Firm- and Plant-level Analysis of Multinationals in Southeast Asia:
the Perils of Pooling Industries and Balancing Panels
Eric D. Ramstetter
International Centre for the Study of East Asian Development
and Graduate School of Economics, Kyushu University
Email: ramst@icsead.or.jp
October 2009
Abstract
This paper uses micro data and published compilations of micro data to estimate shares ofmultinational corporations (MNCs) in Southeast Asian manufacturing. It first shows that
MNC shares tended to be largest in Singapore, intermediate in Malaysia and (recently) in
Vietnam, and lowest in Thailand and Indonesia. Shares tended to decline in Singapore andThailand, were relatively constant in Malaysia, and increased in Indonesia and Vietnam.
Shares of majority foreign MNCs also increased conspicuously in Indonesia and Thailand as
MNCs bought out local partners in joint ventures after the Asian crisis. Second, it highlights
how MNC shares were always lowest in terms of the number of plants or establishments, or inother words, how MNCs tended to be larger on average than local firms or plants. MNCs alsotended to account for larger shares of production than employment, and even larger shares ofexports. Hence MNCs tended to have relatively high labor productivity and export
propensities. Because these simple comparisons do not account for other influences on
productivity, wages, or exporting, for example, the paper also describes how micro-data havebeen used to analyze productivity, wages, and export propensities. This literature suggests that
productivity differentials were generally positive but often statistically insignificant,especially at the industry level. Wage differentials were also positive and more often
significant, but the largest and most consistent differentials are observed in export
propensities. Third, the paper also reviewed literature suggesting positive productivity andwage spillovers in Indonesia, Thailand, and to some extent Vietnam. However, such analyses
need to be treated with caution because unwarranted pooling across manufacturing industries
is common and has the well-known potential to bias estimates. In addition, the paperemphasized that balanced panels can create important sampling biases because of large
turnover that is particularly conspicuous among small non-MNCs in this dynamic region.
Key Words: micro data, manufacturing, Southeast Asia, multinational corporations
JEL Categories: C81, C83, D21, F23, L60, O14, O53
Acknowledgements: This is a substantial revision of a paper prepared for the 2009
Comparative Analysis of Enterprise (Micro) Data (CAED), which was held 2-4 October at
Hitotsubashi University, Tokyo. The author would first like to thank the organizers of theconference, especially Kyoji Fukao and Ryo Kambayashi, for financial support and guidance
regarding this paper’s content. I am also grateful to Shahrazat Binti Haji Ahmad for research
assistance with the Malaysian data used in this paper and to Sadayuki Takii for advice on theIndonesian literature and related issues. However, the standard disclaimer applies: I alone am
responsible for the content of this paper, including any errors and all opinions expressed.
11. Introduction
The study of multinational corporations (MNCs) and their activities has long been
confounded by the lack of data, especially in developing regions like Southeast Asia.
Fortunately, data on such activities have become increasingly available over the last two
decades and the literature using such data to analyze MNC activity has increased markedly asa result. However, there is no recent review highlighting the strengths and weaknesses ofexisting data and analyses using these data. This paper attempts to fill that part of that gap byreviewing the data and literature on Southeast Asia.
More specifically, the paper uses the micro data and published compilations to estimate
shares of foreign-owned multinational corporations (MNCs) in the manufacturing sectors ofthese economies to illustrate important characteristics of the data sets and the related literature.
Because the data sets involved differ greatly across countries, the paper is organized bycountry. Care is taken to describe the characteristics of each data set and to highlightproblems encountered when using the data. Reflecting the availability constraints, the paper
focuses on micro data for Indonesian establishments or plants (Section 2), Malaysian plants(Section 3), Thai plants and large enterprises or firms (Section 4), and Vietnamese firms(Section 5). Shares calculated from official compilations for Singaporean plants are also
examined, although the underlying micro data are not available to researchers (Section 6).
And although there is only one known compilation, potential sources of micro data that could
allow more comprehensive estimation of MNC shares for the Philippines and related studies
are also mentioned in Section 6, before some concluding remarks are offered (Section 7).
To illustrate the roles of MNCs in these economies and some important characteristics ofrelated data sets, the paper addresses three basic questions. First, how have MNC sharesvaried across these economies and time? Second, how do MNC shares depend on the2economic activity being measured and what are the implications of these differences? Third,
how has the economic literature used these data to compare related activities (e.g., laborproductivity, wages, exporting) in MNCs and local firms or plants, or how MNC presence
may affect the activities of local firms or plants through so-called spillovers? Spillover
analyses have become very popular in recent years and sometimes perform estimates usingbalanced panels in large cross sections encompassing many manufacturing industries.
Unwarranted pooling of industries has the well known potential to bias coefficient estimates
and the creation of balanced panels forces the analyst to sacrifice a large amount of
information on new entrants and exiting firms or plants. Correspondingly, the paper highlights
differences between estimates at the industry-level and estimates for all manufacturing
combined, as well as the characteristics of balanced panels in this dynamic region.

2. Indonesia
Indonesia’s main statistical agency, Badan Pusat Statistik (BPS) has compiled relatively
comprehensive, surveys and censuses of manufacturing plants since 1975. 1 The annual
surveys (except in census years) and three censuses conducted in 1986, 1996, 2006 are
relatively comprehensive in coverage. For example, the 1996 census sample was not muchlarger than survey samples in surrounding years (7 percent compared to 1995 3 percent if
relative to 1997, Table 1). BPS’ practice of estimating missing values for several nonreporting plants by using information from surrounding years or other, similar plants, is one
reason that sample size has been rather consistent through survey and census years. However,there is a notable break with the most recent census for 2006, when the sample jumps 42
percent in a single year to 29,468.
1

See Indonesia, Badan Pusat Statistik (various years). BPS was also called Biro Pusat
Statistik in the past, as well as the Central Bureau of Statistics and BPS-Statistics in English.
3Sample coverage rates were lowest in terms of in terms of employment (36 percent or less)in 1990, 1999, 2001-2002, and 2005, but higher (40 percent or more) in 1992-1993, 1995,
1998, and 2006 (Table 1). Comparisons with GDP estimates for the entire economy suggest
the lowest coverage rates (59 percent or less) were in 1990-1991, 1997, 1999, and 2001-2006,
while coverage was higher (65 percent or more) in 1992-1996 and 1998. Likewise the implied
coverage of export estimates was good in 1991-1997 (79-111 percent), but lower (63-69
percent) in 1990 and 2006, and a less than half (41-46 percent) in 1999-2000 and 2004.2
These calculations highlight how smaller plants (19 or fewer employees) are very important inIndonesia, especially for job creation. The relatively large coverage rates in the early- to mid1990s, and the large increase in the number of plants in the 2006 census, both suggest thatsurvey coverage tended to be relatively low in the early 21st century. One possible reason for adrop in coverage was that the post-Suharto era reforms have included a number of measuresthat increased the financial and bureaucratic burden on medium-large plants. This may haveencouraged a number of smaller plants to reduce employment or break themselves up intoplants with fewer than 20 employees. BPS and other government agencies have also been
affected by budget cuts during this period.
From the early 1990s through 1998, BPS prepared two data sets to check the coverage ofthe data, the so-called raw data sets which had a large number of variables as reported andprocessed annually by BPS, and the so-called backcast data sets which had few variables, butwere subject to more rigorous checking for non-reporting or unrealistic values.3 In the raw
data sets, the number of variables collected was greatly expanded in late 1980s and early2

Exports for survey/census plants are estimated as the export propensity times gross output.
Economy-wide estimates are product-based and use a broad definition of manufacturingexports, including many food and mineral products often excluded from narrower definitions(United Nations Industrial Development Organization 2009).
3
See Takii and Ramstetter (2000) for a detailed comparison of data from the 1998 backcast
and the annual raw data for 1985-1998. The 1998 backcast was apparently the last.
4

1990s, and subsequently reduced in the early 21st century. Two notable additions in 1988 and1990, respectively, were estimates of fixed capital stocks and export propensities. However,these important variables have not been collected consistently since 1996 and 2001,
respectively. Problems with extremely low export coverage since 1999 also make analyses ofdata for these years suspect.4 There are also large fluctuations in the amounts of fixed assets
and corresponding MNC shares, which are unrealistic and indicative of severe inconsistencies
in the definition and/or reporting of this variable (Table 1).5
Table 1 also shows the shares of two groups of MNCs, all MNCs and majority-foreign
MNCs, in the number of plants, the number of workers, value added, and exports. Shares of
all MNCs grew from 10 to 24 percent of sample employment and from 22 to 40 percent ofsample value added in 1990-2006. However, again highlighting the importance of small
plants, corresponding MNC shares of total employment, which increased from 3.5 to 10
percent, and manufacturing GDP, which increased from 13 to 22 percent, were markedly
lower. Because these surveys and censuses probably covered most MNCs but do not cover
many small, non-MNCs, the latter shares are a more accurate indication of overall MNC
presence in Indonesian manufacturing.
Since the early 1990s, the micro data collected in the surveys and censuses have become
widely available to researchers, and the literature using these data boomed in the late 1990s
and thereafter. This literature is vast and goes back to important older studies of Indonesia
such as Hill (1988). In recent years, perhaps the most common topic of analysis has been the4

As noted in Table 1, export propensities for 1998 were not plausible for many plants.
Reflecting the problems in the data on fixed assets, many studies (e.g., Lipsey and Sjöholm
2004a, 2004b; Ramstetter and Takii 2006; Sjöholm and Lipsey 2006; Sjöholm and Takii
2006; Takii and Ramstetter 2005) use variables such as electricity consumption as a proxy
fixed capital, because they are thought to have been compiled relatively accurately and arethought to be highly correlated with the use of fixed capital. There is also an investment series
in the data which have been used in conjunction with simplifying assumptions about thenature of technology (Sjöholm, 1998, 1999a, 1999b).
55issue of whether greater foreign presence is associated with relatively high or low productivityof local plants in the same industry, or in other words, the extent to which productivity
spillovers are observed. Assuming relatively simple Cobb-Douglas technology, crosssectional analysis by Blomström and Sjöholm (1999) and Sjöholm (1998, 1999), found thatspillovers tended to be positive, but the extent of spillovers depended on a number ofindustry-characteristics such as the degree of competition, technology gaps between MNCs
and local plants, and to some extent on the degree of foreign ownership. Using panel data andmore flexible, translog functions, Blalock and Gertler (2004) find evidence of positive
spillovers through vertical supply chains and Takii’s (2005, 2006) results suggest that
productivity spillovers have usually been positive, but more prevalent in industries with small
technical gaps and where minority foreign MNCs had relatively large shares.
In related analysis, Takii (2004, 2006) and Takii and Ramstetter (2005) find evidence that
MNCs were generally more productive than non-MNCs, but that these differences often
became insignificant if production functions were estimated for separate industries. Ito(2004a) and Okamoto and Sjöholm (2000) provide evidence for the automobile industry
suggesting that MNCs had relatively high productivity. Here it is important to note that many
analyses, including most spillover analyses, assume identical technology, either in all
manufacturing industries or in groups of many industries. However, the results of estimatingproduction functions at a more disaggregate level often indicate large differences in manyslope coefficients (technology parameters) across industries, suggesting that pooling acrossindustries may be inappropriate.
Lipsey and Sjöholm (2004a, 2004b, 2005, 2006) and Sjöholm and Lipsey (2006) findsubstantial wage differentials between MNCs and local plants and that the wage differentials
persist even after industries are disaggregated and when the educational level of the workforce
6and other controls are considered. Moreover, they also provide evidence of positive wage
spillovers from MNCs both from regressions and from comparisons of wages before and after
takeovers or sales by MNCs. Simultaneity concerns are important when examining spillovers,
especially when using cross sectional data, because MNCs may be attracted to industries
where productivity or wages are high, for example. The concerns can be partially alleviatedby using panel data or by comparing productivity and wages before and after takeovers.
Similarly, Sjöholm (2003) examines the determinants of exporting in the mid-1990s whensurvey coverage was apparently best, emphasizing the finding that MNCs had a greater
tendency to enter exporting and interpreting this as a result of relatively low, export-related,sunk costs in MNCs, who have access to relatively extensive foreign networks. Ramstetter(1999b) and Ramstetter and Takii (2006) also present results for the 1990s in a series of crosssections or pooled-cross sections of 2 years each, suggesting that MNCs have a higher
propensity to export than local plants and that export propensities tend to be highest among
MNCs with large (90%+) foreign ownership shares. On the other hand, panel GMM estimatesby Sjöholm and Takii (2006) for 1990-2000 suggest that MNC takeovers increased theprobability of a higher export propensity while local takeovers had the reverse effect, but thatchanges between minority- and majority-foreign statuses had no significant impact.
It is also important to highlight the fact that, analyses using balanced panels during thisperiod can be hazardous for Southeast Asian economies like Indonesia, primarily because
high entry and exit rates over relatively short periods of time result in substantially lower
coverage for panels than for unbalanced panels or a series of cross section analyses covering
the same period. Correspondingly, most panel studies of Indonesia, including most of thosecited above, rely on unbalanced panels. Because the coverage Indonesian samples tended to7

be relatively comprehensive for most of the 1990s, low panel coverage probably results
primarily from high entry and exit rates.
For example, even if one tries to create a small, two-year, balanced panel for 1990 and
1996, the resulting panel covers less than half of the plants, three fifths of employment, andthree-fourths of value added in 1996, though these shares were larger in 1990 (60%, 77%, and
87% percent, respectively). However, members of the balanced panel for 1990-1996 wereclearly larger and had higher average labor productivity than the average plant. MNC sharesalso tended to be slightly larger in the panel for 1990, with large differences in MNC shares ofexports (23% vs. 7%), which were not well covered in this year. By 1996, this story was
reversed, however, with MNC shares 4 percentage points lower in terms of employment, 2
percentage points lower in terms of value added, and 9 percentage points lower in terms ofexports. In short, the panels understate the growth of MNC shares for the period, in addition
to consisting of plants with relatively high value added and exports per worker.
Comparisons of the balanced panel for 1996-2000 with original data also suggested that thepanel estimates understated MNC shares in the latter year but slightly overstated them in theinitial year. However, similar patterns are not observed in the two panels involving 2006,
beginning in 1996 and 2000, respectively. In principle, one would have the most faith in the
1996-2006 panel because these were census years. This balanced panel resulted in lower
MNC shares for employment, value added, and exports in both years, and the differenceswere larger (in absolute value) in 2006 than in 1996. Moreover, the coverage of these panels
was also limited to about two-thirds or three-fourths of employment, value added and exports.
These low coverage rates reflect the impact of the 1997-1998 economic and political crisis,
which eventually led to substantial restructuring and turnover among firms and plants. Thus,
there are important differences between the balanced panel samples and the overall samples,8

which affect estimates of MNC shares, average labor productivity, and export propensities,
even when care is taken to include census years and thus maximize panel coverage.

3. Malaysia
Changes in Malaysia, particularly those brought on by the 1997-1998 crisis and Malaysia’s
somewhat unorthodox response to the crisis, also resulted in large turnover and low panel
coverage in 1994-1999, 62 percent of employment and 76 percent of value added in 1994, and
76 and 89 percent, respectively, in 1999 (Table 2). Because both of these years were sample
years and Malaysian surveys cover a relatively small portion of plants compared toIndonesian surveys, for example, these coverage rates should probably be consideredrelatively high given the survey design. Coverage of another panel including the census year
2000 and the most recent survey year 2004, was somewhat higher, 75 and 85 percent of
employment and value added, respectively, in 2000, and 87 and 85 percent, respectively, in
2004. Coverage of plant numbers tended to be even smaller (2004 in the 2000-2004 panel
being the major exception), again suggesting that balanced panels generally included
relatively large plants. Higher coverage rates in the 2000-2004 panel also suggest that
Malaysian manufacturing settled down somewhat after the crisis, despite substantial
disruption experienced after the 2001 dot.com crash and the related downturn in theelectronics sector, which is very important to the country (Ramstetter and Haji Ahmad 2009).
The Malaysian panels also consistently overstate MNC shares relative to the cross sections
encompassing all sample or census plants. The degree to which MNC shares were overstated
was relatively large (3 percentage points or more) for employment in 1994, 1999, and 2000 in
related panels, for the number of plants in the same three years, and for fixed assets andexports in 2004 (Table 2). On the other hand, panels yielded relatively small differences9between panel shares for MNCs and cross section shares (1 percent or less in absolute value)
for value added and fixed assets in 1994, fixed assets in 2000, and employment, value added,
and number in 2004. Given the relatively high level of MNC shares in Malaysia, differencesof this scope may not be that important to comparisons of MNCs and non-MNCs, but they doindicate biases which need to be carefully considered in any balanced panel analysis.
One notable change in the 2000 census and subsequent surveys was the addition of aquestion asking for export propensities. Implied exports of sample plants (Table 2) were 76
percent of total manufacturing exports (from Bank Negara Malaysia 2009) in 2000-2002 and73 percent in 2003, but only 52 percent in 2004. In other words, the 14 percent decline inexports of sample plants in 2004 was the result of poor sample coverage in that year; total
manufacturing exports actually increased 20 percent in that year. As in Indonesia, MNC
shares tended to be largest in terms of exports, 68-72 percent in 2000-2003. This share waslower in 2004, suggesting that the 2004 sample omitted export data for some large MNCs.
Historically MNCs have been far more important in Malaysia and Singapore than other
Southeast Asian economies. Moreover, majority-foreign MNC shares were sometimes higher
in the 1970s for these economies than in the 1980s, or even during the FDI boom of the earlyto mid-1990s (Ramstetter 1999a). Majority-foreign MNC shares of employment (about twofifths), value added (a little over two fifths in most years), exports (about two-thirds in 20002003) were all substantial (Table 2). Malaysia’s surveys/censuses also cover a relatively large
portion of overall manufacturing. For example, sample plants accounted for 64 to 84 percent(73 percent mean) of total manufacturing employment in 1994-2004 and ratios of GDP
produced by these plants to total manufacturing GDP ranged between 94 and 103 percent in
1994-1996 and in 1999-2004. In 1997 the ratio ballooned to 201 percent because of an

10

apparent error in the plant-level data obtained. MNC shares were again smallest in terms of
their number (10-15 percent in survey years, 8 percent in the census year).
In other words, MNCs tended to be relatively large by any measure and to have relatively
high average labor productivity and export propensities. MNC-local differences in averagelabor productivity were relatively small (i.e., MNC shares of employment and value added
were similar). They were also inconsistent across manufacturing industries and years during
2000-2004, but differences in size and export propensities were much larger and consistent(Ramstetter and Haji Ahmad 2009). Similar calculations also indicated positive, relatively
large and consistent MNC-local differentials for plant size and compensation per worker.
Access to the Malaysian plant-level data described in this section has been difficult toarrange according to several sources. However, Haji Ahmad (2010), Khalifah and Adam(2009), and Ramstetter and Haji Ahmad (2009) have recently used the micro data and relatedcompilations. Thus, it is likely that more plant-level analyses of the role MNCs play in
Malaysian manufacturing will be forthcoming. Several past studies (Athukorala, and Menon
1996; Menon 1998; Oguchi et al 2002, 2004; Okamoto 1994) were also able to arrange forunpublished industry-level compilations from these data.
As in Indonesia, much of the academic literature has been concerned with trying to evaluate
MNC-local productivity differentials and the extent of productivity spillovers from MNCs tolocal plants. Menon (1998) first provided evidence that total factor productivity (TFP) tended
to grow more slowly in MNCs than in local plants in the late 1990s. Oguchi et al (2002)
added further evidence that TFP growth was relatively slow in MNCs and provided important
evidence that productivity levels tended to be slightly higher in MNCs. More recently
Khalifah and Adam (2009) analyze balanced panel with a simplified Cobb-Douglas
specification (assuming constant returns to scale) and samples of all manufacturing plants
11combined, finding that productivity spillovers were positive when MNC presence is measured
as the share of value added or fixed assets, but insignificant or negative when MNC presence
is measured as the share of employment. Spillovers are also found to depend on the foreign
ownership shares. They also examine productivity differentials with a fixed effects
specification in the same sample, suggesting firms taken over by MNCs often had relatively
low labor productivity. 6 In this case, the failure to account for large differences in slope
coefficients across industries as in Haji Ahmad (2010) may be related to the results obtained.
There are a number of other potentially interesting sources of micro data in Malaysia. The
financial survey of limited companies is no longer published, but it is a valuable source ofdata for 1969-1995 (Ramstetter 1999a). The Malaysia Industrial Development Authority(MIDA) also maintained data on the employment, fixed assets, and paid-up capital of FDI
projects from the 1980s until 1996, but this series has not been published for more recent
years. There are also numerous other surveys (e.g., Lee 2004, 2008; Phang 1998; Rasiah 2003,
2004), which have been conducted periodically for specific purposes, though most of thesedata are less comprehensive than the industrial censuses/surveys and are proprietary.

4. Thailand
Numerous sources of micro data have also been used to study firm and plant behavior in
Thailand. Early studies analyzed firms and projects promoted by the Board of Investment(BOI). 7 These studies generally failed to find statistically significant differences between
various productivity measures for MNCs and local firms. Some of these papers speculated

6Note that the authors misinterpret this result and do not examine the static, cross section
question of whether MNCs had higher productivity at a given point or period in time.
7
See studies of 1973-74 and 1985-86 by Tambunlertchai and Ramstetter (1991), 1982-83 by
Khanthachai et al. (1987) and 1990 by (Ramstetter 1993, 1994).
12

that BOI-promoted firms were among the more productive local firms and that the puzzling
result (of few significant, productivity differentials) would be reversed if a morecomprehensive set of data on Thai firms could be accessed.
More comprehensive samples became available in the National Statistical Office’s (1999)
industrial census for 1996. Tests using these data continued to suggest that MNCs generally
had higher productivity by a number of measures, but that variation was large among both
MNCs and non-MNCs and differentials were often insignificant statistically (Ramstetter
2006). Ito’s (2004b) results for the automobile industry were similar. On the other hand, crosssection results for 1996 from Kohpaiboon (2006a, 2006b) and suggested positive productivity
spillovers from MNCs that were relatively strong in industries with relatively low protection(see also Ramstetter 2006). The 2006 census has recently become available, but access to themicro data is now more limited than before and no known studies use these data yet.
As in the Indonesian and Malaysian cases, MNC-local differences in wages and exporting
were positive and more consistent in 1996 than corresponding differences in productivity.
However, the evidence regarding wage differentials (Movshuk and Matusoka-Movshuk 2006)
is weaker than in the Indonesian case because the 1996 Thai census did not contain data on
worker education levels, making it impossible to control for the influence of this importantcause of wage differentials. Export propensities appear to have been significantly higher in
MNCs than in local plants and higher in heavily-foreign-owned (90%+ ownership share)
MNCs than in other majority- or minority-owned affiliates (Ramstetter 1994, 1998, 2006).
The 1996 and 2006 censuses are the only comprehensive micro data sets on the Thaimanufacturing (National Statistical Office 2006). In 1996, the universe of establishments wasestimated at 32,489 and this grew to 73,391 in 2006 (Table 3). The large increase in thisdecade also appears related to the inclusion of a large number of plants with employment
13

below the 10 person cutoff used in the 1996 census. Census coverage rates were quite high interms of gross output (value added plus intermediate consumption), with the census output
amounting to 91 and 89 percent in 1996 and 2006, respectively, of total income reported in
the national accounts by the National Social and Economic Development Board (NESDB).8
Compared to labor force survey estimates, the coverage of manufacturing employment wasmuch lower, however, and increased from 56 to 68 percent. These censuses indicate that
overall MNC shares of decreased markedly during this period from 39 to 25 percent of census
employment and 54 to 43 percent of census output. However, part of the reason for the
decline was the large increase in the number of small, primarily local establishments coveredin the 2006 census. If MNC shares of all manufacturing are calculated, the decrease was also
substantial, from 22 to 17 percent of employment, and from 49 to 37 percent of gross output.
Although the 1996 and 2006 censuses are by far the most comprehensive data on Thaimanufacturing, researchers faced important problems when trying to use the 1996 data for
analytical purposes and not much is known about the 2006 data yet. First, National Statistical
Office officials said that factory identity codes change from time to time, which precludes thecreation of panels with these data. Second, at least for 1996, the sample included a large
number of duplicates or near duplicates (Ramstetter 2006), many of which appeared tocontain firm-wide estimates for some groups of factories belonging to multi-plant firms.
There were also a large number of plants that report unrealistic (non-positive) values for basic
variables such as employment or revenue in both censuses. Thus, samples used for analytical
purposes have generally been much smaller than the universe of plants in the census. There

8

Gross output as reported by the NSO and total income as reported by the NESDB areconceptually similar and should be of similar magnitude. However, there are often large
differences between NESDB and NSO estimates even for census years (as seen in 2006).
Differences are even more obvious at the industry-level, where there are several large
mismatches despite the use of apparently identical industry definitions.
14were also several surveys between the two censuses, but survey coverage is much more
limited and not very consistent. For example, the employment of surveyed plants amounted toonly two-fifths of 1996 (census) levels in 1998, one-fifth in 1999, and a little under one-thirdin 2000 (Table 3).9 In short, the coverage in survey years is so low and inconsistent that it isdifficult to consider these samples representative.
Partially because access to the industrial census and survey data has not been easy toarrange, and partially because there are no good time series data on the real activities (e.g.,
employment, revenue) of MNCs in Thailand, researchers have used compilations based on
mandatory reports to the Ministry of Commerce. For example, Ramstetter (2003) compiled
employment and revenue data for a rather comprehensive sample of large firms in 1990-1991
and 1994-2000, while Kohpaiboon and Ramstetter (2008) use data from a smaller sample ofthe largest firms in each of 55 3- or 4-digit industries for the census years (1996 and 2006).
Table 4 includes data from the latter dataset for comparison and suggests three importantpatterns. First, large firm revenues grew slightly more rapidly than plant output (113 vs. 101
percent for the decade). By 2006 large firm revenues amounted to 92 percent of plant output
reported in the industrial census and 80 percent of the total income reported by NESDB.
Second, the large firm data contrast with the census’ plant data by suggesting that MNC
shares rose from 58 to 69 percent of large firm revenue and from 35 to 55 percent of total
income reported by the NESDB. These large-firm data may overstate the growth of revenue in
manufacturing MNCs, because large firm revenues includes earnings from non-manufacturing
activities such as services, and it seems likely revenue growth from non-manufacturing was
relatively rapid. Nonetheless, the discrepancies between the firm- and plant-level calculations
9

There was also a survey in 2003 covering 2002 data, which contained a similar number ofplants (8,862) and had coverage issues like those in the surveys of 1998, 1999 and 2000 data,
but is omitted from Table 3 because I have not had the time (or reason) to compile the micro
data from this survey yet.
15

are substantial and not easy to explain. Third, both the plant- and firm-level calculations are
consistent in suggesting a sharp rise in the shares of majority-foreign MNCs, which rose from34 to 52 percent of large firm revenue and from 21 to 41 percent of total income reported by
the NESDB. It other words, these data suggest contrasting trends in the shares of all MNCs,
but similar, sharp increases in the shares of majority-foreign MNCs.
Finally, Table 4 also shows that panels created from the large-firm data cover about threefourths of large firm revenues in both 1996 and 2006. This suggests that exit rates may wererelatively low in large firms. MNC shares were larger for 1996 in the balanced panel than in
the corresponding cross section (e.g., 61 vs. 58 percent for all MNCs) but there was little
difference in 2006. In other words, the creation of balanced panels of large-firms leads tooverestimates of MNC shares in the initial year and underestimates of their growth over theperiod, reflecting lower turnover among large MNCs than among large local firms.

5. Vietnam
Vietnam conducted its first annual enterprise census or survey in 2000, and this author has
since been able to obtain underlying micro data for 2000-2007 (Vietnam, General Statistics
Office, various years a).10 This section focuses on these enterprise data sets because they arequite rich (lots of variables), have good coverage of enterprises in both manufacturing andmost non-manufacturing sectors (household firms are the largest omission), and areincreasingly used to analyze MNC activities in Vietnam. The data contain firm identity codesthat should be useful for constructing panels, but these codes have been increasingly
duplicated over time. By 2005, 1.0 percent of the 24,018 records for manufacturing firms
10

The official name of this publication and the introductions to the more recent issues indicatethat these are survey data, but the original publication for 2000 (Vietnam, General Statistics
Office, 2002) indicated that they were census data.
16

included duplicate identity codes compared to 0.5 percent in 2003-2004, 0.4 percent in 2000,
0.1 percent in 2001, and none in 2000. If the number of duplicate identity codes continues toincrease, panel creation will become increasingly difficult.11
As in Malaysia, MNC shares have been relatively high in Vietnam recently, and increased
from 22 to 39 percent of firm employment and from 41 to 46 percent of firm sales during
2000-2006 (Table 5). 12 In other words, in 2000 there was a relatively large differential
between sales per worker in MNCs and local firms, but by 2006, this differential had
narrowed markedly. Wholly-foreign MNCs have always accounted for the majority of MNC
activity, and their share of employment doubled in 2000-2006, from 17 to 35 percent, but thecorresponding share of sales increased more slowly, from 22 to 32 percent. Thus, as soon as
2003, wholly-foreign MNCs had lower sales per worker than other firms (local firms andother MNCs combined). MNC shares of fixed assets were relatively high throughout,indicating relatively high capital intensity among MNCs, but differences between whollyforeign MNCs and local firms were smaller than differences between other MNCs (minority& other majority-owned MNCs) and local firms. Here again, MNC shares were smallest interms of their number, indicating that MNCs were larger than local firms by any measure.
Because of the rapid growth in the corporate sector during this period, the cost of creatingbalanced panels is extremely high in Vietnam. For example, even a short, balanced panel for2000 to 2004 covers only 70 percent of total sample employment in the earlier year and onehalf in the latter year (Table 5). Coverage of sales was slightly higher, but still only threefourths and three-fifths, respectively. Ramstetter and Phan (2007a, Table 6) show that the low
11

Duplication apparently results primarily because different branches of the same firm arereported as separate records. 2006-2007 data obtained by this author do not yet contain thecorrect identity codes so it is not clear if the trend toward greater duplication has continued.
12
In Table 5, 2000-2005 data come from compilations of micro data and may differ slightly
from official compilations, but not by much. 2006 data are from an official publication and2007 data are omitted because official compilations of sufficient detail are not yet available.
17panel coverage results both from high entry rates among all types of firms (state-owned
enterprises [SOEs], private firms, and MNCs), as well as from high exit rates among SOEs
and private firms (though the exit rate was relatively low for MNCs).
In short, analysis of balanced panel data for Vietnam in recent years tells us very little
about general economic conditions, but instead illustrates the special characteristics ofsurviving firms, which often differ from new entrants and exiting firms. For example, MNC
shares tend to be larger in the panel than in corresponding cross sections. The largest
differences were for 2000, when MNC shares of employment were 6 percentage points higher
than in the corresponding cross section and shares of sales were 7 points higher. Sales’ sharesin 2004, and fixed asset shares in both years were also 5-7 percentage points higher. In otherwords, balanced panels overstate the degree of MNC presence and could therefore have an
influence on estimates of productivity differentials or spillovers involving MNCs.
Nguyen, T.T.A. et al (2006) were among the first to utilize these data to show that MNCs
had relatively high sales per employee in three manufacturing groups ((a) mechanics &electronics, (b) textiles, garments, & footwear, (c) food processing) in 2001-2003. Ramstetterand Phan (2008) calculate value added per worker, revealing similar patterns in mostmanufacturing industries in 2000, 2002, and 2004. However, when they estimate translog
production functions for eight manufacturing industry groups, productivity differentials
between MNCs and private, local firms were statistically insignificant in six of these groups.
Nguyen, T.T.A. et al (2006) examine four channels of potential spillovers, labor turnover,technology diffusion & transfer, production linkages, and competition. They also perform
cross section, Cobb-Douglas estimates of spillovers, concluding that “there is little evidenceof positive spillover effects at the firm level”, though there are also “no signs of negative
spillover effect either” (p. 56). In contrast, Pham’s (2008) cross section, Cobb-Douglas
18

estimates generally suggested positive spillovers that were largest in Hanoi and Ho Chi Minh
City, and from MNCs that were not wholly-foreign. Combining firm-level data for 2000-2005
with the 2000 input-output table, Nguyen, P.L. (2008) estimates cross section Cobb Douglas
functions finding that both horizontal and vertical spillovers were generally positive, andlargest in more advanced regions and in more sophisticated local firms. In analysis using an
unbalanced panel of the same data, Nguyen, N.A. et al. (2008) finds that backward, vertical
spillovers were positive in manufacturing, while horizontal spillovers were positive inservices. Le and Pomfret (2008) also use a similar approach to estimate spillovers in an
unbalanced panel of all industrial firms (including mining and utilities) for 2000-2004,
finding positive backward spillovers in manufacturing but negative horizontal spillovers,
which were relatively strong on private firms, domestic-oriented firms, firms without R&D,
and firms in low technology industries.13 Translog estimates for 2000, 2002, and 2004 from
Ramstetter and Phan (2008) also suggest the existence of positive spillovers from MNCs toprivate firms in cross sections, but no significant spillovers in unbalanced panels. In sum,these results all suggest some degree of positive spillovers, especially in cross sections, but
results vary markedly depending on specification, sample, and productivity measures, andevidence from panel analysis is relatively weak.14
In related research, Ramstetter and Phan (2007b) examine wage differentials between
MNCs and local, private firms for 2000, 2002, and 2004. There is evidence of positive and

13

The use of the 2000 input-output table in these studies may be unrealistic because of largechanges in Vietnam’s industrial structure during 2000-2005, for example.
14
Panel estimates are preferred when estimating spillovers because they examine the questionof how changes in foreign shares are related to changes in local firm productivity. Ramstetterand Phan (2008), Nguyen, N.A. et al. (2008), Nguyen, T.T.A. et al. (2006) use value-addedbased estimates of productivity, while Le and Pomfret (2008) and Nguyen, P.L. (2008) use asales-based measure. Value added data must be compiled from product-level data and omit
some portions of sales, but the coverage of the value added samples seems reasonably goodfor 2000, 2002, and 2004 (Ramstetter and Phan 2008, Table 1, Appendix Tables 1a-1p).
19

significant differentials after firm-level labor skill (measured as shares of scientists andtechnicians and of female workers) was accounted for, both in manufacturing overall and alleight industry groups examined. Cross section evidence suggested that MNC presence waspositively correlated with local firm wages in 2002 and 2004, but fixed effects estimates in
unbalanced panels suggest that changes in MNC presence was not significantly related tochanges in local firm wage levels in 2000-2002-2004.15 Le (2007) combines the 2000 inputoutput data and labor skill proxies from provincial data with a panel of firm data for 20002004, finding evidence of both vertical and horizontal wage spillovers from MNCs.
In addition to the enterprise data highlighted above, there are several other sources of micro
data in Vietnam. First, Phan and Ramstetter (2004) analyze patterns in published compilationsof a firm census for 1995 and an industrial survey for 1998, again suggesting the MNCs
tended to be relatively large and have relatively large sales per worker. Second, Phan and
Ramstetter, (forthcoming) also use a database of FDI projects to show how MNCs with large
foreign ownership shares (90% or more) account for a large share of Vietnam’s exports andhave much higher export propensities than other MNCs. Macro data also suggested thatexport propensities were much higher in MNCs than local firms or plants. Nguyen (no date)analyzes a 1998-1999 survey of 96 textiles and garments firms, first finding that MNCs had
relatively low profit rates after controlling for industry, location, scale, export propensities,
and other firm-level characteristics. He also found relatively high TFP in MNCs, with thedifference generally being strongest in Cobb-Douglas estimates. However, even in translog
estimates, there was weak evidence (significant at the 10 percent level, but not the normal 5
percent level) of higher productivity in MNCs.

15

Data on labor quality were not available for 2001 and 2003, so those years were omitted.
20

6. Singapore and the Philippines
Singapore has long-published compilations of major indicators by ownership category
and/or nationality of major investor for all manufacturing plants combined in its Census of
Manufacturing Activities (previously known as the Census of Industrial Production). These
aggregate compilations have been used in several previous analyses of MNCs (e.g.,
Ramstetter 1999a; Ramstetter and Matsuoka 2001). Calculations of MNC shares from thesedata are provided for recent years in Table 6.
Contrary to trends through the late-1980s noted in previous studies, there has been a fairly
clear and strong, downward trend in MNC shares of employment since 1990, which fell from72 percent to under half in 2007 (Table 6). MNC shares of value added also fell some but
remained relatively high at 79 percent in 2007. In other words, the average product of labor isrelatively high in MNCs in Singaporean manufacturing as well. Similarly, average worker
compensation was also relatively high in MNCs (Ramstetter 1999a, Singapore, Economic
Development Board, various years). And here again, MNC shares were by far the largest interms of exports, especially for majority-foreign plants, indicating that MNCs also tended tohave relatively high export propensities. On the other hand, MNC shares of fixed assets andvalue added were similar (slightly lower in 1993-2000 and slightly larger in 2001-2008),
suggesting that differences in average capital productivity were a good deal smaller thandifferences in average labor productivity, for example.16

16

As far as this author knows, Singapore does not allow access to the micro data underlying
this or other publically managed data sets. Although the process was very time consuming, I
have in the past been able to arrange to unpublished compilations from the underlying data,
but these were extremely simple calculations and I know of no researcher who has been able
to contract the authorities for more sophisticated analyses (e.g., regressions). There is arelatively good set of data on large firms from a publication called the Singapore 1000 (seeRamstetter and Matsuoka 2001 for details), but it contains only a limited number of indicators(sales, profits, assets, equity) and is therefore of limited use academically.
21

In the Philippines, the National Statistics Office conducts the Annual Survey of Philippine
Business and Industry (previously the Annual Survey of Establishments), which contains
ownership information and could potentially be used to perform analyzes of MNC activities
similar to those of other countries reviewed above. However, Hill (2003, p. 236) provides the
only known, limited compilation of these data, showing that MNCs accounted for 56 percent
of manufacturing of the output of surveyed firms in 1995. Using a separate, more limited
survey of firms in food, clothing, and electronics in 2002 which was carried out by the AsianDevelopment Bank, Dueñas-Caparas (2006) examines the determinants of exports, findingthat MNC affiliates had higher export propensities than local firms in all industries andspecifications examined. Lall (2000, p. 10) also provides a list of the top 50 exporters in thePhilippines, showing that most of them were MNCs, many of which were in electronics. Thisevidence is consistent with similar analyses of Indonesia and Thailand, and with thedescriptive statistics presented for Malaysia and Singapore above. However, there are no
known data facilitating calculations of MNC shares or known evidence on other comparisonsof MNCs and local plants or firms in the Philippines.

7. Conclusion
This paper has used micro data and published compilations of these data to estimate sharesof multinational corporations (MNCs) in Southeast Asian manufacturing. It first shows that
MNC shares tended to be largest in Singapore, intermediate in Malaysia and (recently)
Vietnam, and lowest in Thailand and Indonesia. In the decade or so since the mid-1990s,
MNC shares have continued to rise in Indonesia and Vietnam, fluctuated but remained largely
unchanged in Malaysia, fallen in Singapore, and probably fallen in Thailand. Another notable
trend was the tendency for shares of majority-foreign MNCs to rise markedly in Indonesia
22and Thailand as many local partners in joint-ventures sold shares to their MNC partners inorder to cope with financial difficulties related to the 1997-1998 crisis.
Second, there were also clear differences in MNC shares depending on the economic
activity being measured. MNC shares of plant or firm numbers were always smallest,
indicating that MNCs tended to be larger than non-MNCs. Shares of production (value addedor gross output/sales) tended to be larger than shares of employment, reflecting relatively highaverage labor productivity in MNCs. Especially in Malaysia and Thailand, and also to someextent in Vietnam, differences in labor productivity tended to be relatively small and/orinconsistent. However, the evidence for Indonesia suggests a somewhat more consistentpattern of relatively high labor productivity in MNCs. A review of more sophisticated studies
accounting for the influence of related factors such as factor intensity suggested similar
patterns. Related studies have also found substantial and significant wage differentials infavor of MNCs in Indonesia, Thailand, and Vietnam in a relatively large number of industries
and periods. Descriptive data for Malaysia and Singapore are also consistent with theexistence of substantial wage differentials. Perhaps the most consistent differences between
MNCs and local firms or plants were in exporting, however, with MNCs, particularly thosewith high foreign ownership shares, having relatively high export propensities. These results
are also underscored by studies of Indonesia, the Philippines, Thailand, and Vietnam that
account for numerous influences on export propensities ignored in these simple calculations.
Third, the paper also reviewed the growing literature on productivity and wage spillovers,
which tends to find evidence of positive spillovers in Indonesia and Thailand, but lessconsistent evidence regarding spillovers in Malaysia and Vietnam. Unlike some of theliterature on productivity and wage differentials, the spillover literature almost always
assumes identical production function slope coefficients for a wide range of industries, which23can bias estimates. The use of balanced panel samples may also bias the existing results for
Malaysia, for example, because balanced panels often omit substantial information andconsist of relatively large and productive firms or plants, as well as relatively large numbersof MNCs in at least one year. On the other hand, studies of Indonesia and Vietnam generally
use unbalanced panels or cross sections, recognizing that balanced panels often contain a setof firms or plants that differ from the overall samples in ways that may bias estimates.

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29

Table 1: Large- and Medium-Sized Manufacturing Plants in Indonesia
Employees (1,000s)
Value Added (trillion Rupiah)Nat'l Sample MNC shares %Nat'l Sample MNC shares %
Year
total
total
10%+ 50%+
total
total
10%+ 50%+
1990
7,693
2,663
10
7
43.6
25.2
22
13
1991
7,946
2,994
12
9
53.4
29.9
21
15
1992
8,255
3,313
14
11
62.0
41.4
24
18
1993
8,784
3,575
15
12
73.6
49.8
23
16
1994
10,841
3,814
17
13
89.2
59.9
27
19
1995
10,127
4,174
17
14
109.7
73.9
29
21
1996c
10,773
4,215
18
14
136.4
93.3
31
21
1997
11,009
4,155
19
15
168.2
84.1
35
29
1998
9,934
4,124
19
16
238.9
154.7
35
28
1999
11,516
4,124
19
16
285.9
154.7
35
28
2000
11,642
4,367
21
18
385.6
236.9
38
30
2001
12,086
4,386
21
18
506.3
269.6
34
24
2002
12,110
4,365
21
18
523.2
310.0
33
23
2003
11,496
4,274
23
20
568.9
326.8
36
30
2004
11,070
4,325
22
19
644.3
358.9
36
30
2005
11,953
4,227
23
20
760.4
396.4
37
31
2006c
11,578
4,756
24
21
919.5
514.3
40
34
Balanced Panel 1990 & 1996
1990
2,053
11
8
21.9
22
13
1996
2,612
14
10
70.3
29
18
Balanced Panel 1996 & 2000
1996
3,706
19
14
86.4
31
20
2000
3,783
20
17
205.1
35
27
Balanced Panel 2000 & 2006
2000
3,314
20
17
186.3
37
27
2006
3,324
24
20
403.8
41
35
Balanced Panel 1996 & 2006
1996
2,815
18
13
71.1
30
19
2006
2,880
23
19
358.2
39
32

30

Table 1 (continued)
NumberFixed Assets (bil.Rup.)
Exports (bil. Rupiah)
Sample MNC shares % Sample MNC shares % Sample MNC shares %Year
total 10%+ 50%+
total 10%+ 50%+
total 10%+ 50%+
1990
16,536
4
3
167
47
43
11.9
7
6
1991
16,494
4
3
189
8
6
18.8
22
15
1992
17,648
5
4
117
14
10
27.1
33
26
1993
18,163
5
4
155
39
36
29.4
28
23
1994
19,017
6
5
108
29
23
40.3
32
27
1995
21,551
6
5
110
22
16
53.6
33
28
1996c
22,997
6
5
70.4
37
30
1997
22,386
6
5
112
25
21
56.8
39
32
1998
21,423
7
6
318
18
15
1999
22,070
8
7
197
19
16
108.6
47
40
2000
22,174
8
7
245
22
18
166.5
45
40
2001
21,396
8
7
769
17
16
2002
21,146
8
7
409
35
28
2003
20,324
9
8
1,368
69
66
2004
20,685
8
7
437
60
56
178.8
45
42
2005
20,729
8
7
1,045
40
39
2006c
29,4687
7 95,835
34
29
364.3
50
46
Balanced Panel 1990 & 1996
1990
9,937
5
3
156
49
46
10.3
23
16
1996
9,937
5
4
43.7
28
19
Balanced Panel 1996 & 2000
1996
16,897
7
6
64.3
37
29
2000
16,897
7
6
210
14
10
137.7
39
33
Balanced Panel 2000 & 2006
2000
13,476
9
8
208
23
18
124.4
38
32
2006
13,47610
8 61,148
27
19
271.5
47
42
Balanced Panel 1996 & 2006
1996
10,801
8
6
48.3
34
25
2006
10,8018
7 53,169
29
21
236.9
44
39
Note: c=census year; samples consist of plants with 20 or more workers; national totals
come from labor force data for employment and national accounts for value added; exports
estimated as export propensities times gross output and not shown for 1998 because
reported export propensities were not plausible (well above 100%) for many plants; MNC
shares are percentages of sample totals.
Sources: Asian Development Bank (various years); Indonesia, Badan Pusat Statistik(various years).

31

Table 2: Manufacturing Plants in Malaysia 1994-2004
Employees (thousands)
Value added (RM mil)National
SampleMNC %National
Sample
MNC %
Year
1994
1,892
1,225
43
52.1
49.4
44
1995
1,780
1,421
37
58.7
60.0
41
1996
1,912
1,449
38
70.6
73.0
44
1997
2,002
1,395
40
80.0
160.8
35
1999
1,991
1,677
41
93.0
90.7
61
2000c
2,174
1,575
38
110.0
108.7
44
2001
2,184
1,392
39
103.4
97.6
45
2002
2,069
1,489
38
112.1
112.8
43
2003
2,131
1,503
38
125.3
123.2
43
2004
2,023
1,543
39
144.0
140.9
43
Balanced Panel 1994 & 1999
1994
757
46
37.5
46
1999
1,272
46
81.0
58
Balanced Panel 2000 & 2004
2000
1,180
42
92.0
46
2004
1,346
41
119.1
45
NumberSample
MNC %
Year
1994
8,328
15
1995
7,553
12
1996
7,865
11
1997
6,751
13
1999
6,494
13
2000c
20,4558
2001
13,93410
2002
13,482
11
2003
13,67211
2004
12,451
11
Balanced Panel 1994 & 1999
1994
3,629
18
1999
3,629
18
Balanced Panel 2000 & 2004
2000c
9,556
13
2004
9,556
13

Fixed Assets
Sample
MNC %
81
40
99
38
239
44
136
38
144
36
154
38
149
41
167
40
174
35
177
37

Exports
Sample
MNC %
226
69
203
68
218
69
232
72
200
61

65
108

40
38

-

-

141
148

40
39

65
160

50
66

Notes: c=census year; national totals come from labor force data for employment andnational accounts for value added; annual survey totals are calculated from plant-level
data and differ substantially from published figures for 1996-1997 (fixed assets),
1997 (value added), and 1999 (employment); exports estimated as export propensities
times gross output; MNC shares are percentages of sample totals.
Sources: Asian Development Bank (2008); Malaysia, Department of Statistics (2002,
2007, various years)

32

Table 3: Manufacturing Plants in Thailand
Employees (1,000s)
Survey
MNCs %
Year
Total
total 10%+ 50%+1996c
4,329 2,445
39
16
1998
4,264 1,020
43
1999
4,274
466
40
2000
4,650
742
43
2006c
5,504 3,726
25
17

Year
1996
1998
1999
2000
2006c

Surveytotal
32,489
8,552
9,360
9,294
73,931Gross Output (billion Baht)
Survey
MNCs %
Total
total 10%+ 50%+
3,898 3,557
54
22
4,126 1,524
59
4,183
706
64
4,784 1,337
59
8,305 7,147
43
29

NumberFixed Assets
Exports
MNCs %
Survey
MNCs %
Survey
MNCs %
10%+ 50%+
total 10%+ 50%+
total 10%+ 50%+8
3 1,789
53
22
13
879
60
5
404
71
7
774
62
4
2 3,07242
28 2,476
55
43

Note: c=census year; surveys and censuses include all plants with 10 or more employees
to 2002, all plants in the 2006 census; national totals come from labor force data for
employment and national accounts for gross output (proxied with total income); exports
estimated as export propensities times gross output; MNC shares are percentages ofsample totals.
Sources: Asian Development Bank (various years); Thailand, National Statistics Office(various years a, various years b); Thailand, National Economic and Social Development
Board (2008).
Table 4: Large Manufacturing Firms in Thailand
Revenue (bil. Baht)
Number Rev.>0
Fixed assets (bil. Baht)
Nat'l Sample
MNCs %
SampleMNCs %
SampleMNCs %Year
total
total 10%+ 50%+
total 10%+ 50%+
total 10%+ 50%+
1996
2006

3,898
8,305

2,368
6,604

58
69

34
52

1,099
889

44
56

22
39

1,250
1,655

47
58

23
42

Balanced Panel 1996-2006
1996
1,747
2006
5,008

61
70

37
49

514
514

51
57

27
37

898
1,186

47
57

21
38

Note: The sample consists of the largest firms in within 58 3- or 4-digit industries; intra-industry
conglomerates (groups of firms in which the same ultimate parent has a majority-ownership share)
are consolidated when possible, or combined and counted as a single firm; MNC shares are
relative to sample totals.
Source: Compilations from numerous sources detailed Kohpaiboon and Ramstetter (2008).

33

Table 5: Manufacturing Firms in Vietnam
Employees (1,000s)
Nat'l Sample MNC shares %
Year
total
total
10%+
100%
2000
3,550
1,598
22
17
2001
3,887
1,808
24
19
2002
4,160
2,203
29
23
2003
4,560
2,557
31
26
2004
4,832
2,893
33
29
2005
5,249
3,099
36
32
2006
5,656
3,402
39
35
Balanced Panel 2000 & 2004
2000
1,119
28
22
2004
1,438
34
29

Sales (bil. Dong)
Sample MNC shares %
total
10%+
100%
246
41
22
302
39
21
379
41
23
476
42
24
623
43
27
743
43
28
906
46
32
182
366

48
49

NumberFixed Assets (bil. Dong)
Sample MNC shares %Sample MNC shares %
Year
total
10%+
100%
total
10%+
100%
2000
10,405
10
7
115
64
30
2001
13,237
11
8
127
61
32
2002
14,794
11
9
150
58
33
2003
16,915
12
9
171
55
34
2004
20,531
11
9
213
52
34
2005
24,018
11
9
239
54
38
2006
26,863
11
10
Balanced Panel 2000 & 2004
2000
5,479
15
10
92
69
32
2004
5,479
15
11
110
59
33
Notes: Data from censuses or surveys of firms in Vietnam; national
totals come from labor force data for employment; comprehensive
value added data not available (see Ramstetter and Phan 2008); MNC
shares are percentages of sample totals.
Sources: Vietnam, General Statistics Office (various years a; various
years b).

34

26
27

Table 6: Manufacturing plants in Singapore (published data)
Employees (1,000s)
Value Added (S$ billions)Nat'l Sample MNC shares %Nat'l Sample MNC shares %
Year
total
total
1%+ 50%+
total
total
1%+ 50%+
1990
439
352
72
60
17.3
16.5
85
74
1991
430
358
69
58
19.9
17.8
83
73
1992
434
358
69
57
20.6
18.1
82
69
1993
429
355
67
55
23.8
21.1
82
71
1994
423
366
67
55
26.3
23.3
82
70
1995
385
370
67
55
29.5
26.9
83
72
1996
406
368
65
54
30.7
28.2
82
72
1997
414
367
64
53
32.0
29.7
82
72
1998
404
352
63
50
31.5
29.6
83
73
1999
396
339
61
50
31.9
34.9
85
76
2000
434
345
60
48
41.2
39.0
83
73
2001
384
345
59
48
35.2
31.9
80
72
2002
368
357
55
45
38.2
36.4
80
73
2003
365
351
54
44
38.6
37.1
81
73
2004
357
358
54
44
48.0
46.3
82
72
2005
485
370
53
44
51.1
49.0
79
69
2006
517
382
51
42
56.7
54.1
82
73
2007
568
404
48
42
57.7
56.0
79
71
NumberFixed Assets (S$bil.)
Exports (S$bil.)
Sample MNC shares %Sample MNC shares %Sample MNC shares %
Year
total
1%+ 50%+total
1%+ 50%+total
1%+ 50%+
1990
3,703
32
23
18.0
85
72
47.0
93
87
1991
3,785
32
23
18.7
83
71
45.9
91
85
1992
3,917
30
22
20.2
82
70
46.9
92
84
1993
3,993
29
21
22.3
80
69
53.0
91
85
1994
4,013
29
22
24.1
80
68
61.5
91
85
1995
4,036
29
21
26.4
80
68
69.0
92
86
1996
4,068
28
21
29.1
78
61
73.0
93
86
1997
4,108
28
21
37.0
80
67
76.4
93
86
1998
4,004
27
21
37.2
81
68
75.5
94
88
1999
3,928
27
21
38.6
81
69
85.4
93
87
2000
4,044
26
20
42.4
83
68
93.9
93
87
2001
4,041
26
20
48.1
84
72
84.2
93
88
2002
8,609
13
10
47.9
82
71
88.4
92
87
2003
8,597
12
10
45.8
82
71
98.7
93
89
2004
8,725
12
9
47.8
84
71
116.8
92
85
2005
8,037
12
10
47.1
84
71
134.6
90
82
2006
7,892
12
10
47.5
85
72
141.1
89
82
2007
8,166
12
10
47.8
84
72
152.3
86
80
Source: Singapore, Economic Development Board (various years).

35