General Equilibrium Trade Policy Analysis among One Belt One Road Nations Using Structural Gravity Framework

Abstract

Currently, the world is witnessing one of China’s most significant economic integration initiatives–One Belt One Road (OBOR). This article aims to evaluate the general equilibrium (GE) effects of this initiative on member nations. The structural gravity model is used in this study to perform the counterfactual analysis while analysing the conditional and general equilibrium effects of the trade policy of border removal on international trade flow among the member countries. The estimates suggest varied strade gains for the member countries in response to the trade policy changes. Most Asian countries are witnessing an increase in producers’ prices and therefore gaining more from globalisation. We also deduced that the member countries had reached half of their potential to trade gains, with most developing countries witnessing a decrease in multilateral trade resistance (MTR). The findings of this study implicate a debate for the policymakers over continuing support for further trade integration.

JEL Codes: C21, F15, F17

Keywords

China One Belt One Road general equilibrium effects structural gravity model international trade

Introduction

The impact of economic integration among countries has widely been accepted to impact their development and economic growth positively. The economic implications of such trade policies differ across the individual economies (Baier et al., 2015). This variation among the trade partners in response to trade policies is mainly due to different economic backgrounds (Baier et al., 2017). The evidence of trade integration promoting the welfare of the economies highlights that there needs to be continuous support for economic integration among the various countries (Margalit, 2012). The trade policies that involve removing barriers to international trade help in the economic growth of a country through improved infrastructure and better productivity (Ansari & Khan, 2011). The barriers limit the abilities of various economies to reap the benefits of integration and globalisation (Cole & Tenreyo, 2021). The economic integration initiative of China, One Belt One Road (OBOR), aims to change the pattern of international trade by building and improving infrastructure, reducing trade costs and aiding countries that were unable to reap the benefits of globalisation in the past (Enderwick, 2018). In this regard, this article utilises the method counterfactual analysis using the gravity model developed by Anderson et al. (2018) to analyse the effect of economic integration among the OBOR countries. This approach solves the limitations of previous studies as we analyse general equilibrium effects in response to the counterfactual scenario to quantify the integration impact of OBOR.

Since the OBOR project is still undergoing and is yet to be completed, the exact trade volume via this route is not present in the literature. However, some studies have proposed an estimate of the trade improvement and welfare effects of the OBOR. Villafuerte et al. (2016) proposed that improving the network of transport and facilitation of trade along the OBOR route may boost GDP growth by 0.1–0.7 percentage points in West, Central and South Asia. It may also result in a rise in welfare spending from 6 billion USD to 100 billion USD. The aggregate exports of nations via the OBOR route could rise from 5 billion USD to 135 billion USD. The OBOR transportation projects may cut travel and logistics alone by 12 per cent, boost global commerce by 2.7–9.7 per cent, increase income by up to 3.4 per cent and bring around 7.6 million people out of extreme poverty (Maliszewska & van der Mensbrugghe, 2019). Along similar lines, the present study aims to highlight the varied welfare effects that each participating country can have by joining the OBOR initiative.

This article aims to discuss the trade policy implication on the trade flow among 72 OBOR countries (Business & Outlook, 2018) from 1996 to 2016 using the structural gravity model in a general equilibrium environment. The remainder of this article is organised as follows. The second section reviews the literature; The third section presents the methodology adopted. The database and its sources are discussed in this section, followed by results and analysis in the fourth section. The fifth section includes the discussion and future scope.

Literature Review

China’s new Silk Route or OBOR aims to integrate significant world economies. The project aims at building efficient networks and infrastructure to facilitate trade flow among the participating countries. This aim is speculated to be achieved through the twenty-first century Maritime Silk Road traversing the Indian Ocean and continent of Africa to the European Union and the network of roads and railways under the name of Silk Road Economic Belt from China to Turkey and the European countries via Central Asia (Baniya et al., 2020). The route has proved quite lucrative for boosting trade in previous centuries (Hansen, 2012). Despite all the difficulties, the traders in the past manoeuvred the roads along the Silk Route for business. The trade via this route aided in fields such as profit generation, return on investments and employment opportunities for the participating nations. In the current scenario, the participating countries account for more than 45 per cent of the world population and more than 13 trillion USD’ worth of total world trade (Foo et al., 2020). The world nations view the project to have substantial strategic and geopolitical implications.

The facilitation of international trade flow via this trade route is analysed in this article by the structural gravity model. According to the World Bank (2004), the research on modelling flow of trade in the past is based on simulation models and econometric models that analyse the impact of a phenomenon in the former and forecast based on past performance in the latter case. For simulation models analysing the trade flow that also incorporate the cost of transportation, researchers have used input-output models besides general equilibrium models (Ivanova, 2014). For trade flow modelling and investigating the effects of regional trade agreements (RTAs) on trade, researchers have used computable general equilibrium (CGE) models (Augier & Gasiorek, 2003). However, since the CGE model, unlike the gravity model, involves the selection of specific parameters rather than their estimation, its use has been restricted as the results obtained using this model lack any significant statistical properties (World Bank, 2004), although, in combination with some other econometric models, CGE can yield better results (Hertel et al., 2007).

The trade gravity model used in the present study is a structural framework with solid theoretical underpinnings. Because of this trait, the gravity framework is well suited to counterfactual analysis, such as assessing the effects of trade policies. The gravity model depicts a realistic general equilibrium environment that can handle numerous countries, industries and even enterprises at the same time. As a result, the gravity framework can be used to capture the notion that markets are interconnected globally and that trade policy changes in one market will have repercussions in other markets. The gravity setting is a very flexible structure that may be used in a wide range of broader general equilibrium frameworks to investigate the relationships between investment, the environment, trade and labour markets, etc. The gravity model’s predictive power is one of its most appealing features. For both products and services, empirical gravity equations of trade flows routinely yield a great match of 60–90 per cent with aggregate and sectoral data (Yotov et al., 2016). Anderson and van Wincoop (2003) also present an augmented version of Anderson’s (1979) gravity model which includes addition of multilateral resistance terms for the exporter and importer that proxy for the existence of undetected trade barriers. The discussion of multilateral resistance matters for heteroscedasticity considerations (Singh, 2021), a topic not addressed by many trade models, makes this model intriguing overall. Using the Poisson Pseudo Maximum Likelihood (PPML) technique with the gravity model in this study also solves the issue of zero trade flows and heteroscedasticity.

The gravity model concept has its roots in Newtonian physics, suggesting that trade between a country pair depends on their size and distance (Porojan, 2001). Therefore the trade flow between trading partners is affected by the characteristics of destination and source, with distance between the partners acting as impedance to trade. The gravity equation developed independently by Timbergen (1962) and Pulliainen (1963) explained the trade flow between the country pairs using importer and exporter income and the geographical distance. Baldwin and Portes (1994) conclude that the gravity model gives robust empirical results in describing the international trade flow. The model is most popular among the econometric and simulation models for estimating the effects of various trade policies and other factors on the trade flow, mainly due to estimation rather than the assumption of the parameters (Filippini & Molini, 2003).

Literature indicates that the study of trade flow at an aggregate level using a panel and time-series data (Arghyrou, 2000; Elliott, 2007; Lampe, 2008) and the trade flow in specific industries has also been investigated using gravity model (Kangas & Niskanen, 2003). Rose (2000) studied the effect of currency unions on trade flow using the gravity model. Similar studies have been conducted using the gravity model to investigate the impact of economic integrations on trade flows in the Asia-Pacific region (Wilson et al., 2003), African countries (Longo & Sekkat, 2004) and among members of the Organisation for Economic Co-operation and Development (OECD) (Fratianni, 2006). Regarding implications of trade policy on international trade flow, literature illustrates that Nitsch (2000) used the gravity model to study the border effects on trade within the European Union countries. Gopinath and Echeverria (2004) reviewed the impact of foreign direct investments on trade among 85 countries for 1989–1998 using the gravity model. The facilitation of trade among 78 countries from 2000–2004 was studied by Iwanow and Kirkpatrick (2007) using the gravity model.

The gravity model finds its application in time series, cross-sectional and panel data settings (Rajesh, 2018). In the case of panel data, besides the variables used in the traditional gravity setting, some additional variables were added by Baltagi et al. (2003). These dimensions, classified into two categories of main effects and additional effects, included exporter-fixed effects, importer-fixed effects, time-fixed effects in the former and country pair fixed effects in the latter. This model was an improvement of the model given by Mátyás (1997), where the dimension of time reflected the process of globalisation and economic integration. By considering the inward and outward multilateral resistances, the revitalised model explained that besides the bilateral barriers such as distance and tariffs, bilateral trade between country pairs depends on the obstacles to trade flow with other trading partners. On the building blocks of multilateral trade resistance, besides others, Anderson and van Wincoop (2003) contributed towards building a multi-country general equilibrium model using the gravity equation.

As discussed above, the literature indicates that the gravity equation traditionally has been used to estimate the effects of various variables such as RTAs, borders, distance and common language on the international trade flow (Anderson, 2011). The theoretical foundations of the gravity model were further strengthened by Bergstrand et al. (2013). A structural gravity model was developed based on increasing returns to scale of production and monopolistic competition to estimate the general equilibrium counterfactual scenarios (comparative statics) and other equation coefficients. To the best knowledge of the researcher, there is a shortage of literature for the use of the structural gravity model in a general equilibrium environment for the study of trade among OBOR countries. Therefore this study involves combining the partial and general equilibrium with structural gravity for a robust trade policy analysis besides estimating the effect of other gravity variables among the countries belonging to OBOR. In this article, we investigate the impact of the general equilibrium comparative statics on trade flow among the OBOR countries using the methods of Larch and Yotov (2016). The counterfactual scenario used in this study under a general equilibrium environment is complete trade integration by removal of international borders where the producers and consumers are free to sell and buy from global markets while preserving the geographical effects.

Data and Methodology

The structural gravity model for performing counterfactual analysis of conditional and full endowment general equilibrium effects of the trade policy on international trade flow among OBOR nations is implemented to obtain the estimates using PPML. Apart from this, PPML would forecast the member nations’ suggested trade gains. A simple econometric procedure would estimate the general equilibrium effects of trade policy on the standard statistical analysis software such as Stata. Apart from calculating the general equilibrium effect of removing international borders from OBOR, the cross-section gravity model would be implemented to evaluate the efficiency and effectiveness of removing international barriers from an economic policy perspective. This counterfactual scenario would investigate eliminating international borders, thereby removing the trade cost specification differences arising between national and international trade. Using this tool in the research would assist in evaluating the research objective efficiently and, in turn, determine whether the removal of international borders for OBOR is beneficial for the countries adopting the model.

Data

In estimating the effects of some of the gravity variables such as RTAs, panel data are required (Manzoor & Mir, 2022); however, cross-sectional data are needed for counterfactual analysis. Therefore, to analyse the effect of border removal on trade flow among the member countries of OBOR, cross-sectional data for the latest year in our sample, 2016, are employed. The data used in this article are obtained from multiple sources. It involves intranational and international trade data for 72 OBOR countries from 1996 to 2016, as consistent data for each variable used in the study were available for this period. Although data for some of the discussed variables are available for succeeding years, but data for the variable intranational trade is available only till 2016. Therefore, to maintain the consistency of variables in various econometric specifications, the time period is selected for which the data of all the discussed variables are available. Including the data on intranational trade in structural gravity estimates are desirable for various reasons. First, it ensures that gravity theory is followed, with customers choosing and consuming both foreign and domestic varieties. Second, it contributes to theoretically consistent estimation of various impacts of bilateral trade policies (Dai et al., 2014). Third, by assessing the impacts of distance on domestic trade compared to its impact on international trade, it solves the ‘distance puzzle’ (Yotov, 2012). Also, it allows determining and quantifying the impacts of non-discriminatory trade policy (Heid et al., 2015). Moreover, the data for succeeding years could have absorbed the impact of the COVID-19 pandemic and, therefore, paves the way for comparative study in the future using pre and post-COVID-19 scenarios.

The data on international trade is obtained from the Direction of Trade Statistics (DOTS) database by the International Monetary Fund (IMF). The intra-national trade data is obtained from the database of Yotov et al. (2016) and the United States International Trade Commission (USITC)’s International Trade and Production Database for Estimation (ITPD-E). The Centre d’Etudes Prospectives et d’Informations Internationales (CEPII) GeoDist Database provides data for the standard gravity covariates such as distance, shared borders, common official language and colonial ties between the country pairs.

Econometric Specification

The structural gravity model follows a large section of trade models (Arkolakis et al., 2012; Costinot & Rodriguez-Clare, 2014; Head & Mayer, 2014). The bilateral flow of trade X_ij from a country i to country j is given by structural gravity model as follows:

X_{i j} = \frac{Y_{i} E_{j}}{φ_{i} P_{j}} τ_{i j}

where the tariff and non-tariff trade cost between the countries i and j is given by function $τ_{i j}$ . $φ_{i}$ represents outward multilateral resistance (OMR) and inward multilateral resistance (IMR) is given by $P_{j}$ . The total production value of a country i, given by total sales of the country is given by $Y_{i}$ and $E_{j}$ denotes the expenditure of a country j which equals to the sum of all the exports of the country.

The methodology used in this article offers consistency between the terms of structural gravity model and the corresponding country fixed effects ( $π_{i, t}$ and $μ_{j, t}$ ) as given below:

exp ({\hat{π}}_{i, t}) = \frac{Y_{i, t}}{{\hat{φ}}_{i, t}^{1 - σ}} \times E_{r, t} and exp ({\hat{μ}}_{j, t}) = \frac{E_{j, t}}{{\hat{P}}_{j, t}^{1 - σ}} \times \frac{1}{E_{r, t}},

(1)

${\hat{π}}_{i, t}$ and ${\hat{μ}}_{j, t}$ in the above equation denote the importer and exporter fixed effects estimates and ${\hat{φ}}_{i, t}^{1 - σ}$ and ${\hat{P}}_{j, t}^{1 - σ}$ denote the estimates of corresponding multilateral resistance terms respectively. $Y_{i, t}$ denotes the income of country $i$ , $E_{j, t}$ denotes the expenditure of country $j$ , the parameter $σ$ denotes the elasticity of substitution across several varieties and expenditure of the reference nation r in t year is denoted by $E_{r, t}$ . For obtaining the real GDP effects, the value for $σ$ is chosen to be 7 which is in line with the literature discussing the sensitivity of results in various counterfactual gravity frameworks to $σ$ choice Felbermayr et al. (2015). Using these properties and the method developed by Anderson et al. (2018), the following steps are followed to estimate the general equilibrium effects on trade among OBOR countries:

Step1: Estimation of Baseline Gravity Indexes

A baseline structural gravity model is estimated using PPML estimator, directional fixed effects (importer-time and exporter-time fixed effects) and country pair fixed effects ( $ρ_{i j})$ :

X_{i j, t} = exp [{TP}_{i j, t} β + π_{i, t} + μ_{j, t} + ρ_{i j}] + ϵ_{i j, t}

(2)

where ${TP}_{i j, t}$ takes into account both unilateral and bilateral covariates related to trade policy and $ϵ_{i j, t}$ is the error term. PPML estimator also considers the heteroscedasticity, which, if present, plagues the data and results in incorrect estimates (Silva & Tenreyro, 2006). After obtaining the trade costs and gravity model elasticities, importer and exporter fixed effects are estimated. However, the estimation requires one of the fixed effects to be dropped to avoid the inconsistencies arising from perfect collinearity. Besides this, to solve the gravity equation, a set of multilateral resistances ( $π_{i, t} and μ_{j, t}$ ) is normalised by keeping the IMR of a representative country equal to 1 and dropping the country’s importer fixed effect.

Step 1a: Construct Baseline Indexes. The estimates obtained in Equation (2) and the expenditure and output data are used to construct the inward and outward multilateral resistances, achieved using the definition in Equation (1) and the normalisation imposed above

{\hat{φ}}_{i, t}^{1 - σ} = \frac{Y_{i, t}}{exp ({\hat{π}}_{i, t})} \times E_{r, t}

(3)

and

{\hat{P}}_{j, t}^{1 - σ} = \frac{E_{j, t}}{exp ({\hat{μ}}_{j, t})} \times \frac{1}{E_{r, t}}

(4)

where, $Y_{i, t} = \sum_{j} X_{i j, t}$ and $E_{j, t} = \sum_{i} X_{i j, t}$ .

Step 2: Define the Counterfactual Scenario

This step requires defining the counterfactual experiment by altering the trade policy variables explained in the vector ${TP}_{i j, t}$ . It is achieved either by eliminating the border covariate from the trade cost construction or defining an additional variable as a proxy for border covariate set to zero for each pair of member countries. In our study, the counterfactual scenario is abolishing the international borders among the OBOR nations.

Step 3: Estimation of Counterfactual Gravity Model

In this step the constrained gravity model is estimated by using the PPML estimator as follows:

X_{i j, t} = \exp [{TP}_{i j, t}^{CFL} \bar{β} + π_{i, t}^{CFL} + μ_{j, t}^{CFL} + {\bar{ρ}}_{i j}] + ϵ_{i j, t}^{CFL}

(5)

where $X_{i j, t}$ variable denotes nominal international trade flow among the member nations, including international and domestic trade, in the year $t$ . The variables with a bar indicate that the model has several coefficients with values constrained to be the same as their estimates in Equation (2) in Step 1 or their corresponding estimates specified in Step 2.

Step 3a: Construct Conditional General Equilibrium Indexes

In this step, step 1 is repeated with the fixed effects estimated in Equation (5). The data on expenditure and output was initially used to estimate the multilateral resistances under the conditional general equilibrium scenario.

Step 4: Estimation of Full Endowment Gravity Model

This iterative step taking place in a loop delivers the fixed effects estimates corresponding to the full endowment general equilibrium scenario as follows:

Step 4a: Allow for Endogenous Factory Gate Prices

This step involves the usage of market-clearing conditions $p_{i} = {(\frac{Y_{i}}{Y})}^{\frac{1}{1 - σ}} \frac{1}{β_{i} φ_{i}}$ , for the translation of conditional general equilibrium effects on the multilateral resistances estimated in step 3a into the first order alterations in factory gate prices of producers ( $p_{i}$ ):

\frac{p_{i, t}^{CFL}}{p_{i, t}} = {(\frac{exp ({\hat{π}}_{i, t}^{CFL})}{exp ({\hat{π}}_{i, t})})}^{\frac{1}{1 - σ}}

(6)

where ${\hat{π}}_{i, t} = \frac{Y_{i, t} E_{0, t}}{{\hat{φ}}_{i, t}^{1 - σ}}$ .

Step 4b: Allow for Endogenous Expenditure, Income and Trade

This step involves taking into account the endogenous response in the estimates of expenditure $E_{j, t}^{CFL} = (p_{j, t}^{CFL} / p_{j, t}) E_{j, t}$ and income/output $Y_{i, t}^{CFL} = (p_{i, t}^{CFL} / p_{i, t}) Y_{i, t}$ . This endogenous response will result in further changes in the multilateral resistance terms. However, these changes cannot be documented explicitly as fixed effects influence them. Therefore, the structural gravity equation $X_{i j} = \frac{Y_{i} E_{j}}{Y} {(\frac{t_{i j}}{φ_{i} P_{j}})}^{1 - σ}$ is used for the translation of changes in expenditure and output due to the alterations in producers’ prices, into trade flow changes:

X_{i j, t}^{CFL} = \frac{{(t_{i j, t}^{1 - σ})}^{CFL}}{t_{i j, t}^{1 - σ}} \frac{Y_{i, t}^{CFL} E_{j, t}^{CFL}}{Y_{i, t} E_{j, t}} \frac{φ_{i, t}^{1 - σ} P_{j, t}^{1 - σ}}{{(φ_{i, t}^{1 - σ})}^{CFL} {(P_{j, t}^{1 - σ})}^{CFL}} X_{i j, t}

(7)

where ${(t_{i j, t}^{1 - σ})}^{CFL} = exp ({TP}_{i j, t}^{CFL} \bar{β} + {\bar{ρ}}_{i j})$ and $t_{i j, t}^{1 - σ} = exp ({TP}_{i j, t} β + ρ_{i j})$ .

Equation (7) takes into account that changes in trade flows due to factory gate price change is via changes in OMRs and output on the side of the exporter and via changes in IMRs and expenditure on the side of the importer.

Step 4c: Estimation of Structural Gravity Model

The loop then involves repetition of Step 3 with altered trade values using PPML estimator to translate producer prices obtained earlier into alterations in the importer and exporter fixed effects. This, combined with the changes in international trade values, estimates additional changes in the multilateral resistance terms. Step 4a follows it to obtain new values of factory-gate prices. Step 4b is repeated to get a new set of trade values. The model is then re-estimated, and iterations continue until the point of convergence where the producer price change approaches zero.

Step 4d: Construction of Full Endowment General Equilibrium Effects

Following the steps from Step 1a, new full endowment general equilibrium indexes are constructed.

Step 5: Construction of Percentage Changes

This step involves calculating percentage differences between the baseline values estimated in Step 1a and the indexes of the counterfactual scenario obtained in Step 3a to estimate the conditional general equilibrium effects of the counterfactual shock. Full endowment general equilibrium effects of the counterfactual shock on trade flows can be obtained by calculating the difference in percentage changes between the indexes of baseline gravity model estimated in Step1 and indexes of counterfactual gravity model estimated in Step 4d.

The techniques and specifications discussed above are applied to the actual data set on trade flows, between the countries participating in the OBOR, bilateral distance between the pairs and various dummy variables. A cross-section structural gravity equation is specified for the data of the year 2016, which is the latest year in our dataset, to obtain the estimates of border removal effect.

X_{i j} = exp (β_{1} {DIST}_{i j} + β_{2} {CNTG}_{i j} + β_{3} {INTLB}_{i j} + π_{i} + μ_{j}) + ϵ_{i j}

(8)

This specification estimates the effects of trade facilitation on multilateral fronts. In the given equation, the dummy variable ${CNTG}_{i j}$ which takes a value of one if the country pair share a common border and zero otherwise, the logarithm of the distance between the trading pair given by ${DIST}_{i j}$ and the dummy variable ${INTLB}_{i j}$ which takes a value of one for international trade and zero for an intranational trade account for the trade cost $t_{i j}^{1 - σ}$ . The importer time fixed effects and exporter time fixed effects account for the inward and outward multilateral resistances. The country-specific fixed effects also account for the outputs and expenditures and are consistent with the structural gravity model (Larch & Yotov, 2016).

Results

From following the steps discussed in the previous section, the estimates of the effect of contiguity, bilateral distance and international barriers on international trade are obtained. The findings from the first specification are consistent with the expected results except for the coefficient of contiguity $CNTG$ which is positive but statistically insignificant (Table 1). The estimate of the effect of bilateral distance $β_{1}$ = −0.71(standard error 0.11) which is negative and statistically significant is consistent with estimates in previous studies (Head & Mayer, 2014). The estimate of the effect of international borders is negative and statistically significant. The estimate suggests that under constant conditions, international borders decrease the trade flow by 87.7 per cent (standard error 0.33), calculated as $(exp [{\hat{β}}_{3}] - 1) \times 100.$

Table 1.

Estimating the Effects of Gravity Model Covariates and International Borders.

Number of parameters: 140
Number of observations: 4761
Pseudo log-likelihood: −2.498e+09
R-squared: 0.96540181
Option strict is: off
Covariates	Coeffecients	Robust Std. Error	z	P > \|z\|
ln Dist	−0.71	0.11	−6.42	0.00
CNTG	0.13	0.24	0.54	0.588
INTL_BRDR	−2.09	0.33	−6.26	0

Source: The authors.

In the continuation of the first step, the fixed effects estimates obtained are used to construct the baseline general equilibrium indexes. These baseline index values are then used to capture their variations in response to the counterfactual shock, which removes all international borders. The values of the index are not shown in the table for brevity.

In the second step, conditional general equilibrium effects on trade due to abolishing international borders are analysed. After defining the counterfactual shock in the previous step, the third step involves solving the counterfactual model where the values for inward and outward multilateral resistances, outputs and expenditures for the 72 OBOR countries are constructed. Consistent with the specification discussed in step 3 of the previous section, to normalise the multilateral resistances, their values for New Zealand (reference country for which the data available are reliable across all parameters and dimensions) have been set to one. The results indicate considerably significant conditional general equilibrium effects on the trade due to the removal of borders between the member countries, which is analysed by the change in their exports. The percentage change in exports due to the conditional general equilibrium effects of the removal of international borders is given in column (2) of Table 2. The findings reveal that the estimate for the covariate ${INTLB}_{i j}$ is −2.09 (Table 1) which is calculated as $(exp [{\hat{β}}_{3}] - 1) \times 100 = (exp [- 2.09] - 1) \times 100 = 87.7 p e r c e n t$ , indicating a large effect of border removal on trade. Also due to the country heterogeneity such as different country geography and size, there is a wide variation in the estimated results.

Table 2.

Conditional and Full GE Effects of Removing International Borders.

	Conditional General Equilibrium	Full Endowment General Equilibrium
(1)	(2)	(3)	(4)	(5)	(6)	(7)
Country	%Change in Exports	%Change in Exports	%Change in Price	%Change in IMR	%Change in OMR	%Change in GDP
AFG	8.05	54.66	43.29	−9.70	−34.27	58.67
ALB	35.09	85.17	37.06	−4.42	−30.77	43.39
ARE	76.19	133.74	33.13	4.78	−28.39	27.06
ARM	21.91	70.02	39.52	−6.02	−32.19	48.45
AZE	45.17	97.66	36.37	−3.09	−30.36	40.72
BGD	70.92	130.21	35.24	−6.48	−29.68	44.61
BGR	66.25	120.97	32.88	−1.05	−28.23	34.29
BHR	77.62	135.79	32.91	−3.61	−28.25	37.89
BIH	34.63	84.77	37.20	−4.51	−30.86	43.68
BLR	26.27	75.30	38.74	−5.70	−31.75	47.12
BRN	39.46	92.51	38.49	−3.41	−31.61	43.38
BTN	5.03	51.92	44.79	−10.26	−35.07	61.34
CHN	170.02	241.54	26.26	9.90	−23.82	14.89
CZE	136.71	201.89	26.86	4.56	−24.24	21.32
EGY	41.38	93.69	37.15	−5.71	−30.83	45.46
EST	52.07	105.63	35.28	−2.54	−29.71	38.80
GEO	13.73	60.27	40.91	−7.88	−32.98	52.96
HRV	18.93	66.32	39.68	−7.11	−32.29	50.37
HUN	87.44	145.77	30.98	1.24	−27.01	29.37
IDN	120.16	184.14	29.55	4.04	−26.07	24.52
IND	218.36	295.84	20.44	9.53	−19.50	9.95
IRN	36.38	87.67	37.86	−3.08	−31.24	42.24
IRQ	26.64	75.90	39.04	−3.72	−31.92	44.41
ISR	172.93	241.89	23.09	8.06	−21.53	13.92
JOR	17.92	66.25	40.99	−8.17	−33.02	53.54
KAZ	33.83	86.13	39.43	−5.49	−32.15	47.54
KEN	90.42	150.52	31.82	−1.42	−27.55	33.72
KGZ	11.58	58.55	42.22	−8.17	−33.70	54.87
KHM	29.89	81.00	39.68	−6.75	−32.29	49.80
KOR	177.72	251.58	26.43	15.05	−23.94	9.90
KWT	10.86	57.09	41.82	3.31	−33.48	37.28
LAO	16.20	64.69	41.95	−7.76	−33.55	53.89
LBN	40.75	92.79	37.08	−7.44	−30.78	48.10
LKA	123.73	188.86	29.49	−0.50	−26.03	30.14
LTU	53.18	106.96	35.13	−2.91	−29.62	39.18
LVA	49.27	102.26	35.53	−2.74	−29.86	39.34
MAR	47.88	101.32	36.27	−6.01	−30.30	44.99
MDA	18.61	65.73	39.62	−6.28	−32.26	48.98
MDV	55.93	111.61	36.05	−9.17	−30.17	49.78
MKD	9.73	54.77	40.87	−7.56	−32.95	52.39
MMR	14.89	63.47	42.51	−8.43	−33.85	55.63
MNG	9.27	58.43	45.10	−8.54	−35.23	58.65
MYS	74.89	131.43	33.39	5.40	−28.55	26.56
NPL	19.35	69.94	42.71	−9.78	−33.96	58.18
NZL	106.27	169.62	31.06	0.00	−27.06	31.06
OMN	25.16	75.23	40.26	−4.47	−32.61	46.82
PAK	75.99	136.67	35.24	−4.05	−29.69	40.95
PAL	18.96	67.77	41.03	−9.73	−33.04	56.23
PAN	48.19	102.45	36.83	−6.65	−30.64	46.58
PHL	106.85	171.40	31.63	−1.93	−27.43	34.23
POL	150.80	217.32	24.88	6.86	−22.84	16.87
QAT	26.93	76.51	39.42	4.13	−32.14	33.88
ROM	32.94	82.63	37.33	−5.59	−30.93	45.46
RUS	32.68	83.36	38.34	−2.19	−31.52	41.44
SAU	36.30	87.64	37.99	0.15	−31.32	37.78
SGP	513.14	611.15	11.61	0.83	−12.03	10.69
SVK	100.35	160.88	29.99	1.19	−26.36	28.46
SVN	79.91	137.15	31.73	0.58	−27.50	30.97
SYR	3.67	48.22	42.86	−8.86	−34.04	56.73
THA	90.46	150.87	32.29	1.92	−27.85	29.79
TJK	6.98	53.26	43.37	−9.10	−34.31	57.71
TKM	6.05	51.66	43.04	−7.29	−34.14	54.29
TMP	3.99	50.12	44.48	−9.46	−34.90	59.58
TUR	94.48	153.98	30.48	−2.00	−26.68	33.14
UKR	60.75	115.42	34.08	−0.65	−28.98	34.96
UZB	10.96	57.95	42.48	−8.44	−33.84	55.61
VNM	122.31	188.65	30.21	−0.81	−26.51	31.27
YEM	10.91	57.59	42.14	−7.45	−33.65	53.57
ZAF	82.18	141.01	32.69	2.13	−28.11	29.92

Source: The authors.

Notes: The table above reports results from removing international borders. Column (1) lists the OBOR country codes. The percentage changes in total exports for a member country under the scenario of conditional general equilibrium are reported in Column (2). With GDP held constant, the trade cost changes, both direct and indirect, are taken into account in the results reported under the conditional general equilibrium scenario. The results estimated under the Full Endowment General Equilibrium, additionally taking income effects into account are reported under Column (3–7). The percentage changes in the member country’s total exports under Full Endowment General Equilibrium are reported in Column (3). The percentage changes in producer prices are reported in Column (4). Columns (5) and (6) report the percentage changes in IMR and OMR respectively. Column (7) reports the percentage change in real GDP (also considered as a welfare effect). For further details, refer to the text.

The estimation of full endowment general equilibrium effects of international border removal on trade is obtained in the fourth step. The scatter plot of Figure 1 shows the change in exports of each country for the year 2016 under conditional and full endowment general equilibrium scenario in response to the counterfactual shock. It is noteworthy that the conditional general equilibrium effects of border removal on exports are also interpreted as the response of trade costs to the counterfactual shock when expenditure and output are kept constant. Figure 1 clearly indicates increased general equilibrium (GE) effects from the removal of international borders as compared to the conditional GE effects. This is analysed by the percentage change in exports in response to international border removal. These results are complemented by the corresponding numerical values in column (2) and column (3) of Table 2. The results reported in column (2) of Table 2 indicate the conditional general equilibrium percentage change in exports in response to the counterfactual shock ranges between 3.66 per cent for Syria and 513.13 per cent for Singapore. However, these percentage changes in the export value are compared to the numeraire, which is the IMR value of the reference country, New Zealand. The percentage increase in the nominal values of exports of the member countries due to the barrier removal is further strengthened in the scenario of full endowment general equilibrium. This reinforcement is evident from the results in column (3) of Table 2, where the increase in exports in the full endowment scenario ranges from 44 to 98 per cent compared to the conditional equilibrium setting.

The scatter plot of Figure 2 shows the full endowment general equilibrium effect in terms of percentage changes. The percentage change in the real GDP in response to abolishing borders between the OBOR countries is depicted in the scatter plot. The effect of these changes on consumers and producers via the percentage change in inward multilateral resistance and percentage change in factory gate prices for the member countries are also depicted in the sample. The findings from the scatter plot of Figure 2 reveals large full endowment GE effects on real GDP along with a large variation in the real GDP effects across the sample countries. It suggests that the smaller economies and less developed countries would be benefitted significantly due to the border removal in comparison to the large economies and developed countries. These results more importantly highlight that in terms of exports, high income nations would be benefited more from border removal. These interpretations imply that for low income countries, real GDP gain would come largely on consumer end through comparatively favourable prices. Figure 2 also indicates the full endowment GE effects from border removal on producers through factory gate price changes in comparison to the reference country. These effects appear larger than the corresponding GE effects on the consumers suggesting that producers gain more from the globalisation.

Under the scenario of full endowment general equilibrium, in Table 2 the percentage change in exports is reported in column (3), the percentage change in the factory-gate prices for the producers is reported in column (4), the percentage change in inward multilateral resistances is reported in column (5), the percentage change in outward multilateral resistances is reported in column (6) and the real GDP percentage change is reported in column (7) for the member countries. Column (1) of Table 2 lists the OBOR country codes for the member countries. The findings reported in columns (5) and (6) are consistent with the results of Anderson and Yotov (2010), which indicate that the percentage change in IMR is substantially smaller than the percentage change in OMR. The percentage change in IMR of the member countries is estimated relative to the reference country, New Zealand, whose percentage change in IMR is zero. Column (5) of Table 2 suggests that the countries with a smaller percentage rise in real GDP, such as China, India, Israel, Korea, Poland and Singapore, witness increased IMRs relative to the reference country, while the percentage points of OMRs relatively see a decrease. This finding, which is also depicted by the scatter plot of Figure 2, suggests that with decreasing OMR, it would be difficult for the consumers of the member nations to access the world markets while for producers; the cost to sell their produce in the world market will decrease relative to the producers of reference country, after abolishing international borders. Since there is lesser access to the world market for the consumers in these countries, they do not enjoy a considerable decrease in the prices, due to which the real GDP gains are low in those countries. However, countries such as Bhutan, Afghanistan, Mongolia, Nepal, Palestine, Syria, Tajikistan and East Timor, on the other hand, that witness a higher percentage in real GDP gain witness a decrease in inward and outward multilateral resistances. The producers’ price in these countries rises, consistent with decreasing outward multilateral resistances.

Discussion

We employed the structural gravity framework to study the general equilibrium effects of a counterfactual shock on the trade flow of OBOR countries. The results of our study offer a comprehensive guide to the policymakers in analysing the impact of border removal on trade facilitation. This study provides a practical approach while estimating trade cost indexes in a general equilibrium scenario and their response to the changes in trade policy, which is the removal of international borders. The results lead to varied welfare gains for the countries participating in the OBOR initiative to abolish international borders. This response is witnessed via the changes in inward and outward multilateral resistances while the changes in expenditure and output account for the general equilibrium effects on trade. Our results indicate a decrease in the trade between member countries in the presence of international barriers. These results are consistent with Costinot and Rodríguez-Clare (2014), wherein the effect of trade liberalisation on trade gains is discussed under various counterfactual scenarios. To Costinot and Rodríguez-Clare (2014), the international trade gain for China is 11.2 per cent which is close to our estimate of 14.8 per cent. This similarity in estimates of the welfare effects due to the counterfactual shock of border removal holds for the countries involved in both studies. The varied percentage increase in the export value of member countries after counterfactual shock indicates significant conditional general equilibrium effects. These results are consistent with the findings of Larch and Yotov (2016), also suggesting a positive correlation between the size of the country, measured by the output value and international border removal. Due to the counterfactual scenario, some countries witnessed decreased producers’ prices and low real GDP gain (Anderson & Yotov, 2010). Other countries, mainly developing Asian countries, saw a high GDP gain and increased producers’ prices. The member countries with low real GDP gain witness a decreasing OMR, which suggests that it would be difficult for the consumers of the member nations to access the world markets. Also, the cost to sell their produce in the world market will decrease for the producers of these countries relative to the producers of reference country, after abolishing international borders. Since there is lesser access to the world market for the consumers in these countries, they do not enjoy a considerable decrease in the prices, due to which the real GDP gains are low in those countries. However, the member countries with increased real GDP gain witness a decrease in OMR and IMR, indicating that the producer member country gains more from globalisation (Costinot & Rodríguez-Clare, 2014). Therefore, the countries that open their economies to the initiative of OBOR by abandoning the barriers witness a trade gain. Following the study of Costinot and Rodríguez-Clare (2014), who report results from the observed level of trade to autarky, this article provides estimates for a full abolishment of international borders, which can be thought of as the most that we can obtain in terms of liberalisation. As the magnitudes are comparable (ours even a bit larger), this study argues that for the OBOR countries, when economies open and go from autarky to full abolishment of international barriers, there are the total gains from trade. Hence, we conclude that the OBOR countries have enjoyed half of the full gains from the trade, and further integration can lead to more considerable gains from international trade. The present findings of the article can act as a way forward for the policymakers of the countries yet to participate in OBOR to speculate on their status and future of joining OBOR, as the results indicate prosperity for most member nations. For future research directions, the researchers can use the dynamic gravity model to study the general equilibrium effects of removing international borders on individual sectors or a single sector across various countries, enabling the policymakers and other stakeholders to better understand the welfare effects of change in trade policies.

Supplemental Material

Supplemental material for this article is available online.

Supplemental Material

Supplemental material for this article is available online.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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