Profiting from growth: Trade, investment and the ASEAN-China technology gap

Figures & data

Table 1. Linear and threshold causalities from China’s exports and FDI to Southeast Asia’s growth, 1995–2019.

Panel (A): GDP vs. EXPO		Panel (B): GDP vs. FDI				Panel (C): GDP vs. EXPO+FDI
	Nonlinear		Linear		Nonlinear		Linear		Nonlinear
	$d{Y}_t$		$d{Y}_t$		$d{Y}_t$		$d{Y}_t$		$d{Y}_t$
	(1)		(2)		2(3)		(4)		(5)
$d{Y}_{t-1}$	0.4288	$d{Y}_{t-1}$	0.2423	$d{Y}_{t-1}$	0.3551	$d{Y}_{t-1}$	0.3431	$d{Y}_{t-1}$	0.5108*
	(0.2819)		(0.2418)		(0.2508)		(0.2503)		(0.2617)
$d{X}_{t-1}$	–0.0818*	$d{K}_t$	–0.3244	$d{K}_{t-1}$	0.2547	$d{X}_t$	0.0771**	$d{X}_{t-1}$	–0.0956**
	(0.2819)		(0.2418)		(0.2508)		(0.2503)		(0.2617)
$d{X}_{t-1}$	–0.0818*	$d{K}_t$	–0.3244	$d{K}_{t-1}$	0.2547	$d{X}_t$	0.0771**	$d{X}_{t-1}$	–0.0956**
	(0.0415)		(0.3888)		(0.3897)		(0.0340)		(0.0441)
		$d{K}_{t-1}$	–0.0046			$d{X}_{t-1}$	–0.0691	$d{K}_{t-1}$	–0.0388
			(0.3984)				(0.0462)		(0.3861)
						$d{K}_t$	0.2632
							(0.3938)
						$d{K}_{t-1}$	0.0900
							(0.3669)
$Errorterms$
${\epsilon }_{t-1}^x\cdot I^x$	–1.3278	${\epsilon }_{t-1}^k$	–1.0539a	${\epsilon }_{t-1}^k\cdot I^k$	–1.3180a	${\epsilon }_{t-1}^{x,k}$	–1.2455a	${\epsilon }_{t-1}^{x,k}\cdot I^{x,k}$	–1.6064a
	(0.4052)		(0.3233)		(0.3651)		(0.3514)		(0.4032)
${\epsilon }_{t-1}^x\cdot I{\prime }^x$	–0.7429			${\epsilon }_{t-1}^k\cdot I{\prime }^k$	–0.5469			${\epsilon }_{t-1}^{x,k}\cdot I{\prime }^{x,k}$	–0.8712
	(0.6400)				(0.7410)				(0.6749)
$Constant$	–0.3952		0.0303		–0.7104		–0.2736		–0.4448
	(0.7610)		(0.7156)		(0.9350)		(0.6316)		(0.7374)
$Causality$
$Shortrun$	–1.9725*		0.4123		0.6535		3.1847**		2.6486
$Longrun$	6.6466		–3.2604		7.3865		–3.5448		9.1513
$Strong1$	5.8322**		4.8820**		6.5482		5.3606		5.9434
$Strong2$	2.4154				0.6984				1.8637
$Thresholdyears$	Y1998/99				Y1998/99				Y1998/99
	Y2012				Y2009/10				Y2009
					Y2012				Y2012
					Y2014
$\tau$	–1.3831				–0.9558				–1.4323
${\bar{R}}^2$	0.3833		0.3992		0.4019		0.5452		0.4557
$AIC$	5.3471		5.3209		5.3165		5.0992		5.2526
$LM\left(2\right)$	0.0297[0.9708]		1.5140[0.2518]		0.8278[0.4560]		3.2520[0.0716]		0.0746[0.9285]
$ARCH\left(2\right)$	0.2206[0.8043]		0.1381[0.8720]		0.3125[0.7357]		2.5799[0.1051]		0.4609[0.6384]
$JB$	43.2487[0.0000]		12.1161[0.0000]		62.6986[0.0000]		82.4210[0.0000]		17.7347[0.0001]

*** p < 0.01, significant at 1%; ** p < 0.05, significant at 5%; * p < 0.1, significant at 10%.

Table 2. Export- and FDI-driven growth in Southeast Asia.

	GDP vs. EXPO	GDP vs. FDI
	(1)	(2)
ASEAN-6
Indonesia	Yes	Yes
Malaysia	Yes	Yes
Singapore	Yes	Yes
Brunei	–	Yes
Thailand	–	Yes
Philippines	–	–
CLMV
Cambodia	Yes	Yes
Vietnam	Yes	Yes
Laos	–	–
Myanmar	–	–

Source: Compiled by the authors based on the results in Appendix B.

Figure 1. Southeast Asia’s economic links to China and domestic GDP growth.

Sources: ASEAN Statistical Yearbook; China Statistical Yearbook; World Development Indicators.

Appendix A. Summary statistics and unit root tests.

		Southeast Asia’s GDP, growth rate % ($Y_t$)	Southeast Asia’s export to China, growth rate % ($X_t$)	Share of China in Southeast Asia’s FDI inflows, % ($K_t$)
		(1)	(2)	(3)
Panel (A): Summary statistics
Observation		24	24	24
Mean		4.6123	13.9017	3.8201
Standard deviation		2.9519	16.2300	3.8597
Minimum		–7.7745	–9.4541	–0.5667
Maximum		7.7597	48.9411	12.3070
Panel (B): Unit root tests
Level	$\left(i\right)$	–4.2627***	–3.7206**	–0.8958
	$\left(ii\right)$	–4.6263***	–4.3018**	–3.2596*
	$\left(iii\right)$	–1.8920*	–2.5897**	0.1457
First differences	$\left(i\right)$	–13.8043***	–10.4793***	–7.5751***
	$\left(ii\right)$	–13.2896***	–10.6832***	–7.4814***
	$\left(iii\right)$	–14.4180***	–10.5913***	–6.6472***

Note: Phillips-Perron unit root tests are applied in Appendix A to ensure the robustness of serial correlation. All three versions with a) constant, b) with constant and trend, and c) with no constant or tend are included in the model specifications; the optimal lag lengths are chosen based on Schwartz Criterion (SC). *** p < 0.01, significant at 1%; ** p < 0.05, significant at 5%; * p < 0.1, significant at 10%.

Linear and threshold cointegration tests on Southeast Asia’s economic ties with China, 1995–2019.

Panel (A): GDP vs. EXPO				Panel (B): GDP vs. FDI				Panel (C): GDP vs. EXPO and FDI
	Linear		Nonlinear		Linear		Nonlinear		Linear		Nonlinear
	$d{Y}^t$		$d{Y}_t$		$d{Y}_t$		$d{Y}_t$		$d{Y}_t$		$d{Y}_t$
	(1)		(2)		(3)		(4)		(5)		(6)
$Y_{t-1}$	–0.8648**	$Y_{t-1}{I}^x$	–1.9880***	$Y_{t-1}$	–1.0837***	$Y_{t-1}{I}^K$	–1.5783***	$Y_{t-1}$	–1.2314***	$Y_{t-1}{I}^{x,K}$	–1.4661***
	(0.3599)		(0.4741)		(0.3357)		(0.2992)		(0.3469)		(0.2533)
$X_{t-1}$	0.0782	$Y_{t-1}I{\prime }^x$	0.6576	$Y_{t-1}I{\prime }^x$	0.1318	$Y_{t-1}I{\prime }^x$	–0.2266	$Y_{t-1}I{\prime }^x$	0.2756***	$Y_{t-1}I{{ }^{\mathrm{\prime }}}^{x,K}$	0.0832
	(0.0626)		(0.5820)		(0.1990)		(0.3666)		(0.0910)		(0.3557)
$d{Y}_{t-1}$	0.1479	$X_{t-1}{I}^x$	0.6488**	$d{Y}_{t-1}$	0.2518	$d{Y}_{t-1}$	1.1708***	$K_{t-1}$	0.6998**	$X_{t-1}{I}^{x,K}$	0.0593
	(0.2845)		(0.2720)		(0.2480)		(0.3867)		(0.2561)		(0.1543)
$d{X}_t$	0.0784	$X_{t-1}I{\prime }^x$	0.0565	$d{K}_t$	–0.2845	$K_{t-1}I{\prime }^K$	0.0066	$K_{t-1}I{\prime }^K$	0.2374	$X_{t-1}I{{ }^{\mathrm{\prime }}}^{x,K}$	0.1364**
	(0.0479)		(0.0505)		(0.4053)		(0.1604)		(0.2523)		(0.0589)
$d{X}_{t-1}$	–0.0432	$d{X}_t$	0.1231**	$d{X}_t$	–0.0510	$d{X}_t$	–0.3438	$d{X}_t$	0.1462**	$K_{t-1}{I}^{x,K}$	1.8307***
	(0.0446)		(0.0430)		(0.4175)		(0.3214)		(0.0522)		(0.3460)
		$d{Y}_t$	0.1473			$d{Y}_t$	0.2063	$d{X}_{t-1}$	–0.1158**	$K_{t-1}I{{ }^{\mathrm{\prime }}}^{x,K}$	0.2908
			(0.2266)				(0.1993)		(0.0507)		(0.1676)
		$d{X}_{t-1}$	–0.0518			$d{K}_{t-1}$	0.0348	$d{K}_{t-1}$	0.6804	$d{K}_{t-1}$	0.1394***
			(0.0358)				(0.3381)		(0.4372)		(0.0312)
								$d{K}_{t-1}$	0.1204	$d{K}_{t-1}$	0.2818
									(0.3659)		(0.2636)
										$d{Y}_{t-1}$	0.0910
											(0.1475)
										$d{X}_{t-1}$	–0.0561
											(0.0320)
										$d{K}_{t-1}$	0.1742
											(0.2314)
$Constant$	2.8050		–4.5142		4.5300**		0.7008		–1.3320		–3.9509**
	(2.0295)		(2.9701)		(1.6363)		(1.6906)		(2.3302)		(1.5975)
$Cointegration$
$WaldF$-$Stat$	3.9430				5.2116*				5.7630**
$Wald{\chi }^2$-$stat$			24.3396***				29.1031***				84.7644***
${\bar{R}}^2$	0.4622		0.6649		0.3718		0.6149		0.5861		0.8677
$AIC$	5.2405		4.8156		5.3959		4.9548		5.0437		3.9135
$LM\left(1\right)$ ^a	0.8166[0.3805]		0.9009[0.3599]		0.0001[0.9941]		0.2221[0.6453]		0.0389[0.8469]		5.3069[0.0467]
$LM\left(2\right)$	2.6863[0.1029]		0.4166[0.6685]		1.6805[0.2218]		0.1813[0.8364]		0.1364[0.8740]		4.0206[0.0619]
$ARCH\left(1\right)$ ^b	0.0801[0.7802]		0.8395[0.3710]		0.6122[0.4436]		0.0160[0.9007]		0.0686[0.7962]		0.0353[0.8529]
$ARCH\left(2\right)$	2.9999[0.0766]		0.7248[0.4988]		0.0711[0.9316]		0.3114[0.7365]		0.5157[0.6061]		3.3557[0.0591]
$JB$ ^c	129.4889[0.0000]		1.5818[0.4534]		91.6590[0.0000]		2.9859[0.2247]		15.9091[0.0004]		2.1031[0.3494]

Notes: ^a Breusch-Godfrey serial correlation LM test; ^b ARCH heteroscedasticity test; ^c Jacque-Bera normality test; Standard errors are reported in parentheses, while $\rho$ -values in brackets.

*** p<0.01, significant at 1%; ** p<0.05, significant at 5%; * p<0.1, significant at 10%.

Threshold causalities from China’s exports and FDI to Southeast Asia’s economic growth, by country 1995–2019.

Panel (A): GDP vs. EXPO
	ASEAN-6					CLMV
	Brunei	Indonesia	Malaysia	Philippines	Singapore	Thailand	Cambodia	Laos	Myanmar	Vietnam
	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)
$d{Y}_{t-1}$	–	1.1923***	0.5462**	–	0.5257**	–	0.3794	–	–	0.4303**
	–	(0.3780)	(0.2318)	–	(0.2461)	–	(0.2229)	–	–	(0.1818)
$d{X}_{t-1}$	–	–0.0160	–0.0796*	–	–0.1696***	–	–0.0029	–	–	–0.0009
	–	(0.0344)	(0.0444)	–	(0.0460)	–	(0.0032)	–	–	(0.0030)
$Errorterms$
${\epsilon }_{t-1}^x\cdot I^x$	–	–2.2886***	–2.0450***	–	–1.5828***	–	–0.9000**	–	–	–1.0224***
	–	(0.5033)	(0.3450)	–	(0.3435)	–	(0.3891)	–	–	(0.2347)
${\epsilon }_{t-1}^x\cdot I{\prime }^x$	–	1.7717*	–0.9597	–	–1.3721**	–	–0.7736**	–	–	–0.5565*
	–	(0.9181)	(0.7756)	–	(0.5799)	–	(0.2802)	–	–	(0.3141)
$Constant$	–	–2.5160**	–1.0887	–	–0.4269	–	0.1535	–	–	–0.1886
	–	(1.0711)	(0.8464)	–	(0.8713)	–	(0.5767)	–	–	(0.1545)
$Causality$
$Shortrun$	–	–0.4658	–1.7947*	–	–3.6854***	–	–0.9080	–	–	–0.2964
$Longrun$	–	12.1127***	18.5041***	–	11.5977***	–	5.8545**	–	–	11.0592***
$Strong1$	–	10.6993***	17.6063***	–	12.7275***	–	3.1693*	–	–	9.5038***
$Strong2$	–	2.5080	1.8524	–	8.3053***	–	5.0598**	–	–	1.6805
$\tau$	–	–1.3070	–1.3169	–	1.0212	–	–2.9350	–	–	–0.1561
${\bar{R}}^2$	–	0.5263	0.6779	–	0.6006	–	0.3299	–	–	0.4689
$AIC$	–	5.5296	5.3109	–	5.6195	–	4.8567	–	–	2.2429
$LM\left(1\right)$ ^a		0.7399[0.4024]	2.2839[0.1502]		0.0121[0.9137]		1.2680[0.2767]			0.2861[0.6001]
$LM\left(1\right)$	–	0.6086[0.5570]	1.6885[0.2181]	–	0.9857[0.3961]	–	0.6806[0.5213]	–	–	0.2908[0.7518]
$ARCH\left(1\right)$ ^b		1.4197[0.2481]	0.5938[0.4504]		0.3080[0.5854]		0.0887[0.7691]			0.6934[0.4154]
$ARCH\left(2\right)$	–	0.0200[0.9802]	0.3714[0.6952]	–	0.3641[0.7001]	–	0.0930[0.9116]	–	–	5.5537[0.0139]
$JB$ ^c	–	40.6846[0.0000]	9.0553[0.0108]	–	1.4502[0.4842]	–	7.7320[0.0209]	–	–	1.1518[0.5622]
Panel (B): GDP vs. FDI
	ASEAN-6					CLMV
	Brunei	Indonesia	Malaysia	Philippines	Singapore	Thailand	Cambodia	Laos	Myanmar	Vietnam
	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$	$d{Y}_t$
	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)	(19)	(20)
$d{Y}_{t-1}$	0.4422*	0.7189*	0.5394**	–	0.1445	0.1567	0.4159*	–	–	0.3962**
	(0.2377)	(0.3506)	(0.1880)	–	(0.2616)	(0.2315)	(0.2203)	–	–	(0.1795)
$d{K}_{t-1}$	–0.0393**	–0.0624	0.2207	–	0.0127	0.0274	–0.0129	–	–	0.0221
	(0.0158)	(0.1138)	(0.2420)	–	(0.1910)	(0.0905)	(0.0445)	–	–	(0.0825)
$Errorterms$
${\epsilon }_{t-1}^k\cdot I{\prime }^k$	–0.6826**	–1.7152***	–1.9534***	–	–1.3637**	–0.8301**	–1.2198***	–	–	–0.8303***
	(0.2879)	(0.4968)	(0.2974)	–	(0.5096)	(0.3148)	(0.3271)	–	–	(0.1978)
${\epsilon }_{t-1}^k\cdot I{\prime }^k$	–1.1768**	0.9391	–1.7227**	–	–1.2658**	–1.1442*	–0.5492	–	–	–3.7344
	(0.4968)	(0.9600)	(0.8059)	–	(0.4718)	(0.5457)	(0.4536)	–	–	(3.5351)
$Constant$	0.3195	–1.6367	–0.8141	–	–0.2990	0.4484	–0.2916	–	–	–0.1378
	(0.5009)	(1.1319)	(0.9608)	–	(1.1189)	(0.9314)	(0.7187)	–	–	(0.1573)
$Causality$
$Shortrun$	–2.4917**	–0.5480	0.9121	–	0.0666	0.3026	–0.2906	–	–	0.2683
$Longrun$	5.1083**	7.3233***	22.2693***	–	6.0734**	5.0916**	7.7815***	–	–	9.0603***
$Strong1$	5.0384**	6.3212***	21.5680***	–	4.1151**	3.4931*	6.9545***	–	–	8.8280***
$Strong2$	4.8490**	0.4786	3.4164*	–	3.8994**	2.3310	0.7355	–	–	0.6099
$\tau$	1.1819	–1.2776	–2.1574	–	–1.8305	–2.3883	–0.6744	–	–	0.7337
${\bar{R}}^2$	0.3139	0.3828	0.7145	–	0.4503	0.3333	0.3589	–	–	0.4092
$AIC$	4.5123	5.7944	5.1903	–	5.9389	5.5560	4.8125	–	–	2.3494
$LM\left(1\right)$ ^a	0.8003[0.3843]	0.0233[0.8806]	3.5031[0.0797]		4.3436[0.0535]	16.2078[0.0010]	1.1319[0.3032]			1.5012[0.2382]
$LM\left(2\right)$	0.6233[0.5495]	0.1787[0.8381]	2.1891[0.1465]	–	2.1208[0.1545]	7.7229[0.0049]	0.7852[0.4739]	–	–	1.0766[0.3657]
$ARCH\left(1\right)$ ^b	0.6324[0.4363]	0.3055[0.5869]	0.2074[0.6539]		0.7567[0.3952]	2.9188[0.1038]	0.2537[0.6203]			0.3383[0.5676]
$ARCH\left(2\right)$	0.7660[0.4803]	0.0998[0.9056]	0.3107[0.7370]	–	0.3690[0.6969]	5.5378[0.0141]	0.1131[0.8937]	–	–	6.8228[0.0067]
$JB$ ^c	1.2469[0.5361]	109.4851[0.0000]	12.6003[0.0018]	–	0.5090[0.7753]	4.5841[0.1011]	0.7835[0.6759]	–	–	1.7182[0.4236]

Notes: ^a Breusch-Godfrey serial correlation LM test; ^b ARCH heteroscedasticity test; ^c Jacque-Bera normality test; Standard errors are reported in parentheses, while $\rho$-values in brackets.

*** p<0.01, significant at 1%; ** p<0.05, significant at 5%; * p<0.1, significant at 10%.