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Impact of ASEAN-China free trade area on fishery value chain based on difference-in-difference method

Pubblicato online: 16 Aug 2022
Volume & Edizione: AHEAD OF PRINT
Pagine: -
Ricevuto: 04 Mar 2022
Accettato: 15 May 2022
Dettagli della rivista
License
Formato
Rivista
eISSN
2444-8656
Prima pubblicazione
01 Jan 2016
Frequenza di pubblicazione
2 volte all'anno
Lingue
Inglese
Introduction
A brief introduction of Association of Southeast Asian Nations

ASEAN is the acronym for the Association of Southeast Asian Nations. ASEAN has 10 member states, including Cambodia, Myanmar, Laos, Vietnam, Brunei, Singapore, the Philippines, Thailand, Indonesia and Malaysia. Since China and the ASEAN countries are adjacent by sea and connected by rivers, their natural conditions, resources, fishery technology and market demand constitute an extensive foundation for fishery-based cooperation.

Zhang Shi hai, Chen Wan ling., Framework and mechanism of fishery cooperation between China and ASEAN [J]. Marine development and management Ocean Development and Management, 2006, (01): 29–33.

ASEAN countries are located in tropical regions with a warm and humid climate, which leads to rich biodiversity. The interior of ASEAN is the home to many rivers and lakes. Among the Southeast Asian countries, Indonesia, the Philippines, Vietnam, Malaysia and Thailand have long coastlines and rich marine fishery resources. Indonesia is the world's largest archipelagic country, consisting of 17,504 islands with a total coastline of 99,093 km.

Wu Ruo Nan, Research on fishery development of ASEAN countries and their cooperation with China [D]. Xiamen University, 2019.

Their advantageous geographical and climatic conditions determine the plenteous fishery resources of ASEAN countries. As for the climatic conditions, ASEAN countries are located in the tropics with hot and rainy climate and steady sea temperature. Moreover, most countries are seldom affected by natural disasters such as typhoons, making these regions suitable for the growth and reproduction of fish. From the perspective of geographical conditions, these countries have long coastlines, vast areas of sea and numerous inland rivers and lakes, making them ideal places to develop various types of fisheries. However, these countries have different fishery resource endowments, which thus leads to varying levels of fishery activities among these countries.

Among the ASEAN countries, Indonesia has a much higher gross domestic product (GDP) and the strongest overall economy, followed by Thailand, Singapore, Malaysia and the Philippines; Brunei, Cambodia, Laos and Myanmar have a weaker overall economy. Since 2008, only Brunei's GDP growth has been chronically lower than that of the world average, and it has had negative growth rate for many years. Among the ASEAN countries, seven – Myanmar, Cambodia, Laos, the Philippines, Indonesia, Malaysia and Vietnam – have had GDP growth rates well above the world average level. This shows that the overall economic strength of ASEAN countries has been increasing in the past decade.

Content of ASEAN–China Free Trade Area

The ASEAN–China Free Trade Area (ACFTA) is the largest FTA that China has participated in constructing and operating. On 4 November 2002, the two sides passed the Framework Agreement on Comprehensive Economic Cooperation between China and ASEAN (hereinafter referred to as the Framework Agreement), which was put into effect on 1 July 2003, marking the formation of the ACFTA. The ACFTA was formally established in January 2010. It is a huge economy with a population of 1.9 billion and a GDP of 6 trillion US dollars, covering 11 countries. At present, ASEAN has 10 member states, including Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand and Vietnam.

The main objectives of the ACFTA are to minimise barriers and deepen economic ties between China and the ASEAN countries. The liberalisation arrangement of trade in goods between China and ASEAN is composed of the “Early Harvest” plan, the Agreement on Trade in Goods under the Framework Agreement about Comprehensive Economic Cooperation between China and ASEAN (hereinafter referred to as Agreement on Trade in Goods), as well as relevant clauses and annexes, which mainly include the following aspects.

Framework Agreement on Comprehensive Economic Co-Operation Between the Association of South East Asian Nations and The People's Republic Of China: http://fta.mofcom.gov.cn/dongmeng_phase2/dongmeng_phase2_special.shtml.

The “Early Harvest” programme

In 2004, the “Early Harvest” programme of the ACFTA was formally implemented. In order to actively promote the FTA construction plan, the two sides reduce tariffs on the importing and exporting of agricultural products under the Harmonised System (HS) codes HS01–08 in advance according to the original schedule. Among them, the product tax reduction arrangement between China and the original ASEAN members (Brunei, Indonesia, Malaysia, Singapore, Thailand and the Philippines) under the “Early Harvest” programme started from 1 January 2004, with zero tariff achieved on 1 January 2006. China and the newly added ASEAN members (including Vietnam, Laos, Myanmar and Cambodia) finally reached their goals of tax reduction arrangements on 1 January 2010. The trade liberalisation approach of the “Early Harvest” programme has been very successful in accelerating its overall development. Although each member state's level of economic development varies, which has somehow hindered the liberalisation of trade, the programme has adopted a discriminatory manner in the “Early Harvest” programme to promote free trade. A substantial tariff reduction arrangement could be achieved in a relatively short period of time and also in a way that countries could avoid the economic losses that might result from uniform tax reduction.

Rules of origin

The rules of origin signed by China and ASEAN are based on the “value-added standards”. The core of these rules is to determine the standards for the origin of products. According to the Agreement on Trade in Goods, if the Regional Value Content (RVC) of a product is no < 40% of its total value, the product will be regarded as originating from the free trade zone, and the product will satisfy the preferential tax rate of the free trade zone in China and ASEAN member states. When China and ASEAN implemented the “Early Harvest” programme, the vast majority of import and export products were agricultural products, which fit the “full access” standard; a small number of special products, such as wool and textiles, meet the tariff change standard and selectivity standard, respectively. The rules of origin of ACFTA are more feasible; moreover, the relevant provisions are relatively loose and convenient. The “value-added standard” is conducive to promoting and expanding bilateral free trade, making each member state economically complementary. In 2020, in the face of the full impact of the coronavirus disease-2019 (COVID-19) pandemic, the trade in goods between China and the ASEAN countries continued to rise in the first 6 months; meanwhile, the ASEAN countries surpassed the European Union and became China's largest trade partner in goods for the first time.

Articles related to fishery production cooperation in ACFTA

The articles related to fish-related products and production activities in ACFTA are embodied in the investment agreement of the China–ASEAN Comprehensive Economic Cooperation Framework Agreement (referred to as “Investment Agreement”),

Agreement on Investment of the Framework Agreement on Comprehensive Economic Cooperation Between the People's Republic of China and the Association of Southeast Asian Nations: http://fta.mofcom.gov.cn/dongmeng_phase2/dongmeng_phase2_special.shtml.

which was signed on 15 August 2009 in Thailand.

The national treatment clause of Article 4 stipulates that, each Party shall, accord to investors of another party and their investments treatment no less favourable than it accords in its territory, in like circumstances, to its own fellow investors and their investments with respect to management, conduct, operation, maintenance, application, sale, liquidation, or other forms of disposal of such investments. Similarly, in Article 5 of the Most-Favoured-Nation clause, Each Party shall accord to investors of another Party and their investments treatment no less favourable than it accords, in like circumstances, to fellow investors of any other Party or third country and/or their respective investments with respect to admission, establishment, acquisition, expansion, management, conduct, operation, maintenance, use, liquidation, sale, and other forms of disposal of investments. These two clauses are the core clauses of the agreement and play a key role in ensuring the convenience, transparency and fairness of the investment liberalisation of ACFTA.

According to Article 7 of the framework agreement on other areas of economic cooperation, the two sides also agreed to strengthen cooperation in five limited areas, including agriculture, information and communication technology, human resources opening, investment and the development of the Mekong River Basin, through approaches such as regional development cooperation in fishery resources.

Research method
Sources of data and properties of the series

The difference-in-differences (DID) model, input–output table, Kaplinsky index and gravity model have been used for analysis. The sources of data and properties of the series are as described below.

The dependent variable DVAit represents the domestic value added of agriculture, forestry and fishing goods exported by country i to other countries. The data, from the Organisation for Economic Co-operation and Development (OECD) Trade in Value Added (TiVA) database (2018), covers the period 2000–2016, and the logarithmic form is used in the model. The dependent variable GVCposition represents the global value chain (GVC) position index, calculated based on the OECD TiVA Database (2018). The dependent variable Consumeit represents quantity of domestic supply, fish and seafood. The data used from the Food and Agriculture Organization of the United Nations (FAO) Food Balance Sheets (old methodology and population) covers the period 2000–2013, and the logarithmic form is used in the model. The control variable gd pit represents the GDP of the country i (constant prices in 2010); the data used from the World Bank Database covers the period 2000–2016, and the logarithmic form is used in the model. The control variable peoit represents the population in the country i; the data used from the World Bank Database covers the period 2000–2016, and the logarithmic form is used in the model. The control variable disit represents the geographical distance between country i and China; the data used is from the World Bank Database. The control variable iitit represents the index of intra-industry trade in aquatic products between country i and China, calculated based on UN Comtrade data; the data used covers the period 2000–2016, and the logarithmic form is used in the model.

The input–output analysis model was proposed by Leontief, based on data from the World Input–Output Database (WIOD) from 2000 to 2014.

http://www.wiod.org/database/wiots13.

It takes Indonesia as an example and analyses the impact on fisheries and aquaculture after joining the China–ASEAN regional trade agreement. Besides, all the data points used by the Kaplinsky index are calculated based on UN Comtrade. In the gravity model, Tradeij represents the export value of Category-03 aquatic products from ASEAN countries to China. Using the sample data above, the term “ASEAN countries” refers to five countries, including the Philippines, Thailand, Malaysia, Vietnam and Indonesia; the data is from UN Comtrade. GDPi represents China's GDP (calculated at 2010 constant prices); the data comes from the World Bank Database. GDPj represents the GDP of the ASEAN countries (calculated at 2010 constant prices); the data used is from the World Bank Database. DISij represents the distance between China and country j, the data being from the World Bank Database. FTAij represents a dummy variable for the decision regarding whether to join a free trade zone. Among the countries, Philipines, Thailand, Malaysia and Indonesia have joined FTA since 2005, with a value of “1”; Vietnam has joined FTA since 2008, with a value of “1”. All variables in the gravity model are in the logarithmic form, and the data used covers the period 2000–2019.

Models and testing methodologies
DID model

The signing of the ACFTA has produced differences before and after its establishment. On the other hand, there are differences between signed and unsigned agreements at the same time point. Based on this double difference, we can identify the effects brought by the establishment of the ACFTA. Meyer (1995) discussed this method in detail.

Meyer B., “Natural and Quasi-Experiments in Economics”, Journal of Business and Economic Statistics, 1995, 13(2): 151–161.

Feld Stein (1995) used this method to explore the impact of tax rates and tax reform.

Feldstein, M., “The effect of Marginal Tax Rates on Taxable Income: A panel study of the 1986 Tax Reform Act”, Journal of Political Economy, 1995, 103: 551–572.

Tan Zhibo, Zhou Li an and Zhao Yue (2015) used the DID model to study the impact of the reform in provincial management of counties on counties and cities.

Tan Z., Zhou L., Zhao Y., “County Adminstrated by Province” Reform, Fiscal Decentralization and People's Welfare: A Difference-in-Differences Estimation”, China Economics Quarterly, 2015, 14.

The samples were divided into the following groups: (1) the treatment group before the regional trade agreement (RTA) came into effect; (2) the treatment group after the RTA took effect; (3) the control group before the RTA came into effect; (4) the control group after the RTA came into effect. To describe the above samples, we constructed two binary dummy variables, du and dt, to represent them, respectively. The variable du indicates whether to sign the RTA with China. If du is equal to “1”, it means that the county has signed the RTA; if du is equal to “0”, it means that the county has not signed the RTA. The variable dt is the period before and after the RTA takes effect. When the variable dt equals “1”, it indicates the period after the RTA takes effect, and when dt equals “0”, it indicates the period before the RTA takes effect., Stata software is used for estimation, and the fixed-effects model is selected after Hausman test.

Yit=β0+β1duit+β2dtit+β3duitdtit+β4ln(gdpit)+β5ln(peoit)+β6ln(disit)+β7ln(iitit)+εjt {Y_{it}} = {\beta _0} + {\beta _1}d{u_{it}} + {\beta _2}d{t_{it}} + {\beta _3}d{u_{it}}d{t_{it}} + {\beta _4}ln\left( {gd{p_{it}}} \right) + {\beta _5}ln\left( {pe{o_{it}}} \right) + {\beta _6}ln\left( {di{s_{it}}} \right) + {\beta _7}ln\left( {ii{t_{it}}} \right) + {\varepsilon _{jt}}

Description of main variables.

Primary variables and data source
Category Variable Definition Data source
Dependent variable ln(DVAit) The domestic added value of agriculture, forestry and fishing goods exported by country i to other countries OECD TiVA Database (2018)
GVCpositionit GVC position index Calculated based on OECD TiVA Database (2018)
ln(Consumeit) Domestic supply quantity, fish, seafood FAO Food Balance Sheets (old methodology and population)
Control variable ln(gd pit) The logarithm of GDP of country i (constant prices in 2010) World Bank Database
ln(peoit) The logarithm of the population in country i World Bank Database
ln(disit) The logarithm of geographical distance between country i and China World Bank Database
ln(iitit) The logarithm of the index of intra-industry trade in aquatic products trade between the country i and China Calculated based on UN Comtrade

DVA, Domestic value added; FAO, food and agriculture organization of the United Nations; GDP, gross domestic product; GVC, global value chain; OECD, organisation for economic co-operation and development; TiVA, trade in value added.

Input–output analysis model

This paper uses the input-output analysis model proposed by Leontief. The intermediate input rate is the share of raw materials required by one industrial sector from other industries of the entire economy to produce a unit of output, calculated using the following formula: Fj=i=1nxiji=1nxij+Zj(j=1,2,,n), {F_j} = {{\sum\nolimits_{i = 1}^n {x_{ij}}} \over {\sum\nolimits_{i = 1}^n {x_{ij}} + {Z_j}}}(j = 1,2, \ldots ,n), where i=1nxij \sum\nolimits_{i = 1}^n {x_{ij}} denotes the sum of inputs required by each industry from others and itself in the production of the jth industrial sector. Zj denotes the added value of j industries, and j refers to fisheries and aquaculture.

The intermediate demand rate is the ratio of the total intermediate demand for an industry to the total demand for that industry sector for each industry sector in the economy as a whole, calculated as follows: Gi=j=1nxijj=1nxij+Yi(i=1,2,,n), {G_i} = {{\sum\nolimits_{j = 1}^n {x_{ij}}} \over {\sum\nolimits_{j = 1}^n {x_{ij}} + {Y_i}}}\left( {i = 1,2, \ldots ,n} \right), where j=1nxij \sum\nolimits_{j = 1}^n {x_{ij}} denotes the sum of the intermediate demand of the i-th industrial sector by other industries; Yi is the final demand of the i-th industry.

According to the definition of Kaplinsky and Readman (2005), changes in relative price and market share should be considered simultaneously when judging whether an industry or a product is upgraded.

Kaplinsky R, Readman J. “Globalization and upgrading: What can (and cannot) be learnt from international trade statistics in the wood furniture sector?” [J]. Industrial & Corporate Change, 2005, 14(14): 679–703.

Li Chen and Chi Ping (2017) used this method to calculate China's aquatic product export upgrading index and trade status index from 2008 to 2014; they made a comparative analysis with the world's major aquatic product exporters.

Li Chen, Chi Ping., “The study on the upgrading trend and trade positing of China's aquatic product exports: based on the perspective of global value chain” [J]. World Agriculture, 2017, (01): 121–126.

If the price of a product and its market share rise and fall at the same time, it indicates that the industry or product has been upgraded or degraded. Within the territory of the ACFTA, the price and market share of China's aquatic products are calculated as follows:

where RRPt,t1ni RRP_{t,t - 1}^{ni} represents the relative export price of a product i in a country n in a period t compared with the period t – 1. Meanwhile, RPtni RP_t^{ni} and RPt1ni RP_{t - 1}^{ni} represent the ratio of the unit export price of a product i in a country n to the unit export price of the same product in another specific area in the periods t and t – 1, respectively. Ptni P_t^{ni} , Xtni X_t^{ni} and Qtni Q_t^{ni} represent the unit export price, export value and export volume, respectively, of a product i in a country n in a period t. Ptri P_t^{ri} , Xtri X_t^{ri} and Qtri Q_t^{ri} represent the unit export price, export value and export volume, respectively, of a product i in another specific area in the period t. The same goes for the variables representing the period t – 1. RRXt,t1ni=RXtniRXt1ni=XtniXtriXt1niXt1ri RRX_{t,t - 1}^{ni} = {{RX_t^{ni}} \over {RX_{t - 1}^{ni}}} = {{{{X_t^{ni}} \over {X_t^{ri}}}} \over {{{X_{t - 1}^{ni}} \over {X_{t - 1}^{ri}}}}} where RRXt,t1ni RRX_{t,t - 1}^{ni} represents the relative share of a product i in a country n in a period t compared with the period t – 1, and RXtni RX_t^{ni} and RXt1ni RX_{t - 1}^{ni} represent the ratio of the export value of a product i in a country to the total export value in a specific study area in periods t and t – 1, respectively. Xtni X_t^{ni} and Xtri X_t^{ri} represent the export value of a product i in a country n and in the specific study area r, respectively, in a period t. The same goes for Xt1ni X_{t - 1}^{ni} and Xt1ri X_{t - 1}^{ri} . In this paper, the export data of various aquatic products of China, the Philippines, Thailand, Malaysia, Vietnam, Indonesia and Singapore were selected to calculate the change in the export upgrading index. Singapore, Cambodia, Laos, Brunei and Myanmar were excluded because of their low ratios. There are some missing data values for Vietnam in the UN Trade Statistics Database.

DID estimation of domestic added value of exports of aquatic products.

(1) (2) (3) (4)
duitdtit 0.718*** (3.82) 0.505** (2.29) 0.537* (1.71) 0.550* (1.87)
ln(gd pit) 0.38*** (3.42) −0.231 (−1.56) −0.259* (−1.81)
ln(peoit) 0.494*** (4.07) 0.48*** (3.70)
ln(disit) 2.208*** (28.24) 2.155*** (22.21)
ln(iitit) 0.128 (1.16)
_cons 8.614 (32.91) −2.349 (−0.77) −12.891*** (−5.20) −11.318 (−4.60)
N 99 99 99 99
F 11.57 8.08 182.48 159.57
R2 0.08 0.18 0.81 0.82

p < 0.01,

p < 0.05,

p < 0.1.

DID, Difference-in differences.

DID estimation results for export of aquatic products in terms of GVCpositionit.

(1) (2)
duitdtit 0.893 (0.97) −0.046 (−0.76)
ln(gd pit) 0.190*** (3.79) 0.212*** (4.39)
ln(peoit) −0.068 (−1.56) −0.083** (−2.03)
ln(disit) 0.003 (0.15) 0.001 (0.07)
ln(iitit) −0.000 (−0.01) −0.005 (−0.16)
_cons −3.477 (−4.52) −2.349 (−0.77)
N 99 99
F 15.57 15.28
R2 0.38 0.38

p < 0.01,

p < 0.05.

DID, Difference-in-differences; GVC, global value chain.

Results and discussion
Local production (DID model)

Zhou et al. (2014) also focussed on the level of China's manufacturing industry in the GVC, based on the TIVA database.

Zhou Sheng qi, Lan Zhen Xian, Fu Hua., “Division Status of China's Manufacturing Industry in Global Value Chains: A Study Based on Koopman's GVC Position Indices” [J]. Journal of International Trade, 2014, (02): 3–12.

In this paper, we studied the data from TiVA 2018.

https://stats.oecd.org/Index.aspx?DataSetCode=TIVA_2018_C2#.

DVAit represents the domestic added value (added value from the country i) of the agricultural, forestry and fishery products exported by country i to other countries, while FVAit represents the added value of foreign countries (added value from economies other than the country i) of the agricultural, forestry and fishery products exported by the country i to other countries.

The partner countries that have signed the RTA with China are regarded as the treatment group, while the countries that have not signed the RTA with China are regarded as the control group. Five main ASEAN countries (the Philippines, Thailand, Malaysia, Vietnam and Indonesia) were set as the treatment group, while Norway, Canada, the United States and Japan were set as the control group. The reason for choosing these countries as the control group is that there is data showing that they are preference countries that are keen on aquatic product consumption. Moreover, they have a large-scale import and export trade volume of aquatic products with China, and they are distributed in different geographic regions such as Europe, America and Asia. ln(DVAit)=β0+β1duit+β2dtit+β3duitdtit+β4ln(gdpit)+β5ln(peoit)+β6ln(disit)+β7ln(iitit)+εjt {\rm{ln}}\left( {DV{A_{it}}} \right) = {\beta _0} + {\beta _1}d{u_{it}} + {\beta _2}d{t_{it}} + {\beta _3}d{u_{it}}d{t_{it}} + {\beta _4}{\rm{ln}}\left( {gd{p_{it}}} \right) + {\beta _5}{\rm{ln}}\left( {pe{o_{it}}} \right) + {\beta _6}{\rm{ln}}\left( {di{s_{it}}} \right) + {\beta _7}ln\left( {ii{t_{it}}} \right) + {\varepsilon _{jt}}

From the perspective of the domestic added value from the domestic export of aquatic products, the coefficient of duitdtit was around 0.5 and significantly positive, indicating that as the RTA came into effect, the countries’ domestic added value in the treatment group was significantly higher than that of the countries in the control group. It suggests that after the signing of the ASEAN–China regional trade agreements, the trade agreements have played a significant role in promoting the domestic production of countries in the aquatic product industry chain. In addition, the domestic added value of the exports of the aquatic products of the countries under the ASEAN–China agreement increases by about two times on average. As shown in Table 4, the covariates ln(gd pit) and ln(peoit) passed the significance level test of 10% and 1%, respectively, which indicates that a high level of economic development and a large population within ASEAN countries are conducive to domestic production of aquatic products. The coefficient of the variable ln(disit) was positive. In general, the closer the countries are geographically, the better will be the condition for trade. However, the empirical results show that proximity cannot effectively promote domestic production in aquatic products export but restrain the domestic added value. The variable ln(iitit) did not pass the significance level test, indicating that the strength of industrial complementarity could not affect the domestic production level.

DID estimation results of aquatic products consumption in ASEAN countries.

(1) (2)
duitdtit 0.014 (0.24) −0.013 (−0.16)
ln(gd pit) 0.427*** (7.05) 0.432*** (7.26)
ln(peoit) 0.159*** (−1.56) 0.155*** (−2.80)
ln(disit) −0.470*** (−10.02) −0.471*** (−10.01)
ln(iitit) −0.153*** (−0.01) −0.153** (−2.59)
_cons −2.912*** (−2.86) −2.988*** (−3.00)
N 126 126
F 279.05 269.39
R2 0.81 0.81

p < 0.01,

p < 0.05.

ASEAN, Association of Southeast Asian Nations; DID, difference-in-differences.

After the input–output table was developed in the WIOD database, Koopman (2010) constructed a value-added trade decomposition framework based on the inter-country input–output table. Wei Long and Wang Lei (2015) redefined the upgrading direction of the manufacturing industry based on the WIOD database.

Wei Long, Wang Lei., A study of Chinese Manufacturing Industry Transformation and Upgrading in Global Value Chains System [J]. The Journal of Quantitative & Technical Economics, 2015, (6): 71–86.

Based on the WIOD from 2000 to 2014, the indicators such as the upstream degree and the technical complexity of the manufacturing industry in 43 economies around the world were measured, and the direction of upgrading of the manufacturing industry was redefined.

Song Pei, Chen Zhe, Song Dian., Can Green Technology Innovation Promote GVC of China's Manufacturing Industry? [J]. Collected Essays on Finance and Economics, 2021, (1): 1–14.

Figure 1 shows the input–output analysis model proposed by Leontief, based on the WIOD from 2000 to 2014.

http://www.wiod.org/database/wiots13.

It takes Indonesia as an example and analyses the impact on fisheries and aquaculture after joining the China–ASEAN regional trade agreement.

Fig. 1

Intermediate input rate of Indonesia's fisheries and aquaculture. Source: World Input–Output Database.

The WIOD database contains 43 economies, of which only Indonesia, as an ASEAN country, has relevant data. From the above graph, it can be seen that the intermediate input rate of Indonesia's fisheries and aquaculture industry has been decreasing since 2000 and has stabilised at around 15% since 2010. Among them, the share of raw materials supplied by other domestic industries is gradually decreasing, indicating that the productivity of Indonesia's domestic fisheries and aquaculture industry is increasing. As the intermediate input rate decreases, it brings an increase in the value-added rate.

As can be seen from Figure 2, since 2006, the share of fishery and aquaculture products used as raw materials for production in other industries, produced domestically in Indonesia, stabilised between 0.35 and 0.38.

Fig. 2

Intermediate demand rates of Indonesia's fisheries and aquaculture. Source: World Input–Output Database.

Processing (DID model)

Koopman (2012) decomposed the export structure of various countries and proposed the indicators “GVC status index” and “GVC participation degree” based on the value-added measurement method to analyse a country's status in the value chain.

Koopman R, Wang Zhi and Wei S., “Estimating Domestic Content in Exports When Processing Trade is Pervasive” [J]. Journal of Development Economics, 2012, (99): 178–189.

Baldwin (2013) explains that the position in the value chain is determined by the added value of intermediate products. This section adopts the method proposed by Koopman.

Baldwin, R. and Harrigan, J., 2011, “Zeros, Quality, and Space: Trade Theory and Trade Evidence”, American Economic Journal: Macroeconomics [J]. 2011, (3): 60–88.

GVC position index is derived as follows: GVCpositionit=ln(1+(DVE)it)ln(1+(FVE)it), GV{C_{position}}_{it} = {\rm{ln}}\left( {1 + {{\left( {{{DV} \over E}} \right)}_{it}}} \right) - {\rm{ln}}\left( {1 + {{\left( {{{FV} \over E}} \right)}_{it}}} \right), where DV represents the domestic added value of intermediate products exported to a third country after reprocessing by the importing country; E represents the export value; (DVE)it {\left( {{{DV} \over E}} \right)_{it}} in the following represents the proportion of the domestic added value of agriculture, forestry and fishing industries of the country i to its export value. FV represents the foreign added value contained in exports, and (FVE)it {\left( {{{FV} \over E}} \right)_{it}} represents the proportion of the foreign added value of agriculture, forestry and fishing industries of the country i to the export value. The larger the GVC position index, the more upstream a country is in the GVC. Otherwise, the country is closer to the downstream value. Gereffi (1994) explains that producer-driven industries should take climbing up the GVC as the way towards industrial upgrading.

Gereffi G, Korzeniewicz M., Commodity Chains and Global Capitalism [M]. Greenwood Pub Group, 1994.

GVCpositionit=β0+β1duit+β2dtit+β3duitdtit+β4ln(gdpit)+β5ln(peoit)+β6ln(disit)+β7ln(iitit)+εjt \matrix{ {GV{C_{position}}_{it}} \hfill & { = {\beta _0} + {\beta _1}d{u_{it}} + {\beta _2}d{t_{it}} + {\beta _3}d{u_{it}}d{t_{it}} + {\beta _4}{\rm{ln}}\left( {gd{p_{it}}} \right)} \hfill \cr {} \hfill & {\quad + {\beta _5}{\rm{ln}}\left( {pe{o_{it}}} \right) + {\beta _6}{\rm{ln}}\left( {di{s_{it}}} \right) + {\beta _7}ln\left( {ii{t_{it}}} \right) + {\varepsilon _{jt}}} \hfill \cr }

GVCpositionitis calculated according to the OECD TiVA Database (2018). The following DID model includes the Philippines, Thailand, Malaysia, Vietnam and Indonesia as the treatment group, and Norway, Canada, the United States and Japan as the control group.

The policy impact point of (1) was in 2006 and 2008 in Vietnam, and (2) was in 2010. From the GVCs of the aquatic products, the coefficient of duitdtit was not significant, indicating that the entry into force of the RTA does not affect the position of aquatic products of ASEAN countries in the global production chain. The covariates ln(gd pit) and ln(peoit) passed the significance level test, which indicates that when ASEAN countries are highly economically developed, it is beneficial to the ASEAN production chain. The larger the population is, the worse it is for the fishery production chain of ASEAN countries. The coefficients of ln(disit) and ln(iitit) were not significant, indicating that the distance between trading partners and intra-industry trade have no impact on the GVC position of ASEAN countries.

Consumption (DID model)

The dependent variable data originated from FAO Food Balance Sheets. The policy impact point of (1) was in 2006 and 2008 in Vietnam, and (2) was in 2010. From the perspective of the domestic food supply of aquatic products, the coefficient of duitdtit was not significantly positive, indicating that with the implementation of the RTA, there is no direct impact on the local demand for aquatic products. The covariates ln(gd pit) and ln(peoit) passed the significance level test, which indicates that when ASEAN countries are highly economically developed with a large population, it is beneficial to the domestic supply of aquatic products. The coefficient of ln(disit) was significantly negative. The greater the distance between trading partners, the worse it will be for aquatic products’ domestic supply. The coefficient of the variable ln(iitit) was significantly negative, indicating that the stronger the industrial complementarity, the less the domestic food supply of ASEAN countries will be.

From the perspective of the domestic food supply of aquatic products, the coefficient of duitdtit was not significantly positive, indicating that with the enforcement of the RTA, there is no direct impact on the local demand for aquatic products. The covariates ln(gd pit) and ln(peoit) passed the significance level test, which indicates that a developed economy and a large population of an ASEAN country are beneficial to the domestic supply of aquatic products. The coefficient of ln(disit) was significantly negative. The greater the distance between the trading partners, the worse it will be for aquatic products’ domestic supply. The coefficient of the variable ln(iitit) was significantly negative, indicating that the stronger the industrial complementarity, the less the domestic food supply of ASEAN countries will be.

Trade effects

By calculation based on UN Comtrade of the aquatic products with HS four-digit code, the following points can be concluded: (1) for “0301” (including live fish), both the product price and market share in the Chinese market continue to rise at the same time, indicating that the product has been upgraded; (2) for “0302” (fresh or chilled fish), in China, Malaysia, the Philippines and other countries, the product has a trend of upgrading; (3) for “0303” (frozen fish), the tendency of continuous upgrade is not seen; (4) for “0304” (fish fillets and other fish meat – fresh, chilled or frozen), the product has been continuously upgraded in recent years in countries such as Indonesia and the Philippines; (5) for “0305” (dried fish, salted fish or fish in brine, and smoked fish, fish meat fit for human consumption), the current trend of continuous upgrading is not seen; (6) for “0306” (crustaceans, in shell or not, live, fresh, chilled, frozen, dried, salted or in brine), China achieved product upgrades in 2014 and 2016; (7) for “0307” (molluscs, whether in shell or not, live, fresh, chilled, frozen, dried, salted in brine; aquatic invertebrates [not crustaceans and molluscs] live, fresh, chilled, frozen, dried, salted or in brine), no continuous escalation trend is found.

Conclusion
National policy

We found that countries could be both large exporters and importers of fish, as is the case in Thailand, China and Vietnam. However, developing countries mainly export high-value products and import low-value ones. Thus, in some instances, the proceeds from exporting more-expensive fish can be used to import less-expensive, but equally or more nutritious, fish.

FAO, Value chain dynamics and the small-scale sector Policy recommendations for small-scale fisheries and aquaculture trade, 2014, P7.

Therefore, from the trade data, such as import and export data, we might find that the economic effect of ACFTA is quite limited, but the nutritious value of ACFTA should never be underestimated.

Freshness is the priority of seafood. How to transport seafood from one place to another fresh and clear is crucially important. The advances in transportation, especially the cold chain logistics system, add value to the fisheries products across nations. Consolidation technology can save space in container ships. As long as the technical problems related to inspection and quarantine exist, fish and fishery product consolidation is highly suggested. Consolidation of fish and fishery products might be another incentive for the development of international trade of seafood.

Economic framework

Continuing to promote the solid development of the major China–ASEAN trade markets: As aquatic products have corrosive qualities, the possibility of trade friction is high. We need to further expand the trade volume of China–ASEAN aquatic products and enrich the variety of aquatic products trade. We also need to explore potential opportunities of aquatic product trade in various countries in the free trade zone.

Establishing a new pattern of industrial division of labor within the framework of ACFTA: The high intra-industry trade index between China and major ASEAN countries such as the Philippines, Thailand, Malaysia and Indonesia indicates a complementary trade demand for aquatic products. However, for “0305”, “0306”, “0307” and other deeply processed products, China and the major ASEAN countries have not yet formed a competitive edge. In order to avoid the problem of uncoordinated development and losses caused by low cooperation efficiency, a unified guiding policy should be formulated within the framework of ACFTA to promote the cooperation efficiency of trade division. It is also necessary to avoid the decline of labour resources and product competitiveness, in addition to making the intra-regional trade of aquatic products develop towards higher level of intra-industry trade.

Further expand and strengthen fisheries and aquaculture-related industrial chains: From the previous analysis, it can be seen that, in Indonesia, for instance, the proportion of fishery and aquaculture used as intermediate products in other industries is not high and there are not many types of intermediate products. This indicates that there are not many upstream and downstream industries associated with fishery and aquaculture, and further development of high-tech farming technology and deep processing should be carried out.

Further strengthen international cooperation in bilateral industrial chains: From the previous analysis, it can be seen that, in Indonesia, for instance, most of the other industries invested into fisheries and aquaculture are still relatively low in terms of intermediate input rates from their own countries. Bilateral economic and trade cooperation should be deeply strengthened to encourage enterprises related to fisheries and aquaculture to invest in the regions of member states, so that relevant resources can seek their best allocation in the market in a more reasonable manner.

Effect of COVID-19

Affected by the COVID-19 pandemic, the process of aquatic product movement into and out of the enterprises is more hindered. On the one hand, as closely related to the food industry, fisheries and industrial farming industry must pay close attention to the safety of aquatic products in the ACFTA. Meanwhile, the inspection and quarantine of imported and exported aquatic products from China and ASEAN need to be greatly strengthened to prevent non-inspected aquatic products from entering the market. In addition, learning from the lessons of the pandemic, it is necessary to accelerate the construction of an ACFTA fishery product information platform, as well as a quality and safety traceability system, to ensure the quality and safety of products in all aspects of the supply chain, including production, processing and sales.

At the same time, along with the successful signing of the Regional Comprehensive Economic Partnership (RCEP), the ACFTA has an extremely important role in promoting the economic recovery of countries after the global pandemic and in promoting long-term prosperity and development. Further acceleration of the trade liberalisation process will bring a greater boost to regional economic and trade prosperity. The preferential results of the agreement directly benefit consumers as well as fishery- and aquaculture-related enterprises, which will play a critical role in enriching consumer's market choices and reducing trade costs for enterprises.

Fig. 1

Intermediate input rate of Indonesia's fisheries and aquaculture. Source: World Input–Output Database.
Intermediate input rate of Indonesia's fisheries and aquaculture. Source: World Input–Output Database.

Fig. 2

Intermediate demand rates of Indonesia's fisheries and aquaculture. Source: World Input–Output Database.
Intermediate demand rates of Indonesia's fisheries and aquaculture. Source: World Input–Output Database.

DID estimation of domestic added value of exports of aquatic products.

(1) (2) (3) (4)
duitdtit 0.718*** (3.82) 0.505** (2.29) 0.537* (1.71) 0.550* (1.87)
ln(gd pit) 0.38*** (3.42) −0.231 (−1.56) −0.259* (−1.81)
ln(peoit) 0.494*** (4.07) 0.48*** (3.70)
ln(disit) 2.208*** (28.24) 2.155*** (22.21)
ln(iitit) 0.128 (1.16)
_cons 8.614 (32.91) −2.349 (−0.77) −12.891*** (−5.20) −11.318 (−4.60)
N 99 99 99 99
F 11.57 8.08 182.48 159.57
R2 0.08 0.18 0.81 0.82

DID estimation results of aquatic products consumption in ASEAN countries.

(1) (2)
duitdtit 0.014 (0.24) −0.013 (−0.16)
ln(gd pit) 0.427*** (7.05) 0.432*** (7.26)
ln(peoit) 0.159*** (−1.56) 0.155*** (−2.80)
ln(disit) −0.470*** (−10.02) −0.471*** (−10.01)
ln(iitit) −0.153*** (−0.01) −0.153** (−2.59)
_cons −2.912*** (−2.86) −2.988*** (−3.00)
N 126 126
F 279.05 269.39
R2 0.81 0.81

DID estimation results for export of aquatic products in terms of GVCpositionit.

(1) (2)
duitdtit 0.893 (0.97) −0.046 (−0.76)
ln(gd pit) 0.190*** (3.79) 0.212*** (4.39)
ln(peoit) −0.068 (−1.56) −0.083** (−2.03)
ln(disit) 0.003 (0.15) 0.001 (0.07)
ln(iitit) −0.000 (−0.01) −0.005 (−0.16)
_cons −3.477 (−4.52) −2.349 (−0.77)
N 99 99
F 15.57 15.28
R2 0.38 0.38

Description of main variables.

Primary variables and data source
Category Variable Definition Data source
Dependent variable ln(DVAit) The domestic added value of agriculture, forestry and fishing goods exported by country i to other countries OECD TiVA Database (2018)
GVCpositionit GVC position index Calculated based on OECD TiVA Database (2018)
ln(Consumeit) Domestic supply quantity, fish, seafood FAO Food Balance Sheets (old methodology and population)
Control variable ln(gd pit) The logarithm of GDP of country i (constant prices in 2010) World Bank Database
ln(peoit) The logarithm of the population in country i World Bank Database
ln(disit) The logarithm of geographical distance between country i and China World Bank Database
ln(iitit) The logarithm of the index of intra-industry trade in aquatic products trade between the country i and China Calculated based on UN Comtrade

Anderson, J.L., F. Asche and T. Garlock, Globalization and commoditization: The transformation of the seafood market. Journal of Commodity Markets, 2018, (12): pp. 2–8. AndersonJ.L. AscheF. GarlockT. Globalization and commoditization: The transformation of the seafood market Journal of Commodity Markets 2018 12 2 8 10.1016/j.jcomm.2017.12.004 Search in Google Scholar

Crona, B., et al., China at a Crossroads: An Analysis of China's Changing Seafood Production and Consumption. One Earth, 2020, (3): pp. 32–44. CronaB. China at a Crossroads: An Analysis of China's Changing Seafood Production and Consumption One Earth 2020 3 32 44 10.1016/j.oneear.2020.06.013 Search in Google Scholar

Handley, K., & Limão, N. Policy Uncertainty, Trade, and Welfare: Theory and Evidence for China and the United States. The American Economic Review, 2017, (107): pp. 2731–2783. HandleyK. LimãoN. Policy Uncertainty, Trade, and Welfare: Theory and Evidence for China and the United States The American Economic Review 2017 107 2731 2783 10.1142/9789813147980_0005 Search in Google Scholar

Lee T M, Chi P Y, Chang K I. Duration and determinants of fishery trade patterns: Evidence from OECD countries [J]. Marine Policy, 2020, (118): p. 103977. LeeT M ChiP Y ChangK I Duration and determinants of fishery trade patterns: Evidence from OECD countries [J] Marine Policy 2020 118 103977 10.1016/j.marpol.2020.103977 Search in Google Scholar

Li Chen, Chi Ping. “The study on the upgrading trend and trade positing of China's aquatic product exports: based on the perspective of global value chain”. World Agriculture, 2017, (01): pp. 121–126. LiChen ChiPing “The study on the upgrading trend and trade positing of China's aquatic product exports: based on the perspective of global value chain” World Agriculture 2017 01 121 126 Search in Google Scholar

Lieng S, Yagi N, Ishihara H. Global Ecolabelling Certification Standards and ASEAN Fisheries: Can Fisheries Legislations in ASEAN Countries Support the Fisheries Certification? Sustainability, 2018, (10): p. 3843. LiengS YagiN IshiharaH Global Ecolabelling Certification Standards and ASEAN Fisheries: Can Fisheries Legislations in ASEAN Countries Support the Fisheries Certification? Sustainability 2018 10 3843 10.3390/su10113843 Search in Google Scholar

Li L, Song P. Research on the opportunities, countermeasures and legal issues of marine fishery cooperation in Southeast Asia. International Journal of Electrical Engineering Education, 2021, (10): p. 1177. LiL SongP Research on the opportunities, countermeasures and legal issues of marine fishery cooperation in Southeast Asia International Journal of Electrical Engineering Education 2021 10 1177 10.1177/0020720920985052 Search in Google Scholar

Xiao-Fei L, Yong-Hui H. Analysis on the Characteristics of Aquatic Products Trade between China and ASEAN based on the HS Classification [J]. Iop Conference, 2018, (153): pp. 54–56. Xiao-FeiL Yong-HuiH Analysis on the Characteristics of Aquatic Products Trade between China and ASEAN based on the HS Classification [J] Iop Conference 2018 153 54 56 10.1088/1755-1315/153/3/032054 Search in Google Scholar

Sun J. Do Higher-Quality Regional Trade Agreements Improve the Quality of Export Products from China to “One-Belt One-Road” Countries? [J]. Asian Economic Journal, 2021, (35): pp. 12–15. SunJ Do Higher-Quality Regional Trade Agreements Improve the Quality of Export Products from China to “One-Belt One-Road” Countries? [J] Asian Economic Journal 2021 35 12 15 10.1111/asej.12241 Search in Google Scholar

Wei H, Wang S. Analysis of and Theoretical Reflections on China's “Excessive De-Industrialization” Phenomenon [J]. Chinese Journal of Urban and Environmental Studies (CJUES), 2019, (07): pp. 12–15. WeiH WangS Analysis of and Theoretical Reflections on China's “Excessive De-Industrialization” Phenomenon [J] Chinese Journal of Urban and Environmental Studies (CJUES) 2019 07 12 15 10.1142/S2345748119500179 Search in Google Scholar

Li, Q., et al. “Analysis on the Evolution Characteristics of China's Manufacturing Industry Structure—Based on the Perspective of Global Value Chain.” IOP Conference Series: Materials Science and Engineering, 2019, 563: p. 042061. LiQ. “Analysis on the Evolution Characteristics of China's Manufacturing Industry Structure—Based on the Perspective of Global Value Chain.” IOP Conference Series: Materials Science and Engineering 2019 563 042061 10.1088/1757-899X/563/4/042061 Search in Google Scholar

Zhao, Liming, et al. “Trends in the Dynamic Evolution of Corporate Social Responsibility and Leadership: A Literature Review and Bibliometric Analysis.” Journal of Business Ethics, 2022, 4: pp. 1–23. ZhaoLiming “Trends in the Dynamic Evolution of Corporate Social Responsibility and Leadership: A Literature Review and Bibliometric Analysis.” Journal of Business Ethics 2022 4 1 23 10.1007/s10551-022-05035-y Search in Google Scholar

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