The spatial heterogeneity and time-varying nature of FDI determinants: evidence from China

Figures & data

Figure 1. Actual FDI inflows in China, 1979–2018.

Sources: China Statistical Yearbooks (1999–2019); China Statistical Data of 50 Years 1949–1998.

Figure 2. Actual FDI inflows in BRI and non-BRI provinces in China, 1979–2018.

Sources: China Statistical Yearbooks (1999–2019); China Statistical Data of 50 Years 1949–1998.

Table 1. Summary statistics.

Symbols	Observations	Maximum	Minimum	Mean	S.D.
FDI	1159	12.796	−1.578	6.775	2.989
gdp	1160	15.192	7.780	11.746	1.479
Wage	1160	10.988	7.080	8.483	0.980
Export	1160	1.237	−7.623	−2.515	1.012
Transport	1160	7.928	2.743	5.941	0.988
Human capital	1160	−3.184	−7.716	−5.392	1.196
Population	1160	7.987	1.646	5.295	1.236
Exchange rate	1160	1.832	0.602	1.468	0.291
Region
East	1160	1	0	0.379	0.485
Central	1160	1	0	0.345	0.476
West	1160	1	0	0.276	0.447
Period
Pre-1992	1160	1	0	0.325	0.469
1992–2000	1160	1	0	0.225	0.418
2001–2018	1160	1	0	0.450	0.498
BRI	1160	1	0	0.552	0.498
BRI Year	1160	1	0	0.150	0.357

This table shows sample statistics for a panel of 29 provinces and municipalities in China over the 1979–2018 period. All the variables are in logarithmic form. Sources: China Statistical Yearbooks (NBS, 1987–2019, various issues), and China Statistical Data of 50 Years 1949–1998 (NBS, 1999). S.D. = standard deviation. All monetary values are at the 1990 price level.

Table 2. The determinants of FDI in China 1979–2018: Long-run static model.

Variables	Dependent variable: FDI(in logs)
	(1)	(2)	(3)
gdp	−0.062 (0.806)	0.838*** (0.000)	0.869*** (0.000)
wage	1.012*** (0.000)	0.725*** (0.000)	0.236** (0.012)
export	−0.086 (0.192)	0.149** (0.011)	0.213*** (0.000)
transport	0.932*** (0.000)	0.818*** (0.000)	0.794*** (0.000)
human capital	0.419*** (0.000)	0.249*** (0.000)	0.270*** (0.000)
population	−0.219** (0.013)	−0.392*** (0.000)	−0.395*** (0.000)
exchange rate	2.999** (0.018)	0.593 (0.498)	2.553*** (0.000)
constant	−8.295*** (0.000)	−12.209*** (0.000)	−9.877*** (0.000)
Province fixed effect	Yes	No	No
Year fixed effect	Yes	Yes	No
Region fixed effect	No	Yes	Yes
Period fixed effect	No	No	Yes
Observations	1159	1159	1159
R^2	0.925	0.894	0.885

$\mathrm{FD}{I}_{it}={\beta }_0+{\beta }_1{X}_{it}+{\beta }_{2}FE+v_{i,t}$

This table reports the results from the above long-run static model to verify the impact of FDI determinants. FDI is the actual used foreign direct investment. $X_{it}$ is a set of explanatory variables, including gdp, wage, export, transport, human capital, population, and exchange rate. FE is a set of year (or period) and province (or region) fixed effect variables. We consider heteroscedasticity and robust standard errors. p values are in parentheses. The significance levels at 10%, 5%, and 1% are indicated by ^*, ^**, and ^***, respectively.

Table 3. The determinants of FDI in China: Long-run heterogeneity across regions.

Variables	Dependent variable: FDI(in logs)
	(1)	(2)	(3)
gdp	0.838*** (0.000)	0.575*** (0.000)	0.600*** (0.000)
wage	0.725*** (0.000)	1.028*** (0.000)	0.522*** (0.000)
export	0.149** (0.011)	0.414** (0.000)	0.460*** (0.000)
transport	0.818*** (0.000)	1.460*** (0.000)	1.431*** (0.000)
human capital	0.249*** (0.000)	−0.152 (0.155)	−0.134*** (0.196)
population	−0.392*** (0.000)	−0.666*** (0.000)	−0.627*** (0.000)
exchange rate	0.593 (0.498)	1.271 (0.217)	3.519*** (0.000)
Central	−0.520*** (0.000)	9.515*** (0.000)	10.116*** (0.000)
West	−0.961*** (0.000)	5.541*** (0.009)	6.103*** (0.005)
Central$\times$gdp		0.903*** (0.000)	1.061** (0.000)
West$\times$gdp		0.331*** (0.000)	0.312*** (0.000)
Central$\times$wage		−0.767*** (0.002)	−0.956*** (0.000)
West$\times$wage		−0.326* (0.073)	−0.335* (0.074)
Central$\times$export		−0.131 (0.206)	−0.083 (0.459)
West$\times$export		−0.143 (0.270)	−0.124 (0.349)
Central$\times$transport		−1.634*** (0.000)	−1.624*** (0.000)
West$\times$transport		−0.956*** (0.000)	−0.913*** (0.001)
Central$\times$human capital		0.972*** (0.000)	0.978*** (0.000)
West$\times$human capital		0.406*** (0.001)	0.387*** (0.001)
Central$\times$population		0.467** (0.024)	0.383* (0.072)
West$\times$population		0.682*** (0.000)	0.610*** (0.000)
Central$\times$exchange rate		−1.268*** (0.000)	−1.518*** (0.000)
West$\times$exchange rate		−2.161*** (0.000)	−2.281*** (0.000)
constant	−12.209*** (0.000)	−16.801*** (0.000)	−14.847*** (0.000)
Year fixed effect	Yes	Yes	No
Period fixed effect	No	No	Yes
Observations	1159	1159	1159
R^2	0.894	0.917	0.907

$\mathrm{FD}{I}_{it}={\beta }_0+{\beta }_1{X}_{it}+{\beta }_2{X}_{it}\times {\mathit{Region}}_j+{\beta }_3{\mathit{Region}}_j+{\beta }_{4}FE+v_{i,t}$

This table reports the results of the above long-run static model verifying the long-run impact of FDI determinants across regions. Region is a set of dummy variables, including East, Central and West, taking a value of 1 for provinces in the respective region and 0 otherwise. $X_{it}\times {\mathit{Region}}_j$ is the form of the interaction terms between the explanatory variables and Region dummy variables. We consider heteroscedasticity and robust standard errors. p values in parentheses. The significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively.

Table 4. The determinants of FDI in China: Long-run heterogeneity over time.

Variables	Dependent variable: FDI(in logs)
	(1)	(2)	(3)
gdp	0.869*** (0.000)	0.756*** (0.002)	0.893*** (0.000)
wage	0.236** (0.012)	3.140*** (0.000)	2.727*** (0.000)
export	0.213*** (0.000)	−0.033 (0.713)	0.164*** (0.114)
transport	0.794*** (0.000)	0.716** (0.027)	1.487*** (0.000)
human capital	0.270*** (0.000)	0.359*** (0.008)	0.142 (0.295)
population	−0.395*** (0.000)	−0.361 (0.169)	−0.927*** (0.000)
exchange rate	2.553*** (0.000)	1.696*** (0.000)	1.743*** (0.000)
1992–2000	1.515*** (0.000)	29.117*** (0.000)	27.325*** (0.000)
2001–2018	0.373** (0.042)	29.689*** (0.009)	28.193*** (0.000)
1992–2000$\times$gdp		0.039 (0.730)	0.036 (0.775)
2001–2018$\times$gdp		0.015 (0.889)	−0.002 (0.983)
1992–2000$\times$wage		−3.378*** (0.000)	−2.932*** (0.000)
2001–2018$\times$wage		−3.369*** (0.000)	−2.925*** (0.000)
1992–2000$\times$export		0.163 (0.118)	0.285** (0.016)
2001–2018$\times$export		0.109 (0.263)	0.074 (0.509)
1992–2000$\times$transport		−0.003 (0.993)	−0.519 (0.198)
2001–2018$\times$transport		−0.127 (0.722)	−0.936*** (0.009)
1992–2000$\times$human capital		−0.154 (0.274)	−0.198 (0.200)
2001–2018$\times$human capital		−0.038 (0.770)	0.163 (0.273)
1992–2000$\times$population		0.144 (0.570)	0.467* (0.095)
2001–2018$\times$population		0.225 (0.353)	0.773*** (0.002)
1992–2000$\times$exchange rate		−1.904*** (0.003)	−2.140*** (0.002)
2001–2018$\times$exchange rate		−1.974*** (0.003)	−1.475*** (0.018)
constant	−9.877*** (0.000)	−16.801*** (0.000)	−14.847*** (0.000)
Province fixed effect	No	Yes	No
Region fixed effect	Yes	No	Yes
Observations	1159	1159	1159
R^2	0.885	0.921	0.895

$\mathrm{FD}{I}_{it}={\beta }_0+{\beta }_1{X}_{it}+{\beta }_2{X}_{it}\times {\mathit{Period}}_j+{\beta }_3{\mathit{Period}}_j+{\beta }_{4}FE+v_{i,t}$

The table reports the results of the above long-run static model testing the long-run impact of FDI determinants in different periods. Period is a set of dummy variables, including pre-1992, 1992–2000, and 2001–2018, that take a value of 1 for that subperiod and 0 otherwise. $X_{it}\times {\mathit{Period}}_j$ is the form of the interaction terms between the explanatory variables and Period dummy variables. We consider heteroscedasticity and robust standard errors. p values are in parentheses. The significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively.

Table 5. The determinants of FDI in China: Short-run heterogeneity across regions.

Variables	Controlled random without ECM	Controlled random with ECM	Controlled random without ECM	Controlled random with ECM	Controlled random without ECM	Controlled random with ECM	Controlled random without ECM	Controlled random with ECM
	(1)full sample		(2)East		(3)Central		(4)West
Δgdp	4.259*** (0.000)	3.955*** (0.000)	3.682*** (0.000)	4.270*** (0.000)	2.816*** (0.001)	2.834* (0.056)	8.828*** (0.001)	6.046** (0.015)
Δwage	−1.041*** (0.000)	−0.702* (0.088)	−1.343** (0.030)	−0.271 (0.692)	−1.348 (0.002)	−1.170 (0.115)	−0.698 (0.085)	−1.211 (0.345)
Δexport	−0.014 (0.826)	0.045 (0.580)	−0.022 (0.869)	0.024 (0.880)	0.049 (0.628)	0.034 (0.739)	−0.085 (0.399)	0.193 (0.231)
Δtransport	−0.118 (0.304)	0.242* (0.100)	−0.184 (0.374)	0.387 (0.187)	−0.020 (0.909)	0.036 (0.827)	−0.203 (0.332)	−0.017 (0.961)
Δhuman capital	0.079* (0.066)	0.297 (0.211)	0.421** (0.358)	0.158 (0.607)	0.112 (0.697)	−0.126 (0.738)	0.046 (0.232)	0.307 (0.498)
Δpopulation	−0.069 (0.221)	−4.344 (0.156)	0.351 (0.590)	−2.153 (0.197)	2.326* (0.059)	−2.721 (0.495)	−0.084 (0.105)	−1.694 (0.174)
Δexchange rate	0.438* (0.090)	0.204 (0.503)	0.535 (0.138)	−0.170 (0.525)	−0.019 (0.961)	0.188 (0.676)	−0.881 (0.146)	0.841 (0.297)
ECM
FDI_1		−0.373*** (0.000)		−0.338*** (0.000)		−0.383*** (0.000)		−0.509*** (0.000)
gdp_1		2.023*** (0.000)		1.914*** (0.000)		2.419*** (0.000)		2.706*** (0.000)
wage _1		−1.078*** (0.000)		−0.788* (0.059)		−0.428*** (0.235)		−1.685*** (0.000)
export_1		0.278*** (0.009)		−0.174 (0.402)		0.607*** (0.002)		0.291 (0.104)
transport_1		1.215*** (0.000)		0.530 (0.437)		0.385 (0.466)		0.069 (0.866)
human capital_1		−0.636*** (0.000)		−0.816*** (0.002)		−1.005*** (0.000)		0.016 (0.862)
population _1		0.798*** (0.001)		1.489 (0.159)		2.921 (0.217)		−0.667*** (0.001)
exchange rate _1		3.336*** (0.000)		3.312*** (0.000)		4.002*** (0.000)		2.247*** (0.000)
constant	−0.144* (0.083)	−10.077*** (0.000)	−0.108 (0.343)	−10.334*** (0.000)	−0.045 (0.656)	−17.406*** (0.000)	−0.640** (0.014)	−5.252*** (0.000)
Observations	1130	1130	428	428	390	390	312	312
R^2	0.058		0.059		0.034		0.088

$\Delta \mathrm{FD}{I}_{i,t}=f\left(\Delta {\tilde{X}}_{i,t}\right)-\theta ({\mathit{FDI}}_{i,t-1}-f(\Delta {\tilde{X}}_{i,t-1}))+{\epsilon }_{i,t}$

This table reports the results from the abovementioned short-run dynamic model at the regional level to verify the causal relationship between FDI and the major variables. The sample is divided into the total sample and three regions, i.e. Eastern, Central, and Western, without ECM as the treatment group, and ECM as the control group. Δ denotes first difference, $\Delta {\tilde{X}}_{i,t-1}$ is the lagged term of the estimated residual obtained from the first regression, $f\left(\Delta {\tilde{X}}_{i,t}\right)$ denotes the short-run form of the original production function shown in equation. ‘_1’ denotes a lag for one period. We consider heteroscedasticity and robust standard errors. The significance levels at 10%, 5%, and 1% are indicated by ^*, ^**, and ^***, respectively.

Table 6. Short-run and long-run elasticities across regions.

Variables	(1)Full sample		(2)East		(3)Central		(4)West
	Short-run	Long-run	Short-run	Long-run	Short-run	Long-run	Short-run	Long-run
gdp	4.259	5.424	3.682	5.663	2.816	6.316	8.828	5.316
wage	−1.041	−2.890	−1.343	−2.331	−1.348	−1.117	−0.698	−3.310
export	−0.014	0.745	−0.022	−0.515	0.049	1.585	−0.085	0.572
transport	−0.118	3.257	−0.184	1.568	−0.020	1.005	−0.203	0.136
human capital	0.079	−1.705	0.421	−2.414	0.112	−2.624	0.046	0.031
population	−0.069	2.139	0.351	4.405	2.326	7.627	−0.084	−1.310
exchange rate	0.438	8.944	0.535	9.799	−0.019	10.449	−0.881	4.415

This table reports the results from he abovementioned short-run dynamic model to verify the short-run and long-run elasticity. The short-run elasticity is the coefficient of the short-run regression without ECM, and the long-run elasticity is obtained by dividing the lag coefficient involving the ECM regression by θ.

Table 7. The determinants of FDI in China: Short-run heterogeneity over time.

Variables	Controlled random without ECM	Controlled random with ECM	Controlled random without ECM	Controlled random with ECM	Controlled random without ECM	Controlled random with ECM	Controlled random without ECM	Controlled random with ECM
	(1)1979–2018		(2)pre-1992		(3)1992–2000		(4)2001–2018
Δgdp	4.259*** (0.000)	3.955*** (0.000)	4.647*** (0.002)	3.932*** (0.003)	7.193*** (0.000)	5.387*** (0.000)	1.889*** (0.010)	1.457* (0.098)
Δwage	−1.041*** (0.000)	−0.702* (0.088)	−0.998 (0.390)	−0.689 (0.529)	−1.177 (0.011)	−0.372 (0.357)	−0.019 (0.904)	−0.058 (−0.410)
Δexport	−0.014 (0.826)	0.045 (0.580)	−0.171 (0.100)	0.113 (0.368)	0.043 (0.742)	0.192 (0.199)	0.087* (0.088)	0.078* (0.079)
Δtransport	−0.118 (0.304)	0.242* (0.100)	1.031 (0.281)	1.941** (0.037)	−0.144 (0.742)	0.023 (0.941)	0.134 (0.156)	0.116 (0.212)
Δhuman capital	0.079* (0.066)	0.297 (0.211)	0.642** (0.049)	0.614* (0.094)	0.795*** (0.001)	0.964*** (0.001)	0.296* (0.081)	0.019 (0.950)
Δ population	−0.069 (0.221)	−4.344 (0.156)	2.036 (0.483)	3.873 (0.192)	4.006 (0.433)	1.379 (0.754)	−0.077 (0.292)	−0.068 (0.353)
Δexchange rate	0.438* (0.090)	0.204 (0.503)	−0.010 (0.986)	0.160 (0.764)	−0.262 (0.349)	−0.516 (0.184)	−0.527 (0.244)	−0.150 (0.758)
ECM
FDI_1		−0.373*** (0.000)		−0.296*** (0.000)		−0.289*** (0.000)		−0.058* (0.075)
gdp_1		2.023*** (0.000)		0.26*** (0.001)		0.216*** (0.000)		0.088*** (0.009)
wage _1		−1.078*** (0.000)		0.411 (0.332)		−0.809*** (0.000)		−0.127** (0.029)
export_1		0.278*** (0.009)		0.124* (0.090)		0.310*** (0.000)		0.030 (0.150)
transport_1		1.215*** (0.000)		0.656*** (0.004)		0.229 (0.238)		0.135*** (0.001)
human capital_1		−0.636*** (0.000)		0.012 (0.891)		0.045 (0.465)		0.026*** (0.000)
population _1		0.798*** (0.001)		−0.364** (0.026)		−0.110 (0.344)		−0.059* (0.096)
exchange rate _1		3.336*** (0.000)		0.815*** (0.006)		−0.391 (−0.65)		0.236 (0.284)
constant	−0.144* (0.083)	−10.077*** (0.000)	−0.223 (−1.29)	−7.214** (0.042)	−0.513*** (0.000)	6.683*** (0.000)	−0.173* (0.051)	−0.343 (0.690)
Observations	1127	1130	348	348	232	232	493	493
R^2	0.058		0.058		0.160		0.088

$\Delta \mathrm{FD}{I}_{i,t}=f\left(\Delta {\tilde{X}}_{i,t}\right)-\theta ({\mathit{FDI}}_{i,t-1}-f(\Delta {\tilde{X}}_{i,t-1}))+{\epsilon }_{i,t}$

This table reports the results from the abovementioned short-run dynamic model at the period level to verify the relationship between FDI and the major variables. The samples are divided into four periods (1979–2018, 1979–1991, 1992–2000 and 2001–2018),without ECM as the treatment group and with ECM as the control group. Δ denotes first difference, $\Delta {\tilde{X}}_{i,t-1}$ is the lagged term of the estimated residual obtained from the first regression, $f\left(\Delta {\tilde{X}}_{i,t}\right)$ denotes the short-run form of the original production function shown in equation. ‘_1’ denotes a lag for one period. We consider heteroscedasticity and robust standard errors. The significance levels at 10%, 5%, and 1% are indicated by ^*, ^**, and ^***, respectively.

Table 8. Short-run and long-run elasticities over time.

Variables	(1)1979–2018		(2)pre-1992		(3)1992–2000		(4)2001–2018
	Short-run	Long-run	Short-run	Long-run	Short-run	Long-run	Short-run	Long-run
gdp	4.259	5.424	4.647	0.878	7.193	0.747	1.889	1.517
wage	−1.041	−2.890	−0.998	1.389	−1.177	−2.799	−0.019	−2.190
export	−0.014	0.745	−0.171	0.419	0.043	1.073	0.087	0.517
transport	−0.118	3.257	1.031	2.216	−0.144	0.792	0.134	2.328
human capital	0.079	−1.705	0.642	0.041	0.795	0.156	0.296	0.448
population	−0.069	2.139	2.036	−1.230	4.006	−0.381	−0.077	−1.017
exchange rate	0.438	8.944	−0.010	2.753	−0.262	−1.353	−0.527	4.069

This table reports the results from the abovementioned short-run dynamic model to verify the short -run and long-run elasticity. The short-run elasticity is the coefficient of the short-run regression without ECM, and the long-run elasticity is obtained by dividing the lag coefficient involving the ECM regression by θ.

Table 9. Impact of the Belt and Road Initiative (BRI) on FDI.

Variables	Dependent variable: FDI(in logs)
	(1)	(2)	(3)	(4)	(5)	(6)
BRI_region	0.310*** (0.000)	0.334*** (0.000)	0.181*** (0.012)	0.195*** (0.011)	0.446*** (0.000)	0.313*** (0.000)
BRI_ year					0.355*** (0.004)	0.379*** (0.003)
BRI_region$\times$BRI_year					−0.702*** (0.000)	−0.753*** (0.000)
gdp	0.927*** (0.000)	0.936*** (0.000)	0.848*** (0.000)	0.880*** (0.000)	0.938*** (0.000)	0.880*** (0.000)
wage	0.685*** (0.001)	0.118 (0.189)	0.699*** (0.001)	0.216** (0.023)	0.144 (0.207)	0.247** (0.036)
export	0.255*** (0.000)	0.327*** (0.000)	0.135** (0.023)	0.199*** (0.000)	0.323*** (0.000)	0.192*** (0.001)
transport	0.908*** (0.000)	0.844*** (0.000)	0.826*** (0.000)	0.802*** (0.000)	0.814*** (0.000)	0.768*** (0.000)
human capital	0.296*** (0.000)	0.302*** (0.000)	0.254*** (0.000)	0.273*** (0.000)	0.267*** (0.000)	0.234*** (0.000)
population	−0.277*** (0.001)	−0.248*** (0.001)	−0.346*** (0.000)	−0.345*** (0.000)	−0.220*** (0.002)	−0.318*** (0.000)
exchange rate	−0.083 (0.927)	2.474*** (0.000)	0.607 (0.487)	2.548*** (0.000)	2.470*** (0.000)	2.545*** (0.000)
constant	−13.416*** (0.000)	−10.746*** (0.000)	−12.561*** (0.000)	−10.305*** (0.000)	−11.229*** (0.000)	−10.822*** (0.000)
Year fixed effect	Yes	No	Yes	No	No	No
Region fixed effect	No	No	Yes	Yes	No	Yes
Period fixed effect	No	Yes	No	Yes	Yes	Yes
Observations	1159	1159	1159	1159	1159	1159
R^2	0.889	0.880	0.895	0.885	0.882	0.887

$FD{I}_{it}={\beta }_0+{\beta }_1{X}_{it}+{\beta }_{2}BRI\_\mathit{region}+{\beta }_{3}BRI\_\mathit{region}\times \mathit{BR}{I}_{\mathit{year}}+{\beta }_{4}BRI\_\mathit{year}+{\beta }_{5}FE+v_{it}$

This table reports the results from the above long-run static model to examine the impact of BRI on FDI. BRI_region takes a value of 1 for provinces directly on the BRI plan, and 0 otherwise. BRI_year takes a value of 1 for years after 2013, which is when the BRI was announced, and 0 otherwise. BRI_region$\times$BRI_year is the form of the interaction terms between region and year of BRI. We consider heteroscedasticity and robust standard errors. p values are in parentheses. The significance levels at 10%, 5%, and 1% are indicated by *, **, and ***, respectively.