# Analysis Model Design on the Impact of Foreign Investment on China’s Economic Growth

Authors: Lintao Zhang; Robin H. Xang
Journal: Scientific Programming
 (2022)

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## Abstract

This research mainly analyzes the influence of foreign investment in the era of big data on China's economic growth, in the process of analyzing the impact of foreign investment on the national economy, based on the analysis of the current situation of foreign investment and error-correction model, etc., through the correlation coefficient matrix to determine the variables data needed to fit the model; after fitting the model, the residual model is extracted, and the stationarity of the residual sequence is tested . On the basis of the above, this paper analyzes the difference of foreign direct investment in different regions, combined with the coastal areas and central region model and actual situation analysis, analyzes the two foreign direct investment (FDI) development speeds, base development speed, and average development speed, at the same time for the two regions in 2017; the specific direction of FDI do a detailed analysis. Finally, a series of conclusions are obtained.


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## Body

## 1. Introduction

For China, since the reform and opening up in 1978, the economy of the eastern coastal areas has shown a trend of rapid development, while the economic development of the central and western regions is relatively slow compared with the coastal areas [1–5]. The impact of foreign direct investment on China is very far-reaching, but due to China’s large and vast territory, the impact of foreign direct investment on different regions is different [6, 7]. For example, Jiangsu is located in the eastern coastal areas of China, and it has the advantage of the eastern coastal areas. From 1978 to 2017, Jiangsu’s foreign direct investment quota was from $639915 million to $2513541 million; located in the central of Henan; foreign direct investment increased from $495.27 million to $1722428 million. Only from the surface data observation, we can find the foreign direct investment quota in t’e two regions [8–11]. This research is devoted to t7-he analysis model design of the impact of foreign investment on China's economic growth, so as to provide decision support for the formulation of macroeconomic policies and the management and control of microeconomic operation.


## 2. Analysis of the Impact of Foreign Investment on the National Economy

### 2.1. Current Utilization of Foreign Investment in China

Since the reform and opening up, the level of China's export-oriented economy has been continuously improved, the scale of introducing and utilizing foreign capital has been continuously expanding, the level has been continuously improved, and the number of domestic foreign-invested enterprises has also been continuously increasing [9–12]. Next, the statistical analysis of foreign direct investment will be conducted from the change of foreign investment quota, capital source, and industrial investment.


#### 2.1.1. Current Situation of Foreign Direct Investment in Recent Years

Data on the actual utilization of foreign direct investment and foreign investment from 2009 to 2018 and 1999 to 2018 are processed in Figures1 and 2.Figure 1
Actual use of foreign direct investment.Figure 2
Data on the actual utilization of foreign capital.From Figure1, we can see that in the decade from 2009 to 2018, both actual utilized foreign direct investment and actual utilized foreign capital showed an upward trend, but the growth trend of actual utilized foreign investment and actual utilized foreign investment between 2012 and 2018 is slower than before. The actual use of foreign investment includes two parts, one is large foreign direct investment and the other is another foreign investment. From Figure 2, we can see the actual use of foreign direct investment and the actual use of foreign investment during the 20 years from 1999–2018 [13]. This shows that in the actual use of foreign investment, the actual use of foreign investment proportion gradually decreased, reduced to 0 in recent years. For example, in 2015, the actual utilization of foreign investment and the actual utilized foreign direct investment were the US $1,26,267 million, in 2016, both were US $12,6001 million, in 2017, both were US $13,1035 million, and in 2018, both were US $13,49,666 million, indicating that the actual utilization of other foreign investment in recent years was 0.


#### 2.1.2. Analysis of the Source Structure of Foreign Direct Investment

Under the background of the era of economic globalization, WTO was established on January 1, 1995, a total of 162 members, including a large part of the countries and regions in the world, can say the world economy roughly forms a whole, and the arrival of the electronic information age and transportation more and more convenient, our country and superior geographical location, rich resources attract all over the world to invest in our country [14–16]. China's foreign direct investment sources more areas, spread across five continents. Table 1 is the general situation of the sources of foreign direct investment in China from 2016 to 2018.Table 1
Source structure of FDI in China (ten thousand dollars).
National201620172018Actual investmentProportionActual investmentProportionActual investmentProportionSummation12600100100%13103500100%13496600100%Asia988310378.44%1091938783.33%1070131079.29%Hongkong814650864.65%945090172.13%899172466.62%Taiwan1962801.56%1772471.35%1391361.03%Japan3095852.46%3261002.49%3797802.81%Singapore6046684.80%4763183.64%5210213.86%Korea4751123.77%3672532.80%4666883.46%Africa1127200.89%657460.50%610420.45%Europe9434397.49%8836196.74%11193508.29%Latin America12216189.70%6362734.86%9026466.69%North America3104212.46%4285523.27%5147893.81%Oceania1267941.01%1609501.29%1909041.41%As can be seen from Table1, China's foreign direct investment mainly comes from Asia, accounting for about 80%, while Hong Kong is the main source of foreign direct investment in China [17]. From the data, it can be concluded that more than half of China's foreign direct investment in China comes from Hong Kong, accounting for more than 60%. Among the five continents in the world, China's foreign direct investment from Africa is the least, which is less than 1%, which is directly related to the economic situation of Africa. Compared with Asia, the proportion of foreign direct investment from Europe, Oceania, and Latin America is also relatively small. From the above results, we can find that the source of a foreign direct investment structure is not reasonable, mainly foreign direct investment from Hong Kong. This phenomenon is related to the relationship between the mainland and Hong Kong; the transportation between China and Hong Kong region is very convenient, and for Hong Kong, the mainland also has various preferential policies. In addition, foreign direct investment is also directly related to the distance.


#### 2.1.3. The General Situation of Foreign Direct Investment by Industry in China

This summary adopts the data of foreign direct investment in China in 2018 collected by the National Bureau of Statistics. Table2 shows China's foreign direct investment in 2018 is roughly divided by industry (ten thousand dollars).Table 2
China's foreign direct investment in 2018 is roughly divided by industry (ten thousand dollars).
IndexSumActual utilized amount of foreign direct investment13496589The amount of foreign direct investment actually utilized in agriculture, forestry, animal husbandry, and fishery80131The actual amount of foreign direct investment utilized in the mining industry122841The actual amount of foreign direct investment in the manufacturing industry4117421Actual utilization amount of foreign direct investment in the production and supply of electricity, gas, and water442390The amount of foreign direct investment actually utilized in the construction industry148809The actual amount of foreign direct investment in transportation, storage, and postal services472737Actual utilization amount of foreign direct investment in information transmission, computer services, and software industry1166127Actual utilization amount of foreign direct investment in the wholesale and retail industry976689The amount of foreign direct investment is actually utilized in the accommodation and catering industry90107The actual amount of foreign direct investment is utilized in the financial industry870366The actual amount of foreign direct investment is utilized in the real estate industry2246740The actual amount of foreign direct investment in leasing and business services1887459The amount of foreign direct investment actually utilized in scientific research, technical services, and the geological exploration industry681298The amount of foreign direct investment actually utilized by the water conservancy, environment, and public facilities management industry47408The amount of foreign direct investment actually utilized in residential services and other service industries56166The actual amount of foreign direct investment utilized in education7420The amount of foreign direct investment actually used in the health, social security, and social welfare industry30178The actual amount of foreign direct investment is utilized in the cultural, sports, and entertainment industries52290The amount of foreign direct investment actually utilized by public administration and social organizations12Taking out the largest amount of several industries from Table2 into the pie chart, it is more convenient to see the specific direction of FDI in China (Figure 3).Figure 3
The proportion of FDI in various industries in China.It can be seen that China's foreign direct investment is mainly concentrated in the manufacturing, real estate, leasing, and business service industries, which account for about 62%. However, the FDI used for education, public management, and social organizations is particularly small, which shows that the introduction of FDI in China is extremely unbalanced in China, and the proportion of FDI varies greatly among various industries, which is also related to the main development of manufacturing and real estate industry in China.


### 2.2. Model Construction

#### 2.2.1. Determination and Cointegration Test of the Sample Data

First of all, after inquiring about the database of the National Bureau of Statistics, the variables related to foreign direct investment (X1) include the number of contractual utilization of foreign investment projects (X2), the total import and export of foreign-invested enterprises (million US dollars) (X3) net foreign direct investment (X4), and the actual use of foreign direct investment (X5) [18–20]. Collect and collate data from 2009 to 2018 (incomplete data, so only 11 years). The correlation coefficient matrices were calculated between all the variables, such as in Table 3.Table 3
Correlation coefficient matrix between the individual variables.
Covariance correlationX1X2X3X4X5YX11.57E + 121.000000X27.92E +090.5889911.15E +081.000000X32.10E +110.7653977.21E +080.3068134.79E +101.000000X44.41E +120.8256551.56E +100.3405323.75E +110.4013261.82E +131.000000X51.47E +120.9985097.51E +090.5953921.99E +110.7753434.04E +120.8056131.38E +121.000000Y2.03E +110.941857129E +090.6968032.31E +100.6122256.32E +110.8606601.88E +110.9296982.97E +101.000000Table3 shows that from 2009 to 2018, the correlation coefficient between FDI and actual FDI was 0.998509, indicating that the correlation between FDI and FDI is very high, and in the statistical Yearbook, some provinces have no data for FDI, so we can use foreign investment to measure FDI , and the correlation coefficient matrix can provide a theoretical basis for this. For the five variables with FDI, the correlation coefficient between foreign direct investment (X1) and the explained variables is the largest, indicating the strongest correlation between the two sets of data can be used as analyzed sample data.Secondly, use Eviews to make a scatter map of China’s GDP and foreign direct investment, as shown in Figure4.Figure 4
Scatter chart of China's GDP and foreign direct investment.It can be seen from the figure that almost all points are evenly distributed on both sides of the line. GDP (Y) and FD of foreign direct investment I (X1) present a positive trend; combined with the correlation coefficient matrix, we can determine the two sets of time series as the analysis of the required data: economic growth is measured by GDP, recorded as Y: foreign direct investment with the actual use of foreign investment measured as an explanatory variable, recorded as FDI [21].Next, the GDP (Y) and actual FDI from 2000 to 2019 will be taken as the sample data to analyze the problems. Table4 shows China’s GDP and FDI and its development speed.Table 4
China's GDP and FDI and its development speed.
YearGDP/a hundred millionMonth-on-month development speedForeign direct investment/ten thousand dollarsMonth-on-month development speed2000100280.1—4071500—2001110863.11.10553439846878001.1513692742002121717.41.09790723952743001.1251119932003137422.01.12902510253505001.0144474152004161840.21.17768770660630001.1331651252005187318.91.15743121960325000.9949694872006219438.51.17147015165821001.0911065062007270092.31.23083369674768001.1359292632008319244.61.18198334492395001.2357559382009348517.71.09169489590033000.9744358462010412119.31.182491736105735001.1744027192011487940.21.183978037116011001.0971863622012538580.01.103782800111716000.9629776492013592963.21.100975157117586001.0525439512014643563.11.085333963119562001.0168047222015688858.21.070381754126267001.0560796912016746395.11.083525027126001000.9978933532017832035.91.114739231131035001.0399520642018919281.11.104857495134965891.0299987792019990865.01.077869435138100001.023221497Since both sets of GDP and actual FDI are time series, and the time-series data are often nonstable, the stability of GDPs and actual FDI is tested before analyzing the relationship between the two so as to prevent the phenomenon of false regression. After the first-order difference operation between GDP and actual foreign direct investment can pass the stability test of 95% confidence, so Y and FDI are the first-order single integral sequence, which belongs to the same order single integral variable, and may have a long-term stability relationship [22, 23]. Table 5 shows the unit root test results.Table 5
Unit root test results.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%Y1.712258(c, 0, 1)−3.857386−3.040391−2.660551NoDY−5.478450(c, 0, 1)−3.920350−3.065585−2.673459YesFDI−0.801904(c, 0, 1)−3.886751−3.052169−2.666593NoDFDI−3.692204(c, 0, 1)−3.959148−3.081002−2.681330Yes


#### 2.2.2. Sample Data Were Fitted

We take the GDP as the explanatory variable and the actual foreign direct investment as the explanatory variable. The model is set as follows:(1)Yt=C+βFDIt+μt.The model is established by the least-squares method, and the results of the estimated parameters are shown in Table6.Table 6
Regression results of Y versus FDI.
VariableCoefficientStd. Errort-StatisticProb.CFDI−33199410.08295458229.450.005899−570148014.063190.00000.0000R- SquaredAdjusted R-squaredS. E.of regressionSum squared residLog-likelihoodf-statisticprob (F-statistic)0.9165790.911945 85531.891.32E+11–254.4581197.77350.000000Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan–Quinn criteriaDurbin–Watson stat441466.8288237425.6458125.7453825.665240.315429Note. Data are obtained from the Eviews 8.0 regression results.(2)Yt=−331994+0.082954FDIt+μt,R2=0.916579F=197.7735.Extract residuals:(3)μt=Yt+331994−0.082954FDIt.The residual stability (ADF) test is shown in Table7.Table 7
An ADF test for the extracted residuals.
t-StatisticProb.∗Augmented dickey-fuller test statistic−0.3216400.9035Test critical values:1% level5% level10% level−3.857386–3.040391−2.660551The results show that the residual sequence is a nonsmooth sequence, which shows that there is no long-term stable relationship between Y and FDI, contradictory to the previous conclusion. Considering that the GDP unit is billions, and the actual foreign direct investment is dollars, the unit difference is particularly large, so the result may be the difference of units. So, the two groups of sample data measurement to eliminate the effect of the dimension take the logarithm of Y and FDI in Eviews 8.0. For the newly obtained data, Table8 shows the stationarity test after taking the logarithm.Table 8
The stationarity test after taking the logarithm.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t,n)1%5%10%lnY−1.917836(c, 0,1)−3.857386−3.040391−2.660551NoDlnY−5.503037(c, 0,1)−3.920350−3.065585−2.673459YeslnFDI−2.114963(c, 0,1)−3.857386−3.040391−2.660551NoDlnFDI−5.704385(c, 0,1)−3.920350−3.065585−2.673459YesSet the model after eliminating the dimension to the following:(4)lnYt=C+αlnFDIt+μt.Then, Table9 shows the results of the regression of lnY versus lnFDI.Table 9
Results of the regression of lnY versus lnFDI.
VariableCoefficientStd. Errort-StatisticProb.CLNFDI−17.163851.8724531.0958760.068567−15.6622227.308250.00000.0000R-squaredAdjusted R-squaredS.E.of regressionSum squared residLog-likelihoodF- StatisticProb (F-statistic)0.9764320.9751220.1195700.25734715.15187745.74050.000000Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan–Quinn criteriaDurbin–Watson stat12.753700.758087–1315187−1215613–1.2957490.834389Note. Data are obtained from the Eviews 8.0 regression results.The results of the Granger causality test [9] show that the F value of Y = 0.53843 < 0.80041, which shows that gross domestic product (Y) is not the reason for FDI, and similarly, FDI is the reason for gross domestic product (Y).


### 2.3. Establishment of the Error-Correction Model

According to Granger's theorem, the error-correction model can be established with the cointegration relationship between the nonstationary variables, so we can establish the error-correction model between the above two variables. Table10 shows the cointegration regression results of GDP and FDI.Table 10
Cointegration regression results of GDP and FDI.
VariableCoefficientStd. Errort-StatisticProb.CD (LNFDI)ET (−1)0.0957340.4571380.0349500.0111350.1302860.1243098.5978143.5087190.2811560.00000.00430.7834R-squaredAdjusted R-squaredS.E.of regressionSum squared residLog-likelihoodG- StatisticProb (F-statistic)0.5137490.4327070.0331250.01316731.501356.3392960.013218Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan–Quinn criteriaDurbin–Watson stat0.1207980.043980–3.800180 -3.658570–3.8016891.077134The resulting error-correction model is as follows:(5)ΔlnYt∧=0.095734+0.457138ΔlnFDIt+0.034950et−1,R2=0.513749DW=1.077134.The error-correction modelR2=0.513749is relatively bad, but P is far less than 0.05, so the model can be adopted.


### 2.4. Empirical Results Analysis

The empirical results show two aspects: (1) In the short term, FDI has a significant influence on GDP (i.e., economic growth). (2) In the long run, significant impact of FDI on GDP (i. e., economic growth). One percentage point change of FDI will cause a 0.46 percentage point change, and the large introduction of FDI will cause rapid growth of GDP.


### 2.5. Chapter Conclusion

In this section, the variable data needed to fit the model was first determined by the correlation coefficient matrix. After fitting the model, the residue is extracted, and the stationarity of the residue sequence is tested. Granger causality tests the model to find that FDI is the cause of Y. Finally, the model is corrected for error-correction and empirical analysis.


## 2.1. Current Utilization of Foreign Investment in China

Since the reform and opening up, the level of China's export-oriented economy has been continuously improved, the scale of introducing and utilizing foreign capital has been continuously expanding, the level has been continuously improved, and the number of domestic foreign-invested enterprises has also been continuously increasing [9–12]. Next, the statistical analysis of foreign direct investment will be conducted from the change of foreign investment quota, capital source, and industrial investment.


### 2.1.1. Current Situation of Foreign Direct Investment in Recent Years

Data on the actual utilization of foreign direct investment and foreign investment from 2009 to 2018 and 1999 to 2018 are processed in Figures1 and 2.Figure 1
Actual use of foreign direct investment.Figure 2
Data on the actual utilization of foreign capital.From Figure1, we can see that in the decade from 2009 to 2018, both actual utilized foreign direct investment and actual utilized foreign capital showed an upward trend, but the growth trend of actual utilized foreign investment and actual utilized foreign investment between 2012 and 2018 is slower than before. The actual use of foreign investment includes two parts, one is large foreign direct investment and the other is another foreign investment. From Figure 2, we can see the actual use of foreign direct investment and the actual use of foreign investment during the 20 years from 1999–2018 [13]. This shows that in the actual use of foreign investment, the actual use of foreign investment proportion gradually decreased, reduced to 0 in recent years. For example, in 2015, the actual utilization of foreign investment and the actual utilized foreign direct investment were the US $1,26,267 million, in 2016, both were US $12,6001 million, in 2017, both were US $13,1035 million, and in 2018, both were US $13,49,666 million, indicating that the actual utilization of other foreign investment in recent years was 0.


### 2.1.2. Analysis of the Source Structure of Foreign Direct Investment

Under the background of the era of economic globalization, WTO was established on January 1, 1995, a total of 162 members, including a large part of the countries and regions in the world, can say the world economy roughly forms a whole, and the arrival of the electronic information age and transportation more and more convenient, our country and superior geographical location, rich resources attract all over the world to invest in our country [14–16]. China's foreign direct investment sources more areas, spread across five continents. Table 1 is the general situation of the sources of foreign direct investment in China from 2016 to 2018.Table 1
Source structure of FDI in China (ten thousand dollars).
National201620172018Actual investmentProportionActual investmentProportionActual investmentProportionSummation12600100100%13103500100%13496600100%Asia988310378.44%1091938783.33%1070131079.29%Hongkong814650864.65%945090172.13%899172466.62%Taiwan1962801.56%1772471.35%1391361.03%Japan3095852.46%3261002.49%3797802.81%Singapore6046684.80%4763183.64%5210213.86%Korea4751123.77%3672532.80%4666883.46%Africa1127200.89%657460.50%610420.45%Europe9434397.49%8836196.74%11193508.29%Latin America12216189.70%6362734.86%9026466.69%North America3104212.46%4285523.27%5147893.81%Oceania1267941.01%1609501.29%1909041.41%As can be seen from Table1, China's foreign direct investment mainly comes from Asia, accounting for about 80%, while Hong Kong is the main source of foreign direct investment in China [17]. From the data, it can be concluded that more than half of China's foreign direct investment in China comes from Hong Kong, accounting for more than 60%. Among the five continents in the world, China's foreign direct investment from Africa is the least, which is less than 1%, which is directly related to the economic situation of Africa. Compared with Asia, the proportion of foreign direct investment from Europe, Oceania, and Latin America is also relatively small. From the above results, we can find that the source of a foreign direct investment structure is not reasonable, mainly foreign direct investment from Hong Kong. This phenomenon is related to the relationship between the mainland and Hong Kong; the transportation between China and Hong Kong region is very convenient, and for Hong Kong, the mainland also has various preferential policies. In addition, foreign direct investment is also directly related to the distance.


### 2.1.3. The General Situation of Foreign Direct Investment by Industry in China

This summary adopts the data of foreign direct investment in China in 2018 collected by the National Bureau of Statistics. Table2 shows China's foreign direct investment in 2018 is roughly divided by industry (ten thousand dollars).Table 2
China's foreign direct investment in 2018 is roughly divided by industry (ten thousand dollars).
IndexSumActual utilized amount of foreign direct investment13496589The amount of foreign direct investment actually utilized in agriculture, forestry, animal husbandry, and fishery80131The actual amount of foreign direct investment utilized in the mining industry122841The actual amount of foreign direct investment in the manufacturing industry4117421Actual utilization amount of foreign direct investment in the production and supply of electricity, gas, and water442390The amount of foreign direct investment actually utilized in the construction industry148809The actual amount of foreign direct investment in transportation, storage, and postal services472737Actual utilization amount of foreign direct investment in information transmission, computer services, and software industry1166127Actual utilization amount of foreign direct investment in the wholesale and retail industry976689The amount of foreign direct investment is actually utilized in the accommodation and catering industry90107The actual amount of foreign direct investment is utilized in the financial industry870366The actual amount of foreign direct investment is utilized in the real estate industry2246740The actual amount of foreign direct investment in leasing and business services1887459The amount of foreign direct investment actually utilized in scientific research, technical services, and the geological exploration industry681298The amount of foreign direct investment actually utilized by the water conservancy, environment, and public facilities management industry47408The amount of foreign direct investment actually utilized in residential services and other service industries56166The actual amount of foreign direct investment utilized in education7420The amount of foreign direct investment actually used in the health, social security, and social welfare industry30178The actual amount of foreign direct investment is utilized in the cultural, sports, and entertainment industries52290The amount of foreign direct investment actually utilized by public administration and social organizations12Taking out the largest amount of several industries from Table2 into the pie chart, it is more convenient to see the specific direction of FDI in China (Figure 3).Figure 3
The proportion of FDI in various industries in China.It can be seen that China's foreign direct investment is mainly concentrated in the manufacturing, real estate, leasing, and business service industries, which account for about 62%. However, the FDI used for education, public management, and social organizations is particularly small, which shows that the introduction of FDI in China is extremely unbalanced in China, and the proportion of FDI varies greatly among various industries, which is also related to the main development of manufacturing and real estate industry in China.


## 2.1.1. Current Situation of Foreign Direct Investment in Recent Years

Data on the actual utilization of foreign direct investment and foreign investment from 2009 to 2018 and 1999 to 2018 are processed in Figures1 and 2.Figure 1
Actual use of foreign direct investment.Figure 2
Data on the actual utilization of foreign capital.From Figure1, we can see that in the decade from 2009 to 2018, both actual utilized foreign direct investment and actual utilized foreign capital showed an upward trend, but the growth trend of actual utilized foreign investment and actual utilized foreign investment between 2012 and 2018 is slower than before. The actual use of foreign investment includes two parts, one is large foreign direct investment and the other is another foreign investment. From Figure 2, we can see the actual use of foreign direct investment and the actual use of foreign investment during the 20 years from 1999–2018 [13]. This shows that in the actual use of foreign investment, the actual use of foreign investment proportion gradually decreased, reduced to 0 in recent years. For example, in 2015, the actual utilization of foreign investment and the actual utilized foreign direct investment were the US $1,26,267 million, in 2016, both were US $12,6001 million, in 2017, both were US $13,1035 million, and in 2018, both were US $13,49,666 million, indicating that the actual utilization of other foreign investment in recent years was 0.


## 2.1.2. Analysis of the Source Structure of Foreign Direct Investment

Under the background of the era of economic globalization, WTO was established on January 1, 1995, a total of 162 members, including a large part of the countries and regions in the world, can say the world economy roughly forms a whole, and the arrival of the electronic information age and transportation more and more convenient, our country and superior geographical location, rich resources attract all over the world to invest in our country [14–16]. China's foreign direct investment sources more areas, spread across five continents. Table 1 is the general situation of the sources of foreign direct investment in China from 2016 to 2018.Table 1
Source structure of FDI in China (ten thousand dollars).
National201620172018Actual investmentProportionActual investmentProportionActual investmentProportionSummation12600100100%13103500100%13496600100%Asia988310378.44%1091938783.33%1070131079.29%Hongkong814650864.65%945090172.13%899172466.62%Taiwan1962801.56%1772471.35%1391361.03%Japan3095852.46%3261002.49%3797802.81%Singapore6046684.80%4763183.64%5210213.86%Korea4751123.77%3672532.80%4666883.46%Africa1127200.89%657460.50%610420.45%Europe9434397.49%8836196.74%11193508.29%Latin America12216189.70%6362734.86%9026466.69%North America3104212.46%4285523.27%5147893.81%Oceania1267941.01%1609501.29%1909041.41%As can be seen from Table1, China's foreign direct investment mainly comes from Asia, accounting for about 80%, while Hong Kong is the main source of foreign direct investment in China [17]. From the data, it can be concluded that more than half of China's foreign direct investment in China comes from Hong Kong, accounting for more than 60%. Among the five continents in the world, China's foreign direct investment from Africa is the least, which is less than 1%, which is directly related to the economic situation of Africa. Compared with Asia, the proportion of foreign direct investment from Europe, Oceania, and Latin America is also relatively small. From the above results, we can find that the source of a foreign direct investment structure is not reasonable, mainly foreign direct investment from Hong Kong. This phenomenon is related to the relationship between the mainland and Hong Kong; the transportation between China and Hong Kong region is very convenient, and for Hong Kong, the mainland also has various preferential policies. In addition, foreign direct investment is also directly related to the distance.


## 2.1.3. The General Situation of Foreign Direct Investment by Industry in China

This summary adopts the data of foreign direct investment in China in 2018 collected by the National Bureau of Statistics. Table2 shows China's foreign direct investment in 2018 is roughly divided by industry (ten thousand dollars).Table 2
China's foreign direct investment in 2018 is roughly divided by industry (ten thousand dollars).
IndexSumActual utilized amount of foreign direct investment13496589The amount of foreign direct investment actually utilized in agriculture, forestry, animal husbandry, and fishery80131The actual amount of foreign direct investment utilized in the mining industry122841The actual amount of foreign direct investment in the manufacturing industry4117421Actual utilization amount of foreign direct investment in the production and supply of electricity, gas, and water442390The amount of foreign direct investment actually utilized in the construction industry148809The actual amount of foreign direct investment in transportation, storage, and postal services472737Actual utilization amount of foreign direct investment in information transmission, computer services, and software industry1166127Actual utilization amount of foreign direct investment in the wholesale and retail industry976689The amount of foreign direct investment is actually utilized in the accommodation and catering industry90107The actual amount of foreign direct investment is utilized in the financial industry870366The actual amount of foreign direct investment is utilized in the real estate industry2246740The actual amount of foreign direct investment in leasing and business services1887459The amount of foreign direct investment actually utilized in scientific research, technical services, and the geological exploration industry681298The amount of foreign direct investment actually utilized by the water conservancy, environment, and public facilities management industry47408The amount of foreign direct investment actually utilized in residential services and other service industries56166The actual amount of foreign direct investment utilized in education7420The amount of foreign direct investment actually used in the health, social security, and social welfare industry30178The actual amount of foreign direct investment is utilized in the cultural, sports, and entertainment industries52290The amount of foreign direct investment actually utilized by public administration and social organizations12Taking out the largest amount of several industries from Table2 into the pie chart, it is more convenient to see the specific direction of FDI in China (Figure 3).Figure 3
The proportion of FDI in various industries in China.It can be seen that China's foreign direct investment is mainly concentrated in the manufacturing, real estate, leasing, and business service industries, which account for about 62%. However, the FDI used for education, public management, and social organizations is particularly small, which shows that the introduction of FDI in China is extremely unbalanced in China, and the proportion of FDI varies greatly among various industries, which is also related to the main development of manufacturing and real estate industry in China.


## 2.2. Model Construction

### 2.2.1. Determination and Cointegration Test of the Sample Data

First of all, after inquiring about the database of the National Bureau of Statistics, the variables related to foreign direct investment (X1) include the number of contractual utilization of foreign investment projects (X2), the total import and export of foreign-invested enterprises (million US dollars) (X3) net foreign direct investment (X4), and the actual use of foreign direct investment (X5) [18–20]. Collect and collate data from 2009 to 2018 (incomplete data, so only 11 years). The correlation coefficient matrices were calculated between all the variables, such as in Table 3.Table 3
Correlation coefficient matrix between the individual variables.
Covariance correlationX1X2X3X4X5YX11.57E + 121.000000X27.92E +090.5889911.15E +081.000000X32.10E +110.7653977.21E +080.3068134.79E +101.000000X44.41E +120.8256551.56E +100.3405323.75E +110.4013261.82E +131.000000X51.47E +120.9985097.51E +090.5953921.99E +110.7753434.04E +120.8056131.38E +121.000000Y2.03E +110.941857129E +090.6968032.31E +100.6122256.32E +110.8606601.88E +110.9296982.97E +101.000000Table3 shows that from 2009 to 2018, the correlation coefficient between FDI and actual FDI was 0.998509, indicating that the correlation between FDI and FDI is very high, and in the statistical Yearbook, some provinces have no data for FDI, so we can use foreign investment to measure FDI , and the correlation coefficient matrix can provide a theoretical basis for this. For the five variables with FDI, the correlation coefficient between foreign direct investment (X1) and the explained variables is the largest, indicating the strongest correlation between the two sets of data can be used as analyzed sample data.Secondly, use Eviews to make a scatter map of China’s GDP and foreign direct investment, as shown in Figure4.Figure 4
Scatter chart of China's GDP and foreign direct investment.It can be seen from the figure that almost all points are evenly distributed on both sides of the line. GDP (Y) and FD of foreign direct investment I (X1) present a positive trend; combined with the correlation coefficient matrix, we can determine the two sets of time series as the analysis of the required data: economic growth is measured by GDP, recorded as Y: foreign direct investment with the actual use of foreign investment measured as an explanatory variable, recorded as FDI [21].Next, the GDP (Y) and actual FDI from 2000 to 2019 will be taken as the sample data to analyze the problems. Table4 shows China’s GDP and FDI and its development speed.Table 4
China's GDP and FDI and its development speed.
YearGDP/a hundred millionMonth-on-month development speedForeign direct investment/ten thousand dollarsMonth-on-month development speed2000100280.1—4071500—2001110863.11.10553439846878001.1513692742002121717.41.09790723952743001.1251119932003137422.01.12902510253505001.0144474152004161840.21.17768770660630001.1331651252005187318.91.15743121960325000.9949694872006219438.51.17147015165821001.0911065062007270092.31.23083369674768001.1359292632008319244.61.18198334492395001.2357559382009348517.71.09169489590033000.9744358462010412119.31.182491736105735001.1744027192011487940.21.183978037116011001.0971863622012538580.01.103782800111716000.9629776492013592963.21.100975157117586001.0525439512014643563.11.085333963119562001.0168047222015688858.21.070381754126267001.0560796912016746395.11.083525027126001000.9978933532017832035.91.114739231131035001.0399520642018919281.11.104857495134965891.0299987792019990865.01.077869435138100001.023221497Since both sets of GDP and actual FDI are time series, and the time-series data are often nonstable, the stability of GDPs and actual FDI is tested before analyzing the relationship between the two so as to prevent the phenomenon of false regression. After the first-order difference operation between GDP and actual foreign direct investment can pass the stability test of 95% confidence, so Y and FDI are the first-order single integral sequence, which belongs to the same order single integral variable, and may have a long-term stability relationship [22, 23]. Table 5 shows the unit root test results.Table 5
Unit root test results.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%Y1.712258(c, 0, 1)−3.857386−3.040391−2.660551NoDY−5.478450(c, 0, 1)−3.920350−3.065585−2.673459YesFDI−0.801904(c, 0, 1)−3.886751−3.052169−2.666593NoDFDI−3.692204(c, 0, 1)−3.959148−3.081002−2.681330Yes


### 2.2.2. Sample Data Were Fitted

We take the GDP as the explanatory variable and the actual foreign direct investment as the explanatory variable. The model is set as follows:(1)Yt=C+βFDIt+μt.The model is established by the least-squares method, and the results of the estimated parameters are shown in Table6.Table 6
Regression results of Y versus FDI.
VariableCoefficientStd. Errort-StatisticProb.CFDI−33199410.08295458229.450.005899−570148014.063190.00000.0000R- SquaredAdjusted R-squaredS. E.of regressionSum squared residLog-likelihoodf-statisticprob (F-statistic)0.9165790.911945 85531.891.32E+11–254.4581197.77350.000000Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan–Quinn criteriaDurbin–Watson stat441466.8288237425.6458125.7453825.665240.315429Note. Data are obtained from the Eviews 8.0 regression results.(2)Yt=−331994+0.082954FDIt+μt,R2=0.916579F=197.7735.Extract residuals:(3)μt=Yt+331994−0.082954FDIt.The residual stability (ADF) test is shown in Table7.Table 7
An ADF test for the extracted residuals.
t-StatisticProb.∗Augmented dickey-fuller test statistic−0.3216400.9035Test critical values:1% level5% level10% level−3.857386–3.040391−2.660551The results show that the residual sequence is a nonsmooth sequence, which shows that there is no long-term stable relationship between Y and FDI, contradictory to the previous conclusion. Considering that the GDP unit is billions, and the actual foreign direct investment is dollars, the unit difference is particularly large, so the result may be the difference of units. So, the two groups of sample data measurement to eliminate the effect of the dimension take the logarithm of Y and FDI in Eviews 8.0. For the newly obtained data, Table8 shows the stationarity test after taking the logarithm.Table 8
The stationarity test after taking the logarithm.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t,n)1%5%10%lnY−1.917836(c, 0,1)−3.857386−3.040391−2.660551NoDlnY−5.503037(c, 0,1)−3.920350−3.065585−2.673459YeslnFDI−2.114963(c, 0,1)−3.857386−3.040391−2.660551NoDlnFDI−5.704385(c, 0,1)−3.920350−3.065585−2.673459YesSet the model after eliminating the dimension to the following:(4)lnYt=C+αlnFDIt+μt.Then, Table9 shows the results of the regression of lnY versus lnFDI.Table 9
Results of the regression of lnY versus lnFDI.
VariableCoefficientStd. Errort-StatisticProb.CLNFDI−17.163851.8724531.0958760.068567−15.6622227.308250.00000.0000R-squaredAdjusted R-squaredS.E.of regressionSum squared residLog-likelihoodF- StatisticProb (F-statistic)0.9764320.9751220.1195700.25734715.15187745.74050.000000Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan–Quinn criteriaDurbin–Watson stat12.753700.758087–1315187−1215613–1.2957490.834389Note. Data are obtained from the Eviews 8.0 regression results.The results of the Granger causality test [9] show that the F value of Y = 0.53843 < 0.80041, which shows that gross domestic product (Y) is not the reason for FDI, and similarly, FDI is the reason for gross domestic product (Y).


## 2.2.1. Determination and Cointegration Test of the Sample Data

First of all, after inquiring about the database of the National Bureau of Statistics, the variables related to foreign direct investment (X1) include the number of contractual utilization of foreign investment projects (X2), the total import and export of foreign-invested enterprises (million US dollars) (X3) net foreign direct investment (X4), and the actual use of foreign direct investment (X5) [18–20]. Collect and collate data from 2009 to 2018 (incomplete data, so only 11 years). The correlation coefficient matrices were calculated between all the variables, such as in Table 3.Table 3
Correlation coefficient matrix between the individual variables.
Covariance correlationX1X2X3X4X5YX11.57E + 121.000000X27.92E +090.5889911.15E +081.000000X32.10E +110.7653977.21E +080.3068134.79E +101.000000X44.41E +120.8256551.56E +100.3405323.75E +110.4013261.82E +131.000000X51.47E +120.9985097.51E +090.5953921.99E +110.7753434.04E +120.8056131.38E +121.000000Y2.03E +110.941857129E +090.6968032.31E +100.6122256.32E +110.8606601.88E +110.9296982.97E +101.000000Table3 shows that from 2009 to 2018, the correlation coefficient between FDI and actual FDI was 0.998509, indicating that the correlation between FDI and FDI is very high, and in the statistical Yearbook, some provinces have no data for FDI, so we can use foreign investment to measure FDI , and the correlation coefficient matrix can provide a theoretical basis for this. For the five variables with FDI, the correlation coefficient between foreign direct investment (X1) and the explained variables is the largest, indicating the strongest correlation between the two sets of data can be used as analyzed sample data.Secondly, use Eviews to make a scatter map of China’s GDP and foreign direct investment, as shown in Figure4.Figure 4
Scatter chart of China's GDP and foreign direct investment.It can be seen from the figure that almost all points are evenly distributed on both sides of the line. GDP (Y) and FD of foreign direct investment I (X1) present a positive trend; combined with the correlation coefficient matrix, we can determine the two sets of time series as the analysis of the required data: economic growth is measured by GDP, recorded as Y: foreign direct investment with the actual use of foreign investment measured as an explanatory variable, recorded as FDI [21].Next, the GDP (Y) and actual FDI from 2000 to 2019 will be taken as the sample data to analyze the problems. Table4 shows China’s GDP and FDI and its development speed.Table 4
China's GDP and FDI and its development speed.
YearGDP/a hundred millionMonth-on-month development speedForeign direct investment/ten thousand dollarsMonth-on-month development speed2000100280.1—4071500—2001110863.11.10553439846878001.1513692742002121717.41.09790723952743001.1251119932003137422.01.12902510253505001.0144474152004161840.21.17768770660630001.1331651252005187318.91.15743121960325000.9949694872006219438.51.17147015165821001.0911065062007270092.31.23083369674768001.1359292632008319244.61.18198334492395001.2357559382009348517.71.09169489590033000.9744358462010412119.31.182491736105735001.1744027192011487940.21.183978037116011001.0971863622012538580.01.103782800111716000.9629776492013592963.21.100975157117586001.0525439512014643563.11.085333963119562001.0168047222015688858.21.070381754126267001.0560796912016746395.11.083525027126001000.9978933532017832035.91.114739231131035001.0399520642018919281.11.104857495134965891.0299987792019990865.01.077869435138100001.023221497Since both sets of GDP and actual FDI are time series, and the time-series data are often nonstable, the stability of GDPs and actual FDI is tested before analyzing the relationship between the two so as to prevent the phenomenon of false regression. After the first-order difference operation between GDP and actual foreign direct investment can pass the stability test of 95% confidence, so Y and FDI are the first-order single integral sequence, which belongs to the same order single integral variable, and may have a long-term stability relationship [22, 23]. Table 5 shows the unit root test results.Table 5
Unit root test results.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%Y1.712258(c, 0, 1)−3.857386−3.040391−2.660551NoDY−5.478450(c, 0, 1)−3.920350−3.065585−2.673459YesFDI−0.801904(c, 0, 1)−3.886751−3.052169−2.666593NoDFDI−3.692204(c, 0, 1)−3.959148−3.081002−2.681330Yes


## 2.2.2. Sample Data Were Fitted

We take the GDP as the explanatory variable and the actual foreign direct investment as the explanatory variable. The model is set as follows:(1)Yt=C+βFDIt+μt.The model is established by the least-squares method, and the results of the estimated parameters are shown in Table6.Table 6
Regression results of Y versus FDI.
VariableCoefficientStd. Errort-StatisticProb.CFDI−33199410.08295458229.450.005899−570148014.063190.00000.0000R- SquaredAdjusted R-squaredS. E.of regressionSum squared residLog-likelihoodf-statisticprob (F-statistic)0.9165790.911945 85531.891.32E+11–254.4581197.77350.000000Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan–Quinn criteriaDurbin–Watson stat441466.8288237425.6458125.7453825.665240.315429Note. Data are obtained from the Eviews 8.0 regression results.(2)Yt=−331994+0.082954FDIt+μt,R2=0.916579F=197.7735.Extract residuals:(3)μt=Yt+331994−0.082954FDIt.The residual stability (ADF) test is shown in Table7.Table 7
An ADF test for the extracted residuals.
t-StatisticProb.∗Augmented dickey-fuller test statistic−0.3216400.9035Test critical values:1% level5% level10% level−3.857386–3.040391−2.660551The results show that the residual sequence is a nonsmooth sequence, which shows that there is no long-term stable relationship between Y and FDI, contradictory to the previous conclusion. Considering that the GDP unit is billions, and the actual foreign direct investment is dollars, the unit difference is particularly large, so the result may be the difference of units. So, the two groups of sample data measurement to eliminate the effect of the dimension take the logarithm of Y and FDI in Eviews 8.0. For the newly obtained data, Table8 shows the stationarity test after taking the logarithm.Table 8
The stationarity test after taking the logarithm.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t,n)1%5%10%lnY−1.917836(c, 0,1)−3.857386−3.040391−2.660551NoDlnY−5.503037(c, 0,1)−3.920350−3.065585−2.673459YeslnFDI−2.114963(c, 0,1)−3.857386−3.040391−2.660551NoDlnFDI−5.704385(c, 0,1)−3.920350−3.065585−2.673459YesSet the model after eliminating the dimension to the following:(4)lnYt=C+αlnFDIt+μt.Then, Table9 shows the results of the regression of lnY versus lnFDI.Table 9
Results of the regression of lnY versus lnFDI.
VariableCoefficientStd. Errort-StatisticProb.CLNFDI−17.163851.8724531.0958760.068567−15.6622227.308250.00000.0000R-squaredAdjusted R-squaredS.E.of regressionSum squared residLog-likelihoodF- StatisticProb (F-statistic)0.9764320.9751220.1195700.25734715.15187745.74050.000000Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan–Quinn criteriaDurbin–Watson stat12.753700.758087–1315187−1215613–1.2957490.834389Note. Data are obtained from the Eviews 8.0 regression results.The results of the Granger causality test [9] show that the F value of Y = 0.53843 < 0.80041, which shows that gross domestic product (Y) is not the reason for FDI, and similarly, FDI is the reason for gross domestic product (Y).


## 2.3. Establishment of the Error-Correction Model

According to Granger's theorem, the error-correction model can be established with the cointegration relationship between the nonstationary variables, so we can establish the error-correction model between the above two variables. Table10 shows the cointegration regression results of GDP and FDI.Table 10
Cointegration regression results of GDP and FDI.
VariableCoefficientStd. Errort-StatisticProb.CD (LNFDI)ET (−1)0.0957340.4571380.0349500.0111350.1302860.1243098.5978143.5087190.2811560.00000.00430.7834R-squaredAdjusted R-squaredS.E.of regressionSum squared residLog-likelihoodG- StatisticProb (F-statistic)0.5137490.4327070.0331250.01316731.501356.3392960.013218Mean dependent varS.D. dependent varAkaike info criterionSchwarz criterionHannan–Quinn criteriaDurbin–Watson stat0.1207980.043980–3.800180 -3.658570–3.8016891.077134The resulting error-correction model is as follows:(5)ΔlnYt∧=0.095734+0.457138ΔlnFDIt+0.034950et−1,R2=0.513749DW=1.077134.The error-correction modelR2=0.513749is relatively bad, but P is far less than 0.05, so the model can be adopted.


## 2.4. Empirical Results Analysis

The empirical results show two aspects: (1) In the short term, FDI has a significant influence on GDP (i.e., economic growth). (2) In the long run, significant impact of FDI on GDP (i. e., economic growth). One percentage point change of FDI will cause a 0.46 percentage point change, and the large introduction of FDI will cause rapid growth of GDP.


## 2.5. Chapter Conclusion

In this section, the variable data needed to fit the model was first determined by the correlation coefficient matrix. After fitting the model, the residue is extracted, and the stationarity of the residue sequence is tested. Granger causality tests the model to find that FDI is the cause of Y. Finally, the model is corrected for error-correction and empirical analysis.


## 3. The Impact of Foreign Direct Investment on the Economy of Different Regions

### 3.1. Model Construction

When analyzing the impact of foreign direct investment on the economy of different regions, the factors with great impact on economic growth, such as consumption level, net export and labor force level (L), and domestic direct investment, should be combined [24, 25]. The sample data required for the analysis of the economic growth (Y) by FDI, consumption level (CPI), net export (NE), labor (L), (L), domestic investment (K), after the elimination of magnitude, the model can be set as follows:(6)lnYt=α1lnFDIt+α2lnNEt+α3lnKt+α4lnLt+α5lnCPIt+μt.


#### 3.1.1. Economic Growth Model of Coastal Areas (Jiangsu Province as an Example)

Consumption level is measured by per capita consumption (CPI), labor (L) is employed, NE is expressed by the difference between exports and imports, and domestic investment (K) is measured by social fixed asset investment. Data of each variable in Jiangsu Province are collected as shown in Table 11.Table 11
Data of each variable in Jiangsu Province.
YearGDP(Y)/a hundred millionFDI/ten thousand dollarsConsumption per person (CPI)/YuanQuantity of employment (L)/thousands of peopleSocial fixed assets investment(K)/a hundred millionNet export amount(NE)/ten thousand dollars19997697.8263991535944390.712742.6553.5720008553.6964235838734418.142995.4359.0220019456.8471220141234436.453302.9664.01200210606.85103661547084472.843849.2466.55200312442.87158021452614499.975335.8046.10200415003.60121378359134537.076827.5941.37200518598.69131833970664578.758739.71180.23200621742.05174314081824628.9510071.24368.42200726018.48218920695304677.8812268.07577.95200830981.982512001108824700.9615060.45838.04200934457.302532289119934726.5418949.88596.54201041425.482849777140354754.6823184.28753.08201149110.273213173171674758.2326314.66854.87201254058.223575956194524759.5331706.581089.83201359753.373325922235854759.8935982.521068.69201465088.322817416283164760.8341552.751199.76201570116.382427469316824758.5045905.171317.23201677388.282454296358754756.2249370.851290.76201785869.762513541397964757.8053000.211354.58Due to the inconsistent dimensions of each index, the influence of log-eliminating the dimensions and the sample data are all time-series data, so the stability needs to be tested. According to the results in Table11, the first-order difference sequence is stationary [26–35], indicating a long-term relationship between these variables. Table 12 shows the test of the stationarity of the sample data.Table 12
Test of the stationarity of the sample data.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%lnY−1.650678(c, 0, 1)−3.886751−3.052169−2.666593NoDlnY−3.675515(c, 0, 1)−3.959148−3.081002−2.681330YeslnFDI−2.432745(c, 0, 1)−3.920350−3.065585−2.673459NoDlnFDI−5.173851(c, 0, 1)−3.920350−3.065585−2.673459YeslnNE−1.153147(c, 0, 1)−3.886751−3.052169−2.666593NoDlnNE−3.732355(c, 0, 1)−3.959148−3.081002−2.681330YeslnK−2.191837(c, 0, 1)−3.920350−3.065585−2.673459NoDlnK−6.132102(c, 0, 1)−3.920350−3.065585−2.673459YeslnL−2.104357(c, 0, 1)−3.886751−3.052169−2.666593NoDlnL−3.100355(c, 0, 1)−3.959148−3.081002−2.681330YeslnCPI0.304070(c, 0, 1)−3.886751−3.052169−2.666593NoDlnCPI−4.820446(c, 0, 1)−3.959148−3.081002−2.681330YesThe model can therefore be set to the following:(7)lnYt=α1lnFDIt+α2lnNEt+α3lnKt+α4lnLt+α5lnCPIt+μt.The results of fitting these variables at Eviews 8.0 are shown in Table13.Table 13
Fitting results of Jiangsu Province Economic Growth Model.
VariableCoefficientStd. Errort-StatisticProb.LNFDI0.0630490.0433321.4550210.1677LNNE0.0450710.0140363.2110340.0063LNK0.4906810.0792676.1902010.0000LNL0.2231550.0692203.2238510.0061LNCPI0.2685690.0796603.3714300.0046R-squared adjusted R-squared S.E. of regression sum squared resid log-likelihood Durbin–Watson stat0.999152Mean dependent var10233290.811391-4.180496-3.931960-4.1384340.998910S.D. dependent var0.026792Akaike info criterion0.010049Schwarz criterion44.71471Hannan–Quinn criteria1.600222The resulting fitted model is as follows:(8)lnYt=0.063049lnFDIt+0.045071lnNEt+0.490681lnKt+0.223155lnLt+0.268569lnCPIt+μt,R2=0.999152.Table14 shows the Stationarity test of the residuals. It can be seen that the fitting effect is good, and then the residuals of the model are proposed as follows:(9)μt=lnYt−0.063049lnFDIt−0.04507lnNEt−0.490681lnKt−0.223155lnLt−0.268569lnCPIt.Table 14
Stationarity test of the residuals.
t-StatisticProb.∗Augmented Dickey–Fuller test statistic−3.629831−0.0166Test critical values:1% level5% level10% level−3.886751–3.052169−2.666593The results in Table13 show that at the 5% significance level, the value of the t-test statistic is-3.629831, less than the cut-off of-3.052169, rejecting the null hypothesis that the residual root from the model and the residual sequence are a stationary sequence, and the long-term relationship between the explanatory variables and the explained variables can be learned.


#### 3.1.2. Economic Growth Model of the Central Region (Henan Province as an Example)

Data was collected first according to the coastal area operation method. The results obtained are as shown in Table15.Table 15
Data of each variable in Henan Province.
YearGDP(Y)/a hundred millionFDI/ten thousand dollarsConsumption per person (CPI)/YuanQuantity of employment (L)/thousands of peopleSocial fixed assets investment(K)/a hundred millionNet export amount(NE)/ten thousand dollars19994517.9449527190552051206.835.073420005052.9953999221555721377.747.119020015533.0135861238155171544.066.384020026035.4845165255355221725.9310.340120036867.7056149308355362262.9712.444220048553.7987367362555873099.3817.3874200510587.42122960409256624311.6324.6582200612362.79184526453057195904.7134.7399200715012.46306162514157738010.1139.7798200818018.534032665877583510490.6439.5846200919480.464798586607594913704.5012.5457201023092.366246707837604216585.8632.7737201126931.0310082099171619817768.9558.3868201229599.31121177710380628821450.0076.0549201332191.30134565911820638726087.46120.1733201434938.24149268813078652030782.17137.3452201537002.16160863714507663635660.35123.4221201640471.79169931216043672640415.09144.4215201744552.83172242817842676744496.93164.4519All the data in Table15 are done as in the previous section, and Table 16 shows the test of the stationarity of the sample data.Table 16
Test of the stationarity of the sample data.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%lnY−1.385376(c, 0, 1)−3.886751−3.052169−2.666593NoDlnY−4.270420(c, 0, 1)−3.959148−3.081002−2.681330YeslnFDI−0.842845(c, 0, 1)−3.920350−3.065585−2.673459NoDlnFDI−3.119600(c, 0, 1)−3.920350−3.065585−2.673459YeslnNE−0.819215(c, 0, 1)−3.886751−3.052169−2.666593NoDlnNE−4.411203(c, 0, 1)−3.959148−3.081002−2.681330YeslnK−1.851008(c, 0, 1)−3.920350−3.065585−2.673459NoDlnK−2.754575(c, 0, 1)−3.920350−3.065585−2.673459YeslnL2.360425(c, 0, 1)−3.886751−3.052169−2.666593NoDlnL−3.633860(c, 0, 1)−3.959148−3.081002−2.681330YeslnCPI−0.236929(c, 0, 1)−3.886751−3.052169−2.666593NoDlnCPI−5.661558(c, 0, 1)−3.959148−3.081002−2.681330YesNote. The data are obtained from Eviews 8.0.The model-fitting results are as follows:(10)lnYt=0.044262lnFDIt+0.042003lnNEt+0.526400lnKt+0.523185lnLt−0.040786lnCPIt+μt,R2=0.998457.Extract residuals:(11)μt=lnYt−0.044262lnFDIt−0.042003lnNEt−0.0526400lnKt−0.523185lnLt+0.040786lnCPIt.Table17 is the Residual stationarity test. The results in Table 17 show that at the 5% significance level, the value of the t-test statistic is −3.748439, less than the cut-off of −3.052169. Thus, it rejects the null hypothesis that the residual roots from the model and the residual sequence are stationary sequences, and the long-term relationship between the explanatory variables and the explained variables can be learned.Table 17
Residual stationarity test.
t-StatisticProb.∗Augmented Dickey–Fuller test statistic−3.7484390.0131Test critical values:1%level5%level10%level−3.886751–3.052169−2.666593


### 3.2. The Differences Were Analyzed by Combining the Two Regional Models and the Actual Situation

#### 3.2.1. Analysis of the Speed of FDI in the Two Regions

Taking the quota of foreign direct investment from 1999 to 2017 as the research object, the sequential development rate, fixed base development rate, and average development rate are calculated as shown in Table18.Table 18
Speed of FDI development in both regions.
YearHenan ProvinceJiangsu ProvinceFDIMonth-on-month development speedDetermine the development speed of the foundationFDIMonth-on-month development speedDetermine the development speed of the foundation199949527——639915——2000539991.090291.090296423581.003821.003822001358610.664100.724077122011.108731.112962002451651.259450.9119310366151.455511.619932003561491.243201.1337015802141.524402.469412004873671.555981.7640312137830.768111.8967920051229601.407402.4826913183391.086142.0601820061845261.500703.7257717431401.322222.7240220073061621.659186.1817221892061.255903.4210920084032661.317178.1423525120011.147453.9255220094798581.189939.6888225322891.008083.9572320106246701.3017812.6127228497771.125384.45337201110082091.6139920.3567532131731.127525.02125201212117771.2019124.4670035759561.112905.58817201313456591.1104827.1702133259220.930085.19744201414926881.1092630.1388728174160.847114.40280201516086371.0776832.4800024274690.861593.79342201616993121.0563734.3108224542961.011053.83535201717224281.0136034.7775625135411.024143.92793It can be seen from the calculation results in Table18 that the month-on-month development rate of foreign direct investment in Jiangsu Province is basically stable between 0.84 and 1.15, while the floating range of the month-on-month development rate in Henan Province is between 0.66 and 1.67, which is slightly larger than that of Jiangsu Province.The average development speed of FDI in Henan province and Jiangsu Province is calculated as follows:(12)x¯G=x1x2⋯xnn=Rn.According to the formula, the average development rate of FDI in Henan province is 1.20537, while the average development rate of FDI in Jiangsu Province is 1.07466. It can be seen that the average development rate of FDI in Henan province is higher than that of Jiangsu Province, but because its base is far smaller than that of Jiangsu Province, although it has grown too fast in the past 20 years, it is much different from Jiangsu Province.


#### 3.2.2. Analysis of the Industrial Structure of Foreign Direct Investment in the Two Regions

The general situation of FDI by industry is as follows: the total foreign direct investment in 2017 was $2513541 million, of which FDI of manufacturing was $1118072 million, real estate $346007 million, leasing and business services $223912 million, electricity, heat, gas, and water production and supply $578.78 million, construction $2276.35 million, and other industries accumulated $540.34 million, as shown in Figure5.Figure 5
The portion of FDI industries in Jiangsu Province.In 2017, FDI in Henan province showed a total FDI of $1722428 million, including manufacturing utilization FDI of $1034835 million, electricity, heat, gas, and water $2311.5 million, leasing and business services $8788.44 million, real estate $187056 million, construction $27.56 million, and $178,7.87 million in other industries. Figure6 shows the portion of FDI industries in Henan Province.Figure 6
The portion of FDI industries in Henan Province.


#### 3.2.3. Analysis of the Causes of the Difference

The economic growth model of Henan Province is as follows:(13)lnYt=0.044262lnFDIt+0.042003lnNEt+0.526400lnKt+0.523185lnLt−0.040786lnCPIt+μt.The economic growth model of Jiangsu Province is as follows:(14)lnYt=0.063049lnFDIt+0.045071lnNEt+0.490681lnKt+0.223155lnLt+0.268569lnCPIt+μt.It can be seen that when other variables remain unchanged, for every 1 percentage point of FDI growth, the GDP of Henan Province increased by 0.044262 percentage points, while Jiangsu Province increased by 0.063049 percentage points, and the difference between the two regions was 0.018787 percentage points. There are many reasons for this difference. From the analysis of this chapter, we can find some reasons: First of all, the distribution of FDI in Henan province is relatively uneven. FDI has been invested too much in the manufacturing industry, as high as 60%, while the manufacturing industry in Jiangsu Province is 44%, which is also due to the inconsistency between the leading industries in the two regions. Secondly, the construction industry is an important industry in promoting economic development. However, in terms of the construction industry, the FDI utilization in Jiangsu Province accounts for 9%, while the FDI introduced in the construction industry is only 0.0016%. Finally, although the growth rate of FDI introduced in Henan Province is very fast, its amount is far less than that of Jiangsu Province. Jiangsu Province has formed a relatively mature foreign joint venture, while the foreign investment in Henan Province is in the growth period, and the number of foreign direct investment cooperative enterprises is small.


## 3.1. Model Construction

When analyzing the impact of foreign direct investment on the economy of different regions, the factors with great impact on economic growth, such as consumption level, net export and labor force level (L), and domestic direct investment, should be combined [24, 25]. The sample data required for the analysis of the economic growth (Y) by FDI, consumption level (CPI), net export (NE), labor (L), (L), domestic investment (K), after the elimination of magnitude, the model can be set as follows:(6)lnYt=α1lnFDIt+α2lnNEt+α3lnKt+α4lnLt+α5lnCPIt+μt.


### 3.1.1. Economic Growth Model of Coastal Areas (Jiangsu Province as an Example)

Consumption level is measured by per capita consumption (CPI), labor (L) is employed, NE is expressed by the difference between exports and imports, and domestic investment (K) is measured by social fixed asset investment. Data of each variable in Jiangsu Province are collected as shown in Table 11.Table 11
Data of each variable in Jiangsu Province.
YearGDP(Y)/a hundred millionFDI/ten thousand dollarsConsumption per person (CPI)/YuanQuantity of employment (L)/thousands of peopleSocial fixed assets investment(K)/a hundred millionNet export amount(NE)/ten thousand dollars19997697.8263991535944390.712742.6553.5720008553.6964235838734418.142995.4359.0220019456.8471220141234436.453302.9664.01200210606.85103661547084472.843849.2466.55200312442.87158021452614499.975335.8046.10200415003.60121378359134537.076827.5941.37200518598.69131833970664578.758739.71180.23200621742.05174314081824628.9510071.24368.42200726018.48218920695304677.8812268.07577.95200830981.982512001108824700.9615060.45838.04200934457.302532289119934726.5418949.88596.54201041425.482849777140354754.6823184.28753.08201149110.273213173171674758.2326314.66854.87201254058.223575956194524759.5331706.581089.83201359753.373325922235854759.8935982.521068.69201465088.322817416283164760.8341552.751199.76201570116.382427469316824758.5045905.171317.23201677388.282454296358754756.2249370.851290.76201785869.762513541397964757.8053000.211354.58Due to the inconsistent dimensions of each index, the influence of log-eliminating the dimensions and the sample data are all time-series data, so the stability needs to be tested. According to the results in Table11, the first-order difference sequence is stationary [26–35], indicating a long-term relationship between these variables. Table 12 shows the test of the stationarity of the sample data.Table 12
Test of the stationarity of the sample data.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%lnY−1.650678(c, 0, 1)−3.886751−3.052169−2.666593NoDlnY−3.675515(c, 0, 1)−3.959148−3.081002−2.681330YeslnFDI−2.432745(c, 0, 1)−3.920350−3.065585−2.673459NoDlnFDI−5.173851(c, 0, 1)−3.920350−3.065585−2.673459YeslnNE−1.153147(c, 0, 1)−3.886751−3.052169−2.666593NoDlnNE−3.732355(c, 0, 1)−3.959148−3.081002−2.681330YeslnK−2.191837(c, 0, 1)−3.920350−3.065585−2.673459NoDlnK−6.132102(c, 0, 1)−3.920350−3.065585−2.673459YeslnL−2.104357(c, 0, 1)−3.886751−3.052169−2.666593NoDlnL−3.100355(c, 0, 1)−3.959148−3.081002−2.681330YeslnCPI0.304070(c, 0, 1)−3.886751−3.052169−2.666593NoDlnCPI−4.820446(c, 0, 1)−3.959148−3.081002−2.681330YesThe model can therefore be set to the following:(7)lnYt=α1lnFDIt+α2lnNEt+α3lnKt+α4lnLt+α5lnCPIt+μt.The results of fitting these variables at Eviews 8.0 are shown in Table13.Table 13
Fitting results of Jiangsu Province Economic Growth Model.
VariableCoefficientStd. Errort-StatisticProb.LNFDI0.0630490.0433321.4550210.1677LNNE0.0450710.0140363.2110340.0063LNK0.4906810.0792676.1902010.0000LNL0.2231550.0692203.2238510.0061LNCPI0.2685690.0796603.3714300.0046R-squared adjusted R-squared S.E. of regression sum squared resid log-likelihood Durbin–Watson stat0.999152Mean dependent var10233290.811391-4.180496-3.931960-4.1384340.998910S.D. dependent var0.026792Akaike info criterion0.010049Schwarz criterion44.71471Hannan–Quinn criteria1.600222The resulting fitted model is as follows:(8)lnYt=0.063049lnFDIt+0.045071lnNEt+0.490681lnKt+0.223155lnLt+0.268569lnCPIt+μt,R2=0.999152.Table14 shows the Stationarity test of the residuals. It can be seen that the fitting effect is good, and then the residuals of the model are proposed as follows:(9)μt=lnYt−0.063049lnFDIt−0.04507lnNEt−0.490681lnKt−0.223155lnLt−0.268569lnCPIt.Table 14
Stationarity test of the residuals.
t-StatisticProb.∗Augmented Dickey–Fuller test statistic−3.629831−0.0166Test critical values:1% level5% level10% level−3.886751–3.052169−2.666593The results in Table13 show that at the 5% significance level, the value of the t-test statistic is-3.629831, less than the cut-off of-3.052169, rejecting the null hypothesis that the residual root from the model and the residual sequence are a stationary sequence, and the long-term relationship between the explanatory variables and the explained variables can be learned.


### 3.1.2. Economic Growth Model of the Central Region (Henan Province as an Example)

Data was collected first according to the coastal area operation method. The results obtained are as shown in Table15.Table 15
Data of each variable in Henan Province.
YearGDP(Y)/a hundred millionFDI/ten thousand dollarsConsumption per person (CPI)/YuanQuantity of employment (L)/thousands of peopleSocial fixed assets investment(K)/a hundred millionNet export amount(NE)/ten thousand dollars19994517.9449527190552051206.835.073420005052.9953999221555721377.747.119020015533.0135861238155171544.066.384020026035.4845165255355221725.9310.340120036867.7056149308355362262.9712.444220048553.7987367362555873099.3817.3874200510587.42122960409256624311.6324.6582200612362.79184526453057195904.7134.7399200715012.46306162514157738010.1139.7798200818018.534032665877583510490.6439.5846200919480.464798586607594913704.5012.5457201023092.366246707837604216585.8632.7737201126931.0310082099171619817768.9558.3868201229599.31121177710380628821450.0076.0549201332191.30134565911820638726087.46120.1733201434938.24149268813078652030782.17137.3452201537002.16160863714507663635660.35123.4221201640471.79169931216043672640415.09144.4215201744552.83172242817842676744496.93164.4519All the data in Table15 are done as in the previous section, and Table 16 shows the test of the stationarity of the sample data.Table 16
Test of the stationarity of the sample data.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%lnY−1.385376(c, 0, 1)−3.886751−3.052169−2.666593NoDlnY−4.270420(c, 0, 1)−3.959148−3.081002−2.681330YeslnFDI−0.842845(c, 0, 1)−3.920350−3.065585−2.673459NoDlnFDI−3.119600(c, 0, 1)−3.920350−3.065585−2.673459YeslnNE−0.819215(c, 0, 1)−3.886751−3.052169−2.666593NoDlnNE−4.411203(c, 0, 1)−3.959148−3.081002−2.681330YeslnK−1.851008(c, 0, 1)−3.920350−3.065585−2.673459NoDlnK−2.754575(c, 0, 1)−3.920350−3.065585−2.673459YeslnL2.360425(c, 0, 1)−3.886751−3.052169−2.666593NoDlnL−3.633860(c, 0, 1)−3.959148−3.081002−2.681330YeslnCPI−0.236929(c, 0, 1)−3.886751−3.052169−2.666593NoDlnCPI−5.661558(c, 0, 1)−3.959148−3.081002−2.681330YesNote. The data are obtained from Eviews 8.0.The model-fitting results are as follows:(10)lnYt=0.044262lnFDIt+0.042003lnNEt+0.526400lnKt+0.523185lnLt−0.040786lnCPIt+μt,R2=0.998457.Extract residuals:(11)μt=lnYt−0.044262lnFDIt−0.042003lnNEt−0.0526400lnKt−0.523185lnLt+0.040786lnCPIt.Table17 is the Residual stationarity test. The results in Table 17 show that at the 5% significance level, the value of the t-test statistic is −3.748439, less than the cut-off of −3.052169. Thus, it rejects the null hypothesis that the residual roots from the model and the residual sequence are stationary sequences, and the long-term relationship between the explanatory variables and the explained variables can be learned.Table 17
Residual stationarity test.
t-StatisticProb.∗Augmented Dickey–Fuller test statistic−3.7484390.0131Test critical values:1%level5%level10%level−3.886751–3.052169−2.666593


## 3.1.1. Economic Growth Model of Coastal Areas (Jiangsu Province as an Example)

Consumption level is measured by per capita consumption (CPI), labor (L) is employed, NE is expressed by the difference between exports and imports, and domestic investment (K) is measured by social fixed asset investment. Data of each variable in Jiangsu Province are collected as shown in Table 11.Table 11
Data of each variable in Jiangsu Province.
YearGDP(Y)/a hundred millionFDI/ten thousand dollarsConsumption per person (CPI)/YuanQuantity of employment (L)/thousands of peopleSocial fixed assets investment(K)/a hundred millionNet export amount(NE)/ten thousand dollars19997697.8263991535944390.712742.6553.5720008553.6964235838734418.142995.4359.0220019456.8471220141234436.453302.9664.01200210606.85103661547084472.843849.2466.55200312442.87158021452614499.975335.8046.10200415003.60121378359134537.076827.5941.37200518598.69131833970664578.758739.71180.23200621742.05174314081824628.9510071.24368.42200726018.48218920695304677.8812268.07577.95200830981.982512001108824700.9615060.45838.04200934457.302532289119934726.5418949.88596.54201041425.482849777140354754.6823184.28753.08201149110.273213173171674758.2326314.66854.87201254058.223575956194524759.5331706.581089.83201359753.373325922235854759.8935982.521068.69201465088.322817416283164760.8341552.751199.76201570116.382427469316824758.5045905.171317.23201677388.282454296358754756.2249370.851290.76201785869.762513541397964757.8053000.211354.58Due to the inconsistent dimensions of each index, the influence of log-eliminating the dimensions and the sample data are all time-series data, so the stability needs to be tested. According to the results in Table11, the first-order difference sequence is stationary [26–35], indicating a long-term relationship between these variables. Table 12 shows the test of the stationarity of the sample data.Table 12
Test of the stationarity of the sample data.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%lnY−1.650678(c, 0, 1)−3.886751−3.052169−2.666593NoDlnY−3.675515(c, 0, 1)−3.959148−3.081002−2.681330YeslnFDI−2.432745(c, 0, 1)−3.920350−3.065585−2.673459NoDlnFDI−5.173851(c, 0, 1)−3.920350−3.065585−2.673459YeslnNE−1.153147(c, 0, 1)−3.886751−3.052169−2.666593NoDlnNE−3.732355(c, 0, 1)−3.959148−3.081002−2.681330YeslnK−2.191837(c, 0, 1)−3.920350−3.065585−2.673459NoDlnK−6.132102(c, 0, 1)−3.920350−3.065585−2.673459YeslnL−2.104357(c, 0, 1)−3.886751−3.052169−2.666593NoDlnL−3.100355(c, 0, 1)−3.959148−3.081002−2.681330YeslnCPI0.304070(c, 0, 1)−3.886751−3.052169−2.666593NoDlnCPI−4.820446(c, 0, 1)−3.959148−3.081002−2.681330YesThe model can therefore be set to the following:(7)lnYt=α1lnFDIt+α2lnNEt+α3lnKt+α4lnLt+α5lnCPIt+μt.The results of fitting these variables at Eviews 8.0 are shown in Table13.Table 13
Fitting results of Jiangsu Province Economic Growth Model.
VariableCoefficientStd. Errort-StatisticProb.LNFDI0.0630490.0433321.4550210.1677LNNE0.0450710.0140363.2110340.0063LNK0.4906810.0792676.1902010.0000LNL0.2231550.0692203.2238510.0061LNCPI0.2685690.0796603.3714300.0046R-squared adjusted R-squared S.E. of regression sum squared resid log-likelihood Durbin–Watson stat0.999152Mean dependent var10233290.811391-4.180496-3.931960-4.1384340.998910S.D. dependent var0.026792Akaike info criterion0.010049Schwarz criterion44.71471Hannan–Quinn criteria1.600222The resulting fitted model is as follows:(8)lnYt=0.063049lnFDIt+0.045071lnNEt+0.490681lnKt+0.223155lnLt+0.268569lnCPIt+μt,R2=0.999152.Table14 shows the Stationarity test of the residuals. It can be seen that the fitting effect is good, and then the residuals of the model are proposed as follows:(9)μt=lnYt−0.063049lnFDIt−0.04507lnNEt−0.490681lnKt−0.223155lnLt−0.268569lnCPIt.Table 14
Stationarity test of the residuals.
t-StatisticProb.∗Augmented Dickey–Fuller test statistic−3.629831−0.0166Test critical values:1% level5% level10% level−3.886751–3.052169−2.666593The results in Table13 show that at the 5% significance level, the value of the t-test statistic is-3.629831, less than the cut-off of-3.052169, rejecting the null hypothesis that the residual root from the model and the residual sequence are a stationary sequence, and the long-term relationship between the explanatory variables and the explained variables can be learned.


## 3.1.2. Economic Growth Model of the Central Region (Henan Province as an Example)

Data was collected first according to the coastal area operation method. The results obtained are as shown in Table15.Table 15
Data of each variable in Henan Province.
YearGDP(Y)/a hundred millionFDI/ten thousand dollarsConsumption per person (CPI)/YuanQuantity of employment (L)/thousands of peopleSocial fixed assets investment(K)/a hundred millionNet export amount(NE)/ten thousand dollars19994517.9449527190552051206.835.073420005052.9953999221555721377.747.119020015533.0135861238155171544.066.384020026035.4845165255355221725.9310.340120036867.7056149308355362262.9712.444220048553.7987367362555873099.3817.3874200510587.42122960409256624311.6324.6582200612362.79184526453057195904.7134.7399200715012.46306162514157738010.1139.7798200818018.534032665877583510490.6439.5846200919480.464798586607594913704.5012.5457201023092.366246707837604216585.8632.7737201126931.0310082099171619817768.9558.3868201229599.31121177710380628821450.0076.0549201332191.30134565911820638726087.46120.1733201434938.24149268813078652030782.17137.3452201537002.16160863714507663635660.35123.4221201640471.79169931216043672640415.09144.4215201744552.83172242817842676744496.93164.4519All the data in Table15 are done as in the previous section, and Table 16 shows the test of the stationarity of the sample data.Table 16
Test of the stationarity of the sample data.
Variable quantityADFType of inspectionCritical valueWhether smooth(c, t, n)1%5%10%lnY−1.385376(c, 0, 1)−3.886751−3.052169−2.666593NoDlnY−4.270420(c, 0, 1)−3.959148−3.081002−2.681330YeslnFDI−0.842845(c, 0, 1)−3.920350−3.065585−2.673459NoDlnFDI−3.119600(c, 0, 1)−3.920350−3.065585−2.673459YeslnNE−0.819215(c, 0, 1)−3.886751−3.052169−2.666593NoDlnNE−4.411203(c, 0, 1)−3.959148−3.081002−2.681330YeslnK−1.851008(c, 0, 1)−3.920350−3.065585−2.673459NoDlnK−2.754575(c, 0, 1)−3.920350−3.065585−2.673459YeslnL2.360425(c, 0, 1)−3.886751−3.052169−2.666593NoDlnL−3.633860(c, 0, 1)−3.959148−3.081002−2.681330YeslnCPI−0.236929(c, 0, 1)−3.886751−3.052169−2.666593NoDlnCPI−5.661558(c, 0, 1)−3.959148−3.081002−2.681330YesNote. The data are obtained from Eviews 8.0.The model-fitting results are as follows:(10)lnYt=0.044262lnFDIt+0.042003lnNEt+0.526400lnKt+0.523185lnLt−0.040786lnCPIt+μt,R2=0.998457.Extract residuals:(11)μt=lnYt−0.044262lnFDIt−0.042003lnNEt−0.0526400lnKt−0.523185lnLt+0.040786lnCPIt.Table17 is the Residual stationarity test. The results in Table 17 show that at the 5% significance level, the value of the t-test statistic is −3.748439, less than the cut-off of −3.052169. Thus, it rejects the null hypothesis that the residual roots from the model and the residual sequence are stationary sequences, and the long-term relationship between the explanatory variables and the explained variables can be learned.Table 17
Residual stationarity test.
t-StatisticProb.∗Augmented Dickey–Fuller test statistic−3.7484390.0131Test critical values:1%level5%level10%level−3.886751–3.052169−2.666593


## 3.2. The Differences Were Analyzed by Combining the Two Regional Models and the Actual Situation

### 3.2.1. Analysis of the Speed of FDI in the Two Regions

Taking the quota of foreign direct investment from 1999 to 2017 as the research object, the sequential development rate, fixed base development rate, and average development rate are calculated as shown in Table18.Table 18
Speed of FDI development in both regions.
YearHenan ProvinceJiangsu ProvinceFDIMonth-on-month development speedDetermine the development speed of the foundationFDIMonth-on-month development speedDetermine the development speed of the foundation199949527——639915——2000539991.090291.090296423581.003821.003822001358610.664100.724077122011.108731.112962002451651.259450.9119310366151.455511.619932003561491.243201.1337015802141.524402.469412004873671.555981.7640312137830.768111.8967920051229601.407402.4826913183391.086142.0601820061845261.500703.7257717431401.322222.7240220073061621.659186.1817221892061.255903.4210920084032661.317178.1423525120011.147453.9255220094798581.189939.6888225322891.008083.9572320106246701.3017812.6127228497771.125384.45337201110082091.6139920.3567532131731.127525.02125201212117771.2019124.4670035759561.112905.58817201313456591.1104827.1702133259220.930085.19744201414926881.1092630.1388728174160.847114.40280201516086371.0776832.4800024274690.861593.79342201616993121.0563734.3108224542961.011053.83535201717224281.0136034.7775625135411.024143.92793It can be seen from the calculation results in Table18 that the month-on-month development rate of foreign direct investment in Jiangsu Province is basically stable between 0.84 and 1.15, while the floating range of the month-on-month development rate in Henan Province is between 0.66 and 1.67, which is slightly larger than that of Jiangsu Province.The average development speed of FDI in Henan province and Jiangsu Province is calculated as follows:(12)x¯G=x1x2⋯xnn=Rn.According to the formula, the average development rate of FDI in Henan province is 1.20537, while the average development rate of FDI in Jiangsu Province is 1.07466. It can be seen that the average development rate of FDI in Henan province is higher than that of Jiangsu Province, but because its base is far smaller than that of Jiangsu Province, although it has grown too fast in the past 20 years, it is much different from Jiangsu Province.


### 3.2.2. Analysis of the Industrial Structure of Foreign Direct Investment in the Two Regions

The general situation of FDI by industry is as follows: the total foreign direct investment in 2017 was $2513541 million, of which FDI of manufacturing was $1118072 million, real estate $346007 million, leasing and business services $223912 million, electricity, heat, gas, and water production and supply $578.78 million, construction $2276.35 million, and other industries accumulated $540.34 million, as shown in Figure5.Figure 5
The portion of FDI industries in Jiangsu Province.In 2017, FDI in Henan province showed a total FDI of $1722428 million, including manufacturing utilization FDI of $1034835 million, electricity, heat, gas, and water $2311.5 million, leasing and business services $8788.44 million, real estate $187056 million, construction $27.56 million, and $178,7.87 million in other industries. Figure6 shows the portion of FDI industries in Henan Province.Figure 6
The portion of FDI industries in Henan Province.


### 3.2.3. Analysis of the Causes of the Difference

The economic growth model of Henan Province is as follows:(13)lnYt=0.044262lnFDIt+0.042003lnNEt+0.526400lnKt+0.523185lnLt−0.040786lnCPIt+μt.The economic growth model of Jiangsu Province is as follows:(14)lnYt=0.063049lnFDIt+0.045071lnNEt+0.490681lnKt+0.223155lnLt+0.268569lnCPIt+μt.It can be seen that when other variables remain unchanged, for every 1 percentage point of FDI growth, the GDP of Henan Province increased by 0.044262 percentage points, while Jiangsu Province increased by 0.063049 percentage points, and the difference between the two regions was 0.018787 percentage points. There are many reasons for this difference. From the analysis of this chapter, we can find some reasons: First of all, the distribution of FDI in Henan province is relatively uneven. FDI has been invested too much in the manufacturing industry, as high as 60%, while the manufacturing industry in Jiangsu Province is 44%, which is also due to the inconsistency between the leading industries in the two regions. Secondly, the construction industry is an important industry in promoting economic development. However, in terms of the construction industry, the FDI utilization in Jiangsu Province accounts for 9%, while the FDI introduced in the construction industry is only 0.0016%. Finally, although the growth rate of FDI introduced in Henan Province is very fast, its amount is far less than that of Jiangsu Province. Jiangsu Province has formed a relatively mature foreign joint venture, while the foreign investment in Henan Province is in the growth period, and the number of foreign direct investment cooperative enterprises is small.


## 3.2.1. Analysis of the Speed of FDI in the Two Regions

Taking the quota of foreign direct investment from 1999 to 2017 as the research object, the sequential development rate, fixed base development rate, and average development rate are calculated as shown in Table18.Table 18
Speed of FDI development in both regions.
YearHenan ProvinceJiangsu ProvinceFDIMonth-on-month development speedDetermine the development speed of the foundationFDIMonth-on-month development speedDetermine the development speed of the foundation199949527——639915——2000539991.090291.090296423581.003821.003822001358610.664100.724077122011.108731.112962002451651.259450.9119310366151.455511.619932003561491.243201.1337015802141.524402.469412004873671.555981.7640312137830.768111.8967920051229601.407402.4826913183391.086142.0601820061845261.500703.7257717431401.322222.7240220073061621.659186.1817221892061.255903.4210920084032661.317178.1423525120011.147453.9255220094798581.189939.6888225322891.008083.9572320106246701.3017812.6127228497771.125384.45337201110082091.6139920.3567532131731.127525.02125201212117771.2019124.4670035759561.112905.58817201313456591.1104827.1702133259220.930085.19744201414926881.1092630.1388728174160.847114.40280201516086371.0776832.4800024274690.861593.79342201616993121.0563734.3108224542961.011053.83535201717224281.0136034.7775625135411.024143.92793It can be seen from the calculation results in Table18 that the month-on-month development rate of foreign direct investment in Jiangsu Province is basically stable between 0.84 and 1.15, while the floating range of the month-on-month development rate in Henan Province is between 0.66 and 1.67, which is slightly larger than that of Jiangsu Province.The average development speed of FDI in Henan province and Jiangsu Province is calculated as follows:(12)x¯G=x1x2⋯xnn=Rn.According to the formula, the average development rate of FDI in Henan province is 1.20537, while the average development rate of FDI in Jiangsu Province is 1.07466. It can be seen that the average development rate of FDI in Henan province is higher than that of Jiangsu Province, but because its base is far smaller than that of Jiangsu Province, although it has grown too fast in the past 20 years, it is much different from Jiangsu Province.


## 3.2.2. Analysis of the Industrial Structure of Foreign Direct Investment in the Two Regions

The general situation of FDI by industry is as follows: the total foreign direct investment in 2017 was $2513541 million, of which FDI of manufacturing was $1118072 million, real estate $346007 million, leasing and business services $223912 million, electricity, heat, gas, and water production and supply $578.78 million, construction $2276.35 million, and other industries accumulated $540.34 million, as shown in Figure5.Figure 5
The portion of FDI industries in Jiangsu Province.In 2017, FDI in Henan province showed a total FDI of $1722428 million, including manufacturing utilization FDI of $1034835 million, electricity, heat, gas, and water $2311.5 million, leasing and business services $8788.44 million, real estate $187056 million, construction $27.56 million, and $178,7.87 million in other industries. Figure6 shows the portion of FDI industries in Henan Province.Figure 6
The portion of FDI industries in Henan Province.


## 3.2.3. Analysis of the Causes of the Difference

The economic growth model of Henan Province is as follows:(13)lnYt=0.044262lnFDIt+0.042003lnNEt+0.526400lnKt+0.523185lnLt−0.040786lnCPIt+μt.The economic growth model of Jiangsu Province is as follows:(14)lnYt=0.063049lnFDIt+0.045071lnNEt+0.490681lnKt+0.223155lnLt+0.268569lnCPIt+μt.It can be seen that when other variables remain unchanged, for every 1 percentage point of FDI growth, the GDP of Henan Province increased by 0.044262 percentage points, while Jiangsu Province increased by 0.063049 percentage points, and the difference between the two regions was 0.018787 percentage points. There are many reasons for this difference. From the analysis of this chapter, we can find some reasons: First of all, the distribution of FDI in Henan province is relatively uneven. FDI has been invested too much in the manufacturing industry, as high as 60%, while the manufacturing industry in Jiangsu Province is 44%, which is also due to the inconsistency between the leading industries in the two regions. Secondly, the construction industry is an important industry in promoting economic development. However, in terms of the construction industry, the FDI utilization in Jiangsu Province accounts for 9%, while the FDI introduced in the construction industry is only 0.0016%. Finally, although the growth rate of FDI introduced in Henan Province is very fast, its amount is far less than that of Jiangsu Province. Jiangsu Province has formed a relatively mature foreign joint venture, while the foreign investment in Henan Province is in the growth period, and the number of foreign direct investment cooperative enterprises is small.


## 4. Conclusion

Through the analysis of this paper, the study can obtain the following conclusions: First, China's FDI is mainly derived from the Hong Kong region. Second, foreign direct investment has a positive role in promoting China's economic growth but also increases China's domestic employment opportunities. Third, the distribution of foreign direct investment in various industries is very uneven, showing a situation dominated by manufacturing, leasing, and business services, and the real estate industry also accounts for a large proportion but relatively little foreign investment in education, public management, health, and social security. Fourth, in different regions, due to the regional economic law, development level is inconsistent, the introduction of FDI value is very different, and the introduction of the FDI economic benefits (i.e., GDP) because of different leading industries, so each industry introduced FDI also has different, but in each region are manufacturing most FDI. Manufacturing, leasing and business services and real estate are a large part of the FDI. These three major industries have contributed to economic growth after the introduction of FDI.



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*Source: 4489430-2022-07-08.xml*