Discussion Papers In Economics
And Business
Does It Matter Where You Invest? The Impact of FDI
on Domestic Job Creation and Destruction

Bin Ni
Hayato Kato
Yang Liu

Discussion Paper 20-18

January 2021

Graduate School of Economics
Osaka University, Toyonaka, Osaka 560-0043, JAPANDoes It Matter Where You Invest?
The Impact of FDI on Domestic Job Creation and Destruction*
BIN NI†, HAYATO KATO‡, and YANG LIU§
†Faculty of Economics, Hosei University, Machida, Tokyo, Japan. (email:jiadaniel@hotmail.com)‡Graduate School of Economics, Osaka University, Toyonaka 1-7, Machikaneyama,
Toyonaka, 560-0043, Osaka, Japan. (email: hayato.kato@econ.osaka-u.ac.jp)§Research Institute of Economy, Trade and Industry (RIETI), Chiyoda, Tokyo. Japan. (email:liu-yang@rieti.go.jp)

Abstract
This study uses unique division-level data of Japanese firms to examine how foreign direct
investment (FDI) affects domestic employment. Contrary to most previous studies focusing on
the effect on net employment growth, we decompose it into gross job creation and gross job
destruction. We find that FDI destination plays an important role: FDI to Asia increases job
creation, while FDI to Europe or North America decreases it. A frictional search-and-matchingmodel with heterogeneous jobs can explain the differential effects. The model provides
additional predictions on job creation and destruction by job type, which are also empiricallyconfirmed.
JEL classification numbers: F23; J21; J23
Keywords: Outward FDI, firm-establishment-division-level data, multinational enterprises(MNEs), large-firm search model, high/low-skilled jobs

* This study was conducted as a part of project undertaken at the Research Institute of Economy, Trade andIndustry (RIETI). The current revised version is conducted as a part of project supported by JSPS KAKENHI(Grant Number JP18K12777, JP19K13693, JP99K13693). It utilises the data based on the “Basic Survey ofJapanese Business Structure and Activities” (BSJBSA) which is conducted by the Ministry of Economy, Trade andIndustry (METI). We are very grateful to seminar and conference participants at JEA 2019 Spring Meeting,
European Trade Study Group 2019, Sophia University, Kyoto University, KER Conference 2020, and JSIE
Workshop.

I. Introduction
Along with the rapid globalization since the late nineties, multinational firms have increased
their influence on the domestic labor market. In the period between 1990 and 2015, world
foreign direct investment (FDI) flows increased 9.4-fold or 2.8 times faster than world grossdomestic product (GDP) and 1.9 times faster than world trade.1 This trend evoked a skeptic
view in developed countries that domestic jobs would be offshored to low-wage developing
countries. Whether outward FDI contributes to home employment has drawn much academic
and policy interest. The empirical literature thus far has provided mixed evidence, dependingon the sample countries and performance measures of foreign activities. Due to data limitations,most of the studies focus on the effect of FDI on net employment growth, which is the difference
between gross job creation (total employment gains in expanding establishments) and gross job
destruction (total employment losses in contracting establishments).
Even on finding a positive effect of FDI on net employment growth, one should becautious in concluding that FDI is always good for home employment. Net employment growth
can be positive in both cases: (i) where gross job creation increases and gross job destruction isunchanged and (ii) where job creation is unchanged and gross job destruction decreases. The
two cases illustrate very different labor markets: case (i) shows an active labor market favoring
job seekers, while case (ii) shows a stable one rewarding existing employees. For a country
benefitting from globalization, case (i) would be more preferable than case (ii), because welfare
gains are obtained through the reallocation of factors between sectors and firms to their mostproductive uses (Melitz, 2003; Autor, Dorn and Hanson, 2013; Dix-Carneiro and Kovak, 2017).
This study examines the impact of outward FDI on job creation and job destruction
using unique Japanese firm-establishment-division level panel data from 1996 to 2016.
Contrary to most existing studies, we construct a measure of job creation and destruction within

1

The data are from the World Bank Development Indicators: https://databank.worldbank.org/home.aspx.
1

an establishment by exploiting information on division level employment. Specifically, we
count the number of newly added jobs for all divisions within a firm with multiple
establishments and use it to define the job creation of the firm. Similarly, we define the job
destruction of a firm as the number of newly eliminated jobs for all divisions within the firm.
This definition helps interpret our empirical results by elucidating firm-decision-making basedon which we build a frictional search-and-matching model with heterogeneous jobs. The theory
highlights the roles of different jobs (or divisions), and its mechanism is further confirmed
empirically.
We also pay special attention to the destinations of FDI, which are strongly associatedwith the purpose of the FDI. Multinationals tend to invest in developing countries in Asia toseek low-price factors, which is known as vertical FDI. Contrastingly, those investing in Europe
and/or North America tend to be motivated by gaining better access to the local market, known
as horizontal FDI.2 Apart from the availability of unique data, the case of Japan is particularly
worth investigating because the destination of Japanese FDI is geographically dispersed.
Japan’s FDI into Europe and North America and Asia accounted for 65% and 25% in 2015,
respectively, both concerning the value of outward FDI and the number of affiliates.3 In thisway, Japanese FDI provides an ideal example to compare the various impacts of vertical andhorizontal FDI on the labor market of developed countries.
We specifically examine the effect of FDI into Asia and Europe/North America,
measured by the log number of foreign affiliates, on their domestic job creation and destruction.
Our identification strategy uses industry-mean FDI as an instrument for firm-level FDI.4 ThereSee Markusen (2004, Ch. 8) on theoretical accounts for vertical and horizontal FDI.
The data on FDI values are from JETRO (in Japanese):https://www.jetro.go.jp/world/japan/stats/fdi.html. The data on the number of affiliates, which corresponds
to “Number of enterprises,” are from OECD Statistics: https://stats.oecd.org.
4
Our strategy is partly inspired by studies on intergenerational persistence of economic status (Shea, 2000)
and money and happiness (Luttmer, 2005; Pischke, 2011; Li et al., 2014). For example, to see whether higher
232

are two reasons why we believe this is a plausible instrument. First, industry-mean FDI is anaggregate measure that is correlated with FDI by individual firms but is beyond individual
influences. Second, part of the variation in industry-mean FDI is attributed to industry
characteristics rather than firm characteristics. For example, some industries such as chemical
and machinery are by nature easier to fragment their production processes into finer stages andoffshore processes than other industries (Hummels et al., 2001 suggestive evidence). Anotherexample to indicate industry-specific attributes is that factor costs within and across countries
differ widely by industry (Du Caju et al., 2010). Du Caju et al. (2010) conclude thatinterindustry wage differentials in eight European countries could reflect the difference in labormarket institutions. We further address potential concerns about the self-selection of firms into
particular industries by controlling for measures of firm characteristics such as revenue,
productivity, research and development (R&D), and capital-labor ratio.
The results indicate that investments in Asia and Europe/North America have a positiveeffect on net employment growth in Japan. The effect on gross employment changes, that is,job creation and destruction, may differ by destination. Investment in Asia has a positive effecton domestic job creation, whereas investment in European/North American countries has anegative effect. Regarding job destruction, the impact is negative regardless of the FDI
destination.
We then construct Wasmer’s (1999) based frictional job-search-and-matching model
to illustrate the mechanism by which FDI can affect domestic job creation and destruction
differently in different destinations.5 In the model, there are two types of jobs: high-skilled andincome raises happiness, Pischke (2011) and Li et al. (2014) use industry-average wage as an instrument forfamily/individual income. In this context, Pischke and Schwandt (2012) give a cautionary note on theindustry-level instrument. Applications in the context of international trade can be found in, e.g., Hoekstra(2013).
5
While the flow of jobs created must be equal to that of jobs destroyed in the steady state where all
adjustments are done, these may differ in the short run where state variables such as capital and(un)employment rate do not change.
3

low-skilled. Firms face a trade-off between paying high search costs and enjoying a stablematch with high-skilled workers, or paying low search costs but having an unstable match withlow-skilled workers. An exogenous increase in the FDI of a firm requires more support from
home, thereby making the match of both job types more stable. This setting is motivated by thefact that outward FDI by Japanese multinationals in automobile industry is complementary toexports of intermediate parts from Japan (Nishitateno, 2013). It can explain the empirical resultof why FDI, regardless of its destination, accounts for lower job destruction.
The effect of FDI in different regions on the duration of domestic job match is assumed
to vary, given the fact that the purpose of FDI by Japanese multinationals differs regionally.
Japanese multinationals investing in Asia tend to export intermediate goods from home toaffiliates for low-cost assembly (Fukao et al., 2003; Fujita and Hamaguchi, 2012).6 Thus, FDI
to Asia is thought to be complementary to low-skilled domestic workers engaged in production
and related services, creating more low-skilled jobs and fewer high-skilled jobs. Due to its low
hiring cost, the increase in low-skilled job creation raises the overall job creation despite thedecline in high-skilled job creation.
Contrastingly, Japanese multinationals investing in Europe/North America tend to
substitute exports from home for local production and services to save transportation costs. FDI
to Europe/North America is thought to be substitutable to domestic low-skilled workers, making
firms create fewer low-skilled jobs and more high-skilled jobs. Because of the high hiring cost,the magnitude of increase in high-skilled job creation is not as high as that of the decrease in
low-skilled jobs. Thus, overall job creation declines. We further test this theoretical mechanism
by dividing divisions into high-skilled and low-skilled ones and measuring job creation andAccording to Nishitateno (2013), outward FDI in automobile industry shows a stronger complementarity
to exports from Japan to Asia than exports from Japan to other regions. Fukao et al. (2003) find that trade insimilar but quality-differentiated products (i.e., vertical intra-industry trade), which is likely to be driven byfactor-cost differences, are more prominent in Asia than in Europe.
4
6

destruction of each division.

Relation to the literature
There is a wide body of literature on the impact of outward FDI on the home labor market usingfirm/establishment-level data. Existing studies find mixed evidence (Brainard and Riker, 1997;
Desai et al., 2009; Muendler and Becker, 2010; Kovak et al., 2018).7 Using data on UnitedStates (US) multinationals, Desai et al. (2009) and Kovak et al. (2018) find a positive effect ofaffiliate employment on parent employment. Contrastingly, Muendler and Becker (2010) use
data on German multinationals to estimate the labor demand system and find negative
elasticities of home employment with respect to foreign wage.
These mixed findings have motivated subsequent studies to take a deeper look at thedestinations of FDI (Debaere et al., 2010; Navaretti et al., 2010; Harrison and McMillan, 2011;
Hijzen et al., 2011). By dividing the destinations of US multinationals into high- and lowincome countries, Harrison and McMillan (2011) find that affiliate employees in low-comecountries are substitutable for parent employees in the US. Debaere et al. (2010) echo theirresults using data on South Korean multinationals: starting operation in less-advanced countries
decreases parent employment growth. Hijzen et al. (2011) contrastingly find that FDI in lowincome (or high-income) countries has no significant (or positive) effect for French
multinationals. These studies measure the labor variation by applying the level or growth rateof employment of parent firms.
We take one step further, and decompose net employment growth into gross job
creation and destruction by utilizing unique firm-establishment-division level data. We follow

See Hummels et al. (2018) for comprehensive surveys. There is another growing literature on the impactof import competition on domestic labor market, starting from Autor et al. (2013). Subsequent studies focus
on the role of service sector (Feentsra and Sasahara, 2018), export exposure (Feenstra et al., 2019), worker
heterogeneity (Endoh, 2017), global value chains (Choi and Xu, 2019), and intermediate inputs (Taniguchi,2019).
57

the approach of Davis and Haltiwanger (1999) to calculate job creation and job destruction. Our
approach differs from theirs in that the calculations of job creation/destruction are conducted at
the division level rather than at the establishment level, which allows us to take advantage ofthe detailed information on labor variation for each division within firms. Job creation anddestruction constructed from division-level data also motivated us to build a simple frictional
search-and-matching theory with heterogeneous jobs, whose implications are further verified
by additional empirical tests. There are a few exceptional studies examining the relationshipbetween job creation and destruction of parent firms and establishments (Moser et al., 2010;
Boehm et al., 2020). However, their focus is not on the differential impact of FDI on different
destinations, which is of primary interest.
When it comes to the studies using data on Japanese multinationals, Hijzen et al. (2007)
and Yamashita and Fukao (2010) find that outward FDI has a positive effect on net employment
growth and firm performance.8 The closest study to ours is Hayakawa et al. (2013), examining
the role of FDI destinations.9 More specifically, they find that starting operations both in lowincome countries (vertical FDI) and high-income countries (horizontal FDI) has a positiveeffect on net employment. We echo their findings in that an increase in the number of affiliates
in both Asia (vertical FDI) and Europe/North America (horizontal FDI) has a positive effect on
net employment growth. We further advance their findings by taking a closer look at gross job
flows, which can be decomposed into job creation and destruction. Although there are a few
studies measuring job creation and destruction using data on Japanese multinationals (Andoand Kimura, 2015; Kodama and Inui, 2015), they focused on aggregated job flows such asindustry-level or sector-level job creation and destruction based on firm-establishment level

Other studies examining the impact of FDI by Japanese multinationals on domestic labor market focus on
the entry and exit of establishments (Ito and Ikeuchi, 2017) and non-regular workers (Tanaka, 2017).
9 Kambayashi and Kiyota (2015) also highlight the role of FDI destinations. However, their main focus ison the prices of final and investment goods in destination countries, rather than the direct impact of FDI.
6
8

data rather than firm-establishment-division level data. Furthermore, econometric analysis of
job creation and destruction were not provided in those studies.
Apart from empirical practice, our simple theory to explain the differential effects of
FDI on job creation and destruction also contributes to the theoretical literature on the largefirm version of the frictional search-and-matching model (Pissarides, 2000, Ch. 3; Wasmer,
1999; Cahuc and Wasmer, 2001).10 Contrary to the standard matching model, where there is a
one-to-one match between workers and jobs/firms, the large-firm setting allows one firm tomatch with multiple workers and is, thus, more suitable for interpreting reduced-form empiricalresults than the standard setting. Wasmer (1999) extends it to incorporate two types ofheterogeneous jobs: one with high hiring cost and low job-separation rate, and the other withlow hiring cost and high job-separation rate. He examines the effect of the growth of laborproductivity on job composition in a steady state. We simplify his framework and conduct
different comparative statics: how changes in job-separation rate, which is assumed to be causedby FDI, affect job creation and destruction of each type of job in the short run, where statevariables remain unchanged. Although our framework is similar to that of Wasmer (1999), thefull analytical characterization of comparative statics reveals the exact relationship between theaggregate job creation and the job creation of each job type. We believe the results obtained inthis study would be a useful benchmark when sorting out the empirical results of FDI’s impact
on domestic job creation and destruction.
The remainder of this study is organized as follows. The next section introduces thedata and estimation strategy. Section 3 presents the empirical results. To propose a possible
mechanism behind the results, Section 4 builds a frictional search-and-matching model. The
model provides additional testable implications, which are empirically confirmed. The final

Subsequent studies using the large-firm setting (multi-worker firm setting in general) examine thegeneralization of intra-firm bargaining (Cahuc et al., 2008; Acemoglu and Hawkins, 2014), business cycle(Mandelman and Zanetti, 2014; Dossche et al, 2019; Kudoh et al., 2019), and many other issues.
710

section concludes the study.

II. Data and methodology
Data, job creation, and job destruction
This study uses firm-level data collected through the Basic Survey of Japanese Business
Structure and Activities (BSJBSA), which is conducted annually by the Ministry of Economy,
Trade, and Industry, Japan. The survey covers almost all medium and large firms in Japan; small
firms who employ ≥ 50 workers with ≥ 30,000,000 yen worth of capital are also included. The
response rate is over 80%, with around 30,000 firms completing the questionnaire each year.
The samples of manufacturing and non-manufacturing firms were used for this study, covering
the years 1995–2017. Summary statistics of the data are reported in Table A1 of Appendix 1.
We removed outliers that recorded negative value terms such as R&D, revenue, or export.
The approach for calculating job creation and destruction is similar to that used by
Davis and Haltiwanger (1999); the difference is that our calculations occur at the division level
and, thus, capture the job creation and destruction within the firm. Job creation in a firm isdefined as the sum of all new jobs in the firm’s expanding and newly opened divisions, whilejob destruction in a firm is defined as the sum of all eliminated jobs in the firm’s downsizing or
closed divisions. Furthermore, the firm’s branches or plants are considered to be similar todivisions. Newly set up and closed firms are excluded; they are not within the scope of thisstudy’s objectives because such job creation/destruction instances are quite different from thosein existing firms.
First, the magnitude of job creation in firm i in year t is defined as the sum of all newjobs in expanding divisions in firm i in year t, represented as follows (the number of divisions
in firm i is d):8𝑆𝐶
𝐽𝐶𝑖,𝑡 = ∑ ∆𝑁𝑖,𝑑,𝑡
𝑑=1

where
𝐶
∆𝑁𝑖,𝑑,𝑡= 𝑁𝑖,𝑑,𝑡 − 𝑁𝑖,𝑑,𝑡−1

conditioned on
𝑁𝑖,𝑑,𝑡 − 𝑁𝑖,𝑑,𝑡−1 > 0.

In the above equations, S is the number of divisions in firm i, and 𝑁𝑖,𝑑,𝑡 is the number ofworkers employed in division d in firm i in year t.
The magnitude of job destruction in firm i in year t is defined as the sum of all
diminished jobs in diminishing divisions in firm i in year t, represented as follows (the numberof divisions in firm i is d):𝑆𝐷
𝐽𝐷𝑖,𝑡 = ∑ ∆𝑁𝑖,𝑑,𝑡
𝑑=1

where
𝐷
∆𝑁𝑖,𝑑,𝑡= −(𝑁𝑖,𝑑,𝑡 − 𝑁𝑖,𝑑,𝑡−1 )conditioned on
𝑁𝑖,𝑑,𝑡 − 𝑁𝑖,𝑑,𝑡−1 < 0.

In practice, we use JC and JD as our main dependent variables. Furthermore, to make theanalysis comparable to the previous literature, we also calculate the within-firm net
employment and investigate how the Japanese multinational firms’ overseas investments willaffect these measurements.

Estimation strategy
9Our baseline regression of job creation takes the following form:

𝐽𝐶𝑖𝑡 = 𝛾1 𝐴𝑠𝑖𝑎_𝑎𝑓𝑓𝑖𝑙𝑖𝑎𝑡𝑒𝑖𝑡 + 𝛾2 𝐸𝑈_𝑁𝐴_𝑎𝑓𝑓𝑖𝑙𝑖𝑎𝑒𝑖𝑡 + 𝛾3 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑡 + 𝛾𝑖 + 𝛾𝑡 + 𝑒𝑖𝑡 (1)𝐽𝐶𝑖𝑡 = 𝛿1 𝐴𝑠𝑖𝑎_𝑎𝑓𝑓𝑖𝑙𝑖𝑎𝑡𝑒𝑖𝑡 + 𝛿2 𝐸𝑈_𝑁𝐴_𝑎𝑓𝑓𝑖𝑙𝑖𝑎𝑡𝑒𝑖𝑡 + 𝛿3 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑡 + 𝛿𝑖 + 𝛿𝑡 + 𝜀𝑖𝑡(2)

The regressions for job destruction and net employment, that is, job creation minus job
destruction, are analogously defined. 𝐴𝑠𝑖𝑎_𝑎𝑓𝑓𝑖𝑙𝑖𝑎𝑡𝑒 is the log of the number of Asian
affiliates of firm i in year t, and 𝐸𝑈_𝑁𝐴_𝑎𝑓𝑓𝑖𝑙𝑖𝑎𝑡𝑒 is the log of the combined number ofaffiliates that are located in Europe or North America for firm i in year t. Controls is the vector
of control variables including the capital-labor ratio, R&D expenditure share concerning
revenue, foreign capital share, firm age, revenue (log), and total factor productivity.11 Firm andyear fixed effects are also included.
Because both FDI decisions and domestic employment decisions are made by the same
firm, our estimation may be subject to endogeneity bias. One might consider that firms actively
engaged in foreign investment need to make adjustments to within-firm employment more
frequently because these firms are more sensitive to cost variation and labor reallocation is anefficient way to alleviate cost shocks. If this is the case, our baseline estimation may suffer fromself-selection biases. To mitigate this problem, we apply a two-stage instrumental variable (IV)method. An ideal instrument is the one that is closely related to firms’ FDI decision but does
not affect the employment dynamics within firms. Thus, the instrument we can think ofnaturally is the industry-level FDI trend. In practice, we use the (log) average number of Asian
affiliates and European/North American affiliates in industry j in year t, andIn the baseline specification, we use the method as in Levinsohn and Petrin (2003). For robustness checks,
we apply Olley and Pakes (1996), and stochastic frontier methods as well.
1011mean_Asia_affiliatejt and mean_EU_NA_affiliatejt as an instrument for Asia_affiliateit and
EU_NA_affiliateit, respectively. The fitted value obtained in the first stage will be used in thesecond stage to measure the elasticity of within-firm employment regarding FDI.
The industry-mean FDI is correlated with but is not directly affected by individual firm
FDI as long as the industry is sufficiently large. Besides, at least part of the variation in industrymean FDI comes from industry characteristics rather than from firm characteristics; thus, theinstrument is plausibly exogenous to firm-decision making on domestic employment. There are
two arguments why we think this is the case. First, how easily firms expand foreign activities
crucially depends on the nature of the products and services of their industry. Some industries
are more amenable to the spatial separation of production processes and, thus, tend to establish
more foreign affiliates than other industries (Baldwin, 2016). Suggestive evidence for theindustry variation of the easiness of the so-called unbundling reported that the use of imported
intermediates in producing goods exported, which they call vertical specialization, varies
widely across industries within a country in 10 Organisation for Economic Co-operation andDevelopment (OECD) countries and four emerging economies (Hummels et al., 2001; Johnson
and Noguera, 2012). 12 Second, inter-industry factor costs, particularly wages, vary widely
across countries, mainly due to differences in institutions (Du Caju et al., 2008; 2010). Du Caju
et al. (2010) observed huge inter-industry wage differential across eight member countries inthe European Union (EU) and attributed this to the difference in rent-sharing rule determinedby countries’ labor market institutions.

A concept related to vertical specialization is vertical intra-industry trade (Fontagne and Freudenberg,
2002 for a survey).
11
12

III. Estimation results
Table 1 demonstrates the baseline estimation results, as in Eqs. (1) and (2): This shows that FDI
to Asian countries has a positive effect on domestic job creation, but the effect is negative for
FDI to European/North American countries. As indicated in columns (3) and (4), FDI to Asian
countries prevents firms from removing the jobs, and so does the investment in Europe/North
American countries. When we combine these two effects, as presented in columns (5) and (6),
FDI to Asia has an overall positive impact on the net employment of Japanese firms, which iseasy to follow because the job creation effect is much larger. In the meantime, FDI toEurope/North America is also associated with net employment growth. If we compare themagnitude of the coefficient of EU_NA_affiliate between the case of JC and JD, it can beconcluded that the decrease in JD surpasses that in JC, which leads to positive net employment
growth.
TABLE 1Baseline results
Dependent
variable
Asia_affiliate
EU_NA_affiliate
Capital_labor_ratio
R&D share

(1)
JC

(2)
JC

(3)
JD

(4)
JD

(5)
Net

(6)
Net

20.68***(3.627)-8.457*(4.568)-50.71***(2.809)-8.84217.24***(3.633)-10.44**(4.570)-46.48***(2.856)6.761-1.204(3.712)-41.34***(4.674)9.713***(2.874)9.776-1.256(3.719)-41.55***(4.678)
11.59***(2.924)7.041

29.27*** 25.80***(3.653)(3.662)50.14*** 48.46***(4.532)(4.536)-68.94*** -66.80***(2.903)(2.954)-22.951.454(18.72)(18.69)(19.16)(19.13)(20.58)(20.55)
Foreign_capital_share-0.0899*** -0.0904*** -0.0976*** -0.0985*** -0.0439* -0.0413(0.0212) (0.0212) (0.0217) (0.0217) (0.0257) (0.0257)
Firm_age-0.00673 -0.00665 -0.00309 -0.00321 -0.00333 -0.00277(0.0151) (0.0151) (0.0154) (0.0154) (0.0223) (0.0223)
TFP_LP-2.84325.15***-37.98***(4.431)(4.534)(4.566)12

ln_Revenue

29.94***

19.25***

5.855

Firm FE
Year FE

Yes
Yes(3.905)
Yes
Yes

Yes
Yes

(3.997)
Yes
Yes

Yes
Yes

(4.036)
Yes
Yes

Observations
R-squared
Number of firms

151,727
0.007
23,368

151,727
0.007
23,368

151,727
0.006
23,368

151,727
0.006
23,368

128,763
0.009
20,579128,763
0.009
20,579Standard errors are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at
10%.
Notes: “Net” in columns (5) and (6) is defined as the difference between JC and JD.
As for the instrumental variable estimation, to test the credibility of the instruments,
we calculated the correlation between IVs and major variables of interest. As can be seen from
Table 2, mean_Asia_affiliate and mean_EU_NA_affiliate have almost no correlation with thedependent variables, whereas the correlation with the instrumented variables is relatively high.
The first-stage results are presented in Table 3-A. mean_Asia_affiliate is shown topositively affect Asia_affiliate and EU_NA_affiliate, but the same does not apply tomean_EU_NA_affiliate. When it comes to the second-stage estimation, as indicated in Table 3B, Asia_affiliate has a positive effect on job creation, and the effect is negative for
EU_NA_affiliate. As indicated in columns (3) and (4), both the investment in Asian andEuropean/North American countries negatively affects JD. If we combine these two effects(impact on JC and JD) because the magnitude of JC is larger than that on JD, for bothAsia_affiliate and EU_NA_affiliate, we should expect their impact on the net employment tofollow the trend of JC. The results in columns (5) and (6) verify our predictions.

TABLE 2
Correlation between the IVs and variables of interest
JC

JD
13

Net

mean_Asia_affiliate-0.0107-0.0093-0.0026

mean_EU_NA_affiliate-0.00320.001-0.0048

Asia_affiliate

EU_NA_affiliate

mean_Asia_affiliate

0.24410.1591

mean_EU_NA_affiliate

0.22020.1666

14

TABLE 3-A
IV estimation: First stage results
(1)

(2)

(3)
JC/JD

Dependent
variable

Asia_affiliate

EU_N Asia_aA_affil ffiliate
iate

EU_N Asia_aA_affil ffiliate
iate

EU_N Asia_aA_affil ffiliate
iate

EU_N
A_affil
iate

mean_As
ia_affiliate0.0734***0.0181***0.0163***0.0171***0.0152***(24.96) (7.72)

0.0709
***

(4)

(5)

0.0680
***

(6)

(7)
Net

0.0651***(8)(24.17) (7.00)(21.58) (6.72)(20.70) (5.97)

mean_E
U_NA_af 0.0324
filiate***0.0127***0.0306***0.0139***0.0309***0.0141***0.0287***0.0155***(10.52)0.0333***(-5.16)(-9.97)(-5.67)(-9.44)(-5.34)(-8.81)(-5.9)

0.0207***0.0501***0.0303***0.0303***0.0190***0.0470***0.0286***

Capital_l
abor_rati
o
R&D
share
Foreign_capital_s
hare

Firm_age

TFP_LP(14.49) (11.31) (21.55) (16.34) (11.79) (9.19)
0.135* 0.0895 0.139* 0.0944 0.158* 0.123*(18.07) (13.60)
0.164* 0.129***(8.80)
0.000191***(11.01)0.0000229(1.85)***(7.35)
0.000115***(-8.33)0.0000151(1.54)***(7.78)
0.000117***(-8.54)**(8.70)
0.000113***(-4.98)**(8.38)
0.000132***(-7.18)**(9.04)
0.0001
16***(-5.12)**(8.82)
0.0001
33***(-7.26)0.0000147(1.50)

0.0000523**(2.65)

0.0000331*(2.08)

0.0000526**(2.68)

0.0000334*(2.10)

0.157***(44.46)
Yes

0.0880***(30.88)Yes

0.148* 0.0777*****(40.99) (27.10)

ln_RevenueFirm FE**(9.12)
0.000196***(11.34)0.0000221(1.79)

Yes

Yes

0.145* 0.0760*****(36.01) (23.46)
0.157***(49.66)
Yes

0.0881***(35.07)
Yes
Yes
15

Yes

Year FE

Yes

Observati 14715
ons
2

Yes

Yes

Yes

Yes

Yes

Yes

Yes

14715
2

14715
2

14715
2

12475
8

12475
8

12475
8

12475
8

t statistics are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at 10%.
TABLE 3-B
IV estimation: Second stage results
(1)
JC

(2)
JC

(3)
JD

(4)
JD

(5)
Net

Asia_affiliate

185.5**(73.31)144.1**(72.90)-9.466(74.02)-14.01(74.03)

197.1*** 154.1**(73.67)(74.04)

EU_NA_affiliate-339.2*** -301.6** -241.5* -239.3* -71.84-32.92(130.0)(128.2)(131.3)(130.2)(117.7)(116.4)-49.02*** -43.71*** 14.52*** 18.66*** -71.70*** -70.59***

Dependent variable

Capital_labor_ratio(3.305)(3.776)(3.337)(3.834)(3.302)
R&D share-1.332
16.93
30.04
28.73
-35.23(20.34)(20.37)(20.54)(20.69)(22.66)
Foreign_capital_share-0.0964***-0.0998***-0.123*** -0.125*** -0.0403(0.0233) (0.0233) (0.0235) (0.0237) (0.0280)
Firm_age-0.00544 -0.00511 0.000261 9.79e-05 -0.00832(0.0155) (0.0154) (0.0156) (0.0156) (0.0227)
TFP_LP-1.948(7.816)ln_Revenue
Firm FE
Year FE

Yes
Yes

Observations
147,152
Number of firms
18,793
Cragg-Donald Wald F76.68
statistic

42.37***(7.891)(6)
Net(3.777)-9.651(22.77)-0.0366(0.0279)-0.00701(0.0226)-53.81***(8.075)35.48***(8.079)
Yes
Yes
Yes
Yes

39.12***(8.204)
Yes
Yes
Yes
Yes

-7.736(8.465)
Yes
Yes

147,152
18,793
78.00

147,152
18,793
78.00

124,758
16,574
74.38

147,152
18,793
76.68

124,758
16,574
73.57

Standard errors are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at
10%.
Notes: We use mean_Asia_affiliate and mean_EU_NA_affiliate as IVs.

Robustness checks and further issues
16For robustness checks, we use alternative instruments―exchange rate and lagged values ofAsia_affiliate and EU_NA_affiliate (Alfaro et al., 2004; Keller and Yeaple, 2009). From
theoretical and empirical standpoints, exchange rates are known as one of the significant
determinants of FDI (Froot and Stein, 1991; Blonigen, 1997).13 Among others, Froot and Stein(1991) emphasize that real exchange rates affect the attitudes of foreign investors by changing
their relative wealth. A depreciation in the real exchange rate of the host country makes assets
relatively cheap. Thus, foreign multinationals invest more in the host country under imperfectcapital markets, where external financing is more costly than internal financing. The validity of
our instruments rests on such theories. Similar identification strategies can be found in Alfaro
et al. (2004), who examine the effect of FDI on economic growth, and in Keller and Yeaple(2009), who examine the effect of FDI on firm productivity. We will follow these practices anduse real effective exchange rates of the Japanese Yen against Asian and European/North
American regions as verification instruments.14 The results are presented in Table 4. Generally,
the impact of Asia_affiliate and EU_NA_affiliate on JC and JD has the same signs as in theprevious results and is statistically significant. Both have a positive influence on net
employment, which is also consistent with previous findings.
TABLE 4-A
First stage results using alternative IVs (exchange rates and lagged FDI measurements)

Depende
nt

(1)

(2)

Asia_affiliate

EU_N Asia_aA_affil ffiliate

EU_N Asia_aA_affil ffiliate

EU_N Asia_aA_affil ffiliate

EU_N
A_affil

iate

iate

iate

iate

variable
RER_EU/NA

13
140.0005
89

(3)
JC/JD

0.000230.0005
32

(4)

(5)

0.0002080.0005
89

(6)

(7)
Net

0.000230.000532

See also Görg and Wakelin (2002); and Qi et al. (2019) for subsequent developments.
See Appendix 2 for how we construct the real effective exchange rates by region.
17(8)0.000208

(0.88)
Lag_Asia 0.741*_affiliate **(345.90)
Lag_EU_ 0.0343
NA_affili ***ate(12.85)(0.40)(0.80)

0.0357 0.740******(19.46) (344.8
4)0.710* 0.0331*****(0.36)(0.88)

0.0348 0.741******(18.95) (345.9
0)0.709* 0.0343*****(0.40)(0.80)(0.36)

0.0357 0.740******(19.46) (344.8
4)0.710* 0.0331*****

0.0348***(18.95)0.709***

Capital_l0.0042(310.57)0.0029abor_rati
o

2*

8*

9***

4***

2*

8*

9***

4***

(2.42)
0.0498***(4.02)
0.0000511**(2.00)
0.0405***(3.82)
0.0000148(5.47)
0.0524***(4.23)
0.0000497**(3.78)
0.0426***(4.03)
0.0000142(2.42)
0.0498***(4.02)
0.0000511**(2.00)
0.0405***(3.82)
0.0000148(5.47)
0.0524***(4.23)
0.0000497**(3.78)
0.0426***(4.03)
0.0000142(3.27)(1.11)(3.18)(1.07)(3.27)(1.11)(3.18)(1.07)

0.0000223(1.67)
0.0451***(16.45)0.00000287(0.25)
0.0206***(8.80)

0.0000225(1.68)

0.00000296(0.26)

0.0000223(1.67)
0.0451***(16.45)0.00000287(0.25)
0.0206***(8.80)

0.0000225(1.68)

0.00000296(0.26)

0.0247***(11.93)
Yes
Yes

Yes

0.0501***(20.68)Yes

0.0247***(11.93)
Yes

R&D
share
Foreign_capital_s
hare
Firm_age

TFP_LP

ln_Revenue(12.38) (310.1
6)0.0096 0.0057(12.85) (310.5
7)0.0042 0.0029(12.38) (310.1
6)0.0096 0.0057

Firm FE

Yes

Yes

0.0501***(20.68)Yes

Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

11885
9

11885
9

11885
9

11885
9

11885
9

11885
9

11885
9

Observati 11885
ons
9t statistics are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at 10%.
TABLE 4-B
Second stage results using alternative IVs (exchange rates and lagged FDI measurements)
18

Dependent variable

(1)

(2)

(3)

(4)

(5)

(6)

JC

JC

JD

JD

Net

Net

Asia_affiliate

16.44*** 11.98** -13.25** -13.40** 26.58*** 22.09***(5.903)(5.919)(6.080)(6.098)(5.171)(5.187)
EU_NA_affiliate-33.64*** -36.28*** -43.24*** -43.56*** 6.981
4.542(7.609)(7.616)(7.838)(7.845)(6.666)(6.674)
Capital_labor_ratio -63.69*** -59.32*** 5.587
7.449** -69.94*** -67.17***(3.469)(3.532)(3.573)(3.639)(3.039)(3.096)
R&D share-17.82
4.052
5.886
2.965
-18.407.094(24.68)(24.64)(25.43)(25.38)(21.62)(21.59)Foreign_capital_share-0.0507-0.0490-0.0128 -0.0138 -0.0473* -0.0446
Firm_age
TFP_LP(0.0311)0.0313(0.0266)(0.0311)0.0317(0.0266)-11.59**(5.490)ln_Revenue(0.0320)0.0357(0.0274)(0.0320)0.0358(0.0274)

24.49***(5.655)(0.0272) (0.0272)-0.00146 -0.00104(0.0233) (0.0233)-35.38***(4.809)Firm FE
Year FE

Yes
Yes

27.94***(4.868)
Yes
Yes
Yes
Yes

19.33***(5.015)
Yes
Yes
Yes
Yes

10.58**(4.266)
Yes
Yes

Observations

118,859

118,859

118,859

118,859

118,859

118,859

R-squared
0.005
Number of firms
15,535
Cragg-Donald Wald F30630
statistic
Sargan statistic
0.332
Sargan test p-value 0.565

0.005
15,53530633

0.004
15,53530630

0.004
15,53530633

0.008
15,53530630

0.008
15,535
30633

0.254
0.615

0.198
0.656

0.170
0.680

1.101
0.294

0.856
0.355

Standard errors are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at
10%.
Notes: We use RER_Asia, RER_EU/NA, Lag_Asia_affiliate and Lag_EU/NA_affiliate as IVs.
In the meantime, changes in the exchange rate may directly affect domestic
employment dynamics through changes in export prices and the export behavior of firms (Klein
et al., 2003). To shut down this direct channel from exchange rate to job creation and destruction
through exports, we rerun Eqs. (1) and (2), while including the export intensity of firms, which19

is defined as the export value over total revenue. The results are shown in Table A2 of Appendix
1. To control for the potential impact from past employment status, we also include the first lagof employment (in log) in the estimation, the results of which are shown in Table A3. Both of
the above additional tests provide findings that are consistent with the previous practice.
One might also argue that FDI firms might have different decision-making regarding
their investment behavior from firms that are fully home operated. To address this concern, we
remove the samples that do not have any overseas affiliates and repeat the aforementioned
practice. The OLS estimation results are presented in Table A4 of Appendix 1, the predictionsof which remain consistent with the previous results.15
Another point is the coverage of industries. In the previous estimations, we have beenusing the full sample, which includes both manufacturing and non-manufacturing industries(we use two-digit codes, and there are 27 industries in total). However, the impact of FDI on
employment can be considered different between manufacturing and non-manufacturingindustries. To confirm whether our previous findings are robust to industry heterogeneity, we
limit the estimation to firms that are located in manufacturing industries only. The results arepresented in Table A5 of Appendix 1, where the predictions remain unchanged.
In summary, the empirical findings suggest that the effect of FDI on job creation anddestruction may differ depending on where investment goes: an increase in FDI to Asia raises
job creation and reduces job destruction, while that to Europe/North America reduces both job
creation and destruction. But through what kind of channels do the causality exist? A further
explanation from the theoretical perspective can help us disentangle the empirical findings.

We also conduct the IV estimation using both the initial (mean_FDI_affiliate) and the new instruments(exchange rate & lag_FDI) and come up with robust results.
20
15

IV. Theoretical model
We provide a simple model to explain the empirical findings on the effect of FDI on domestic
job creation and destruction. The base model is a search-and-matching model withheterogeneous jobs developed by Wasmer (1999). Firms have two types of jobs in domestic
activities: high-skilled and low-skilled. We assume that an exogenous increase in FDI raises
labor demand in a way that makes the separation (or destruction) of both types of domestic jobs
less likely.16 The expansion of foreign activities requires the support of headquarters and homebranches through administration, customization, production for exports, and so forth. How
much demand for a high-skilled job increases relative to the other, however, depends on thedestination of FDI. This differential impact of FDI on demand for heterogeneous jobs is the key
to our theoretical mechanism. Here, we provide a sketch of the model and relegate derivations
to the Theory Appendix.

Overview of a search-and-matching model with two types of jobs
The economy consists of a continuum of risk-neutral, infinitely lived workers of size 𝐿 and acontinuum of risk-neutral, infinitely lived firms of size one. The representative firm produces
output 𝑌 from capital stock 𝐾 and employment 𝑁. The production function, 𝑌 = 𝐺(𝐾, 𝑁),
is specified as a constant-returns-to-scale for both factors.17 The employment 𝑁 consists of
two types of workers: 𝑁 = 𝑁ℎ + 𝑁𝑙 , where 𝑁𝑗 is the mass of type-𝑗 ∈ {ℎ, 𝑙} workers. The
firm posts 𝑉𝑗 of vacancies for 𝑗 job type at a cost 𝑐𝑗 . The matching process between firms
and workers is costly in that unemployed workers and vacancies meet each other randomly. The
existing match of type 𝑗 job breaks with an exogenous Poisson separation (destruction) rate

This assumption is in line with the fact that outward FDI by Japanese multinationals is complementary toexports of intermediate goods from home, which is likely to raise the demand for workers in home
(Nishitateno, 2013).
17 Because all firms are symmetric and the mass of firms is one, variables for individual firms are also
aggregate ones. Thus, we omit the index of firms.
21
16

𝑠𝑗 . The two types of jobs differ in the following way: the firm finds it costlier to search for highskilled workers than low-skilled workers: 𝑐ℎ > 𝑐𝑙 but tends to continue the match with highskilled workers longer than that with low-skilled workers: 𝑠ℎ < 𝑠𝑙 . The higher hiring cost for
the high-skilled worker pays off in the longer continuation of match, while the lower hiring costfor the low-skilled worker comes at the expense of the shorter continuation.
The matching process is governed by a constant-returns-to-scale matching function,𝑚(𝑈, 𝑉) = 𝑈 𝜂 𝑉 1−𝜂 , where 𝑈 is the mass of unemployed workers, 𝑉 = 𝑉ℎ + 𝑉𝑙 is the massof vacancies and 𝜂 ∈ (0,1) the matching elasticity. 𝑚(⋅) is the Poisson arrival rate so that
there are on average 𝑚Δ𝑡 matches during a short time interval Δ𝑡 . We assume that thematching is formed sequentially. First, 𝑚(𝑈, 𝑉) matches are formed on average per unit oftime between ex ante identical unemployed workers and total vacancies. Then, 𝑚(𝑈, 𝑉ℎ )matched workers out of 𝑚(𝑈, 𝑉) get employed in the high-skilled job, while the remaining𝑚(𝑈, 𝑉) − 𝑚(𝑈, 𝑉ℎ ) matched workers in the low-skilled job. The Poisson arrival rate ofmatching for a vacancy 𝑗 ∈ {ℎ, 𝑙} with an unemployed worker, denoted by 𝑞𝑗 , is thus:𝑞ℎ ≡𝑚(𝑈, 𝑉) 𝑚(𝑈, 𝑉ℎ )⋅= (𝜃𝑣ℎ )−𝜂𝑉ℎ𝑚(𝑈, 𝑉)

(3)
1−𝜂

𝑞𝑙 ≡

𝑚(𝑈, 𝑉) 𝑚(𝑈, 𝑉) − 𝑚(𝑈, 𝑉ℎ ) 𝑞 − 𝑣ℎ 𝑞ℎ 𝜃 −𝜂 (1 − 𝑣ℎ⋅==𝑉𝑙𝑚(𝑈, 𝑉)
1 − 𝑣ℎ1 − 𝑣ℎ)(4)where 𝜃 ≡ 𝑉/𝑈 ; 𝑣𝑗 ≡ 𝑉𝑗 /𝑉 ; and 𝑞 ≡ 𝑚(𝑈, 𝑉)/𝑉 = 𝑚(1/𝜃, 1) = 𝜃 −𝜂 . Given the vacancyunemployment share 𝜃, both arrival rates decrease with the share of high-skilled vacancy 𝑣ℎ .
Higher 𝑣ℎ makes each high-skilled vacancy more difficult to match with an unemployedworker (lower 𝑞ℎ ). It also raises the mass of high-skilled match (higher 𝑚(𝑈, 𝑉ℎ )/𝑉 = 𝑣ℎ 𝑞ℎ )and, thus, crowds out low-skilled matches (lower 𝑞𝑙 ). The Poisson arrival rate of matching for
an unemployed worker with a vacancy 𝑗 is defined similarly.
22

Job creation is measured by the number of vacancies that find workers. Aggregate job
creation and high/low-skilled job creations (per unit of time) are given by:

𝐽𝐶 = 𝑞 ⋅ 𝑉 = 𝜃 −𝜂 𝑉

(5)𝐽𝐶ℎ = 𝑞ℎ ⋅ 𝑉ℎ = 𝜃 −𝜂 𝑣ℎ 1−𝜂 𝑉

(6)

1−𝜂

(7)

𝐽𝐶𝑙 = 𝑞𝑙 ⋅ 𝑉𝑙 = 𝜃 −𝜂 (1 − 𝑣ℎ)𝑉,

Similarly, job destruction is measured by the mass of newly separated matches. Aggregate job
destruction and high/low-skilled job destructions (per unit of time) are given by:

𝐽𝐷 = 𝑠ℎ ⋅ 𝑁ℎ + 𝑠𝑙 ⋅ 𝑁𝑙

(8)

𝐽𝐷𝑗 = 𝑠𝑗 ⋅ 𝑁𝑗

(9)where 𝑛𝑗 ≡ 𝑁𝑗 /𝑁 is the employment share of job 𝑗.

Firm’s problem and labor demand
Each firm maximizes the expected value of discounted lifetime profits by choosing timeschedules of capital investment 𝐼, capital stock 𝐾, each type of vacancy 𝑉𝑗 , and each type of
employment 𝑁𝑗 . 𝐼 and 𝑉𝑗 are the control variables and can change instantaneously, while 𝐾
and 𝑁𝑗 are the state variables and can change only gradually. 18 Solving the maximization
problem gives the usual marginal productivity condition for each type of job:

𝐺𝑁 = 𝑤ℎ +(𝑟 + 𝑠ℎ )𝑐ℎ
𝑞ℎ

(LDh)

Letting 𝑁𝑗̇ be the time derivative of employment 𝑗, employment 𝑗 evolves according to 𝑁𝑗̇ = 𝐽𝐶𝑗 − 𝐽𝐷𝑗 ,where 𝐽𝐶𝑗 is job creation of 𝑗 defined (Eqs. (6) and (7)) and 𝐽𝐷𝑗 is job destruction of 𝑗 (Eq.(9)).
1823𝐺𝑁 = 𝑤𝑙 +(𝑟 + 𝑠𝑙 )𝑐𝑙
𝑞𝑙

(LDl)

where 𝐺𝑁 ≡ 𝜕𝐺/𝜕𝑁 = 𝜕𝐺/𝜕𝑁𝑗 is the marginal product of labor, 𝑤𝑗 is the wage of labor, 𝑗𝑟 is the exogenous interest rate, and 𝑞𝑗 is the filling rate of the job 𝑗 defined in Eqs. (3) and(4). The marginal product of labor 𝑗, 𝐺𝑗 , must be equal to the marginal cost of hiring a worker
𝑗 that consists of the wage, 𝑤𝑗 , and the expected recruitment cost for the worker,(𝑟 + 𝑠𝑗 )𝑐𝑗 /𝑞𝑗 , since the expected duration of a high-skilled-job vacancy finding a worker is
1/𝑞𝑗 . Given the share of high-skilled vacancy, 𝑣𝑗 , both equations show a downward sloping
curve in (𝜃, 𝑤𝑗 ) space. Higher wages 𝑤𝑗 discourage firms from posting vacancies, leading toa less tighter labor market (lower 𝜃). We call the two equations the labor demand curves.

Wage setting and the share of high-skilled vacancy
When a new match is formed, the firm and the worker engage in a bargain to determine wages
in a way of generalized Nash bargaining in which the equilibrium wage maximizes a weighted
product of each party’s return from the job match. The resulting outcome is:

𝑤𝑗 = (1 − 𝛽)𝑧 + 𝛽 [𝐺𝑁 + ∑ (𝜃𝑣𝑗 )𝑐𝑗 ]

(WS)

𝑗=ℎ,𝑙

where 𝛽 ∈ (0,1) is a parameter capturing the worker’s bargaining power and 𝑧 is an
unemployment benefit. ∑𝑗=ℎ,𝑙 𝜃𝑣𝑗 𝑐𝑗 = ∑𝑗=ℎ,𝑙 𝑐𝑗 𝑉𝑗 /𝑈 represents the average cost of postingvacancies for each unemployed worker and increases with the high-skilled-vacancy share 𝑣ℎbecause 𝑐ℎ > 𝑐𝑙 . The worker demands a higher wage when her outside payoff is greater (higher𝑧) and/or the firm’s opportunity cost of keeping vacancies unfilled is greater (higher 𝐺𝑁 and∑𝑗=ℎ,𝑙 𝜃𝑣𝑗 𝑐𝑗 ). We also note that the right-hand side of (WS) does not depend on the job type,24

implying 𝑤ℎ = 𝑤𝑙 = 𝑤, partly because both types of jobs have the same marginal product. The
equation (WS), which we call the wage setting curve, has an upward-slope in (𝑤, 𝜃) space.
The steady-state equilibrium is the intersection of the labor supply and demand curves, as
shown in Figure 1.

Figure 1. Labor demand and wage setting curves

From (LDh) and (LDl), we have:𝐺𝑁 −(𝑟 + 𝑠ℎ )𝑐ℎ(𝑟 + 𝑠𝑙 )𝑐𝑙= 𝐺𝑁 −
𝑞ℎ
𝑞𝑙

or,
𝜂

𝑣ℎ + (𝑐̃ − 1)𝑣ℎ − 𝑐̃ = 0(10)

where 𝑐̃ ≡ (𝑟 + 𝑠𝑙 )𝑐𝑙 /[(𝑟 + 𝑠ℎ )𝑐ℎ ] measures the hiring cost of low-skilled jobs relative tohigh-skilled jobs: it is greater when the relative hiring cost is high (higher 𝑐𝑙 /𝑐ℎ ) and/or therealistic duration of match is shorter (higher 𝑠𝑙 /𝑠ℎ ). Eq. (10) is the condition under which thefirm is indifferent to posting either a high-skilled vacancy or a low-skilled vacancy in a steadystate. The marginal product of the labor net of the expected flow of hiring cost must be equal
between the two jobs, determining the high-skilled-vacancy share 𝑣ℎ . If the relative hiring costof a low-skilled worker is sufficiently low such that 𝑐̃ < 1 − 𝜂, Eq. (10) has a solution of 𝑣ℎ ∈25

(0,1). In the following, we assume this inequality for the two types of jobs to coexist in thesteady state. As 𝑐̃ is higher, the firm shifts emphasis on recruiting high-skilled workers rather
than low-skilled ones, leading to higher 𝑣ℎ .
In steady state, the outflows from and the inflows to the unemployment pool for eachtype of worker must be equal. That is, for each type 𝑗, 𝐽𝐶𝑗 = 𝐽𝐷𝑗 must hold.

(𝜃𝑣ℎ )1−𝜂 𝑢𝐿 = 𝑠ℎ 𝑛ℎ (1 − 𝑢)𝐿1−𝜂

𝜃1−𝜂 (1 − 𝑣ℎ)𝑢𝐿 = 𝑠𝑙 (1 − 𝑛ℎ )(1 − 𝑢)𝐿

where we note 𝐽𝐶𝑗 = 𝑞𝑗 𝑉𝑗 = 𝜃𝑗 𝑞𝑗 𝑈. These equations are also known as the Beveridge Curves(Pissarides, 2000). In summary, the high-skilled-vacancy share 𝑣ℎ is pinned down by theindifference condition (8); the vacancy–unemployment ratio 𝜃 and the wage 𝑤 are by labor
supply (LS) and labor demand (LD); the high-skilled-employment share 𝑛ℎ and theunemployment rate 𝑢 are determined by the Beveridge Curves.

Effect of FDI on domestic job creation and destruction
We model FDI as an exogenous shock to the firm. We assume that FDI decreases the separation
rate 𝑠𝑗 because FDI increases the demand for both types of domestic jobs. Domestic
headquarters and branches need to support expanding foreign activities concerning both highand low-skill intensive activities through administration, product/service customization,
production for exports, and so forth.
However, the effect is assumed to vary in the destination of FDI. Japanese firms
establish affiliates in Asian countries mainly for seeking low-cost factors such as labor and land,
which is known as vertical FDI. Headquarters and plants in Japan concentrate on the productionof high value-added parts and components and export the intermediate goods to their plants in26

Asia for assembly (Fujita and Hamaguchi, 2012). This assumption is motivated by the facts thatvertical intra-industry trade driven by factor-cost difference is prominent in Asia than in otherregions (Fukao et al., 2003; Kimura et al., 2007). FDI to Asia promotes exports and domestic
production, thereby making low-skilled jobs more valuable than high-skilled jobs. The relative
reduction in the separation rate of low-skilled jobs can be considered sufficiently high, that is,𝜕𝑠𝑙 /𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎 << 𝜕𝑠ℎ /𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎 < 0.19
Conversely, Japanese multinationals engage in FDI to European and North Americancountries mainly for saving trade costs and seeking new markets, known as horizontal FDI.
They tend to replace exports by local production to save transportation costs and reduce
domestic production. In fact, Nishitateno (2013) finds that complementarity between Japanese
FDI and exports to Europe/North America is weaker than that between Japanese FDI andexports to Asia. FDI to Europe and North America does not significantly increase the need forlow-skilled jobs relative to that for high-skilled jobs.20 This implies that the relative reductionin the separation rate of high-skilled jobs is sufficiently high, that is, 𝜕𝑠ℎ /𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴 <<𝜕𝑠𝑙 /𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴 < 0.21
Responding to a decline in the separation rate, the high-skilled-vacancy share 𝑣ℎ andthe vacancy–unemployment ratio 𝜃 change immediately because firms can instantaneously
adjust vacancies 𝑉𝑗 . By contrast, the adjustment of employment 𝑁𝑗 and the unemployment
rate 𝑢 take time and change gradually. In the following, we will consider the effect of FDI in
the short run, where vacancies can react while un/employment remains unchanged.

𝜕𝑠

𝜕𝑠

𝑙
The exact condition is given by 𝜕𝐹𝐷𝐼ℎ𝐴𝑠𝑖𝑎 / 𝜕𝐹𝐷𝐼𝐴𝑠𝑖𝑎∈ [0, 𝑆 𝑎 ) , where 𝑆 𝑎 ≡ Γ2 /Γ1 ; Γ1 ≡ 𝑐ℎ (𝑟 +𝑠ℎ )[𝑥{𝜂𝑐̃ − 𝑣ℎ (1 − 𝜂)(1 − 𝑐̃ )} + 𝛽𝜃𝑐𝑙 𝑐̃ (1 − 𝑣ℎ )] ; Γ2 ≡ 𝛽𝜃𝑐𝑙2 (𝑟 + 𝑠ℎ )(1 − 𝑣ℎ ) ; and 𝑥 ≡ (1 − 𝛽)(𝐺𝑁 −𝑧) − 𝛽𝜃 ∑𝑗 𝑣𝑗 𝑐𝑗 , which we assume to be positive. See Theory Appendix for details.

19

Hayakawa et al. (2013) find that Japanese firms that started horizontal FDI, defined as FDI to developed
countries, increased demand for non-production workers in home.
20

𝜕𝑠

𝜕𝑠

ℎ
𝑙
The exact condition is given by 𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴/ 𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴∈ [𝑆 𝑑 , ∞), where 𝑆 𝑑 ≡ Θ2 /Θ1; Θ1 ≡ 𝑣ℎ 𝑐ℎ (𝑟 +𝑠ℎ )[𝛽𝜃(1 − 𝑣ℎ ) 𝑐̃ (𝑐ℎ − 𝑐𝑙 ) + 𝑥] ; and Θ2 ≡ 𝑐𝑙 (𝑟 + 𝑠ℎ )(1 − 𝑣ℎ )[𝜂𝑥 + 𝛽𝜃𝑣ℎ (𝑐ℎ − 𝑐𝑙 )] . See Theory
Appendix for details.
27

21

FDI to Asia
When the separation rate of the low-skilled match declines more sharply than that of the highskilled match as a result of FDI to Asia, firms find it more profitable to match low-skilledworkers relative to high-skilled ones. To equalize the profitability of hiring the two types ofworkers, firms increase the share of low-skilled vacancy, 𝑣𝑙 = 1 − 𝑣ℎ , with a lower search costof 𝑐𝑙 (< 𝑐ℎ ). This change in the composition of vacancies reduces the average search cost per
unemployed worker (lower ∑𝑗=ℎ,𝑙 𝑐𝑗 𝜃𝑣𝑗 = ∑𝑗=ℎ,𝑙 𝑐𝑗 𝑉𝑗 /𝑈 ) and strengthens the bargaining
position of firms against workers. Workers are unable to demand higher wages than before,making (WS) shift down. Besides, based on the setting where high-skilled vacancies are filled
first and the low-skilled ones next, the filling rate of low-skilled vacancy 𝑞𝑙 increases owing
to the fewer high-skilled vacancies. Thus, firms increase low-skilled vacancies more than they
reduce high-skilled vacancies, making the labor market tighter, as reflected in the rightward
shift of (LD). Both shifts of (WS) and (LD) result in a higher vacancy–unemployment ratio 𝜃,
as shown in Figure 2.

Figure 2.

The effect of FDI to Asia

28

From Eqs. (5) and (8), the effect of FDI on domestic job creation and destruction is
given by:𝜕𝐽𝐶
= (1 − 𝜂)𝜃 −𝜂 𝜃 ′ 𝑈 > 0
𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎
𝜕𝐽𝐷= ∑ 𝑠𝑗 ′ 𝑁𝑗 < 0
𝐴𝑠𝑖𝑎
𝜕𝐹𝐷𝐼
𝑗=ℎ,𝑙

where 𝜃 ′ ≡ 𝜕𝜃/𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎 > 0 and 𝑠𝑗′ ≡ 𝜕𝑠𝑗 /𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎 < 0 . Because of FDI to Asia, bothtypes of job matches are likely to continue longer so that fewer existing matches are destroyed.
The effect of longer duration is greater for low-skilled jobs with lower search costs. The total
number of vacancies firms post increases, resulting in more job creation. This result is in linewith our empirical findings.
By decomposing the effect into job creation and destruction of each type of job (Eqs.
(6), (7) and (9)), we can see:𝜕𝐽𝐶ℎ= 𝑞ℎ′ 𝑉ℎ + 𝑞ℎ 𝑉ℎ′ = (1 − 𝜂)(𝜃𝑣ℎ )1−𝜂 (𝜃′/𝜃 + 𝑣ℎ′ /𝑣ℎ )𝑈 < 0
𝐴𝑠𝑖𝑎
𝜕𝐹𝐷𝐼

(11)

𝜕𝐽𝐶𝑙
𝜕𝐽𝐶
𝜕𝐽𝐶ℎ
=
−
>0
𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎 𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎 𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎

(12)

𝜕𝐽𝐷𝑗
= 𝑠𝑗 ′ 𝑁𝑗 < 0 for 𝑗 ∈ {ℎ, 𝑙}𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎

(13)

𝜂−1

where 𝑣ℎ ′ ≡ 𝜕𝑣ℎ /𝜕𝐹𝐷𝐼 𝐴𝑠𝑖𝑎 < 0; 𝑣ℎ> 1; and 𝜃 ′ /𝜃 + 𝑣ℎ′ /𝑣ℎ > 0. The proofs are given in

the Theory Appendix. Although FDI to Asia increases total job creation, it decreases thecreation of high-skilled jobs by shifting the share of vacancies from high-skilled to low-skilled
jobs.

29

FDI to Europe/North America
Contrary to FDI to Asia, FDI to Europe and North America causes a much greater decline in
the separation rate of the high-skilled match than that of the low-skilled match. The effect here
is exactly opposite to the effect of FDI to Asia. Due to the higher profitability of high-skilledjobs, firms raise their share of total vacancies (higher 𝑣ℎ ). Higher 𝑣ℎ increases the average
search cost per unemployed worker and, thus, the firms’ opportunity cost of keeping vacancies
unfilled. Firms must agree on a higher wage demanded by workers, shifting (WS) up. Becauseof the higher 𝑣ℎ reducing the filling rate of high-skilled vacancies, firms reduce both highskilled and low-skilled vacancies, making (LD) shift leftward. These shifts translate into a lower
vacancy–unemployment ratio 𝜃, as shown in Figure 3.

Figure 3.

The effect of FDI to Europe/North America

From Eqs. (5) and (8), the effect of FDI on total job creation and destruction is𝜕𝐽𝐶= (1 − 𝜂)𝜃 −𝜂 𝜃 ′ 𝑈 < 0𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴
𝜕𝐽𝐷= ∑ 𝑠𝑗 ′ 𝑁𝑖 < 0𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴
𝑗=ℎ,𝑙

30

where′≡ 𝜕𝜃/𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴 < 0 and 𝑠𝑗′ ≡ 𝜕𝑠𝑗 /𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴 < 0. This is consistent with ourempirical findings.
From Eqs. (6), (7), and (9), we derive the effect of FDI on each type of job creationand destruction as.
𝜕𝐽𝐶ℎ= 𝑞ℎ′ 𝑉ℎ + 𝑞ℎ 𝑉ℎ′ = (1 − 𝜂)(𝜃𝑣ℎ )1−𝜂 (𝜃′/𝜃 + 𝑣ℎ′ /𝑣ℎ )𝑈 > 0
𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴

(14)

𝜕𝐽𝐶𝑙
𝜕𝐽𝐶
𝜕𝐽𝐶ℎ
=
−
<0
𝐸𝑈,𝑁𝐴
𝐸𝑈,𝑁𝐴
𝜕𝐹𝐷𝐼
𝜕𝐹𝐷𝐼
𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴

(15)

𝜕𝐽𝐷𝑗
= 𝑠𝑗 ′ 𝑁𝑗 < 0 for 𝑗 ∈ {ℎ, 𝑙}𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴

(16)

𝜂−1

where 𝑣ℎ′ ≡ 𝜕𝑣ℎ /𝜕𝐹𝐷𝐼𝐸𝑈,𝑁𝐴 < 0; 𝑣ℎ> 1; and 𝜃 ′ /𝜃 + 𝑣ℎ′ /𝑣ℎ > 0. The proofs are givenin the Theory Appendix. Contrary to FDI to Asia, FDI to Europe/North America induces firms
to create more high-skilled jobs and fewer low-skilled jobs.

Empirical evidence on the mechanism
To explain why the effect of FDI on domestic jobs varies in its destination, the theoretical model
highlights the role of heterogeneous jobs and gives new testable implications. That is, (i) an
increase in FDI to Asia creates more low-skilled jobs and fewer high-skilled jobs (Eqs. (11) and(12)); (ii) an increase in FDI to EU/North America creates more high-skilled jobs and fewer
low-skilled jobs (Eqs. (14) and (15)); (iii) an increase in FDI to either destination reduces thedestruction of both types of jobs (Eqs. (13) and (16)): 22
We take a step further to empirically investigate the three predictions from (i) to (iii).
Because detailed information on the skill level of employees is not available, we instead use

We start from our theory by assuming that FDI has different impacts on the job separation rate, 𝑠𝑖 . Ideally,
we need to check this to verify the mechanism. However, our limited data does not enable us to further
investigate 𝑠𝑖 . We take an indirect approach instead; we derive new theoretical predictions on FDI impact ofdomestic job creation and destruction of each type of job and then empirically test them.
22

31

the information on division-level characteristics. We suppose that employees working in somedivisions are high-skilled labor, while those in other divisions are low-skilled. The classification
closely follows that proposed by Autor and Dorn (2013) and is given in Table 5.
TABLE 5Classification of low/high skilled jobs
Division

Skill typeResearch & planning

High

Information

High

Research & development

High

International businessHigh

Human resources, accounting, other management

Low

Manufacturing, mining, electricity, gas

Low

Commerce

Low

Restaurants

Low

Research

High

Services

Low

Warehouse, transportation, delivery

Low

Other domesticLow

We then construct the measures of firm-skill level job creation and destruction andrepeat the same regressions as in Section 3. The results are summarized in Table 6. The signsof the coefficients of interest are consistent with our predictions, although some of them arestatistically insignificant. From columns (1) and (2), we see that an increase in the number of
Asian affiliates has a negative effect on high-skilled job creation and a positive effect on lowskilled job creation, which is in line with (i). As predicted by (ii), we also see that an increasein the number of European/North American affiliates has an exact opposite effect on high/lowskilled-job creation. We confirm the third prediction: the negative effect of FDI into
Europe/North America on high/low skilled-job distribution from columns (3) and (4).
TABLE 6
Analysis by high skilled and low skilled divisions (IV method)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Dependent variable JC_high JC_high JC_low JC_low JD_high JD_high JD_low JD_low
Asia_affiliate-0.396-0.85916.86** 12.86** -1.081
32-1.257-12.19**-12.16**

*(2.009) (2.015) (5.408) (5.424) (1.897) (1.903) (5.753) (5.770)
EU_NA_affiliate
40.24** 42.56**
43.20** 43.40**
6.619** 6.297** **-0.0560 -0.165 **(2.590) (2.592) (6.972) (6.978) (2.446) (2.448) (7.416) (7.423)
Capital_labor_ratio 5.654** 4.841** 58.06** 54.50**
9.366******-2.108* -1.903* 7.709** *(1.181) (1.202) (3.179) (3.237) (1.115) (1.136) (3.381) (3.443)
R&D share
3.277
4.793 -20.99 -0.640 2.310
3.106
3.494 -0.228(8.402) (8.387) (22.62) (22.58) (7.934) (7.921) (24.06) (24.02)
Foreign_capital_sha re0.0228* 0.0228***-0.0279 -0.0261 0.0101 0.0102 -0.0229 -0.0239(0.00998 (0.00998(0.0106) (0.0106) (0.0285) (0.0285) ))(0.0303) (0.0303)
Firm_age
0.00097 0.00093
940.0323 0.0327 -0.00430 -0.00429 0.0400 0.0400(0.00906 (0.00906(0.00855 (0.00855)

Firm FE
Year FE(0.0244) (0.0244) ))15.61**
3.977***
0.00725(1.869)(5.031)(1.765)6.743**
21.18****
1.450(1.657)(4.461)(1.565)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Observations
Number of firms

122,578 122,578 122,578 122,578 122,578 122,578 122,578 122,578
19,254 19,254 19,254 19,254 19,254 19,254 19,254 19,254

TFP_LP

ln_Revenue)(0.0259) (0.0259)
24.52***(5.351)

Yes
Yes

17.90***(4.746)
Yes
Yes

Standard errors are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at
10%.
Notes: We use mean_Asia_affiliate and mean_EU_NA_affiliate as IVs.

33

V. Conclusion
We examined the effect of outward FDI on home employment using unique firm-establishmentdivision level panel data in Japan. Contrary to most previous studies focusing only on net
employment growth, we have decomposed it into job creation and job destruction. This
decomposition tells us where net employment growth comes from, that is, positive net growth
resulting from more jobs created, from fewer jobs destructed, or both. Such information isessential for ensuring the flexibility of the labor market, which is a key factor for a country
benefiting from globalization.
The results show that although both investments in Asia and Europe/North America
have a positive effect on the net employment growth of a firm, they have opposite effects on
job creation, positive and negative, respectively. Compared with investment in Europe andNorth America, investment in Asia favors unemployed workers and/or existing employees inother firms, contributing to active adjustment in the Japanese labor market. To explain the
results, we have modeled heterogeneous jobs, high-skilled and low-skilled ones, in a frictional
search-and-matching framework and argued that FDI to different regions may lead to different
labor reallocation decisions between the two types of jobs. FDI to Asia raises the demand forlow-skilled jobs with low hiring costs more than demand for high-skilled jobs with high hiringcosts, leading to more aggregate job creations. Contrastingly, FDI to Europe/North America
increases the demand for high-skilled jobs more, reducing aggregate job creations. Such amechanism is also verified empirically.
The limitation of this study is that the data does not include very small firms who
employ <50 workers or with < 30,000,000 yen worth of capital. Most firms in this category
could be immature firms or ventures, whose behaviors and FDI effects could differ from thoseof large and mature firms. Thus, the findings are only limited to medium-sized and large firms
in Japan. Furthermore, detailed FDI activities and the motivation for foreign investment are
unavailable in the current data. We will leave these for future studies.

34

Appendix
Appendix 1. Additional tables
TABLE A1
Summary statistics
Variable

Obs

Mean

Std.Dev.

Min

Max

Job creation (person)
Job destruction (person)
Net employment (person)

642463642463546261

47.48
44.54
3.64

379.43351.13388.4812623799996
126132

Revenue (million yen)64246322869.31

177307.10

0
0
99691
1

R&D expense/revenue
Firm age
Total regular employee

2864156424616424630.01
44.53
432.450.16
96.36
1775.64

0
0
50

57.10
2005
153405642384

2.09

12.23

0

100

642463642463284125284125

-0.28
6.71
2.92
2.32

1.22
1.12
18.33
18.41

-7.34
-1.74
0
0

7.96
13.28
1346
1327

284125284125

1.29
0.38

7.21
4.57

0
0

524
360

284125

0.42

5.54

0

735

642463642463

97.47
0.02

15.97
0.10

71.28
0

130.911(person)Foreigncapitalshare(100%)
Capital/labor ratio (log)
TFP_LP (log)
Total number of affiliates
Total number of overseas
affiliates
Number of Asian affiliates
Number of European
affiliates
Number of North American
affiliates
Exchange rate
Export/revenue

1.59E+07

TABLE A2
Baseline results with export control
Dependent variable
Asia_affiliate
EU_NA_affiliate

(1)
JC

(2)
JC

(3)
JD

20.53***(3.649)-8.509*17.31***(3.654)-10.41**-0.373-0.45128.28***(3.734)(3.740)(3.677)-41.07*** -41.29*** 49.79***
35

(4)
JD

(5)
Net

(6)
Net
25.10***(3.685)48.21***

(4.570)(4.572)(4.676)(4.679)(4.534)

Capital_labor_ratio-50.75*** -46.46*** 9.907*** 11.84*** -69.18***(2.810)(2.859)(2.876)(2.926)(2.905)
R&D share-8.925
6.795
10.22
7.454
-23.52(18.72)(18.70)(19.16)(19.14)(20.58)Foreign_capital_share -0.0900*** -0.0904*** -0.0969*** -0.0979*** -0.0446*(0.0212) (0.0212) (0.0217) (0.0217) (0.0257)
Export_intensity-0.00672 -0.00665 -0.00312 -0.00325 -0.00322(0.0151) (0.0151) (0.0154) (0.0154) (0.0223)
Firm_age
5.134-2.238-27.78** -27.25** 31.78**(13.26)(13.26)(13.57)(13.57)(13.36)
TFP_LP-2.955(4.441)

ln_Revenue

25.76***(4.544)

Firm FE
Year FE

Yes
Yes

29.99***(3.914)
Yes
Yes

Observations
R-squared
Number of firms

151,727
0.007
23,368

151,727
0.007
23,368(4.538)-67.02***(2.957)1.079(20.55)-0.0417(0.0257)-0.00269(0.0223)
22.89*(13.36)-38.72***(4.576)

Yes
Yes

19.79***(4.006)
Yes
Yes

Yes
Yes

5.379
(4.046)
Yes
Yes

151,727
0.006
23,368

151,727
0.006
23,368

128,763
0.009
20,579128,763
0.009
20,579Standard errors are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at
10%.
Notes: “Net” in columns (5) and (6) is defined as the difference between JC and JD.
TABLE A3
Baseline results with Lag_employment control
Dependent variable
Asia_affiliate
EU_NA_affiliate
Lag_employment
Capital_labor_ratio
R&D share

(1)
JC

(2)
JC

(3)
JD

(4)
JD

(5)
Net

(6)
Net

16.96*** 13.07*** -22.48*** -19.22*** 42.96***(4.201)(4.201)(4.293)(4.292)(3.606)-15.45*** -17.01*** -67.61*** -66.21*** 63.37***35.69***(3.592)60.37***(5.206)-59.07***(5.697)-70.90***(3.517)-10.20(23.63)(4.449)-352.6***(5.280)-120.5***(3.005)82.48***(20.18)(5.203)-93.06***(6.175)-69.18***(3.514)22.95(23.60)(5.321)220.8***(5.822)52.65***(3.595)-28.40(24.15)36(5.317)254.6***(6.310)49.68***(3.591)-53.42**(24.11)(4.469)-283.7***(4.890)-125.3***(3.019)23.34(20.28)Foreign_capital_share -0.0217-0.0167-0.0294(0.0295)
0.0247(0.0256)
4.551(5.468)(0.0295)
0.0254(0.0256)

Firm_age
TFP_LP
ln_Revenue-0.0330-0.0134-0.00468(0.0302) (0.0301)
0.03100.0304(0.0261) (0.0261)-35.21***(5.588)-80.14***(5.352)
Yes
Yes
Yes
Yes

(0.0253)-0.00368(0.0220)40.08***(4.693)(0.0252)-0.00221(0.0219)

Yes
Yes

146.1***(4.479)Yes
Yes

Firm FE
Year FE

Yes
Yes

63.94***(5.238)Yes
Yes

Observations

128,762

128,762

128,762

128,762

128,762

128,762

R-squared
Number of firms

0.006
20,5780.007
20,5780.018
20,5780.019
20,5780.039
20,5780.048
20,578

Standard errors are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at
10%.
Notes: “Net” in columns (5) and (6) is defined as the difference between JC and JD.
TABLE A4
OLS results using samples for FDI-firms only
Dependent variable

(1)
JC

(2)
JC

(3)
JD

(4)
JD

(5)
Net

(6)
Net

Asia_affiliate

25.33*** 19.93*** -2.889-3.37439.78*** 34.60***(6.222)(6.239)(6.164)(6.183)(6.134)(6.155)
EU_NA_affiliate-14.80** -17.51*** -41.33*** -41.72*** 43.08*** 40.83***(6.245)(6.252)(6.187)(6.196)(6.082)(6.090)Capital_labor_ratio -56.53*** -52.06*** 11.39** 13.08*** -78.30*** -75.50***(4.971)(5.042)(4.926)(4.997)(4.967)(5.041)
R&D share-15.16
6.051
3.063
2.624
-24.330.608(31.71)(31.66)(31.42)(31.37)(32.39)(32.35)
Foreign_capital_share-0.168*** -0.171*** -0.169*** -0.170*** -0.0923* -0.0943*(0.0382) (0.0382) (0.0379) (0.0379) (0.0490) (0.0490)
Firm_age
TFP_LP
ln_Revenue
Firm FE

0.000476 0.00150(0.0273) (0.0273)-11.58(8.193)34.17***(7.223)
Yes
Yes

0.00501(0.0270)
19.43**(8.117)Yes
37

0.00495(0.0270)

18.35**(7.158)Yes

0.00299 0.00396(0.0391) (0.0391)-40.42***(8.088)12.10*(7.151)Yes
Yes

Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Observations
Number of firms

69,1690.007

69,1690.008

69,1690.008

69,1690.008

61,4710.010

61,471
0.010

Standard errors are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at
10%.
TABLE A5
OLS results using samples in manufacturing industries only
Dependent variable

(1)
JC

(2)
JC

(3)
JD

(4)
JD

(5)
Net

(6)
Net

Asia_affiliate

17.75*** 15.34*** -0.823-1.10527.44*** 25.16***

EU_NA_affiliate(3.516)(3.523)(3.462)(3.469)(3.835) (3.844)-14.42*** -15.76*** -34.12*** -34.36*** 31.41*** 30.29***(4.435)(4.437)(4.366)(4.369)(4.738) (4.741)

Capital_labor_ratio-36.41*** -32.83*** 6.399** 7.701** -50.71*** -48.29***(3.050)(3.103)(3.002)(3.055)(3.396) (3.456)
R&D share-31.95-20.72-5.664-6.570-35.81-19.94(25.55)(25.51)(25.15)(25.12)(30.19) (30.15)
Foreign_capital_share-0.119*** -0.120*** -0.136*** -0.137*** -0.0474 -0.0471(0.0220) (0.0220) (0.0216) (0.0216) (0.0293) (0.0293)
Firm_age-0.000735 -0.000454 0.00645 0.00644 -0.00443 -0.00388

Firm FE
Year FE

Yes
Yes(0.0141) (0.0141)
14.70***(4.491)23.40***
13.57***(4.025)(3.964)
Yes
Yes
Yes
Yes
Yes
Yes

Observations
R-squared
Number of firms

93,0400.007
12,27993,0400.008
12,279TFP_LP(0.0144)1.299(4.562)

ln_Revenue(0.0144)

93,0400.008
12,27993,0400.008
12,279(0.0235) (0.0235)-18.94***(5.029)7.984*(4.446)
Yes
Yes
Yes
Yes
80,6260.008
11,01780,6260.008
11,017

Standard errors are in parentheses. *** Significant at 1%. ** Significant at 5%. * Significant at
10%.

Appendix 2. Real effective exchange rate
We construct the regional real exchange rate as follows. The bilateral real exchange rate ofJapan against country 𝑐 in year 𝑡 is38

𝑅𝐸𝑅𝑐,𝑡 =𝑁𝐸𝑅𝑐,𝑡 × 𝐶𝑃𝐼𝑐,𝑡
𝑁𝐸𝑅𝐽𝑃𝑁,𝑡 × 𝐶𝑃𝐼𝐽𝑃𝑁,𝑡

𝑁𝐸𝑅𝑐 : US dollar per country 𝑐´s currency,
𝐶𝑃𝐼𝑐 : Consumer price index in country 𝑐,where 𝑁𝐸𝑅𝑐 and 𝐶𝑃𝐼𝑐 are taken from IMF International Financial Statistics. Supposing 𝑐 isin Asia, its real exchange rate weighted by FDI stock is
𝐹𝐷𝐼𝑐,𝑡𝐴𝑠𝑖𝑎
𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑_𝑅𝐸𝑅𝑐,𝑡=× 𝑅𝐸𝑅𝑐,𝑡∑𝑘∈𝐴𝑠𝑖𝑎 𝐹 𝐷𝐼𝑘,𝑡
𝐹𝐷𝐼𝑐 : Japanese outward FDI stock to country 𝑐,where FDI stock data are from JETRO. The regional real exchange rate is then𝐴𝑠𝑖𝑎∑𝑘∈𝐴𝑠𝑖𝑎 𝑤 𝑒𝑖𝑔ℎ𝑡𝑒𝑑_𝑅𝐸𝑅𝑘,𝑡
𝐴𝑠𝑖𝑎
𝑅𝐸𝑅𝑡
= 100 ×.
𝐴𝑠𝑖𝑎∑𝑘∈𝐴𝑠𝑖𝑎 𝑤 𝑒𝑖𝑔ℎ𝑡𝑒𝑑_𝑅𝐸𝑅𝑘,1996
We normalize the 1996 level to 100.

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42

Theory Appendix to“Does it matter where you invest? The impact of FDI on
domestic job creation and destruction”
Bin Ni1

Hayato Kato Yang Liu
January 25, 2021

Setting

We here provide the full analysis of the theoretical part of the paper. Some explanations are overlapped with those in the text. The base model is a search-and-matching model with heterogeneousjobs developed by Wasmer (1999). Firms have two types of jobs in domestic activities: skilled
and unskilled. We assume that an exogenous increase in FDI raises labor demand in a way thatmakes the separation (or destruction) of both types of domestic jobs less likely.1 The expansion offoreign activities requires the support of headquarters and home branches through administration,customization, production for exports, and so forth. How much demand for a skilled job increases
relative to the other, however, depends on the destination of FDI. This differential impact of FDI
on demand for heterogeneous jobs is the key to our theoretical mechanism.
The economy consists of a continuum of risk-neutral, infinitely lived workers of size L and acontinuum of risk-neutral, infinitely lived firms of size one. The representative firm produces output
Y from capital stock K and employment N . The production function, Y = G(K, N ), is specified asa constant-returns-to-scale for both factors.2 The employment N consists of two types of workers:
N = Nh + Nl , where Nj is the mass of type-j ∈ {h, l} workers. The firm posts Vj of vacancies for j
job type at a cost cj . The matching process between firms and workers is costly in that unemployedworkers and vacancies meet each other randomly. The existing match of type j job breaks with anexogenous Poisson separation (destruction) rate sj . The two types of jobs differ in the following
way: the firm finds it costlier to search for high-skilled workers than low-skilled workers: ch > cl but
tends to continue the match with high-skilled workers longer than that with low-skilled workers:
sh < sl . The higher hiring cost for the high-skilled worker pays off in the longer continuation ofmatch, while the lower hiring cost for the low-skilled worker comes at the expense of the shorter
continuation.
The matching process is governed by a constant-returns-to-scale matching function, m(U, V ) =U η V 1−η , where U is the mass of unemployed workers, V = Vh + Vl is the mass of vacancies andη ∈ (0, 1) the matching elasticity. m(·) is the Poisson arrival rate so that there are on average
1This assumption is in line with the fact that outward FDI by Japanese multinationals is complementary to exports
of intermediate goods from home, which is likely to raise the demand for workers in home (Nishitateno, 2013).
2
Because all firms are symmetric and the mass of firms is one, variables for individual firms are also aggregate
ones. Thus, we omit the index of firms.

m∆t matches during a short time interval ∆t. We assume that the matching is formed sequentially.
First, m(U V ) matches are formed on average per unit of time between ex ante identical unemployedworkers and total vacancies. Then, m(U, Vh ) matched workers out of m(U, V ) get employed in thehigh-skilled job, while the remaining m (U, V ) − m(U, Vh ) matched workers in the low-skilled job.
The Poisson arrival rate of matching for a vacancy j ∈ {h, l} with an unemployed worker, denotedby qj , is thus:
qh ≡
ql ≡

m(U, V ) m(U, Vh )·= (θvh )−η ,Vhm(U, V )m(U, V ) m(U, V ) − m(U, Vh )q − v h qh·==Vlm(U, V )1 − vhθ−η 1 − vh1−η
1 − vh,where θ ≡ V /U ; vj ≡ Vj /V ; and q ≡ m (U, V ) /V = m(1/θ, 1) = θ−η . Given the vacancy-unemployment
share θ, both arrival rates decrease with the share of high-skilled vacancy vh . Higher vh makes eachhigh-skilled vacancy more difficult to match with an unemployed worker (lower qh ). It also raises themass of high-skilled match (higher m(U, Vh )/V = vh qh ) and, thus, crowds out low-skilled matches(lower ql ). The Poisson arrival rate of matching for an unemployed worker with a vacancy j isdefined similarly.
Job creation is measured by the mass of vacancies that match with unemployed workers. Aggregate job creation and high/low-skilled job creations (per unit of time) are given by:
JC = q · V = θ−η V = θ1−η ,JCh = qh · Vh = θ−η vh1−η V = θ1−η vh1−η U,JCl = JC − JCh = θ−η 1 − vh1−η V = θ1−η 1 − vh1−η U.
Similarly, job destruction is measured by the mass of newly separated matches. Aggregate job
destruction and high/low-skilled job destructions (per unit of time) are given by:
JD =X

sj · Nj ,j=h,lJDj = sj · Nj ,where nj ≡ Nj /N is the employment share of job j.

2

Firm’s problem

The representative firm chooses the infinite-time schedule of capital investment I, capital stock K,
the mass of each type of vacancy Vj , and each type of employment Nj , to maximize the discounted2sum of the stream of future profits:
Z ∞max{I(t),K(t),Vh (t),Vl (t),Nh (t),Nl (t)} 0e−rt G(K(t), N (t)) −X{wj (t)Nj (t) + cj Vj (t)} − I(t) dt,

j=h,lsubject to the law of motion capital and the evolution of employment j ∈ {h, l}:K̇(t) = I(t) − δK(t),Ṅj (t) = qj (t)Vj (t) − sj Nj (t),where the dot represents the time derivative: ẋ ≡ dx/dt. The notations are defined as follows:r : interest rate,
XN=
Nj : total employment,
j=h,lwj : wage of type-j labor,
cj : vacancy cost for type-j job,qh = (θvh )−η : rate of a high-skilled vacancy matching with a unemployed worker,
ql = θ−η (1 − vh−η )/(1 − vh ) : rate of an low-skilled vacancy matching with a unemployed worker,
vh = Vh /V = Vh /(Vh + Vl ) : share of high-skilled vacancy,
sj : separation rate of type-j match.
Set the Hamiltonian as
H = G(K, N ) −X(wj Nj + cj Vj ) − I +

j=h,l

X
j=h,l

3

λj (qj Vj − sj Nj ) + µ(I − δK),

where λj and µ are associated co-state variables. The necessary conditions are∂H= −cj + λj qj = 0,∂Vj
∂H= λ̇j + GN − wj − λj sj ,rλj = λ̇j +∂Nj→ λ̇j = (r + sj )λj − (GN − wj ),∂H= −1 + µ = 0,∂I
∂Hrµ = µ̇ += µ̇ + GK − µδ,∂K→ µ̇ = (r + δ)µ − GK ,lim e−rt λj (t)Nj (t) = 0,

t→∞

(A1)

(A2)
(A3)

(A4)

lim e−rt µ(t)K(t) = 0,t→∞where GK and GN respectively represent the derivative of G(·, ·) with respect to its first and second
argument. In what follows, we will focus on steady states where λ̇h = λ̇l = µ̇ = 0 and suppress thetime index unless otherwise noted. From (A1) and (A2), we have
wj = GN − (r + sj )cj /qj .

(A5)

GK = r + δ,

(A6)

From (A3) and (A4), we have

which pins down the capital-labor ratio: k ≡ K/N . Letting g(k) ≡ G(K/N, 1), the property of
constant-returns-to-scale implies that GK = g 0 (k) and GN = g(k) − kg 0 (k). Once k is pinned downby (A6), GN is also determined.

3

Wage bargaining

Let πj be a marginal value to the firm from hiring one type-j ∈ {h, l} worker. The marginal value,denoted by Jj , must be equal to the discounted sum of the flow profit and the expected future
profits, Letting ∆t be a small time interval, the following must hold:1[(GN − wj )∆t + (1 − sj ∆t) · Jj + sj ∆t · 0],
1 + r∆t
G N − wj
→ Jj =.
r + sjJj =(A7)On the other hand, the marginal value to the firm from posting a vacancy for type-j worker is zero
because all vacancy opportunities are exhausted by many atomistic firms.

4

The value to the type-j worker Wj and the value of an unemployed worker WU are respectivelyrWj = wj + sj (WU − Wj ),X
rWU = z +θj qj (Wj − WU ),

(A8)
(A9)

j=h,l

where z is an unemployment benefit. Once matched, the firm and the worker engage in the generalized Nash bargaining, resulting in the following sharing rule:
Wj − WU : Jj − 0 = β : 1 − β,→ Wj − WU =

βJj
,
1−β

(A10)

where β ∈ (0, 1) represents the worker’s bargaining power and as noted earlier the value of themarginal vacancy is zero.
From (A5) and (A7) to (A10), we have(r + sj )(Wj − WU ) = wj − rWU ,→ (r + sj ) ·∵ (A8)

β G N − wj
= wj − rWU ,1 − β r + sj

∵ (A7)&(A10)→ β(GN − wj ) = (1 − β)(wj − rWU ),X→ wj = βGN + (1 − β) · z +
θj qj (Wj − WU )

∵ (A9)

j=h,l





β G N − wj 
1 − β r + sj
j=h,l


X
(r + sj )cj /qj→ wj = βGN + (1 − β)z + βθ j qj ·,r + sjj=h,lX→ wj = βGN + (1 − β)z + β
θj cj = w.
→ wj = βGN + (1 − β) · z +X

θ j qj ·∵ (A7)&(A10)∵ (A5)
(A11)

j=h,l

The wage turns out to be common between the two types of workers. This result partly comes fromthe fact that both types of job have the same productivity.

5

4

Coexistence of the two types of jobs

4.1

Steady-state equilibrium

From (A5), we have
wh = GN − ch (r + sh )(θvh )η ,(A12)

wl = GN − cl (r + sl )θη (1 − vh )/(1 − vh1−η ).

(A13)

We see that the wage of each type of job, wj , decreases with the share of vacancy for the job, vj ,implying that these two equations can be thought of as the labor demand curves for each type ofjob.3 The wage-setting curves are given by (A11):
wh = βGN + (1 − β)z + βθX

vj cj ,

(A14)

vj cj .

(A15)

j=h,l

wl = βGN + (1 − β)z + βθ

X
j=h,l

Equations through (A12) to (A15) determine the steady-state values of (wh , wl , θ, vh ).
We use (A12) and (A14) to obtainch (r + sh )(θvh )η − cl (r + sl )θη (1 − vh )/(1 − vh1−η ) = 0,
→ ch (r + sh )vhη (1 − vh1−η ) − cl (r + sl )(1 − vh ) = 0,
→ ch (r + sh )vhη + [cl (r + sl ) − ch (r + sh )]vh − cl (r + sl ) = 0,c − 1)vh − ec = 0,→ vhη + (e(A16)

where ec ≡ cl (r + sl )/ch (r + sh ),which determines vh . We impose restrictions on parameters to ensure that both types of jobs coexist
at the steady states. Let us define a function such that H(evh ) = vehη + (ec − 1)evh − ec. From the facts
that H(evh = 0) = −ec < 0 and H(evh = 1) = 0, the condition for H(evh ) = 0 to have a solution forve ∈ (0, 1) is that an infection point of H(evh ) must be in [0, 1]. The unique infection point is derivedas
0 = H 0 (evh ) = ηevhη−1 + ec − 1,
 11−ηη→ v# =.
1−ecv # is in (0, 1) if η + e
c < 1, in which case G(ṽh ) = 0 has a unique solution such that vh ∈ (0, v # ).
It can be checked that ∂wh /∂vh = −ηch (r + sh )θη vhη−1 < 0; ∂wl /∂vl = −∂wl /∂vh = −cl (r + sl )θη vhη (1 − η −ec)(1 − vh )/(vh − vhη )2 , noting that vhη + (ec − 1)vh − ec = 0 and 1 − η − ec > 0, as we will see shortly.
3

6

The condition will be assumed in the following analysis.

4.2

Stability

We will check that (a) vh is a stable steady state and that (b) the corner solutions, vh ∈ {0, 1}, areunstable. From (A1) and (A2), we have the laws of motion for qh and ql :q̇h /qh = (GN − wh )(θvh )−η /ch − (r + sh ),q̇l /ql = (GN − wl )θ−η (1 − vh1−η )/[cl (1 − vh )] − (r + sl ),Noting that wages are exogenous to individual firms, q̇j /qj decreases with vj . This ensures thestability of the steady state at which q̇j /qj = 0 (or equivalently (A16)).
Next, we consider two endpoints. At vh → 0, we have
lim q̇h /qh = ∞ > (GN − wl )θ−η − (r + sl ) = q̇l /ql ,vh →0in which case the firm post more vacancies for high-skilled job so that vh ' 0 is unstable. Similarly,
we can check that vh ' 1 is unstable. We thus conclude that vh satisfying (A16) is the unique stablesteady state.

5Effect of FDI

We model FDI as a decrease in the separation rate: ∂sj /∂F DI < 0. We consider the effect of FDI
in the short run where only jump variables, i.e., vacancies Vj , wages wj , respond to changes in FDI.
Given Nj , FDI decreases job destruction of both types of jobs:
∂sj
∂JDj
=
Nj < 0,∂F DI∂F DI
which immediately implies that FDI also decreases aggregate job destruction:
X ∂sj
∂JD
=
Nj < 0.
∂F DI
∂F DI
j=h,l

To see the effect of FDI on job creation, we need to know how FDI affects the share of highskilled vacancy, vh , and the vacancy-unemployment ratio, θ. Applying the implicit function theorem7to (A16), we obtainvh0 =ec0 (1 − vh ),
ηvhη−1 + e
c−1
r + sh s0h s0l cl (r + sl )1 − vh=− 0
r + slsl ch (r + sh )2 ηec/vh − (1 − η)(1 − e
c)
{z
}
|
{z
}|

(A17)

<0

R0


< 0 if s0 /s0 < S b ≡ (r + sh )/(r + sl )h l=,≥ 0 if s0 /s0 ≥ S b
h l
where

vh0 ≡

∂vh
,
∂F DI

s0j ≡

s0 cl (r + sl )
∂e
c
e
c ≡
= l
∂F DI
ch (r + sh )2

∂sj
< 0,
∂F DI

0



r + sh s0h− 0
r + slsl

.

The sign of vh0 is determined by whether s0h /s0l is greater than S b ≡ (r + sh )/(r + sl ).
From (A12) and (A14), we havech (r + sh )(θvh )η + βθ[ch vh + cl (1 − vh )] − (1 − β)(GN − z) = 0.
Differentiating both sides with respect to F DI gives
ch s0h (θvh )η + ηch (r + sh )(θvh )η−1 (θvh0 + vh θ0 ) + βθ0 [ch vh + cl (1 − vh )] + βθvh0 (ch − cl ) = 0,
where θ0 ≡

∂θ
.
∂F DI

This is simplified to[ηvh0 /vh + s0h /(r + sh )][(1 − β)(GN − z) − βθg] + βθvh0 (ch − cl ),(1 − β)(GN − z)η/θ + βf (1 − η)
Θ1 s0h − Θ2 s0l=,ch (r + sh )2 [(1 − β)(GN − z)η/θ + βf (1 − η)][ηec − vh (1 − η)(1 − ec)]> 0 if s0 /s0 < S d ≡ Θ2 /Θ1h l=,≤ 0 if s0 /s0 ≥ S d

θ0 = −hwhere f ≡

X

(A18)

l

vj cj ,x ≡ (1 − β)(GN − z) − βθf > 0,j=h,l

Θ1 ≡ vh ch (r + sh )[βθ(1 − vh )ec(ch − cl ) + x(1 − η − ec)] > 0,
Θ2 ≡ cl (r + sh )(1 − vh )[ηx + βθvh (ch − cl )] > 0,
and where we used the following relation:ch (r + sh )(θvh )η = (1 − β)(GN − z) − βθf > 0.

8

∵ (A12)&(A14)

Job creation, defined as JC = θq(θ)U = θ1−η U , is an increasing function of θ given U and thus
decreases (or increases) with F DI if s0h /s0l < S d (or s0h /s0l > S d ).
With these in hand, we can see the effect of FDI on job creation of the two types of jobs. It canbe checked that JCh = (θvh )1−η U always increases with F DI:∂[(1 − η)(θvh )−η (vh θ0 + θvh0 )U ]∂JCh=,∂F DI 
∂F DI

∂JCh
= sign{θ0 /θ + vh0 /vh },→ sign
∂F DI




c − vh (1 − η)(1 − ec)}]= sign (Γ2 s0l − Γ1 s0h )/ [ch (r + sh )2 (ηx + βθf ){ηe
|
{z
}

>0


= sign Γ2 s0l − Γ1 s0h


> 0
≤ 0

if s0h /s0l > S a ≡ Γ2 /Γ1,

if s0h /s0l ≤ S a

where Γ1 ≡ ch (r + sh )[x{ηec − vh (1 − η)(1 − ec)} + βθcl ec(1 − vh )] > 0,
Γ2 ≡ βθc2l (r + sh )(1 − vh ),Sb − Sa =r + sh Γ2xch (r + sh )2 [ηec − vh (1 − η)(1 − e
c)]
−
=
> 0.
r + slΓ1Γ1 (r + sl )JCh increases (or decreases) with F DI if s0h /s0l > S a (or s0h /s0l < S a ).
On the other hand, JCl = JC − JCh = θ1−η (1 − vh1−η )U may increase or decrease with F DI
depending on s0h /s0l :∂[(1 − η)θ−η {(1 − vh1−η )θ0 − θvh−η vh0 }U ]∂JCl=,
∂F DI 
∂F DI

∂JCl
→ sign= sign{ec(1 − vh )θ0 − θvh0 }
∂F DI







0
0
−1
2
3
= sign (Ω1 sh − Ω2 sl )/ {cl (1 − vh )} · vh ch (r + sh ) (ηx + βθf ){ηec − vh (1 − η)(1 − e
c)}

|
{z
}


>0


> 0 if s0 /s0 < S c ≡ Ω2 /Ω1h l= sign Ω1 s01 − Ω2 s02,
≤ 0 if s0 /s0 ≥ S c
h l



r + sl−ec> 0,where Ω1 ≡ vh ch (r + sh )(r + sl ) (1 − ec){x + βθvh (ch − cl )} + βθcl 1 +r + shr + sl22Ω2 ≡ ch (r + sh ) βθ(ch − cl )(1 − e−ecvh + ηxec > 0,
c)vh + ηx(1 − ec) + βθcl 1 +r + shxvh c2h (r + sh )3 (ηx + βθf )[ηec − vh (1 − η)(1 − e
c)]
Θ2 Ω2
Sd − Sc =
−
=
> 0.
Θ1 Ω1
Θ1 Ω1
We summarize the results in the following proposition.

9

Proposition.

Assume that (i) an exogenous increase in FDI decreases the job-separation rate

sj ; (ii) the relative cost of high-skilled vacancy is sufficiently high, i.e., η + e
c < 1 or ch /cl >(r + sl )/[(1 − η)(r + sh )]; and (iii) the marginal product of labor is sufficiently high, i.e., x > 0 orP
GN > z + β j vj cj /(1 − β). Depending on the magnitude of the effect of FDI on sh relative to thaton sl , i.e., s0h /sl , we obtain the following comparative-statics results.

s0h
∂sh
∂sl
≡
s0l
∂F DI ∂F DI[0, S a )

∂JC
∂F DI
+

∂JCh
∂F DI
−

∂JCl
∂F DI
+

∂vh
∂F DI
−

[S a , S b )

+

+

+

−

(ii)

[S b , S c )

+

+

+

+

(iii)

[S c , S d )

+

+

−

+

(iv)

[S d , ∞)

−

+

−

+

(v)

Case
(i)

We note that the effects of FDI on aggregate job destruction, high-skilled job destruction, and lowskilled job destruction are always negative.

We suppose that FDI to Asia decreases sl much significantly than sh , corresponding to case (i).
Then it leads to an expansion of total job creation and low-skilled job creation, and an contractionof high-skilled job creation. On the other hand, we suppose that FDI to Europe or North America
has an exactly opposite effect of job separations rates, corresponding to case (v). It results in an
opposite effect on total job creation, and high/low-skilled job creations.

References
Nishitateno, S. (2013). Global production sharing and the FDI–trade nexus: New evidence from theJapanese automobile industry. Journal of the Japanese and International Economies, 27:64–80.
Wasmer, E. (1999). Competition for jobs in a growing economy and the emergence of dualism.
Economic Journal, 109(457):349–371.

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