China’s Export Potential to ASEAN Countries

Introduction

Since the 1997 Asian financial crisis, closer regional economic integration and cooperation have been widely used for economic survival in the region. In the case of East Asia, ASEAN and China have strategic roles in preserving growth momentum in the region. Since 2002, ASEAN and China have signed a series of free trade agreements, including the agreement on trade in goods and services, investments, dispute settlement mechanism, and ASEAN-China Free Trade Area (ACFTA) formally set up in 2010 (Yang and Martinez-Zarzoso, 2014). It is argue that both China and ASEAN would get benefit from the formation of ACFTA. It can help member countries of ASEAN to access the wealthy Chinese market, fostering their economic growth. At the same time China could get benefit from it because ASEAN member countries are commonly resource rich countries that can provide and support China’s production and help it to expand its market to all countries in the Southeast Asia, strengthening economic cooperation and trade activities among ACFTA member countries.

However, the continuous impact of the 2008 global financial crisis has forced governments of ASEAN countries to introduce nontariff barriers (e.g. green barriers and technical barriers) in order to protect domestic industries. Such barriers significantly influenced China’s export to ASEAN countries. Under such a circumstance, in October 2013 the Chinese government proposed the updated version of ACFTA with the aim to strengthen trade cooperation among them (Zhang and Wang, 2015). In order to know whether this is an effective way for China to promote its export and whether there exists room for further improvement, a good understanding of China’s trade potential to ASEAN countries is necessary. This is the main objective of this research essay.

The rest of this essay is organized as follows. Following the literature review, the methodology and measurement of China’s export potential are explained. The empirical results of the analysis including the export performance is presented in the next section before the concluding comments.

Literature review

The stochastic frontier gravity model was employed to analyse the Chinese trade flows to the ASEAN countries. This model is used by many scholars and researchers as benchmark to estimate the trade flows between countries. It is used extensively in explaining the consequences of diverse policy and other trade flow determinants with two key variables (the size of the economy and distance) always included (Armstrong et al. 2008).

As pointed out by Kalirajan and Singh (2008), trade flows can be constrained by three factors: (i) natural constraints like population, income, geographical distance and transport costs; (ii) ‘behind the border’ constraints, including institutional and infrastructural rigidities in the exporting country; and (iii) ‘beyond the border’ constraints, which consist of explicit constraints such as import tariffs, and implicit ones, such as institutional and infrastructural rigidities in the importing country, which are often hard to measure. The non-negative error term in the stochastic frontier gravity model represents the ‘behind the border’ inefficiencies in the exporting country that prevents it from reaching its trade frontier, and the random term captures all other disturbances including ‘implicit beyond the border’ (Armstrong et al. 2008). Knowing the factors hindering trade and undertaking reforms or measures to addressing them could potentially lead to trading at the frontier.

An exporting country reaching its trade frontier indicates that this country has achieved its trade potential, hence, trading in the most efficient manner. In other words, it has achieved both technical and allocative efficiency (Kalirajan and Shand 1999), leading to higher trade flows. As a result, economic growth could be enhanced, leading to the improvement of people’s welfare.

Methodology and measurement

Trade potential is the trade achieved at a frontier that estimates a level of trade that might potentially be achieved through open trade and frictionless trade possible given current trade, transport and institutional technologies or practices (Armstrong et al. 2008, p.3). In order to see China’s export potential to 10 ASEAN countries (Brunei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Viet Nam), this research paper constructs the export trade equation on the basis of the stochastic frontier gravity model using a panel data across a 4-year time span (2012-2015). This model is considered useful to answer the research question more effectively and accurately since its aim is to capture trade resistances not only the explicit resistances but also beyond the explicit ones, though the implicit resistances are sometimes difficult to measure (Armstrong et al. 2008; Kalirajan and Singh, 2008). This will further explain the reasons of why production is not at the technological frontier (Margono et al. 2011, p.664).

The data on GDP, population, exchange rate, exports and tariffs are sourced from the World Bank World Development Indicators data, World Bank Integration Trade Solution (WITS), and from the International Trade Centre (ITC). The data on the distance between China and the ASEAN countries is gathered from the GeoDist (CEPII database). Table 3 in the appendix shows this data. In this study, the stochastic frontier (Frontier 4.1) production function is used to do the regression with the basic gravity model converted to log form.

The gravity equation for exports (Kalirajan 1999) can be estimated as

lnX_ij = ln f(Z_i; β) exp(v_i-u_i)

where the term X_ij represents the actual exports from country i to country j. The term f(Z_i; β) is a function of the determinants of potential bilateral trade (Zi) and β is a vector of unknown parameters. The u_i and v_i represents the single sided error term and the double-sided error term, respectively. u_i which is assume to follow normal distribution, N(μ, σ²_u) is the combined effect of the economic distance referred by Anderson and Roemer, which emanates due to different socio-political-institutional factors across countries. Whereas v_i is usually assumed to be N(0, σ²_v) , it captures the effect on trade flows of other left out variables, including errors in measurements that are randomly distributed across observations in the sample. Maximum likelihood methods can be applied on both cross section and panel data to estimate the gravity model discussed above and to verify the importance of the socio-political-institutional factors in constraining trade from reaching their potential levels between countries.

To analyse the determinants of trade between China and the ASEAN countries, the stochastic frontier approach of the gravity model employed in this research is as follows:

ln X_ijt = α₀ + α₁lnGDP_it + α₂lnGDP_jt + α₃lnPop_jt + α₄lnDist_ijt + α₅lnER_jt + α₆lnTar_jt + α₇lnD_ijt                           

where the variables are defined as follows: X_ijt – is the total value of exports from country i (in this case China) to country j (ASEAN countries) in year t; GDP_it– Gross Domestic Product of country i at year t; GDP_jt– Gross Domestic Product of country j at year t; Pop_jt – population of importing country j; Dist_ijt – is the geographical distance between the capital cities of country i and country j; ER_{jt –} is the exchange rate of country j in U.S. dollars; Tar_jt is the tariff applied by country j; and D_ij – as dummy variable for importing country and this could be any trade agreement between country i and country j.

In the model given above, period t = 2012 to 2015 and all data are in yearly aggregates except the relative distance between countries. Country i refers to the exporting country, in this case its China and country j is the 10 ASEAN countries as mentioned earlier. This model assumes that there are no significant constraint for exports of these countries. The results of this model are presented in Tables 1 and 2 in the appendix. In Table 1, the coefficient estimates for all the variables are at the 5 per cent level. In this estimation, the eta coefficient is restricted to zero where impact of country specific effects are considered to be constant over time (Kalirajan 2007).

Empirical results of the analysis

The estimation results of the model are shown in Table 1 and 2 in the appendix section.  In Table 1, it shows that the both the estimated sigma-squared (σ²) and gamma (γ) are highly significant.  The estimated sigma-squared (σ²) which measures the mean total variation over the four year time period of the study is highly significant at 0.6658 indicating that Chinese exports to these ASEAN countries have varied greatly over this time period. The estimated gamma (γ) of 0.9783 and the small value of log likelihood function shows that this variation is due to technical inefficiency in the model.  The large proportion of the mean total variation in the model is the result of country specific socio-political-institutional factors concerning exporting countries and the importing countries (Kalirajan 2007). Thus, further analysis of variables into the model is needed to understand this variation.

The Table 1 shows that the distance, China’s gross domestic product (GDP) and the ASEAN countries GDP significantly affect Chinese exports. The income elasticity of 1.26% of the ASEAN country significantly affects the Chinese exports at the 5% significance level. The bilateral distance between countries have negative effect on the flow of exports which therefore, affect trade.  The results presented shows that one percent increase in trade distance between China and the ASEAN countries will affect Chinese export by 0.3%. This is a proxy associated with transport costs.  However, this does not significantly impact on China’s export performance as China may have increase their production and volume of trade. China’s large manufacturing volume might be the key reason of why its production process can absorb the effects of distance costs to its trading partners. This argument is supported by Kalirajan and Singh (2008, p. 15) in their analysis that argue that cost advantages China has are good tools to boost exports.

Export Performance

Table 2 in the appendix shows the estimation results of technical efficiency estimates of Chinese export performance with each individual ASEAN countries. It covers the technical efficiency of China export to the 10 ASEAN countries over the period of study.

The mean technical efficiency of 61% is significantly high. This indicates that overall China trade well with the ASEAN countries. This is partly because China’s export composition has changed significantly from agriculture sector to a more advanced manufactured goods, such as consumer electronics, appliances, and computers (Amiti and Freund, 2010). In addition, the reduction in costs (transportation and communication) and elimination of tariff barriers as stipulated under the ASEAN-China Free Trade Agreement (see ACFTA, n.d.) has promoted trade expansion and technological transfer from China to ASEAN countries, developing sophisticated production networks and reducing costs (Armstrong et al. 2008, p.11). Not surprisingly then the mean trade performance (technical efficiency) is high.

 

Amongst the 10 ASEAN countries, the results shows that Vietnam, Malaysia and Singapore recorded highest technical efficiency with 92%, 91% and 89%, respectively. This shows that these countries are the most efficient countries that China trade with. This might be because of the high demand of machinery and electrical products in those countries, which is the comparative advantage of China. This can be seen from a larger share of their import from machinery and electrical products from China (57% of Malaysia’s total import, 38% of Singapore’s total import and 44% of total Vietnam’s total import, 27% of Myanmar’s total import, and 21% of Cambodia’s total import) (Salidjanova and Koch-Weser, 2015).

 

The technical efficiency recorded for other ASEAN countries like Thailand, Lao PDR, Philippines and Indonesia is relatively low. This implies that China are not significantly exporting to these countries, particularly Lao PDR. However, similarly to Vietnam, Malaysia, Singapore, Cambodia, and Myanmar, machinery and electrical products imported from China are also accounted for a larger share of their total import (50% of Thailand’s total import, 47% of Lao’s total import, 33% of Philippines’ total import, and 47% of Indonesia’s total import, and 21% of Cambodia’s total import) (Salidjanova and Koch-Weser, 2015). This indicates that there could be other trade barriers, such as political, economic or social factors, that undermine China’s export potential to achieve technical efficiency to those countries. Hence, further analysis might be necessary for future research. Despite the promotion of a range of trade reforms, including the ACFTA, there might be still trade resistances that have not been addressed by the reform. Identifying and measuring all these frictions is difficult as they can be country-specific social, economic, political, and institutional factors (Kalirajan 2007, p. 86)

Conclusion

From the above discussion of the estimation results, it can be concluded that China’s international trade with ASEAN countries has grown steadily since the implementation of the opening-up policy and the inception of ACFTA agreement with the ASEAN economies. China has become a big trader not only in the Asian markets but also in the world markets.

Although there still remains other factors affecting China’s export potential to ASEAN, the most important finding of our analysis is the fast growth of China’s trade from labor-intensive to capital and technology intensive exports. The massive technological transfer particularly through intermediate goods has contributed significantly to the improvement in China’s manufactured exports particularly to Malaysia, Singapore and Vietnam. In addition, progressive removal of non-tariff barriers and obstacles to trade in services and investment as well as increasing relocation of Chinese companies to ASEAN countries could lead to further growth in trade between China and ASEAN.

Appendices

Table 1: Maximum likelihood estimates of the coefficients stochastic frontier gravity model for China export to ASEAN, 2012-2015

Variables	Est. Coefficient	Std. error	t-ratio
Constant	-45.1708	9.8443	-4.5885
GDP of China	1.2117	0.2303	5.2609
GDP of ASEAN countries	1.2612	0.1382	9.1278
Population	-0.2582	0.1341	-1.9263
Distance	-0.3192	1.0089	-0.3164
Exchange rate	0.1177	0.0457	2.5761
Tariff	0.0354	0.0154	2.2964
Sigma-squared	0.6658	0.2905	2.2923
gamma	0.9783	0.0108	90.2444
mu	0.0000
log likelihood function	8.2674
LR test of one sided error	71.4400

 

 

 

Table 2: Technical efficiency estimates

Trading partners	Efficient est.
Brunei Darussalam	0.5389
Cambodia	0.8698
Indonesia	0.1520
Lao PDR	0.3684
Malaysia	0.9128
Myanmar	0.5907
Philippines	0.5057
Singapore	0.8922
Thailand	0.4224
Vietnam	0.9264
mean efficiency	0.6179

 

 

 

 

 

 

 

 

 

 

 

Table 3: China’s export determinants (2012-2015)

YEAR 2012	Trading partners	Exports (000)	China GDP (USD million)	ASEAN GDP (USD million)	Population	Distance (km)	Exchange rate (USD)	Tariff (%)
1	Brunei Darussalam	1,252,438	8,462,000	16,954	405,512	3650.79	1.25	2.39
2	Cambodia	2,708,103	8,462,000	14,038	14,832,255	2594.53	4033.00	9.91
3	Indonesia	34,285,244	8,462,000	917,870	248,037,853	4202.83	9386.63	2.59
4	Lao PDR	937,094	8,462,000	9,359	6,473,050	1993.52	8007.76	13.20
5	Malaysia	36,525,700	8,462,000	314,443	29,021,940	3530.76	3.09	2.81
6	Myanmar	5,673,756	8,462,000	74,690	52,543,841	1745.99	640.65	2.75
7	Philippines	16,732,210	8,462,000	250,092	96,017,322	3106.22	42.23	4.37
8	Singapore	40,750,209	8,462,000	289,269	5,312,437	3841.78	1.25	0.00
9	Thailand	31,196,768	8,462,000	397,291	67,164,130	2248.06	31.08	4.78
10	Vietnam	34,212,587	8,462,000	155,820	88,809,200	2461.15	20828.00	5.66

 

 

YEAR 2013	Trading partners	Exports (000)	China GDP (USD million)	ASEAN GDP (USD million)	Population	Distance (km)	Ex rate (US$)	Tariff (%)
1	Brunei Darussalam	1,703,776	9,491,000	16,111	411,499	3650.79	1.25	2.39
2	Cambodia	3,409,507	9,491,000	14,450	15,078,564	2594.53	4027.25	9.91
3	Indonesia	36,930,490	9,491,000	912,524	251,268,276	4202.83	10461.24	2.27
4	Lao PDR	1,722,577	9,491,000	11,192	6,579,985	1993.52	7860.14	13.20
5	Malaysia	45,930,593	9,491,000	323,343	29,465,372	3530.76	3.15	2.81
6	Myanmar	7,338,689	9,491,000	58,653	52,983,829	1745.99	933.57	2.75
7	Philippines	19,868,125	9,491,000	271,927	97,571,676	3106.22	42.45	4.37
8	Singapore	45,831,865	9,491,000	300,288	5,399,162	3841.78	1.25	0.00
9	Thailand	32,717,904	9,491,000	419,889	67,451,422	2248.06	30.73	4.78
10	Vietnam	48,586,298	9,491,000	171,222	89,759,500	2461.15	20933.42	3.54

 

YEAR 2014	Trading partners	Exports (000)	China GDP (USD million)	ASEAN GDP (USD million)	Population	Distance (km)	Ex rate (US$)	Tariff (%)
1	Brunei Darussalam	1,746,814	10,351,000	17,105	417,394	3650.79	1.27	0.46
2	Cambodia	3,275,454	10,351,000	16,778	15,328,136	2594.53	4037.50	4.85
3	Indonesia	39,060,020	10,351,000	890,487	254,454,778	4202.83	11865.21	2.27
4	Lao PDR	1,838,932	10,351,000	11,716	6,689,300	1993.52	8048.96	5.22
5	Malaysia	46,355,435	10,351,000	338,104	29,901,997	3530.76	3.27	2.81
6	Myanmar	9,367,605	10,351,000	64,330	53,437,159	1745.99	984.35	2.75
7	Philippines	23,474,238	10,351,000	284,777	99,138,690	3106.22	44.40	4.37
8	Singapore	48,911,898	10,351,000	306,344	5,469,724	3841.78	1.27	0.00
9	Thailand	34,293,357	10,351,000	404,320	67,725,979	2248.06	32.48	3.59
10	Vietnam	63,731,485	10,351,000	186,205	90,728,900	2461.15	21148.00	3.43

 

 

YEAR 2015	Trading partners	Exports (000)	China GDP (USD million)	ASEAN GDP (USD million)	Population	Distance (km)	Ex rate (US$)	Tariff (%)
1	Brunei Darussalam	1,411,657	10,866,000	15,492	423,188	3650.79	1.37	0.46
2	Cambodia	3,770,011	10,866,000	18,050	15,577,899	2594.53	4067.75	4.85
3	Indonesia	34,375,284	10,866,000	861,934	257,563,815	4202.83	13389.41	2.27
4	Lao PDR	1,212,287	10,866,000	12,327	6,802,023	1993.52	8147.91	5.22
5	Malaysia	44,192,327	10,866,000	296,218	30,331,007	3530.76	3.91	2.81
6	Myanmar	9,429,575	10,866,000	64,866	53,897,154	1745.99	1162.62	2.75
7	Philippines	26,692,700	10,866,000	291,965	100,699,395	3106.22	45.50	4.37
8	Singapore	53,139,141	10,866,000	292,739	5,535,002	3841.78	1.37	0.00
9	Thailand	38,310,626	10,866,000	395,282	67,959,359	2248.06	34.25	3.59
10	Vietnam	66,381,154	10,866,000	193,599	91,703,800	2461.15	21698.80	3.43

 

References

ACFTA, see ASEAN-China Free Trade Agreement

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<http://data.worldbank.org/indicator/SP.POP.TOTL/countries/AU?display=graph&gt;.

<http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG/countries/all?display=graph>.

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