The Spillover Effects of Chinese Shocks on the Belt and Road Initiative Economies: New Evidence Using Panel Vector Autoregression

Abstract

This paper investigates the spillover effects of Chinese real and monetary sector shocks on the Belt Road Initiative (BRI) economies. The study adopted a panel vector autoregression (PVAR) estimation technique to analyse the dynamic propagation of Chinese shocks in the real sector (gross domestic product (GDP) and trade openness (OPEN)) and monetary sector (nominal interest rate (NIR)) for a sample of 50 BRI countries from 2000 to 2017. The main results revealed that Chinese income shocks positively spill over to all macroeconomic variables except BRI countries’ consumer price index (CPI). However, the Chinese trade openness shock only has a temporary positive spillover to BRI international trade and a temporary negative spillover on its monetary policy. In addition, the Chinese monetary policy shock has a negative spillover on GDP and a positive spillover on CPI in BRI economies. Chinese shocks, however, do not constitute a significant source of variation in any interest variable. As explained by the Chinese income shock, the BRI interest rate is the highest percentage of variable variation accumulated over time. Further, the highest variation of Chinese trade shock is BRI trade openness, and lastly, the highest variation of Chinese interest rate shock is CPI in BRI economies. The beggar-thy-neighbour effect may dominate the positive trade effect and is a negative impact of the Chinese shocks. Hence, BRI economies should alleviate the adverse shocks since the upcoming rapid growth from the Chinese has disturbed the BRI economies. Our results reveal the importance of Chinese development on BRI partners’ economies and the significance of Chinese shocks in real and monetary sectors in assisting policymakers in designing international and monetary policy for BRI economies.

1. Introduction

China is now internationally recognised as an economic superpower. It is globally acknowledged that the Chinese economy has undergone tremendous and rapid development since 1978 [1,2,3]. The purchasing power parity indicators signified that the Chinese economy had been the leading economic superpower since 2014 [4]. The Chinese economy has projected to outstrip the United States (U.S.) economy by 2030 [4]. The Chinese economy registered at USD 25.3 trillion, approximately 18.7 percent of the total world gross domestic product (GDP) of USD 135.4 trillion, surpassing the U.S. GDP recorded at USD 20.6 trillion [4]. The purchasing power of China is likely to grow since they have a market of 1.4 billion in population for its goods and services [4]. In addition, China is the major investor in the colossal Belt and Road Initiative (BRI) global infrastructure development projects, which will ultimately connect the world’s markets with the potential of accruing long-term economic expansion and integration and expanding her influences worldwide. Thus, the current development of the Chinese economic superpower has raised the question of how the effects of Chinese real and monetary sector shocks are propagated to the BRI economies.

The Chinese soft power influence along the ancient Silk Road over millennia will again significantly impact present-day BRI economies in terms of culture, science, trade, and migration. Numerous macroeconomic forces can further significantly affect the BRI countries [5]. The rapid development of Chinese BRI projects, beginning in 2013, has generated a massive investment in BRI economies, mainly for global infrastructure development of ports, rail lines, and telecommunications networks [6]. The initiatives visualise that the performance of participant countries will be sensitive to the Chinese ongoing colossal economic growth due to its vast and demanding projects on infrastructure, which concurrently present a pathway to gaining friends and propagating influences. Consequently, a production chain between the BRI and China means that Chinese intermediate goods will become inputs for other economies.

Conversely, BRI resources and intermediate goods will also serve as inputs to sustain Chinese production capacity [7]. In addition, the BRI initiatives involved millions of workers and millions of emergent and affluent customers who will eventually produce a massive demand for imported consumer products. The BRI gigantic program covers 71 countries, including China, representing 62 percent of the world’s population and 38.8 percent of the world’s GDP [8]. Since its completion has been projected in 2049, the BRI will stretch its influences and affect a growing list of countries that will account for 64.2 percent of the world’s population and 40 percent of its economic output [8].

Economic and trade integration presents a new opportunity for China to discover through adopting the BRI projects. Since the early 1980s, China underwent structural market reforms, annulling trade protectionism policies and regulations to open its economic door to international markets. From 2013 to 2019, the total trade value of goods between China and BRI countries surpassed 7.8 trillion dollars [9], a remarkable and sustained result. In the past two years, China and BRI countries have grown up to 10.8%, surpassing the aggregated trade growth of China by 7.4% [10]. As predicted by the Mundell–Fleming model, home monetary policy can affect foreign economic conditions. The home country’s monetary expansion will raise the import demand and increase the foreign countries’ national outputs and prices. Since the Chinese government controls its capital, the Chinese monetary policy shock will be transmitted through trade channels. In the interest of an ever-expanding significance of Chinese foreign trade into BRI economies, our paper is thus motivated to study the potential Chinese real and monetary sector shock on the dependent BRI economies.

Given that the BRI projects are successful, these will provide a bounteous opportunity for a large proportion of the global poor and vast swaths of the world’s weak economies, with a significant locomotive effect (positive spillover) on universal welfare. Presently, BRI countries’ economic development is below the world average, with an average GDP per capita of USD 3815, accounting for 37.4 percent of the global average [11]. The [12] showed that the total contribution of export from BRI countries to world export (Figure 1) has increased compared to export in the past twenty years except for Europe and the Middle East. China contributes the lion’s share to exports from BRI countries [12]. Trade activities face constraints in countries with insufficient infrastructures, facilities, or mediocre policy institutions, such as Afghanistan, Nepal, Tajikistan, and Laos. In these countries, their considerable potential for economic development remains underexploited. The BRI initiatives in this context could enhance economic and trade integration and open new opportunities for weak economies.

Given the background, this present study may contribute to policy formulation and the extant literature in the following aspects: First, it could leverage the Chinese authority to design the appropriate stabilisation policies and incentivise to solve any conflict peacefully. The country should understand that its growing economic power can enhance economic integration by reducing trade barriers and coordinating monetary policies. Better trade liberalisation will trigger market expansion, cross-border investment, and technology sharing, thus boosting a more robust political and economic cooperation between China and the BRI countries. Since BRI countries are sensitised to Chinese rising global demand and investment, identifying important macroeconomic impulses from the economic superpower would enhance their domestic competitiveness. The BRI countries should formulate and implement national policies to strengthen their economic growth if superpowers like China intentionally propagate adverse shocks (beggar-thy-neighbour effect). According to the Mundell–Fleming framework, if the exchange rate for the home and host countries is flexible, the beggar-thy-neighbour effect might dictate a positive trade effect and thus negatively impact the BRI regions. In addition, BRI monetary authorities could stabilise the short-run output fluctuations by focusing more on the Chinese monetary policy development because it may create variances in prices/outputs.

Secondly, the study should also leverage the literature where some studies only focus on one-sector analysis [13,14,15]. The VAR model has been widely used in this research area, such as by [16], who investigated the impact of the BRI on regional labour markets. However, the present study is different from other studies that use the PVAR model to analyse an emerging market, namely the BRI economies, which may contribute to a new area in the research field.

Our main results revealed a significant positive relationship between the Chinese GDP shock on the GDP, trade, and interest rate of BRI economies. However, the BRI’s CPI is not significantly affected by the Chinese GDP shock. Secondly, Chinese trade openness shock showed a positive spillover to BRI’s trade and interest rate but only over a temporary period. However, both GDP and CPI of BRI countries had shown non-significant relationships. Finally, Chinese monetary policy has shown a positive (negative) and significant impact on BRI’s CPI (BRI’s GDP). In contrast, the impacts on the BRI countries’ trade openness and interest rate are negative and only influential in the short run; however, the impacts tend to disappear as the time horizon increases.

This article is structured as follows: The second section reviews the relevant theoretical and empirical literature, whereas the following section describes the data and the baseline empirical model using panel vector autoregression (PVAR) estimation. Section four will discuss the main empirical findings, and the fifth and final section concludes the study.

2. Theoretical Background and Literature Review

2.1. Theoretical Background

It is generally accepted that a small open economy country has been exposed to foreign trade and financial shocks. This idea can be traced back to the Mundell–Fleming theory, which shows how the key macrovariables (GDP, trade openness, and interest rate) are determined and interact with each other in the world of interdependence. This framework can be analysed using the standard IS/LM/BP model that shows how the small open economy is affected by foreign exogenous shocks, such as shocks in financial assets, trade, and macroeconomic policies from other countries [17].

Uncovered interest parity (UIP) theory will represent the integrated capital market between IS/LM/BP model in examining the interdependence of economies across countries. This reflects the UIP conditions in which discrepancies in the nominal exchange rate between both countries must equal the discrepancies in interest rates between the two economies. For example, an interest gain in a country will equal an expected depreciation in the exchange rate. If both countries have the same interest rate with i* = i, where i* = foreign interest rate and i = home interest rate, then there will be no change in exchange rate where the expected exchange rate is equivalent to the pre-existing exchange rate [18]. The exchange rate will remain unchanged when both interest rates are equal, as shown in the equation below:

$$i-i\ast =\frac{\log e_{t+1}^E-\log e_t}{\log e_t}=\frac{\log e_{t+1}^E}{\log e_t}-\frac{\log e_t}{\log e_t}=\log e_{t+1}^E-\log e_t$$

where $\log e_{t+1}^E$ is expected exchange rate and $\log e$ is the pre-existing exchange rate. The spillover from the policy in the home country to outcomes in a foreign country depends naturally on the exchange rate regime.

2.2. Literature Review

Economic interdependence across countries interests the policymakers in considering their behaviour or actions following the policies other influential countries adopt. According to [18], two possible spillover scenarios exist from these international activities. The first is a negative spillover effect that develops a beggar-thy-neighbour circumstance. An expansionary policy in a country’s exports will trigger a cutback in output or increase unemployment in another country. For example, expansionary home monetary policy under a flexible exchange rate produces a depreciation of the home currency, hence an appreciation for the foreign currency, which depresses its net export and output. The second scenario is the positive spillover effect or locomotive impact, which portrays shock from the home country positively impacting other countries’ output and export. For example, expansionary home monetary policy under a fixed exchange rate pushes down the world interest rate, so output expands in home and foreign countries.

Studies on economic linkages between countries were greatly enhanced following the sub-prime crisis in the U.S., which affected the global economy. The ensuing profuse discussions in the body literature were mainly focused on the transmission of external shock to other economies [19,20,21,22,23,24,25,26]. Specifically, they refer to the economic literature on the propagation of large economies in domestic and external contexts [27,28,29,30,31]. Since 2008, the demand for exports from the Asia-Pacific region has been sluggish due to the recession and weak growth in the U.S. and the Eurozone. However, this was compensated by the surge in Chinese domestic demand, which greatly benefited Asia-Pacific countries. Exports from the region to China doubled in the past five years. Consequently, China has become the most prominent market in the Asian economies, having surpassed the export volume to Japan and the U.S. [32].

Limited studies on Chinese economic shocks, as transmitted to other countries, emanate from its unprecedentedly rapid economic recovery over the last 25 years, even though it is still an emerging economy. China has only recently begun to build its network of gigantic projects through the BRI. Since then, the spillover of Chinese growth has increasingly benefited the BRI participant nations over the years, although the effect from the U.S. economic growth is relatively greater by comparison. A recent study by [33] showed significant spatial dependence on economic development across BRI countries due to the spillover effect from Chinese high institutional quality and economic openness. Other similar studies, such as [34], demonstrated that China is relatively more sensitive than the U.S. to the adverse impact of her growth spillover on other dependent economies. Using the structural vector autoregression (SVAR) model, the author discovered that the commodity price is prominent in transmitting shock from China to New Zealand. China has a rising influence on commodity prices, thus increasingly impacting the economy of New Zealand, particularly in terms of domestic interest rate and real exchange rate.

Most studies conducted on Chinese economic shocks established that her spillovers are more impactful on emerging economies than advanced ones. A negative shock to the Chinese growth rate negatively impacted the market growth rate of ASEAN. Further, a positive inflation shock outturns a long-run and continuous effect on ASEAN’s inflation rate, raising its import and export rate. When China suffers from adverse trade shocks, it causes the economic growth rate in the ASEAN countries to decrease. Conversely, if the ASEAN countries suffer a negative shock, the renminbi will depreciate rapidly, although the Chinese economic growth rate, foreign trade, and inflation are only moderately affected [35]. In a similar study, ref. [32] showed that a negative shock from Chinese GDP elicited a severe impact on commodity exporters in ASEAN, such as Indonesia. Other export-dependent countries from East Asia, such as Japan, Malaysia, Singapore, and Thailand, are greatly affected by real GDP, crude oil prices, metals prices, and agricultural products. Additionally, ref. [36] evaluated the effect of real GDP shock transmitted from China on other dependent economies and discovered that the emerging countries are more strongly impacted than advanced ones. Besides, refs. [14,37] support that Chinese economic growth shocks may reduce the world growth rate, particularly in its partner country.

Research on monetary stability or policy and macroeconomics variables is essential and helpful in expanding economic restoration from its slowdown and avoiding the inflationary economic gap during its peak time. Ref. [38] studied the macroeconomic effects of the monetary and fiscal policy shock to elucidate the development and possible spillover effect between the two policies in BRIC countries. Both monetary policy contraction and fiscal policy show the influence of the spillover effect where a higher bank rate and a declining government spending were shown to intensify the contraction of monetary policy. Ref. [39], who similarly studied the monetary policy co-movement and spillover of shocks in BRIC countries, discovered a significant degree of relatedness in the movement of interest rates. Additionally, a study on policy transmission by [40] showed the substantial impact of a monetary shock on actual output activities between countries. However, the effect of fiscal policy appears much weaker than that of monetary policy.

Ref. [13] also examined policy transmission in the developing countries of Sub-Sahara Africa. They revealed a bi-causal relationship between both monetary policy and financial inclusion. Further, ref. [41] study on monetary policy, fiscal policy, and exchange rate may help explain the external imbalances in Africa’s three largest economies (Nigeria, South Africa, and Egypt). They established that the current account surplus emanates from shocks is attributed to three policy approaches. Between money supply and real interest rate (monetary policy stance), the latter significantly influenced the stock market and inflation. Ref. [42] earlier showed that the stock market of African countries was positively and simultaneously affected by their specific monetary policy through an interest rate channel. The study, however, could not prove the reverse effect. Both money supply and real interest rate respectively respond to a decline in positive and negative shock from the stock market, whereas inflation reacts positively to a negative stock market shock.

Given the above background, this paper can potentially close the literature gap in the following ways: First, a shock originating from a foreign country is an essential factor in determining the country’s business cycle. An unpredictable shock can significantly impact a country’s macroeconomic activity through negative growth and employment rate and reduced consumption and investment [23,43]. The spillover effect of real and monetary policy from markets of economic superpowers to other dependent markets is presently under-researched [1]. Secondly, most of the relevant literature reported differences in the analysis of international spillover effects, especially findings from the U.S., the world’s largest exporter. Consequently, some researchers have studied the impact of economic integration related to trade connections at the regional level, including relationships between countries in the subregions [23,44,45].

The main focus of this study is to investigate the international spillovers from China, as the initiator of the BRI projects, toward BRI member countries. The study potentially contributes to existing literature and thus narrows the existing information gap. China was initially not included in most of the earlier studies by western researchers since their focus was mainly on emerging economies. For example, ref. [26], who adopted the PVAR analysis, examined the spillover effect of uncertainty from the U.S. on emerging markets. He proved that shocks from the leading country, which also involved risks, will influence the consumers and firms’ economic players to delay their consumption and investment decisions. It is thus beneficial to elucidate the BRI member countries, since most studies focus mainly on developing, emerging, and Asian countries. Although the BRI is a fast-growing entity, a historic milestone in economic development, studies on member countries are still limited. Thirdly, the study also extended the simulation model by analysing different sectors, notably real sector shock and monetary policy shock, whereas, in stark contrast, past authors have mainly focused on the primary sector [14,35,38].

3. Methodology

3.1. Data and Variables Explanation

This study investigates the spillover effects of Chinese real and monetary sector shocks to BRI economies. The data were sourced from 50 BRI countries from the year 2000 until the year 2017 (yearly data). The sample period did not consider the COVID-19 period to represent the shocks from China significantly. In addition, the data for nominal interest rates are mostly available up to 2018 for many BRI economies. The countries included in the sampling are listed in Appendix A. However, the remaining data for other BRI countries were not included since the data were incomplete.

The different shocks from the real sector and monetary policy sector for each model are represented in our baseline panel VAR model in the following vectors:

$$V_{it}^n=\left(dlncgd{p}^{China},\Delta dlngd{p}_{it}^{BRI},\Delta dope{n}_{it}^{BRI},\Delta dni{r}_{it}^{BRI},\Delta dlncp{i}_{it}^{BRI}\right)$$

(1)

$$X_{it}^n=\left(dcope{n}^{China},\Delta dln{\mathrm{gdp}}_{it}^{BRI},\Delta dope{n}_{it}^{BRI},\Delta dni{r}_{it}^{BRI},\Delta dlncp{i}_{it}^{BRI}\right)$$

(2)

$$Y_{it}^n=\left(dcni{r}^{China},\Delta dlngd{p}_{it}^{BRI},\Delta dope{n}_{it}^{BRI},\Delta dni{r}_{it}^{BRI},\Delta dlncp{i}_{it}^{BRI}\right)$$

(3)

This approach employs both the standard VAR models, which consider all variables in the system as endogenous. In Equations (1)–(3), dlncgdp^China, dcopen^China, and dcnir^China represent Chinese foreign income, trade openness and nominal interest rate. Other endogenous variables used in the baseline model reflect the responses from the economies of the host countries. The acronym for each endogenous variable is shown in

Table 1. Chinese Shock and BRI’s Macroeconomic Variables.

Variable	Descriptions	Hypothesis	Data Sources
dlncgdp	Chinese GDP represents foreign income or output, and the data are in constant 2010 U.S. dollars. Variable has been used by [33].	−	World Bank database
dcopen	Chinese trade openness represents the summation of total imports and total exports of Chinese GDP, and the data are in percentage of GDP. Variable has been used by [33].	−	World Bank database
dcnir	Chinese nominal interest rate is used to represent foreign monetary policy shock. The data are in percentage form. Variable has been used by [38,46].	−	Datastream database
dlngdp	BRI’s GDP represents domestic income or domestic output, and the data are in constant 2010 U.S. dollars.	H1: The Chinese shock’s spillover effect on BRI’s gross domestic product.	World Bank database
dopen	BRI’s trade openness represents the summation of BRI GDP’s total imports and exports. The data are in the percentage of GDP.	H1: There is a spillover effect of Chinese shock to BRI’s Trade Openness.	World Bank database
dnir	BRI’s nominal interest rate is used to represent domestic monetary policy shock. The data are in percentages.	H1: There is a spillover effect of Chinese shock to BRI’s interest rate.	Datastream database
dlncpi	BRI’s consumer price index. Their base year was 2010. Variable has been used by [35]	H1: There is a spillover effect of Chinese shock to BRI’s consumer price index.	World Bank database

. GDP is entered into our vector, in logarithm form (ln), to represent the domestic output in host countries. Trade openness is included in the vector to capture the effect of international trade transmission, whereas the nominal interest rate represents the monetary policy stance. The domestic consumer price index is used to study the country’s inflation in response to the foreign shock, with the variable in logarithm form. Gross domestic product and consumer price index data are measured in log form to reduce large values and interpreted in percentage form. Table 1 summarises the data descriptions and the hypothesis according to economic theory, information in academic journals, and data sources.

Table 2. Descriptive Statistic for Chinese Shocks.

Variables	Observations	Mean	Std. Dev	Min	Max
dlncgdp	850	26.80686	0.3738683	25.95207	27.20918
dcopen	850	−0.0945376	5.050646	−12.8475	9.056587
dcnir	750	0.05	0.8363408	−1.67	1.84
dlngdp	760	21.91002	1.607096	16.03365	25.87996
dnir	737	−0.3650882	3.511494	−32	46.14
dopen	822	0.0946641	9.503419	−80.6718	53.68657
dlncpi	717	1.319684	1.003376	−3.40259	4.092627

Note: The real sector shock denotes dlncgdp = first difference of ln gross domestic product shock from China and dcopen = first difference of ln trade openness shock from China. The monetary policy sector shock denotes dcnir = first difference of Chinese nominal interest rate. The BRI variables denote the following: dlngdp = first difference of ln of gross domestic product, dnir = first difference of nominal interest rate, dopen = first difference of trade openness, and dlncpi = first difference of ln consumer price index.

shows descriptive statistics for the following variables used in our objectives. Generally, we generate all variables into the first difference to produce stationary data in the estimation, as shown in

Table 3. Panel Unit Root for Chinese Shocks.

Variables	ADF Statistics		PP Statistics
	Level	1st Diff	Level	1st Diff
cgdp	0.35970	143.826 ***	4.2 × 10−5	157.294 ***
cnir	143.065 ***	398.684 ***	132.463 **	772.687 ***
copen	130.910 **	194.699 ***	59.9979	194.699 ***
gdp	40.6906	238.564 ***	41.1424	242.452 ***
nir	304.899 ***	427.855 ***	442.500 ***	473.857 ***
open	132.522 **	430.533 ***	132.326 **	572.446 ***
cpi	45.0383	239.750 ***	62.6071	242.074 ***

Note: ** and *** represented the significant at 5% and 1% respectively.

[46].

3.2. Estimation Strategies

The Panel Vector Autoregression (PVAR) Model

There are three considerations taken in developing this empirical study. First, identify variables customised in previous studies on the real sector and monetary policy shock. Ref. [38] examined the impact of a negative Chinese GDP shock as a real sector shock. Ref. [35] investigated the interaction mechanism of economic and trade shocks between China and ASEAN countries using real output as the real GDP index and foreign trade fluctuation by using import and export growth. Refs. [47,48] examined interest rates to formulate monetary policy shocks. Second, the data source for the Chinese shocks to the BRI economic activities for these empirical studies needs to be restricted to certain important variables within this framework to describe all the shocks due to data availability. Lastly, the model should identify potential endogeneity problems for all the variables used.

Following a few articles that investigate the interaction of variables [14,38,39], our econometric methodology is based on the panel VAR framework, and we utilise a panel VAR generalised method of moments (GMM) estimator to estimate the model. The PVAR model is given by:

$$Z_{it}=Z_{it-1}{A}_1+Z_{it-2}{A}_2+\dots +Z_{it-p}{A}_p+{\mu }_i+e_{it}$$

i Є {1,2,….50}, t Є {1,2,….,T_i}]

(4)

and to be simplified into:

Z_it = A(L)Z_it−1 + μ_i + e_it

(5)

Z_it is an endogenous variables matrix relating to the real sector (cgdp_t, copen_t, gdp_i,t, cpi_i,t, open_i,t) and monetary policy (cnir_t, dnir_i,t) shocks. A(L) is a polynomial matrix in the lag operator L, with country i = 1, …50, μ_i is a vector of unobserved fixed effects, and e_it is a vector of random errors. Their parameter will suffer from estimation bias if the traditional mean difference is adopted due to μ_i being correlated to the lag term. Hence, forward orthogonal deviation (Helmert Transformation) is used to eliminate the μ_i.

Let $\overline{Z_{i, t}}={\displaystyle \sum }_{s=t+1}^{T_i}{Z}_{i,s}^m/\left(T_i-t\right)$ be the means obtained from the future values of $Z_{i,s}^m,$ which is a vector variable in the vector $Z_{i, t}=\left(Z_{i,t}^1,Z_{i,t}^2, \dots ., Z_{i,t}^M\right),$ with $T_i$ is the data sample last period. In addition, $\overline{e_{i, t}}={\displaystyle \sum }_{s=t+1}^{T_i}{e}_{i,s}^m/\left(T_i-t\right)$ is the mean obtained from the future values of $e_{i,s}^m,$ which is a vector variable in the vector $e_{i, t}=\left(e_{i,t}^1,e_{i,t}^2, \dots ., e_{i,t}^M\right)$. Next, the transformed variable will become $Z_{i,t}^{*}=(Z_{i,t}^m$ − $\overline{Z_{i,t}}) \sqrt{(T_{i,t}/T_{i, t}+1)}$ and $e_{i,t}^{*}=(e_{i,t}^m$ − $\overline{e_{i,t}}) \sqrt{(T_{i,t}/T_{i, t}+1)}$ respectively. Considering there is no future value for creating the forward mean, we cannot derive the transformation value of the last period data. Consequently, the final transformed model is as follows:

$${\tilde{Z}}_{it}=A\left(L\right){\tilde{Z}}_{it-1}+{\mu }_i+{\tilde{e}}_{it}$$

(6)

where ${\tilde{Z}}_{i, t}=\left({\tilde{Z}}_{i,t}^1,{\tilde{Z}}_{i,t}^2, \dots ., {\tilde{Z}}_{i,t}^M\right)$ and ${\tilde{e}}_{i, t}=\left({\tilde{e}}_{i,t}^1,{\tilde{e}}_{i,t}^2, \dots ., {\tilde{e}}_{i,t}^M\right)$.

The advantage of the PVAR method stems primarily from its remedy on the considered potential endogenous variable. PVAR is said to be able to gain a degree of freedom by analysing a country’s panel. Additionally, we can identify a spillover effect from one country to another because it captures unobserved country-level heterogeneity [49]. Furthermore, the PVAR method can provide dynamic interaction between Chinese shocks from the real sector, monetary policy, and other macroeconomic factors from the BRI countries. The technique is parsimonious and straightforward and appears to fit the estimation suitably.

The estimation process in the PVAR model above (5) requires four steps. The initial step is to ensure all variables discussed have suitable temporal properties where panel unit root will be implemented [50]. In the second step, we need to set the PVAR lag order using the optimal moment and model selection criteria (MMSC). For this, three criteria can be considered as proposed by [51], namely the Bayesian information criterion (MBIC), the Akaike information criterion (MAIC), and the Hannan-Quinn information criterion (MQIC). The GMM estimator is then applied in the PVAR technique to solve the endogeneity problem in estimating the model. Forward mean-differencing or Helmert transformation is used to trace out the individual fixed effects as suggested by [49]. The Helmert transformation can conserve the orthogonally transformed variables with the lagged dependent variables used as an instrumental variable in this GMM estimation. The third step is to verify the stability condition for all the variables used in the PVAR estimation. Finally, the dynamic relationship between Chinese shocks and other factors in the BRI countries has been examined through the impulse response function (IRF) and the forecast error variance decomposition (FEVD).

Both shocks from the real sector and monetary policy need to be ordered correctly into structural shocks in the PVAR system using the Cholesky decomposition of the variance-covariance matrix method. Hence, the sequence will start with Chinese shocks from its real sector (cgdp and copen) and its monetary policy shocks (cnir) and transmitted to the macroeconomics variables (gdp, open, nir, and cpi) of BRI countries. This comprises a total of seven variables. The real sector shock is placed first for orderliness since it usually has no immediate effect and responds very slowly. Next is the monetary policy since the government usually has a time lag for transmitting information, thus not appropriate as the initial response. Thus the ordering system adopted is initiated with the least endogenous variable and ends with the most endogenous one.

In each model, Chinese shocks (represented respectively by ${\mathsf{\epsilon }}_{it}^A=$ gdp, trade openness, and nominal interest rate) will be ordered first because, being a large country, it has the least endogenous variable, as shown in Equation (7). The assumption is that a large country like China is highly exogenous towards a smaller one. Subsequently, the gross domestic product will be in the first order on the small country variable since it usually has no immediate effect and responds very slowly. Monetary policy will be next in line since the government usually has a time lag for the information to respond initially. The consumer price index follows it since it is the least responsive among the remaining two variables, given its slow evolution over time. The consumer price index is assumed to react with lags to innovations in the monetary policy. Finally, trade openness is placed last because of its response according to the exchange rate and the most responsive to all shocks. The matrix below does the Cholesky decomposition as follows:

$$\left(\begin{array}{c}{\mathsf{\epsilon }}_{it}^A\\ \begin{array}{c}{ \mathsf{\epsilon }}_{it}^{dlngdp}\\ { \mathsf{\epsilon }}_{it}^{dnir }\\ \begin{array}{c}{ \mathsf{\epsilon }}_{it}^{dlncpi}\\ {\mathsf{\epsilon }}_{it}^{dopen}\end{array}\end{array}\end{array} \right) =\left[\begin{array}{ccccc} a& 0& 0& 0& 0\\ b& c& 0& 0& 0\\ d& e& f& 0& 0\\ g& h& i& j& 0\\ k& l& m& n& o\end{array} \right]\left(\begin{array}{c} e_{it}^1\\ e_{it}^2\\ \begin{array}{c}{e}_{it}^3\\ e_{it}^4\\ \begin{array}{c}{e}_{it}^5\\ \end{array}\end{array}\end{array} \right)$$

(7)

Based on the Cholesky decomposition method, the value of the matrix in the diagonal form is set to zero, and the free parameters are below the diagonal. In this case, a, c, f, j, and o are diagonals representing their shocks in the system. Other alphabets in the matrix are the free parameters. The error terms of structural shock in Equation (7) are isolated variables in the system (recursive structure), becoming orthogonal as explained by the alphabetically ordered weights linked to the model’s structural shock and retaining all other responses at zero value. This concept confines the variables at first ordering, which will affect other variables contemporaneously and with an exogenous lag. At the same time, delayed factors will affect the front variables with an endogenous lag. Equation (7) specified the immediate shocks from ${\mathsf{\epsilon }}_{it}^A$ = dlncgdp, dcopen, and dcnir will have a contemporaneous effect on dlngdp, dnir, dlncpi, and dopen. The dlngdp, dnir, dlncpi, and dopen will deliver a lagged effect on the dlncgdp, dcopen, and dcnir, respectively.

Following the panel VAR estimation, the IRF and FEVD analyses will be conducted. The variables will be in an alternative ordering in the Cholesky decomposition to check the robustness of previous findings. The IRF measures the impact of the shock from one variable on another future value of the endogenous variables while holding other shocks constant. The different graphs of the IRF are shown with 95% confidence interval bands implemented by Monte Carlo simulation. FEVD is used to assess the role of each random variable in affecting the variables in the VAR system. In contrast, IRF describes the responses by the variable to the effect of a shock in VAR, which is the percentage of variable variation accumulated over time as explained by the shock from another variable.

4. Results

The PVAR method was applied empirically to test the interaction between China shocks on variables of small dependent countries using IRF and variance decomposition (V.D.). In this empirical framework, all the variables generate into first-difference to ensure stationary conditions, except for trade openness and nominal interest rate [49].

4.1. Whole Sample

Table 4. Moment and Model Selection Criteria (MMSC) for GDP Shock.

Lag	CD	J	J-Value	MBIC	MAIC	MQIC
1	0.99841	106.7681	0.3032448	−427.4653	−93.23189	−228.3644
2	0.9980342	80.87627	0.3008597	−319.7988	−69.12373	−170.4731
3	0.9989307	58.6977	0.1868073	−208.419	−41.3023	−108.8686
4	0.999312	21.81642	0.6463216	−111.7419	−28.18358	−61.96671

,

Table 5. Moment and Model Selection Criteria (MMSC) for Trade Shock.

Lag	CD	J	J-Value	MBIC	MAIC	MQIC
1	0.9923378	80.75924	0.3040302	−336.2919	−69.24076	−176.5989
2	0.9930039	45.06584	0.6712465	−232.9682	−54.93416	−126.5062
3	0.9927698	17.60846	0.8585533	−121.4086	−32.39154	−68.17757

and

Table 6. Moment and Model Selection Criteria (MMSC) for Interest Rate Shock.

Lag	CD	J	J-Value	MBIC	MAIC	MQIC
1	0.8894227	105.0335	0.3456514	−398.6618	−94.96651	−218.3268
2	0.9490399	78.85301	0.3580768	−298.9184	−71.14699	−163.6672
3	0.9628064	62.40207	0.1120307	−189.4456	−37.59793	−99.27806
4	0.981486	24.10557	0.5132806	−101.8182	−25.89443	−56.7345

present the optimal MMSC developed by [51] for all three shock models, and

Table 7. Generalised Method of Moments (GMM) Panel Vector Autoregression Model for GDP Shock.

Response to	Response of
	dlncgdp(t−1)	dlngdp(t−1)	dnir(t−1)	dlncpi(t−1)	dopen(t−1)
dlncgdp	0.8207862	0.0700047	−0.0001021	−0.0199699	−0.0017008
dlngdp	−0.0377286	0.816613	0.0012829	−0.2617765	−0.0121675
dnir	1.689785	2.09795	−0.0649291	−1.542436	0.0041527
dlncpi	0.138204	0.4642755	−0.0009887	0.4669544	−0.0011638
dopen	0.8699948	3.217191	−0.1197427	−4.903296	0.1241017

,

Table 8. Generalised Method of Moments (GMM) Panel Vector Autoregression Model for Trade Shock.

Response to	Response of
	dcopen(t−1)	dlngdp(t−1)	dnir(t−1)	dlncpi(t−1)	dopen(t−1)
dcopen	0.4973735	−6.4362	−0.2482989	−2.71856	−0.0366721
dlngdp	0.0043512	0.759306	−0.0048142	−0.2851654	−0.0112747
dnir	−0.0484188	1.67729	0.0789566	−0.9682496	0.0029706
dlncpi	−0.0031155	0.6033344	−0.0089672	0.4615791	−0.0015149
dopen	0.1124231	0.3016464	−0.2388937	−2.682266	0.1669837

and

Table 9. Generalised Method of Moments (GMM) Panel Vector Autoregression Model for Interest Rate Shock.

Responseto	Response of
	dcnir(t−1)	dlngdp(t−1)	dnir(t−1)	dlncpi(t−1)	dopen(t−1)
dcnir	−0.1292096	0.6810323	−0.2084713	0.4597205	−0.0033477
dlngdp	−0.0033477	0.9241607	−0.0207594	−0.1145027	−0.0026333
dnir	−0.1646203	0.0974779	0.4048713	−0.6148199	−0.0036143
dlncpi	0.1050466	0.1553173	−0.0281866	0.2842186	0.000291
dopen	0.3640861	0.740003	−0.3638609	−5.606146	0.0945059

the fitted coefficient in VAR model. However, the estimated coefficient in the PVAR model cannot be interpreted directly because VAR is an atheoretical model. The PVAR model with lagged 1 (PVAR (1)) is used for the baseline model because it has a minimum value of all information criteria. Subsequently, the three shock models will have accomplished the stability condition test in this estimated PVAR system as shown in Figure 2 since the roots of the companion matrix are inside the unit circle diagram for all three models. As a consequence, the estimation will be further to IRF, and this will be duly discussed for each case.

Figure 3a–c plots the impulse-response function with 95% confidence intervals (using 200 Monte-Carlo iterations) on the propagation of China shocks (GDP, trade, and interest rates) upon BRI economic variables. If the plotted 95% confidence interval does not have zero value, the responses to shocks are therefore significant. For example, if the upper and lower bounds do not contain zero, the response is considered positively significant. The main IRF in this study is plotted in the first row of the figure, where it traces the impacts of the Chinese GDP shock on the macroeconomics of BRI countries.

The BRI countries’ trade openness and interest rate beyond the first year increased approximately by 3.2% and 1.1%, respectively, following the one standard innovation shocks in Chinese GDP. This figure suggests that the positive response of the two variables conforms to expectation and indicates a significant impact. Normally, the propagation effect due to the Chinese economic growth is usually positive since the growth in national income will spur the exports of goods from their trading partners. Increasing exports will ultimately increase the interest rate in the respective countries due to the growing demand for their goods and services. Thus, the impact of Chinese GDP on BRI countries is economically crucial to her trade openness and interest rate, as predicted from the model.

Interestingly, the price level of BRI countries does not seem to impact the economic horizons negatively. However, Chinese growth significantly increased those of the BRI countries but only for the first year’s horizon, beyond which the development is no longer significant. This suggests that Chinese initiatives could only impact the output of BRI countries in the short run. It may also be perceived as mirroring the dynamics of the opposing forces. The spillover effect is usually transmitted through international trade, financial markets, and commodity prices [52]. The economic integration fostered through the BRI countries is still influential enough to stimulate their economic growth. Ref. [53] studied the impact of East Asia integration and established that the integration had promoted development. The consequential rise in higher-income gain would also generate extensive reformation of non-discriminatory impediments. Hence, the propagation of Chinese GDP may be insufficient to sustain BRI countries in the long run. Indeed, there is little dependency on the BRI partners for the ultimate Chinese aim to increase the value-added of production.

To assess the impact of Chinese shock on trade openness, their corresponding IRF is plotted in Figure 3b. The response to trade openness and interest rate shows a similar significant response of up to ten- and three-year horizons. As expected, countries with high linkages with China would be affected by its short-term and long-term impacts. For example, the spillover to Asia is most affected by its increased trade linkages with the Chinese markets [14]. Undoubtedly, the interrelations of economy and trade between China and other dependent economies have gradually become more apparent. Therefore, China is simultaneously becoming progressively negligible in the BRI countries. When a country has a greater degree of openness due to trade liberalisation like China, it will likely change the influence of the exchange rate through the demand for exports and the linkage with demand for imports. Thus, macroeconomic variables fluctuate due to price changes through export and import activities [35]. Therefore, it should transpire that monetary policy will stabilise the prices and output of the affected countries.

However, CPI and GDP in the BRI countries show significance only over the first year’s horizon. This subsequently led to a controversial argument that trading between countries eventually will boost the economies since an increase in demand will encourage the consumption of domestic and foreign products. The trade balance dynamics between China and the BRI countries should move in the same direction if the currency exchange rate were constant. GDP will therefore increase due to the rising net export of the country. However, the export portfolio of BRI countries is rather undiversified, and selling low-value-added goods will limit its export growth. Although exports to China are rising, there is little effect on her economic growth. China may become an important trading partner for the BRI countries, but it does not assume a dominant role. In other words, the demand from China must be sufficiently large to impact the GDP of the BRI countries [54].

The ultimate goal of monetary policy is to support the real sector in an economy. Since China is an open economy, the internal and external equilibrium has become essential to the BRI countries. It is important to note that the spillover of interest rates in China, namely monetary policy, significantly affects all interest variables in BRI countries except for the interest rate. BRI interest rates were not significant in all time horizons. Surprisingly, Chinese monetary policy could affect the BRI price level and its growth for the longer term and trade openness for the shorter term. When China expands the money supply, its interest rate will reduce and ultimately trigger its economic growth. Since i < i* (home interest rate is lower than the host interest rate), the exchange rate will continue to depreciate, and the foreign exchange rate will continue to appreciate and propel the economy until it achieves equilibrium (i = i*). This expansion ultimately influences output growth, price level, and other countries’ trade. China has achieved a welfare state through higher output and at the expense of diminished production in the BRI countries. This is, in essence, the classical beggar-thy-neighbour policy. In short, an economic shock from Chinese monetary policy will ultimately impact total world output and adversely affect the exchange rate market.

The Variance decomposition will assess the role of each random innovation in affecting the variables in the VAR system. This is in contrast to the IRF, which explains the effect of a shock on the interest variable in the VAR. This suggests that variance decomposition presents the percentage of variable variation accumulated over time and explained by the shock to interest variables.

Table 10. Variance Decomposition PVAR (10 periods ahead) for GDP Shock.

Response Variable	Impulse Variable
	dlncgdp	dcopen	dcnir
dlncgdp	0.343419	−	−
dcopen	−	0.3758926	−
dcnir	−	−	0.5633458
dlngdp	0.0112354	0.0060957	0.0493517
dnir	0.0603445	0.0338055	0.0719597
dlncpi	0.0119951	0.0077407	0.1109484
dopen	0.0545841	0.0770966	0.0818801

summarises that the highest variation is shown in the interest rate (6.03%), followed by trade openness (5.45%), CPI (1.19%), and GDP (1.12%) for the cumulative ten years of a Chinese income shock. Following the cumulative ten years of Chinese trade shock, the highest variation is trade openness (7.7%), and the lowest variation is GDP (0.6%). Lastly, the highest variation in Chinese interest rate shock is CPI (11%), followed by trade openness (8.17%), interest rate (7.2%), and GDP (4.9%).

4.2. Robustness Checking

For robustness checking, the baseline model has been re-estimated by considering BRI’s macroeconomic variables to react as exogenous variables, whereas the Chinese variables are endogenous. These assumptions are reasonable given that the BRI economies and China are interconnected in international trade and investment [7]. Thus, we experiment with a different ordering of the variables for this simulation of IRF. Figure 4 below depicts the responses of (a) China’s gross domestic product, (b) trade openness and (c) consumer price index to BRI’s macroeconomic variables. The results show an insignificant relationship between BRI’s trade openness and China’s GDP. However, it reveals a significant negative relationship between the BRI’s GDP and BRI’s interest rate with China’s GDP.

In contrast, all shocks from BRI’s variables do not significantly impact China’s trade openness except for BRI’s interest rate for a temporary period, as shown in Figure 4b. Figure 4c shows that the BRI countries’ interest rate and gross domestic product show negative relationships and significantly affect China’s interest rate. Interestingly, the shocks to BRI’s trade openness and CPI are only influential in the short run, but the impact tends to weaken in the future. The significant relationships found between the BRI countries’ variables and China’s variables reveal the interdependence of resources on each other in BRI initiatives.

5. Summary and Conclusions

Understanding how foreign exogenous shocks from a large country spread to other countries is critical for policymakers to determine whether there is a locomotive impact (positive effect) or a beggar-thy-neighbour effect (adverse effect) on the home economy in response to these shocks. Thus, this study examines the effect of Chinese real and monetary sector shocks on the BRI economic variables, including the GDP of host countries, trade openness, monetary policy, and consumer price index. A panel vector autoregression (PVAR) method was used to analyse the propagation of the Chinese shocks upon BRI economic variables of interest using panel IRF and panel variance decomposition.

The study potentially contributes to relevant macroeconomic policy direction from the perspective of China and BRI policymakers. The exceptional growth rate of the Chinese economy and the accompanying economic shocks may magnify their impacts on BRI economies that can be perceived through a few channels. In designing its policies, China should maintain steady growth by identifying the shocks emanating from the various channels to positively impact BRI economies and promote healthy economic integration. In policy design, it is crucial to identify both the origins of shocks and the associated channels to eliminate and minimise the potential impacts of future economic disturbances. In order to build a stable and controllable market economy following the colossal BRI projects, the member countries must prepare for future negative spillovers when China is exposed to economic slowdown or recession.

Our results confirm that Chinese foreign income has a substantial role in the cross-country transmission of output shock, and the associated negative spillover should be alleviated or eliminated. Since Chinese trade shocks will invariably take effect, BRI policymakers should judiciously manage the international tariff or strategic trade policy to attract and encourage more business transactions, including imports and exports. The BRI authorities should also pay closer attention and be sensitive to the development of Chinese monetary policy to stabilise the short-run output and price fluctuations since the Chinese shocks can influence about 0.5% of the BRI economies.

This study has data availability limits to 50 countries using the PVAR technique. We believe our results may impact and contribute to the authorities, albeit we do not take the heterogeneous aspect such as political structure, development level, and geographical interconnections. If the countries consider the homogeneous grouping, it will be small and affect the validity of the result. Thus, this becomes one of the limitations of the study. BRI encompasses all the participants as a whole and not as individual countries.