The role of foreign direct investment in Indonesia's manufacturing exports

Abstract

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, classified by factor intensity. Our analysis reveals that FDI promotes exports in most panel observations, especially exports from physical-capital-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 influence of other determinants of export performance – namely, private domestic capital investment, GDP growth and exchange rates. Our findings have implications for policymakers seeking to sustain Indonesia's export performance.

Tulisan ini berisikan analisis mengenai kontribusi penanaman modal asing (FDI) terhadap struktur sektor manufaktur Indonesia yang berorientasi ekspor. Data yang digunakan merupakan data pada tingkat industri dari tahun 1990 sampai 2008, yang digolongkan berdasarkan intensitas faktor produksi. Berdasarkan pengamatan terhadap data panel, kajian ini mengungkap bahwa FDI berhasil meningkatkan ekspor, khususnya ekspor dari industri padat modal fisik (physical-capital-intensive/PCI), industri padat modal manusia (human-capital-intensive/HCI), dan industri padat teknologi (technology-intensive/TI). Dengan menggunakan model efek tampang lintang (cross-section-effect model), ditemukan bahwa potensi ekspor dari penanaman modal asing lebih besar pada industri padat modal fisik, industri padat modal manusia, dan industri padat teknologi dibandingkan dengan industri padat sumber daya alam atau industri padat tenaga kerja tidak terampil – keduanya merupakan keunggulan komparatif yang dimiliki Indonesia. Selain dari variabel penanaman modal asing, dikaji juga dampak dari beberapa determinan lain, seperti penanaman modal swasta dalam negeri, pertumbuhan PDB, dan nilai tukar. Temuan dalam tulisan ini relevan bagi pembuat kebijakan yang menginginkan kinerja ekspor Indonesia berkelanjutan.

Keywords:

• export performance
• foreign direct investment
• panel analysis

* 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 financial support under phase three of the Professional Human Resource Development Project. The usual disclaimers apply.

Notes

¹ FDI may take many forms, including greenfield 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 outflows on exports is beyond the scope of this analysis, which uses the terms FDI and foreign investment interchangeably to refer to FDI inflows.

² According to Indonesia's Investment Coordinating Board (Badan Koordinasi Penanaman Modal BKPM), which excludes foreign investment in oil and gas and the financial 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.

⁵ SITC, or the Standard International Trade Classification of imports and exports. SITC 5 = chemicals and related products, not elsewhere specified. SITC 6 = manufactured goods classified chiefly by material. SITC 7 = machinery and transport equipment. SITC 8 = miscellaneous manufactured articles.

⁶ We acknowledge the advantage of using micro-level data to reveal underlying mechanisms at the firm or plant level in scrutinising variations in the export-generating effect of FDI across industries, while using less-disaggregated FDI data may suffice 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).

⁷ Many studies, including Pangestu (2002) and Thee (2006), provide detailed explanations of the effects of the 1997–98 Asian financial 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 classified else-where’) 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 unclassified.

⁹ Using net FDI and domestic investment stocks by industry is beyond our extent here, owing to the lack of any records of capital outflow or disinvestment by project. Since the realised flows into the projects take place and accumulate for more than a year, they should be sufficient 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 official 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.

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 classification of industries by factor intensity, our attempt indicates approximate differences in technology levels between those two industry groups. We retain this arbitrary classification to reveal any relative influence of some macro-level determinants on manufacturing export performance in various industry groups. Any interpretation of the results should take such limitations into account.

¹² Export price indices are not available for disaggregated industries, so we have used Indonesia's GDP deflator as a proxy. This is justified, since merchandise exports represent the largest share of total exports (Kee and Hoon 2005). The use of a GDP deflator with an international tradable price index can be found in the literature (for example, Heien 1968; Goldstein and Khan 1976). Our experiment in using CPI and the producer price index as export price deflators gave poor results, and data from the International Financial Statistics export price index are available only up to 2005. In addition, we use the gross-capital-formation (GCF) price index – calculated by dividing the current GCF value of Indonesia, in dollars, by its constant value – as a proxy for the investment deflator. Both values are from the World Bank's World Development Indicators.

¹³ See <http://stats.oecd.org/Index.aspx?DataSetCode = STAN_IO_TOT_DOM_IMP>.

¹⁴ This part, however, should be interpreted with caution, since export figures do not perfectly measure an industry's technological development. For instance, industrial classifications based on levels of technological intensity may be misleading wherever low-technology products can use relatively complex technological processes, or when high-technology exports include low-value-added assembled products (Okamoto and Sjöholm 2001). Nevertheless, such export figures can still act as rough indicators of technological competence (Thee 2006).