The Impacts of Bilateral Political Relations on Foreign Direct Investment: Evidence from China

Existing research on the determinants of Foreign Direct Investment (FDI) in China has paid insufficient attention to the role of bilateral political relations. Utilizing panel data on FDI from 61 countries into China from 1998 to 2021 and employing the “political ideal point distance” metric constructed from UN General Assembly voting data, this paper empirically analyzes the impact of bilateral political relations on inward FDI (IFDI) in China. The findings indicate a significant correlation between bilateral political relations and the scale of IFDI, with a widening gap in political stances significantly inhibiting the inflow of foreign capital. This conclusion remains robust after a series of robustness checks, including replacing core variables, excluding samples from specific years, winsorizing, and employing instrumental variable methods. Heterogeneity analysis reveals that firms from developed countries are more sensitive to political relations, while major powers may exhibit a “cold politics, hot investment” phenomenon due to strategic considerations; investments from non-major powers, however, strictly align with the proximity of political relations as expected. The research provides empirical evidence for China to optimize its foreign investment policies within a complex international environment.

• Bilateral Political Relations
• Foreign Direct Investment
• Political Ideal Point Distance

1. Introduction

Foreign Direct Investment (FDI) serves as a crucial driver of host-country economic growth and global integration. Over the past three decades, leveraging its vast market size, comprehensive industrial system, and continuously deepening opening-up policies, China has successfully attracted substantial FDI inflows. China’s Inward FDI (IFDI) scale has achieved leapfrog growth, surging from USD 4.366 billion in 1991 to USD 180.906 billion in 2021, with an average annual growth rate exceeding 13%. However, the international political and economic environment has grown increasingly complex in recent years. Factors such as geopolitical conflicts, trade frictions, and institutional differences exert a growing influence on multinational investment decisions. Particularly against the backdrop of Sino-US strategic competition and global supply chain restructuring, the role of bilateral political relations, as a representative non-economic factor, urgently requires in-depth investigation.

Existing research on FDI has developed a relatively comprehensive theoretical framework, primarily covering three dimensions: home-country firm motivations, host-country locational advantages, and bilateral interactions. However, traditional studies have predominantly focused on economic factors, with insufficient attention paid to bilateral political relations. As an emerging economy with institutional environments differing from developed nations, political relations may serve as a crucial substitute mechanism for China, potentially compensating for institutional shortcomings by reducing policy uncertainty and enhancing market access ([21]). Although some scholars have noted the positive impact of political relations on outward FDI (OFDI), empirical research specifically focusing on their effect on IFDI into China remains scarce. This paper, grounded in a China-centric perspective, systematically analyzes the impact of bilateral political relations on the scale of IFDI in China, aiming to address this research gap.

The innovation of this paper lies in its in-depth examination of the direct impact of bilateral political relations on IFDI and its heterogeneous characteristics. Based on panel data of investments from 61 countries to China from 1998 to 2021, this study employs the “political ideal point distance” indicator developed by [6] from UN General Assembly voting data to quantify bilateral political relations, using a two-way fixed effects model to control for unobservable factors at the country and time levels. The findings indicate that bilateral political relations correlate with IFDI scale, where increased political divergence significantly suppresses capital inflows. Firms from developed countries exhibit higher sensitivity to political relations, while major powers may display a “politically cold but investment hot” phenomenon due to strategic considerations. This research holds both theoretical and practical value. Theoretically, it expands the boundaries of traditional locational advantage theory by integrating political relations into the IFDI analytical framework, revealing their moderating role in cross-border investment. Practically, the conclusions provide a basis for China to stabilize foreign investor expectations through diplomatic means and implement differentiated investment attraction strategies.

2. Literature Review and Research Hypotheses

2.1. Literature Review

Foreign Direct Investment (FDI), as a core form of international capital flows, has always been a significant topic in international economics research. Existing studies have formed a relatively comprehensive theoretical framework, primarily explored from three dimensions: first, from the perspective of home-country firms, examining the subjective motivations for overseas investment; second, from the perspective of the host country, analyzing objective factors such as locational advantages that attract FDI; and third, focusing on bilateral country relations, studying how interactions and conditional differences between home and host countries influence investment flows.

At the home-country firm level, early theories focused on the motivations and capabilities of firms engaging in overseas investment. The Monopolistic Advantage Theory ([23]) posits that home-country firms are inclined to invest abroad when they possess monopolistic advantages in the host country. The Product Life Cycle Theory ([34]) suggests that firms establish overseas production facilities during the growth and maturity stages of a product to prevent imitation and market competition from developing countries. The Internalization Theory ([11]) argues that the purpose of foreign investment is to gain internalization advantages, thereby reducing transaction costs and protecting intellectual property. The Eclectic Theory of International Production ([18]) analyzes the reasons for outward investment based on ownership advantages, internalization advantages, and locational advantages. The Comparative Advantage Theory ([25]) posits that home-country firms transfer their marginal industries when they hold advantages in the host country.

At the host-country level, the locational advantages possessed by the host country form the objective basis for attracting FDI inflows. Extensive research confirms that a large market size and growth potential ([18]; [27]; [15]), quality infrastructure ([36]), abundant natural and human resources ([9]), and knowledge spillovers and economies of scale arising from industrial agglomeration ([10]) are traditional core factors attracting FDI. As research deepens, the roles of factors such as labour costs and production costs ([7]; [19]), technological level and productivity ([31]) have also been widely verified. Beyond economic variables, the institutional and environmental quality of the host country has received increasing attention, including the soundness of the political and legal system ([3]), the intensity of environmental regulations ([16]; [29]), and information transparency ([37]), all of which have been shown to significantly influence the location choices of foreign capital.

With the deepening of globalization, research focusing on bilateral country interactions has gradually increased. On one hand, “distance” between countries constitutes investment barriers; for example, institutional and cultural distances affect the management costs and legitimacy of multinational enterprises ([30]), while greater corruption distance similarly inhibits FDI flows ([22]). On the other hand, bilateral interactions aimed at strengthening ties can promote investment; for instance, exchange rate volatility increases uncertainty in transnational operations ([8]), while signing Bilateral Investment Treaties (BITs) can encourage capital flows by providing stable institutional guarantees ([12]).

Notably, as a high-level form of interstate interaction, the economic effects of bilateral political relations have been extensively discussed in recent years, with its research scope extending far beyond traditional diplomacy. A substantial body of literature indicates that fluctuations in political relations significantly affect bilateral trade flows such as bulk commodities ([38]), often with short-term effects ([28]; [32]). Studies have also shown that relations between major powers can affect exchange rate volatility ([2]). In the financial sector, political relations influence not only the investment decisions of sovereign wealth funds ([26]) but are also closely related to the stability of stock markets ([13]) and commodity markets ([14]). Furthermore, their effects extend to multiple dimensions such as international tourism ([1]) and global supply chain governance ([35]; [20]).

In summary, existing literature has laid a solid foundation for understanding the mechanisms of FDI flows from multiple dimensions—firm motivation, locational conditions, and bilateral relations—and has conducted useful explorations into the broad economic impact of bilateral political relations. However, a noticeable gap remains: most studies examining the influence of bilateral factors on FDI focus on analyzing a country’s outward investment (OFDI) or traditional variables like the host country’s institutional environment, while empirical research that places bilateral political relations at the center and systematically tests their impact on Inward Foreign Direct Investment (IFDI) in China remains scarce. Therefore, this paper aims to address this research gap, adopting a China-centric perspective to empirically examine the direct impact of bilateral political relations on IFDI scale and its heterogeneous characteristics.

2.2. Research Hypotheses

Based on the above literature review, FDI is influenced by multiple factors. Inward and outward FDI are two sides of the same coin in cross-border investment. The proximity of bilateral political relations affects a country’s outward investment and is bound to influence other countries’ investment in China.

From the perspective of the stability of the institutional environment, good political relations between China and foreign countries can establish a more complete foreign investment protection mechanism and promote long-term capital investment by reducing transaction costs; on the contrary, fluctuations in political relations may lead to policy uncertainty, which will significantly increase investment risks especially in emerging markets. In terms of market access, political mutual trust can prompt the host country’s government to provide loose market access conditions, preferential tax policies and convenient administrative procedures. The improvement of political relations can reduce foreign investment entry barriers, relax shareholding ratio restrictions and promote the reduction of the negative list, making it easier for foreign investors to enter China. In terms of strategic cooperation, under the framework of solid political mutual trust, investors not only have the confidence to invest in local production enterprises but also are more likely to invest in R&D centers for long-term strategic planning. Political mutual trust can reduce enterprises’ concerns about asset security and make them more willing to invest in strategic projects with high sunk costs. At the information communication level, good political relations are conducive to building diversified formal and informal communication channels, including meetings between leaders and chamber of commerce exchanges. These channels can transmit policy information in a timely and accurate manner, prevent mistakes in foreign investment decisions, enable foreign investors to reduce information acquisition costs and compliance risks, and improve the success rate of decisions and operational performance. From the perspective of macro-coordination, friendly bilateral relations can promote the coordination of monetary policy and fiscal policy. For example, the alignment of exchange rate policies can reduce foreign exchange risks, and the coordination of industrial policies can create a more favorable investment environment, thereby accelerating the process of foreign investment entry.

Therefore, based on previous literature and practical theories, this paper puts forward the following research hypothesis: The proximity of bilateral political relations will significantly affect the scale of foreign direct investment into China.

3. Research Design

3.1. Variable Design and Explanation

This study utilizes panel data from 61 countries’ direct investment into China from 1998 to 2021. The variable design is as follows:

The dependent variable is the flow of inward foreign direct investment (lnfdi), sourced from the China Foreign Investment Statistical Bulletin published by the Ministry of Commerce, directly measuring the actual investment scale from each source country to China. The core explanatory variable is the political ideal point distance (ipd) between two countries. Following the method of [6], this variable is constructed by applying Item Response Theory (IRT) and a spatial model to United Nations voting data to estimate the ideal points of each country’s political stance and then calculating the absolute difference between China’s position and that of each investing country. The UN voting data covers issues with direct relevance to international political reality, such as nuclear weapons and materials, arms control and disarmament, colonial issues, human rights, and development. Therefore, a larger IPD value indicates greater divergence in political stances and more distant bilateral political relations.

Past research focusing on China’s outward investment often selected control variables based on host country factors, potentially overlooking home country (investor) characteristics. Since this study examines investment into China, control variables are selected from three dimensions: the home country’s (investor’s) domestic economic conditions, the host country’s (China’s) economic conditions, and bilateral interactions. From the home country dimension, variables include economic size (lngdp), economic growth rate (growth), and import/export price indices (xp, mp). Economic size reflects a country’s capital stock and outward investment potential; the growth rate captures the impact of domestic economic cyclical fluctuations on capital export; the import/export price indices influence investment decisions through cost and competitiveness channels: rising import prices may increase the cost of sourcing from China, while rising export prices may weaken the competitiveness of its products in the Chinese market, both potentially incentivizing firms to shift to direct investment in China to circumvent trade costs. From the host country (China) dimension, control variables include China’s economic size (lngdpc), average wage (wage), infrastructure level (measured by the logarithm of railway operating mileage, lntrain), and R&D intensity (rd, R&D expenditure as a share of GDP). Market size is a traditional core factor attracting foreign investment; labor costs directly affect production costs and investment attractiveness; infrastructure level relates to logistical efficiency and operational convenience for firms; R&D intensity represents the host country’s technological innovation environment and potential knowledge spillovers, particularly important for high-tech sector investment. From the bilateral relationship dimension, variables include economic openness towards China (open, measured by bilateral trade volume as a share of the home country’s GDP), the institutional stability gap (politysafe, the difference between China and the home country in the “Political Stability and Absence of Violence” index—a higher index value indicates greater political stability, so a larger positive difference suggests China is relatively more stable), and the real exchange rate level (reer, the exchange rate of RMB relative to the home country’s currency). Economic openness reflects the closeness of bilateral economic ties and the degree of policy leniency towards capital flows; the institutional stability gap captures risk aversion or capital-seeking stability effects arising from differences in institutional quality; the real exchange rate directly affects the initial investment cost for foreign capital and the returns on subsequent profit repatriation, where currency appreciation typically discourages foreign investment inflows.

All data are sourced from authoritative databases including China’s National Bureau of Statistics, the United Nations, the World Bank, and the Organisation for Economic Co-operation and Development (OECD). Logarithmic transformations are applied to continuous variables to mitigate heteroskedasticity. Table 1 reports the descriptive statistics of the main variables, covering the mean, standard deviation, and range of each variable during the sample period, providing a data foundation for subsequent empirical analysis.

Table 1.Descriptive statistics.

Table 1

Variable Attribute	Variable Name	Variable Meaning	Mean	Standard Deviation	Minimum Value	Maximum Value
Dependent Variable	lnfdi	Scale of IFDI	7.804258	3.099975	0	13.84814
Independent Variable	ipd	Political Ideal Point Distance	1.064415	0.755903	0.0005349	3.868605
Control Variables
(Home Country)	lngdp	GDP	26.10113	1.95863	18.8428	30.67163
	growth	Economic Growth Rate	3.201948	4.097956	−21.3999	24.61557
	xp	Import Price Index	71.01779	22.22167	21.42717	138.4568
	mp	Export Price Index	78.03924	24.4301	11.12	205.65
(Bilateral)	open	Degree of Openness to China	0.8005459	5.451792	1.76e−06	60.63092
	politysafe	Institutional Stability Gap	−0.6782618	0.9593244	−2.252375	2.207981
	reer	Relative Exchange Rate	95.20904	407.0039	0.03144	3587.575
(China)	wage	Average Wage	37065.67	26172.2	7064	92459
	rd	R&D Intensity	1.612851	0.5289139	0.64689	2.4326
	lntrain	Railway Construction Mileage (Logarithm)	7.854762	0.8302348	6.12905	9.121431
	lngdpc	GDP of China	29.52229	0.6038829	28.4946	30.39427

3.2. Model Design

3.2.1. Hausman Test

To determine the appropriate model specification, a Hausman test was conducted. The results strongly reject the null hypothesis favoring the random effects model (p = 0.0002), justifying the adoption of a fixed effects model. Furthermore, a joint significance test for year dummy variables indicates the presence of significant time effects (p = 0.0000). Consequently, the final model specification is a two-way fixed effects panel model incorporating both country and time fixed effects, with clustering of standard errors at the country level. (Table 2)

3.2.2. Empirical Model

The model references the original investment gravity model proposed by [5] and the investment gravity model used by [24]. However, due to potential collinearity issues of the bilateral distance variable (distance) in the two-way fixed effects model, it is excluded. Control variables for the home (investing) country and host (China) country are introduced. The final model is as follows:

lnfdi i t = β 0 + β 1 Ipd i t + β 2 Controls 1 i t + β 3 Controls 2 i t + β 4 Controls 3 t + λ t + μ i + ε i

In this model: lnfdi_it denotes the scale of direct investment flows from country i to China in year t; Ipd_it represents the proximity of political relations between country i and China in year t; Controls1_it stands for the control variables of country i’s domestic conditions in year t; Controls2_it indicates the control variables of the bilateral relationship between country i and China in year t; Controls3_t refers to the control variables of China’s domestic conditions in year t; μ_i and λ_t represent individual (country-level) fixed effects and time fixed effects, respectively; ε_i is the error term.

Table 2.Hausman test and joint significance test of annual dummy variables.

Table 2

H0: Random effects are consistent and efficient
chi2(9) = (b-B)’[(V_b − V_B) ^ (−1)] (b-B) = 32.47Prob > chi2 = 0.0002
(1) 1999.year = 0	(13) 2011.year = 0
(2) 2000.year = 0	(14) 2012.year = 0
(3) 2001.year = 0	(15) 2013.year = 0
(4) 2002.year = 0	(16) 2014.year = 0
(5) 2003.year = 0	(17) 2015.year = 0
(6) 2004.year = 0	(18) 2016.year = 0
(7) 2005.year = 0	(19) 2017.year = 0
(8) 2006.year = 0	(20) 2018.year = 0
(9) 2007.year = 0	(21) 2019.year = 0
(10) 2008.year = 0	(22) 2020.year = 0
(11) 2009.year = 0	(23) 2021.year = 0
(12) 2010.year = 0
F (23,60) = 4.60	Prob > F = 0.0000

4. Empirical Results

4.1. Benchmark Regression

Table 3 presents the results of the two-way fixed effects model with control variables added stepwise, analyzing the panel data of investment flows from 61 countries over 24 years. The R² remains stable at around 0.8, indicating a good overall model fit. Under the two-way fixed effects, the coefficient of the core explanatory variable (ipd) on China’s IFDI is stable at approximately −0.9, and remains significant at the 1% level after progressively adding home-country and bilateral relationship control variables. Even when only individual fixed effects are included, the coefficient changes but remains significant. The negative coefficient for the political ideal point distance indicates that bilateral political relations are positively correlated with investment scale from a given country into China: closer political relations significantly increase direct investment, while more distant relations significantly decrease it.

Among the home-country control variables, the coefficient for home-country economic scale (lngdp) is positive, suggesting that economic development strengthens the willingness to invest abroad, though its insignificance may be due to the absorption of country-specific effects by individual fixed effects. The coefficient for economic growth rate (growth) is negative with a small absolute value, indicating that short-term home-country economic fluctuations have a limited direct impact on investment in China, likely because outward investment decisions depend more on long-term structural factors. The coefficient for the home-country import price index (xp) is significantly negative, which contradicts conventional theoretical expectations. However, a lower import price index may reflect economic downturn and shrinking demand in the home country, leading to reduced outward investment. The coefficient for the home-country export price index (mp) is positive but small, suggesting that higher export prices affecting competitiveness might encourage outward investment to circumvent tariffs, but its overall impact on investment in China is limited.

Among the bilateral relationship control variables, openness towards China (open) shows a positive but statistically insignificant coefficient, suggesting that expanding bilateral trade may only weakly promote investment. The institutional stability disparity between China and the home country (politysafe) has a positive effect, implying that a larger disparity (China being more stable) may attract capital flight. The relative exchange rate (reer) coefficient is significantly negative, indicating that RMB appreciation inhibits foreign capital inflows, consistent with traditional theory: a stronger host currency increases investment costs or reduces arbitrage opportunities.

Among the China control variables, China’s economic scale (lngdpc) coefficient is positive but insignificant, suggesting that the appeal of market size may be correlated with other variables. China’s labor cost (wage) coefficient is significantly negative, confirming that rising wage levels in China deter foreign investment, aligning with the reality of capital relocation to Southeast Asia. R&D intensity (rd) is significantly positive, indicating that technological advancement in China effectively attracts FDI, consistent with theories of industrial agglomeration and knowledge spillovers. The negative coefficient for the railway infrastructure indicator (lntrain) reflects a measurement discrepancy between annual newly added mileage (a flow variable) and the overall level of infrastructure development (a stock variable). Large-scale construction phases are often accompanied by short-term negative externalities, and this indicator also fails to capture the long-term positive effects that emerge after the railway network matures. However, this does not affect the core findings, as the significance and robustness of the political relations variable (IPD) remain consistent across multiple tests. Future research could re-examine this relationship using a more comprehensive stock-based infrastructure indicator. (Table 3)

4.2. Robustness Tests

4.2.1. Replacement of Core Variable

To avoid spurious results due to a specific variable, this paper tests robustness by replacing the core explanatory variable. Following [33], we replace the original core variable with an interaction term between the bilateral political relationship (IPD) and China’s political stability level (politychn). Data for polity come from the Political Stability and Absence of Violence Index, where a lower value indicates worse political stability. As China’s index value remains stable around −1, we interact its absolute value with the bilateral political distance, so that a larger result indicates greater bilateral political distance, consistent with the trend of the original core variable. This interaction term not only preserves the measurement of bilateral political positions but

Table 3.Baseline regression results.

Table 3

Variables	lnfdi	lnfdi	lnfdi	lnfdi
Ipd	−0.901***	−0.910***	−0.885***	−0.620**
	(0.300)	(0.299)	(0.297)	(0.272)
Lngdp		0.0297	0.306	0.343
		(0.589)	(0.686)	(0.680)
Growth		−0.00565	−0.00384	−0.00225
		(0.00885)	(0.00887)	(0.00877)
xp		−0.0346***	−0.0334**	0.000789
		(0.0128)	(0.0130)	(0.00589)
mp		0.000589	0.000717	0.00386
		(0.00600)	(0.00606)	(0.00532)
open			0.0113	0.00649
			(0.00895)	(0.00805)
politysafe			0.174	0.248
			(0.219)	(0.205)
reer			−0.000728**	−0.000887***
			(0.000324)	(0.000316)
lngdpc				1.097
				(0.681)
wage				−5.21e−05***
				(9.67e−06)
rd				1.231*
				(0.641)
lntrain				−0.159*
				(0.0886)
Constant	8.764***	10.43	3.257	−31.80
	(0.319)	(15.41)	(17.98)	(21.40)
Individual Fixed Effects	Yes	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	Yes	No
Observations	1464	1462	1462	1462
R-squared	0.805	0.807	0.808	0.800

***p < 0.01, **p < 0.05, *p < 0.1. Values in parentheses are country-level clustered standard errors.

also captures the institutional effect of political relations on investment by incorporating China’s own political stability. As shown in Table 4, the coefficient of the interaction term is significantly negative and larger in absolute value, verifying the institutional channel through which political relations affect foreign investment and indicating that China’s institutional stability amplifies the negative effect of political divergence. The coefficient sign remains unchanged and significant after variable replacement, confirming the robustness of the findings.

Table 4.Robustness test regression results.

Table 4

Variables	lnfdi	lnfdi	lnfdi
Ipd·politychn	−1.387***	−1.342***	−0.842***
	(0.343)	(0.356)	(0.278)
lngdp	−0.116	0.154	0.191
	(0.579)	(0.684)	(0.682)
growth	−0.00673	−0.00502	−0.00263
	(0.00882)	(0.00881)	(0.00849)
xp	−0.0329**	−0.0315**	0.00133
	(0.0132)	(0.0134)	(0.00581)
mp	0.00149	0.00163	0.00477
	(0.00591)	(0.00595)	(0.00514)
open		0.00533	0.00201
		(0.00986)	(0.00884)
politysafe		0.143	0.156
		(0.233)	(0.227)
reer		−0.000767**	−0.000925***
		(0.000328)	(0.000319)
wage			−5.71e−05***
			(1.03e−05)
rd			1.684**
			(0.662)
lntrain			−0.203**
			(0.0904)
lngdpc			1.082
			(0.672)
Constant	13.72	6.712	−27.99
	(15.18)	(17.93)	(21.60)
Individual Fixed Effects	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	No
Observations	1462	1462	1462
R-squared	0.807	0.808	0.801

***p < 0.01, **p < 0.05, *p < 0.1. Values in parentheses are country-level clustered standard errors.

4.2.2. Exclusion of Specific Years and Sample Trimming

Considering that specific years and outliers may interfere with the estimation results, this paper implements a dual sample purification strategy: excluding specific years and trimming samples. From the time dimension, four key crisis years are selected for exclusion: 1998 (affected by the Asian Financial Crisis), 2008 (impacted by the U.S. Subprime Mortgage Crisis), and 2020-2021 (severely affected by the pandemic). This treatment effectively eliminates the “noise” caused by extreme external shocks. Although the coefficient of bilateral political relations fluctuates to a certain extent, the result remains significantly negative, confirming that the impact of political relations exhibits cross-cycle persistence.

From the cross-sectional dimension, this paper conducts 5% winsorization on all continuous samples to control the interference of outliers. The regression results fluctuate slightly but remain significantly negative. Therefore, it can be concluded that the impact trend of bilateral political relations on foreign investment into China is highly robust, and the impact of major events has not significantly altered the effect of political relations on investment. (Table 5)

Table 5.Robustness test regression results (year exclusion & sample trimming).

Table 5

	Excluding Specific Years		5% Winsorization
Variables	lnfdi	lnfdi	lnfdi	lnfdi
ipd	−0.916***	−0.651**	−0.771**	−0.505*
	(0.282)	(0.251)	(0.298)	(0.267)
lngdp	0.397	0.416	0.257	0.269
	(0.718)	(0.723)	(0.505)	(0.505)
growth	0.00484	0.00607	−0.00446	−0.00346
	(0.00897)	(0.00853)	(0.00865)	(0.00864)
mp	−0.00318	−0.00173	−0.00710	−0.00350
	(0.00671)	(0.00573)	(0.00513)	(0.00577)
xp	−0.0162	0.0100	−0.0269**	0.00451
	(0.0125)	(0.00655)	(0.0117)	(0.00620)
politysafe	0.177	0.212	0.118	0.198
	(0.213)	(0.200)	(0.222)	(0.209)
open	0.0110	0.00712	−0.636	−0.852
	(0.00804)	(0.00742)	(0.831)	(0.825)
reer	−0.000793**	−0.000957***	−0.00256	−0.00265
	(0.000344)	(0.000327)	(0.00440)	(0.00431)
wage		−3.67e−05***		−5.10e−05***
		(1.01e−05)		(9.27e−06)
lntrain		−0.226***		−0.144*
		(0.0825)		(0.0779)
rd		1.807**		0.644
		(0.712)		(0.544)
lngdpc		−0.00888		1.689***
		(0.842)		(0.608)
Constant	0.0966	−2.205	4.661	−46.24**
	(18.91)	(24.08)	(13.28)	(19.30)
Individual Fixed Effects	Yes	Yes	Yes	Yes
Year Fixed Effects	Yes	No	Yes	No
Observations	1220	1220	1462	1462
R-squared	0.830	0.823	0.837	0.829

***p < 0.01, **p < 0.05, *p < 0.1. Values in parentheses are country-level clustered standard errors.

4.2.3. Endogeneity Test

To mitigate endogeneity issues arising from other factors, this study employs an Instrumental Variable (IV) approach, selecting the one-period lag of the endogenous variable—bilateral political relations—as the instrument and applies the two-stage Least Squares (2SLS) method for testing. Although using the one-period lag as the sole instrumental variable has limitations, it satisfies the relevance condition, as the lagged explanatory variable does not constitute a weak instrument. As shown in Table 6, after controlling for endogeneity, the coefficient for the core explanatory variable (ipd) remains significantly negative, with only a slight decline in significance. This indicates that endogeneity has a limited impact on the authenticity of the regression results, and the research findings remain robust.

Table 6. Endogeneity test regression results.

Table 6

Variables	lnfdi	lnfdi	lnfdi
ipd	−1.485**	−1.448**	−1.256*
	(0.653)	(0.652)	(0.759)
lngdp	0.152	0.396	0.398
	(0.576)	(0.658)	(0.660)
growth	−0.00439	−0.00298	−0.000810
	(0.00932)	(0.00943)	(0.00952)
xp	−0.0342**	−0.0339**	0.00245
	(0.0139)	(0.0141)	(0.00619)
mp	−0.000573	−0.000384	0.00287
	(0.00698)	(0.00704)	(0.00617)
open		0.0167*	0.0120
		(0.00852)	(0.00772)
politysafe		0.244	0.295
		(0.216)	(0.200)
reer		−0.000410	−0.000573*
		(0.000336)	(0.000332)
wage			−5.32e−05***
			(1.00e−05)
rd			1.543*
			(0.814)
lntrain			−0.188**
			(0.0961)
lngdpc			0.729
			(0.745)
Constant	6.412	0.148	−22.62
	(15.37)	(17.56)	(23.09)
Individual Fixed Effects	Yes	Yes	Yes
Year Fixed Effects	Yes	Yes	No
Observations	1403	1403	1403
R-squared	0.812	0.813	0.804

***p < 0.01, **p < 0.05, *p < 0.1. Values in parentheses are country-level clustered standard errors.

4.3. Heterogeneity Analysis

This paper divides the entire sample into developed countries and developing countries for heterogeneity analysis, aiming to distinguish the political sensitivity of investment in China among countries with different economic development levels. As shown in the regression results in Table 7, the impact of bilateral political relations on investment in China by developed countries is more significant and robust, while the impact on developing countries is negative but not significant. This difference proves that foreign investment decisions of developed

Table 7.Heterogeneity analysis by economic development level.

Table 7

	Developed Countries		Developing Countries
Variables	lnfdi	lnfdi	lnfdi	lnfdi
ipd	−0.501*	−0.230*	−0.694	−0.481
	(0.286)	(0.130)	(0.437)	(0.431)
lngdp	2.077*	2.109*	0.0407	0.151
	(1.189)	(1.192)	(1.196)	(1.212)
growth	−0.00355	−0.00557	−0.00742	−0.00176
	(0.00846)	(0.00838)	(0.0129)	(0.0129)
mp	−0.0137	−0.0101	0.00857	0.0105*
	(0.0125)	(0.0106)	(0.00701)	(0.00596)
xp	−0.00146	0.0131	−0.0375**	−0.00352
	(0.0237)	(0.0101)	(0.0142)	(0.00789)
politysafe	0.362	0.337*	0.0407	0.142
	(0.270)	(0.188)	(0.271)	(0.268)
open	0.0104	0.00633	−0.0703	−0.0633
	(0.00720)	(0.00734)	(0.173)	(0.167)
reer	−0.0166	−0.0192	−0.000406	−0.000535
	(0.0154)	(0.0156)	(0.000356)	(0.000349)
wage		−1.98e−05**		−7.42e−05***
		(7.44e−06)		(1.59e−05)
lntrain		0.0219		−0.173
		(0.0862)		(0.118)
lngdpc		0.468		2.923**
		(0.410)		(1.067)
Constant	−43.96	−59.83**	7.929	−79.72***
	(31.32)	(26.66)	(30.62)	(26.35)
Individual Fixed Effects	Yes	Yes	Yes	Yes
Year Fixed Effects	Yes	No	Yes	No
Observations	694	694	768	768
R-squared	0.871	0.868	0.695	0.677

***p < 0.01, **p < 0.05, *p < 0.1. Values in parentheses are country-level clustered standard errors.

countries are more sensitive to political risks: enterprises from developed countries usually have mature international investment evaluation systems. When there are obvious differences in political stances with China, their compliance departments tend to take the initiative to avoid potential risks; in contrast, enterprises from developing countries are more driven by market opportunities, and the marginal impact of political relations is relatively limited. In addition, governments of developing countries are more willing to see their enterprises expand in the international market to feed back their domestic economies. Therefore, even if there are differences in political stances with China, their willingness to block domestic enterprises’ investment in China will be relatively low.

When measuring political relations, the Institute of International Relations at Tsinghua University (2012) defines countries such as the United States, Japan, Russia, Germany, the United Kingdom, France, and India as major powers. Following this definition, this paper divides the sample into major powers and non-major powers. As shown in Table 8, the coefficient for the core explanatory variable (ipd) in the major-power sample is positive but insignificant, exhibiting a “politics-cool, investment-hot” pattern—contrary to the expected result. This phenomenon can be explained from the perspectives of the eclectic theory of international production and political economy. The core logic lies in the fact that multinational corporations’ investment decisions involve a comprehensive trade-off between multiple strategic benefits and political risks. First, China’s vast market size and well-developed industrial chain constitute irreplaceable location advantages, prompting firms to engage in strategic asset locking to secure long-term market positioning, even in the face of political friction, rather than sacrificing growth potential. Second, foreign direct investment can serve as a risk management tool: localization of production helps circumvent potential trade barriers, while deepening economic ties reduces the likelihood of bilateral relations deteriorating, thereby playing a stabilizing role. Third, in emerging fields such as new energy and the digital economy, investment becomes a key means of competing for future technological standards and industrial ecosystem dominance, with strategic value outweighing short-term financial considerations. Finally, China’s relatively stable and consistent policy environment allows firms to partially separate and manage country-level political risks from host-country operational risks. Overall, the investment behavior of firms from major powers reflects their higher tolerance for political friction costs and proactive risk management capabilities in pursuit of core benefits such as strategic asset acquisition, risk hedging, and long-term positioning. Against the backdrop of tense political relations, such as the U.S.-China trade friction (2018) and the China–South Korea THAAD dispute (2017), firms like Tesla, Qualcomm, and Samsung still chose to make major strategic investments in China—a direct manifestation of the “politics-cool, investment-hot” pattern. Thus, due to the strategic considerations of firms from major powers, when political stances diverge, expanding direct investment helps maintain the stability of bilateral economic and trade relations and hedge political risks, which aligns with the “politics-cool, economics-hot” pattern noted by [28]. In the non-major-power sample, the coefficient for the core explanatory variable (ipd) is significantly negative with a larger absolute value, consistent with theoretical expectations: firms from non-major powers lack risk-hedging capabilities and government support, and tend to reduce investment in China when political divergence increases, indicating that political factors impose stronger constraints on their investment decisions.

Table 8.Heterogeneity analysis by national size.

Table 8

	Major Powers		Non-Major Powers
Variables	lnfdi	lnfdi	lnfdi	lnfdi
ipd	0.584	0.461	−0.961***	−0.717**
	(0.650)	(0.458)	(0.318)	(0.297)
lngdp	2.179**	2.211***	0.118	0.176
	(0.622)	(0.590)	(0.756)	(0.759)
growth	−0.00988	−0.0149	−0.00131	−9.59e−05
	(0.0173)	(0.0130)	(0.00922)	(0.00919)
mp	0.0241	0.0214	0.00133	0.00307
	(0.0320)	(0.0187)	(0.00621)	(0.00540)
xp	−0.0312*	−0.0271	−0.0284**	0.00351
	(0.0134)	(0.0200)	(0.0140)	(0.00573)
politysafe	−0.410	0.0902	0.142	0.192
	(0.468)	(0.156)	(0.230)	(0.213)
open	−4.444***	−4.238***	0.00844	0.00395
	(0.969)	(0.881)	(0.00982)	(0.00882)
reer	−0.227*	−0.175*	−0.000703**	−0.000879**
	(0.0956)	(0.0844)	(0.000340)	(0.000332)
wage		−2.98e−05		−5.28e−05***
		(2.38e−05)		(1.00e−05)
lntrain		0.190		−0.191*
		(0.144)		(0.0983)
rd		−3.717		1.696**
		(3.017)		(0.637)
lngdpc		4.287		0.803
		(3.403)		(0.646)
Constant	−50.67**	−172.3	7.401	−19.66
	(17.24)	(99.79)	(19.58)	(20.20)
Individual Fixed Effects	Yes	Yes	Yes	Yes
Year Fixed Effects	Yes	No	Yes	No
Observations	168	168	1,294	1,294
R-squared	0.881	0.861	0.781	0.772

***p < 0.01, **p < 0.05, *p < 0.1. Values in parentheses are country-level clustered standard errors.

5. Research Conclusions and Policy Recommendations

5.1. Research Conclusions

Based on panel data of direct investment from 61 countries into China over the period 1998-2021, this paper constructs a two-way fixed-effects model to conduct an in-depth analysis of the impact of bilateral political relations on the scale of Foreign Direct Investment (FDI) into China. The study finds that the proximity of bilateral political relations exerts a significant influence on FDI inflows into China: an expansion of differences in political stances inhibits the scale of foreign capital inflows, while a reduction in such differences promotes it. This conclusion remains valid after a series of robustness tests, including replacing the explanatory variable, excluding samples from specific years, winsorization, and the instrumental variable method, confirming a high correlation between bilateral political relations and foreign investment into China.

The study also reveals heterogeneous characteristics in the sensitivity of different types of countries to political relations: Enterprises from developed countries are more susceptible to fluctuations in bilateral political relations when making investment decisions; in contrast, enterprises from developing countries are relatively less constrained by political factors.

From the perspective of country size, investment in China by small and medium-sized countries (non-major powers) aligns with theoretical expectations—closer political relations correspond to larger investment scales, and vice versa. However, investment in China by major powers deviates from theoretical expectations: even amid divergences in political stances, major powers may still expand investment to maintain the stability of economic and trade relations, presenting a unique phenomenon of “cold politics but hot investment”.

In summary, bilateral political relations are a key factor affecting FDI inflows into China, and their impact varies across different types of countries.

5.2. Policy Recommendations

Based on the research conclusions, to better leverage the promotional role of bilateral political relations in attracting foreign direct investment, we propose improving the policy system in the following aspects:

1. Deepen Political Mutual Trust and Foster a Stable Environment. Strengthen high-level engagement and regular consultations with various countries, with a focus on narrowing the political distance with non-major powers and developing countries. Use multilateral platforms to signal openness and reduce the interference of political uncertainty on foreign investment decisions.

2. Implement Differentiated Investment Attraction Strategies and Precisely Match Needs. For developed countries, enhance institutional alignment and technological cooperation. For non-major powers, provide customized policy conveniences. For major powers, use major projects as leverage to guide strategic investment, thereby hedging the impact of political differences.

3. Improve Institutional Safeguards and Optimize the Business Environment. Strengthen foreign investment laws and regulations, clarify standards for property rights protection and contract enforcement, shorten the negative list for market access, and enhance long-term infrastructure planning to reduce institutional investment barriers.

4. Establish Communication Platforms to Reduce Information Asymmetry. Rely on governments, chambers of commerce, etc., to build regular information exchange mechanisms, release policy and market updates, and organize industry matchmaking events to lower information acquisition costs and decision-making risks for foreign investors.

Fund Project

This study was supported by the Humanities and Social Sciences Fund Project of the Ministry of Education of China (Grant No. 22YJA790066), “Welfare Effects of Actively Expanding Imports of Consumer Goods: Theory, Evidence and Policy Design”.

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