Hi-Stat

Discussion Paper Series
No.251The Effects of Overseas Operations on
Home Employment of Japanese Multinational Enterprises
Nobuaki Yamashita
Kyoji Fukao
June 2008

Hitotsubashi University Research Unit
for Statistical Analysis in Social Sciences
A 21st-Century COE Program

Institute of Economic Research
Hitotsubashi University
Kunitachi, Tokyo, 186-8603 Japan
http://hi-stat.ier.hit-u.ac.jp/

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The Effects of Overseas Operations on Home Employment ofJapanese Multinational Enterprises∗

Nobuaki Yamashita
Faculty of Economics and Finance, La Trobe University
E-mail: N.Yamashita@latrobe.edu.auand
Kyoji Fukao
Institute of Economic Research, Hitotsubashi University
E-mail: k.fukao@srv.cc.hit-u.ac.jp

Abstract
This paper examines the hypothesis that expansion of overseas operations of Japanesemanufacturing multinational enterprises (MNEs) reduces home employment. While theexisting studies are mainly based on the industry level, this paper presents the evidence
using newly constructed firm-level panel data set over the period 1991-2002. In spite ofconcerns expressed about the adverse effects of FDI on the domestic economy, theevidence does not support the view that overseas operations expand at the cost of home
employment in Japan. On the contrary, the findings suggest that overseas operations
have somewhat helped to maintaining the level of home employment in Japanesemanufacturing during the period under study. However, the results are sensitive to theestimation method used and whether the estimation is based on the panel data set isbalanced or unbalanced.

Key Words: Multinational Enterprises, FDI, Labour demand, hollowing out of
manufacturing, JapanJEL Classification: F14, F16, J31∗

This study was conducted as part of the project on industry and firm-level productivity in Japan
undertaken by Research Institute of Economy, Trade and Industry (RIETI). The authors would like tothank RIETI and the Ministry of Economy, trade and Industry (METI) for making dataset available to thisstudy. The authors are grateful to Prema-chandra Athukorala for useful comments and suggestions. The
part of this research was undertaken when Yamashita was visiting Institute of Economic Research (IER)
at Hitotsubashi University. He would like to thank the supports and a hospitable environment provided at
IER during his visit.

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The Effects of Overseas Operations on Home Employment of JapaneseMultinational Enterprises
1

Introduction

This paper undertakes firm-level analysis of the effects of the expanded overseas
operations of Japanese manufacturing multinational enterprises (MNEs) on home(domestic) employment.

An econometric analysis estimates the labour demand

equation for home employment by allowing for the effects of foreign affiliates
employment and outputs. In addition, industry aggregation and geographic locations offoreign affiliates are both controlled in order to control for the specific regional andcharacteristics of overseas operation of MNEs.

The controversy over the possible adverse effects of overseas production by
MNEs on home employment first arose in the US in the late 1960s and it has gained
increased attention in policy circles of industrial countries in recent years with thegrowing importance of international fragmentation of production (Lipsey 1995;
Harrison and McMillan 2006).

The possible substitution of home employment of

MNEs with increased overseas production is known as the ‘exporting jobs’ in theliterature (Kravis and Lipsey 1988). It also became the subject of heated policy debate
in Japan under the label of ‘manufacturing hollowing-out’ (sangō kudouka) following asurge of Japanese FDI outflow associated with the spread of production networks to low
cost countries in East Asia from the mid-1980s.

In spite of the policy importance, only a few systematic empirical studies areavailable and they are based on readily-available FDI data at the industry level (Fukao
1995; Fukao and Amano 1998; Fukao and Yuan 2001). There is virtually no evidenceof how Japanese MNEs adjust home employment in response to changes in the
production capacity of foreign affiliates at the firm-level. This is certainly an area
where studies on Japanese MNEs lag behind those of the US and Sweden-based MNEs
(Lipsey 1995; Brainard and Riker 1997a, 1997b; Braconier and Ekholm 2000; Fors andKokko 2000; Desai et al. 2005; Harrison and McMillan 2006). 1 This paper aims to fillthis gap. The analysis explores panel data set compiled from the unpublished returns to

1This could be partly due to the stringent Japanese government policy on access to the original returns offirm-level information, which has been eased to some extent in recent years. However, the access to theoriginal METI survey database is still limited to protect private firm information.

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two firm-level surveys, the Basic Survey of Business Structure and Activity and the
Basic Survey of Overseas Japanese Business Activity, collected by Japan Ministry of the
Economy, Trade and Industry (METI) over the period 1991-2002. 2

The next section describes the data used in this chapter. This is followed bysection 3 discussing patterns and trends of home and overseas operations of MNEs.
Section 4 undertakes a survey of the existing empirical evidence for the relationshipbetween the overseas and domestic operations of MNEs.

Section 5 depicts theempirical framework before explaining variable construction and the estimation
methodology. Section 6 interprets the results and the final section concludes.

2

The Effect of the
Operations of MNEs

Overseas

Operations

on

Domestic

In principle, there is little guidance from the theory of MNEs on the effects of theiroverseas operations on home economic activity. One view argues that for a fixed levelof overall production including parent and affiliate production, any expansion in theoverseas operations of MNEs simultaneously reduces domestic operations (thesubstitution effect). However, this simplistic substitution story ignores the positiveeffects of overseas expansion on domestic activity. It is equally possible that increased
overseas operations might enhance the scale of home economic activity due to better
resource allocations and the expanded market overseas (the scale effect). Therefore, thenet impact of increased overseas operation on home economic activity can be either
positive or negative, depending on the magnitude of the scale and the substitution
effects (Hanson et al. 2003).

Different types of MNEs can also complicate the net effect of overseas
operations (Caves 1996).

In general, the theory postulates two types of MNEs,

depending on the investment motivation: vertical or horizontal. Vertical type of MNEs
vertically separates the integrated production process between parent MNEs and theirforeign affiliates. This type is usually motivated to take advantage of the existence ofinternational factor prices differentials between the home and host country. Under

2

Fortunately, Japan is one of the few countries, besides the US and Sweden, where detailed informationon the overseas operations of national firms has been collected systematically over a long period of time.
Recently, these firm-level surveys containing direct measures of Japanese MNEs’ performance havebecome increasingly available to researchers (Kimura and Ando 2003, 2005; Ando and Kimura 2005;
Hijzen et al. 2006; Kimura and Kiyota 2006; Shimizutani and Todo 2007; Todo and Shimizutani 2008).

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operations of vertical MNEs, overseas and domestic employment can be substitutes,
since some domestic operations are relocated to overseas operations. However, it isequally possible the domestic operation is expanded due to vertical specialisation.

The horizontal type of MNE overseas operation is motivated by the objective of
expanding overall sales. In this sense, expanded overseas operations may have little
effect on the scale of the domestic operation of MNEs. However, it is equally possiblethat the domestic operations of these MNEs might be facilitated by the expanded
worldwide scale of production. 3 Beyond this theoretical classification of MNE types,
the postulated relationship between overseas and domestic employment might also
depend on the extent to which overseas operations are located in developed as opposed
to developing countries, and also whether foreign affiliates have plant-level or firmlevel economies of scale. To date, the theory of MNEs does not provide clear-cut
predictions about the possible effects of foreign production on home operations.

A large amount of the empirical research on the effects of overseas operations
on home operations is based on US MNEs (Kravis and Lipsey 1988; Lipsey 1995;
Brainard and Riker 1997a, 1997b; Hanson et al. 2003; Desai et al. 2005; Harrison and
McMillan 2006). These studies make use of firm-level survey data, conducted by the
Bureau of Economics Analysis (BEA), The US Department of Commerce. The BEA
data is a comprehensive and integrated data set for tracking the operations of US MNEs
non-bank foreign affiliates in host countries and the operation of parent firms in theirhome countries. 4

The survey format includes information on the classification ofindustry, sales, trade in goods and services, employment, wages, assets, expenditure forplant and equipment, and R&D expenditure.

Among such researchers, Kravis and Lipsey (1988) and Lipsey (1995) makeinitial attempts to examine the impact of foreign production on the home employment of
US MNEs. A higher level of foreign affiliate production in developing countries is3Complex integration is another type of MNEs (UNCTAD 1998, 2002; Yeaple 2003). This type sharescertain features of both the vertical and horizontal type. Any MNE might set up integrated production toserve a foreign country market, and might also choose to operating another host foreign country for the
purpose of assembling their products.
They establish foreign affiliates to conserve on transportation
costs, and also establish affiliates in other foreign countries in order to benefit from international factor
price differentials. As a result, the net impact of all of these overseas operations depends on the extent
and magnitude between the vertical and horizontal type of MNE.
4
The survey began in 1929, but its scope was limited to one question – the value of foreign commercial
assets controlled by US companies (See Mataloni (1995) for more detail).

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found to be associated with lower home employment for a given level of home
production.

Brainard and Riker (1997a) develop a more systematic analysis and estimate theforeign affiliate cross-wage elasticities of parent firms’ labour demand for the period
1983-1992. They find evidence of a substitution relationship between foreign anddomestic employment, although the degree of substitution is low. On the other hand, astrong substitution relationship was found among the various foreign affiliates of MNEs,
operating in developing countries. The evidence indicates any employment substitution
effect predominantly takes place between the foreign affiliates of MNEs operating inoverseas locations rather than between parents and their foreign affiliates. If anything,parent firms adjusted employment very little in response to changes in foreign affiliate
wages.

Hanson et al. (2003) find expansion in the sales of foreign affiliates of US
MNEs raise the labour demand for their home operations, although the quantitative
effect is small.

This finding supports a hypothesis of a mild complementary

relationship between increased overseas sales and parent employment. Their second
main finding is that the relationship between the parent and its foreign affiliates appears
to depend on the skilled/unskilled labour costs of foreign affiliates. When the cost of
skilled labour is lower in foreign affiliates, the demand for home labour appears toincrease. This result suggests changes in the prices of high-skilled employment inforeign affiliates tend to increase overall employment, both in foreign affiliates andparent firms. On the other hand, where the cost of unskilled labour for foreign affiliates
is lower, the US parent firms decrease the demand for home employment.

Desai et al. (2005) find evidence of increased overseas operations of MNEs
enhancing the scale of home operations. A 10 percent greater accumulation of foreign
property plant and equipment is associated with a 2.2 percent increase in domestic net
property plant and equipment.

Similarly, a 10 percent rise in foreign employeecompensation is associated with a 4 percent greater domestic employee compensation,
and a 10 percent higher number of foreign employees with a 2.5 percent higher numberof domestic employees. In sum, the results amply support the hypothesis that expanded
operations of US MNEs’ foreign affiliates have stimulated the domestic activity of US
parent firms over the last two decades.
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Harrison and McMillan (2006) explore the BEA data sets, but cast back the data
series back to 1977. They find strong evidence the employment of foreign affiliates indeveloping countries substitutes for the home employment of parent firms in US
manufacturing. However, the effect is quantitatively small. On the other hand, home
employment in the US and the employment of foreign affiliates in developed countries
are found to be complementary, characterised by a decline in employment both at home
and in developed countries. In other words, any decline in employment of foreign
affiliates in developed countries leads to some contraction in employment of the parent
firm in the US. By and large, the finding of Harrison and McMillan (2006) is consistent
with that of Brainard and Riker (1997a).

Of the available studies on Japanese MNEs, a disproportionately large numberof studies have focussed on the relationship between expanded overseas production andexports of home countries in Japan (Fukao and Amano 1998; Lipsey et al. 1999; Headand Ries 2001; Kimura and Kiyota 2006). Fukao (1995) makes an early attempt to
examine the possible impacts of foreign affiliate production on domestic employment.
Fukao and Yuan (2001) develop a 3-digit level of cross-industry data, concerning theimpact of FDI on the employment growth rate over the period 1989 to 1998. The
unique feature of their study is the differentiation of FDI by investment motivation andregion of the host country. They find that Japanese FDI in East Asia led to shedding
around 600,000 workers in home country employment. They also find that marketoriented FDI in East Asia seemed to increase the amount of home country employment.

3

Construction of the Panel Data 5

The data set was constructed by using the information on parent firms extracted fromthe Basic Survey of Business Structure and Activity (Kigyo Katsudou Kihou Chosa inJapanese) and the information on their corresponding foreign affiliates from the Basic
Survey of Overseas Japanese Business Activity (Kaigai Gigyou Katsudou Kihon Chosain Japanese). Both surveys are conducted by the Ministry of Economy, Trade andIndustry (METI) (Appendix 1 discusses each survey in detail). For brevity, the former
will henceforth be called the ‘METI Firm survey’ and the latter the ‘METI Foreign
Affiliates survey’.
5

During work on this data set, I have extensively referred to Matsuura and Kiytao (2004) and theresources available from the RIETI website at http://www.rieti.go.jp/jp/database/d02.html#01.

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The starting point of the panel data is 1991 when the first METI Firm survey
was conducted. The second survey was undertaken in 1994 and it has been conducted
continuing from then. The most recent data for both METI surveys available for thispaper are 2002 (note that 1992 and 1993 are missing since the METI Firm survey was
not conducted in these two years). The panel data set only includes parent firms thathave both more than 50 employees and capital of more than 30 million yen. The
industry classification is available at 2-digit level of Japan Standard Industrial
classification (JSIC) (see Appendix 3 for the industry classification). The panel set isunbalanced due to the ‘entry/exit’ of parent firms.

Creating the matched panel data using these two METI surveys involved thefollowing steps. First, information from both surveys was restricted to manufacturingindustry by excluding non-manufacturing industry data.

This necessarily removed

information on any foreign affiliates whose industry classification is not manufacturing.
It is possible this process somewhat underestimates the overseas operations of Japanese
MNEs, since some parent manufacturing firms set up foreign affiliates in nonmanufacturing industries. However, such downward bias is considered to be minimal.
After limiting the data to the manufacturing sector, the consistent 3-digit level of the
manufacturing industry classification was assigned to each parent. This is importantbecause there were some changes in the 3-digit manufacturing industry classification
over the time period 1991-2002.

Second, the two surveys was linked by using the permanent identifier assigned
to each individual parent firm of the METI Firm survey to the same code reported byeach individual foreign affiliate from the METI Foreign Affiliate survey. To ensure
successful matching, a careful cross-checking was implemented by examining the nameand the address of parent firms and the ownership structure. As a result, this proceduresystematically combined information on the overseas operations of Japanese MNEs
with domestic economic activity.

Third, following Hanson et al. (2003) and Harrison and McMillan (2006), sales
weighted averages of foreign affiliate variables were constructed (see section 5.1 for the7

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construction of foreign affiliate variables). 6 This is essential to make the panel data
estimation operational, because Japanese parent firms often own more than one foreign
affiliate operating in multiple locations. For example, Toyota has foreign affiliates inThailand as well as in England.

Lastly, about 1 percent of the data was excluded from the main dataset sincesome parent firms in the METI Firm survey had reported abnormally large or smallvalues. Any parent firms were also dropped if at least one of the values of employment,
sales, industry classification, and identification code was missing.

The pooled data are disaggregated into four regions of host countries, East Asia,
North America, the EU, and South America. The main motivation of the regional
disaggregation was to control for the level of the host country’s stage of development,
the geographic proximity to Japan, and the possible characteristics of foreign affiliate
production. Foreign affiliates of Japanese MNEs operating in developing countries(East Asia and South America) are most likely to be the vertical type of MNEs, whereas
those in developed countries (North America and the EU) are most likely motivated byhorizontal MNEs. The postulated employment relationship between home and abroad
critically depends on the location of foreign affiliates (Brainard and Riker 1997a;
Harrison and McMillan 2006).

In addition to these considerations, the firm-level data are also aggregated up to
the 3-digit industry level. While the firm-level investigation brings about moreadvantages, the industry-level also has several merits. First, the industry level data
capture not only the within-firm employment changes, but also the across-firm
employment changes. It is possible that some parent firms reduce home employment,
whereas other parent firms expand foreign affiliate employment. The firm-level data do
not track the across-firm employment substitutions (Harrison et al. 2007). Second, theestimated labour demand at industry level is likely to reflect changes in employment
resulting from the entry and exit of firm to the industry (Roberts and Skoufias 1997).
Third, the industry-level aggregation mitigates the potential problem of small
6

In principle, it would be possible to include variables for each host country where foreign affiliates
potentially operate without aggregating foreign affiliate variables. However, this creates the problem ofrepeating the same information for the corresponding parent firms, making it difficult to interpret theestimated results (Brainard and Riker 1997a). It would be particularly daunting to repeat the same home
employment in the dependant variable.

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employment changes that are relatively symmetrically distributed around the origins(see Figure 7.1). The industry-level data only show the net effects of employmentchanges between home and foreign affiliate employment.

One limitation of the panel set is that some parent firms disappear in one year in
the data coverage and reappear in another presumably because of varying sample
restrictions imposed. This means that the entry and exit of firms in this survey do notnecessarily correspond to the standard definitions of origin and termination of firms(Nishimura et al. 2005). The matched panel data set also excludes small scale Japanesefirms, which do not meet the sample selection criteria of the METI firm survey even if
they do have foreign affiliates. However, their omissions do not affect the overall
trends of MNEs’ operations.

4

Patterns and Trends of the Home and Overseas Operations of
MNEs

Home Operations
Selected key indicators of home operations of MNEs, using the METI Firm survey data
for the period 1991-2002 are summarised in Table 1. Total domestic sales by Japaneseparent firms rose from 128 trillion yen in 1991 to 136 trillion in 2002. Accordingly, thenumber of parents firms increased from 616 in 1991 to 1,114 firms in 2002. On theother hand, the employment figure contracted from about 2.2 million in 1991 to 2
million in 2002. This indicates that about 180 thousand jobs were shed in the home
employment of MNEs over the period. However, this loss of home employment by
MNEs parent firms could be considered relatively small, compared with the 3 million
jobs lost in total Japanese manufacturing during the same period.

The share of parent firms of MNEs in total manufacturing accounted for an
average of about 6.6 percent over the period 1991-2002. While this seems small, butthese parent firms of MNEs contributed the majority of economic activity to total
manufacturing over 1991-2002. In 2002 parent firms of MNEs accounted for close to
55 percent of aggregate manufacturing outputs and over 40 percent of aggregate
manufacturing employment as well as more than half of aggregate capital stock.
Almost half of manufacturing workers’ compensation was also paid by MNEs. Not
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surprisingly, parent firms conducted the major proportion of international trade,
accounting for over 80 and 60 percent of exports and imports, respectively, andcontributed over a 75 percent share of the research and development (R&D) expenditure
in total manufacturing over the same period. These figures suggest that any effects on
the operations of MNEs are likely to be deeply felt in the home economy.

The MNE dominance in domestic manufacturing is not unique to Japan. For
example, MNEs in the US manufacturing accounted for over 60 percent of total
manufacturing sales, over 70 percent of total exports, almost 60 percent of
manufacturing employment, and 82 percent of domestic R&D expenditure during 19821999, although the number of US MNEs also looked small (Harrison and McMillan
2006). Similar figures are reported for Swedish MNEs (Fors and Kokko 2000).
Overseas Operations
Table 2 summarises the data on the key performance indicators of foreign affiliates ofmanufacturing MNEs over the period 1989-2002. The number of foreign affiliates
steadily increased from 2,656 in 1989 to over 10,000 in 2000, but the following two
years (2001 and 2002) saw some declines in the number of foreign affiliate. Similarly,
sales of foreign affiliates have achieved a five-hold increase between 1989 and 2000,
but they significantly dropped between 2001 and 2002. Employment of foreign affiliate
continuously expanded since 1989, and it has reached close to 3 million in 2002. This
indicates the number of workers employed in foreign affiliates is higher than that ofworkers employed by parent firms of MNEs (see Table 1). The data in Table 7.2 also
indicates an increase in the size of foreign affiliates in terms of average employment andoutput over the period under study. On average, sales ratios both to Japan and othercountries have also been increasing since 1989, while the local sales ratio has remained
stable at around 65-70 percent over the period. There is also some indication of
upgrading in the technological capacity of the foreign affiliates of Japanese MNEs.
This is consistent with the finding from Odagiri and Yasuda (1996) that overseas R&D
activity has been rising rapidly despite a slow beginning.

Foreign affiliates of Japanese MNEs are heavily concentrated in generalmachinery, electronics, information and communication, and transport equipment
industries (Table 3). There is an increasing share of sales in the transport equipment
industry, growing from about 30 percent in 1989 to 37 percent in 2002. The similar10

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expansion of sales can be seen for information and communication industry. While theelectronics machinery industry has been one of the most important components inJapanese outward FDI, its sale share stagnated over the whole period. There was even aslight decline in the employment and sales share from 1989 to 2002.

Foreign affiliates of Japanese MNEs were overwhelmingly concentrated in East
Asian countries in the period under review (Table 4). About 60 percent of the foreign
affiliates of Japanese MNEs were located in East Asia in 2002, up from 49 percent in
1989, with corresponding employment share growth. Within East Asia, the rise of
China as a destination for foreign affiliates is impressive. Only 3.6 percent of Japanese
foreign affiliates were operating in China in 1989, but that figure had jumped sharply toover 19 percent by 2002. Accordingly, 1.8 percent of the employment share had grown
to 22 percent in 2002. This geographical shift of overseas operation has facilitated thecreation of international production network by Japanese MNEs in East Asia (Kimura
and Ando 2005).

Foreign affiliates of Japanese MNEs began to turn away from North American
and the EU over the years. For example, the employment share of foreign affiliates in
the US fell from 24 percent in 1989 to 16.5 percent in 2002; 22.5 percent of the foreign
affiliates of Japanese MNEs were located in the US in 1989, but by 2002 this had fallen
to 17 percent. Other developed country regions such as the EU countries experienced aslight decline or no change in foreign affiliate and employment share. This shift in thelocation of overseas operations of Japanese manufacturing MNEs coincides withpatterns of Japan’s fragmentation trade, discussed in Chapter 3.

Interestingly, while there is strong evidence of increased concentration of theoperations of foreign affiliates in East Asian countries over time, the geographicalcomposition of overseas sales are still concentrated in the US. The share of foreign
affiliate sales in East Asian countries remained at around 30 percent, although with anotable increase of sales share in the Chinese market. The sales share in North America
also remained at virtually the same level between 1995 and 2002, at about 42 percent.
For the EU, the share of foreign affiliate sales actually grew from 13.5 percent in 1989
to 16.5 percent in 2002, despite a decline in the share of employment and foreign
affiliates.
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5

The Analytical Framework

The baseline specification is based on a reduced form of labour demand function widelyused in this strand of literature (Brainnard and Rikcer 1997; Hanson et al. 2003;
Harrison and McMillan 2006).

(1)

ln Liht = α 0 + β1 ln wiht + β 2 ln Qiht + β3 ln rzht + β 4 ln R &Diht + β5 ln IMPzht +β 6 ln L jft + β 7 ln Q jft + β8 ln GDPft + fi + ϕ z + γ t + ε i,twhere subscripts i, h, z and t denote parent firm, home country, industry and time.
Subscripts j and f represent foreign affiliate and host country. A symbol ln means
natural logarithm. The dependent variable (Liht) is the quantity of home employment.
The log-linear specification offers the direct interpretation of elasticity between factors(i.e., own-wage elasticity and cross-wage elasticity). The explanatory variables arelisted below with the expected sign of each regression coefficient given in the bracket:

Estimating of labour demand (1) includes the indicators of foreign affiliate
employment and output. The estimated coefficient on these variables should provide a
direct test of the effect of overseas operations on home employment of MNEs. A
complementary relationship between home and foreign affiliate employment is expected
if the overseas operations of MNEs have scale effects. In this case, the positive sign is
expected. On the other hand, the negative sign indicates that home and foreign affiliate
employment are substitutes to each other. This means that the expanded overseas
operation of MNEs has reduced the home operation of MNEs by shedding employment
and shutting down the establishments.

Estimation of equation (1) also allows to

examine the nature of the relationship between the scale of output by MNEs’ foreign
affiliates and home employment.

The wage rate of home employment is expected to be negatively related to thenumber of home employment, given the downward sloping labour demand curve(Hamermesh 1993). The sign suggests that as the cost of home country workers rises,
profit-maximizing firms substitute away from labour towards other production inputs.

Product demand shocks both at home and in host countries are included in themodel (Brainard and Riker 1997a; Braconier and Ekholm 2000; Harrison and McMillan
2006). They are expressed by gross sales (denoted as Q), time-specific dummy and123/06/2008
GDP per capita of host countries. Any shocks to product demand are likely to movelabour demand in the same direction (Hasan et al. 2007). Positive shocks on productdemand are likely to raise the demand for the factor of production under the assumptionof constant returns to scale (one of the four Hicks-Marshallian laws of factor demand).
The inclusion of the output scale of parent firms is also treated as holding the size ofparent firms constant when estimating the labour demand equation (Kravis and Lipsey
1988). Time-specific dummies are also included to capture pure random shocks to thelabour demand equation common to all firms, but varying over time. Similarly, foreign
demand is proxied by the sales output of foreign affiliates as well as the GDP per capita
of host countries. The positive impact of the product market in foreign countries should
also translate positively into an increase in home employment (the market expansion
effect).

The labour demand on the condition of output also depends on the cost of capital
service. The sign of cross-factor price indicates the nature of relationship between
labour and capital. The positive sign is expected if they are substitutes, and the negative
sign is expected for a complementary relationship.
The level of technology is proxied by the intensity of R&D (denoted as R&D)
and by unobserved firm- and industry-specific characteristics.

The sign of R&D

depends on the nature of technological progress. It can substitute for employment of
parent firms since the introduction of technology (e.g., machinery equipment) might
require fewer operational workers. At the same time, technological progress increases
demand for skilled workers, engineers and IT related personnel. Therefore, a priori the
expected sign for R&D is ambiguous.

The unobserved heterogeneity across firms canarise from differences in organisation, the aging of capital equipment, the extent ofunionization, the quality of output produced, or the quality of management inputs.
Failing to take them into account might lead to permanent observable differences in
output, employment and wages (Westbrook and Tybout 1993). Additionally, industryspecific effects take into account for industry-wide technological shocks.

Another factor for influencing labour demand is the force of international
competition.

Tomiura (2004); Bernard et al. (2006); Ito (2005) confirm that

manufacturing employment growth in developed country is negatively related to a rapid
increase of imports from low-wage countries.

To control for this effect, import13

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penetration (IMP) at the industry level is included in the model. The expected sign of
IMP is negative, because the entrance of imports works against domestic manufacturing
employment.

5.1

Variable Construction

The dependant variable is measured by the average number of regular employees. The
METI Firm survey only collects information on the number of workers, not on hours
worked. While fluctuations in hours per worker are crucial for understanding short-run
labour demand, in the long run variation the number of workers is the primaryadjustment method (Hamermesh 1993; Roberts and Skoufias 1997). Therefore, a focus
on employment, rather than hours worked, is consistent with the objective of explaining
long-run labour demand differences at the firm-level. Unfortunately, the skill
composition of home employment is not available in the original METI data. Hence,
there is no distinction made between skilled or unskilled labour.

Foreign Affiliate Variables
Following the standard procedure in the literature, for each parent firm (i) in year t, aweighted average of employment of all foreign affiliates for a given parent firm iscomputed as follow:m(2)

Li, j = ∑ wgt j ,i L j , f
j =1

Subscript i refers to the individual parent firm, and j denotes the corresponding foreign
affiliate at multiple locations. The weight (wgt) is the share of foreign affiliate j in theaggregate foreign affiliate sales of parent firm i. The (sales share) weighted averages offoreign sales and GDP per capita are computed in the same fashion. GDP per capita istaken from the World Bank Development Indicators. In the experimental stage, analternative weighting scheme was attempted using the employment share, but obtained
similar results. Therefore, the results reported below are based on the sales share offoreign affiliates.

Parent Firm Variables
Output (Q) is the reported total sales by parent firms. The nominal gross outputs are
deflated by wholesale price index (WPI) at industry level taken from the Bank of14

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Japan. 7 The home wage rate is computed by dividing the annual wages and salaries by
the annual number of regular workers. Wages and salaries include bonus payments aswell as non-wage compensations. The nominal wage series is deflated by the total
Consumer Price Index (CPI) is also taken from the Bank of Japan. The user cost ofcapital (r) is proxied by wholesales index of investment goods obtained from the same
online database of the Bank of Japan. 8

The remaining variables for parent firms are obtained directly from the METI
survey.

R&D expenditure refers to average values of R&D expenditure spent on

knowledge creation and technological upgrading activity by firms, excluding R&D
activities done by other firms. R&D intensity is then computed by taking the share of
R&D expenditure of the total sales of parent firms. The import penetration ratio (IMP)is computed taking the ratio of imports to apparent domestic adsorption, which isdefined as (Outputs + Imports) – Exports.

IMP is constructed at the 3-digit

manufacturing industry level.

5.2

Estimation Method

Estimation of labour demand equation (1) takes into account for the presence of thefirm-level and time-specific specific effects. Both the within-transformation and firstdifference estimators of the fixed effect model are employed to sweep out the firmsspecific effects and the estimations results are compared between two estimators.9 The
heteroscedasticity-robust standard errors clustering for each firm is used to compute thestandard errors, which is also robust to serial correlation. The OLS estimator is also
performed to provide a benchmark comparison for results based on the other estimators.

The first-difference estimator provides the better treatment for the endogenity
problem, which is common to the firm-level data, as compared to the withintransformation estimator (Westbrook and Tybout 1993). However, the first-difference
7Complied from the online database at http://www.boj.or.jp/type/stat/dlong/price/cgpi/index.htm
They are available for the following industries, textile products, iron and steel, non-ferrous metals, metal
products, general machinery, electrical machinery, transport equipment, precision instruments, and othermanufacturing industry products.
9
The within-transformation estimator performs OLS on variables expressed in terms of deviations fromthe firm-specific means: for any variable xit, the within transformed variable can be written as follow,
8

xi ,t = xi ,t − (T )−1 T

∑ xi ,t i=1,….N, where i and t represent individual firm and time, respectively. The

t =1

difference estimator applies OLS on time-differenced data: Δxi ,t = xi ,t − xi ,t − j t=1,..j…T.

15

3/06/2008
might suffer from the potential selectivity bias because this estimator excludes firms notpresenting in the period t and t-1. It is also known that the first-difference estimator can
exacerbate the bias due to measurement errors by reducing the amount of systematic
variations in the data (Griliches and Hausman 1986). Therefore, the first-difference andwithin-transformation estimators are treated as complementary estimation procedures.

The most important estimation issue is the potential endogeneity problem forsome explanatory variables in equation (1). MNEs make a decision on the overseas anddomestic operations in terms of employment and outputs simultaneously rather thanindependently. Therefore, the common factor, which is excluded from the model, could
influence either the positive or negative correlation of the OLS regression in theconditional labour demand equation (Desai et al. 2005). In this regard, a generalised
method of moments (GMM) instrumental variable (IV) procedure is employed(Griliches and Hausman 1986; Arellano and Bond 1991). This procedure essentiallyapplies instrumental variables to the first-differenced data using the moment conditions.
It is often shown in the literature that the lagged values of the potentially endogenous
variables in level are potentially useful instruments for the time-differenced variables(Griliches and Hausman 1986; Hasan et al. 2007).

Instrument variables for employment and output of foreign affiliates in a hostcountry are the lagged employment output and wage rates of foreign affiliate, thepercentage of the manufacturing labour force and the percentage of national income
spent on education. The last two exogenous variables are considered to determine thesupply side of labour in the host country, and should only affect home labour market
outcomes through their impact on the choice of employment in host country. Thesevariables are taken from online version of the World Bank Development Indicators foreach host country. 10

There is also a concern for possible correlation between the output variable (Q)of parent firm and the error term in equation (1). The use of time-dummies, industryand firm-specific fixed effects to some extent alleviates the potential endogeneity
problem (Roberts and Skoufias 1997; Hasan et al. 2007). The time-dummies take careof such an unobserved economy-wide shock, while the industry-specific and firm-10

http://devdata.worldbank.org/dataonline/16

3/06/2008
specific effect accounts for unobserved technology shocks specific to industry-wise andindividual firms, respectively.

Those fixed effects should take care of the bulk of the endogenity problem
between output and the error term. However, it is still possible that the output variable
is correlated with some parts of the error term which are not covered by the fixed effects.
In this case, the instrument variables (IV) approach is employed to deal with thispotential endogeneity problem on domestic output. Instruments include the lagged
capital stock and the lagged intermediate inputs as well as lagged output. The first twovariables are directly taken from the METI Firm survey. The capital stock is measured
by the book value of the stock of tangible assets, such as capital, machinery andproperty.

The nominal capital stock is transformed into the real term using thewholesale prices index of machinery and equipment as a deflator. This deflator is
obtained from the Bank of Japan. 11 In the original METI Firm survey, there are no
readily available data for the intermediate input expenditures. Hence, they are definedas the sum of the cost of goods sold and general administrative costs minus wage bills,
the rate of depreciation as well as the rental costs.

There is also a concern for the endogenous problem of home wages in
estimating the condition labour demand equation (1). However, the firm-level data isless prone to this problem. This is, based on the reasonable assumption labour supply
which a firm is facing is perfectly elastic, so that wages are exogenously determined(Griliches and Hausman 1986; Hamermesh 1993; Roberts and Skoufias 1997; Slaughter2001). Both labour supply and demand depend on wages observed. However, when
labour supply is perfectly elastic, shifts in the labour supply schedule, measured by
movement in wages, trace out the labour demand schedule. In this case, the position of
the labour demand is purely controlled for by non-labour factor prices and output or
product demand shocks. Additionally, labour demand elasticity estimated at firm levelis more accurate than that obtained from industry-level data.

The sample selection is also an important estimation issue that should be
addressed. As descried in section 3, the entry and exit of firm in the METI data iscomplicated by the disappearing and re-appearing of firm.

In order to check theimportance and nature of potential sensitivity bias, econometric estimations are11

See the footnote 71.

17

3/06/2008
performed for two sets of the data. One sample allows entry/exit (and re-appearance) of
parent firms at any time, hence, it is unbalanced panel data. The other data set isrestricted to only ‘surviving’ parent firms observed for the entire period 1991-2002.
Under the complicated nature of the entry/exit of firms, the standard sample selectivity
correction using the Heckman (1979) two-step procedure is not considered. 126

Results

Table 7a reports the estimations results of the labour demand equation (1) based on theunbalanced panel data set. Results based on the balanced panel set are reported in Table
7b for the purpose of comparison. Model 1 of Table 7 presents the estimation results
based on OLS, and Model 2 performs the within-transformation of fixed effect model.
Model 3 employs the first-difference and Model 4 with the first-difference IVs estimator.
Table 8 presents the estimated results for the 3-digit of 59 manufacturing level data.
Finally, Tables 9a to 9d present results for each of the four regions - East Asia, North
America, the EU and South America. Time dummies and industry-specific dummies
are included for all regressions performed except for OLS (Model 1), but the results aresuppressed for brevity.

Table 7a contains some evidence of a positive complementary relationshipbetween foreign affiliate employment and outputs and home employment within
Japanese manufacturing MNEs for the period 1991-2002, but the magnitude of theestimated coefficient is very small. Model 2 (within-transformation) suggests a 10
percent increase of foreign affiliate employment leads to a 0.13 percent increase ofhome employment.

Foreign affiliate output variable also indicates the statisticallysignificant positive effect on home employment, although the magnitude of theestimated coefficient on this variable is also small.

Additionally, foreign demand

shocks, captured by GDP per capita, have no causal statistical relationship with change
in home employment (Model 2, Table 7a).

The first-difference estimator (Model 3) in Table 7a also suggests a positive
impact of expanded foreign affiliate sales on home employment, but not foreign affiliate
employment. The IVs procedure in Model 4 in Table 7a improves the results forforeign affiliate employment, but the correction of endogeneity for foreign affiliate sales
12This procedure amounts to estimating a probit model of firm’s survival at the current period by using
information from the previous period (See Harrison and McMillan 2006 for an application).

18

3/06/2008
loses the statistical significance of this variable. The overidentifying test statistics for
instruments amount to 3.69, which does not reject the null hypothesis that all
instruments are uncorrelated with the error term at 5-percent significant level( χ q2= 4 =9.49). In other word, the selected instruments are indeed valid instruments withno direct correlation with the error term in equation (4). The first stage of regression
also finds a strong correlation between the selected instruments and the endogenous
variables (the results are suppressed for brevity).

The OLS result in Model 1 indicates a positive complementary relationshipbetween foreign affiliates and home employment and the negative impact of foreign
affiliate output on home employment (Model 1, Table 7a). The evidence also indicatesa positive impact of foreign market demand shock on home employment. However,comparing the estimation results between OLS (Model 1) and the fixed effects
estimation in Model 2 and 3 points to the importance of controlling for the firm-fixed
effects.

The OLS results without considering the firm-fixed effects largely

overestimated the statistical significance of labour demand variables.

Table 7b reports the same set of regression results, but the sample is restricted tothe balanced panel data for 1991-2002. The estimation results are somewhat different
from the unbalanced panel data.

The expanded overseas operations in terms of

employment and output have virtually no impact on demand for home employment of
parent firms which exist the entire period of 1991-2002. The only exception is that
estimated coefficient of foreign affiliate employment by first-difference IVs estimator in
Model 4 in Table 7.9b indicates a positive effect on home employment at a 5 percent ofstatistical level. These contrasting findings between balanced and unbalanced panel
data might offer two interpretations.

First, the estimation results obtained in theunbalanced panel data might be driven by the sample selection biases introduced byparent firms’ appearing and disappearing in the sample. Second, the surviving parent
firms in the balanced panel data set well adjust to changes in overseas operations
without changing the scale of the home operations.

Table 8 reports results for 59 manufacturing industries at three-digit level overthe period 1991-2002. While the magnitudes of the estimated coefficient for most ofvariables have increased to some degree as compared to the firm-level data, the general19

3/06/2008
findings do not change from Table 7b. The estimated coefficients of foreign affiliate
employment and their sales have no impact on change in home employment, apart fromthe OLS procedure.

Table 9 presents results for each region by dividing total foreign affiliates into 4
different regions of East Asia (Table 9a), North America (Table 9b), the EU (Table 9c)
and South America (Table 9d). Employment and sales of foreign affiliate operating in
East Asia are found to have no impacts on home employment, despite the rapidly grownoverseas operation in the region for the past 15 years. This suggests international
fragmentation of production in East Asia have no implication for the home employment
adjustments. In North American (Table 9b), foreign affiliate operations do not have any
impact on change in home employment, despite their long history of foreign affiliate
operations. By contrast, the increased foreign affiliate outputs in the EU countries(Table 9c) show small positive effects on home employment with a 5-percent ofsignificant level (Model 2 and 3). In other words, the expansion of sales in the EU has
scale effects on home employment in Japan.

In sum, there is no clear-cut evidence of ‘exporting jobs’, despite the concerns
expressed in the public debates. Instead, there are some findings to suggest that the
expanded overseas operations have actually helped to maintain the level of home
employment, but it depends on the estimation method used and whether the panel data
is balanced or unbalanced.
Other determinants of labour demand by parent firms can be summarised asfollows. Wage elasticity of labour demand consistently has the expected negative sign.
This indicates any increase in the wage rates results in a decrease in hiring more home
employment. The own-wage elasticity is consistently reported in the range of -0.1 to 0.5. The range is comfortably consistent with the finding in Hamermesh (1993) that theown wage elasticity of labour demand usually varies from -0.3 to -0.6 on average. The
output elasticity is significant both in the within-transformation and the first-difference
estimators (Model 2 and 3 in Table 7a). However, this result changes once correcting
for the endogeneity problem in Model 4. In contrast, the output elasticity is found to bepositive and statistically very significant in the industry level panel regression with andwithout the IV correction (Table 8).

20

3/06/2008
The estimated coefficient of r (the user cost of capital) shows mixed results,
making it impossible to infer whether capital and home employment are substitute for orcomplementary to each other. While the estimated coefficient for R&D intensity showssome significance in explaining change in home employment in the unbalanced panel
data (Table 7a), the statistical significance is lost when the analysis moves into thebalanced panel set (Table 7b). This could mean the entry and exit of parent firms ishighly correlated with their observable technological level. Import penetration is hardly
ever significant. Apart from the industry level regression in Table 7.8, the signs ofimport penetration overwhelmingly show an unexpected positive sign. This finding
contrasts with the US study where there is a robust negative relationship to change inparent employment with the degree of import penetration (Harrison and McMillan
2006; Bernard et al. 2006).

Overall, this chapter found that the expanded overseas operations of Japanesemanufacturing MNEs have no adverse effects on home employment, in contrast to the‘exporting job’ concerns. Relating to the finding of the previous chapter, international
fragmentation of production has had only the effects of changing the skills structure of
employment, but few effects on the amount of employment in Japanese manufacturing.
By and large, there is no strong indication that overseas operations expand at the cost ofworkers in Japanese manufacturing over the period 1991-2002.

7

Concluding Remarks

This paper has examined the hypothesis that expansion of overseas operations ofJapanese manufacturing MNEs reduces home employment within the MNEs’ operations.
A standard labour demand equation is estimated by allowing the effects of foreign
affiliates employment and sales on home employment.

In addition, industry

aggregation and geographic locations of foreign affiliates are both controlled in order tocontrol for the specific regional and characteristics of MNEs. The empirical exercise isbased on the newly constructed panel data set, covering information for both home andforeign affiliates’ operations within matched manufacturing Japanese MNEs for theperiod 1991-2002.

Despite concerns expressed about the adverse effects of FDI, the evidence does
not support the view that overseas operations expand at the cost of home employment inJapan. On the contrary, the findings suggest that overseas operations have somewhat
21

3/06/2008
helped to maintaining the level of home employment in Japanese manufacturing.
However, the results are sensitive to the estimation method used and whether theestimation is based on the panel data set is balanced or unbalanced.

22

3/06/2008

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Table 1Year

Selected indicators of parents firms of Japanese manufacturing MNEs, 1991-2002
Number
of:

Sales
of:

Employmentof:

MNE parent firms

Unit
1991
1994
1995
1996
1997
1998
1999
2000
2001
2002
Average

616
863
782
902
950
914
989
926
984
1144
907.0

Trillionof yen
128.8
124.2
128.5
143.2
142.7
131.4
138.6
144.8
139.3
136.8
135.8

in 1,000
2245
2275
2267
2328
2292
2188
2261
2215
2121
2066
2225.8

Share in total Japanese manufacturing of:
Number of
MNEs

Output of
MNEs

Employment
of MNEs

Workers
earnings
of MNEs

Capitalstockof MNEs

Exportsof MNEs

Imports
of
MNEs

R&D
of
MNEs

(%)

(%)

(%)

(%)

(%)

(%)

(%)

(%)

4.5
6.3
5.4
6.3
6.7
6.5
7.1
7.6
7.3
8.7
6.6

48.2
49.6
49.0
51.7
52.8
52.0
54.4
57.2
55.8
54.6
52.5

37.2
38.3
38.0
39.4
40.1
39.2
41.1
44.0
41.4
42.1
40.1

43.9
45.2
44.6
46.5
47.6
46.7
48.6
51.9
49.5
50.8
47.5

47.1
49.3
48.7
50.5
51.9
50.4
52.4
55.7
53.5
52.9
51.2

86.2
80.8
82.5
82.5
82.8
84.8
86.5
83.2
86.1
83.9

60.6
60.7
58.5
60.3
60.9
62.2
69.1
65.0
64.8
62.5

73.5
74.6
75.7
77.2
77.8
77.8
79.0
81.2
78.4
80.6
77.6

Source: Based on the METI database, which is explained in section 2 and Appendix 1.
Note: See the definition of MNEs in main text. The survey data are not available for year 1992 and 1993.

27

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Table 2Year

Key indicators of foreign affiliates of Japanese manufacturing MNEs, 1989-2002

Number
of

Unit

1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
Level
change%changeEmployme
nt of:

in 1000

Sales
of:

Averagesales

Average
employment

trillionsof yenforeign affiliate of manufacturing MNEs
billons
Unit
Millions
output perof yenof yenworkerAverage
wage
rates

Averagelabourproductivity

Average
R&D
intensity

Average
local
sales
ratio

Averagesales
ratio toJapan

Averagesales
ratio toothercountries

Averagepurchaseratio from
local

Averagepurchaseratio fromJapan

Averagepurchaseratio fromothercountry

%

%

%

%

%

%

%

2656
3407
3535
3040
4548
7992
7345
7626
9279
9069
9828
10549
7068
8006
5350

914
1242
1261
986
1516
1972
1986
2258
2540
2339
2812
3049
2645
2844
1930

22.4
26.2
25.4
25.1
29.2
85.2
87.1
106.7
110.8
109.6
102.9
112.8
64.2
64.9
42.5

8.4
7.7
7.7
9.0
6.9
11.7
13.2
15.2
13.0
13.3
11.7
12.3
10.4
9.8
1.41

344.1
364.4
369.6
371.0
357.0
267.4
310.6
296.6
274.5
294.3
289.8
289.9
398.8
410.3
66.2

2.7
3.0
3.1
4.2
3.7
4.2
4.1
3.6
4.1
3.7
3.4
0.7

24.5
21.1
20.8
24.3
19.4
43.6
42.6
51.2
47.4
45.2
40.2
42.4
26.1
24.0
-0.5

1.2
3.7
0.6
1.0
3.0
1.1
1.1
1.2
4.2
3.3
1.7
0.5

74.8
69.5
67.0
75.1
65.8
68.7
74.5
70.8
68.7
71.6
72.2
70.7
68.6
66.4
-8.4

17.9
15.3
17.8
25.0
18.1
15.6
29.3
21.8
22.7
24.6
23.6
21.8
28.5
25.4
7.5

18.2
17.7
18.7
27.3
17.3
15.8
30.5
24.6
22.9
34.0
25.1
23.2
37.9
24.9
6.7

52.9
48.9
50.3
55.1
50.9
41.0
58.8
48.8
47.1
51.3
50.7
49.6
58.2
56.2
3.3

44.9
41.2
40.9
49.9
38.8
43.0
53.7
44.4
45.5
47.6
46.9
46.0
43.4
39.2
-5.7

13.4
11.1
12.5
22.9
11.4
16.3
21.1
25.9
23.7
32.1
25.8
23.3
32.8
20.0
6.6

201.4

211.2

189.7

16.7

19.2

25.9

-2.0

41.7

-11.2

41.9

36.8

6.2

-12.7

49.3

Source: Based on the METI dataset, which is explained in section 2 and Appendix 1.
Note: Average labour productivity is measured by output per worker. R&D intensity refers to the ratio of R&D expenditures to output. Worker compensation and
R&D expenditures for 1990, 1991, and 1993 are not contained in the original return to the METI Foreign Affiliate survey. The METI Foreign Affiliate survey is available
from 1989.

28

3/06/2008

Table 3

Industry distributions of foreign affiliates of Japanese manufacturing MNEs (%), 1989-2002

TextileChemicals
Primary metals
Metal goods
General machinery
Electronics machinery
Information and communication
Transport equipment
Scientific equipment
Other mfg
Total manufacturing
Unit

1989
6.9
13.6
6.5
3.2
11.0
7.2
15.6
12.1
3.8
20.1

Firm
1995
8.5
13.6
6.4
3.0
10.6
6.3
16.9
12.1
3.4
19.4

2002
6.0
14.4
5.4
2.9
10.6
7.0
16.3
15.9
3.6
17.9

100
100
100
Number of Firms
5834
8637
8014

1989
7.8
7.7
7.5
1.2
6.3
8.8
23.2
17.8
2.6
17.3

Employment
1995
8.2
7.5
5.8
1.2
6.4
6.8
26.2
19.9
2.4
15.7

2002
5.5
6.3
3.9
1.2
5.5
7.3
27.1
23.7
3.1
16.3

100
100
100
Employment in 1,000
1485
2618
3251

1989
2.1
8.9
6.7
0.7
9.7
5.6
21.3
30.5
1.9
12.8

Sales
1995
1.9
9.3
5.8
0.5
6.3
4.6
28.5
28.6
1.7
12.6

2002
1.3
10.2
3.9
0.5
5.5
4.6
24.2
36.7
2.2
10.9

100
100
100
Sales in billions of yen
32816
50591
72295

Source: Based on the METI dataset, which is explained in section 2 and Appendix 1.

29

3/06/2008

Table 4

Geographical distribution of foreign affiliates of Japanese MNEs (%) 1989-2002

East Asia
China
South Asia
Oceania
North America
US
South
America
The EU
Eastern & Central
EuropeAfricaWorld
Unit

Number of:
Employment of:Sales of:foreign affiliates of manufacturing MNEs
1989 1995 2002 1989
1995 2002
1989 1995
49.2 56.8
59.0 48.5
58.8
64.6
30.4
33.0
3.6 16.4
19.3
1.8
12.7
22.2
0.4
2.1
1.5
1.2
1.7
2.2
2.0
1.9
0.8
0.8
3.3
2.9
2.4
3.1
1.5
1.0
4.5
2.4
24.7 20.2
18.4 25.3
20.5
17.3
46.0
41.9
22.5 18.6
17.2 23.8
19.4
16.5
43.2
39.7
6.4
4.7
3.7 10.2
6.1
3.7
3.5
4.3

2002
32.6
6.1
1.1
2.4
41.9
38.9
3.4

12.7
0.6

16.5
0.9

12.0
0.8

11.6
1.1

8.9
0.3

9.7
0.4

8.5
0.8

13.5
0.4

16.0
0.7

0.7
100
6197

0.4
0.4
0.6
0.1
0.5
0.2
0.1
0.4
100
100
100
100
100
100
100
100
9042 8265 1533
2603 3285 35886 52429 73886
unit
in 1,000
billions of yen
Source: Based on the METI database, which is explained in section 2 and Appendix 1.

30

Table 5

Symbols ofvariables
L
W
Q
K
R&D
IMP
L (foreign)
Q (foreign)
GDPP

Summary statistics of selected variables used in regressions forunbalanced panel data

Description

Obs.

Mean

Std. Dev.

Log Parent firms employment
Log Wage rate
Log Output
Log Capital priceLog R&D intensity
Log Import penetration
Log Foreign Affiliates Employment
Log Foreign Affiliates Sales
Log GDP per capita of host countries

8432
8428
7837
8419
7179
7853
8058
8110
7849

6.81
-2.84
5.36
4.57
-3.99
-3.56
4.90
3.19
9.22

1.36
0.33
1.71
0.06
1.31
1.03
1.57
1.84
1.31

IMP

L (foreign)

Table 6

Min

Max

3.91
-5.65
-1.13
4.35
-10.81-11.11
-4.91
-9.48
0.71

11.32
-0.50
11.21
4.65
-0.46
-0.66
10.53
10.41
10.45

Correlation Matrix
w

W
K
Q
R&D
IMP
L (foreign)
Sales
GDPP

Coeff.
Var.
0.20
-0.12
0.32
0.01
-0.33
-0.29
0.32
0.58
0.14

K
1
-0.06
0.41
0.26
-0.06
0.16
0.32
0.16

Q
1
-0.01
-0.13
-0.24
-0.06
-0.08
0.02

R&D

1
0.23
0.04
0.49
0.71
0.26

1
0.08
0.10
0.26
0.20

1
0.13
0.16
0.01

1
0.66
-0.06

Sales

GDPP

1
0.42

1

Source: Based on the METI database, which is explained in section 7.2 and Appendix 7.1.

31

Table 7

Labour demand by parent firms of MNEs, 1991-2002(a) Results from Unbalanced panel data

Dependent variable = log (home employment)
Model 1Model 2Model 3Model 4
OLS
WithinFirstFirsttransformation difference difference, IVs
Log Wage rate
-0.257-0.117-0.123-0.119(0.047)***(0.019)***(0.015)*** (0.016)***
Log Capital prices
1.013
0.367
0.086
0.101
(0.227)***(0.137)***(0.103)(0.136)Log Output
0.689
0.135
0.043
0.065
(0.014)***(0.022)***(0.014)*** (0.064)
Log R&D intensity
0.154
0.022
0.009
0.009
(0.013)***(0.005)***(0.003)*** (0.005)*
Log Import penetration-0.033
0.001
0.007
0.009
(0.013)***(0.006)(0.003)*(0.004)**Log Foreign Affiliates Employment 0.088
0.013
0.004
0.027
(0.014)***(0.005)**(0.003)(0.014)**Log Foreign Affiliates Sales-0.051
0.011
0.006-0.009(0.015)***(0.005)**(0.003)** (0.008)
Log GDP per-capita of host country 0.081
0.002-0.0040.005(0.013)***(0.005)(0.003)(0.005)Constant-2.6574.161-0.048-0.084(1.046)**(0.650)***(0.045)(0.052)
Observations
The number of parent firm
Adjusted R-squared
F-Statistics

6170
1290
0.86
473.77***

6170
1290
0.30
170.11***

4289
1023
0.07
9.06***

3691
952
7.43***

Note: Time- and industry-dummy variables (three-digit level) are included for all estimations,but the results are suppressed here. Standard errors based on White’s heteroscadasticity
correction clustered by individual firm are given in brackets, with statistical significance (twotailed test) denoted as: *** 1per cent, ** 5 per cent, and * 10 per cent. The instruments
variables for output, foreign affiliates output and employment used in estimating Model 4 arediscussed in the main text. The overidentifying test statistic for instruments used is 3.69, which
does not reject the null hypothesis that all instruments are uncorrelated with the error term at 5percent significant level ( χ q2= 4 =9.49).

32

Table 7 (continued)(b)Results from balanced panel data

Log Wage rate
Log Capital prices
Log Output
Log R&D intensity
Log Import penetration
Log Foreign Affiliates
Employment
Log Foreign Affiliates Sales
Log GDP per-capita of hostcountry
Constant

Dependent variable = log (home employment)
Model 1Model 2Model 3Model 4
OLS
WithinFirstFirsttransformation difference difference, IVs-0.539-0.098-0.132-0.07(0.104)*** (0.035)***(0.027)*** (0.028)**0.769-0.004-0.160-0.064(0.480)(0.255)(0.164)(0.225)
0.687
0.102
0.026
0.162
(0.035)*** (0.040)**(0.016)(0.105)0.195
0.018-0.001
0.005(0.032)*** (0.011)(0.004)(0.007)-0.027
0.010
0.006
0.017
(0.030)(0.012)(0.006)(0.008)**0.088(0.031)***-0.040(0.040)0.012(0.010)0.014(0.012)0.004(0.006)0.005(0.008)0.049(0.019)**-0.005(0.028)0.107(0.030)***-2.461-2.096-0.011(0.013)7.076(1.193)***-0.004(0.004)0.037(0.034)0.012(0.010)0.057(0.043)Observations
1459
1459
1254
1070
The number of parent firms
178
178
177
168
Adjusted R-squared
0.90
0.38
0.08
F-statistics
165.72*** 15.15***
4.63***
4.25***
Note: Time- and industry-dummy variables (three-digit level) are included for all estimations,but the results are suppressed here. Standard errors based on White’s heteroscadasticity
correction clustered by individual firm are given in brackets, with statistical significance (twotailed test) denoted as: *** 1per cent, ** 5 per cent, and * 10 per cent. The instrumental
variables for output, foreign affiliates output and employment used in estimating Model 4 arediscussed in the main text.

33

Table 8

Labour demand by parent firms of MNEs at three-digit level, 1991-2002

Log Wage rate
Log Capital prices
Log Output
Log R&D intensity
Log Import penetration
Log Foreign Affiliates
Employment
Log Foreign Affiliates Sales
Log GDP per-capita of hostcountry
Constant

Dependent variable = log (home employment)
Model 1Model 2Model 3Model 4
OLS
WithinFirstFirsttransformation difference
difference, IVs-0.4220.065
0.055-0.351(0.209)**(0.114)(0.122)(0.238)-0.286-0.061-0.1270.182(0.736)(0.470)(0.491)(0.477)
0.556
0.685
0.717
0.549
(0.048)*** (0.053)***(0.036)*** (0.070)***
0.096
0.05
0.046
0.060
(0.051)*(0.031)(0.028)(0.038)-0.025-0.020-0.020-0.045(0.028)(0.023)(0.022)(0.021)**
0.191
0.014
0.077
0.032
(0.043)*** (0.046)(0.038)(0.105)-0.012
0.034
0.007
0.066
(0.055)(0.022)(0.054)(0.036)*
0.269
0.067
0.052
0.064
(0.049)*** (0.039)(0.032)**(0.039)0.928
0.018-0.0051.930(3.407)(0.018)(0.033)(2.270)Observations
459
459
388
387
Adjusted R-squared
0.95
0.79
0.81
F-Statistics
330.76*** 31.97***
76.60***
30.39***
Note: Time-dummy variables are included for all estimations, but the results are suppressed
here. Standard errors based on White’s heteroscadasticity correction clustered by individual firm
are given in brackets, with statistical significance (two-tailed test) denoted as: *** 1per cent, **5 per cent, and * 10 per cent. The instrumental variables for output, foreign affiliates output andemployment used in estimating Model 4 are discussed in the main text.

34

Table 9

Labour demand by parent firms of MNEs by region, 1991-2002(a) - East Asia

Log Wage rate

Log Capital prices
Log Output
Log R&D intensity

Log Import penetration
Log Foreign Affiliates
Employment
Log Foreign Affiliates Sales
Log GDP per-capita of hostcountry
Constant

Observations
The number of parent firm
Adjusted R-squared
F-statistics

Dependent variable = log (home employment)
Model 1Model 2Model 3Model 4
OLS
WithinFirstFirsttransformation difference difference, IVs-0.258-0.123-0.128-0.125(0.017)**(0.048)*** (0.021)****(0.020)***
1.043
0.303
0.043
0.125
(0.243)*** (0.147)**(0.100)(0.147)
0.737
0.121
0.036
0.111
(0.012)*** (0.025)***(0.015)** (0.070)
0.173
0.029
0.011
0.015
(0.004)***(0.013)*** (0.006)***(0.006)**-0.026
0.000
0.002
0.006
(0.014)*(0.006)(0.003)(0.004)0.046(0.014)***-0.061(0.015)***0.006(0.005)0.003(0.004)0.001(0.003)0.002(0.003)0.021(0.011)**-0.009(0.008)0.004-0.011-2.076(1.145)*-0.006(0.005)5.615(0.732)***-0.002(0.003)-0.004(0.009)0.000(0.005)-0.011(0.010)

4947
1058
0.88
458.61***

4947
1058
0.32
839.02***

3426
829
0.07
8.66***

2898
767
7.14***

Note: Time- and industry-dummy variables (three-digit level) are included for all estimations,but the results are suppressed here. Standard errors based on White’s heteroscadasticity
correction clustered by individual firm are given in brackets, with statistical significance (twotailed test) denoted as: *** 1per cent, ** 5 per cent, and * 10 per cent. The instrumental
variables for output, foreign affiliates output and employment used in estimating Model 4 arediscussed in the main text.

35

Table 9 (continued)(b) - North America
Dependent variable = log (home employment)
Model 1OLS
Log Wage rate
Log Capital prices
Log Output
Log R&D intensity
Log Import penetration
Log Foreign Affiliates
Employment
Log Foreign Affiliates Sales
Log GDP per-capita of hostcountry
Constant

Observations
The number of parent firm
Adjusted R-squared
F-Statistics-0.276(0.062)***0.425(0.312)0.688(0.019)***0.16(0.020)***-0.014(0.017)

Model 2
Withintransformation-0.121(0.022)***0.200(0.173)0.100(0.026)***0.015(0.007)**0.002(0.007)

Model 3Firstdifference-0.121(0.018)***0.082(0.170)0.031(0.015)**0.004(0.003)0.002(0.004)Model 4Firstdifference, IVs-0.102(0.021)***
0.058(0.214)0.063(0.068)0.004(0.005)0.003(0.005)0.097(0.017)***-0.052(0.021)**0.008(0.007)0.020(0.008)**0.004(0.005)0.005(0.005)0.001(0.020)-0.002(0.015)-0.044(0.031)
1.295(1.416)-0.048(0.020)**6.176(0.852)***-0.018(0.010)*-0.004(0.009)-0.002(0.044)-0.011(0.013)

3996
812
0.84
252.06***

3996
812
0.25
386.67***

2785
662
0.11
6.09***

2198
589
4.06***

Note: Time- and industry-dummy variables (three-digit level) are included for all estimations,but the results are suppressed here. Standard errors based on White’s heteroscadasticity
correction clustered by individual firm are given in brackets, with statistical significance (twotailed test) denoted as: *** 1per cent, ** 5 per cent, and * 10 per cent. The instrumental
variables for output, foreign affiliates output and employment used in estimating Model 4 arediscussed in the main text.

36

Table .9 (continued)(c) – The EU

Log Wage rate
Log Capital prices
Log Output
Log R&D intensity
Log Import penetration
Log Foreign Affiliates
Employment
Log Foreign Affiliates
Sales
Log GDP per-capita ofhost country
Constant

Observations
The number of parent firm
Adjusted R-squared
F-statistics

Dependent variable = log (home employment)
Model 1Model 2Model 3Model 4
OLS
WithinFirstFirsttransformation difference
difference, IVs-0.201-0.128-0.126-0.103(0.061)*** (0.027)***(0.022)*** (0.024)***
1.155
0.180
0.087
0.045
(0.349)*** (0.216)(0.189)(0.272)
0.695
0.089
0.018
0.025
(0.026)*** (0.032)***(0.020)(0.085)
0.21
0.012
0.004
0.003
(0.023)*** (0.008)(0.005)(0.007)-0.013
0.005
0.006
0.01
(0.020)(0.008)(0.005)(0.007)0.074(0.017)***0.005(0.006)0.002(0.004)0.032(0.018)*-0.039(0.028)0.029(0.015)**0.016(0.008)**-0.011(0.027)-0.04(0.038)-1.606(1.620)-0.063(0.030)**6.61(1.107)***-0.04(0.017)**0.001(0.011)-0.02(0.053)0.002(0.009)

2432
493
0.86
233.09***

2432
493
0.29
2876.28***

1715
399
0.07
3.85***

1271
342
2.05**

Note: Time- and industry-dummy variables (three-digit level) are included for all estimations,but the results are suppressed here. Standard errors based on White’s heteroscadasticity
correction clustered by individual firm are given in brackets, with statistical significance (twotailed test) denoted as: *** 1per cent, ** 5 per cent, and * 10 per cent. The instrumental
variables for output, foreign affiliates output and employment used in estimating Model 4 arediscussed in the main text.

37

Table 9 (continued)(d) – South America
Dependent variable = log (home employment)
Model 2Model 3Model 4
WithinFirstFirsttransformation difference difference, IVs-0.482-0.242-0.253-0.176(0.132)***(0.066)***(0.072)*** (0.103)*
0.677
0.842
0.239
-0.171(0.724)(0.352)**(0.261)(0.505)
0.731
0.205
0.067
0.190
(0.044)***(0.077)***(0.075)-0.209
0.156
0.003
0.000
0.001
(0.032)***(0.015)(0.007)(0.009)Model 1OLS

Log Wage rate
Log Capital prices
Log Output
Log R&D intensity
Log Importpenetration
Log Foreign Affiliates
Employment
Log Foreign Affiliates
Sales
Log GDP per-capita ofhost country
Constant

Observations
The number of parent
firm
Adjusted R-squared
F-statistics-0.021(0.043)0.036(0.013)***0.003(0.010)0.010(0.012)0.023(0.032)0.009(0.012)0.009(0.008)-0.002(0.030)0.045(0.043)0.047(0.019)**0.008(0.008)0.016(0.028)-0.122(0.081)-0.344(3.385)(0.069)(0.035)*2.738(1.819)-0.014(0.019)0.03(0.012)***-0.017(0.069)-0.038(0.043)

764
154

764
154

546
129

320
96

0.88
108.16***0.46
128.83***0.16
2.83***

2.14**

Note: Time- and industry-dummy variables (three-digit level) are included for all estimations,but the results are suppressed here. Standard errors based on White’s heteroscadasticity
correction clustered by individual firm are given in brackets, with statistical significance (twotailed test) denoted as: *** 1per cent, ** 5 per cent, and * 10 per cent. The instrumental
variables for output, foreign affiliates output and employment used in estimating Model 4 arediscussed in the main text.

38

Appendix 1 METI Surveys
METI Firm Survey (the Basic Survey of Business Structure and Activity):This survey, first conducted in 1991 has become an annual survey since 1994. It covers
all firms in both manufacturing and non-manufacturing including mining, wholesale,agriculture, retail, and construction as well as the service sector that have both morethan 50 employees and capital of more than 30 million yen. It collects sufficient
information to quantify details on the domestic operations of Japanese firms, including
total sales, total purchases, employment, workers’ compensation, fixed tangible andnon-tangible assets, capital, number of establishments, R&D expenditure, year of
establishment, exports, and imports.

Most key variables have been reported

continuously since 1991 except for the years in 1992 and 1993. This survey also covers
limited information about the operation of foreign affiliates such as the number ofaffiliates, employment and value of sales, if the parent firm engages in FDI.
Transactions are recorded in millions of Japanese yen and measure the amounts paid or
received by individual firms. All individual firms are assigned unique identifiers,
making it possible to track operations of the same firms over time. The survey is
mandatory 13 and hence the response ratio is very high (around 90 percent).

The well know limitation of the METI Firm survey is that the entry and exit offirms in this survey does not necessarily correspond to the standard definitions of origin
and termination of firms due to the sample selection criteria (Nishimura et al. 2005).

METI Foreign Affiliates survey (the Basic Survey of Overseas Japanese Business
Activity)
The METI Foreign Affiliates survey is designed to trace the scale and functions offoreign affiliates of Japanese MNEs operating overseas. The survey are sent out to theirparents firms located in Japan. There has been a relatively long history of conducting
this survey commencing in 1971, a detailed survey every three years since 1981 and astandard one each year in other years. However, data are available by electronics means
for this project only from 1989. Most importantly, each individual affiliate is assigned
its own unique code as well as the parent firm identifier. This makes it possible to link
between the METI Firm survey and the METI Foreign Affiliate survey.

13This means firms failing to return the survey to the METI face heavy fines.

39

The METI Foreign Affiliate survey contains the main variables such as sales
output distinguished by destinations such as local market, Japan or other countries, totalpurchase distinguished by sources, wages and salaries, employment, fixed tangible
assets, capital, and R&D spending. However, not all have been reported consistently
since 1989. For instance, wage and salaries only appear continuously from 1994, andfixed tangible assets are only available for years 1989, 1992, 1995, 1998, and 2001.
The METI Foreign Affiliates survey also reports limited information about theoperations of parent firms, such as sales, purchases, employment, and capital.

While the METI Foreign Affiliates survey has been a very useful and valuable
data source for evaluating the overseas operations of Japanese MNEs, its quality has
been questioned from time to time (Ramstetter 1996). 14

These problems can besummarised as follows. Unlike the METI Firm survey, responding to this survey is nota mandatory requirement. This yields a wide fluctuation in sample coverage from year
to year (Ramstetter 1996). The response rate varied from 33 percent in 1980 to 51
percent during 1983-1992, but has increased somewhat in more recent years. In 2005,
the questionnaire was sent to 4,564 Japanese firms, and 3,176 completed and thecorresponding return rate accounts for 69.6 percent. Information on foreign affiliates
operating in developing host countries is far less satisfactory than from those operating
in developed host countries.

There is also a wide variation in the reported coverage of variables from year toyear, making it difficult to track the same variable over time (Matsuura 2004). 15
However, the key variables, including sales, employment, and the year when foreign
affiliates were established are available for each year. Other items, such as intermediate
inputs expenditure and capital stock have not been reported on a consistent basis. In
addition, the fluctuation in the survey response rate also significantly influences thestability of variables over time (Matsuura 2004). Some key variables, such as sales andemployment, are found to follow a smooth time-series pattern, while variables such as
workers compensation and R&D expenditure behave less consistently over time.

14

An alternative data source is available from a private publishing company, Toyo Keizai. However,
Ramstetter (1996) concluded the data from Toyo Keizai have even more serious coverage and quality
problems for the sample collection and variables.
15
See also http://www.rieti.go.jp/jp/database/d02.html#01.

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Appendix Table 1

List of two-digit Japan Statistical Industry Classification (JSIC)

JSIC CodeIndustry Description

05-10
11-14
15-16
17-18
19
20-21
22-24
25-33
34-35
36
37-39
40-41
42-43
44-46
47
50-56
57-58
59-62
63-64

Foods
textiles (silk, spinning, fabrics and other textiles, apparel and accessories)
Clothing
Lumber and wood products
Furniture and fixtures
Pulp and papers
Publishing and printing
Chemical and allied products
Rubber products
Leather products and fur skins
Ceramic, stone and clay products
Iron and steel
Non-ferrous metals and products
Fabricated metal products
General machinery equipment
Electrical machineryTransport equipment
Precision machinery and equipment
Other manufacturing

Note: This industry classification refers to Matsuura and Kiyota (2004).

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