With the continuous advancement of the globalization process, economic and trade cooperation between countries has become increasingly close. Both the “going out” strategy and the “One Belt, One Road” initiative have shown that China actively participates in cooperation among countries in the world. As an important part of the globalization process, OFDI plays a vital role in international economic and trade cooperation. Since 2002, China’s foreign direct investment has maintained a momentum of continuous growth for 14 consecutive years. After the “One Belt, One Road” initiative was launched in 2013, China’s outward foreign direct investment grew rapidly. However, while the scale of investment continues expands, problems such as unregulated investment and even false investment also follow. At the end of 2016, the government increased the review of the authenticity and compliance of outward foreign direct investment. Investment entities have gradually become rational. According to “the 2017 China Foreign Investment Bulletin”, China’s OFDI flow in 2017 reached $158.29 billion, ranking third in the world. The global share has exceeded 10% for two consecutive years and the investment structure has been further optimized. The investment industry is widely distributed, mainly in manufacturing, wholesale, retail, and financial industries. The investment flow to countries along the “Belt and Road” is $20.17 billion. On the one hand, with the promotion of the “Belt and Road Initiative” and the improvement of related systems, it is foreseeable that the future cooperation between China and the countries along the “Belt and Road” will be broad, but on the other hand, we must pay attention to some shortcomings while seeing the gratifying results of China’s investment. From the comparison of the stock of outward foreign direct investment, we can see that China accounts for only about one-fifth of the United States. There is still a considerable gap. Along with the expansion of China’s investment scale, the distribution of investment locations appears to be concentrated in a few regions, such as Vietnam and Russia. The excessive concentration of investment limits the potential of China’s OFDI and is detrimental to the risk of diversification. Deng Ning decomposed the location advantage into the factor advantage and the investment environment advantage. The former is the internal motivation of investment, which mainly includes three aspects: natural resource, technical element and labor factor. The latter is reflected in the host country’s external investment environment, which mainly includes the host country’s politics, culture, rule of law, institutional environment and government policies. The improvement of the host country’s environment will greatly reduce China’s investment risk. Better institutional environment will significantly enhance its attractiveness to investors.
The research on location selection can be traced back to the cost theory. In addition to the traditional location factors, foreign investors have a disadvantage of foreign identity compared with host enterprises. They face greater uncertainty in purchasing materials, acquiring skilled labor and managing the business. So they need to pay higher information costs than local companies. In this paper, Habib and Zubawicki (2002)  proposed the theory of “institutional close preference”, which indicated that investors often prefer countries or regions similar to their own systems when selecting investment objects. Large institutional differences between the investment home country and the host country will inhibit investment to a certain extent. Kolstad and Wiig (2010)  indirectly supported this conclusion by analyzing China as a research object. They found that institutional differences may hinder companies from conducting cross-border investment. Cui and Jiang (2009)  explored the differences in the determinants of investment between China and developed countries. They proposed that Chinese companies are deeply influenced by the host country system and pay more attention to government support  .
Domestic research on location selection started late, but related research has increased in recent years. Liu Haiping, Song Yihong, and Wei Wei (2014)  found that the host country’s resource elements, historical factors, institutional conditions and joining relevant organizations have an important impact on foreign capital inflows. Fu Shaojun (2018)  empirically analyzed the influence of the host government’s governance level on China’s OFDI location selection. Ji Shengbao, Li Shuhui, and Ma Shujuan (2018)  empirically studied the effect of multi-dimensional distances on the distribution of investment. Tian Yuan and Li Jianjun (2018)  analyzed the location preference of China’s investment in the countries along the “Belt and Road” from the perspective of resources and systems. He Yaping and Xu Kangning (2018)  focused on the influence of the economic system by studying the location distribution of China’s OFDI in countries along the “Belt and Road”. Di Yuna, You Linqing (2018)  used Heckman two-stage estimation method to empirically study the economic motive and multi-dimensional distance factors in China’s investment location selection. Liu Shuangqin and Li Minyan’s (2018)  research results show that: the normative institutional distance will inhibit China’s OFDI, while the regulatory system distance will positively affect the scale of investment. Liu Juan (2018)  systematically examined the relationship between the institutional environment of the host country, the orientation of market, resource investment and OFDI. Zhang Yabin (2016)  proposed that the improvement of the host country’s economic environment has the greatest impact on OFDI. Peng Dongdong and Lin Hong (2018)  empirically tested the influence of the institutional system of the host country on the choice of China’s OFDI location driven by different motives. Lin Liangpei, Jie Xiaowen (2017)  compared the influence of host country’s market size, government governance and financial capital on China’s OFDI.
In the study of multi-dimensional distance factors, the empirical results of geographical distance are consistent, that is, China is more inclined to invest in neighboring countries, mainly in Southeast Asia. However, there are many differences in other distance studies such as politics, culture and economics. Deng Ming (2012)  and Ji Xiangbao (2014)   both adopted six measures of the political system in the world governance indicators, but the two people’s research conclusions were very different. Deng Ming proposed that institutional distance has a restraining effect on China’s OFDI, while Ji Xiangbao believed that China tends to invest in countries with a large distance from the domestic system. This divergence is partly caused by the difference between the research object and the time period.
Compared with previous studies, the main differences of this paper are two points: First, the countries along “the Belt and Road” are mostly at different stages of development. The gap in economic development is large. Existing researches either analyze the distribution of investment in a particular host country or analyze all countries as a research entity. They fail to study the distribution of investment in host countries with certain common characteristics. Based on the availability of data, this paper divides 57 countries along the “Belt and Road” into three categories according to the 2017 World Bank per capita income standard: high-income, middle-income and low-income countries. Among them, there are 20 high-level countries with per capita income exceeding $12,055; Per capita income in 19 middle-income countries ranges from $996 to $12,055, and 18 countries with per capita income below $996 are low-income countries. Secondly, most of the existing researches have analyzed investment motivation and institutional distance as separate influencing factors. This paper combines the two to consider how institutional distance is used as a regulatory variable to affect the investment under different motivations. Finally, according to the research conclusions, we provide targeted suggestions for Chinese investment entities in order to reduce the risks.
Since the empirical part of this article involves a large number of variables, the nomenclature is introduced as follows for the convenience of reading: the dependent variable, the foreign direct investment is named OFDI; six independent variables include the natural resource variable named na, the labor resource variable named la, the technical element variable named tec, the market size variable named gdp, the normative system distance variable named gd, the regulatory system distance variable named gud; the three control variables include the bilateral economic and trade tightness variable named tight, the inflation rate variable named ci, the trade distance variable named trdis.
2. Model Design and Data Source
2.1. Indicator Selection Description
The selection of variables and related descriptions are now shown in Table 1.
2.2. Model Design
By expanding the traditional trade gravity model of Anderson (1979)  to construct the investment gravity model, the investment motives such as nature resource, labor cost, market size and technical factor are taken into consideration.
Table 1. Indicator selection instructions and data sources.
For the consideration of the distance factor, the institutional distance is added on the basis of the spatial geographical distance. The empirical model of this paper is as follows:
The subscript i stands for the country, t stands for the year, is the intercept term, is the investment motivation variable, including the market size, the natural resource variable, the labor resource variable, the technical factor variable; is the institutional distance variable, including the normative and regulatory institutional distance; is error term.
3. Variable Selection and Analysis Hypothesis
3.1. Dependent Variable
China’s Outward Foreign Direct Investment (OFDI): This paper selects the investment stock data of 57 countries along the Belt and Road in China from 2008 to 2017 as an indicator to measure the level of China’s investment. Since the annual investment flow data varies greatly, some are negative or vacant, and the stock data is ultimately selected.
3.2. Independent Variables
This article draws on the practice of Liu Jing (2012)  and other scholars to subdivide the institutional distance into the distance between the regulatory system and the normative system, which is used to measure the institutional environmental gap between China and the countries along the “Belt and Road”. The specific calculation method is to measure the regulatory and normative institutional distance by the absolute value of the difference between the average values of the three indicators between China and the countries along the “Belt and Road” in each year.
Discourse rights and accountability distance, political stability and non-riot distance and corruption distance are used to measure the normative institutional distance. The normative system is mainly informal. It is the system that people form in their lives to guide their behavior and decision-making. The normative system distance is mainly reflected in the gap between the investment country and the host country in terms of customs and personal behaviors. Generally speaking, the smaller the distance of the normative system is, the closer the normative system of the two countries is. The transaction cost of the investment is smaller.
Hypothesis 1: Normative institutional distance has a negative impact on the expansion of investment scale.
The regulatory system distance is usually measured by the government efficiency distance and the distance between supervision and the rule of law. On the one hand, the regulatory system of developed countries is perfect, and the relevant requirements for investment will be more stringent. If the regulatory system distance is small, it means high consistency with China. It will help reduce the “Liability of Foreignness” costs brought about by differences in regulatory systems and promote investment; On the other hand, most of the countries along the “Belt and Road” are developing countries. Problems such as inadequate supervision by government, the inefficiency of the relevant departments and imperfect rule of law systems are still serious, so investment risks are high. However, China’s investment entities have not reduced or stopped investing in these countries. We can understand from the following two aspects: First, under the framework of “One Belt, One Road”, most of China’s investment projects in these countries are aimed at helping them to improve domestic infrastructure construction, so they are welcomed by the government and the majority. Second, countries such as Pakistan, whose abundant natural resource elements can offset the negative impacts caused by the regulatory system environment.
Hypothesis 2: The regulatory system distance between China and the developed host countries has a negative effect on the expansion of China’s OFDI scale. Hypothesis 3: The regulatory system distance between China and the underdeveloped host countries has a positive effect on the expansion of China’s OFDI scale.
While China’s economy is developing at a rapid pace, the scarcity of natural resources is becoming more and more serious. The goal of nature resource-seeking investment is to obtain the natural resources at a low cost.
Hypothesis 4 is proposed: Host country’s natural resource abundance is positively correlated with the scale of OFDI.
With the gradual disappearance of the demographic dividend, the low-cost advantage of the manufacturing industry has not existed. So manufacturing industry begins to seek a lower-cost transformation, which is the purpose of labor resources-seeking OFDI.
Hypothesis 5 is proposed: the labor cost of the host country is negatively correlated with the scale of OFDI.
In recent years, more and more multinational companies in China have chosen developed countries as the target of investment. The main purpose is to learn the advanced technology and management experience of the host country.
Hypothesis 6 is proposed: the development of the technical level of the host country is positively correlated with the scale of OFDI.
The main goal of market-oriented investment is to occupy overseas markets and cross trade protection barriers. On the one hand, in order to alleviate the anti-dumping pressure, more and more enterprises choose OFDI as another way to participate in international economic and trade cooperation. On the other hand, as a big manufacturing country in the world, China’s domestic market is still immature and the competition is fierce. It is far from meeting the needs of many enterprises’ products. In addition to the national “going out” strategy, some enterprises respond to the government’s call for transnational operations.
Hypothesis 7 is proposed: the market size of the host country is positively correlated with the scale of OFDI.
3.3. Control Variables
Based on the existing research, this paper draws on Wu Xianming and Hu Cuiping  ’s approach to select the three factors of economical trade closeness, inflation rate and geographical distance as the control variables of this paper.
Bilateral economic and trade tightness (tight): The close economic and trade cooperation between the two countries helps the investment entities to obtain various types of information. Therefore, it is expected that the bilateral trade and economic tightness will be positively correlated with the scale of OFDI.
Inflation rate (ci): A country’s high inflation rate means that its macro economy is relatively unstable, which in turn increases the risk of investment. Therefore, it is expected that the inflation rate will be negatively correlated with the scale of OFDI.
Trade distance (trdis): Trade distance is considered in combination with geographical distance and oil price in transportation costs. In general, the farther the trade distance is, the higher the transportation cost is. Therefore, it is expected that the trade distance will be negatively correlated with the scale of OFDI.
4. Empirical Analysis and Model Checking
4.1. Model Test and Regression Method Selection
This paper selects the 2008-2016 China’s OFDI panel data for regression analysis. Firstly, in order to reduce the influence of heteroscedasticity, the data other than the percentage is logarithmically processed. Secondly, the Hausman test results show that the corresponding chi-square value is greater than 100 and the p-value is 0.000, so the null hypothesis is rejected and the fixed-effect panel regression method is finally selected. In order to ensure the robustness of the results, the linear correlation analysis of the main variables is first carried out. The maximum coefficient of the correlation coefficient matrix of the main variables is 0.584, and there is no variable group exceeding 0.6. Further multicollinearity tests are performed on each model. The relevant test parameters indicate that there were no serious multicollinearity problems among the variables in all models.
In order to further compare the differences in investment motives among different income countries and the effect of institutional distances in different types of host countries, the sub-sample analysis is carried out separately based on the analysis of the whole sample countries.
Since the direction of the influence of the regulatory system distance is uncertain, the interaction term between the investment motivation and the regulatory system is added to the model to examine the effect of the regulatory system distance on the OFDI driven by different motives.
4.2. Analysis of Empirical Results
According to Table 2, it can be seen that motivation for seeking the technical elements of the host country in the whole sample is not significant (p > 0.1), while the other three factors: the influence of the host country’s labor force, natural resources, and market size on the scale of investment are consistent with the previous assumptions. After being divided into three groups, it can be found that the coefficient of natural resource is positive in each sample, but in the middle and the high-income country sample is not significant (p > 0.1). The coefficients of the technical elements are all positive, which is very significant in the middle and high-income countries (β = 0.037, 0.072; p < 0.01, p < 0.01), indicating that the motivation for the technical elements of investment in middle and high income countries is very strong. The labor cost coefficient is negative, and the labor cost in low and middle-income countries is significantly negatively correlated with the location choice of China’s investment entities (β = −0.825, −4.419; p < 0.05, p < 0.05), indicating that the motivation for seeking labor resources is
Table 2. Empirical results of institutional distance and investment motivation.
Note: The numbers in parentheses are t values; “*”, “**” and “***” indicate the significance of the regression coefficient by 1%, 5% and 10%.
very strong. The market size of the host country is significantly positively correlated with the location choice of China’s investment (β = 7.865, 1.4, 2.954; p < 0.01, p < 0.01, p < 0.05), indicates that when choosing the investment location in China, it is more inclined to choose a host country with a larger market size. The natural resource factor coefficient is positive in each sample, and is more significant in low-income countries (β = 0.122, p < 0.05), indicating that the abundant natural resources of low-income countries have a great positive appeal to investment. This shows that the motivation for seeking natural resources in the more developed countries is not significant (p > 0.1). In addition, the labor resources factors of high-income countries and the technical elements of countries of low-income countries are not the main considerations for investment. It is worth noting that the correlation coefficient of the market size in the whole sample and the three sub-samples is significantly positive (β = 7.865, 1.4, 2.954; p < 0.01, p < 0.01, p < 0.05), indicating that the market size of the countries along the “Belt and Road” is an important decision-making factor.
The normative institutional distance between low-income countries and high-income countries is significantly negative for China’s investment location choice (β = −0.67, −1.103; p < 0.1, p < 0.1), indicating that investors are more willing to choose a host country with similar cultural practices to invest to reduce investment costs. The regulatory institutional distance from low-income countries (β = 0.06, p < 0.1) is significantly positively correlated with investment, while the regulatory institutional distance from middle and high-income countries is significantly negatively correlated with investment (β = −0.051, −1.177; p < 0.05, p < 0.1).The normative system distance correlation coefficient is significantly negative at the level of 1% (β = −0.67, −1.103; p < 0.1, p < 0.1), and the conclusions of the whole sample and the subsample are consistent. That is, the investment entity is more willing to choose the host country that is closer to China’s normative system to invest. In the full-sample and low-income host countries, the regulatory institutional distance is significantly positively correlated with the size of the investment (β = 1.346, 0.06; p < 0.01, p < 0.1). However, in the sample of medium and high-income countries, the result is opposite (β = −0.051, −1.177; p < 0.05, p < 0.1). The smaller the regulatory system distance is, the larger the investment scale is. The hypothesis 2 is verified.
The results for the control variables are roughly consistent with the expected assumptions. The correlation coefficient of trade distance is negative as expected, indicating that investors tend to choose neighboring countries as investment targets. The inflation rate is a substitute for the macroeconomic stability of the host country. Its correlation coefficient is also negative, indicating that investment tends to flow into a host country with relatively stable macroeconomics. The economic and trade tightness is significantly positively related to China’s investment. Therefore, the host country with closer economic and trade ties with China is more attractive to investment entities.
4.3. Empirical Analysis of the Effect of Regulatory System Distance
In order to further analyze the effect of regulatory system distance on different internal investment motives, the interaction between institutional distance and investment motivation is introduced into the model to conduct empirical research. The interaction variables are averaged and centralized in advance to avoid multicollinearity with the main variables. The results are shown in Table 3.
Firstly, for the three groups of host countries at different stages of economic development, the coefficient of the interaction between the regulatory system distance and the market size is significantly negative, (β = −1.343, −3.804, −1.106; p < 0.01, p < 0.05, p < 0.01) which means that the regulatory system distance has a reverse effect on the market-seeking OFDI. Investors are more inclined to invest in host countries similar to China’s regulatory system to obtain their market share. Under similar regulatory circumstances, investment entities are relatively
Table 3. Regulatory system distance adjustment effect.
Note: The numbers in parentheses are t values; “*”, “**” and “***” indicate that the regression coefficients pass the 1%, 5%, and 10% significance test. The variables gn, gg, gl, and gt represent the interactions between the regulatory system distance and natural resources, market size, labor costs, and technical elements.
more familiar with the government and legal system. Investors can use low transaction costs to increase their market share in the host country and increase their international influence.
Secondly, for high-income host countries, the coefficients of interaction between the regulatory system distance and the investment motive are mostly negative, the coefficient of interaction with technical factors is significantly negative at 1% (β = −0.066, p < 0.01), which means that for high-income countries, the regulatory system distance has an adverse impact on the investment driven by different motivations, especially for technology-seeking investment.
For middle-income countries, the coefficient of interaction between labor cost and regulatory system distance is significantly positive at 5% (β = 0.898, p < 0.05), which means that the distance between the host country and China’s regulatory system has a significant reverse regulation effect on labor-seeking investment. It shows that investors will also consider the impact of differences in regulatory systems while considering cheap labor resources. When the regulatory system is relatively close, multinational corporations can relatively easily adapt to the local regulatory environment, so they will choose to expand their investment scale.
Finally, for low-income countries, the cross-term correlation coefficient between regulatory system distance and natural resources is significantly positive at the level of 1% (β = 0.0009, p < 0.01), which means that the regulatory system distance has a very significant positive adjustment effect on natural resource-seeking investment. Even if the host country’s regulatory system is not perfect, abundant natural energy such as ore metal will be very attractive to Chinese investors, which can offset the negative impact of the regulatory system to some extent.
5. Conclusions and Recommendations
Firstly, this paper analyzes the impact of investment motivations on the scale of OFDI. The motivation for seeking the technical elements of the host country is not significant in the whole sample, and the other three motivation factors are consistent with the previous assumptions. After being divided into three groups, we can find that China’s investment motivations for middle and high-income countries are mainly technical elements. The natural and labor resources of low-income countries are the main considerations for investment. As an important decision-making factor that positively influences the choice of investment location, market size has great appeal to investment entities.
Secondly, it analyzes the influence of institutional distance on the overall investment scale. The normative system distance correlation coefficient is significantly negatively correlated with the investment. The conclusions of the whole sample and the group sample are the same. That is, investors are more willing to choose a host country with similar cultural practices to invest to reduce investment costs. In the whole sample and the sample of low-income host countries, the regulatory institutional distance is significantly positively correlated with the investment scale, but in the sample of medium and high-income countries, the result is opposite. The regulatory system distance has a negative effect on the investment scale.
Finally, it discusses the adjustment effect of the regulatory system distance on the investment. In all the samples, the regulatory system has a reverse adjustment effect on the market-oriented investment. For high-income countries, the regulatory system distance has a negative impact on the investment driven by different motivations, especially for technology-seeking investment. For middle-income countries, the regulatory system distance has a significant reverse adjustment effect on labor-seeking investment. For low-income countries, the regulatory system distance has a very significant positive effect on natural resource-seeking investment.
Based on the above conclusions, the following suggestions are proposed for the location selection of Chinese investment entities:
First, China’s investment entities should consider the institutional distance between China and the host country when making investment location choices. In views of the fact that the normative system distance is not conducive to increasing investment, the investment entity should strengthen communication and cooperation with local enterprises and the public and make good use of the advantages of the Chinese population in the host country. They can learn about local customs and habits through intermediaries or organize relevant personnel to conduct overseas exchanges and other means to promote China’s excellent culture in order to obtain the cultural identity of local people and reduce transaction costs caused by differences in normative systems.
Second, investment entities need to maximize the use of institutional distance adjustments in response to their different investment motivations. In view of the fact that the regulatory system distance has a negative effect on market-seeking investment, it also has a significant hindrance to the labor-seeking and technology-seeking investment in middle and high-income countries. It is necessary to reduce the regulatory system distance between China and the host countries in order to expand the scale of OFDI. On the one hand, it requires investment entities to passively choose investment objects with similar regulatory environment. On the other hand, it is also necessary for the government to draw attention. Relevant departments should improve the environment of their own regulatory systems to reduce the gap with the host countries.
Third, it is necessary to establish a relevant risk prediction and prevention system from the beginning of the selection of investment objects to the overseas operations after investment. Most of the countries along the “Belt and Road” are still developing countries. Some countries are facing the threat of violence and terrorist attacks, which greatly increases the possibility of uncertainty in the investment process. Therefore, it is necessary to establish a relatively complete risk assessment and control system, and to do a good job of pre-forecasting, in-process control and post-processing.
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