While implementing the strategy of “the Belt and Road” in China, different countries along the belt demonstrate huge differences from China. Such differences are an important reason that hinders foreign investments of Chinese enterprises. Based on the perspective of differences between host nations’ systems and Chinese system, the paper discusses the theoretic mechanism of labor exports and foreign direct investments from Chinese enterprises. According the institutional differences between 65 countries along “The Belt and Road” and China, active degree of overseas Chinese and the number of corporate investment projects, the paper adopts zero-inflated negative binomial regression method and empirical research, and finds out: 1) Institutional differences are negatively related to OFDI of Chinese enterprises. In terms of investment on “the Belt and Road” nations, Chinese enterprises are also more inclined to make investments in nations with governance levels more similar to China. 2) The activity of ethnic Chinese in host countries made up the negative impact of the cultural differences on OFDI location selection of Chinese enterprise. 3) Regardless of institutional differences and the host country Chinese network, as for OFDI enterprises motivated by technology research and development, they do not pose significant impacts. The paper contains important enlightenment on how to make the best use of system and labor services export during the process of implementing “the Belt and Road”.
Outward foreign direct investment (hereinafter referred to as OFDI) refers to Chinese domestic investors’ business activities such as establishing, purchasing foreign enterprises in Hong Kong, Macao and Taiwan or foreign areas in forms of cash and intangible assets, aiming to gain control over management and administrative rights of enterprises. According to 2015 World Investment Report released by UNCTAD, in 2014, the outflow of global OFDI was 1.35 trillion dollars. The year-end stock was 25.87 trillion dollars. Taking this number as the base, in 2014, OFDI of China accounted for 9.1% and 3.4% of global OFDI and stock respectively. The outflow of global OFDI ranked third in global nations (regions) for three consecutive years. The proportion increased by 1.5% compared with last year. The monetary stock ranked eighth, and advanced by three positions compared with last year.
In September and October 2013, while visiting countries in Central Asia and Southeast Asia, Chinese President Xi Jinping proposed strategies of “Economic Belt of Silk Road” and “21st Century Maritime Silk Road” (hereinafter referred to as The Belt and Road). In 2014, during the APEC Beijing Summit, China announced the establishment of the Asia Infrastructural Investment Bank and the Silk Road Fund, in order to support and enhance connectivity and construction among countries along “The Belt and Road”, which attracted a lot of international attention. In March 2015, China formally issued Vision And Action For Promoting Joint Development Of Economic Belt Of Silk Road” And “21st Century Maritime Silk Road”. It signified that the initiative of “The Belt and Road” began the stage of comprehensive development.
Under the initiative of “The Belt and Road”, how to develop and enhance Chinese direct investments on nations along “The Belt and Road” is an important and realistic issue. This issue involves important significance for properly promote the implementation of “The Belt and Road” initiative, as well as reference values for enhancing Chinese direct investments on nations along “The Belt and Road”. In recent years, research on the “The Belt and Road” initiative has aroused extensive attention in the academic community. However, literary studies and research based on the perspective of solid evidence are still inadequate. We can’t help wondering: are direct investments on nations along “The Belt and Road” dominated by the government, or corporate choices based upon profit maximization? As most developing countries do not indicate many institutional differences from China, will they pose unique influences on such direct investment? What are the roles and influences of ethnic Chinese in this initiative?
According to previous research on institution, [
Compared with current research and studies, innovation in this paper involves the two following aspects: 1) In research and studies concerning influences of institutional differences on Chinese enterprises’ investment on countries in “The Belt and Road” strategy, the paper introduces the informal institutional factor of ethnic Chinese in host countries, in order to explore regulatory effects of institutional differences and Chinese enterprises’ choice on OFDI locations. 2) While studying how institutional differences and ethnic Chinese in host countries affect Chinese investments on countries in “The Belt and Road”, we conduct tests based on corporate classification. According to investment motivations, we divide companies into different types: commercial services, local production and sales, technology research and development, and resource development, in order to verify different influences of institutional disparity and ethnic Chinese on different types of enterprises.
[
Research conducted by [
Hence, based on above theories, the paper proposes two assumptions:
Assumption 1: China and host countries indicate increasingly obvious institutional differences. Chinese enterprises are less likely to invest in such countries.
Assumption 2: in host countries, over Chinese network indicates more vitality, it is more like to reduce negative impacts of institutional differences on Chinese enterprises’ OFDI.
1) Data samples
This paper considers the number of Chinese OFDI projects in countries along “The Belt and Road” as the proxy variable in Chinese enterprises’ OFDI. Data source is Statistical Bulletin Of Chinese Foreign Direct Investment. It selects data concerning investments in 65 countries along “The Belt and Road” from 2003 to 2014. “The silk road economic belt” and “Marine silk road economic belt” has a long history, “The Belt and Road” involved 65 countries and regions (
2) Variable declarations (
Linearization of several variables, there will be heteroscedasticity, affect the
Countries | Northeast Asia: Mongolia, Russia, Japan, South Korea; |
---|---|
Southeast Asia: Indonesia, Thailand, Malaysia, Vietnam, Singapore, Philippines, Burma, Kingdom of Cambodia, Laos, Brunei, east Timor; | |
South Asia: India, Pakistan, Bangladesh, Sri Lanka, Afghanistan, Nepal, Maldives, the Kingdom of Bhutan; | |
the West Asia and the North Africa: Saudi Arabia, UAE, Oman, Iran, turkey, Israel, Egypt, Kuwait, Iraq, Qatar, Jordan, Lebanon, Bahrain, Republic of Yemen, Syria, Palestine | |
the Commonwealth of the Independent States: Ukraine, The Republic of Belarus, Georgia, Azerbaijan, Armenia, Moldova; | |
Central and Eastern Europe: Poland, Rumania, Czech Republic, Slovakia, Bulgaria, Hungary, Latvia, Republic of Lithuania, Slovenia, Estonia, Croatia, Albania, serbia, Macedonia, Bosnia and Herzegovina, Republic of Montenegro; | |
Central Asia: Kazakhstan, Uzbekistan, Turkmenistan, Kyrghyzstan, Tajikistan; |
Dependent variable | |||
---|---|---|---|
Number of foreign investment projects (ninvest) | Lists of overseas investment enterprises (agencies) issued by Ministry of Commerce | The number of Chinese enterprises’ investments in 65 countries along “The Belt and Road “from 2003 to 2014―a counting variable used to measure the levels of Chinese enterprises’ investments in along “The Belt and Road” | |
independent variable and regulated variable | |||
Institutional differences(cgzcfl) | Worldwide governance indicators of World Bank | We use “worldwide governance indicators” (WGI) to assess legal and institutional environment in host countries. WGI is an index number system that includes political stability government effectiveness, regulatory quality, rule of law, voice and accountability and control of corruption. The evaluation range of these six indicators is −2.5 - 2.5. Higher positive scores represent better government administration and institution quality. Negative scores represent bad institution quality. Based on the method of principal component analysis, this paper obtains scores of institution quality aggregative indicators in countries along “The Belt and Road”. Institution differences are the absolute values obtained by host countries’ institution quality score minus the institution quality score of China. | |
Overseas Chinese network (llaborc) | This data originate from National Bureau of Statistics | Past literature materials used the percentage of ethnic Chinese in national population as proxy variables in network of overseas Chinese, so as to assess their vitality in host countries. This data originates from specialized database and the Yearbook of Chinese Economy in Ohio University. Considering that it is cross-section data, this paper uses the stock number of labor dispatching to 65 countries along “The Belt and Road” from 2011 to 2014. In light of bidirectional causal relationship existed in OFDI and transnational labor flow, in order to avoid endogeneity, laborers dispatched to host countries need to adjust to local environment, the process of taking effects may be delayed. As a result, we deal with the data in the next delayed phase. | |
Control variable | |||
GDP of host countries(lngdp) | World Development Indicators of World Bank database | On the basis of host countries’ GDP calculated according to dollar prices in 2015, use them as proxy variables to assess market sizes of host countries. Larger market sizes represent more chances for corporate investments. Take the logarithms of related data. | |
Wage levels of host countries (lnhgdp) | This data originates from International Monetary Fund | This paper uses national income per capita as the replacement variable of labor costs. National income per capita not only measures wealth levels of a nation, but also reveals its wage levels. | |
China’s export to host countries(lnex) | State Statistics Bureau website | Take the logarithm of China’s export to host countries. In theory, export can promote outward foreign direct investment. As result, China’s increasingly export to host countries can also promote outward foreign direct investment of Chinese enterprises. | |
Resources of host countries (re) | World Development Indicators of World Bank database | An important reason for Chinese enterprises’ foreign investment is to seek for resources. This variable uses percentages of minerals, metals and fuels in GDO as proxy variables. | |
Technological level of host countries(highex) | World Development Indicators of World Bank database | This variable uses percentages of high-tech products export in GDO as proxy variables. | |
Geographical distance(lndistance) | CEPII distance database | Use the spherical distance between two countries’ capitals to represent with the unit of kilometers. Take its logarithm. | |
China’s GDP(lncgdp) | World Development Indicators of World Bank database | Calculate China’s annual GDP based on dollar prices in 2005. | |
Cultural distance(cd) | Hofstede official websites | Geert・Hofstede, a Holland scholar, proposed four cultural dimensions, including power distance, uncertainty avoidance, individualism/collectivism, male/female, to assess cultural differences and value orientation in different countries. This paper adopts Kought and Singh’s method [ | |
variables | Observies | mean | Sd | Min | Max |
---|---|---|---|---|---|
ninvest | 780 | 9.46 | 19.13 | 0.00 | 131.00 |
lndistance | 780 | 8.54 | 0.46 | 6.86 | 9.25 |
lngdp | 780 | 24.34 | 1.81 | 19.95 | 29.20 |
lnhgdp | 780 | 8.16 | 1.34 | 5.48 | 11.04 |
lncgdp | 780 | 10.83 | 0.34 | 10.27 | 11.32 |
cd | 780 | 2.59 | 1.72 | 0.45 | 8.25 |
lnex | 780 | 12.19 | 2.38 | 2.77 | 17.35 |
highex | 780 | 8.48 | 13.09 | 0.00 | 73.64 |
re | 780 | 31.43 | 32.68 | 0.00 | 99.79 |
cgzcfl | 780 | 0.86 | 0.63 | 0.00 | 2.90 |
llaborc | 195 | 7369.36 | 23,162.43 | 0.00 | 177,664.00 |
N | 780 |
stability of statistical results. The natural logarithm of these variables can eliminate the phenomenon of heteroscedasticity. Since the logarithm is strictly monotone increasing function, it will not change the causality between the data, and can avoid the violent fluctuation between the variables.
Descriptive statistics of different variables are shown in
3) Model construction
This paper constructs basic models based on following formulas:
Chinese enterprises’ investments in countries along “The Belt and Road” = f (institution difference, overseas Chinese network, market demands, wage levels, technical levels, resource level, export, geographical distance, economic development, cultural distance).
Considering that explained variable selected in this paper is Chinese enterprises’ investment projects in countries along “The Belt and Road” in 2013 and 2014, which is nonnegative integer of discrete variable. Poisson’s regression model is used in this enumeration data. After calculation, sample variance is greater than expected value, indicating excessive decentralization. If Poisson’s regression is still used at this point, it may generate significant errors. So it adopts the negative binomial regression model:
λ i t = exp ( β i x i t + μ i t ) (2)
μ i t is unobserved individual effects. Assume that exp ( μ i t ) conforms to Gam- ma distribution with ( 1, δ ) as parameters, and is independent identically distributed. Then, y i t conforms to the negative binomial distribution:
Prob ( Y i t = y i t | x i t ) = Γ ( λ i t + y i t ) [ δ ] λ i t Γ ( λ i t ) Γ ( ( y i t + 1 ) [ 1 + δ ] λ i t + y i t (3)
Mean value and variance are E ( Y i t | x i t ) = λ i t and
Var ( Y i t | x i t ) = λ i t ( 1 + 1 δ λ i t ) . When δ is any nonzero constant, conditional variance is greater than conditional mean. When δ is close to infinity, negative binomial distribution converges in Poisson distribution model. As a result, Poisson distribution model is a unique example of negative binomial distribution. Estimated values of β and δ can be obtained through the following log-likelihood function of negative binomial distribution:
ln L ( β ) = ∑ i = 1 N ∑ t = 1 T [ Γ ( λ i t + y i t ) − ln Γ ( λ i t ) − ln Γ ( y i t + 1 ) + λ i t ln ( δ ) − ( λ i t + y i t ) ln ( 1 + δ ) ] (4)
As zero value accounts for big proportions in explained variables of samples, which may impact estimation results. So zero-expansion negative binomial regression is adopted. The mixed distribution is as follows:
P ( Y = y ; ϕ , α ) = { ϕ + ( 1 − ϕ ) ( α α + λ ) α , y = 0 ( 1 − ϕ ) Γ ( y + α ) Γ ( y + 1 ) Γ ( α ) ( α α + λ ) α ( λ α + λ ) y , y > 0 (5)
Zip option is added in regression, so as to provide a likelihood-ratio test to determine whether parameter α of excessive decentralization is 0. The result shows that parameter α is not 0. So it proves that zero-expansion negative binomial regression should be adopted.
To sum up, the model should be set as:
E ( ninvest | X i t ) = exp ( α i + β 1 cgzcfl + β 2 llaborc + β 3 cgzcfl * llaborc + β 4 lndistance + β 5 ln g d p + β 6 lnhgdp + β 7 lnhighex + β 8 lnre + β 9 lnex + β 10 cd + β 11 cgdp + ξ i t ) (6)
i and t represents year and country, ninvest represents Chinese enterprises’ investments in countries along “The Belt and Road”, α is a constant of national differences, cgzcfl represents institution distance, llaborc represents the delay stock of labor dispatching in host countries. cgzcf *llaborc is introduced to verify Assumption 2, lndistance represents geographical distance, lngdp represents market demands, lnhgdp represents labor costs, lnex represents export, lnhighex represents technological levels of host countries, lnre represents resource of host countries, lncgdp represents economic development level of China, cd represents cultural distance between China and host countries. As for (6), through the stata 12 panel data, obtain the estimated parameters based on negative regression estimation method.
1) Analysis of regression results
The paper adopts the method of hierarchical regression. Model 1 conducts regression on control variables. Model 2 adds independent variables and regulated variables to Model 1. Model 3 adds interactive terms to Model 2. Results are shown in
From regression results, we can see that for control variables, geographical
Model 1 | Model 2 | Model 3 | |
---|---|---|---|
ninvest | ninvest | ninvest | |
ninvest | |||
lndistance | −0.713*** | −0.241 | −0.155 |
(−4.60) | (−1.00) | (−0.63) | |
lngdp | −0.355*** | −0.347*** | −0.331*** |
(−4.56) | (−2.90) | (−2.81) | |
lnhgdp | −0.208*** | −0.187** | −0.188** |
(−4.52) | (−1.99) | (−2.02) | |
lncgdp | 0.484*** | 1.829 | 1.891 |
(2.63) | (1.46) | (1.52) | |
cd | −0.0306 | −0.0678 | −0.0965* |
(−0.91) | (−1.22) | (−1.71) | |
lnex | 1.013*** | 0.933*** | 0.864*** |
(13.11) | (7.90) | (7.20) | |
highex | −0.0201*** | −0.00100 | 0.00665 |
(−4.89) | (−0.15) | (0.84) | |
re | −0.00301* | −0.00669** | −0.00838*** |
(−1.83) | (−2.46) | (−2.95) | |
cgzcfl | −0.511*** | −0.451** | |
(−2.95) | (−2.57) | ||
llaborc | 0.0000173*** | 0.0000433*** | |
(3.15) | (2.95) | ||
cgzcflxllaborc | −0.0000171* | ||
(−1.95) | |||
_cons | 0.118 | −18.05 | −19.08 |
(0.05) | (−1.27) | (−1.36) | |
inflate | |||
_cons | −4.795 | −3.643*** | −3.782*** |
(−1.59) | (−3.64) | (−3.12) | |
lnalpha | |||
_cons | 0.157 | −0.295* | −0.310* |
(1.47) | (−1.81) | (−1.85) | |
N | 780 | 195 | 195 |
t statistics in parentheses *p < 0.05, **p < 0.01, ***p < 0.001.
distance coefficient in Model 1 are obviously negative, indicating that geographical distance is still an important factor prohibiting Chinese enterprises’ investments in countries along “The Belt and Road”. Wage levels of host countries (lnhgdp) and export to host countries (lnex) coefficient symbols are identical to expectations, indicating that labor costs of host countries is still an important factor for Chinese enterprises’ outward investments. They may reduce investments in countries with higher levels of average per capita income in order to save costs. Export can promote foreign investments to some extent. Host countries’ GDP coefficient (lngdp) is negative. The possible reason is that in primary stages, investments in countries along “The Belt and Road” are guided by state-owned enterprises, which are mainly controlled by national government. As a result, Chinese OFDI in “The Belt and Road” tend to reveal diplomatic policies and government expectation. Natural resources of host nations (lnre) are negative, which is in conflict with expectations. The possible reason is that we merely regard mineral, metal and fuel export proportion in GDP as proxy variables of resources. In practical investments on countries along “The Belt and Road”, many enterprises do not include resources such as forest and rubber as proxy variables in host countries. Technological level coefficient (lnhighex) is negative. Through comparing lists, we can find that many foreign investment projects provide services in technical support and technology consulting for host countries. It also indicates that Chinese enterprises do not prefer to make investments on nations with high technological levels. Cultural difference coefficient (cd) between host countries and China is not obvious. The proxy variable(lncgdp)of China’s economic development level is obviously positive. It means that the enhancement of China’s economy also promotes increase in corporate outward and foreign investments.
Based on Model 2 and Model 3, it can be seen that institutional difference coefficient is obviously negative. It means that institutional differences between countries along “The Belt and Road” and China can reduce Chinese enterprises’ direct investments. As a result, Chinese enterprises are more likely to make investments in countries with less institutional differences compared with China. It is identical to research results of [
2) Tests on institutional preference
According to research conducted by [
Negative | Positive | |
---|---|---|
ninvest | ||
cgzcfl | 0.323 | −1.430*** |
(1.14) | (−3.51) | |
llaborc | 0.00000604 | 0.0000975*** |
(1.40) | (4.16) | |
lndistance | −0.442* | −0.767 |
(−1.80) | (−1.39) | |
lngdp | 0.0307 | −0.0346 |
(0.17) | (−0.19) | |
lnhgdp | −0.377* | −0.492** |
(−1.93) | (−2.10) | |
lncgdp | −0.0962 | 2.244 |
(−0.06) | (1.37) | |
cd | −0.273*** | 0.246** |
(−4.03) | (2.30) | |
lnex | 0.647*** | 0.418** |
(3.83) | (2.51) | |
highex | −0.0138 | −0.00219 |
(−1.17) | (−0.17) | |
re | −0.00529 | −0.00470 |
(−1.01) | (−0.78) | |
_cons | 1.233 | −17.44 |
(0.07) | (−0.95) | |
inflate | ||
_cons | −704.5 | −2.823*** |
(.) | (−4.31) | |
lnalpha | ||
_cons | −0.550** | −0.853*** |
(−2.50) | (−3.56) | |
N | 123 | 72 |
t statistics in parentheses *p < 0.05, **p < 0.01, ***p < 0.001.
that as for countries along “The Belt and Road” that have better institutions than China, Chinese enterprises indicate preference of institution differences in outward investments. As for countries along “The Belt and Road” that have worse institutions than China, Chinese enterprises indicate preference in host countries with less institutional differences. Positive institution difference coefficients are obviously negative. Such results are identical to results of studies conducted by [
3) Test on investment motivations
According to main purposes of Chinese enterprises’ OFDI, the paper divide them into different types: commercial services, local production and sales, resource development, technology research and development. Commercial service companies means that investments mainly provide business services to enterprises, so as to facilitate domestic and foreign production and investment. Local production and sales OFDI focuses on production based on cheap local labor and other special elements, so as to reduce production costs and enhance competition. Resource development OFDI aims to development unique local resources, so as to ensure domestic production and gain materials. Technology research and development OFDI aims to make use of advanced production factors, as well as research and development contexts, so as to enhance R&D efficiency for subsidiary corporations in host countries and parent companies in home countries.
Based on regression results in
This paper conducts robustness tests on labor dispatch (laborl) and empirical results. Test results are shown in
Invest 1 | Invest 2 | Invest 3 | Invest 4 | |
---|---|---|---|---|
cgzcfl | −0.259 | −0.715*** | −0.851*** | 0.0509 |
(−1.49) | (−2.93) | (−2.82) | (0.20) | |
llaborc | 0.0000268** | 0.0000641*** | 0.0000679*** | 0.0000210 |
(1.97) | (3.52) | (3.00) | (1.30) | |
cgzcflxllaborc | −0.00000883 | −0.0000310*** | −0.0000398*** | −0.00000849 |
(−1.09) | (−2.74) | (−2.79) | (−0.83) | |
lndistance | −0.0577 | 0.0507 | 0.282 | −0.585* |
(−0.26) | (0.16) | (0.73) | (−1.88) | |
lngdp | −0.208* | −0.456*** | −0.761*** | 0.00287 |
(−1.85) | (−2.77) | (−4.10) | (0.02) | |
lncgdp | 1.998 | 0.860 | 2.705 | −0.121 |
(1.63) | (0.49) | (1.26) | (−0.05) | |
lnhgdp | −0.0973 | −0.429*** | −0.484*** | −0.0329 |
(−1.06) | (−3.27) | (−3.24) | (−0.23) | |
lnex | 0.710*** | 1.112*** | 1.151*** | 0.591*** |
(6.10) | (5.86) | (5.35) | (2.95) | |
cd | −0.142*** | 0.0336 | −0.200* | −0.0819 |
(−2.58) | (0.42) | (−1.84) | (−1.06) | |
highex | 0.00699 | −0.00195 | 0.00877 | 0.0125 |
(0.92) | (−0.18) | (0.86) | (1.17) | |
re | −0.00706** | −0.0149*** | 0.00587 | −0.00450 |
(−2.52) | (−3.70) | (1.23) | (−1.08) | |
_cons | −23.35* | −8.923 | −25.42 | −1.864 |
(−1.68) | (−0.45) | (−1.06) | (−0.07) | |
inflate | ||||
_cons | −2.970*** | −3.682*** | −3.297** | −706.9 |
(−3.28) | (−2.98) | (−2.19) | (.) | |
lnalpha | ||||
_cons | −0.519** | −0.0107 | −0.0181 | −0.475 |
(−2.39) | (−0.06) | (−0.06) | (−1.19) | |
N | 195 | 195 | 195 | 195 |
t statistics in parentheses *p < 0.05, **p < 0.01, ***p < 0.001.
ninvest | ninvest 1 | ninvest 2 | ninvest 3 | ninvest 4 | |
---|---|---|---|---|---|
main | |||||
cgzcfl | −0.396*** | −0.361** | −0.796*** | −0.401 | 0.0119 |
(−2.72) | (−2.42) | (−3.75) | (−1.44) | (0.05) | |
laborl | 0.0000578*** | 0.0000377** | 0.0000820*** | 0.0000881*** | 0.0000372** |
(3.84) | (2.52) | (4.40) | (3.69) | (2.05) | |
cgzcflxlaborl | −0.0000251** | −0.0000118 | −0.0000464*** | −0.0000652*** | −0.0000197 |
(−2.42) | (−1.14) | (−3.59) | (−3.36) | (−1.47) | |
lndistance | −0.634*** | −0.461** | −1.010*** | 0.00296 | −0.853*** |
(−3.26) | (−2.37) | (−3.89) | (0.01) | (−3.11) | |
lngdp | −0.157 | −0.113 | 0.00513 | −0.585*** | 0.0460 |
(−1.60) | (−1.12) | (0.04) | (−3.64) | (0.31) | |
lnhgdp | −0.239*** | −0.104 | −0.453*** | −0.583*** | −0.113 |
(−3.24) | (−1.35) | (−4.14) | (−4.51) | (−0.91) | |
lncgdp | 0.824 | 1.073 | 0.595 | −0.227 | 1.484 |
(1.16) | (1.44) | (0.59) | (−0.17) | (1.38) | |
cd | 0.0239 | −0.0107 | 0.232*** | −0.124 | 0.0487 |
(0.48) | (−0.21) | (3.27) | (−1.27) | (0.62) | |
ch | 0.0347*** | 0.0290*** | 0.0642*** | 0.0356* | 0.0326*** |
(3.97) | (3.23) | (5.40) | (1.89) | (3.03) | |
lnex | 0.729*** | 0.652*** | 0.634*** | 1.005*** | 0.669*** |
(7.32) | (6.32) | (4.23) | (5.42) | (3.64) | |
highex | −0.0157** | −0.0128* | −0.0352*** | −0.0000425 | −0.0121 |
(−2.53) | (−1.91) | (−3.95) | (−0.00) | (−1.21) | |
re | −0.00424* | −0.00398* | −0.00725** | 0.00490 | −0.00630* |
(−1.89) | (−1.67) | (−2.26) | (1.17) | (−1.65) | |
_cons | −5.387 | −11.26 | −2.210 | 7.904 | −19.26 |
(−0.67) | (−1.33) | (−0.20) | (0.54) | (−1.57) | |
inflate | |||||
_cons | −3.169*** | −3.117*** | −3.244*** | −3.647* | −32.06 |
(−5.58) | (−3.81) | (−3.32) | (−1.67) | (−0.00) | |
lnalpha | |||||
_cons | −0.545*** | −0.584*** | −0.303 | −0.00904 | −0.729** |
(−3.76) | (−3.16) | (−1.46) | (−0.03) | (−2.29) | |
N | 260 | 260 | 260 | 260 | 260 |
t statistics in parentheses *p < 0.05, **p < 0.01, ***p < 0.001.
Based on zero-expansion negative binomial regression method for corporate OFDI project data from 2003 to 2014, this paper conducts empirical analysis on influence of institutional differences on Chinese enterprises’ outward foreign direct investment on countries in “The Belt and Road”, as well as regulatory effects between labor dispatching of these countries and regional choices for Chinese enterprises’ outward foreign direct investment. Moreover, according to four main purposes of Chinese enterprises’ outward foreign direct investment, the paper conducts related classification, tests and studies on commercial services, local production and sales, resource development, as well as technology research and development OFDI enterprise, as well as basic samples. Studies reveal: firstly, institutional differences and Chinese enterprises’ OFDI are negatively related. Even if for investments countries in “The Belt and Road”, Chinese enterprises’ outward foreign direct investment still involves “institutional proximity”. Secondly, overseas Chinese network in host countries reduces impacts of institutional differences on regional choices for Chinese enterprises’ outward foreign direct investment. In host countries with more active network of overseas Chinese, despite bigger institutional differences, Chinese enterprises are still more inclined to make investments. Overseas Chinese network in host countries can help Chinese enterprises overcome business barriers caused by institutional differences, so as to expand investments. Lastly, impacts on institutional differences, overseas Chinese network, commercial service, technology research and development enterprises’ outward foreign direct investment are not significantly obvious. It means that these enterprises tend to focus on market volume, technical context, as well as research and development conditions, instead of labor dispatching in host countries.
Based on the above conclusion, we should respond to the national initiative of opening to the outside world, comply with trends of world multipolarization, economic globalization, cultural diversification and social informationalization, adhere to the spirit of regional cooperation, and dedicate to maintaining the global free trade system. For investments on countries in “The Belt and Road”, Chinese enterprises also takes the regional element of institutional differences into consideration, so as to reduce investment costs, and risks of legal disputes. Due to economic globalization, as well as political and economic integration, underdeveloped countries learn from advanced countries. As a result, over the long run, reducing the institutional differences can promote China’ outward foreign direct investment on the whole. In the meantime, as for institutional differences, we can attempt to reduce them from non-economic perspectives. For instance, increase labor dispatching to countries in “The Belt and Road” and enhance labor flow across countries. Moreover, setting up more convenient and fast convenient and fast, as well as improving investment context can also provide more resources and information for Chinese enterprises investments on countries in “The Belt and Road”. In addition, for enterprises that focus on the motivation of technology research and development, they are also less likely to be negatively influenced by institutional differences and related economic losses. Instead, they can focus more attention on selecting the most optimal location for product research and development among regions and countries in “The Belt and Road”. In this way, it can better promote and stimulate innovation, technical enhancement, as well as research and development efficiency for parent enterprises in China.
Gao, Y. (2017) Influences of Institutional Differences on Chinese Enterprises’ Investment on Countries in “The Belt and Road” Strategy. Mo- dern Economy, 8, 551-566. https://doi.org/10.4236/me.2017.84042