Impact of Hong Kong-ASEAN Free Trade Agreement: an assessment from the trade creation and trade diversion perspectives

Figures & data

Table 1. Summary of latest literature review.

Study	Period	Study object	FTA/Regions	Method	Main findings
Khurana and Nauriyal (2017)	2000–2015	33 countries (11 countries of ASEAN, India, India’s top trading partners	India-ASEAN FTA	OLS, PPML, only time effects, time and country specific effects	FTA has led to rise trade diversion effects.
Timsina and Culas (2022)	1996–2017	Wheat export of Australia	Australia’s FTA	OLS, PPML	Trade creation effects is higher than Intra block export diversion.
Gharleghi and ShaFigurehi (2020)	2000–2017 2000–2007 2008–2017	Asia-Pacific region	AANZFTA AFTA APTA	2SLS, OLS	Trade has widened between AFTA and APTA members, but not between AANZFTA members.
Lateef et al. (2018)	2001–2014	110 partners countries, China, Pakistan	CPFTA	PPML	On Pakistan’s agricultural exports, CPFTA had a significant TC  effect
Koh et al. (2017)	1995–2014	10 member of ASEAN and 39 trading partners	AFTA	REM	AFTA encourages trade between ASEAN members to have  TC influences on exports and imports, as well as TD effects on imports.
Mareta (2018)	1990–2015	Indonesia and 20 trading countries partner	AKFTA	LSDV, OLS, FE	AKFTA have TC and TD effect on Indonesian export.
Taguchi (2015)	1993–2013	14 countries/regions	AKFTA AJFTA ACFTA	FEM	While the TD effects were typically negative in all three agreements, the TC effects in the ACFTA were much larger compared to the ones in the ASEAN-Korea FTA (AKFTA) and ASEAN-Japan FTA (AJFTA).
Zolin and Uprasen (2018)	1999–2015	38 countries including EU and Selected Asian countries	EU	FE-PPML, PPML, RE, FE	The TC effects in the ACFTA were significantly bigger compared to those in the AKFTA and ASEAN-Japan FTA (AJFTA), while the TD effects were typically adverse in all three agreements.
Muñoz et al. (2023)	Quanitative method (1996–2018) Qualitative method (October 2019 and March 2020)	Chile and Korea	Chile–Korea FTA	Mixed method (quanitative and qualitative method)	Due to limitations in the public and private sectors, Chile has been unable to change the level of concentration of its exports.
Geda and Yimer (2023)	1993–2017	North and South-Eastern African countries and the West and Central African countries	AfCFTA	OLS, PPML	The AfCFTA might increase intra-African exports by approximately US$ 13.7 billion (by around 19%) from an average yearly value of US$ 72.7 billion (2015–2017).
Alhassan and Payaslioğlu (2023)	2000–2018	ASEAN countries	FTAs in ASEAN	PPMLHDFE	The degree of the TC and TD effects of the FTAs on trade flows in ASEAN Depene upon the strength of he political and economic institutions in ASEAN.
(Kashif)	n.a.	ASEAN and India countries	ASEAN- India FTA	SMART	ASEAN-India FTA has resulted TD from non-member countries in favor of India.

Table 2. List of countries used in this study.

Member countries of AHKFTA	Top 23 trade partners of Hong Kong in 2020
Brunei	USA
Cambodia	Taipei
Indonesia	India
Laos	Netherlands
Myanmar	Korean
Malaysia	UK
Philipines	UAE
Singapore	Germany
Thailand	Macao
Vietnam	Switzerland
	Mexico
Hong Kong	Russian Federation
	France
	Australia
	Italy
	Brazil
	Poland
	Belgium
	Spain
	Hungary
	Israel
	Bangladesh
	Japan

Source: Authors elaboration using UNCOMTRADE database and information from the World Trade Organization. Website: https://www.wto.org.

Table 3. Data definitions and sources.

Variables	Descriptions	Expected sign	Source
${\mathit{\text{EX}}}_{\mathit{\text{ijt}}}$	Bilateral exports from exporter $i$ to importer $j$ in period $t$		Trademap
${\mathit{\text{GDP}}}_{\mathit{\text{it}}}$; ${\mathit{\text{GDP}}}_{\mathit{\text{jt}}}$	Nominal gross domestic product in countries $i$, $\mathit{j }$	+	WDI
${\mathit{\text{POP}}}_{\mathit{\text{jt}}};{\mathit{\text{POP}}}_{\mathit{\text{jt}}}$	the populations of countries $i$ and $j$ in period $t$	+	WDI
${\mathit{\text{DIST}}}_{\mathit{\text{ij}}}$	distance between the capital cities	–	CEPII
${\mathit{\text{LANG}}}_{\mathit{\text{it}}}$	speaking the same official language	–	CEPII
$\mathit{\text{LANDLOCKij}}$	being a landlocked country	+	CEPII
${\mathit{\text{BORDER}}}_{\mathit{\text{ij}}}$	sharing common border	+	CEPII
${\mathit{\text{FTA}}}_{1\mathit{\text{ijt}}}$	takes a value of 1 if both countries $i$ and $j$ in year $t$ belong to the AHKFTA and zero otherwise	+	WTO
${\mathit{\text{FTA}}}_{2\mathit{\text{ijt}}}$	takes a value of one if exporter $i$ belongs to the AHKFTA in year $t$ and destination country $j$ does not and zero otherwise	+	WTO
${\mathit{\text{FTA}}}_{3\mathit{\text{ijt}}}$	takes a value of one if exporter $i$ is a non-AHKFTA member in year $t$ and destination country $j$ belongs to the AHKFTA and zero otherwise	–	WTO

Note. UN Comtrade Database; World Bank World Development Indicators (WDI); the gravity data set available at the Centre d’Etudes Prospectives etd’Informations Internationales (CEPII); Website: https://www.wto.org (WTO).

Table 4. Panel data gravity estimatdata gravity estimation.

VARIABLES	(1)	(2)	(3)	(4)
	OLS	FE	RE	HTM
$\mathit{\text{lnGD}}{P}_{\mathit{\text{it}}}$	1.174***	0.485***	0.804***	0.582***
	(0.0155)	(0.123)	(0.0664)	(0.0469)
$\mathit{\text{lnGD}}{P}_{\mathit{\text{jt}}}$	1.059***	0.410***	0.680***	0.440***
	(0.0152)	(0.120)	(0.110)	(0.0379)
$\mathit{\text{lnPO}}{P}_{\mathit{\text{it}}}$	0.179***	−2.648***	0.394***	−0.820***
	(0.0150)	(0.729)	(0.0737)	(0.236)
$\mathit{\text{lnPO}}{P}_j$	0.0322**	0.769	0.287***	1.504***
	(0.0148)	(0.687)	(0.0946)	(0.227)
$\mathit{\text{lnDIST}}$	−1.118***		−1.052***	−0.921***
	(0.0207)		(0.0672)	(0.177)
$\mathit{\text{LANG}}$	0.678***		0.987***	1.119**
	(0.0584)		(0.181)	(0.489)
$\mathit{\text{LANDLOCK}}$	0.190***		0.459**	0.872*
	(0.0494)		(0.194)	(0.446)
$\mathit{\text{BORDER}}$	−0.0284		−0.0541	0.262
	(0.0903)		(0.236)	(0.786)
${\mathit{\text{FTA}}}_{1\mathit{\text{ijt}}}$	1.097***	0.221**	0.105	0.165***
	(0.129)	(0.0870)	(0.0822)	(0.0518)
${\mathit{\text{FTA}}}_{2\mathit{\text{ijt}}}$	0.395***	0.228***	0.133**	0.192***
	(0.0853)	(0.0659)	(0.0620)	(0.0348)
${\mathit{\text{FTA}}}_{3\mathit{\text{ijt}}}$	−0.0102	−0.0755	−0.0863*	−0.0732**
	(0.0857)	(0.0528)	(0.0521)	(0.0349)
Constant	−41.51***	21.80	−30.13***	−19.16***
	(0.427)	(18.78)	−2.235	−3.686
Observations	13,464	13,464	13,464	13,464
R-squared	0.675	0.062
Number of Country		1,122	1,122	1,122
Country FE	NO	YES	YES
Year FE	NO	YES	YES

Robust standard errors in parentheses.

***p < 0.01, **p < 0.05, *p < 0.1.

Table 5. Panel data gravity estimation by using PPML and LASSO technique.

VARIABLES	Full sample		Subsample
	(1)	(2)	(3)	(4)
	PPML	LASSO	PPML	LASSO
$\mathit{\text{lnGD}}{P}_{\mathit{\text{it}}}$		1.166***		1.207***
		(0.0220)		(0.0283)
$\mathit{\text{lnGD}}{P}_{\mathit{\text{jt}}}$		1.050***		1.099***
		(0.0220)		(0.0322)
$\mathit{\text{lnPO}}{P}_{\mathit{\text{it}}}$		0.199***		0.209***
		(0.0211)		(0.0315)
$\mathit{\text{lnPO}}{P}_{\mathit{\text{jt}}}$		na		na
$\mathit{\text{lnDIST}}$		−1.170***		−1.445***
		(0.281)		(0.264)
$\mathit{\text{LANG}}$		na		na
$\mathit{\text{LANDLOCK}}$		na		na
$\mathit{\text{BORDER}}$		na		na
${\mathit{\text{FTA}}}_{1\mathit{\text{ijt}}}$	0.00751	1.067***	0.0574	1.047***
	(0.0567)	(0.292)	(0.0684)	(0.285)
${\mathit{\text{FTA}}}_{2\mathit{\text{ijt}}}$	0.0269	0.356***	0.0596	0.696***
	(0.0577)	(0.137)	(0.0688)	(0.141)
${\mathit{\text{FTA}}}_{3\mathit{\text{ijt}}}$	−0.0202*	0.0996	−0.0167**	0.331***
	(0.0113)	(0.118)	(0.00807)	(0.124)
Observations	13428	13,464	5,580	5,610
R-squared	0.963		0.978

Standard errors in parentheses.

***p < 0.01, ** p < 0.05, * p < 0.1. na: not applicable because not selected by the LASSO technique.

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