Export Composition and Long-run Economic Growth Impact: A Cointegration Analysis for ASEAN ‘Latecomer’ Economies

Abstract

Export expansion can be a significant engine of economic growth for developing economies. The size of the growth effect of exports depends not only on volume but also on the sectoral composition of exports. By explicitly considering the sectoral export composition, this article examines over a 31-year period the long-run growth impact of exports in four transition and emerging economies of the Association of Southeast Asian Nations (ASEAN), namely Cambodia, Laos, Myanmar and Vietnam. More specifically, for each country, a long-run equilibrium analysis within a cointegration framework is conducted for three broad sectors (agriculture, mining/natural resources and manufacturing) as well as for 22 manufacturing industries during 1987–2017. The results indicate that there has been sizeable export expansion (especially in Vietnam), but export composition differs substantially across economies, which has an effect on the overall growth impact of the country’s exports. Clear evidence of export-led growth is found only for some sectors. Countries that rely heavily on primary goods have experienced relatively lower export-led growth. Export restructuring in manufacturing industries is associated with a larger long-run growth effect for a country’s exports. Our findings suggest that policies encouraging diversification away from traditional export sectors would be expected to lead to higher long-run growth effects of exports. Furthermore, our analysis implies a steady strengthening of export dynamism in ASEAN ‘latecomer’ economies and their rising significance in the global trade system.

JEL Classification: F14, F43, F63, O11, O47

Keywords

Exports Export composition Economic growth Cambodia Lao PDR Myanmar Vietnam Cointegration analysis

1. Introduction

It is generally accepted by economic theory, mainstream politics and various international institutions that trade liberalisation and increased integration into the world economic system are associated with beneficial economic outcomes (International Monetary Fund, The World Bank, & World Trade Organization, 2017; F. L. Rivera-Batiz & Rivera-Batiz, 2018; The World Bank, 2015; Van den Berg & Lewer, 2015). Particularly in the context of developing countries, it is argued that trade openness can lead to considerable export expansion which in turn promotes economic growth and development through various channels.

This has become known as the export-led growth hypothesis postulated by a long-established and influential line of theoretical literature on international trade and development economics (Ahumada & Sanguinetti, 1995; Baldwin & Robert-Nicoud, 2008; Coe & Helpman 1995; Edwards, 1989; Feder, 1983; Feenstra & Kee, 2008; Grossman & Helpman, 1991, 2018; Helpman & Krugman, 1985; Rivera-Batiz & Romer, 1991). There is rich empirical literature as well, which initially observed and linked differential economic growth outcomes to export expansion. Numerous empirical studies have documented and evidenced an export-led growth scenario (Ahmad, Umar Draz, & Yang, 2018; Balassa, 1978; Dreger & Herzer, 2013; Eicher & Kuenzel, 2016; Feenstra & Kee, 2008; Islam, 1998; McNab & Moore, 1998; Moschos, 1989; Ram, 1985; Reppas & Christopoulos, 2005).

In a nutshell, the various ways in which export expansion can contribute to economic growth and development are via: an expanded international market that allows domestic firms to take full advantage of their productive capacity; efficient resource allocation and exploitation of comparative advantage leading to productivity gains; specialisation and utilisation of scale economies; increased product mix and export diversification; increased efficiency and innovation as a response to international competitive pressures; export-financed imported technology that increases productivity and innovation capacity; and export activity-induced knowledge spillovers to exporting and non-exporting sectors of the domestic economy.

The member countries of the Association of Southeast Asian Nations (ASEAN) have a long history of export orientation and share a vision of strengthening trade liberalisation and economic cooperation. Since its foundation, ASEAN members see enhanced intra-regional and extra-regional trade integration as an essential and effective national development strategy. ASEAN is considered a prime example of successful regional economic integration contributing significantly to the development efforts of its members (ADBI, 2014). There is a considerable body of empirical work on the export-led growth experience for the five founding ASEAN economies (Ahmad et al., 2018; Bhatt, 2014; Fakher, 2012; Santosa, 2018; Sothan, 2016; Tan & Tang, 2016).

However, the literature has paid much less attention to the newest member countries which joined the ASEAN in the mid- and late 1990s (Cambodia, Laos, Myanmar and Vietnam), and there is scarce evidence on the subject (e.g., Bhatt, 2013). These can be regarded as ‘latecomer’ economies, as they joined the ASEAN at a much later point in time and, compared to the other ASEAN members, are at a lower level of economic development and free-market institutions. On the latter point, these economies have been going through a transition process from a centrally planned economy towards a more market-oriented one for many years. Some catching up has been achieved in this area over the years: more specifically; before the 1990s, the four ASEAN latecomers exhibited low levels of economic openness and free-market development. However, by the mid- and late 1980s, they started introducing reforms, liberalising their economies and opening up to the world economy. This resulted in increasing international trade relations and ultimately in their integration into the ASEAN. Since then, they have experienced increasing export activity.

Given the limited evidence of their experience, there is scope to fill the gap and produce detailed long-run country-specific information for this group of developing Southeast Asian economies. In addition, a study on their experience can provide policy-relevant insights on the critical issue of economic growth due to export expansion for other comparable developing and transition economies. A particularly pertinent aspect on economic policy design that has to be taken into account is the sectoral export composition. More recent literature on the export-led growth hypothesis has shown that the positive productivity effects, technology spillovers and overall economic growth impacts from greater export activity might depend considerably on the types of goods exported (Aslan & Topcu, 2018; Borgersen & King, 2014; Dunusinghe, 2009; Hausmann, Hwang, & Rodrick, 2007; Jarreau & Poncet, 2012; Shafiullah et al., 2017; Munir & Javed, 2018; Sachs & Warner, 1999; Sheridan, 2014). Without delving into the intricate details, relying on export growth in primary and naturals resource goods for various reasons can be associated with weaker economic growth compared to a shift to manufacturing goods. It has to be stressed, though, that this does not necessarily hold uniformly across all developing countries, as it also depends on certain underlying sectoral characteristics and country conditions that affect the economic growth contribution of exports in the different sectors.

The discussion suggests that from an economic policy perspective, not only export volume, but also the composition of exports, are crucially important for a country’s growth potential due to increased exporting activity. Thus, information on which sectors are associated with a higher export-led growth impact is relevant to policymakers (e.g., for policies related to industrial development, economic diversification and export specialisation and promotion). Therefore, our empirical analysis explicitly takes into account export composition in assessing the growth effect of export expansion. First, in addition to total merchandise exports, we examine the long-run economic growth effect of exports for three broad economic sectors: agricultural and related goods; quarrying, mining, raw materials and other natural resource goods; and manufacturing. Second, we apply a detailed analysis within the manufacturing sector, by examining long-run, export-led growth for 22 manufacturing industries.

The main objectives of this article are to examine for each of the four ASEAN latecomer economies (Cambodia, Laos, Myanmar and Vietnam) whether there exists a long-run equilibrium relationship between exports and economic growth and to estimate the long-run growth effect of exports in different sectors. This long-run analysis, which is applied for each of the three broad sectors as well as for each of the 22 manufacturing industries considered, allows us to derive country-specific and sector-specific information and to contrast the various findings. The sectoral long-run analysis of the effect of export expansion on real GDP per capita is conducted for a period spanning over three decades (1987–2017).

The remainder of the article is structured into the following sections. Section 2 discusses the data and variables and provides a descriptive analysis of the long-run trends and patterns during the sample period. Section 3 presents the research methodology. Section 4 presents and discusses the empirical results, and finally Section 5 provides concluding remarks.

2. Data, Variables and Long-run Sectoral Patterns

For each of the four ASEAN countries under study (Cambodia, Laos, Myanmar and Vietnam), we examine the following series over 1987–2017:

Real GDP per capita (DEV)

Agricultural, hunting, forestry and fishing exports (AX)

Quarrying, mining, raw materials and other natural resources goods exports (QX)

Manufacturing exports (MX)

Total merchandise exports (TX)

DEV is taken as a proxy for economic development and is defined as GDP per capita in US dollars and constant 2,000 prices, deflated using each country’s GDP deflator obtained by the World Development Indicators (WDI) database. The export data are expressed in US dollars and obtained from World Integrated Trade Solution (WITS) database as mirror data (imports of all countries in the world from the four ASEAN countries), due to the unavailability of sectoral export data for our countries over the long sample period. The data are classified according to the International Standard Industry Classification (ISIC), and we aggregate the data to three broad sectors (AX, QX and MX) as well as TX. The GDP deflator is used to convert the series to constant 2,000 prices. Finally, all series are transformed to natural logarithms.

As already mentioned, in addition to the sectoral analysis for the aforementioned three broad sectors, we also examine developments within the manufacturing sector. Thus, export data for 22 individual two-digit ISIC manufacturing industries, which are shown in Table 1, are considered as well.

Having classified exports according to these sectors, we first examine the relevant export developments during the 1987–2017 period, in order to discern patterns and trends with respect to export structure and dynamism of the four economies. In addition to relevant long-run trends, this examination provides an overall picture that can be useful for the findings of the long-run analysis of the economic growth impact of exports in these four countries.

The long-run dynamics in total merchandise exports are shown for all of the four economies in Figure 1 panel (a) and for three economies (excluding Vietnam) in panel (b). This is because Vietnam’s scale of exports far exceeds that of the other countries, making the graph less clear. This can be seen in the left side of the figure (panel [a]). It is evident that there is an upward trend in all the countries; however, the export expansion is particularly strong in Vietnam. Cambodia and Myanmar also show significant export growth (though Myanmar exhibits a drop in the last 2 years). Laos, the smallest country in the group, has the lowest export quantity (value) but still exhibits an important increasing trend, as it started off from a very low export value base initially when it was almost a closed economy. Generally, though all the countries went through export expansion, it is evident that they have different export scales as well as export growth dynamics. This justifies a long-run equilibrium analysis on a country-specific basis.

As mentioned, export composition and its evolution matter, and thus we look at this issue next. Here, there have been changes over time (Figure 2): countries in which exports initially were mostly concentrated in the agricultural sector witnessed a significant decline in the share of agricultural exports and an increase in other sector exports. Cambodia and Myanmar (and to some extent Laos) particularly had a large share in agricultural exports. In 2017, Cambodia’s exports are dominated by manufactures, while in Myanmar besides manufacturing, mining also represents an important export share. Laos exhibits a large manufacturing share, but this does not dominate its overall export composition. In Vietnam, the largest exporting country, even in the initial period (31 years ago), the share of MX was substantial (58%), which increased to a share of about 95 per cent. Thus, manufacturing clearly dominates its export composition (as is the case for Cambodia in recent years).

If we group the four countries’ exports together and examine the individual country shares in total ASEAN-4 exports, it is not surprising that Vietnam has the largest share in total exports, as it is the largest country and its export value is enormous compared to the other economies (Figure 3). However, at the initial time point, Vietnam accounted for about 56 per cent of total ASEAN-4 exports. This share has increased to 86 per cent in 2017, an indication of the impressive large-scale export expansion the country has experienced compared to the others in this time frame. The changes over time by sector are also informative. For instance, in 1987, Vietnam’s export share in agricultural exports amounted to less than a quarter, but after three decades it has increased to over 62 per cent. It is also apparent that Vietnam dominates the overall ASEAN-4 exports differently in the different sectors. Myanmar used to be the second-most important export country in this group, but Cambodia and Laos have caught up. Within the ASEAN-4 group, this has left Myanmar the most important exporter of mining and agricultural goods.

Table 1
Twenty-two International Standard Industry Classification (ISIC) Manufacturing Industries

Industry Code—Description

15—Manufacture of food products and beverages

16—Manufacture of tobacco products

17—Manufacture of textiles

18—Manufacture of wearing apparel; dressing and dyeing of fur

19—Tanning and dressing of leather; manufacturing of luggage, handbags, saddlery, harness and footwear

20—Manufacture of wood and of products of wood and cork (except furniture)

21—Manufacture of paper and paper products

22—Publishing, printing and reproduction of recorded media

23—Manufacture of coke, refined petroleum products and nuclear fuel

24—Manufacture of chemicals and chemical products

25—Manufacture of rubber and plastics products

26—Manufacture of other non-metallic mineral products

27—Manufacture of basic metals

28—Manufacture of fabricated metal products, except machinery and equipment

29—Manufacture of machinery and equipment n.e.c.

30—Manufacture of office, accounting and computing machinery

31—Manufacture of electrical machinery and apparatus n.e.c.

32—Manufacture of radio, television and communication equipment and apparatus

33—Manufacture of medical, precision and optical instruments, watches and clocks

34—Manufacture of motor vehicles, trailers and semi-trailers

35—Manufacture of other transport equipment

36—Manufacture of furniture; manufacturing n.e.c.

Industry Code—Description
15—Manufacture of food products and beverages
16—Manufacture of tobacco products
17—Manufacture of textiles
18—Manufacture of wearing apparel; dressing and dyeing of fur
19—Tanning and dressing of leather; manufacturing of luggage, handbags, saddlery, harness and footwear
20—Manufacture of wood and of products of wood and cork (except furniture)
21—Manufacture of paper and paper products
22—Publishing, printing and reproduction of recorded media
23—Manufacture of coke, refined petroleum products and nuclear fuel
24—Manufacture of chemicals and chemical products
25—Manufacture of rubber and plastics products
26—Manufacture of other non-metallic mineral products
27—Manufacture of basic metals
28—Manufacture of fabricated metal products, except machinery and equipment
29—Manufacture of machinery and equipment n.e.c.
30—Manufacture of office, accounting and computing machinery
31—Manufacture of electrical machinery and apparatus n.e.c.
32—Manufacture of radio, television and communication equipment and apparatus
33—Manufacture of medical, precision and optical instruments, watches and clocks
34—Manufacture of motor vehicles, trailers and semi-trailers
35—Manufacture of other transport equipment
36—Manufacture of furniture; manufacturing n.e.c.

Source: United Nations Statistics Division.

Note: The reported manufacturing industries are based on the International Standard Industrial Classification of the United Nations Statistics Division (https://unstats.un.org/unsd/classifications/unsdclassifications/).

Figure 1

Source: The author.

Figure 2

Source: The author.

Figure 3

Source: The author.

Figure 4

Source: The author.

Figure 5

Source: The author.

Table 2

Top Three Manufacturing Industries in Terms of Balassa Index in Each Country (2017)

Cambodia
Manufacture of wearing apparel; dressing and dyeing of fur
Manufacture of textiles
Manufacture of other transport equipment
Laos
Manufacture of tobacco products
Manufacture of basic metals
Manufacture of wood and products of wood and cork (except furniture)
Myanmar
Manufacture of basic metals
Manufacture of tobacco products
Manufacture of wood and products of wood and cork (except furniture)
Vietnam
Manufacture of other non-metallic mineral products
Manufacture of office, accounting and computing machinery
Manufacture of radio, television and communication equipment

Source: The author.

Although Figure 3 shows the country’s export shares, the large country sizes make this measure less indicative specifically for a country’s export specialisation within the group. For this reason, we calculate the Balassa indices of relative export specialisation, also known as revealed comparative advantage, based on the following formula:

B A L A S S A_{i j} = \frac{\frac{E X P_{i j}}{\sum_{i = 1}^{n} E X P_{i j}}}{\frac{\sum_{j = 1}^{m} E X P_{i j}}{\sum_{i = 1}^{n} \sum_{j = 1}^{m} E X P_{i j}}},

(1)

where EXP denotes exports and subscripts i, j, n and m denote sectors, countries, the total number of sectors and total number of countries in the country group (ASEAN-4), respectively. Hence, EXP_ij is exports of sector i in country j, ΣEXP_ij (summation across i = 1,…,3) is total exports in country j, ΣEXP_ij (summation across j = 1,…,4) is exports of sector i in the ASEAN-4 group (all countries together) and ΣΣEXP_ij (summation across i and j) is total exports in the ASEAN-4 group.

The Balassa index is a measure of relative specialisation, because it examines a country’s export share in a sector relative to the corresponding export share in that sector of the country group. The index ranges between 0 and infinity. Index values above 1 show export specialisation or revealed comparative advantage in a given sector, while values below 1 indicate low relative export specialisation and comparative disadvantage.

In Figures 4 (a)–(c), we show the Balassa indices during 1987–2017 in agriculture, mining and manufacturing, respectively. First, it is evident that there is a substantial drop in the relative export specialisation index in the agricultural sector in all countries. The least specialised country in agriculture within this country group is Vietnam, while the most specialised countries appear to be Myanmar and Laos. In the mining sector, the revealed comparative advantage dynamics generally do not move in the same direction. There is a clear and significant upward trend in Myanmar and Laos (which exhibit the greatest export specialisation in the ASEAN-4 group), while Vietnam experiences a substantial [strong] downward trend in the Balassa index, making it (the country which initially exhibited the highest index) the country with the least comparative advantage (along with Cambodia) in this sector.

It is evident that there is a general increasing trend in export specialisation in manufacturing. Vietnam and Cambodia exhibit the highest revealed comparative advantage, while Myanmar shows the lowest specialisation (and rather a comparative disadvantage in this sector as the index value is below 1). In summary, the analysis on the long-run dynamics and patterns reveals that, though there is an upward trend in all the countries, export composition across these economies differs to a significant extent. Some appear to specialise more in manufactures, while others more in primary products. Second, there have been significant changes over time. Most importantly, there is generally evidence of ‘industrialisation,’ as export quantities and export specialisation in manufacturing products have increased, while those in agricultural goods have fallen.

Finally, a detailed analysis of the manufacturing sector through an examination of relevant export trends across 22 manufacturing industries reveals deeper insights into each country’s export composition. Figure 5 shows the export shares of the manufacturing industry in 1988, 2002 and 2017 in each country. It is evident that generally exports are concentrated in a few industries such as clothing and wearing apparel (ISIC 18), leather products and footwear (19) and manufacture of textiles (17). However, there is evidence of export diversification, as the export shares in these industries drop over time, while that of some other industries increase. It has to be noted though that some ‘traditional’ sectors in most of the four ASEAN latecomer economies still maintain their high relative importance. The most notable exceptions are Vietnam which has witnessed a significant rise in the export shares in machinery and electronics-related industries, such as radio, television and communication equipment (32); office, accounting and computing machinery (30); miscellaneous electrical machinery and apparatus (31); and medical, precision and optical instruments, watches and clocks (33). Laos has also experienced a substantial reorientation towards export share growth in some industries such as the manufacture of basic metals (27); radio, television and communication equipment (32); and manufacture of chemicals and chemical products (24).

Table 2 shows the manufacturing industries with the highest (top three) Balassa index for each country in 2017. In general, the export specialisation patterns have slowly changed over the 31-year period (though this is not shown in the table as not all years are reported due to the large number of data points needed). However, some of the ‘traditional’ labour-intensive and resource-intensive industries still exhibit very high Balassa indices. Countries have, though, become more specialised in other manufacturing industries, notably Vietnam and Laos, but to some extent Myanmar too. In Vietnam, for instance, industries such as office, accounting and computing machinery as well as radio, television and communication equipment are among the top three manufacturing industries in terms of the country’s export specialisation, as evidenced by the Balassa index.

3. Research Methodology

In order to assess the long-run effect of exports (in each sector) on economic development, we have first to establish that there exists a long-run equilibrium relationship between the two variables over the 31-year period. Since, in each analysis, there are two series (for examination across the three broad sectors and overall exports: DEV and either AX, QX, MX or TX; for the examination within the manufacturing sector: DEV and either of the 22 ISIC industries, that is, ISIC15, ISIC16,…, ISIC36), there is only one cointegrating vector, and thus we employ a single-equation, long-run (cointegration) analysis. In such an analysis, the aim is to establish and estimate the stable (error/equilibrium correcting) relationship between two series over time, and thus methodologically it is a time-series analysis for a single cross-section (e.g., country, firm, and equity stock). When multiple cross-sections are considered in one long-run analysis (panel time-series analysis), the estimation is still methodologically time series, where there is a time-series estimation for each cross-section, and then the average is taken as the estimate of the panel (e.g., the pooled mean group estimator). In our case, we have four countries and conduct four analyses to derive country-specific information (the panel estimate for the long-run analysis can be derived by averaging the four country-specific estimates).

The long-run equilibrium (cointegration) modelling framework that is to be adopted depends on the stationarity properties of the series. Therefore, we perform the augmented Dickey–Fuller (ADF) unit root tests for all the series:

Δ s_{t} = α + β s_{t - 1} + δ t + ζ_{1} Δ s_{t - 1} + ζ_{2} Δ s_{t - 2} + \dots + ζ_{k} Δ s_{t - k} + ε_{t} ​

(2)

where s_t represents any series at time t, β is the regression coefficient of s_t₋₁, which is the main focus of the test, and ε_t is the residual of the test regression equation. For the ADF, the test equation can include a time trend term δt as well as lagged values of Δs_t, depending on the appropriate serial correlation structure and dynamic specification that we determine through the Bayesian information criterion (BIC). The selected specification of Equation (2) is the one with the minimum value of BIC. The null hypothesis of the test is the series that contains a unit root (i.e., is non-stationary), which in terms of Equation (2) is evidenced by β = 0. Stationarity is confirmed if the null hypothesis is rejected.

As we will show later, both series are found to be I(1), integrated of order 1 (i.e., non-stationary in levels and stationary in first differences). With I(1) variables, a long-run relationship can be concluded, if there is a linear combination of those variables that is stationary. In order to obtain more robust evidence, we perform several cointegration testing procedures, such as the Engle–Granger’s (EG) residual test (Engle & Granger, 1987), Phillips–Ouliaris (PO) alternative residual test (Phillips & Ouliaris, 1990), Hansen instability test (Hansen, 1992) and the Pesaran et al. (2001) bounds test. In a nutshell, the EG and PO procedures are residual-based tests, where the residual of the assumed cointegrating (long-run equilibrium) equation is tested for stationarity by means of an ADF unit root test that does not include a trend term. Evidence in favour of cointegration is obtained, if the null hypothesis of a unit root of the residual (non-stationarity/no cointegration) is rejected.

In Hansen’s instability test (which is based on a Lagrange multiplier test), the estimated long-run parameter (coefficient) of the assumed cointegrating equation is tested for time variation and, thus, instability. If there is a stable long-run relationship, the estimated long-run coefficient should be stable during the sample period and not exhibit variations. The null hypothesis is cointegration.

The bounds procedure essentially tests the joint significance (through an F-test) of all long-run parameters, including the error correction term. It also conducts a single significance t-test for the error correction term. These two tests are based on an estimated autoregressive distributed lag (ARDL) model with error correction that is appropriately dynamically specified. Evidence in favour of cointegration is found, if both tests reject the null hypothesis (coefficients equal zero).

One of the benefits of the Pesaran et al. (2001) bounds test is that the estimated long-run equilibrium relationship is obtained through a dynamic error correction model, which is a re-parameterised ARDL that provides more precise coefficient estimates of short-run, long-run and speed of adjustment (error/equilibrium correction). Therefore, though we perform all the different cointegration tests, our empirical analysis draws conclusions on the long-run effects of exports on real GDP per capita by means of the ARDL model. The long-run equilibrium relationship between exports and economic development is, thus, based on the estimation of the following model:

\begin{matrix} \begin{matrix} Δ Y_{t} = c_{0} + c_{1} t - α (Y_{t - 1} - β E X P_{t - 1}) + {\sum^{​}}_{p - 1}^{i = 1} ψ_{i} Δ Y_{t - i} + η Δ E X P_{t} \\ + {\sum^{​}}_{q - 1}^{i = 1} ξ_{i} Δ E X P_{t - i} + u_{t} \end{matrix} \\ t = 1987, 1988, ...., 2017 \end{matrix}

(3)

with the speed of adjustment (or error correction) represented by α and the long-run coefficient of exports given by $\frac{β}{α}$ .

Depending on the optimal specification selection (based on BIC), the ARDL error correction model might contain a trend term t, and several lags of the dependent variable (ΔY_t−i) and the first differenced explanatory variable (ΔEXP_t−i), which capture the short-run effects and overall dynamics that control for serial correlation and allow for more efficient long-run estimates. Finally, since we aim at assessing the experience and providing evidence for each sector in each country (and total merchandise exports), we conduct cointegration tests and estimate long-run equilibrium relationships, as outlined earlier in this article, for each of the four countries and sectors separately, and contrast the findings.

4. Empirical Results

Table 3 reports the ADF unit root tests. It is confirmed that all variables are non-stationary in levels and stationary in first differences. Thus, all of our series are integrated of order one, I(1), and therefore the long-run analysis is conducted within a cointegration framework. The next step, hence, is to test whether real GDP per capita and exports (of each sector) are cointegrated. Table 4 shows the results of the EG and PO residual-based cointegration tests. The cointegration findings of the Hansen parameter instability and the bounds cointegration test are reported in Tables 5 and 6, respectively. From all the tests, we derive the same overall conclusion. More specifically, in all countries evidence is found of cointegration between total exports (TX) and economic development (DEV).

However, the sectoral cointegration analysis reveals that in most cases, only exports in specific sectors are cointegrated with DEV. First, in Cambodia, evidence of cointegration is found only for MX at the 1 per cent significance level. This implies that the cointegration found for total merchandise exports is mostly due to MX. Laos represents the exception with regard to the cointegration findings, as we find strong evidence of cointegration between real GDP per capita and exports in all three sectors (AX, QX and MX). In Myanmar, DEV is found to be cointegrated with MX at the 1 per cent level and with quarrying, mining, raw materials and other natural resources goods exports (QX) but only at the 5 per cent level of significance according to the EG, PO and bounds cointegration tests.

Table 3
Stationarity/Unit Root Tests

Cambodia
Laos
Myanmar
Vietnam

Level 1st diff. Level 1st diff. Level 1st diff. Level 1st diff.

DEV −1.583 −4.882^a −1.752 −5.331^a −0.235 −3.987^a −2.092 −4.065^a

AX −0.729 −3.079^b −0.772 −3.916^a 0.325 −2.937^b −1.244 −6.530^a

QX −0.883 −4.595^a −0.921 −5.369^a −1.421 −3.990^a −1.557 −3.121^b

MX −2.382 −6.773^a −0.626 −5.583^a −1.034 −3.554^a −0.412 −5.203^a

TX −1.936 −4.046^a −0.333 −3.692^a 0.712 −3.983^a −1.616 −9.176^a

	Cambodia	Laos	Myanmar	Vietnam
DEV	−1.583	−4.882^a	−1.752	−5.331^a	−0.235	−3.987^a	−2.092	−4.065^a
AX	−0.729	−3.079^b	−0.772	−3.916^a	0.325	−2.937^b	−1.244	−6.530^a
QX	−0.883	−4.595^a	−0.921	−5.369^a	−1.421	−3.990^a	−1.557	−3.121^b
MX	−2.382	−6.773^a	−0.626	−5.583^a	−1.034	−3.554^a	−0.412	−5.203^a
TX	−1.936	−4.046^a	−0.333	−3.692^a	0.712	−3.983^a	−1.616	−9.176^a

Source: The author.

Notes: Specification of ADF test regression equation is based on BIC. Superscripts a and b denote significance at 1% and 5% levels, respectively. Null hypothesis is the series that contains a unit root.

Table 4

EG and PO Cointegration Tests Between DEV and Exports

	Engle–Granger				Phillips–Ouliaris
	AX	QX	MX	TX	AX	QX	MX	TX
Cambodia	−2.707	−2.966	−4.575^a	−4.662^a	−2.600	−2.833	−4.365^a	−4.408^a
Laos	−5.387^a	−5.634^a	−6.070^a	−5.653^a	−5.291^a	−5.501^a	−5.819^a	−5.429^a
Myanmar	−2.788	−3.980^b	−4.883^a	−5.024^a	−2.121	−3.614^b	−4.487^a	−4.637^a
Vietnam	−4.790^a	−2.393	−5.136^a	−5.543^a	−4.595^a	−2.096	−4.852^a	−5.205^a

Source: The author.

Notes: Superscripts a and b denote significance at 1% and 5% levels, respectively. Null hypothesis is no cointegration.

Table 5

Hansen Cointegration Test

	AX		QX		MX		TX
	L_c	Prob.	L_c	Prob.	L_c	Prob.	L_c	Prob.
Cambodia	0.465	0.050	0.417	0.069	0.188	> 0.2	0.202	> 0.2
Laos	0.215	> 0.2	0.305	> 0.2	0.121	> 0.2	0.124	> 0.2
Myanmar	0.578	0.026	0.468	0.117	0.349	> 0.2	0.132	> 0.2
Vietnam	0.387	0.184	0.585	0.025	0.193	> 0.2	0.351	> 0.2

Source: The author.

Note: Null hypothesis is cointegration. Prob. refers to p-values.

Table 6

Bounds Cointegration Test

	AX		QX		MX		TX
	F	t	F	t	F	t	F	t
Cambodia	2.452	−1.052	2.422	−1.354	8.572^b	−3.888^b	9.439^b	−4.087^b
Laos	13.75^a	−4.209^a	14.671^a	−4.483^a	16.634^a	−4.942^a	15.458^a	−4.762^a
Myanmar	2.597	−0.806	9.489^b	−4.065^b	11.143^a	−4.381^a	11.727^a	−4.947^a
Vietnam	15.301^a	−4.918^a	2.109	−2.312	16.73^a	−5.023^a	17.69^a	−5.135^a

Source: The author.

Notes: Superscripts a and b denote significance at 1% and 5% levels, respectively, for I(1) variables. Null hypothesis is no cointegration.

In Vietnam, all the cointegration tests (especially the Bounds test) strongly confirm the existence of a long-run equilibrium relationship between DEV and exports in agricultural, hunting, forestry and fishing (AX) industries as well as with exports of manufacturing products. These cointegration results conform to some of the long-run developments in the export patterns analysed earlier. For instance, although in Vietnam agricultural exports have fallen dramatically in the country’s total goods exports, they do show a significant increasing upward trend (the absolute value of real exports, at 2,000 constant prices, is growing over time). This is also evident as Vietnam has increased its ASEAN-4 share in agricultural exports. Thus, it is not unreasonable to find that AX and DEV are bound together by a stable relationship over time (though this does not necessarily need to be the case). On the contrary, in quarrying and mining-related exports, the country has witnessed a sharp decline, not only in terms of share but also in absolute value. Consequently, DEV and QX not only do not exhibit the same upward trend (in growth over time) but in fact show diverging trends at certain periods. This is an indication that the two series are not likely to be cointegrated.

Having established the presence of cointegration in specific sectors, we next estimate the long-run equilibrium relationship between exports and DEV. As, in addition to total exports, we conduct a long-run analysis for each sector (that was found to be cointegrated), we obtain estimates of the sectoral long-run effect of exports on real GDP per capita growth for each country. Table 7 reports the results of the long-run analysis which are obtained by estimating a dynamic error correction model, based on the ARDL of Equation (3). Since the variables (dependent, DEV and the independent, EXP) are in logs, the long-run regression coefficient is interpreted as an elasticity.

In general, it is apparent that all countries, in which exports in a sector were found to be cointegrated with DEV, show a statistically significant positive long-run effect on economic development. In Cambodia, the long-run elasticity of total exports on DEV indicates that a 1 per cent increase in exports leads to a 0.37 per cent increase in real GDP per capita. However, we have already established that this long-run relationship is mostly due to MX, which exhibit an elasticity of about 0.406. For this country, only MX show a stable equilibrium relationship and contribution to long-run real GDP per capita growth. On the other hand, in Laos exports in all three sectors contribute to long-run economic development. The MX in particular show the strongest effect with the long-run coefficient suggesting that a 1 per cent increase in MX results in an almost 0.9 per cent increase in the development variable. The long-run elasticity of agricultural exports is much lower with an elasticity estimate of about 0.2, while quarrying and mining exports show a very small effect with an estimate of about 0.1. In Myanmar, MX exhibit the highest long-run elasticity, while that of QX is also statistically significant and sizeable. Thus, Myanmar’s quarrying and mining exports seem to have a rather substantial effect, as a 1 per cent increase in exports in this sector is associated with an increase in the country’s real GDP per capita by about 0.44 per cent.

In Vietnam, MX have a strong long-run impact on development, but interestingly the effect of agricultural exports is even larger. This suggests that, besides the manufacturing sector, exports relating to agriculture, hunting, forestry and fishing have a substantial positive long-run contribution to economic development. The long-run elasticities indicate that real GDP per capita would increase by 0.71 per cent and 0.57 per cent from a 1 per cent increase in AX and MX, respectively.

The detailed long-run analysis for the 22 manufacturing industries reveals, first, that not all industries’ exports are cointegrated with real GDP per capita and second, that the long-run effect of exports (elasticity) varies substantially across manufacturing sectors and countries. For ease of presentation, Table 8 reports the top five manufacturing industries in each country in terms of the estimated long-run coefficient. In most cases, the elasticity estimate of an individual manufacturing sector is around the elasticity estimate of the manufacturing sector as a whole. However, there are some instances (most importantly in Vietnam and Laos) where some industries exhibit higher long-run elasticity estimates. In general, it is evident that the long-run growth effect of exports is particularly high in all countries in industries such as wearing apparel, clothing and textiles, which constitute the traditional manufacturing activities and important export sectors for those economies. However, there are some other industries that do not belong in this category with high long-run elasticities, such as food products and beverages (in Myanmar, Laos and Cambodia). Interestingly, exports in machinery and ICT-related manufacturing industries also show a high long-run economic growth effect. This is particularly true for Vietnam and Laos and to some extent Cambodia.

Table 7

Long-run Equilibrium Relationship Between DEV and Exports

	AX		QX		MX		TX
	Coefficient	Prob.	Coefficient	Prob.	Coefficient	Prob.	Coefficient	Prob.
Cambodia
LR effect					0.4063	0.000	0.3721	0.000
ECT					−0.3452	0.000	−0.3615	0.000
BG test					2.272	0.685	4.114	0.391
Laos
LR effect	0.2005	0.000	0.1036	0.013	0.8947	0.000	0.6607	0.000
ECT	−0.2269	0.000	−0.2075	0.000	−0.2742	0.000	−0.2461	0.000
BG test	3.621	0.459	2.335	0.674	2.303	0.682	1.865	0.760
Myanmar
LR effect			0.4469	0.000	0.5394	0.000	0.5185	0.000
ECT			−0.1917	0.000	−0.2072	0.000	−0.3267	0.000
BG test			2.567	0.632	3.675	0.452	4.012	0.404
Vietnam
LR effect	0.7137	0.000			0.5709	0.000	0.6243	0.000
ECT	−0.2868	0.000			−0.3185	0.000	−0.3754	0.000
BG test	3.533	0.473			3.269	0.514	3.527	0.474

Source: The author.

Notes: LR effect refers to the long-run regression coefficient (elasticity). ECT is the error correction term, indicating the speed of adjustment to equilibrium. BG test refers to the Breusch–Godfrey test of autocorrelation with null hypothesis of no serial correlation.

Table 8

Manufacturing Industries with Highest Long-run Elasticity

Cambodia
Manufacture of wearing apparel; dressing and dyeing of fur
Manufacture of textiles
Tanning and dressing of leather; manufacturing of luggage, handbags, saddlery, harness and footwear
Manufacture of food products and beverages
Manufacture of other transport equipment
Laos
Manufacture of wearing apparel; dressing and dyeing of fur
Manufacture of food products and beverages
Manufacture of basic metals
Manufacture of radio, television and communication equipment
Manufacture of furniture; manufacturing not classified elsewhere
Myanmar
Manufacture of food products and beverages
Manufacture of wearing apparel; dressing and dyeing of fur
Tanning and dressing of leather; manuf. of luggage, handbags, saddlery, harness and footwear
Manufacture of textiles
Manufacture of furniture; manufacturing not classified elsewhere
Vietnam
Tanning and dressing of leather; manuf. of luggage, handbags, saddlery, harness and footwear
Manufacture of wearing apparel; dressing and dyeing of fur
Manufacture of radio, television and communication equipment
Manufacture of furniture; manufacturing not classified elsewhere
Manufacture of electrical machinery and apparatus not classified elsewhere

Source: The author.

Overall, exports do significantly contribute to long-run economic development in all four ASEAN latecomer economies. However, as the results show, there are some important differences with the regard to the long-run effects. More specifically, total goods exports as well as MX have the smallest long-run effect in Cambodia. In Laos and Vietnam, the long-run impact of total exports is the highest among the four countries. Myanmar exhibits a rather sizeable long-run elasticity, though it is lower than in Laos and Vietnam. Hence, in summary, the findings of the long-run equilibrium analysis suggest that Laos and Vietnam have particularly gained from export expansion compared to the other two countries. Finally, the error correction models in all countries perform well. The error correction terms are significant and negative as they should be. There is no problem of autocorrelation, which would render the long-run coefficients (and other relevant statistics) less precise and reliable. This is evidenced by the Breusch–Godfrey serial correlation test that is conducted for first-, second-, third- and fourth-order autocorrelation.

5. Conclusions

For developing economies, export expansion can play an important role and can constitute a significant engine of economic growth and development. However, in addition to the volume of exports, the sectoral composition of exports determines to a large extent the magnitude of the positive long-run effect of export expansion on economic growth. This suggests that with respect to economic growth outcomes, it matters what a country exports. Thus, obtaining information on how exports (and which sectors) affect growth and development is highly relevant for economic policy. For instance, certain sectors might be associated with higher economic growth outcomes, and thus specific policy measures could be taken to induce an expansion of exports in those sectors.

This article has examined the issue of long-run export-led growth, by explicitly taking into account the relevant aspect of sectoral export composition. More specifically, based on a long-run equilibrium relationship framework, it has analysed the effect of exports on economic growth for four ASEAN latecomer economies during the 1987–2017 period. Within this conceptual framework, a cointegration analysis has been conducted in order to verify the presence of a stable long-run equilibrium relationship between exports and economic development and to assess the long-run growth effect of exports. The empirical analysis has been applied for each country in order to obtain country-specific information and provide a comparative view.

The results indicate that all four countries have experienced significant export expansion during the 31-year period. However, export growth has been particularly strong in Vietnam, which represents by far the most important exporting economy and dominates total ASEAN-4 exports. Export composition and its evolution over time have been found to differ significantly among the four economies. Certain countries (e.g., Myanmar) exhibit a higher export specialisation in primary goods, while others (most notably Vietnam) show a much higher export specialisation and revealed comparative advantage in manufacturing goods. There was, though, a general trend towards ‘industrialisation’ (as evidenced by increased MX) in all countries during 1987–2017.

The cointegration analysis has confirmed that total goods exports and real GDP per capita are cointegrated in all countries. For individual sectors, though, it has been found that countries differ. Our cointegration analysis on individual sectors revealed that a long-run equilibrium relationship between development and exports is present in some sectors. Only in Laos the exports in every sector are cointegrated with the economic development variable. However, the one ‘constant’ finding is that MX in all countries exhibit an equilibrium relationship with real GDP per capita.

Although generally exports have been found to contribute to long-run development in all countries, the analysis indicates that there are important differences in the long-run effect of exports. In Laos (especially in manufacturing) and in Vietnam (especially in agriculture, followed by manufactures), the long-run growth impact of exports is particularly strong, while Cambodia exhibits the smallest long-run effect of (overall and manufacturing) exports.

Overall, our empirical analysis has revealed that the long-run trends and patterns in export expansion and composition as well as the economic growth contribution of exports are not uniform across the four ‘latecomer’ economies. Over the period 1987–2017, countries which started off as relatively more industrialised and experienced further specialisation in manufacturing (such as Vietnam and to some extent Laos) have benefited to a higher extent from export expansion compared to countries where primary goods represented (and to some degree still represent) a large export share. This implies that a shift towards manufacturing and export diversification/restructuring seems to have been a relevant factor for the long-run economic growth effect of export expansion. However, this does not mean that primary goods do not have a positive growth contribution. In certain countries, there is a strong long-run growth impact of primary goods. For instance, in Vietnam, this is the case with agricultural goods (within which fishing is important), though the country does not specialise in the agricultural sector as it represents a tiny share in total exports. This suggests that a country’s economic (and export) structure is likely to be an important issue. Countries in which export share and the degree of the economy’s reliance of primary goods are very high have experienced relatively lower export-led growth.

Finally, the findings of our long-run analysis during 1987–2017 for the four emerging ASEAN economies have broader economic implications with respect to the dynamics of trade patterns in the Southeast Asian region as well as in the global economy. In particular, the substantial export expansion that has been achieved (especially in Vietnam) has led to increased export shares of the Southeast Asian economies considered in our analysis and created a shifting of export dynamism towards those economies. Furthermore, this export expansion is not limited to primary products but includes manufacturing products. Although traditional labour-intensive activities such as clothing and textiles represent an important share, there are many other non-traditional manufacturing industries on the rise. In fact, our analysis suggests a scenario of industrialisation across the four latecomer economies. In some of those countries, this industrialisation process also extends to relatively more sophisticated manufacturing industries, such as machinery, transport equipment, electronics and ICT-related industries. This implies an increasing trend of changing world export patterns in those industries in favour of the emerging ASEAN economies and possibly at the expense of more developed and higher labour cost countries in the Southeast Asian region and the world in general.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

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