The Impact of FDI on China’s Urban–Rural Income Gap

Abstract

Since the reform and opening up some forty years ago, China has suffered from a capital shortage problem. To both solve this problem and satisfy its economic and social needs, China has been bringing in foreign investment, much of which has gone toward economic reconstruction. However, with the continuous inflow of foreign direct investment (FDI) into China, not only has the gap between rich and poor become increasingly wide but, due to China’s unique dualistic economic structure, the gap between urban and rural areas also appears to be widening. This aspect of the problem has attracted the attention of scholars around the world, as it may affect the future sustainable development of China’s economy and society. Cognizant of the need for practical solutions, this study conducts a more in-depth study of the relationship between foreign direct investment and the urban–rural income gap in China. Based on a review of previous studies and panel data from 30 Chinese provinces, this paper argues that FDI will affect this income gap through mechanisms such as employment structure, industrial structure and international trade. The findings of this study suggest that FDI inflows will first improve income inequality between urban and rural Chinese at a point in time but then have a worsening effect. In addition, China’s three major economic zones are examined, with results showing that the overall impact of FDI on the urban–rural income gap in China displays an inverted ‘U’ curve. Today, the impact of FDI on the urban–rural income gap is on the right-hand side of the curve, with regional differences in its impact. Urban–rural income inequality in the eastern region responds the most to FDI.

1. Introduction

Since 1978, not only has China’s capital account been continuously deregulated but, for some time now, the government has also been encouraging international capital flows. The total amount of foreign direct investment (FDI) into China, an important component of international mobility, has been increasing year by year. In 1983, China’s FDI was only USD 916 million [1], but barely twenty years later, in 2002, China was experiencing the world’s highest FDI inflows and had started to see structural imbalances in domestic economic and social development, particularly in terms of income distribution [2].

In addition, China’s economy has also achieved high growth, with a total GDP of USD 14.36 trillion in 2019, accounting for 16.38% of the global economy and the second largest economy in the world in terms of purchasing power parity (PPP) [3]. In addition, FDI, as an important economic factor, is increasingly being recognized as one of the strongest drivers of China’s economic growth [4]. FDI has been influencing China in three areas: economic growth, social development and environmental protection [5]. However, China’s unique dualistic economic structure of high productivity and high wages in the urban industrial sector, alongside low productivity and low-income levels in the traditional agricultural sector, has led to a large inflow of FDI, which has led to the emergence of an urban–rural income gap in China. The actual utilization of foreign investment in China has given rise to a number of problems, particularly the uneven regional distribution and investment structure of FDI. Currently, income inequality in China is much higher than the world average and income distribution issues pose many challenges to social governance [6].

Since the topic of FDI and the urban–rural income gap first arose, various scholars have been studying the link between FDI on a number of levels: labor, economic performance and host-country wage levels since the 1970s [7,8,9,10,11,12,13]. For Chinese scholars, research in this area has been conducted on the basis of China’s particular dualistic economic structure [14,15,16,17]. However, due to differences in the indicators, the countries and both the times and the methods of measurement chosen by scholars in their studies, there has been little consensus on the results of the impact. Although there has been much discussion and research on this topic, most of the literature has used simple linear regression methods, ignoring the existence of regional heterogeneity in China. In reality, China’s total outward FDI has been growing consistently and this has contributed significantly to the country’s economic development. This paper therefore hopes to explore the specific status and impact of FDI on urban–rural income inequality across regions in China through an empirical study based on previous research.

Therefore, this study both analyzes the impact of FDI on the urban–rural income gap in China from different paths and over time and establishes a fixed-effects panel model to more objectively study the relationship between the two. It also enriches and expands this research proposition by fully examining the actual impact of FDI on specific regions. according to the classification of China’s three major economic zones. The focus on regional heterogeneity in China in this study, especially the empirical evidence and analysis of FDI and urban–rural income inequality in the three major economic regions of China at this stage, is very important for the economic development and social welfare improvement and sustainability of different regions in China in the postepidemic era. This study helps local governments to both refer to and introduce targeted policy recommendations to help regions establish a more equitable and effective distribution system and, in this way, it has important practical implications for China’s rural revitalization strategy.

2. Theory and Literature

2.1. Theoretical Basis

The Kuznets hypothesis, with its inverted ‘U’ curve, is at the heart of modernization theory and will form one of the central tenets of the research conducted for this paper. The paper will also use some of the important variables mentioned in this hypothesis, a hypothesis first formulated by Kuznets [17]. He argued that economic development both “creates” and “destroys” economic and social structures, a process which then affects income distribution. In the course of national economic development, the distribution of income changes according to certain laws. The most important law of change is that the income gap in a country goes through three stages: from increasing to stable development and then to reduction. These three stages of development can be explained as follows: In the initial stages of a society’s development, a large amount of wealth is concentrated in a few people, which leads to a growing income gap. However, as a society moves to the next stage of development, the economic structure shifts again. With external factors such as institutional and administrative intervention, the income distribution gap gradually stabilizes and then narrows. In short, there is a trend of “deterioration followed by improvement”. The inverted ‘U’ theory was originally used to explain the relationship between a country’s economic level and income distribution, but many scholars have extended this theory and found that there is also an inverted U-shaped curve between FDI and income distribution [18].

This suggestion that there is an inverted U-shaped curve between FDI and income distribution can be illustrated using the example of Taiwan. In the early stages of industrialization, the government of Taiwan brought in large amounts of foreign capital.

Initially, income inequality in Taiwan worsened. From the 1950s to the 1970s, Taiwan’s economy grew rapidly and during this period GDP per capita also grew. However, the Gini coefficient began to increase significantly, and the problem of income disparity grew.

There are, however, scholars who oppose the Kuznets hypothesis, with many using the economic development of various developed regions around the world to refute the hypothesis. Similar to the Taiwan example, there is some evidence that the gap between the rich and the poor in China continues to widen, even though China has entered the late stage of industrialization [19] when the Gini coefficient should be falling.

The research in this paper will also use some of the important variables mentioned in this theory, together with the McDougall–Kemp model, one of the key underlying theories of this study. The McDougall–Kemp model was established by Hobson, McDougall and Kemp to analyze the consequences of international capital flows [20,21,22] and looked at the impact of international capital flows on the welfare and income of the investing and investee countries from the perspective of supply and demand of capital elements. To explain the relationship between FDI and income distribution, this theory maintains that FDI will reduce the income level of the owners of capital elements in the inflow countries and increase the income level of the owners of the supporting labor force. In theory, developing countries are usually countries with relatively abundant labor and relatively scarce capital, so the inflow of FDI may increase the real income of labor owners, thus narrowing the income gap between workers and capitalists.

2.2. Literature Review

The findings of previous studies on the impact of FDI on the urban–rural income gap can be divided into three categories. Some scholars believe that FDI will help narrow the income gap between urban and rural areas. There are relatively few scholars who maintain this viewpoint. Their research focuses on factors such as factor rewards and knowledge spillovers. Early on, Rodrik studied the topic from the perspective of labor income [23]. He thought that, in countries with relatively low labor income and relatively high average rates of return on capital, the income gap of different factor owners would be reduced due to FDI inflows. This was due to an increase in labor remuneration and a reduction in the income gap of different groups. Sylwester, on the other hand, felt that, in the process of FDI flowing into developed countries, unskilled workers in those countries could be fully absorbed into the labor force, which would not only improve their incomes, but would also promote the equalization of factor rewards in the countries [24]. Another scholar, Chen, believed that, during the inflow process, FDI created many employment opportunities due to knowledge-spillover effects, economic-growth effects and FDI itself, which was also beneficial in narrowing the income gap [25]. Phyo used panel data to prove that FDI had created labor demands and that the income gap would narrow through international migration [26].

Many scholars, on the other hand, think that FDI will enlarge the urban–rural income gap. Some scholars use data from various regions and industries to conduct research and explain the reasons from the perspectives of wages and the investment environment. Atkinson and Brandolini believed that FDI generally tends to enter cities and towns [27], with the FDI inflow allowing these already developed regions to further their economic and trade growth. However, due to the relatively backward investment environment in the more rural areas, which are less attractive to FDI, when it flows into two regions at the same time, the impact on their economic growth is different. The speed of development in rural areas is obviously slower than that in cities and towns, so the gap between the two regions will widen. Some other scholars have also taken a country or a region as the research object, with their research reaching the same conclusion. Aitken studied the wages of residents in Mexico, Venezuela and the United States and found that an inflow of FDI will increase the wages of domestic employees and foreign employees in the host country. This means that foreign direct investment will widen the income gap of residents in these three countries [28]. Mah took South Korea as the research object and concluded that FDI would bring more serious income inequality to South Korea [29]. Diaconu et al. also found evidence from Romania’s domestic labor market that FDI will widen the income gap of employees in different industries [30]. This research shows that FDI inflow has the potential to have a ‘widening income gap’ effect on Romania’s domestic labor market.

Kuznets proposed the concept of a “U” shape for FDI and the income gap between urban and rural areas. He studied relevant data on the United States, Britain and Germany and found the following: With the development of the economy, the distribution of income inequality in these countries will first expand and then gradually narrow [17]. This inverted ‘U’ shape is also the so-called “Kuznets phenomenon”. Based on the theory, some scholars have further studied the relationship between FDI and the urban–rural income gap. Todaro found that, when FDI occurs in developing countries, there will be a period of heightened performance. At the beginning, due to the profit-driven nature of foreign capital, FDI tends to prioritize those urban areas with developed human capital, perfect infrastructure and large market scale [31]. In an empirical study comparing the OECD and a non-OECD group of developing countries, Figini and Görg found that FDI exerted an inverted U-shaped impact on income inequality in the developing countries, particularly where the impact of FDI on income inequality was found to be nonlinear [31]. As China is the largest developing country in the world, there is no consensus on the impact of FDI on its urban–rural income gap either in terms of method of inquiry or in terms of presentation of results. Most scholars have studied the impact of FDI in China either by looking at China as a whole or at FDI in a specific region [32,33,34,35]; as a result, very little attention has been paid to the issue of regional heterogeneity in these studies. This is especially true for China, which has a dualistic economy. For empirical analysis and study, this paper therefore focuses not only on China as a whole, but also on the three major economic regions of China.

3. Analysis of the Current Situation

3.1. Regional Distribution of FDI

This study finds that, from 2000 to 2019, there were differences in the distribution of FDI across the three major regions of China. China has three major economic regions, namely, the eastern coastal regions with 12 provinces, municipalities and autonomous regions, the central region with 9 provinces and autonomous regions and the western region with 10 provinces and autonomous regions. Figure 1 and Figure 2 show not only that the distribution of FDI in China is extremely uneven, but that there are also obvious regional differences. Although the trend of development of FDI inflow into these three regions is different, it is developing towards a balanced development.

FDI is mostly concentrated in the eastern coastal economic zone of China. Compared with the other two regions, the eastern region has an advantage in terms of both geographical location and factors which favor foreign trade and FDI. Such factors include, inter alia, infrastructure, human capital and institutional environment [36]. In addition, the Chinese government has, for some time now, favored the eastern region, making it easier for FDI to flow in. Over the past 20 years, the central region has been less able to absorb FDI and, consequentially, there is still a large gap between the central and eastern regions. FDI in the central region, however, is more stable than that in the eastern region and, in fact, is slowly and steadily increasing. The central region’s advantage lies in its lower labor and land costs. Due to support from the Chinese government, in recent years, a considerable number of industries have moved there from the east. The central region has become increasingly attractive to FDI and has gradually become a strong competitor for FDI inflows.

The western economic belt is restricted by its own geographical factors and economic development. The development of FDI lags far behind the eastern and central economic belts. FDI in the western region is mainly concentrated on natural resource development projects, but with the strengthening of environmental protection efforts both in China and internationally, governments all over the world have begun to advocate the concept of green and sustainable development. This has weakened the natural resource endowment advantage in the western region [37]. Although the Chinese government has been trying to strengthen the development of the western region, due to its own factors and other various factors, the development of foreign direct investment in the western region has been slow or has even been declining in recent years. The inflows of the three areas all show an increasing trend, but from the dynamic change, the development of FDI in China is gradually becoming balanced in regional distribution.

3.2. The Current Situation of Income Gap between Urban and Rural Areas

Figure 3 shows that the disposable income of China’s urban and rural residents can be divided into the following four stages from 1978–2018: the fluctuation stage (1978–1997), the steady increase stage (1998–2003), the steady development stage (2004–2009) and the slow descent stage (2009–2018).

From 1978 to 2019, even though the PCDI of Chinese residents showed uneven growth, the material living standard of Chinese people did improve significantly. Although the PCDI trendline is generally growing, the gap always remains large in numerical value. China’s ‘dual structure’ is very prominent. The gap problem has always existed and the dividends of reform and opening up enjoyed by rural residents has been limited.

Figure 4 shows the income gap generally increases first and then decreases. Although the income ratio did fluctuate due to various factors in the years after 1978, it did not affect the trend of this increasing gap between 1978 and 2003.

Figure 5 shows the broad income ratios of urban and rural residents in each of China’s provinces, respectively.

The height of the line indicates the provinces with relatively large urban–rural income, i.e., the provinces with a large urban–rural income gap. These are Guangxi, Chongqing, Guizhou, Qinghai, Xinjiang, Yunnan, Shaanxi, Gansu and other provinces, most of which belong to the western economic region. In general, the areas with smaller income gaps are Beijing, Tianjin, Shanghai, Jiangsu and other provinces that mostly belong to the eastern region. Judging from the changes in the trendline of these provinces, most of the 30 provinces exhibit an inverted U-shape, with the more obvious provinces being Shanxi, Guangxi, Chongqing, Xinjiang, Yunnan and Gansu. Figure 5 also shows that in other provinces, the inverted U-shaped curve on the right is not obvious, that is, the downward trend after 2010 is relatively stable, resulting in an inverted U-shaped characteristic that is not obvious in other provinces, such as Jiangsu and Liaoning. There are also individual areas that still show an upward or steady development trend at this stage, such as Beijing and Shanghai. Different provinces have different development characteristics in their income gaps.

Judging from the above analysis of China’s current situation, not only is the income gap between urban and rural areas gradually widening, but the inflow of FDI is also increasing. In the process of looking for the correlation between the two, the development level of FDI is expressed by the proportion of the actual utilization of foreign capital to GDP.

As can be seen from Figure 6, the trendline changes of the two indicators are relatively similar. From 1983 to 1994, both indicators showed obvious trends in growth, but, from 1994, the proportion began to decline, although with a relatively short cycle. It was only in the four years from 1994 to 1997 that the proportion began to increase. After a stable period from 2003 to 2009, the income gap began to slowly and continuously narrow. After 1994, the development level of foreign direct investment began to show a steady downward trend. From this trend chart, there is a strong correlation between these two indicators, and FDI may be one of the important variables to change this relationship.

4. Empirical Analysis

4.1. Model Setting

From the above discussion, FDI may act on China’s urban–rural income gap through factors such as employment structure, industrial structure and international trade. At the same time, it may also be affected by factors such as economic openness, education level and government financial intervention. Therefore, based on a consideration of various factors, this study further explores the possible relationship between the two areas. Using data from 2000 to 2019 and both referring to the models of He and Xu [35] and based on the basic Kuznets [17] model and provincial Chinese panel data, a static panel of FDI and urban–rural income gap is constructed by introducing FDI and other important control variables as follows:

lnIG_it = β₀ + β₁lnLFDI_it + β₂lnLFDI_it² + β₃lnPGDP_it + β₄lnTO_it + β₅lnEMP_it + β₆lnGOV_it + β₇lnEDU_it + β₈lnUR_it + γ_i + δ_t + ε_it

where i represents different provinces (i = 1, 2, 3, …, 30), t represents different times (t = 2001, 2002, 2003, …, 2019), lnIG_it represents the income gap between urban and rural areas, lnLFDI_it represents the level of FDI, lnLFDI_it² represents the quadratic term of the level of LFDI, lnPGDP_it represents the level of local economic development, lnTO_it is the degree of trade openness, lnEDU_it is the level for education, lnEMP_it is the employment structure, lnGOV_it represents the level of government intervention and lnUR_it represents urbanization. γ_i is the province fixed effect, δ_t is the time fixed effect and ε_it represents the independent and identically distributed random disturbance term. This research tries to solve or alleviate the problems brought by endogeneity by selecting the lag variable after taking a logarithm of FDI as the tool variable.

Table 1. Descriptive statistics.

	(1)	(2)	(3)	(4)	(5)
VARIABLES	N	Mean	Sd	Min	Max
lnIG	600	1.027	0.188	0.613	1.560
lnLFDI	600	−4.146	1.093	−9.142	−2.103
lnPGDP	600	11.26	0.550	10.13	12.09
lnEDU	600	−4.308	0.576	−6.154	−3.246
lnGOV	600	−1.639	0.440	−2.672	−0.277
lnTO	600	−1.699	0.987	−4.360	0.537
lnEMP	600	−0.576	0.349	−2.120	0.167
lnUR	600	3.884	0.301	3.140	4.500

is a descriptive statistic for the data used in the model [38,39,40,41].

4.2. Model Testing

Due to the potential for multicollinearity in the empirical study, a matrix of correlation coefficients between the variables in the model and the variance expansion factor VIF were calculated through Stata (

Table 2. Correlation coefficient matrix of each variable.

	LnLFDI	LnPGDP	LnEDU	LnGOV	LnTO	LnEMS	LnUR
LnLFDI	1.0000
LnPGDP	0.1041	1.0000
LnEDU	0.3031	−0.6742	1.0000
LnGOV	−0.4742	−0.4993	0.2069	1.0000
LnTO	0.5703	0.0170	0.3969	−0.4276	1.0000
LnEMS	0.5253	−0.3891	0.7355	−0.2313	0.7220	1.0000
LnUR	0.4323	−0.5123	0.7616	0.0709	0.6377	0.8278	1.0000

and

Table 3. VIF values for each variable.

Variable	VIF	1/VIF
LnLFDI	1.94	0.514304
LnPGDP	2.90	0.344538
LnEDU	4.09	0.244315
LnGOV	2.65	0.377247
LnEMS	6.29	0.159079
LnUR	5.30	0.159079
Mean VIF	3.78

). The results showed that the model developed for the study did not have serious problems of multicollinearity.

In order to test the robustness of the model developed and whether serious endogeneity problems could arise, a regression analysis with all explanatory variables delayed by one stage was conducted. The specific regression results are presented in

Table 4. Results of Robustness Test.

	(1)	(2)	(3)	(4)
Variable	RE	RE	FE	FE
L.LnLFDI	−0.0631185 *	−0.0666235 ***	−0.048658 **	−0.0619125 ***
	(0.0222286)	(0.0209429)	(0.0219007)	(0.0206988)
L.LnLFDI2	−0.0050329 **	−0.0057608 ***	−0.0043053 *	−0.0056101 ***
	(0.0022591)	(0.0021049)	(0.0022172)	(0.0020808)
L.LnPGDP	0.1284587 ***	0.1630254 ***	0.1272801 ***	0.1570297 ***
	(0.0061529)	(0.0139704)	(0.0059799)	(0.0141202)
L.LnEDU		0.1264516 ***		0.1328454 ***
		(0.0142274)		(0.014561)
L.LnGOV		−0.0324421		−0.0789349 ***
		(0.02388954)		(0.0256594)
L.LnEMS		−0.09197824 ***		−0.064135 **
		(0.0317674)		(0.0338466)
L.LnTO		−0.0566467 ***		−0.0525889 ***
		(0.0107098)		(0.0115845)
L.LnUR		−0.099064 ***		−0.0821654 ***
		(0.0307616)		(0.0312396)
Constant	−0.5919029 ***	−0.2555726 **	−0.532188 ***	−0.2624786
	(0.0916817)	(0.2339526)	(0.0878301)	(0.2341468)
Number of obs	570	570	570	570
R-squared	0.4581	0.5295	0.4602	0.5347
Number of regions	30	30	30	30

Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01.

. After such regressions, the regression coefficients for the core explanatory variables were all significant, indicating that the estimates were robust.

4.3. Data Sources and Variable Selection

The original data used in the empirical study were derived from data collected by the National Bureau of Statistics of China and documents such as the China Labor Statistics Yearbook. The time span of the data is 2000–2019 and the cross-section units are the provincial panel data of China; data on Tibet, Taiwan and the Hong Kong and Macao Special Administrative Regions were excluded, giving a total of 30 provinces for analysis.

IG (income gap between urban and rural areas): IG is used to represent the urban– rural income gap. In fact, the Gini coefficient is a widely accepted indicator of a region’s income gap. However, in the application process, in addition to the controversial system for calculating the Gini coefficient, the relevant data of the Gini coefficient of each province in China are also difficult to obtain. Therefore, the general methods of current research are adopted, with the ratio of urban resident per capita disposable income (PCDI) to rural resident PCDI taken as the measurement index. The greater the value, the greater the income gap between the two places.

As impacts are a continuous, long-term, dynamic process, the direct use of flow data may not accurately reflect such long-term disturbances [35]. In addition, because of the lack of directly available FDI stock data, this study uses the LFDI ratio, taking into account the exchange rate and inflation, to express the level of FDI. Using TO to express a region’s trade openness also draws lessons from the research of Robinson [42], which holds that the dependence of such a region on the external market can reflect the economic control and influence of FDI on a country. The level of economic development is expressed in terms of PGDP, with the nominal PGDP adjusted using the 2000-based consumer price index in the calculation to calculate the actual PGDP of a province (

Table 5. Dependent and independent variable.

IG (Income Gap Between Urban and Rural Areas)	Per capita disposable income of urban residents/Per capita net income of rural residents
LFDI (Level of Investment)	Actual utilized foreign capital/Total regional GDP
TO (Trade Openness)	Total regional imports and exports/Total regional GDP
PGDP (Local Economic Development Level)	The actual per capita GDP of each province
EMS (Employment Structure)	Total number of employees in secondary and tertiary industries/Total number of employees in each province
EDU (Educational Level)	Number of college students in each province/Resident population in each province
UR (Degree of Urbanization)	Urban population/Total population of each province
GOV (Degree of government intervention)	Annual government expenditure/Total regional GDP

).

4.4. Full Sample Analysis

Table 6. Full-sample Empirical Regression Results.

	(1)	(2)	(3)	(4)	(5)	(6)
Variable	LnIG	LnIG	LnIG	LnIG	LnIG	LnIG
LnLFDI	0.0146538 **	−0.0468034 **	−0.0821913 ***	−0.0755567 ***
	(0.0066521)	(0.0230469)	(0.0211699)	(0.0205463)
LnLFDI2		−0.0046158 **	−0.0068333 ***	−0.0070584 ***
		(0.002354)	(0.0021468)	(0.0020821)
LnLFDI(t−1)					−0.1035722
					(0.0297001)
LnLFDI2(t−1)					−0.008888
					(0.0030239)
LnLFDI(t−2)						−0.0847121
						(0.033003)
LnLFDI2(t−2)						−0.0047681
						(0.0034882)
LnPGDP		0.1019913 ***	0.1890119 ***	0.1732942 ***	0.1132807 ***	0.1121396 ***
		(0.0059573)	(0.0095623)	(0.0128259)	(0.1132807)	(0.0149877)
LnEDU			0.1210766 ***	0.1803402 ***	0.0043809	−0.0339057
			(0.0109714)	(0.0146556)	(0.0171471)	(0.0182626)
LnGOV				−0.0274676	0.1246605 ***	0.1089145 ***
				(0.0247877)	(0.0180365)	(0.0180188)
LnEMS				−0.0847088 **	−0.0312737	−0.009189
				(0.0340688)	(0.0252334)	(−0.009189)
LnTO				−0.0553557 ***	−0.0036272	−0.0073726
				(−0.0553557)	(0.0094799)	(0.0099006)
LnUR				−0.1121132 ***	−0.2219606 ***	−0.2144328 ***
				(0.031646)	(0.0411647)	(0.0442591)
Constant	1.087828 ***	−0.230189 **	−0.7940616 ***	−0.0825227	0.5191348	0.369477
	(0.0278611)	(0.0903879)	(0.0966744)	(0.2299536)	(0.296838)	(0.3156405)
Number of obs	600	600	600	600	570	540
R-squared	0.0085	0.3484	0.4638	0.5019	0.5922	0.6308

Standard errors in parentheses ** p < 0.05, *** p < 0.01.

shows the results of the full-sample empirical regression.

In model (1), the impact coefficient of LnLFDI on LnIG is 0.0146538. This is significant at the 5% level, indicating that the impact of FDI on China’s urban–rural income gap is negative; the more FDI flows in, the more the gap widens. However, the value of the coefficient R2 to be determined, which ultimately includes all the control variables under the fixed effect, is only 0.0085. The R value of model (1) is too small compared with other models, indicating that the overall estimation effect of this model is unsatisfactory. Therefore, the research focus of this study is mainly on other, more explanatory, empirical results. From models (2) to (4), control variables and intermediate variables are introduced one after another. Not only were the coefficient levels before LnLFDI and LnLFDI² more significant, but the value of R² was also increasingly large, indicating both that both the overall estimation effect of the model was more ideally suited and that the explanation of the correlation between the two was more reliable. This research will thus make a detailed analysis and explanation of the numerical results of model (4) and its corresponding economic implications. As it takes some time to implement and generate the effects of possible FDI, a one-period lag model (5) and a two-period lag model (6) were also added, but by looking at the time lag of the effect of FDI as reflected in both models, the results were not significant, and the lag effect was not significant. One of the main reasons for this is likely to be that FDI generates income for enterprises and employees, but since income is current and enterprises tend to increase their income to employees through wage income rather than through asset income, the lagged effect of FDI is not significant.

First, the most important two variables, LnLFDI and LnLFDI², are analyzed. The coefficients in front of them are β₁ = −0.0756 and β₂ = −0.0071, respectively. It is worth mentioning that both parameters pass the 1% significance level test, which indicates that it is feasible and necessary to add the square term of dependence on foreign investment into the measurement model of this study. It also shows that there is a strong and complex correlation between FDI and the gap in China as a whole. Specifically, the coefficient of LnLFDI² is negative, indicating that the opening of the equation parabola is downward; there is a so-called “inverted U” curve effect, in theory, between the two. From the specific numerical value, according to the nature of the quadratic function, when LnLFDI = −5.32, the effect of FDI on the urban–rural income gap is the largest, but at the same time, this node is also a turning point. When LnLFDI starts to be greater than −5.32, FDI plays a positive role. From a practical point of view, the research needs to know at what stage the current utilization of foreign capital in China is, so as to better put forward effective suggestions. According to the previous descriptive statistics of LnLFDI, although the minimum value in China’s LnLFDI is only −9.142, the maximum value is −2.103 and the median value is −4.146. Therefore, it can be judged that most of the LnLFDI in the sample is already greater than −5.32. The impact of foreign investment on this gap is on the right side of the inverted “U” shape in China as a whole. The coefficient before LnLFDI was −0.0755567, which also passes the 1% significance test. This negative correlation shows that the development of foreign capital in China will aggravate the inequality of its income distribution, which provides evidence for the analysis results of its square term. With increases in FDI inflows, the income distribution problem in many regions can be gradually improved.

LnPGDP has a coefficient of 0.1732942, which is positive. As China’s economic level grows, the income inequality between two areas will become more serious. The coefficient before LnEDU is β3 = 0.1803402, with the parameters passing the 1% significance level test; this shows not only that there is a significant positive correlation between education level and IG in China, but also that, with improvements in education levels, this gap will become larger. Due to the requirement of labor quality, FDI may be more likely to flow into urban areas with higher education levels during the inflow process, which pushes the overall higher education levels in China to continuously improve. However, with the unbalanced development of education between urban and rural areas, inequality will become more and more serious. The negative LnGOV coefficient shows that the bias of national fiscal policy towards FDI has indeed played a role in improving both income equity and people’s living standards by helping to narrow it. Although it also shows that the greater the government intervention, the easier it will be to improve income inequality, the results of this variable are far from passing the significance test. Further specific empirical results need to be tested.

The results indicate that the coefficient of LnEMS passes the 5% significance test, showing a significant negative correlation. Research shows that the employment structure changes brought about by FDI are helpful in narrowing the gap. At present, the labor force in related secondary and tertiary industries is gradually moving to the countryside. This can also be combined with the results before LnUR. The coefficient passes the 1% test and is significantly negative, indicating that the higher the urbanization rate, the smaller the income gap. With the gradual improvement of the urbanization level in various regions in recent years, the more the nonurban population has moved to cities, the more the urban and rural population structure has changed and the more the labor force in primary industry has gradually been replaced by workers in secondary and tertiary industries. From the perspective of LnTO, even if trade is open, the increase in the proportion of China’s imports and exports will also play a positive role. The coefficient is negative at 1%. This shows that, with the further opening to the outside world, China’s trade structure has been optimized. This may be because the more open to the outside world, the more obvious the improvement in the trade structure will be to increase the income levels of rural residents as compared with those of cities and towns, all of which will help to narrow the gap.

4.5. Regional Heterogeneity Analysis

Before the empirical analysis, a Hausman test is also used for model selection. The monitoring results of the three regions show that the fixed-effect model is the most appropriate. The analysis of the region is also carried out from the two important indicators of LnLFDI and LnLFDI². From the grouped regression results in

Table 7. Grouped Regression Results.

	(1)	(2)	(3)
Variable	East Region	Central Region	Western Region
	LnIG	LnIG	LnIG
LnLFDI	−0.3223774 ***	−0.0200382	0.0317607
	(0.0533342)	(0.0825782	(0.0296606)
LnLFDI2	−0.0489572 ***	0.0010483	0.0037542
	(0.0072472)	(0.0092949)	(0.0026318)
LnPGDP	0.0337128	0.198031 ***	0.2091752 ***
	(0.0219989)	(0.0176657)	(0.0319034)
LnEDU	0.15699 ***	0.2922242 ***	0.2955815 ***
	(0.0256288)	(0.0179884)	(0.0269604)
LnGOV	−0.0972482 **	−0.1426093 ***	−0.0581463 **
	(0.0452359)	(0.0358438)	0.0286611
LnEMS	−0.2079261 **	−0.2612171 ***	−0.0658214 **
	(0.1016075)	(0.0766731)	0.0304838
LnTO	0.0157759	−0.0542924 ***	−0.05496 ***
	(0.0246567)	(0.0141957)	0.0120308
LnUR	−0.0420828	0.0021282	−0.4862211 ***
	(0.0370277)	(0.0562525)	0.1235487
Constant	0.6219656 **	−0.6181278	1.796162 **
	(0.2782408)	(0.3783981)	(0.8800926)
Number of obs	240	180	180
R-squared	0.3984	0.7879	0.8154

Standard errors in parentheses ** p < 0.05, *** p < 0.01.

, only the coefficient before the LnLFDI² index in the eastern economic belt passes the 1% significance test, indicating a significant negative correlation, i.e., the relationship between them shows an inverted “U” shape.

Combining the critical values of LnLFDI for each region in Table 7 with the values in the descriptive analysis of LnLFDI for each region in

Table 8. Descriptive Analysis of LnLFDI by Region.

LnLFDI	(1)	(2)	(3)	(4)	(5)
	N	Mean	Sd	Min	Max
Whole country	600	−4.146	1.093	−9.142	−2.103
Eest region	240	−3.497425	0.7720801	−6.374607	−2.103296
Central region	180	−4.031635	0.6023556	−5.894789	−2.989486
West region	180	−5.124873	1.140138	−9.141968	−2.703433

, nearly half of the provinces in the eastern economic belt are now on the left side of this shape.

As can be seen from the above, there are some provinces in the east which are much higher and more improved than other provinces in the east, both in terms of FDI inflow and income inequality. Therefore, although the number is small, it is sufficient to make the overall impact of foreign capital development in the east negatively correlated to the income gap. There is a big gap between the provinces under the influence of the eastern economic belt and there is even a fault phenomenon. Neither the central nor the western regions pass the significance test. This may be due to some practical reasons. For example, in the previous analysis of the current situation, it was mentioned that FDI will always flow into the developed eastern economic region on a large scale, but for the central and western economic regions, the time limit for large-scale inflow of FDI is relatively short and the phenomenon of the inverted “U” relationship, which is a medium-term and long-term research phenomenon, is not obvious. LnLFDI² is significantly negative in the eastern region, showing an inverted “U” shape, but the linear relationship is not significant in the other two regions. The analysis results of the whole sample can be proved better. On the other hand, it can be said that the eastern region plays a leading role in China in all aspects of FDI.

From the perspective of other variables, the coefficient before LnPGDP, whether significant or not, is positively correlated in the three regions. With the development of the regional economy, the income gap in each region will become larger and larger, which corresponds to the analysis results of the whole sample. The test results of LnGOV and LnEMS are consistent with the analysis results of the whole sample, which are all significantly negative. The higher the level of government intervention and the better the employment structure, the more serious the problem of income inequality. The three regions of LnEDU are also significantly positive. The higher the education level in all parts of the country, the wider the income gap. LnTO performance in the three regions is different. In the case of significant results in the central and western regions, there is a negative correlation, while in the eastern region there is an insignificant positive correlation. LnUR is only negatively correlated in the west. The deeper the urbanization, the more inequality in this region can be improved, while the opposite may be true in other regions. All these have strengthened the rationality of the analysis results of the whole sample to a certain extent.

5. Conclusions and Recommendations

5.1. Conclusions

Based on the current situation in China and the important classical theories of relevant research papers, this study builds a model to conduct empirical research on the relationship between research objects and draws the following conclusions: From the perspective of the current development of FDI in China, total FDI has been fluctuating and growing rapidly. Manufacturing is the industry with the highest concentration of foreign direct investment and the east has always attracted the bulk of FDI inflows. For a long time, the PCDI coefficient of urban residents has been much higher than rural residents. The income level of the country, especially in the eastern region, has increased, and the income gap has shown an inverted “U” trend of first expanding and then narrowing with the inflow of FDI. At present, most parts of China are on the right side of this curve. In other words, at this stage, the income distribution problem in most areas of China is gradually improving. The findings related to the impact of FDI on the overall urban–rural income gap in China are the same as those of some previous studies [34,40].

In addition to the results for China as a whole, this paper also presents results on the impact of various influencing mechanisms on various regions. While most of the studies have been conducted in the context of China as a whole, some have argued that Chinese policies should focus more on increasing other factors, such as investment in infrastructure development and public education, which could reduce rural–urban income inequality while attracting more FDI inflows [43,44]. From a regional perspective, the findings of this paper show that the impact varies by region. As the eastern region of China becomes more economically and educationally advanced, more open to trade and more urbanized, the problem of income inequality becomes more acute with the inflow of FDI. The central region’s level of economy, education and urbanization has led to rising overall incomes, but with greater openness to trade and a restructuring of employment. Income disparities are slowly being addressed in the west, but this region is still in its infancy relative to the Middle East. These results have important implications for future measures and crisis response by regional governments in China for sustainable economic and social development in the context of the changing world landscape.

5.2. Limitations

The research in this study is, of necessity, limited. This study does not cover all provinces in China in the selection of sample data. From a time perspective, the Chinese economy began to be affected by the epidemic after the end of 2019 or the beginning of 2020. This study, thus, only analyzes the previous data and does not include a study of the impact of COVID-19. From the perspective of measuring the impact mechanism, this study considers such control variables as education, urbanization, trade openness, employment structure and government intervention. Although some work has been conducted to deal with the missing variables in the model design, some deviations will inevitably occur.

5.3. Recommendations

FDI has a long-term and sustained impact [45]. The government can continue the process of opening up to the outside world, continue to encourage reasonable inflows of foreign investment, enact relevant foreign investment laws and measures and stabilize China’s domestic economy, society and politics. The inflow of foreign direct investment also needs to be reasonably controlled [46]. Too many foreign companies can boost economic growth in the short term, but they can also create fierce competition for domestic companies [47]. The government needs to find the scale and form of foreign investment inflows that can be adapted to the current stage of the Chinese economy and society. The government should pay attention to heterogeneity and choose the appropriate scale and form of foreign investment inflows according to the different characteristics of different regions so as to promote a balanced and coordinated allocation of foreign investment between regions. The regional distribution of FDI should be reasonably adjusted [48]. The government can reasonably guide the flow of FDI and promote a balanced flow of FDI across regions. In the specific case of China, for example, the central government can formulate policies on the introduction of foreign investment from a macroperspective that is suitable for the development of each region. Depending on local conditions, efforts can be made to promote the coordinated development of urban and rural areas in China’s three major regions. In addition, local governments should develop local foreign investment policies based on national macro foreign investment policies according to the situation of each province, as a way to promote sustainable economic and social development.