The heterogeneity of the impact of investing abroad: Evidence frommatched Japanese firmsҰ
Laura Hering•, Tomohiko Inui∇, and Sandra Poncet♦
This draft: 8 Mai 2009
Long abstract
In the present paper we analyze the effects of investing abroad using firm-level data for Japanfor the period 1994-2004. We adopt propensity score matching techniques in combination
with a difference-in-differences (DID) estimator to evaluate the causal effect of establishing aforeign affiliate on a set of domestic outcome variables of interest. We focus on firms thatswitch from being a purely domestic firm to being a multinational firm.
Our identification strategy of switching firms, i.e. Japanese firms investing for the first time
abroad between 1995 and 2003, relies on the confrontation of information coming from two
different data sets: the basic survey on Overseas Business Activities conducted annually by
the Ministry of Economy, Trade and Industry (METI) and the basic survey of JapaneseBusiness Structure and Activities (BSBSA). The basic survey on Overseas Business Activities
provides yearly data on more than 27,000 Japanese investments in operation between 19952004 containing information on the starting date, sector, the country of location and otherdetails of the nature and objective of the investment. The data set also allows the attribution ofaffiliates to their parent firm via a parent identification code. We use the information on when
the operation started to identify affiliates (and their related parent firms) that appear to
become multinationals after 1994.
Our final data set then combines information on manufacturing and non-manufacturing
internationalizing Japanese firms with characteristics of their subsidiaries and allows us toinvestigate the heterogeneity of the effect of moving abroad on employment, investment,
productivity and trade performance. This data set thus allows to contribute to the literaturesince until today while much is known about parent firms characteristics (Greenaway D. and
R. Kneller, 2007), little is known about characteristics of subsidiaries in international
economics literature. Our data set gives us new interesting insight into characteristics of theaffiliates and their relation with the parent firms that will help us to identify investment
strategies of the Japanese firms and the according impact on the parent firm.
Whereas preliminary results at the aggregated level are in line with existing literature (Hijzen
et. al 2007), suggesting that Japanese outward FDI has limited effects on domesticҰ

The METI database used in this paper was prepared and analyzed in cooperation with the Research Institute of
Economy, Trade and Industry (RIETI). Sandra Poncet gratefully acknowledges financial support from Nihon
University during her stay in Japan. We thank Toshiyuki Maatsura for the assistance with the database.
•Centre d’Economie de la Sorbonne (CES), University of Paris 1, Paris School of Economics and CREST. Email: laura.hering@gmail.com.
∇
Corresponding author: College of Economics, Nihon University. Email: inui.tomohiko@nihon-u.ac.jp.
♦
University of Paris 11 and CEPII. Email: sandra.poncet@cepii.fr.

1

employment and the performance of internationalizing firms on average, we see that results
differ significantly depending on the sector (manufacturing versus non manufacturing),
country of location and the FDI type (vertical or horizontal).
We find horizontal FDI more likely to spur productivity growth but at the expenses of export
performance. Fears of job losses associated with production relocation are rejected on average
but results strongly vary depending on the country of destination.
Japanese manufacturers that established a foreign affiliate in rich countries (especially North
America) enjoyed significant white collar employment creation. Although horizontal FDI
seems to create TFP gains on average, increase in productivity for vertical FDI is only
witnessed in low income countries. Fears of “Hollowing out” effects seem to be justified in
the case of relocation to low income countries, for which a contraction of employment andinvestment is observed.

Keywords: FDI, multinationals, offshoring, propensity score matching
JEL Code: F14, F21, F23
1-Introduction
The often advanced claims of a link between relocation and lay-offs in the public debate
contrast sharply with the emerging empirical evidence suggesting limited effects of relocationof production on domestic employment and performance of internationalizing firms (Aubert
and Sillard, 2005; Brown and Spletzer, 2005; Barba Navaretti and Castellani, 2004 andKleinert and Toubal, 2008).
In the context of Japan, results on manufacturing firms moving abroad even suggest thatJapanese outward FDI tends to strengthen the domestic economic activities ofinternationalizing firms in terms of both output and employment (Hijzen, Inui and Todo,2007). This finding is held to be in line with the stylized fact in the literature that FDI andexports are complements. As far as the effect on productivity is concerned, no significant
effect is found in manufacturing (Hijzen, Inui and Todo, 2007). However productivity gains
seem to occur in services (Ito, 2007) suggesting a heterogeneous impact across industries ofmoving abroad.
Previous results might be partly driven by an inappropriate estimation technique that does nottake into account this heterogeneity. More precisely, findings of limited impact of relocationof production abroad on average may coexist with the fact that the aftermath of moving

2abroad varies considerably across sectors and depends on a variety of conditions related to thelocation of the affiliates and the FDI motives.
Therefore, this paper aims at studying how the effect of moving abroad on domestic
employment and performance (investment, productivity and trade performance) ofinternationalizing firms depends on conditions related to the parent’s sector of activities(manufacturing versus non manufacturing), FDI motives and their affiliates’ characteristics.
Despite the recent boost of FDI in the service sector, comparison between manufacturing andservice internationalizing firms has scarcely been done.1 While most of the literature
concentrates on manufacturing because concerns about job losses in that sector have been
widespread, it is of interest to investigate also the influence on relocation on services. Indeed,
one potential effect of relocation is productivity improvements, typically expected in amarket-seeking FDI setup. Improving the productivity of service industries is seen as a toppriority for achieving sustainable growth in the Japanese economy at a time when the nation's
population is set to decline (Morikawa, 2007a and 2007b). Despite recent efforts tounderstand the sources of productivity in the services sector (Kim, Kwon, and Fukao, 2007),the potential effect of FDI has not been studied.2 Since internationalization may be thought ofas a strategy to boost TFP, this paper aims at measuring the effects of relocation abroad on
Japanese firms in the recent period which is a crucial prerequisite to formulating strategies tohelp Japanese retailers and wholesalers to internationalize in order to boost their productivity.
We rely on a new dataset that combines information on manufacturing and nonmanufacturing internationalizing Japanese firms with characteristics of their subsidiaries toinvestigate the heterogeneity of the effect of moving abroad on employment, investment,
productivity and trade performance. This dataset thus allows to contribute to the literaturesince until today while much is known about parent firms characteristics (Greenaway D. and
R. Kneller, 2007), little is known about characteristics of subsidiaries in international
economics literature. Our data set gives us new interesting insight into characteristics of the1

Ito (2007) is the only exception applied to the Japanese context we know of.

2

Amiti and Wei (2006) show that service outsourcing by manufacturing sector has positive influence on value
added of that industry, using US manufacturing data of 1992-2000. But they do not analyze activities bydomestic service industries.

3

affiliates and their relation with the parent firms that will help us to identify investment
strategies of the Japanese firms and the according impact on the parent firm.
One first sign that FDI impact on domestic performance of Japanese firms’internationalization is context-specific comes from the cross-country variation in the
allocation of affiliates’ sales. While 87% of the sales of manufacturing Japanese affiliates
located in North America is made locally, it is only 53% in China. The figures for sales toJapan shares are 19 and 47% respectively for those two countries. In contrast, the share ofpurchases that are local is 50% in North America, but with an analogous figure of 64% for
China. This arguably attests to the role of vertical motives in China and of horizontal ormarket-seeking motives in the context of North American markets.
These contrasting cases reflect diverging FDI motives for establishing an affiliate abroad byinternationalizing firms.
In order to evaluate the potential effects of the role of outward FDI on the economic
performance of the firm in Japan and how these effects depend on characteristics of theaffiliate as the host country or its sector, we would ideally like to compare the output of firmsthat go global with the counterfactual outcome these firms would have had if they hadn’t
decided to become a multinational.
Since this counterfactual outcome is per definition unobservable, we use propensity scorematching techniques to construct a valid control group of domestic Japanese firms. Matching
involves re-constructing the missing information ex post for those who become multinational
had they not decided to do so when a randomised control group is not available. The
comparison of the performance of the firms that have turned into multinationals with thedomestic firms identified by the matching procedure as having similar characteristics (assynthesized by the propensity score) will allow us to extract the pure effect of becoming a
multinational. We combine the propensity score matching with a difference-in-difference
estimation to compare the performance of the two types of firms. This method was first
employed to the estimation of the effect of investing abroad by Barba Navaretti and Castellani

4(2004).3 Kleinert and Toubal (2008) refine this technique in their analysis of growth in
output, employment and productivity on German firms.
In the empirical section of the paper, we will differentiate between different sectors anddifferent types of investment. This approach has been adapted also by Hijzen et al. (2006).
Also when differentiating between industries, these authors do not find any statisticaldifferences for French firms between sectors or types of FDI.
Also in our case, preliminary results suggest that Japanese outward FDI has limited effects on
domestic employment and performance of internationalizing firms on average. However, we
see that results differ significantly depending on the sector (manufacturing versus non
manufacturing), country of location and the FDI type (vertical or horizontal). We findhorizontal FDI more likely to spur productivity growth but at the expenses of export
performance. Fears of job losses associated with production relocation are rejected on average
but results strongly vary depending on the country of destination.
Japanese manufacturers that established a foreign affiliate in rich countries (especially North
America) enjoyed significant white collar employment creation. Although horizontal FDI
seems to create TFP gains on average, increase in productivity for vertical FDI is only
witnessed in low income countries. Fears of “Hollowing out” effects seem to be justified in
the case of relocation to low income countries, for which a contraction of employment andinvestment is observed.
The remainder of this paper is structured as follows. Section 2 provides some background. In
Section 3 we provide a detailed discussion of the methodology and present the data used forthis study. Section 4 analyses the determinants of becoming a multinational in order toconstruct an appropriate counterfactual that we need to evaluate the effects of investing
abroad. Section 5 presents the results. Finally, Section 6 concludes.

2-Horizontal versus vertical FDI
3

These authors apply this method to a data set of Italian firms for the years 1994 to 1998. They find that theforeign investments improve growth of total factor productivity and output, but not of employment.

5

In this paper, we attempt to disentangle the effect of FDI on the parent firm according to thetype of FDI the firm undertakes. We distinguish between two main motives for establishing
an affiliate abroad that have been both broadly discussed in the literature on FDI. The first is
the market-seeking or horizontal FDI. In this case, the parent firm typically chooses to
produce in a foreign country in order to serve the foreign local market by its production there
instead of paying high transport costs for exporting. This type of investment usually allows afirm to reach more markets and is supposed to lead to the replication of identical activities indifferent locations.
The second motive is the factor-seeking or vertical FDI, where a firm decides to localize all orsome of its production processes abroad because prices for factors or intermediate goods arecheaper there then in the home country.
The consequences of relocation are thus depending on its chosen FDI type. Both horizontal
and vertical investment strategies may result in job losses when domestic production forexports or domestic consumption is substituted to the foreign affiliate. However, one mightadvance that the displacement effect of vertical FDI is likely to be more pronounced than thatof horizontal FDI. Pure horizontal FDI is only expected to lead the relocation of that part ofproduction that was previously exported to a specific foreign market.
But becoming a multinational does not necessarily have to result in job losses at the firmlevel. Jobs might be created when the establishment of foreign plants: i) represents
expansionary investment, ii) involves scale effects due to productivity improvements, or when
there are iii) important production complementarities.
Another consequence of the relocation of domestic production to a foreign country is apotential reduction in efficiency through a decreasing plant-level scale effect (Barba Navaretti
and Venables, 2004). This effect would apply both to a vertical and a horizontal FDI due tothe loss of a production stage or to the decrease in exports which would also result in lower
production capacities.
For vertical FDI, moving abroad may induce the contraction of home activities following
relocation (“Hollowing-Out Effect”) but may fuel productivity gains as the less productive
assembling activities are sent abroad.

6

When we look at the Japanese oversea affiliates, a first indicator, that both types of FDI
motives are important for Japanese firms, can be found in analyzing the share of local sales intotal sales for the affiliates in our sample. For this variable, we observe a strong heterogeneity
across countries in the Japanese affiliates’ share of local sales in total sales. As shown in
Table A-1 in the Appendix A, while this ratio is on average 76% in developed countries (withper capita GDP above 10,000 US$), it drops to a mean of 48% for the poorest countries withper capita GDP below 1,500 US$. In contrast, the average share of sales to Japan is 27% fordeveloped countries and rises to 48% for the poorest countries.
These contrasting cases reflect the two diverging FDI motives for establishing an affiliateabroad by internationalizing firms. The sales figures for developed countries are typical of thefirst main motive that is market-seeking or horizontal FDI.
The figures for poorest countries are in contrast suggestive of the second motive that is thefactor-seeking or vertical FDI,In this paper we wish to shed some more light on these possible positive or negative effects ofan investment abroad on the parent firm and we will in particular look at the difference
between horizontal and vertical FDI.
3- Econometric Methodology

3-1 The Matching procedure
In order to evaluate the impact of investing abroad on the economic outcome of Japanesefirms, we adopt a propensity score matching technique in combination with a difference-indifference (DID) estimator. This allows us to construct via a non-parametric method themissing counterfactual observation of the outcome of a switching firm if it hadn’t decided toengage in outward FDI. This approach classifies firms into two categories: those that haveinvested abroad between 1995 and 2004, called the treated group, and those who neverinvested abroad. This is the group of the untreated and observations of this category will be
used to construct the counterfactual of the outcome of a switching firm.
An important feature in the accurate construction of the counterfactual is the selection of avalid control group which has comparable observable characteristics as the treated group. The
7

purpose of matching in this context is to pair each exiting firm with a firm that continues toexport on the basis of some observable variables. By ‘matching’ firms from the group of
untreated firms (those who didn’t invest abroad) that are very similar in their pre-treatment
observable characteristics to the treated (those who invested abroad), we can compare themean difference in outcomes between the treated and the untreated.
Once matched the only observable difference between treated and untreated individuals istheir treatment status. Using our matched control group, we analyze the average effect of thetreatment on the treated (ATT):αˆ ATT = E ( y1 − y 0 D = 1) = E ( y1 D = 1) − E ( y 0 D = 1)

(1)where y1 and y0 are the treated and non-treated outcomes, respectively and D is a dummy
variable, which equals 1 when a firms is treated and 0 otherwise.
Since matching involves comparing multinational and national firms across a number ofobservable pre-internationalization characteristics (notably productivity, size, age) it isdifficult to determine along which dimension to match the firms, what type of weighting
scheme to use.
The presented matching method relies on two assumptions: the conditional mean
independence assumption (CIA) and the common support assumption (CS).
The CIA is a strong assumption that requires that conditional on observables the non-treatedoutcomes are independent of treatment status. Since firms normally self-select into the groupof multinational firms based on firm characteristics as size, age or productivity, thisassumption is violated. Rosenbaum and Rubin (1983) have shown that CIA remains valid
when using the propensity score-matching. This method matches firms according to theirprobability of switching into a multinational, which is conditional on the pre-switching
characteristics of firms. This reduces this dimensionality problem since matching is thenperformed on the basis of a single index that captures all the information from the(observable) characteristics of the firm before investing abroad.
E ( D | y, X ) = E ( D = 1| X )In Section 4, we will estimate the propensity score for each firm, i.e. the probability of
becoming a multinational via a logit model.

8The literature proposes various matching methods. Since we can draw from a large control
group, we use the three nearest neighbors matching method. Each switching firm will bematched to the three firms in the same sector and year that have the most similar propensity
score.
Nevertheless, the propensity score is conditional on only a limited number of observablecharacteristics. If a firm bases its investment decisions for example on future expected profits,
which are unobserved by the econometrician, then the CIA assumption would still be violated.
In order to reduce the risk of the violation of the CIA, for the estimation of the treatment
effect on the treated, we employ a difference-in-differences estimator following Heckman etal. (1997) and Blundell et al. (2004). By comparing growth rates instead of levels before andafter the year of the switch, we control to some extent for selection on unobservablecharacteristics. We thus compare differences in growth rates after the year of the switch,taking into account potential differences in growth rates that existed already before switching.

3-2-Data and variables

3-2-a-Identification of switchers
In this paper, we focus on firms that switch from being a purely domestic firm to being a
multinational firm. Our identification strategy of switching firms, i.e. Japanese firms investingfor the first time abroad between 1995 and 2003, relies on the confrontation of information
coming from two different datasets: the basic survey on Overseas Business Activities
conducted annually by the Ministry of Economy, Trade and Industry (METI) and the basic
survey of Japanese Business Structure and Activities (BSBSA).
The basic survey on Overseas Business Activities4 provides yearly data on more than 27,000
Japanese investments in operation between 1995-2004 containing information on the starting
date, sector, the country of location and other details of the nature and objective of theinvestment. The data set also allows the attribution of affiliates to their parent firm via aparent identification code. We use the information on when the operation started to identify
affiliates (and their related parent firms) that appear to become multinationals after 1994.

4

We obtain access to the answers for the period consecutive years 1995-2004.

9

Some data limitations have to be considered nevertheless: whereas firms report relatively wellto the BSBSA, information of the affiliates is not compulsory and so we have a high numberof Japanese affiliates that does not report regularly and the exact number of affiliates notsending back the survey at all is not known.
The initial selection of 601 parents initiating relocation projects5 abroad for the first time overthe period 1995-2003 was essentially driven by the requirement of covering only affiliates
providing consistent information over time (notably on the country of location, the date ofentry and the sector of operation). We compute outward FDI characteristics at the parent level(such as the allocation of purchases and sales between local and Japanese markets) as theaverage features of its affiliate’s characteristics over the period from the establishment year to
2004.
Obtained information on the Japanese switching firms’ overseas activities is merged withJapanese firms’ domestic information (such as size and productivity) from the Basic survey ofJapanese Business Structure and Activity through the Japanese parent identification code. The
strength of the survey is its sample coverage and the reliability of data, as the survey iscompulsory for manufacturing and non-manufacturing firms with more than 50 employees
and with capital of more than 30 million yen.6 Out of the 601 parents identified in the basic
survey on Overseas Business Activities, only 4 were not present in the BSBSA.7 This survey
provides information on overseas activities which allows to double check the “first time
abroad after 1994” character of the Japanese firms. We use information on loans andinvestments in a related firm abroad reported in 1991 and yearly since 1994 to identify
switching firms as firms which report positive loans and investments in a related firm abroadfor the first time after 1994 (and not prior). Consequently we exclude from our switcher groupthe 153 Japanese firms that report positive investment in a related firm abroad in 1991 or
1994 and the 23 Japanese firms that never report positive investment in the BSBSA. We
further exclude from the 421 identified switchers 9 which report that more than 33% of theircapital is hold by a foreign company.
5

These include 121 investments in the wholesale and retail sector, 75 in other services, 5 in the primary sector
and 400 in the manufacturing sector.
6
The results of the Basic Survey of Japanese Business Structure and Activities are prepared annually by the
Research and Statistics Department, METI (1994-2003). This survey was first conducted in 1991, then again in
1994, and annually thereafter. The main purpose of the survey is to paint a comprehensive statistical picture ofJapanese corporate firms, including their diversification-, globalization-, R&D- and information technologyrelated activities. The sample firms account for about one-third of the total national workforce, 99 % of totalexports, and 69 % of total imports for Japan in 2002 (Kiyota and Uruta, 2007).
7
One rationale for missing parents is that the affiliates survey has no sample restriction in terms of firm size or
sector, while the Basic survey of Japanese Business Structure and Activity excludes firms small firms and nonmanufacturing sectors such as finance, insurance and software services.

10

The impact of investing abroad on the economic outcome of these switching firms will beassessed based on a propensity score matching technique in combination with a difference-indifference (DID) estimator. The method which will be detailed in the following section
consists in comparing switchers with domestic firms8 (no overseas subsidiaries at any point
during the sample period) that have similar observable characteristics (as synthesized by thepropensity score) before the switch.
Due to the unavailability or inconsistency of parent statistics, our final sample covers 410
Japanese switching firms in non-primary sectors, of which 372 provide the necessary
information to compute propensity scores. These include 264 manufacturing firms, 92 firms
in the wholesale and retail sector and 16 in other services.
Further details concerning the data used in the estimations can be found in the data appendix(Appendix A), which includes Tables A-2 and A-3 showing the number of Japanese switchingfirms by year and by country and sectors9 respectively.
Map 1 uses this data to plot the accumulated number of switching Japanese firms in the
countries in which they invested between 1995 and 2003. A number of important features ofJapanese investment patterns are immediately apparent: the concentration in Asia (especiallyChina), the attraction of the US, and their quasi absence from the non-Asian developingworld.

3-2-b-Determinants of switching and outcome variables
In a first step, we estimate a logit model that evaluates the probability for a domestic firm tobecome a multinational. This will give us the propensity score for each firm needed for thematching. Our logit specification follows the literature on the determinants of FDI by
including the firm’s profit over sales ratio, its age, the total factor productivity (calculated
following Olley and Pakes, 1996), the capital/labor ratio; and its mean wage level (Kleinert
and Toubal, 2006; Hijzen et al., 2006).
It would be more sensitive to use lags of at least one year for the independent variables.
However, given the short time span of our sample, this would result in an important loss ofobservations, so we renounce to the lagging of the right hand side variables.
8

We exclude from our domestic sample firms that report that more than 33% of their capital is hold by a foreigncompany.
9

The decomposition into sectors follows a 3-sector nomenclature including manufacturing (light industries,
heavy industries, machinery, electronics &automobile), wholesale and retail sales, and other non-manufacturing(utilities & construction, and other services).

11

For the DID estimations, we will concentrate on certain outcome variables that are ofparticular interest for the firm; the employment level, the total factor productivity, theinvestment, total sales and trade performance.
Our data set contains rich information on different type of employment within the parent firm.
We distinguish between i) the total employment the firm in all its plants within Japan, emp
and ii) the administrative employment of the headquarter, emp_adm. Typically, we would
expect that when creating a new affiliate, more administrative jobs would be needed to ensure
the communication and the administration of the new plant. This last variable can also beinterpreted as an indicator for white collar workers, whose evolution is linked to the evolutionof skill intensity in the parent firm. In the case of vertical FDI, when unskilled labor is
replaced by low wage workers in developing countries, one would expect an increase in theskill intensity associated with a decline in blue and white collar employment, where theformer is supposedly being more affected. For market-seeking FDI, to the extent that
headquarter services are retained at home and become in charge of the administration offoreign affiliates, administrative employment is expected to increase.
The outcome variable sales is defined as total sales of the parent firm. In addition, since ourdata set includes also detailed trade data, we can look at the impact of FDI on imports,
imports, and exports, exports. The latter allows us in particular to test for the theoreticalprediction of export substitution in the case of horizontal FDI.
Investment is defined as “Investement and other assets” and is called inv in our regressions.
In the DID estimations we use the same TFP variable, tfp, as in the logit model, which isconstructed following the method of Olley and Pakes (1996). As a robustness check, we also
use labor productivity (value added over total employment). Results stay qualitatively thesame.
For all these variables, we calculate the yearly growth rates. In the DID estimations we thencompare the differences between the growth rates of the switching firm and its matcheddomestic firms before and after the switch.

4-Propensity score matching12

To obtain the propensity score for each firm, treated or untreated, we first estimate a logit
model, where we estimate the probability of switching.
Since we are interested in the probability of switching from a purely domestic to a
multinational firm, we limit our sample to i) firms that never switch and stay domestic all thetime and ii) firms that switch from being a domestic to being a multinational within the timespan of our sample period 1994 to 2004. Our logit model takes the following form:

The dependent variable FDI takes the value 0 if the firm is non-entrant into FDI at year t. Thisis valid for domestic firms as well as multinationals before their entry during 1995 -2004. We
define the observation as 1 at year t when the firm starts FDI during that year, and any
observation after the entry is not coded (Ito, 2007).
In order to compare the propensity scores of firms that have similar characteristics and to
avoid matching of a firm in the textile sector in 1996 with a firm in electronic machineries in
2002, we classify our firms into 7 sectors and allow only matching for observations from thesame year (as proposed by Kleinert and Toubal (2006)). We thus obtain 66 sector-year pairs.
Our logit model is therefore estimated for each of this sector-year pair separately.
When looking at Table A-4, where we display a pooled logit estimation for all sectors andyears, we see that all explanatory variables have the expected signs and the coefficients are all
significant. The propensity of domestic firms to establish a foreign presence abroad depends
positively on the level of TFP, the level of profits, the size of the firm (proxied by
employment), the capital to labor ratio and age. These results are very much line with themodel presented in Helpman, Melitz and Yeaple (2004) which suggests that more productive
and larger firms self-select into multinationals.
The obtained coefficients of the regressions are then used to predict the probability of a firm
to become a multinational in each year. This predicted probability is called the propensity
score and will be the matching criterion. Each treated firm is then matched according to its
propensity score to its 3 nearest neighbors within its sector-year sub-sample.10 Note that we

10

The advantage of using 3 instead of only one nearest neighbor reduces the impact of outliers in the control
group sample

13ensure that a switcher is allowed to match only a purely national firm and not a firm that willswitch later during our sample period.
Table 1 displays the Balancing test for the 3 nearest neighbors matching method. It reports themeans of a range of variables. The two groups of firms vary substantially in the reported
characteristics: the means of profit/sales, employment as well as the K/L ratio are
significantly different for the treated and the control observations in the unmatched sample.
However, after matching, we see that the difference in the mean is insignificant for all
variables, indicating that the balancing condition is satisfied in our matched sample.
The correcting impact of matching is reflected in the bias reduction, which is 93% forprofit/sales.

5-Results

In the following sections we discuss the results from our difference-in-difference estimations.
After having obtained the propensity scores for the matching of the two firm groups, we firstestimate the impact of FDI in general on the different outcome variables (growth of
employment, investment, productivity, sales and trade performance) before we go into moredetail by differentiating the impact according to different criteria.
Having verified that our control group is valid, we can start to look at the impact of outward
FDI on the performance of the parent firm. As most other papers on this subject, we aremainly interested in the impact on the growth of employment and total factor productivity
growth, but we are also looking at growth rates for investment and for exports and forimports.

5-1 Total sample

14

Table 2 reports the DID results for all outcome indicators for the total sample.11 Our sample
size varies from 252 treated for administrative employment to 278 for sales and total
employment.
The estimates show that the average causal effect of locating production abroad on the growthrate of employment at home is positive but not significant in the first after locating theactivities abroad. We observe a positive and significant effect of FDI on TFP growth. In
contrast, the effect on growth in investment and in export is significantly negative in the firstyear. The growth of sales and imports are not significantly affected by the internationalization
decision.
These first results differ from Hijzen et al. (2006) since the former finds a strengthening ofdomestic employment following FDI but no productivity effect. These differences might bedue to the limitation of our sample to switching firms with information on their affiliates or to
the inclusion of nonmanufacturing firms. In the next section, we will thus separate between
these two sectors to see whether impacts vary across industries.

3-2 Manufacturing versus Nonmanufacturing
In Table 3, we thus split sample into manufacturing and nonmanufacturing firms. We find that
results for the total sample are mainly driven by the manufacturing sector. This shouldn’t
come as a surprise as this sector accounts for nearly 70% of our total sample.
As found above, we observe a negative impact on investment and exports growth, as well as asignificant effect on the TFP growth rate. No such positive impact is present in thenonmanufacturing sector but the split uncovers a strong decrease in the imports for this sector.

5-3 Location country heterogeneity
In a next step, we look at the location of the affiliates. We will split our sample between Asianand non-Asian countries. In addition, we focus on affiliates in China and the US, since thesetwo countries attract a particular high number of affiliates (28% and 20% of the affiliates inour sample are located in China and in US, respectively).
11

From Table 2 on, we report results from DID estimations.

15

Our outcome variables of interest in Table 4 are employment and TFP. The results for theaggregate sample do not reveal any significant differences between countries. However, whenreducing the sample to manufacturing firms, we see that the TFP gains identified previously
come mainly from Non-asian countries and are strongest for North America.
Although on average there is no significant effect of FDI on employment, employment losses
seem to follow implantation in China and job creation is identified in Non Asian countries andNorth America.
Those results may suggest a link between the FDI impact and the development level of hostcountries. High income countries are typically known to attract market-seeking investments,
whereas low income countries have the comparative advantage to propose lower wages andoften also lower prices for intermediate goods than Japan.
In Table 5, we thus split our sample into high income countries that are defined as having an
income per capita superior to 1500 $.12
We see that effects vary substantially according to the development level of the country inwhich the FDI takes place.
For FDI in high income countries, we find a positive and significant impact on the total
employment and the administrative employment in the high income countries. This would bein line with the expectation that more administrative jobs are created, when complementary or
horizontal FDI occurs.
The effect is the reverse when the FDI is located in a low income country, indicating asubstitution of Japanese employment by foreign workers. Further, our estimates show thatwhile no productivity gains are apparent in the high income countries, a positive impact on
the TFP is obtained from FDI in low income countries.
The split of the sample into these two groups shows also, that the strong negative impact on
investment is mainly driven by the FDI in low income countries. Fears of “Hollowing out”
effects seem to be justified in the case of relocation to low income countries, for which acontraction of employment and investment is observed. However, these negative effects areaccompanied by TFP gains.

12

The countries’ gdp per capita used for the definition of the threshold are the means of the gdp per capita overthe period 1995 to 2004. This guaranties, that a country will stay throughout the sample period in the same
category.

16

One possible interpretation of these results would be that the low income countries are mainly
vertical FDI. To pursue this idea, in the next section, we attempt to classify our FDI projects
into horizontal and vertical.

5-4 Vertical versus horizontal: the role of FDI motives
In this section, we create two indicators to determine whether an affiliate is horizontal orvertical FDI. Horizontal FDI is associated with high share of sales to the local market, and a
low share to the home country, whereas vertical FDI is characterized by a high share of local
purchases and also a high share of its sales that goes back to the home country.
We classify a firm as doing vertical FDI when the share of local purchases of the affiliate issuperior to 70% and its share of sales to Japan is superior to 60%. A firm is considered toengage in horizontal FDI if the affiliate’s share of local sales is equal or superior to 70% andits share of sales to Japan is equal or inferior to 20%.13
Table 6 reports results for these categories on labor productivity and trade performance. As
expected from theory that claims horizontal FDI to be a substitute for exports, we can see astrong negative and significant impact of horizontal FDI on the export variable.
No such impact can be found for vertical FDI. Instead, vertical FDI is characterized by a verystrong increase in imports. This is also expected, as intermediate production stages aredelocalized into a foreign country and Japanese firms import intermediate goods from theseaffiliates. This increase in imports is thus likely to be due to an increase in intra-firm trade.
In the last lines of Table 6, we see a positive and significant impact of horizontal FDI on TFP.
Not much research has yet been conducted on the channels by which the parent TFP could be
influenced in the case of horizontal FDI. One possible explanation is that the expansion of theactivities could increase efficiency through scale effects. Moreover, productivity gains could
derive from information sharing between the parent and its affiliate (Ito, 2006). This would
also be in line with the existence of an additional advantage of FDI over exports which is the13

The results are maintained also when changing the thresholds for the two indicators and also, when horizontal

is defined only as the share of local sales superior to 0.7.

17enhanced capacity of firms to adapt their products and processes to the requirements offoreign consumers. TFP gains may be obtained from the foreign R&D activity meant to suitthe local conditions.
However, no such a positive impact is observed for vertical FDI. These results seem tocontradict our previous findings of significant productivity gains of FDI projects in low
income countries, which we considered to be in majority vertical FDIs.
To better understand the impact on TFP, we further split our vertical FDI projects between
low and high income countries. Even if the limited sample size calls for caution in theinterpretation, we find that vertical FDI in low income countries has indeed a positive andsignificant effect on TFP.
When performing the same decomposition of our horizontal FDI projects, we see that here, itis the fact that FDI is located in high income countries that drives the TFP gains for horizontal
projects.
These findings underline the heterogeneity of the impact of investing abroad due to country
location that goes further then the simple horizontal/vertical dichotomy.

6-Conclusion
In this paper, we analyze how the effect of moving abroad on domestic employment andperformance (investment, productivity and trade performance) of internationalizing firms
depends on conditions related to the parent’s sector of activities (manufacturing versus non
manufacturing), FDI motives and their affiliates’ characteristics.
This paper starts with the observation that although emerging empirical evidence suggest
limited effects of relocation on domestic employment and performance of internationalizing
firms on average, the aftermath of relocation abroad varies considerably across firms anddepends on a variety of conditions related to the FDI motives and their affiliates’
characteristics. In the context of Japan, results on manufacturing firms moving abroad evensuggest that Japanese outward FDI tends to strengthen the domestic economic activities ofinternationalizing firms in terms of both output and employment (Hijzen, Inui and Todo,2007).

18

In this study, we confirm previous results but find that positive effects may coexist with thefact that the aftermath of moving abroad is negative in some contexts, typically verticalprojects in low income countries.
We indeed find that horizontal FDI is more likely to spur productivity growth but at theexpenses of export performance. Fears of job losses associated with production relocation are
rejected on average but results strongly vary depending on the country of destination.
Japanese manufacturers that established a foreign affiliate in rich countries (especially North
America) enjoyed significant white collar employment creation. Although horizontal FDI
seems to create TFP gains on average, increase in productivity for vertical FDI is only
witnessed in low income countries. Fears of “Hollowing out” effects seem to be justified in
the case of relocation to vertical FDI projects in low income countries, for which a contractionof employment and investment is observed.
Our findings thus underline the heterogeneity of the impact of investing abroad due to country
location that goes further then the simple horizontal/vertical dichotomy.

19

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21

APPENDIX A
Table A-1
Strategy of FDI: average share of local/Japan purchase and local/Japan sales
Share of sales
Share of purchases
local
Japan
local
Japan
Total sample
0.63
0.35
0.58
0.48
Sector of Japanese firms
Manufacturing sector only
Share of sales
Share of purchases
local
Japan
local
Japan
Total manufacturing sample
0.61
0.36
0.56
0.49
Developed countries
0.76
0.27
0.52
0.58
(GDP per capita>10.000 $)Low income countries
0.48
0.48
0.56
0.45
(GDP per capita<1.500 $)
China
0.53
0.47
0.64
0.41
North America
0.87
0.19
0.50
0.60
Europe
0.61
0.35
0.77
0.53
Asia
0.54
0.41
0.57
0.46
Note: These figures are based on average over the FDI projects. Sales and purchases are typically divided into
local, Japan and other markets origin or destination. The importance of other markets should correspond to theresidual share after local and Japan market shares are deduced. The fact that the sum of local and Japan markets
shares is sometimes higher than 100% in this table is due to rounding up issues.

Strategy of FDI: average share of local/Japan purchase and local/Japan sales
Sector of Japanese firms
Sales
Sales
Share of sales
Share of purchases
Local
Japan
local
Japan
Total sales sample
0.72
0.28
0.59
0.49
Developed countries
0.82
0.12
0.53
0.57
(GDP per capita>10.000 $)Low income countries
0.52
0.52
0.71
0.33
(GDP per capita<1.500 $)
China
0.44
0.54
0.82
0.25
North America
0.94
0.07
0.39
0.76
Europe
0.79
0.31
0.90
0.38
Asia
0.66
0.33
0.61
0.43
Note: These figures are based on average over the FDI projects. Sales and purchases are typically divided into
local, Japan and other markets origin or destination. The importance of other markets should correspond to theresidual share after local and Japan market shares are deduced. The fact that the sum of local and Japan markets
shares is sometimes higher than 100% in this table is due to rounding up issues.

22

Table A-2: Number of switchers by establishment year
Year
Total 1995 1996 1997 1998
Switchers372
107 7941
17
of which inmanufacturing
264
87
60
29
9

1999
18

2000
22

2001
36

2002
29

2003
23

12

11

22

21

13

Table A-3: Summary Statistics on switching firms: 288 observations in sample (difference in
employment evolution)
Manufacturing Non manufacturing
Sales (wholesale & retail)
Total sample
203
85
73
Developed countries
75
48
41(GDP per capita>10.000 $)Low income countries
95
24
21(GDP per capita<1.500 $)
China
66
15
13
US
39
18
14
North America
42
20
15South America
110Europe
9
3
2
Asia
151
61
56

23

Table Table A-4: Logit estimation. whole sample
Tfp
0.2561***(8.96)
Profit/sales
0.1591***(8.59)
Emp
0.3667***(13.88)
K_L_ratio
0.0007**(2.57)
Age
0.0008***(4.91)
Constant-8.4497***(11.78)Time and sector fixed effects
Yes
Observations
188031
Pseudo R-squared
0.07

24

TABLES:
Table 1: Balancing test: 3-nearest neighbor matching
Mean

%bias

Variable

SampleTreatedControl

Tfp

Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched2.05772.05775.29285.29285.43675.436716.96716.96739.39539.3952.04712.0573
5.21425.28725.35785.4062
15.272
16.41438.72638.691

profit/sales
Employment
K/L-ratioAge25% biast-testreduction t
1.2
0.0
4.4
0.3
8.1
3.1
5.2
1.7
1.5
1.6

96.4
92.9
61.4
67.4
-5.3

0.49
0.01
1.85
0.11
3.40
1.07
2.22
0.54
0.63
0.56

p>|t|
0.623
0.988
0.064
0.914
0.001
0.285
0.026
0.588
0.531
0.574

Table 2: Growth of domestic activities: difference-in-difference analysis: All Sample
DID 1year
Employt
CoefficientStandard deviation
Treated nb
Control group nb

DID 1year
DID 1year
DID 1year TFP DID 1 year
DID 1 year
DID 1 year
Employment
Investment
Sales
Exports
Imports
Admin.
Sample based on combined information of Japanese parents and affiliates (used sample in this paper)
0.0112
0.06
-0.01
-0.13
-0.1130.0751-0.53(0.0149)(0.08)(0.015)(0.38)(0.051)**(0.029)**(0.19)***
278
252
268
271
278
246
237
4691
3942
4328
4573
4691
4328
4227

Table 3 : Manufacturing versus Non-Manufacturing
DID 1year
Employt

DID 1year
Employment
Admin.

CoefficientStandard deviation
Treated nb

0.015(0.011)
196-0.057(0.099)177

CoefficientStandard deviation
Treated nb-0.026(0.0313)82

0.26(0.17)
75

DID 1year
Investment

DID 1year TFP

Manufacturing-0.0500.09(0.065)***(0.038)***
196
189
Nonmanufacturing-0.0790.03(0.11)(0.044)
79
81

26

DID 1 year
Sales

DID 1 year
Exports

DID 1 year
Imports-0.003(0.014)196-0.62(0.24)**
182

0.26(0.49)
174-0.021(0.027)82-0.13(0.33)
64-0.97(0.49)**
63

Table 4: Split between different destinations
All sample
DID 1year
Labor
productivity
Coefficient
Treated Nb
Control Nb

China

US

North
america

Asia

Asia w/o
China

Non Asia

0.04(0.047)
80
4863

0.012
(0.047)
57
4863

0.015
(0.045)
62
4863

0.044
(0.034)
211
4863

0.046
(0.044)
131
4863

0.013
(0.042)
75
4863

US

North
america

Asia

Asia w/o
China

Non Asia

0.080(0.049)*
39
3193

0.091
(0.046)*
42
3193

0.038
(0.043)
150
3193

0.038
(0.061)
85
3193

0.076
(0.04)*
52
3193

Manufacturing only
DID 1year
China
Labor
productivity
Coefficient
0.038(0.056)
Treated Nb
65
Control Nb
3193
All sample
DID 1year
employment
Coefficient-.037(.018)**
81
4,918

North
america.018(.022)
62
4,918

Manufacturing only
DID 1year
China
employment
Coefficient-.028(.016)*
Treated Nb
71
Control Nb
3,266

North
america.040(.021)*
42
3,266

Treated Nb
Control Nb

China

Asia

Non Asia-.0141(.0167)
212
4,918

.028
(.021)
76
4,918

Asia

Non Asia.0009(.012)
151
3,266

.050
(.024)**
52
3,266

27

Table 5: High versus Low income countries
Split between high income countries: High gdp per capita: >2000 Dollar

Coefficient
Treated Nb
Control Nb

Coefficient
Treated Nb
Control Nb

Coefficient
Treated Nb
Control Nb
Coefficient
Treated Nb
DID 1year Invest
Coefficient
Treated Nb
Control Nb

DID 1year Employment
High GDP per
Low
capita.0152-.0250(.0162)(.0147)*168 (of which 93
119 (all in Asia)
Asian)
4918
4,918
DID 1year Employ admin
High
Low
0.19
-0.195(0.088)**(0.10)*
152
100
3942
3942
DID 1year TFPHigh GDP per
Low
capita0.03
0.13(0.029)(0.06)**
161
109 (all in Asia
4573
4573
Asia
Non
Asia
.0471
.048
(.0642)(.053)
93High-0.021(0.07)
157
4328

Low
-0.16
(0.06)***
110
4328

28

DID 2years Employment
High
Low
0.028
(0.001)*157-0.018(0.022)
103

3891

3891

DID 2years Employ adminHigh
Low-0.1430.26(0.18)(0.013)**
139
85
3088
3088
DID 2years TFP
High
Low-0.0004(0.05)
153
3763

0.16
(0.06)**
1103763

Table 6: Horizontal versus Vertical
Split between horizontal : share of local sales >=0.7 and share of sales to Japan<=0.2
Vertical : share of local purchases>=0.7 and share of sales to Japan>=0.6
DID 1year Exports
Coefficient
Treated Nb
Control Nb
DID 1year Labor
productivity
Coefficient
Treated Nb
Control Nb

All sample-0.69(0.18)***
254
3558

Horizontal-0.94(0.31)***
91
3558

Vertical
0.37(0.47)
213558

All sample

Horizontal

Vertical

0.036(.027)
287
4863

0.073
(0.04)*
105
4863

.002
(0.072)254863

Split between horizontal : share of local sales >=0.6 and share of sales to Japan<=0.35
Vertical : share of local purchases>=0.6 and share of sales to Japan>=0.5
DID 1year Exports
Coefficient
Treated Nb
Control Nb
DID 2years
Imports
Coefficient
Treated Nb
Control Nb
DID 1year Labor
productivity
Coefficient
Treated Nb
Control Nb

All sample-0.69(0.18)***
254
3558

Horizontal-0.91(0.28)***
111
3558

Vertical
0.59(0.50)
273558

All sample

Horizontal

Vertical-0.18(0.43)
214
2359

-0.59
(0.73)
792359

2.5(1.0)**
20
2359

All sample

Horizontal

Vertical

0.036(0.027)
287
4863

0.075
(0.043)*
127
4863
High
Low income
incomen.s.
0.12(0.057)**-0.0059(0.066)
33
4863
High
Low income
incomen.s
0.09(0.05)*

29