLM-Error	No random effects (${\sigma }_{\mu }^2$=0) assuming the possible existence of spatial autocorrelation (λ ≥ 0)	3.0366	0.0814

Table 4. Spatial panel data model

Variables	Spatial autoregressive model (SAR)		Spatial error model (SEM)
	Fixed effects	Random effects	Fixed effects	Random effects
Constant		34.9975*** (10.3858)		56.8755*** (15.6734)
Inc	0.6836*** (3.5314)	0.7056*** (3.9843)	0.9854*** (4.4822)	1.1138 (4.8709)
UDEm	−0.4412* (−1.8330)	−0.3658 (−1.3559)	−0.4691* (−1.7702)	−0.3164 (−1.0572)
Land	2.3137 (0.6653)	1.3088 (1.2735)	3.5912 (1.0261)	1.1981 (1.1032)
Inter	0.0725 (1.3041)	0.0850** (2.0981)	0.1090 (1.5980)	0.1254*** (2.6605)
Pop	0.4420** (2.0354)	0.3558* (1.7593)	0.5555** (2.1579)	0.3695 (1.4749)
GPMan	0.0158 (0.7727)	0.0027 (0.1841)	0.0140 (0.6263)	−0.0025 (−0.1459)
Spatial lag for the dep. variable (λ) Lambda	0.2946*** (3.9454)	0.3442*** (4.7738)
Spatial lag for error components (ρ) Rho			0.2765*** (3.5568)	0.3417*** (3.5832)
Robust Hausman Test	43.985***		89.844***
Log-Likelihood	−411.2406	−608.0976	−752.0547	−612.5632
AIC	964.4812		1646.1090
BIC	1215.0710		1896.6990

Notes: Standard deviations are shown in brackets. Significance: *p < 0.1, ** p < 0.05, *** p < 0.01.

Table 5. Direct, indirect and total effects (estimation results for SAR model)

	Direct effects		Indirect effects		Total effects
	Coeff	Standard errors	Coeff	Standard errors	Coeff	Standard errors
Inc	0.7000***	0.2072	0.2691**	0.1253	0.9691***	0.3069
UDEm	−0.4518*	0.2593	−0.1737	0.1266	−0.6255*	0.3697
Land	2.3693	3.5184	0.9109	1.4106	3.2802	4.8394
Inter	0.074221	0.058	0.0285	0.0283	0.1028	0.0838
Pop	0.4526**	0.2143	0.174	0.1132	0.6266**	0.3111
GPMan	0.0162	0.0208	0.0062	0.0084	0.0224	0.0286

Notes: Standard deviations are shown in brackets. Significance: *p < 0.1, ** p < 0.05, *** p < 0.01 .