00074918.2013.850632
Bulletin of Indonesian Economic Studies
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The role of foreign direct investment in Indonesia's
manufacturing exports
Rudy Rahmaddi & Masaru Ichihashi
To cite this article: Rudy Rahmaddi & Masaru Ichihashi (2013) The role of foreign direct
investment in Indonesia's manufacturing exports, Bulletin of Indonesian Economic Studies,
49:3, 329-354, DOI: 10.1080/00074918.2013.850632
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Date: 17 January 2016, At: 23:48
Bulletin of Indonesian Economic Studies, Vol. 49, No. 3, 2013: 329–54
THE ROLE OF FOREIGN DIRECT INVESTMENT IN
INDONESIA’S MANUFACTURING EXPORTS
Rudy Rahmaddi and Masaru Ichihashi*
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Hiroshima University, Japan
This article examines whether foreign direct investment (FDI) has contributed to
the changing structure of Indonesia’s manufacturing exports. It uses industry-level
data from 1990 to 2008, classiied by factor intensity. Our analysis reveals that FDI
promotes exports in most panel observations, especially exports from physicalcapital-intensive (PCI), human-capital-intensive (HCI) and technology-intensive
(TI) industries. Yet by applying a differentiated cross-section-effect model, we determine that the export-generating potential of FDI is stronger in PCI, HCI and TI
industries than in natural-resource-intensive or unskilled-labour-intensive industries, in which Indonesia has a comparative advantage. We also assess the inluence
of other determinants of export performance – namely, private domestic capital
investment, GDP growth and exchange rates. Our indings have implications for
policymakers seeking to sustain Indonesia’s export performance.
Keywords: export performance, foreign direct investment, panel analysis
INTRODUCTION
Following the collapse of global oil prices in the mid-1980s, Indonesia liberalised its trade policies in a bid to increase exports. This strategy replaced that of
import-substituting industrialisation, which could no longer be counted on to
promote high growth into the 1990s. Indonesia realised that it needed a new
source of foreign-exchange earnings and economic growth; its policy pendulum
swung towards export-oriented industrialisation, led by the private sector and
based on inputs other than natural resources. Indonesia’s economy subsequently
saw large increases in foreign direct investment (FDI)1 and continuous growth in
* We wish to thank the two anonymous referees of this article, for their invaluable comments on an earlier draft. We are also indebted to R.S. Hanung Harimba Rachman for
providing (and explaining) data on permanent business licences in Indonesia. Rahmaddi
would also like to thank the Ministry of Finance of the Republic of Indonesia for providing
full inancial support under phase three of the Professional Human Resource Development
Project. The usual disclaimers apply.
1 FDI may take many forms, including greenield investment, horizontal and vertical
mergers and acquisitions, and portfolio investment via the capital market (aimed at exercising control). The data referred to in this article do not cover the last of these. In addition,
the effect of FDI outlows on exports is beyond the scope of this analysis, which uses the
terms FDI and foreign investment interchangeably to refer to FDI inlows.
ISSN 0007-4918 print/ISSN 1472-7234 online/13/030329-26
http://dx.doi.org/10.1080/00074918.2013.850632
© 2013 Indonesia Project ANU
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330
Rudy Rahmaddi and Masaru Ichihashi
manufacturing exports. A closer look at the latter indicates that although the commodities of Indonesia’s natural-resource-intensive (NRI) and unskilled-labourintensive (ULI) industries – such as cork and wood, textiles and garments, and
leather and footwear – occupied most of the total value (in real dollars) of manufacturing exports during 1990–2008, their average annual growth rate of 2.4%
was lower than that of the commodities of Indonesia’s physical-capital-intensive
(PCI), human-capital-intensive (HCI) and technology-intensive (TI) industries
(8.2%), owing to increased export growth in road vehicles and other transport
equipment (including components) and in electronics goods. Meanwhile, foreign
investment in the manufacturing sector dominated total realised FDI in Indonesia: more than 75%, or $108.9 billion, of all foreign investments lowed into the
PCI, HCI and TI industries.2 This suggests a plausible linkage between FDI and
industry-based export performance, and implies that the structure of Indonesia’s
manufacturing exports has changed. Research into the effect of FDI on industrybased manufacturing exports therefore deserves attention.
Given the importance of differential analysis in assessing the scale and performance of FDI lows in exports across industries, past studies that scrutinise the
relationship between FDI and trade at the aggregated level may not be suficient.
Although useful, such studies may fail to capture variations in the interactions of
FDI and exports at the disaggregated level (Kawai and Urata 1998). In contrast,
cross-industry variation analyses of FDI’s export-generating potential may inluence those designing development strategies or promoting the beneits of FDI
to speciic industries, especially in countries that rely on foreign direct and indirect investment to transform their industrial sectors. Such analyses could even be
expanded on, to seek out policy implications that dovetail into sustaining export
performance through export-led growth 2.0 (Haddad and Shepherd 2011).3 Yet
few empirical studies of Indonesia have examined the linkage between FDI and
manufacturing exports at the disaggregated level.4
In this article, we attempt to close the gap in the empirical research by analysing the effect of FDI on Indonesia’s manufacturing exports by different industries
during 1990–2008. To do so, we use realised FDI data, which have an advantage
over approved FDI data in measuring the degree to which FDI affects export performance, because they better capture actual inlows of foreign investment into
the domestic economy (after this investment has been implemented in projects).
We also ask whether the growth in Indonesia’s manufacturing exports can be
attributed to FDI, and whether FDI has different export-generating effects in different manufacturing industries (classiied by factor intensity). We then examine
2 According to Indonesia’s Investment Coordinating Board (Badan Koordinasi Penanaman
Modal BKPM), which excludes foreign investment in oil and gas and the inancial sector.
3 Export-led growth 2.0 refers to the next generation of export- or outward-oriented policies that aim to sustain export performance. These include, for example, managing the
external and internal risks that come with outward-oriented policies; diversifying exports;
liberalising South–South trade; encouraging FDI in export-oriented industries, to promote technology transfer and spillover; and promoting international integration. See also
Haddad and Shepherd (2011).
4 Ramstetter (1999), Van Dijk (2002), Narjoko (2009), and Narjoko and Maidir (2009) are
exceptions.
The role of foreign direct investment in Indonesia’s manufacturing exports
331
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other macroeconomic determinants of manufacturing exports – namely, private
domestic capital investment, GDP growth and the exchange rate. We focus here
on the manufacturing sector, which has the dominant share of the value of Indonesia’s merchandise exports and accounts for more than 90% of Indonesia’s FDI.
The rest of this article is organised as follows. Following a brief overview of
FDI and trends in the performance and structure of Indonesia’s manufacturing
exports, we review the related literature on the linkage between FDI and exports.
We then describe our methodology and choice of data, before elucidating on our
empirical results and providing some possible policy implications.
INDONESIA: FOREIGN DIRECT INVESTMENT AND
MANUFACTURING EXPORTS
Indonesia’s strategy of promoting exports has seen FDI increase rapidly, owing
to a bold and decisive series of economic reforms that started in the mid-1980s.
These reforms covered exchange-rate management, including two large nominal
depreciations, in 1983 and 1986; prudent iscal policy; comprehensive tax reform;
a more open posture towards foreign investment; and inancial deregulation (Hill
1996; Ishida 2003). Below, we discuss liberalisation packages relevant to investment and trade.
To attract more foreign investment, in 1985–86 the Indonesian government
relaxed foreign proprietary restrictions and divestment requirements for exportoriented investment and for irms in bonded zones. Later, Regulation 17/1992 on
the Requirements for Share Ownership in Foreign Capital Investment Companies, followed by further programs that facilitated investment, allowed for 100%
foreign proprietorships and less stringent divestment requirements for investments in certain regions, bonded zones and sectors, with descending investment
thresholds. The government also used iscal measures to attract foreign capital,
introducing tax incentives and duty exemptions as well as providing legal protection for foreign investments. These timely pull factors coincided with a wave of
manufacturers relocating to East Asian economies in search of lower production
costs to mitigate push factors such as appreciating currencies, the abolition of
foreign-exchange controls, and rising labour costs at home (Aziz 1998; Pangestu
2002; Thee 2005).
To promote manufacturing exports, the government sought to liberalise trade
by relaxing restrictions on foreign investment in export-oriented industries, tackling ineficient levels of bureaucracy (including reforming Indonesia’s customs
system), minimising trade protections such as non-tariff barriers, and reducing
import tariffs. The average (unweighted) tariff rate was cut from 27% in 1986 to
15% by 1995, and the proportion of tariff lines subject to these barriers fell from
32% to 12% (Snodgrass 2011). Indonesia’s ongoing liberalisation of import tariffs
complemented the ASEAN Common Effective Preferential Tariff (CEPT) scheme,
which generally reduced existing tariff rates to 20% or below within ive to eight
years, starting on 1 January 1993. Some of these covered more than 37,600 tariff lines in the manufacturing sector (tables 1a and 1b). Exporters were also provided with a drawback system for import duties, under which tariffs imposed on
imported raw materials and parts were refunded when companies exported the
inished products.
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Rudy Rahmaddi and Masaru Ichihashi
TABLE 1a Indonesia’s Manufacturing Tariffs (NRI–ULI Industry Group) under
ASEAN’s Common Effective Preferential Tariff Scheme, 1996–2008
(%)
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1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Footwear
Other mfg
Textiles
Wood
Leathers
NMM
26.0
22.0
15.0
13.0
9.2
3.2
23.2
19.6
12.5
13.0
7.6
3.2
18.7 18.3 13.8 13.6
15.8 15.2 11.2 11.0
10.5 8.7 4.9 4.9
11.1 9.3 7.5 6.5
6.1 4.7 3.0 3.0
3.1 3.0 2.9 2.9
Indonesia
ASEAN
14.7 13.2 10.9
10.2 9.3 7.9
9.9
7.0
7.2
5.3
7.0
5.1
9.3
8.0
4.9
5.4
2.9
2.9
5.0
4.7
4.9
4.3
2.8
2.9
5.0
2.8
1.9
0.2
3.1
3.5
3.3
2.4
1.5
0.2
2.8
3.1
3.3
2.4
1.5
0.2
2.8
3.1
2.9
2.3
1.4
0.4
3.0
3.0
2.3
1.9
0.0
0.4
3.0
1.9
5.6
4.2
4.1
3.4
2.8
3.7
2.2
2.9
2.2
2.3
2.2
1.8
1.6
1.3
Source: ASEAN Secretariat.
Note: NRI = natural-resource intensive. ULI = unskilled-labour intensive. Mfg = manufacturing. NMM
= non-metallic minerals. See table 3 for full commodity titles. Figures for Indonesia and ASEAN are
averages.
TABLE 1b Indonesia’s Manufacturing Tariffs (PCI–HCI–TI Industry Group) under
ASEAN’s Common Effective Preferential Tariff Scheme, 1996–2008
(%)
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Plastics
Paper
Metals
Optics
Machinery
Vehicles
Chemicals
Indonesia
ASEAN
11.5 9.5
11.2 11.2
9.9 9.8
9.7 9.1
8.7 8.2
5.8 5.7
5.7 5.6
8.9
6.7
8.4
6.3
7.6
7.8
8.2
7.6
6.6
4.7
5.0
6.6
7.6
7.6
7.0
6.3
4.4
4.7
5.1
4.7
6.3
5.7
5.0
3.6
4.1
5.1
4.7
6.1
5.7
5.0
3.4
4.1
4.7
4.7
5.3
5.0
4.5
2.8
3.7
4.2
4.6
4.3
4.3
3.9
2.1
3.2
3.7
4.1
2.5
1.7
1.7
4.1
2.3
3.7
4.0
2.1
0.5
0.9
4.0
2.0
3.7
4.0
2.1
0.5
0.9
4.0
2.0
3.7
4.1
1.8
0.6
1.0
3.4
1.8
2.5
3.7
1.5
0.2
0.4
1.4
1.3
6.8
5.2
6.3
4.9
4.9
3.9
4.9
3.7
4.4
3.2
3.8
2.7
2.9
3.0
2.5
2.5
2.5
1.9
2.3
2.1
1.6
1.4
Source: ASEAN Secretariat.
Note: PCI = physical-capital intensive. HCI = human-capital intensive. TI = technology-intensive. See
table 3 for full commodity titles. Figures for Indonesia and ASEAN are averages.
As a result of those investment and trade-liberalisation packages, foreign
investment and export performance improved considerably during the mid1980s. The amount of net FDI as recorded in the balance of payments climbed
from $0.4 billion in 1986 to $6.2 billion in 1996. Following negative net inlows
from 1998 to 2003 – triggered primarily by the 1997–98 Asian inancial crisis, and
later exacerbated by local economic disruptions – these numbers have increased
further since 2004. Total realised foreign investment from 1990 to 2008 accounted
for 9,378 projects and $108.9 billion (table 2).
The role of foreign direct investment in Indonesia’s manufacturing exports
333
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TABLE 2 Cumulative Foreign Direct Investment (Realised), 1990–2008
Japan
Singapore
Mauritius
United Kingdom
United States
South Korea
Netherlands
Taiwan
Hong Kong
Malaysia
Germany
Australia
France
Switzerland
Seychelles
Others (combined)
Total
Value
($ billion)
Projects
20.9
13.0
10.4
8.2
7.4
4.7
4.0
3.9
3.8
1.7
1.4
1.3
1.2
0.7
0.7
25.6
108.9
1,783
1,035
49
493
420
1,179
308
532
332
320
227
298
144
102
9
2,147
9,378
Source: Indonesia’s Investment Coordinating Board (BKPM).
Note: BKPM data do not cover foreign investment in oil and gas or the inancial sector; the above data
include investment in Indonesia’s manufacturing and non-manufacturing sectors only (that is, in the
primary and service sectors). Only the manufacturing, or primary, sector, is relevant to this article.
Total accumulated FDI in manufacturing for 1990–2008 was $61.9 billion, 69% of which came from
the top 10 countries above. The data were not disaggregated enough to show accumulated FDI in the
manufacturing industry by country of origin.
Japanese investment accounted for the largest portion of all realised FDI during this period, with most investment taking place in the higher-value-added
industries (such as basic metals; metal goods, machinery and electronics (MME);
road vehicles and other transport equipment; and chemicals and pharmaceuticals). During 1990–2008, the PCI, HCI and TI industries were the main destinations for foreign investment in manufacturing (igure 1); most of it targeted
chemicals and pharmaceuticals and MME.
Manufacturing exports (SITC5 5–8) grew by an average of 21.3% (compounded)
per year from the onset of the trade-liberalisation era, in 1987, until 1996 – from
$4.6 billion to more than $26.2 billion – a nearly sixfold increase over nine years.
While the proportion of oil and gas to total merchandise exports diminished from
50.0% in 1987 to 25.4% in 2007, the share of manufactured goods increased from
5 SITC, or the Standard International Trade Classiication of imports and exports.
SITC 5 = chemicals and related products, not elsewhere speciied. SITC 6 = manufactured goods classiied chiely by material. SITC 7 = machinery and transport equipment.
SITC 8 = miscellaneous manufactured articles.
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Rudy Rahmaddi and Masaru Ichihashi
FIGURE 1 Foreign Direct Investment (Realised) by Industry, 1990–2008
($ billion)
6
PCI & TI
HCI
NRI & ULI
5
4
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3
2
1
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Source: Indonesia’s Investment Coordinating Board (BKPM).
Note: PCI = physical-capital intensive. TI = technology intensive. HCI = human-capital intensive.
NRI = natural-resource intensive. ULI = unskilled-labour intensive. NRI–ULI comprises cork and
wood, textiles and garments, leather and footwear, non-metallic minerals and other manufacturing
industries. HCI comprises rubber and plastics, road vehicles and other transport equipment, and pulp
and paper/paperboard. PCI–TI comprises chemicals and pharmaceuticals; medical instruments and
optics; and metal goods, machinery and electronics.
27.5% to 46.8% (igure 2). The composition of manufactured exports also changed
drastically. Endowed with abundant natural resources and labour, Indonesia has
historically had a comparative advantage in NRI and ULI products. The rise in
ULI exports during 1991–2008 can be attributed to the rise in exports of cork and
wood products (mainly plywood) and textiles and garments; the rise in TI exports
to the rise in exports of electronics. While the value of textile and garment exports
increased more than sixfold during 1987–96, and accounted for 24.8% of all manufactured exports, the growth of electronics exports increased from negligible
amounts to $3.9 billion, or 14.8% of all manufactured exports. Most of the growth
in electronics exports occurred between 1990 and 1996, related to the realisation of
foreign investment in technologically complex, higher-value-added industries (as
discussed).
The upward trend of export growth among PCI, HCI and TI commodities also
saw an increase in the competitiveness of their related industries, whereas between
1993 and 2002 NRI and ULI commodities inlicted a recurrent negative competitiveness effect on total manufacturing export growth (Rahmaddi and Ichihashi
2012). From 1987 to 2005, the share of NRI exports fell from 44.0% to 8.0%, while
that of ULI (such as textiles and garments) and TI (such as MME) exports increased
from 26.1% to 32.2% and from 5.4% to 27.2%, respectively. Pangestu (2002) argues
that such a shift in Indonesia’s export structure, from NRI to TI products, may
explain the performance of manufactured exports. Within 18 years, total manufacturing exports had increased from a small base of $4.6 billion in 1987 to more than
$42.9 billion in 2005, a compounded annual average growth rate of 13.2%.
The role of foreign direct investment in Indonesia’s manufacturing exports
335
FIGURE 2 Foreign Direct Investment (Realised) (lhs) and Exports (rhs), 1990–2008
($ billion)
16
160
Service (lhs)
Industry (lhs)
Primary (lhs)
Merchandise (rhs)
Manufacturing (rhs)
Oil & gas (rhs)
Non-oil primary (rhs)
14
12
10
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8
140
120
100
80
6
60
4
40
2
20
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
0
Source: Authors’ calculations, based on data from Indonesia’s Investment Coordinating Board (BKPM)
and UN Statistics.
Note: FDI = foreign direct investment.
LITERATURE SURVEY
The linkage between FDI and exports
The linkage between inward FDI and a host country’s export performance has
long been recognised in the literature. Yet applied research faces the dificult task
of investigating theoretical predictions on the effect of shifts in production (and
exports of factor services) by studying multinational enterprises (MNEs) and
their effects on the nature and structure of a host economy’s trade (exports) (Wang
et al. 2007). Indonesia’s recent experience may be valuable in investigating how
developing countries could realise their export potential when factors and services are internationally mobile. The substantial differences in factor endowments
between Indonesia and more developed countries drive the exports of technology, management skills and networks and services – as FDI – from these countries
to Indonesia, which, according to theory, could stimulate exports from the labourabundant host country.
Kojima (1973, 1975) and Blomstrom and Kokko (1998) acknowledge the role
of complete ‘FDI-carried packages’ of capital, global networks, technology, and
management and entrepreneurial skills in improving host economies. Such packages increase productive capacity and enhance irms’ operating eficiency; they
also have industry-wide beneits, by disseminating technology and managerial
innovation, which can help average irms be better able to compete in international markets. FDI should thus increase the volume of a host country’s exports.
Yet FDI can also (a) lower or replace domestic savings and investment; (b) transfer
technologies that are at a low level or inappropriate, or both, in relation to a host
country’s factor proportions; (c) target a host country’s domestic market and not
increase exports; (d) inhibit the expansion of indigenous irms that might otherwise become exporters; or (e) hinder the development of a host country’s dynamic
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Rudy Rahmaddi and Masaru Ichihashi
comparative advantages when there is a narrow focus on local, cheap labour and
raw materials (Zhang 2005). The relationship between FDI and a host country’s
export performance therefore depends on the nature and motivation of the FDI in
question. In general, we may distinguish between the direct and indirect effects of
FDI: the former refers to the export activities of MNEs themselves, while the latter refers to the effect of FDI on the export activities of local irms (Helleiner 1989;
Zhang and Song 2000; Caves 2007).
The direct effects of FDI on a host country’s exports can be classiied into three
categories, based on their production characteristics: (a) the processing of local
raw materials, (b) the export of new, labour-intensive products, and (c) labourintensive processes and component specialisation within vertically integrated
international industries (Zhang and Song 2000). First, in processing local raw
materials, MNEs may have better competitive positioning than export-oriented
indigenous irms, given that they are likely to have global networks in place, as
well as more advanced marketing skills, superior technology (in both products
and processes) and better business acumen. Second, in their attempts to become
successful exporters of new labour-intensive inal commodities such as textiles,
footwear and other consumer goods, indigenous labour-intensive exporting irms
face several obstacles to expanding their exports to world markets: setting up
a global distribution network (and keeping its members up to date with rapid
changes in consumer tastes), mastering the technicalities of industrial norms and
safety standards, and increasing the competitiveness of their products. MNEs may
help indigenous export-oriented irms to penetrate world markets, by providing
links to inal buyers. Third, exports of labour-intensive commodities within vertically integrated industries rely on the participation of MNEs; many such exports
take the form of ‘arm’s length’ transactions between MNEs and indigenous irms
in a host country (Zhang and Markusen 1997), in which host countries import
uninished and intermediate goods and re-export them after additional processing and assembling. All of these MNE operations in export activities diversify a
host country’s export bundle.
FDI can also inluence a host country’s manufacturing exports indirectly. For
example, indigenous export-oriented irms can beneit from the presence of MNEs
by observing and learning from their export activities, so that they themselves can
then penetrate export markets; and local irms can expand their exports by using
existing transport, communications and inancial-services infrastructure (Haddad
and Harrison 1993). FDI can also increase the competitiveness of a host country
and accelerate the diffusion of new technologies: MNEs often have irm-speciic
advantages in product or process technology and in management and marketing,
so the entry of MNEs into a host country’s markets may increase competition and
encourage existing irms to adopt more-eficient methods, and hence to become
more productive (Zhang and Song 2000). The more productive domestic exporters there are in a host country, the more likely it is that they will survive in export
markets. Thus, the increased competition stemming from the presence of MNEs
encourages indigenous exporters to perform well in world markets.
FDI may also create linkages between MNEs and local irms, which may have
another indirect effect on a host country’s exports – technology spillover – often
through an MNE’s outsourcing practices. If export-oriented foreign subsidiaries
increase their purchases of local inputs or shift their production activities to
The role of foreign direct investment in Indonesia’s manufacturing exports
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local irms, they will improve these irms’ productivity and competitiveness. As
irms in host countries acquire new production capabilities, they will not only be
inclined to participate in export markets but they will also be able to produce and
export more diverse and sophisticated products. As such, the diversiication of a
host country’s exports can be seen as another indirect advantage of FDI (Iwamoto
and Nabeshima 2012).
Hill and Athukorala (1998) argue that any linkage between FDI and exports
may also depend on the nature of a host country’s speciic investment and trade
regimes. Such a linkage is therefore worth examining empirically.
Selected empirical literature review
Many empirical studies have analysed the linkage between FDI and a host country’s exports, yet their results have somehow been inconclusive. In an aggregate
analysis, Horst (1972) measured the effect of US FDI on US manufacturing exports
to Canada, using three-digit SITC cross-sectional data from 1963; he found that
FDI reduced US exports to Canada, and that Canadian tariffs increased US FDI
(owing to tariff jumping, which drives foreign irms to localise production within
the destination market to avoid tariff or non-tariff barriers). Using annual data
from 1970 to 1998, Sharma (2003) found no statistically signiicant evidence of
FDI having any effect on India’s exports. In contrast, other single-country studies,
including O’Sullivan (1993), of Ireland, and Blake and Pain (1994), of the UK, have
found that FDI increases a host country’s export performance.
Some cross-country literature using disaggregated data has indicated that
FDI’s effect on a host country’s export performance may differ by country, region
or industry. Using cross-country data from 1971 to 1992, Pain and Wakelin (1998)
found evidence of FDI inluencing exports in 10 of 11 OECD countries: seven countries saw their exports increase, and three (Japan, Italy and Denmark) saw them
fall. In explaining the latter result, the authors argued that foreign investment had
targeted relatively closed domestic markets, rather than using each country as
an export base. Investigating the effect of inward FDI on regional export performance in China during 1984–97, Sun (2001) found that the FDI effect was higher
in coastal regions than in inland ones. Taking into account differences in factor
proportions (that is, of comparative advantage) within manufacturing industries
in China, Wang et al. (2007), using 1983–2002 data, found that the effect of FDI on
the manufacturing exports of ULI industries was greater than that on those of PCI
industries.
Recent advances in the literature on the linkages between international trade
and investment have emphasised the effect on trade of changes in the comparative advantages brought about by foreign investment (Sun 2001). As FDI helps to
create an international division of labour and increase the mobility of production
factors – that is, not only of capital but also (and more important) of technology,
management skills and other knowledge – it may reallocate economic resources
and productive capacities according to the relative cost of production in various
countries. This may change countries’ comparative advantages, leading to shifts
in the structure and patterns of international trade. Sun (2001) suggests examining the industry-based linkages between FDI and exports as a plausible means
of studying FDI’s inluence on the structure of exports. Recent empirical studies
have also been devoted to examining FDI’s role in diversifying host countries’
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Rudy Rahmaddi and Masaru Ichihashi
exports, or in making them more sophisticated – for example, Banga (2006), on
India; Xu and Lu (2007), on China; and Iwamoto and Nabeshima (2012), on a
panel database of 175 countries.
Yet empirical research examining industry-based linkages between FDI and
manufacturing exports in the case of Indonesia has been limited, excepting Ramstetter (1999) and Van Dijk (2002), both of which consider the effect of MNE activities on export propensity by using irm-level manufacturing data. Both studies
found that, in general, foreign investment increased Indonesia’s exports. More
speciically, Ramstetter (1999), investigating the effect of foreign ownership on the
export propensity of 15,949 irms in 1990, 1992 and 1994, determined that irms
with high proportions of foreign ownership had a high ratio of exports to output. Van Dijk (2002), using data from Indonesia’s 1995 industrial survey, found
that MNEs mostly increased industry-based exports, except in beverages, footwear and instruments. Using census data of medium and large manufacturing
establishments in 1996 and 2006, Narjoko (2009) revealed that the contribution of
MNEs in expanding technological knowledge increased the likelihood of domestic irms entering export markets.
Narjoko and Maidir (2009) examined irm-level data from Statistic Indonesia’s
annual manufacturing surveys (Survei Industri) from 1990 to 2001. They showed
that a higher degree of foreign ownership of irms is associated with improved
export performance but that its effect varies across industries, and that its effect is
greatest among labour-intensive industries. A recent study by Jongwanich (2010)
on the determinants of the export performance of eight Asian economies (including Indonesia) conirms this. Using quarterly data from 1993 to 2008, Jongwanich
analysed the impact of total net FDI inlows on total merchandise exports, manufacturing exports (SITC 5–8), and exports of machinery and transport equipment
(SITC 7). The last is expected to capture the growing incidence of international
product fragmentation and trade in parts and components. Jongwanich concluded that the positive impact of FDI tends to be greatest for SITC 7 products in
the short run, and he found substantial evidence that FDI has a long-run exportgenerating effect on total manufacturing exports only. Nevertheless, most of the
above studies do not account explicitly for industry-based variation in the effect
of FDI on manufacturing exports classiied by factor intensity. This classiication
allows us to investigate whether FDI has changed the structure of manufacturing exports. This article uses and, to a lesser degree, emphasises disaggregated
FDI data and macroeconomic determinants. This approach supports our objective
of revealing the differential export-generating effect of FDI across industries and
particular macroeconomic determinants of export performance.6
In summary, the results in the studies above seem to support the positive role
of FDI in increasing exports. However, given the ambiguous linkage between FDI
6 We acknowledge the advantage of using micro-level data to reveal underlying mechanisms at the irm or plant level in scrutinising variations in the export-generating effect of
FDI across industries, while using less-disaggregated FDI data may sufice for pinpointing the export-generating effect of FDI and other macroeconomic determinants of export
performance across regions or sectors (see Leichenko and Erickson 1997; Zhang and Song
2000; Sun 2001).
The role of foreign direct investment in Indonesia’s manufacturing exports
339
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and a host country’s exports, it is unclear whether FDI affects the export performance of industries that hold a variety of comparative advantages. Industrybased analysis is perhaps the most appropriate method of elucidating the true
scale and performance of the linkage between FDI and manufacturing exports.
This article explores this relationship empirically, using data on Indonesian manufacturing exports and inward FDI.
METHODOLOGY AND DATA
Methodology
FDI is not the only factor that inluences the export performance of host countries.
We use a reduced form of the export equation (Goldstein and Khan 1978; Rose
1990; Athukorala 2004; Jongwanich 2010) to show that the level of real manufacturing exports (in 2000 constant prices), for example, is determined by the realworld income (Y*), a country’s production capacity (represented by gross capital
formation determined by foreign and domestic investment, and GDP growth)
and the real exchange rate (which represents the price of exports):
(
X = x Y*, FDIF, DCIF, GDPG, REER
)
(1)
While real-world income shifts demand, production capacity shifts supply.
Yet a small-country assumption implies that as many exports as a country could
offer would be absorbed by the world market. Thus, exports should be supplydriven, in this sense (Athukorala and Riedel 1996, inter alia). In other words, the
coeficient attached to real-world income should be insigniicant. Such an assumption allows us to estimate some export determinants (including FDI) whenever
data on industry-based export price indices are unavailable.
Since FDI tends to affect exports from the supply-side channel – whether
directly, via increases in productive capacity in export-oriented industries, or
indirectly, via export spillover (Markusen and Venables 1989) – we specify FDI
alongside other export determinants: domestic capital investment, GDP growth
and the exchange rate (including economic shocks). We do so by modifying an
export model used by Goldberg and Klein (1997), Zhang and Song (2000), and
Sun (2001):
X it = α i + β1FDIFit−1 + β 2 DCIFit−1 + β3GDPGt + β 4 REERt
+ β5 Dcr s st + β6 D roct + ε it
(2)
where the subscript i and t denote cross-sectional unit and time, respectively; ε is
the disturbance term; β1 to β4 are the parameters to be empirically estimated; Xit is
the level of manufacturing export value of industry i in year t; FDIFit–1 and DCIFit–1
account for stock levels of FDI and domestic capital investment, respectively, in
industry i in year t; GDPGt is the growth rate (as a percentage) of real GDP in year
t; and REERt is the index level of the real effective exchange rate (export-weighted)
in year t expressed in year 2000 constant price. To capture the effect of shocks that
may inluence the linkage between FDI and exports, we use two binary dummy
variables: Dcrisist captures the effects of the 1997–98 Asian inancial crisis and
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340
Rudy Rahmaddi and Masaru Ichihashi
other supply disruptions on manufacturing exports (using the value of unity for
1997–2003, and 0 otherwise), which lasted until 2003;7 Dproct captures the effect
of a change of export procedure (using the value of unity for 1997–2000, and 0
otherwise), which may lead to export misclassiication (for example, see Mudge
1999).8 All variables, except for GDP growth (in percentage form) and the dummy
variables (in binary form), are in natural logarithms.
The beta coeficients β1 to β4 are the elasticity of exports with respect to FDI,
domestic capital investment, GDP growth and the export-weighted foreignexchange rate, respectively. The value of the coeficient on FDIFit (β1) is of particular interest in this study, because this coeficient depicts changes in the proportion
of manufacturing exports to total exports in response to a percentage change in
FDI. Lagging explanatory variables FDI and domestic capital investment by one
year is justiied, for three reasons: (a) following Leichenko and Erickson (1997),
the effects of investments (such as the modernisation of production facilities,
adjustments in production structure and the dissemination of new technology)
on export performance are unlikely to take place immediately, since they require
time to inluence production; (b) a lag structure will mitigate any endogeneity
between exports and FDI (Zhang and Song 2000; Sun 2001) and will represent an
appropriate sequence, whereby investment precedes production and production
precedes exports; and (c) although the simple irst-order lag structure may not
be entirely appropriate in capturing potential feedback between investments and
exports, the relatively short period for the study (19 years) requires a simple lag
approach.
In addition to FDI, we specify other variables that may determine the performance of manufacturing exports. First, our inclusion of domestic investment in
the analysis of exports is intended to hold constant the effect of other investments
in general. We expect the coeficient of β2 to be positive, since increases in domestic
capital formation will augment productive capacity, thus enabling producers to
expand their output. Some previous studies (Leichenko and Erickson 1997; Zhang
and Song 2000; Sun 2001) indicated the importance of domestic investment to
export performance. Second, we include the GDP growth rate (GDPGt) in order to
capture in supply capacity the export-enhancing effect of increased economic performance. Thus, we expect the coeficient β3 also to be positive. We use the rate of
GDP growth, rather than its level, to reduce plausible direct simultaneity between
GDP and investment. Ideally, we would use growth in gross industry-based products to capture the effect of industry-based economic performance on manufacturing exports, but our methodology is limited by the availability of industry-based
7 Many studies, including Pangestu (2002) and Thee (2006), provide detailed explanations
of the effects of the 1997–98 Asian inancial crisis on exports and investment, as well as of
other economic disruptions that follow such a crisis. We use a dummy structure similar to
that of Adiningsih et al. (2009).
8 The authors are grateful to an anonymous reviewer for this invaluable insight. As indicated by UN-COMTRADE data, the value of the SITC 9 category (‘Not classiied elsewhere’) in Indonesia’s exports increased from $0.09 billion (0.2% of total exports) in 1996 to
$6.7 billion (12.5%) in 1997 and $8.0 billion (16.4%) in 1998, before decreasing to $0.4 billion
(0.6%) in 2000. Thus, about one-sixth of exports in 1998 were unclassiied.
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The role of foreign direct investment in Indonesia’s manufacturing exports
341
GDP data that can be matched appropriately with existing data on industry-based
FDI. Last, the exchange-rate variable, REERt, is another typical trade-related variable that may inluence exports, since it represents the competitive factor (the price
effect) of export commodities. Sugema (2005) found evidence of the positive effect
of exchange-rate depreciation on Indonesia’s non-oil exports. In our model, REERt
represents an index of the real effective exchange rate (year 2000 = 100), based
on the consumer price index and weighted by the currencies of Indonesia’s 15
main export partners. It is constructed in such a way that an increase in REERt
denotes real currency depreciation. As conventional export-demand theory predicts, the depreciation of a country’s currency may give impetus to further export
expansion. The depreciation (or appreciation) of a currency makes that country’s
export commodities more (or less) competitive, leading to increased (or decreased)
demand in the world market. Thus, we expect the coeficient β4 also to be positive.
Panel data imply that different methods can be used in estimations, including
the ordinary-least-squares (OLS) method and those of ixed effects and random
effects. The main problems inherent in the pooled OLS method are that it does
not allow for industry-based heterogeneity and that it assumes that all industries
are homogeneous. The ixed-effects method, in contrast, can capture the industrybased effect of FDI on manufacturing exports, since it models each effect explicitly. Likewise, the random-effects method can acknowledge heterogeneity in the
cross-section. Yet rather than explicitly modelling the predetermined heterogeneous effect by using an industry-based dummy, the random-effect method assumes
that the effects are random, independent and identically distributed over the error
term, so that uit = vi εit , where vi denotes the ith industry’s year-invariant unobserved heterogeneity, and where εit is the remaining disturbance (Zhang and Song
2000). Such a random effect can be estimated using a generalised-least-squares
(GLS) method. Hsiao (1986) argues that even though its results might be inconsistent when the number of observations is small and if the initial values correlate with the effects, the asymptotic bias of GLS is smaller than that of OLS. To
obtain the most appropriate inferences based on the ixed-effect or random-effect
method, we use Hausman statistics to test the null hypothesis that the regressors
and individual effects are not correlated. On one hand, a failure to reject the null
hypothesis implies that the random-effect method is preferable to the ixed-effect
method; on the other, a rejection of the null hypothesis implies that the ixed effect
is appropriate.
We irst estimate equation (2) by using the full sample of manufacturing industries (N = 11) for which we have relevant data to investigate whether growth in
Indonesia’s manufacturing exports in general can be attributed to FDI. To analyse the existence, scale and performance of any such relationship at the industry
level, we apply equation (2) to two subsample manufacturing groups classiied by
factor intensity: (a) NRI and ULI, which comprise ive industries (table 3); and (b)
PCI, HCI and TI, which comprise six. This method enables us to analyse the effect
of FDI on exports across different industries.
Our analytical model can be expanded to elucidate the effect of FDI on each
industry, by relaxing the equal-effect restriction on both groups. The results may
have imperative implications for the design of development strategy and guide
FDI in speciic industries.
342
Rudy Rahmaddi and Masaru Ichihashi
Thus, we now assume that such an effect varies, as follows, across 11 industries:
11
11
X it = α1 + ∑ α i Dni + β1FDIFit + ∑ γ i (Dni FDIFit ) + β 2 DCIFit + β3GDPGt
n
=2
n
=2
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+ β 4 REERt + β5 Dcrisist + β6 Dproct + uit
(3)
where D is an industry dummy; n is a dummy number; i is the industry (say,
D2i is 1 for textiles and garments, and 0 otherwise); and γi is a differential slope
coeficient, just as αi is a differential intercept that captures an industry’s speciic
effect. If one or more of the γi coeficients is statistically signiicant, then one or
more slope coeficients will differ from the base group (for example, if β1 and γ2
are statistically signiicant, then (β1 γ2) will give the value of the FDI coeficient
for industry 2) (Gujarati 2004). We estimate equation (3) on the full sample, using
the ixed-effect model. In the following subsection, we describe the data in more
detail and provide a list of the industries in the two main categories.
Data
In this article, we have drawn on industry-based realised FDI datasets (Ijin
Usaha Tetap, IUT) obtained from BKPM, which holds three types of FDI data: (a)
approved FDI, the oficial approval of an initial investment plan; (b) an annual
investment activity report (Laporan Kegiatan Penanaman Modal, LKPM); and
(c) the IUT, which records the actual (realised) foreign investment disbursed to a
speciic project until it is ready to be initiated. BKPM registers such IUT datasets
as quasi-accumulated (gross) stock data, by industry. A similar typology applies
for data on domestic capital investment.9 Since exports are more likely to depend
on production capacity or capital (stock) than on additional capital (investment
low), the IUT data are the most appropriate for analysis.10
First we grouped industries by factor intensity, deriving ive main categories:
NRI, ULI, PCI, HCI and TI. This typology aligns with that of Aswicahyono and
Pangestu (2000). To synchronise with BKPM’s data on realised FDI and domestic
ixed-capital investment by industry, we regrouped the datasets into two main
categories: the NRI–ULI group, and the PCI–HCI–TI group. The former represents industries in which Indonesia has a comparative advantage (the NRI,
9 Using net FDI and domestic investment stocks by industry is beyond our extent here,
owing to the lack of any records of capital outlow or disinvestment by project. Since the
realised lows into the projects take place and accumulate for more than a year, they should
be suficient for capturing the accumulated effects of FDI and domestic investment on exports. The IUT datasets of FDI and domestic investment, which are available from BKPM,
are thus the most complete data on quasi-accumulated (gross) stocks, by industry. The
related explanation is based on the oficial statement of Ir. Hanung Harimba, a former head
of BKPM’s Center for Investment Data and Information (Pusdatin). The use of gross rather
than net FDI stocks may result in estimation bias. Any interpretation of results should be
made with caution.
10 The datasets are published but are not publicly available; BKPM granted the authors
access to the datasets and permission to use them.
The role of foreign direct investment in Indonesia’s manufacturing exports
343
TABLE 3 Commodity Classiication Based on Factor Intensity
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NRI–ULI group
Cork & wood
Non-metallic minerals
Textiles & garments
Leather & footwear
Other manufactured commodities
PCI–HCI–TI group
Chemicals & pharmaceuticals
Rubber & plastics
Pulp & paper/paperboard
Metal goods, machinery &
electronics
Road vehicles & other transport
equipment
Medical & optical instruments
Abbreviation
SITC
W
NMM
TEX
LF
OI
63
66
65, 84
61
89
CP
RP
P
51, 52, 54, 59
57, 58, 62, 893
64
MME
67–69, 72–74, 76, 77, 751, 752, 759
RV
MO
78, 79
87, 88
Source: Indonesia’s Investment Coordinating Board (BKPM).
Note: SITC = Standard International Trade Classiication (rev. 2). NRI = natural-resource intensive.
ULI = unskilled-labour intensive. PCI = physical-capital intensive. HCI = human-capital intensive. TI
= technology intensive. Initial categorisation follows Aswicahyono and Pangestu (2000) but has been
reclassiied to make it compatible with sector-based FDI and domestic investment data available from
BKPM.
low-labour-cost and low-technology industries); the latter a comparative disadvantage (the capital-intensive and technologically complex industries).11
We match the value in dollars, by industry, of realised FDI (IUT) and domestic
ixed capital investment with the export value of each commodity, by SITC (rev.
2), obtained from the UN Statistics Database of Commodity Trade (Comtrade).
The panel datasets cover 11 manufacturing industries during 1990–2008, yielding
11 Combining the NRI–ULI and PCI–HCI–TI industries into two single homogeneous
groups, based on factor intensity, even for econometric purposes, may lead to potential
analytical bias, since any given industry can use mixed-factor intensity. For instance, the
electronics industry includes labour-intensive assembly processes, and the textiles and
garments industry is capital- and labour-intensive. Lall (2000) distinguishes between
manufactured exports, based on the technology categories involved: resource-based, low
technology, medium technology and high technology. With some exceptions for particular
commodities, products of the NRI (resource-based) and ULI industries tend to be labour
intensive and use a low level of technology, whereas those of the PCI, HCI and TI industries use the bulk of Indonesia’s skill- and scale-intensive technologies to produce capital
and intermediate goods. Although not a perfect classiication of industries by factor intensity, our attempt indicates approximate differences in technology levels between those two
industry groups. We retain this arbitrary classiication to reveal any relative inluence of
some macro-level determinants on manufacturing export performance in various industry
groups. Any interpretation of the results should take such limitations into account.
344
Rudy Rahmaddi and Masaru Ichihashi
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182 observations within the full sample (unbalanced), 87 for the NRI–
ISSN: 0007-4918 (Print) 1472-7234 (Online) Journal homepage: http://www.tandfonline.com/loi/cbie20
The role of foreign direct investment in Indonesia's
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To cite this article: Rudy Rahmaddi & Masaru Ichihashi (2013) The role of foreign direct
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49:3, 329-354, DOI: 10.1080/00074918.2013.850632
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Date: 17 January 2016, At: 23:48
Bulletin of Indonesian Economic Studies, Vol. 49, No. 3, 2013: 329–54
THE ROLE OF FOREIGN DIRECT INVESTMENT IN
INDONESIA’S MANUFACTURING EXPORTS
Rudy Rahmaddi and Masaru Ichihashi*
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Hiroshima University, Japan
This article examines whether foreign direct investment (FDI) has contributed to
the changing structure of Indonesia’s manufacturing exports. It uses industry-level
data from 1990 to 2008, classiied by factor intensity. Our analysis reveals that FDI
promotes exports in most panel observations, especially exports from physicalcapital-intensive (PCI), human-capital-intensive (HCI) and technology-intensive
(TI) industries. Yet by applying a differentiated cross-section-effect model, we determine that the export-generating potential of FDI is stronger in PCI, HCI and TI
industries than in natural-resource-intensive or unskilled-labour-intensive industries, in which Indonesia has a comparative advantage. We also assess the inluence
of other determinants of export performance – namely, private domestic capital
investment, GDP growth and exchange rates. Our indings have implications for
policymakers seeking to sustain Indonesia’s export performance.
Keywords: export performance, foreign direct investment, panel analysis
INTRODUCTION
Following the collapse of global oil prices in the mid-1980s, Indonesia liberalised its trade policies in a bid to increase exports. This strategy replaced that of
import-substituting industrialisation, which could no longer be counted on to
promote high growth into the 1990s. Indonesia realised that it needed a new
source of foreign-exchange earnings and economic growth; its policy pendulum
swung towards export-oriented industrialisation, led by the private sector and
based on inputs other than natural resources. Indonesia’s economy subsequently
saw large increases in foreign direct investment (FDI)1 and continuous growth in
* We wish to thank the two anonymous referees of this article, for their invaluable comments on an earlier draft. We are also indebted to R.S. Hanung Harimba Rachman for
providing (and explaining) data on permanent business licences in Indonesia. Rahmaddi
would also like to thank the Ministry of Finance of the Republic of Indonesia for providing
full inancial support under phase three of the Professional Human Resource Development
Project. The usual disclaimers apply.
1 FDI may take many forms, including greenield investment, horizontal and vertical
mergers and acquisitions, and portfolio investment via the capital market (aimed at exercising control). The data referred to in this article do not cover the last of these. In addition,
the effect of FDI outlows on exports is beyond the scope of this analysis, which uses the
terms FDI and foreign investment interchangeably to refer to FDI inlows.
ISSN 0007-4918 print/ISSN 1472-7234 online/13/030329-26
http://dx.doi.org/10.1080/00074918.2013.850632
© 2013 Indonesia Project ANU
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330
Rudy Rahmaddi and Masaru Ichihashi
manufacturing exports. A closer look at the latter indicates that although the commodities of Indonesia’s natural-resource-intensive (NRI) and unskilled-labourintensive (ULI) industries – such as cork and wood, textiles and garments, and
leather and footwear – occupied most of the total value (in real dollars) of manufacturing exports during 1990–2008, their average annual growth rate of 2.4%
was lower than that of the commodities of Indonesia’s physical-capital-intensive
(PCI), human-capital-intensive (HCI) and technology-intensive (TI) industries
(8.2%), owing to increased export growth in road vehicles and other transport
equipment (including components) and in electronics goods. Meanwhile, foreign
investment in the manufacturing sector dominated total realised FDI in Indonesia: more than 75%, or $108.9 billion, of all foreign investments lowed into the
PCI, HCI and TI industries.2 This suggests a plausible linkage between FDI and
industry-based export performance, and implies that the structure of Indonesia’s
manufacturing exports has changed. Research into the effect of FDI on industrybased manufacturing exports therefore deserves attention.
Given the importance of differential analysis in assessing the scale and performance of FDI lows in exports across industries, past studies that scrutinise the
relationship between FDI and trade at the aggregated level may not be suficient.
Although useful, such studies may fail to capture variations in the interactions of
FDI and exports at the disaggregated level (Kawai and Urata 1998). In contrast,
cross-industry variation analyses of FDI’s export-generating potential may inluence those designing development strategies or promoting the beneits of FDI
to speciic industries, especially in countries that rely on foreign direct and indirect investment to transform their industrial sectors. Such analyses could even be
expanded on, to seek out policy implications that dovetail into sustaining export
performance through export-led growth 2.0 (Haddad and Shepherd 2011).3 Yet
few empirical studies of Indonesia have examined the linkage between FDI and
manufacturing exports at the disaggregated level.4
In this article, we attempt to close the gap in the empirical research by analysing the effect of FDI on Indonesia’s manufacturing exports by different industries
during 1990–2008. To do so, we use realised FDI data, which have an advantage
over approved FDI data in measuring the degree to which FDI affects export performance, because they better capture actual inlows of foreign investment into
the domestic economy (after this investment has been implemented in projects).
We also ask whether the growth in Indonesia’s manufacturing exports can be
attributed to FDI, and whether FDI has different export-generating effects in different manufacturing industries (classiied by factor intensity). We then examine
2 According to Indonesia’s Investment Coordinating Board (Badan Koordinasi Penanaman
Modal BKPM), which excludes foreign investment in oil and gas and the inancial sector.
3 Export-led growth 2.0 refers to the next generation of export- or outward-oriented policies that aim to sustain export performance. These include, for example, managing the
external and internal risks that come with outward-oriented policies; diversifying exports;
liberalising South–South trade; encouraging FDI in export-oriented industries, to promote technology transfer and spillover; and promoting international integration. See also
Haddad and Shepherd (2011).
4 Ramstetter (1999), Van Dijk (2002), Narjoko (2009), and Narjoko and Maidir (2009) are
exceptions.
The role of foreign direct investment in Indonesia’s manufacturing exports
331
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other macroeconomic determinants of manufacturing exports – namely, private
domestic capital investment, GDP growth and the exchange rate. We focus here
on the manufacturing sector, which has the dominant share of the value of Indonesia’s merchandise exports and accounts for more than 90% of Indonesia’s FDI.
The rest of this article is organised as follows. Following a brief overview of
FDI and trends in the performance and structure of Indonesia’s manufacturing
exports, we review the related literature on the linkage between FDI and exports.
We then describe our methodology and choice of data, before elucidating on our
empirical results and providing some possible policy implications.
INDONESIA: FOREIGN DIRECT INVESTMENT AND
MANUFACTURING EXPORTS
Indonesia’s strategy of promoting exports has seen FDI increase rapidly, owing
to a bold and decisive series of economic reforms that started in the mid-1980s.
These reforms covered exchange-rate management, including two large nominal
depreciations, in 1983 and 1986; prudent iscal policy; comprehensive tax reform;
a more open posture towards foreign investment; and inancial deregulation (Hill
1996; Ishida 2003). Below, we discuss liberalisation packages relevant to investment and trade.
To attract more foreign investment, in 1985–86 the Indonesian government
relaxed foreign proprietary restrictions and divestment requirements for exportoriented investment and for irms in bonded zones. Later, Regulation 17/1992 on
the Requirements for Share Ownership in Foreign Capital Investment Companies, followed by further programs that facilitated investment, allowed for 100%
foreign proprietorships and less stringent divestment requirements for investments in certain regions, bonded zones and sectors, with descending investment
thresholds. The government also used iscal measures to attract foreign capital,
introducing tax incentives and duty exemptions as well as providing legal protection for foreign investments. These timely pull factors coincided with a wave of
manufacturers relocating to East Asian economies in search of lower production
costs to mitigate push factors such as appreciating currencies, the abolition of
foreign-exchange controls, and rising labour costs at home (Aziz 1998; Pangestu
2002; Thee 2005).
To promote manufacturing exports, the government sought to liberalise trade
by relaxing restrictions on foreign investment in export-oriented industries, tackling ineficient levels of bureaucracy (including reforming Indonesia’s customs
system), minimising trade protections such as non-tariff barriers, and reducing
import tariffs. The average (unweighted) tariff rate was cut from 27% in 1986 to
15% by 1995, and the proportion of tariff lines subject to these barriers fell from
32% to 12% (Snodgrass 2011). Indonesia’s ongoing liberalisation of import tariffs
complemented the ASEAN Common Effective Preferential Tariff (CEPT) scheme,
which generally reduced existing tariff rates to 20% or below within ive to eight
years, starting on 1 January 1993. Some of these covered more than 37,600 tariff lines in the manufacturing sector (tables 1a and 1b). Exporters were also provided with a drawback system for import duties, under which tariffs imposed on
imported raw materials and parts were refunded when companies exported the
inished products.
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Rudy Rahmaddi and Masaru Ichihashi
TABLE 1a Indonesia’s Manufacturing Tariffs (NRI–ULI Industry Group) under
ASEAN’s Common Effective Preferential Tariff Scheme, 1996–2008
(%)
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1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Footwear
Other mfg
Textiles
Wood
Leathers
NMM
26.0
22.0
15.0
13.0
9.2
3.2
23.2
19.6
12.5
13.0
7.6
3.2
18.7 18.3 13.8 13.6
15.8 15.2 11.2 11.0
10.5 8.7 4.9 4.9
11.1 9.3 7.5 6.5
6.1 4.7 3.0 3.0
3.1 3.0 2.9 2.9
Indonesia
ASEAN
14.7 13.2 10.9
10.2 9.3 7.9
9.9
7.0
7.2
5.3
7.0
5.1
9.3
8.0
4.9
5.4
2.9
2.9
5.0
4.7
4.9
4.3
2.8
2.9
5.0
2.8
1.9
0.2
3.1
3.5
3.3
2.4
1.5
0.2
2.8
3.1
3.3
2.4
1.5
0.2
2.8
3.1
2.9
2.3
1.4
0.4
3.0
3.0
2.3
1.9
0.0
0.4
3.0
1.9
5.6
4.2
4.1
3.4
2.8
3.7
2.2
2.9
2.2
2.3
2.2
1.8
1.6
1.3
Source: ASEAN Secretariat.
Note: NRI = natural-resource intensive. ULI = unskilled-labour intensive. Mfg = manufacturing. NMM
= non-metallic minerals. See table 3 for full commodity titles. Figures for Indonesia and ASEAN are
averages.
TABLE 1b Indonesia’s Manufacturing Tariffs (PCI–HCI–TI Industry Group) under
ASEAN’s Common Effective Preferential Tariff Scheme, 1996–2008
(%)
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Plastics
Paper
Metals
Optics
Machinery
Vehicles
Chemicals
Indonesia
ASEAN
11.5 9.5
11.2 11.2
9.9 9.8
9.7 9.1
8.7 8.2
5.8 5.7
5.7 5.6
8.9
6.7
8.4
6.3
7.6
7.8
8.2
7.6
6.6
4.7
5.0
6.6
7.6
7.6
7.0
6.3
4.4
4.7
5.1
4.7
6.3
5.7
5.0
3.6
4.1
5.1
4.7
6.1
5.7
5.0
3.4
4.1
4.7
4.7
5.3
5.0
4.5
2.8
3.7
4.2
4.6
4.3
4.3
3.9
2.1
3.2
3.7
4.1
2.5
1.7
1.7
4.1
2.3
3.7
4.0
2.1
0.5
0.9
4.0
2.0
3.7
4.0
2.1
0.5
0.9
4.0
2.0
3.7
4.1
1.8
0.6
1.0
3.4
1.8
2.5
3.7
1.5
0.2
0.4
1.4
1.3
6.8
5.2
6.3
4.9
4.9
3.9
4.9
3.7
4.4
3.2
3.8
2.7
2.9
3.0
2.5
2.5
2.5
1.9
2.3
2.1
1.6
1.4
Source: ASEAN Secretariat.
Note: PCI = physical-capital intensive. HCI = human-capital intensive. TI = technology-intensive. See
table 3 for full commodity titles. Figures for Indonesia and ASEAN are averages.
As a result of those investment and trade-liberalisation packages, foreign
investment and export performance improved considerably during the mid1980s. The amount of net FDI as recorded in the balance of payments climbed
from $0.4 billion in 1986 to $6.2 billion in 1996. Following negative net inlows
from 1998 to 2003 – triggered primarily by the 1997–98 Asian inancial crisis, and
later exacerbated by local economic disruptions – these numbers have increased
further since 2004. Total realised foreign investment from 1990 to 2008 accounted
for 9,378 projects and $108.9 billion (table 2).
The role of foreign direct investment in Indonesia’s manufacturing exports
333
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TABLE 2 Cumulative Foreign Direct Investment (Realised), 1990–2008
Japan
Singapore
Mauritius
United Kingdom
United States
South Korea
Netherlands
Taiwan
Hong Kong
Malaysia
Germany
Australia
France
Switzerland
Seychelles
Others (combined)
Total
Value
($ billion)
Projects
20.9
13.0
10.4
8.2
7.4
4.7
4.0
3.9
3.8
1.7
1.4
1.3
1.2
0.7
0.7
25.6
108.9
1,783
1,035
49
493
420
1,179
308
532
332
320
227
298
144
102
9
2,147
9,378
Source: Indonesia’s Investment Coordinating Board (BKPM).
Note: BKPM data do not cover foreign investment in oil and gas or the inancial sector; the above data
include investment in Indonesia’s manufacturing and non-manufacturing sectors only (that is, in the
primary and service sectors). Only the manufacturing, or primary, sector, is relevant to this article.
Total accumulated FDI in manufacturing for 1990–2008 was $61.9 billion, 69% of which came from
the top 10 countries above. The data were not disaggregated enough to show accumulated FDI in the
manufacturing industry by country of origin.
Japanese investment accounted for the largest portion of all realised FDI during this period, with most investment taking place in the higher-value-added
industries (such as basic metals; metal goods, machinery and electronics (MME);
road vehicles and other transport equipment; and chemicals and pharmaceuticals). During 1990–2008, the PCI, HCI and TI industries were the main destinations for foreign investment in manufacturing (igure 1); most of it targeted
chemicals and pharmaceuticals and MME.
Manufacturing exports (SITC5 5–8) grew by an average of 21.3% (compounded)
per year from the onset of the trade-liberalisation era, in 1987, until 1996 – from
$4.6 billion to more than $26.2 billion – a nearly sixfold increase over nine years.
While the proportion of oil and gas to total merchandise exports diminished from
50.0% in 1987 to 25.4% in 2007, the share of manufactured goods increased from
5 SITC, or the Standard International Trade Classiication of imports and exports.
SITC 5 = chemicals and related products, not elsewhere speciied. SITC 6 = manufactured goods classiied chiely by material. SITC 7 = machinery and transport equipment.
SITC 8 = miscellaneous manufactured articles.
334
Rudy Rahmaddi and Masaru Ichihashi
FIGURE 1 Foreign Direct Investment (Realised) by Industry, 1990–2008
($ billion)
6
PCI & TI
HCI
NRI & ULI
5
4
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3
2
1
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Source: Indonesia’s Investment Coordinating Board (BKPM).
Note: PCI = physical-capital intensive. TI = technology intensive. HCI = human-capital intensive.
NRI = natural-resource intensive. ULI = unskilled-labour intensive. NRI–ULI comprises cork and
wood, textiles and garments, leather and footwear, non-metallic minerals and other manufacturing
industries. HCI comprises rubber and plastics, road vehicles and other transport equipment, and pulp
and paper/paperboard. PCI–TI comprises chemicals and pharmaceuticals; medical instruments and
optics; and metal goods, machinery and electronics.
27.5% to 46.8% (igure 2). The composition of manufactured exports also changed
drastically. Endowed with abundant natural resources and labour, Indonesia has
historically had a comparative advantage in NRI and ULI products. The rise in
ULI exports during 1991–2008 can be attributed to the rise in exports of cork and
wood products (mainly plywood) and textiles and garments; the rise in TI exports
to the rise in exports of electronics. While the value of textile and garment exports
increased more than sixfold during 1987–96, and accounted for 24.8% of all manufactured exports, the growth of electronics exports increased from negligible
amounts to $3.9 billion, or 14.8% of all manufactured exports. Most of the growth
in electronics exports occurred between 1990 and 1996, related to the realisation of
foreign investment in technologically complex, higher-value-added industries (as
discussed).
The upward trend of export growth among PCI, HCI and TI commodities also
saw an increase in the competitiveness of their related industries, whereas between
1993 and 2002 NRI and ULI commodities inlicted a recurrent negative competitiveness effect on total manufacturing export growth (Rahmaddi and Ichihashi
2012). From 1987 to 2005, the share of NRI exports fell from 44.0% to 8.0%, while
that of ULI (such as textiles and garments) and TI (such as MME) exports increased
from 26.1% to 32.2% and from 5.4% to 27.2%, respectively. Pangestu (2002) argues
that such a shift in Indonesia’s export structure, from NRI to TI products, may
explain the performance of manufactured exports. Within 18 years, total manufacturing exports had increased from a small base of $4.6 billion in 1987 to more than
$42.9 billion in 2005, a compounded annual average growth rate of 13.2%.
The role of foreign direct investment in Indonesia’s manufacturing exports
335
FIGURE 2 Foreign Direct Investment (Realised) (lhs) and Exports (rhs), 1990–2008
($ billion)
16
160
Service (lhs)
Industry (lhs)
Primary (lhs)
Merchandise (rhs)
Manufacturing (rhs)
Oil & gas (rhs)
Non-oil primary (rhs)
14
12
10
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8
140
120
100
80
6
60
4
40
2
20
0
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
0
Source: Authors’ calculations, based on data from Indonesia’s Investment Coordinating Board (BKPM)
and UN Statistics.
Note: FDI = foreign direct investment.
LITERATURE SURVEY
The linkage between FDI and exports
The linkage between inward FDI and a host country’s export performance has
long been recognised in the literature. Yet applied research faces the dificult task
of investigating theoretical predictions on the effect of shifts in production (and
exports of factor services) by studying multinational enterprises (MNEs) and
their effects on the nature and structure of a host economy’s trade (exports) (Wang
et al. 2007). Indonesia’s recent experience may be valuable in investigating how
developing countries could realise their export potential when factors and services are internationally mobile. The substantial differences in factor endowments
between Indonesia and more developed countries drive the exports of technology, management skills and networks and services – as FDI – from these countries
to Indonesia, which, according to theory, could stimulate exports from the labourabundant host country.
Kojima (1973, 1975) and Blomstrom and Kokko (1998) acknowledge the role
of complete ‘FDI-carried packages’ of capital, global networks, technology, and
management and entrepreneurial skills in improving host economies. Such packages increase productive capacity and enhance irms’ operating eficiency; they
also have industry-wide beneits, by disseminating technology and managerial
innovation, which can help average irms be better able to compete in international markets. FDI should thus increase the volume of a host country’s exports.
Yet FDI can also (a) lower or replace domestic savings and investment; (b) transfer
technologies that are at a low level or inappropriate, or both, in relation to a host
country’s factor proportions; (c) target a host country’s domestic market and not
increase exports; (d) inhibit the expansion of indigenous irms that might otherwise become exporters; or (e) hinder the development of a host country’s dynamic
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Rudy Rahmaddi and Masaru Ichihashi
comparative advantages when there is a narrow focus on local, cheap labour and
raw materials (Zhang 2005). The relationship between FDI and a host country’s
export performance therefore depends on the nature and motivation of the FDI in
question. In general, we may distinguish between the direct and indirect effects of
FDI: the former refers to the export activities of MNEs themselves, while the latter refers to the effect of FDI on the export activities of local irms (Helleiner 1989;
Zhang and Song 2000; Caves 2007).
The direct effects of FDI on a host country’s exports can be classiied into three
categories, based on their production characteristics: (a) the processing of local
raw materials, (b) the export of new, labour-intensive products, and (c) labourintensive processes and component specialisation within vertically integrated
international industries (Zhang and Song 2000). First, in processing local raw
materials, MNEs may have better competitive positioning than export-oriented
indigenous irms, given that they are likely to have global networks in place, as
well as more advanced marketing skills, superior technology (in both products
and processes) and better business acumen. Second, in their attempts to become
successful exporters of new labour-intensive inal commodities such as textiles,
footwear and other consumer goods, indigenous labour-intensive exporting irms
face several obstacles to expanding their exports to world markets: setting up
a global distribution network (and keeping its members up to date with rapid
changes in consumer tastes), mastering the technicalities of industrial norms and
safety standards, and increasing the competitiveness of their products. MNEs may
help indigenous export-oriented irms to penetrate world markets, by providing
links to inal buyers. Third, exports of labour-intensive commodities within vertically integrated industries rely on the participation of MNEs; many such exports
take the form of ‘arm’s length’ transactions between MNEs and indigenous irms
in a host country (Zhang and Markusen 1997), in which host countries import
uninished and intermediate goods and re-export them after additional processing and assembling. All of these MNE operations in export activities diversify a
host country’s export bundle.
FDI can also inluence a host country’s manufacturing exports indirectly. For
example, indigenous export-oriented irms can beneit from the presence of MNEs
by observing and learning from their export activities, so that they themselves can
then penetrate export markets; and local irms can expand their exports by using
existing transport, communications and inancial-services infrastructure (Haddad
and Harrison 1993). FDI can also increase the competitiveness of a host country
and accelerate the diffusion of new technologies: MNEs often have irm-speciic
advantages in product or process technology and in management and marketing,
so the entry of MNEs into a host country’s markets may increase competition and
encourage existing irms to adopt more-eficient methods, and hence to become
more productive (Zhang and Song 2000). The more productive domestic exporters there are in a host country, the more likely it is that they will survive in export
markets. Thus, the increased competition stemming from the presence of MNEs
encourages indigenous exporters to perform well in world markets.
FDI may also create linkages between MNEs and local irms, which may have
another indirect effect on a host country’s exports – technology spillover – often
through an MNE’s outsourcing practices. If export-oriented foreign subsidiaries
increase their purchases of local inputs or shift their production activities to
The role of foreign direct investment in Indonesia’s manufacturing exports
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local irms, they will improve these irms’ productivity and competitiveness. As
irms in host countries acquire new production capabilities, they will not only be
inclined to participate in export markets but they will also be able to produce and
export more diverse and sophisticated products. As such, the diversiication of a
host country’s exports can be seen as another indirect advantage of FDI (Iwamoto
and Nabeshima 2012).
Hill and Athukorala (1998) argue that any linkage between FDI and exports
may also depend on the nature of a host country’s speciic investment and trade
regimes. Such a linkage is therefore worth examining empirically.
Selected empirical literature review
Many empirical studies have analysed the linkage between FDI and a host country’s exports, yet their results have somehow been inconclusive. In an aggregate
analysis, Horst (1972) measured the effect of US FDI on US manufacturing exports
to Canada, using three-digit SITC cross-sectional data from 1963; he found that
FDI reduced US exports to Canada, and that Canadian tariffs increased US FDI
(owing to tariff jumping, which drives foreign irms to localise production within
the destination market to avoid tariff or non-tariff barriers). Using annual data
from 1970 to 1998, Sharma (2003) found no statistically signiicant evidence of
FDI having any effect on India’s exports. In contrast, other single-country studies,
including O’Sullivan (1993), of Ireland, and Blake and Pain (1994), of the UK, have
found that FDI increases a host country’s export performance.
Some cross-country literature using disaggregated data has indicated that
FDI’s effect on a host country’s export performance may differ by country, region
or industry. Using cross-country data from 1971 to 1992, Pain and Wakelin (1998)
found evidence of FDI inluencing exports in 10 of 11 OECD countries: seven countries saw their exports increase, and three (Japan, Italy and Denmark) saw them
fall. In explaining the latter result, the authors argued that foreign investment had
targeted relatively closed domestic markets, rather than using each country as
an export base. Investigating the effect of inward FDI on regional export performance in China during 1984–97, Sun (2001) found that the FDI effect was higher
in coastal regions than in inland ones. Taking into account differences in factor
proportions (that is, of comparative advantage) within manufacturing industries
in China, Wang et al. (2007), using 1983–2002 data, found that the effect of FDI on
the manufacturing exports of ULI industries was greater than that on those of PCI
industries.
Recent advances in the literature on the linkages between international trade
and investment have emphasised the effect on trade of changes in the comparative advantages brought about by foreign investment (Sun 2001). As FDI helps to
create an international division of labour and increase the mobility of production
factors – that is, not only of capital but also (and more important) of technology,
management skills and other knowledge – it may reallocate economic resources
and productive capacities according to the relative cost of production in various
countries. This may change countries’ comparative advantages, leading to shifts
in the structure and patterns of international trade. Sun (2001) suggests examining the industry-based linkages between FDI and exports as a plausible means
of studying FDI’s inluence on the structure of exports. Recent empirical studies
have also been devoted to examining FDI’s role in diversifying host countries’
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Rudy Rahmaddi and Masaru Ichihashi
exports, or in making them more sophisticated – for example, Banga (2006), on
India; Xu and Lu (2007), on China; and Iwamoto and Nabeshima (2012), on a
panel database of 175 countries.
Yet empirical research examining industry-based linkages between FDI and
manufacturing exports in the case of Indonesia has been limited, excepting Ramstetter (1999) and Van Dijk (2002), both of which consider the effect of MNE activities on export propensity by using irm-level manufacturing data. Both studies
found that, in general, foreign investment increased Indonesia’s exports. More
speciically, Ramstetter (1999), investigating the effect of foreign ownership on the
export propensity of 15,949 irms in 1990, 1992 and 1994, determined that irms
with high proportions of foreign ownership had a high ratio of exports to output. Van Dijk (2002), using data from Indonesia’s 1995 industrial survey, found
that MNEs mostly increased industry-based exports, except in beverages, footwear and instruments. Using census data of medium and large manufacturing
establishments in 1996 and 2006, Narjoko (2009) revealed that the contribution of
MNEs in expanding technological knowledge increased the likelihood of domestic irms entering export markets.
Narjoko and Maidir (2009) examined irm-level data from Statistic Indonesia’s
annual manufacturing surveys (Survei Industri) from 1990 to 2001. They showed
that a higher degree of foreign ownership of irms is associated with improved
export performance but that its effect varies across industries, and that its effect is
greatest among labour-intensive industries. A recent study by Jongwanich (2010)
on the determinants of the export performance of eight Asian economies (including Indonesia) conirms this. Using quarterly data from 1993 to 2008, Jongwanich
analysed the impact of total net FDI inlows on total merchandise exports, manufacturing exports (SITC 5–8), and exports of machinery and transport equipment
(SITC 7). The last is expected to capture the growing incidence of international
product fragmentation and trade in parts and components. Jongwanich concluded that the positive impact of FDI tends to be greatest for SITC 7 products in
the short run, and he found substantial evidence that FDI has a long-run exportgenerating effect on total manufacturing exports only. Nevertheless, most of the
above studies do not account explicitly for industry-based variation in the effect
of FDI on manufacturing exports classiied by factor intensity. This classiication
allows us to investigate whether FDI has changed the structure of manufacturing exports. This article uses and, to a lesser degree, emphasises disaggregated
FDI data and macroeconomic determinants. This approach supports our objective
of revealing the differential export-generating effect of FDI across industries and
particular macroeconomic determinants of export performance.6
In summary, the results in the studies above seem to support the positive role
of FDI in increasing exports. However, given the ambiguous linkage between FDI
6 We acknowledge the advantage of using micro-level data to reveal underlying mechanisms at the irm or plant level in scrutinising variations in the export-generating effect of
FDI across industries, while using less-disaggregated FDI data may sufice for pinpointing the export-generating effect of FDI and other macroeconomic determinants of export
performance across regions or sectors (see Leichenko and Erickson 1997; Zhang and Song
2000; Sun 2001).
The role of foreign direct investment in Indonesia’s manufacturing exports
339
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and a host country’s exports, it is unclear whether FDI affects the export performance of industries that hold a variety of comparative advantages. Industrybased analysis is perhaps the most appropriate method of elucidating the true
scale and performance of the linkage between FDI and manufacturing exports.
This article explores this relationship empirically, using data on Indonesian manufacturing exports and inward FDI.
METHODOLOGY AND DATA
Methodology
FDI is not the only factor that inluences the export performance of host countries.
We use a reduced form of the export equation (Goldstein and Khan 1978; Rose
1990; Athukorala 2004; Jongwanich 2010) to show that the level of real manufacturing exports (in 2000 constant prices), for example, is determined by the realworld income (Y*), a country’s production capacity (represented by gross capital
formation determined by foreign and domestic investment, and GDP growth)
and the real exchange rate (which represents the price of exports):
(
X = x Y*, FDIF, DCIF, GDPG, REER
)
(1)
While real-world income shifts demand, production capacity shifts supply.
Yet a small-country assumption implies that as many exports as a country could
offer would be absorbed by the world market. Thus, exports should be supplydriven, in this sense (Athukorala and Riedel 1996, inter alia). In other words, the
coeficient attached to real-world income should be insigniicant. Such an assumption allows us to estimate some export determinants (including FDI) whenever
data on industry-based export price indices are unavailable.
Since FDI tends to affect exports from the supply-side channel – whether
directly, via increases in productive capacity in export-oriented industries, or
indirectly, via export spillover (Markusen and Venables 1989) – we specify FDI
alongside other export determinants: domestic capital investment, GDP growth
and the exchange rate (including economic shocks). We do so by modifying an
export model used by Goldberg and Klein (1997), Zhang and Song (2000), and
Sun (2001):
X it = α i + β1FDIFit−1 + β 2 DCIFit−1 + β3GDPGt + β 4 REERt
+ β5 Dcr s st + β6 D roct + ε it
(2)
where the subscript i and t denote cross-sectional unit and time, respectively; ε is
the disturbance term; β1 to β4 are the parameters to be empirically estimated; Xit is
the level of manufacturing export value of industry i in year t; FDIFit–1 and DCIFit–1
account for stock levels of FDI and domestic capital investment, respectively, in
industry i in year t; GDPGt is the growth rate (as a percentage) of real GDP in year
t; and REERt is the index level of the real effective exchange rate (export-weighted)
in year t expressed in year 2000 constant price. To capture the effect of shocks that
may inluence the linkage between FDI and exports, we use two binary dummy
variables: Dcrisist captures the effects of the 1997–98 Asian inancial crisis and
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Rudy Rahmaddi and Masaru Ichihashi
other supply disruptions on manufacturing exports (using the value of unity for
1997–2003, and 0 otherwise), which lasted until 2003;7 Dproct captures the effect
of a change of export procedure (using the value of unity for 1997–2000, and 0
otherwise), which may lead to export misclassiication (for example, see Mudge
1999).8 All variables, except for GDP growth (in percentage form) and the dummy
variables (in binary form), are in natural logarithms.
The beta coeficients β1 to β4 are the elasticity of exports with respect to FDI,
domestic capital investment, GDP growth and the export-weighted foreignexchange rate, respectively. The value of the coeficient on FDIFit (β1) is of particular interest in this study, because this coeficient depicts changes in the proportion
of manufacturing exports to total exports in response to a percentage change in
FDI. Lagging explanatory variables FDI and domestic capital investment by one
year is justiied, for three reasons: (a) following Leichenko and Erickson (1997),
the effects of investments (such as the modernisation of production facilities,
adjustments in production structure and the dissemination of new technology)
on export performance are unlikely to take place immediately, since they require
time to inluence production; (b) a lag structure will mitigate any endogeneity
between exports and FDI (Zhang and Song 2000; Sun 2001) and will represent an
appropriate sequence, whereby investment precedes production and production
precedes exports; and (c) although the simple irst-order lag structure may not
be entirely appropriate in capturing potential feedback between investments and
exports, the relatively short period for the study (19 years) requires a simple lag
approach.
In addition to FDI, we specify other variables that may determine the performance of manufacturing exports. First, our inclusion of domestic investment in
the analysis of exports is intended to hold constant the effect of other investments
in general. We expect the coeficient of β2 to be positive, since increases in domestic
capital formation will augment productive capacity, thus enabling producers to
expand their output. Some previous studies (Leichenko and Erickson 1997; Zhang
and Song 2000; Sun 2001) indicated the importance of domestic investment to
export performance. Second, we include the GDP growth rate (GDPGt) in order to
capture in supply capacity the export-enhancing effect of increased economic performance. Thus, we expect the coeficient β3 also to be positive. We use the rate of
GDP growth, rather than its level, to reduce plausible direct simultaneity between
GDP and investment. Ideally, we would use growth in gross industry-based products to capture the effect of industry-based economic performance on manufacturing exports, but our methodology is limited by the availability of industry-based
7 Many studies, including Pangestu (2002) and Thee (2006), provide detailed explanations
of the effects of the 1997–98 Asian inancial crisis on exports and investment, as well as of
other economic disruptions that follow such a crisis. We use a dummy structure similar to
that of Adiningsih et al. (2009).
8 The authors are grateful to an anonymous reviewer for this invaluable insight. As indicated by UN-COMTRADE data, the value of the SITC 9 category (‘Not classiied elsewhere’) in Indonesia’s exports increased from $0.09 billion (0.2% of total exports) in 1996 to
$6.7 billion (12.5%) in 1997 and $8.0 billion (16.4%) in 1998, before decreasing to $0.4 billion
(0.6%) in 2000. Thus, about one-sixth of exports in 1998 were unclassiied.
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The role of foreign direct investment in Indonesia’s manufacturing exports
341
GDP data that can be matched appropriately with existing data on industry-based
FDI. Last, the exchange-rate variable, REERt, is another typical trade-related variable that may inluence exports, since it represents the competitive factor (the price
effect) of export commodities. Sugema (2005) found evidence of the positive effect
of exchange-rate depreciation on Indonesia’s non-oil exports. In our model, REERt
represents an index of the real effective exchange rate (year 2000 = 100), based
on the consumer price index and weighted by the currencies of Indonesia’s 15
main export partners. It is constructed in such a way that an increase in REERt
denotes real currency depreciation. As conventional export-demand theory predicts, the depreciation of a country’s currency may give impetus to further export
expansion. The depreciation (or appreciation) of a currency makes that country’s
export commodities more (or less) competitive, leading to increased (or decreased)
demand in the world market. Thus, we expect the coeficient β4 also to be positive.
Panel data imply that different methods can be used in estimations, including
the ordinary-least-squares (OLS) method and those of ixed effects and random
effects. The main problems inherent in the pooled OLS method are that it does
not allow for industry-based heterogeneity and that it assumes that all industries
are homogeneous. The ixed-effects method, in contrast, can capture the industrybased effect of FDI on manufacturing exports, since it models each effect explicitly. Likewise, the random-effects method can acknowledge heterogeneity in the
cross-section. Yet rather than explicitly modelling the predetermined heterogeneous effect by using an industry-based dummy, the random-effect method assumes
that the effects are random, independent and identically distributed over the error
term, so that uit = vi εit , where vi denotes the ith industry’s year-invariant unobserved heterogeneity, and where εit is the remaining disturbance (Zhang and Song
2000). Such a random effect can be estimated using a generalised-least-squares
(GLS) method. Hsiao (1986) argues that even though its results might be inconsistent when the number of observations is small and if the initial values correlate with the effects, the asymptotic bias of GLS is smaller than that of OLS. To
obtain the most appropriate inferences based on the ixed-effect or random-effect
method, we use Hausman statistics to test the null hypothesis that the regressors
and individual effects are not correlated. On one hand, a failure to reject the null
hypothesis implies that the random-effect method is preferable to the ixed-effect
method; on the other, a rejection of the null hypothesis implies that the ixed effect
is appropriate.
We irst estimate equation (2) by using the full sample of manufacturing industries (N = 11) for which we have relevant data to investigate whether growth in
Indonesia’s manufacturing exports in general can be attributed to FDI. To analyse the existence, scale and performance of any such relationship at the industry
level, we apply equation (2) to two subsample manufacturing groups classiied by
factor intensity: (a) NRI and ULI, which comprise ive industries (table 3); and (b)
PCI, HCI and TI, which comprise six. This method enables us to analyse the effect
of FDI on exports across different industries.
Our analytical model can be expanded to elucidate the effect of FDI on each
industry, by relaxing the equal-effect restriction on both groups. The results may
have imperative implications for the design of development strategy and guide
FDI in speciic industries.
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Rudy Rahmaddi and Masaru Ichihashi
Thus, we now assume that such an effect varies, as follows, across 11 industries:
11
11
X it = α1 + ∑ α i Dni + β1FDIFit + ∑ γ i (Dni FDIFit ) + β 2 DCIFit + β3GDPGt
n
=2
n
=2
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+ β 4 REERt + β5 Dcrisist + β6 Dproct + uit
(3)
where D is an industry dummy; n is a dummy number; i is the industry (say,
D2i is 1 for textiles and garments, and 0 otherwise); and γi is a differential slope
coeficient, just as αi is a differential intercept that captures an industry’s speciic
effect. If one or more of the γi coeficients is statistically signiicant, then one or
more slope coeficients will differ from the base group (for example, if β1 and γ2
are statistically signiicant, then (β1 γ2) will give the value of the FDI coeficient
for industry 2) (Gujarati 2004). We estimate equation (3) on the full sample, using
the ixed-effect model. In the following subsection, we describe the data in more
detail and provide a list of the industries in the two main categories.
Data
In this article, we have drawn on industry-based realised FDI datasets (Ijin
Usaha Tetap, IUT) obtained from BKPM, which holds three types of FDI data: (a)
approved FDI, the oficial approval of an initial investment plan; (b) an annual
investment activity report (Laporan Kegiatan Penanaman Modal, LKPM); and
(c) the IUT, which records the actual (realised) foreign investment disbursed to a
speciic project until it is ready to be initiated. BKPM registers such IUT datasets
as quasi-accumulated (gross) stock data, by industry. A similar typology applies
for data on domestic capital investment.9 Since exports are more likely to depend
on production capacity or capital (stock) than on additional capital (investment
low), the IUT data are the most appropriate for analysis.10
First we grouped industries by factor intensity, deriving ive main categories:
NRI, ULI, PCI, HCI and TI. This typology aligns with that of Aswicahyono and
Pangestu (2000). To synchronise with BKPM’s data on realised FDI and domestic
ixed-capital investment by industry, we regrouped the datasets into two main
categories: the NRI–ULI group, and the PCI–HCI–TI group. The former represents industries in which Indonesia has a comparative advantage (the NRI,
9 Using net FDI and domestic investment stocks by industry is beyond our extent here,
owing to the lack of any records of capital outlow or disinvestment by project. Since the
realised lows into the projects take place and accumulate for more than a year, they should
be suficient for capturing the accumulated effects of FDI and domestic investment on exports. The IUT datasets of FDI and domestic investment, which are available from BKPM,
are thus the most complete data on quasi-accumulated (gross) stocks, by industry. The
related explanation is based on the oficial statement of Ir. Hanung Harimba, a former head
of BKPM’s Center for Investment Data and Information (Pusdatin). The use of gross rather
than net FDI stocks may result in estimation bias. Any interpretation of results should be
made with caution.
10 The datasets are published but are not publicly available; BKPM granted the authors
access to the datasets and permission to use them.
The role of foreign direct investment in Indonesia’s manufacturing exports
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TABLE 3 Commodity Classiication Based on Factor Intensity
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NRI–ULI group
Cork & wood
Non-metallic minerals
Textiles & garments
Leather & footwear
Other manufactured commodities
PCI–HCI–TI group
Chemicals & pharmaceuticals
Rubber & plastics
Pulp & paper/paperboard
Metal goods, machinery &
electronics
Road vehicles & other transport
equipment
Medical & optical instruments
Abbreviation
SITC
W
NMM
TEX
LF
OI
63
66
65, 84
61
89
CP
RP
P
51, 52, 54, 59
57, 58, 62, 893
64
MME
67–69, 72–74, 76, 77, 751, 752, 759
RV
MO
78, 79
87, 88
Source: Indonesia’s Investment Coordinating Board (BKPM).
Note: SITC = Standard International Trade Classiication (rev. 2). NRI = natural-resource intensive.
ULI = unskilled-labour intensive. PCI = physical-capital intensive. HCI = human-capital intensive. TI
= technology intensive. Initial categorisation follows Aswicahyono and Pangestu (2000) but has been
reclassiied to make it compatible with sector-based FDI and domestic investment data available from
BKPM.
low-labour-cost and low-technology industries); the latter a comparative disadvantage (the capital-intensive and technologically complex industries).11
We match the value in dollars, by industry, of realised FDI (IUT) and domestic
ixed capital investment with the export value of each commodity, by SITC (rev.
2), obtained from the UN Statistics Database of Commodity Trade (Comtrade).
The panel datasets cover 11 manufacturing industries during 1990–2008, yielding
11 Combining the NRI–ULI and PCI–HCI–TI industries into two single homogeneous
groups, based on factor intensity, even for econometric purposes, may lead to potential
analytical bias, since any given industry can use mixed-factor intensity. For instance, the
electronics industry includes labour-intensive assembly processes, and the textiles and
garments industry is capital- and labour-intensive. Lall (2000) distinguishes between
manufactured exports, based on the technology categories involved: resource-based, low
technology, medium technology and high technology. With some exceptions for particular
commodities, products of the NRI (resource-based) and ULI industries tend to be labour
intensive and use a low level of technology, whereas those of the PCI, HCI and TI industries use the bulk of Indonesia’s skill- and scale-intensive technologies to produce capital
and intermediate goods. Although not a perfect classiication of industries by factor intensity, our attempt indicates approximate differences in technology levels between those two
industry groups. We retain this arbitrary classiication to reveal any relative inluence of
some macro-level determinants on manufacturing export performance in various industry
groups. Any interpretation of the results should take such limitations into account.
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Rudy Rahmaddi and Masaru Ichihashi
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182 observations within the full sample (unbalanced), 87 for the NRI–