Japan and China in a Two-Hub Formation of Regional Integration in East Asia

Abstract

East Asian countries are looking for an institutional scheme to deepen the initial market-led regional integration. This chapter measures the existing bilateral trade relations among East Asian countries by calculating BTR index (de facto Bilateral Trade Relation), economic distance and HM index (Hub-ness index). Based on the comparison of de facto trade preferences among East Asian countries, it investigates East Asian regionalism in a two-hub formation (an ‘East Asian Bicycle’), of which Japan and China might be the two potential hub candidates. The ASEAN-China free trade zone may spur regional integration in East Asia by motivating other East Asian nations to join the ASEAN-China FTA early and at the same time liberalize trade amongst each other.

Appendix A: Measuring BTR Index and Economic Distance

Chen (2008) generates BTR index and ‘economic distance’ based on the gravity model. The gravity equation simplifies the determinants of bilateral trade between countries (or regions) into two categories: partner countries’ properties, such as GDP, population, GDP per capita, etc. and trade cost of trade between partners, such as transport costs, tariff barriers, quotas, etc.. Lower trade costs will encourage bilateral trade between countries while higher costs will discourage it. Loosely speaking, there are two types of trade costs. One is related to ‘natural frictions’, such as the geographical distance, level of technology, etc., and the other is related to ‘policy frictions’, primarily the degree of bilateral liberalization. Assuming that the marginal effect of either GDP or GDP per capita on bilateral trade is identical over a certain time period, the marginal effect of distance can be magnified or shrunk by individual bilateral trade relations. This can be seen in eq. (2.1):

$$ Trad{e}_{ij}=\frac{{\left( GD{P}_i\cdot GD{P}_j\right)}^{\alpha}\cdot {\left({K}_i\cdot {K}_j\right)}^{\beta }}{Dis{t_{ij}}^{\gamma_{ij}}} $$

(2.1)

where α, β and γ _ij represent the marginal effect of GDP, GDP per capita(K) and distance respectively. The coefficients, α and β, are assumed to be identical while the coefficient γ _ij may vary for each pair of countries. γ _ij is the variable in the equation that contains the country specific propensity. Imagining that policy frictions can affect a country’s bilateral trade flow through their impact on the geographic distance, the economic distance (ED _ij ) can be defined as geographic distance (Dist _ij ) multiplied by a parameter A _ij , which essentially reflects the relative degree of bilateral trade liberalization.

$$ \mathrm{EDij}=\mathrm{Aij}\times \mathrm{Distij} $$

(2.2)

Accordingly a revised version of gravity equation looks like.

$$ {M}_{ij}=\frac{\left({Y_i}^{\beta_1}\cdot {Y_j}^{\beta_2}\right)\cdot \left({y_i}^{\beta_3}\cdot {y_j}^{\beta_4}\right)}{{\left({A}_{ij}\times Dis{t}_{ij}\right)}^{\beta_5}} $$

(2.3)

It transforms into eq. (2.3) by taking in logarithm form.

$$ {\displaystyle \begin{array}{l}\log {\left({M}_{ij}\right)}_{\mathrm{t}}={\beta}_1\times \log {\left({Y}_i\right)}_{\mathrm{t}}+{\beta}_2\times \log {\left({Y}_j\right)}_{\mathrm{t}}+{\beta}_3\times \log {\left({y}_i\right)}_{\mathrm{t}}\\ {}+{\beta}_4\times \log {\left({y}_j\right)}_{\mathrm{t}.}+{\beta}_5\times \log \left( Dis{t}_{ij}\right)+{\beta}_5\times \log \left({\hat{A}}_{ij}\right)\end{array}} $$

(2.4)

We therefore use two-step regressions to estimate the parameters in the equation above based on the fixed-effect regression on panel data.

$$ {\displaystyle \begin{array}{c}\mathrm{Step}\ \mathrm{one}:\log {\left({M}_{ij}\right)}_{\mathrm{t}}={\hat{\beta}}_1\times \log {\left({Y}_i\right)}_{\mathrm{t}}+{\hat{\beta}}_2\times \log {\left({Y}_j\right)}_{\mathrm{t}}+{\hat{\beta}}_3\times \log {\left({y}_i\right)}_{\mathrm{t}}\\ {}+{\hat{\beta}}_4\times \log {\left({y}_j\right)}_{\mathrm{t}}+F{E}_{ij}+{v}_{ij,t}\end{array}} $$

(2.5)

$$ \mathrm{Step}\ \mathrm{two}:F{E}_{ij}=C+{\hat{\beta}}_5\times \log \left( Dis{t}_{ij}\right)+u $$

(2.6)

where ${\hat{\beta}}_i$ (i = 1 … 5) denotes the estimated marginal effect of each independent variable, FE _ij denotes the fix effect, v _ij,t and u are the disturbance terms.

Combining equation (2.5) and (2.6) with the targeted function (2.4), we have${\mathrm{BTR}}_{\mathrm{ij}}={\widehat{\mathrm{A}}}_{\mathrm{ij}}=\frac{\exp \left({\mathrm{FE}}_{\mathrm{ij}}/{\widehat{\beta}}_5\right)}{{\mathrm{Dist}}_{\mathrm{ij}}}$ _. The smaller value of BTR _ij hints the higher degree of market openness of country i to country j. Furthermore, BTR _ij <1 can be interpreted as a signal of ‘pro-trade’ effects of bilateral trade policy that encourage country i to import more from country j, while BTR _ij >1 is a signal of ‘anti-trade’ effects showing the additional cost of country i’s import from country j due to political frictions.

With further assumption that countries i and j are treating each other reciprocally and therefore the bilateral trade preference between them is symmetric, BTR _ij =BTR _ji . Accordingly the economic distance is calculated based on the definitionED _ij =BTR _ij ⋅ Dist _ij . One country’s import from country j is not only affected by its own import policy but also by its trading partner, country j’s export policy. Empirically it would very difficult to distinguish the different effects from these two parties accurately. Holding this assumption, the symmetric BTR index is estimated based on equation (2.7):

$$ {T}_{ij}={T}_{ji}=\left(I{M}_{ij}+I{M}_{ji}\right)=\frac{{\left({Y}_i+{Y}_j\right)}^{\alpha}\cdot {\left({y}_i+{y}_j\right)}^{\beta }}{{\left({A}_{ij}\times Dis{t}_{ij}\right)}^{\gamma }} $$

(2.7)

where T _ij and T _ji is the overall trade volume between the two countries (aggregate imports plus aggregate exports).