Multinational Firm’s Location Decision: Distancevs. Connectivity
Trang Tran∗
Prepared for The 78th JSIE Annual Meeting on September 29,
2019 @ IDE-JETRO

Abstract
Using Japanese foreign and domestic affiliates’ data, we examine how theinter-relationship within firms and groups of firms affects the firm’s decisionon the location of a new affiliate. We find that in general, the presence ofthe firm or group’s prior affiliate increases the possibility of the firm having
a new affiliate in the region. However, the type of affiliate matters in thesense that firms cluster their affiliates by sectors. In addition, changing thesize of regions does not affect the results quantitatively but shows different
patterns among affiliates’ sectors.

JEL Classification: F23,L22,R3
Keywords: Firm Location, Multinational Firms, Vertical links

1 Introduction
The determinants of Foreign Direct Investment (FDI) locations have been of greatinterest not only to researchers but also to politicians from both developed anddeveloping countries. With the increasing availability of detailed data, researchers
have been able to look into the complexity of multinational firms’ international
location strategies. Most firms are now doing both horizontal and vertical FDI,
export-platform FDI or hybrid FDI (Antras and Yeaple, 2015). The decision on
where to place foreign affiliates as well as what types of affiliates to open has beenstudied extensively in both literature of FDI locations and New Economic Geography in terms of market access, supplier access, trade costs and factor costs ofentering the market. (See Head and Mayer (2004), Duranton and Puga (2005),∗

Graduate School of Economics, Hitotsubashi University

1

Blonigen et al. (2007), Defever (2012) for recent literature.) In particular, spatial
agglomeration has been found not only across-firm within industries (Head et al.
(1995), Strauss-Kahn and Vives (2009) for example) but also within-firm (Mayer
et al. (2010) and Defever (2012) for example). There are also many studies on
global city networks which emphasize the connectivity among cities through firms’
flows of services (Taylor, 2001). With this connectivity among cities, firms canovercome spatial distances to place their headquarters in different regions (Belderbos et al., 2015).
In this paper, we examine these effects on the affiliates’ location choice of manufacturing firms by building indices that reflect the agglomeration effects within-firm
and within-financial groups of firms1 , and the importance of a region in Japanesefinancial, services, transportation and wholesale networks. Our contribution is tocombine these two types of ideas in a framework that allows us to compare thesizes of these effects.
Our paper is different from Mayer et al. (2010) and Defever (2012) in the scopeof study and the construction of relevant indices. The former examines Frenchfirms investing abroad where within-firm connection is defined as belonging to thesame financial group. They found this contributes to a large portion of French firms
locating affiliates domestically rather than investing abroad. Our paper focusedsolely in location decision of foreign affiliates. The latter investigates the case offoreign firms investing in the European Union (the EU) and focuses on the distance
between affiliates of different functions. Our paper, while including interactions
among affiliates of different functions, separates the industry-level, financial-grouplevel and firm-level agglomeration effects.
Differing from Belderbos et al. (2017) regarding city connection index, we examine not only headquarters but also other functions of affiliates. In addition,
they use a composite measure of connectivity based on producer services, airport
passenger flows, and the intensity of international co-invention. We use activities
of Japanese firms in different sectors. We should point out that our paper resembles Goerzen et al. (2013) in using Japanese data but they only focus on a few
global cities and do not take into account different sectors. Recent papers suchas Alfaro and Chen (2014) also raise the issue that spatial boundaries may affectthe agglomeration levels in the sense that these boundaries are not continuous likedistance. Earlier, Blonigen et al. (2007) also suggests aggregation of country level
FDI may affect their results on the US’s FDI to European countries. Therefore,
we test the robustness our results by using different administrative levels.
Within-firm agglomeration can be explained in the context of the global value
chain. With the reduction of trade costs and communication costs, firms can fragment their production into different tasks or functions while at the same time1

By group of firms, we mean firms that are linked together through cross ownership.

2

cities evolve to accommodate certain functions (for example, Grossman and RossiHansberg (2008), Duranton and Puga (2005)). However, face-to-face meetings arestill very crucial for production design, marketing, planing and so on (StraussKahn and Vives, 2009).The level of communication depends on the type of affiliates, where headquarters and services will need more face-to-face meetings than
production so that they are more likely to be located near each other. While daily
face-to-face meetings among the firm’s affiliates make production run smoothly,communication among firms within a financial groups could lower transaction costs
for the firm in entering a new region. A representative of a foreign exploration andproduction company in Jakarta stressed “to keep in touch very closely with theauthorities here. You need to be physically there. [. . . ] You have to meet people
daily from all departments” (Breul, 2019). On the other hand, cities of high connectivity may mitigate the necessity for face-to-face meetings by their advanced
producer services (Goerzen et al. (2013), Belderbos et al. (2017)).2
Our results show that, in general, within-industry agglomeration in a region
significantly increases the possibility of a new affiliate in that region. Group andfirm level agglomeration are only significant in certain cases. The first results arequite intuitive and coincide with Defever (2012) and Mayer et al. (2010). The
results on group and firm somewhat contradict those of Defever (2012) where thepresence of existing affiliates, except for sales, increases the probability of a new
affiliate from the same or different sectors. It also contradicts that of Mayer et al.
(2010) since their group level agglomeration index always increases the possibilityof a new investment. We further test our results by examining different levelsof spatial boundaries, and some qualitative conclusions still hold. With regards
to city connectivity, we find very strong effects for cities connected by the nonmanufacturing members of the firms’ financial groups and wholesale companies.
However, cities connected by finance, services and transportation companies do
not show significant effects in most cases.
The remainder of this paper includes data and descriptive statistics, empiricalmodels, estimation method, results and conclusion.

2 Data and descriptive statistics
We use the Overseas Japanese Companies Data and Domestic Affiliates Databasefrom the Toyo Keizai Database to collect information about Japanese affiliates
abroad and groups of Japanese firms. We consider each affiliate that has at least
2

By connectivity, we mean the interlocking model as in Taylor (2001), where firms connect
cities through their activities. Two cities are connected if they have subsidiaries of the same
firm, counted as one link. A city with more active firms are more likely to have more links, hence
more likely to be well connected.

3

more than 10 percent ownership from Japanese firm(s) as an investment in a foreigncountry. We use the year that the affiliate was established as the entry year. (Weuse the word “having new affiliate”, “subsidiary” and “investing” interchangeably.)
To gather information about the spatial locations of affiliates, we use an algorithm to retrieve postal codes from affiliates’ addresses to calculate their coordinates. In the case where postal codes are not available, we search for cities inaddresses.3 We start with 4, 890 firms and 26, 748 affiliates. We end up with 527
firms and 1, 233 foreign affiliates after excluding those with less than 10 percent
ownership by Japanese firms, those outside the EU and those whose parent companies are not manufacturing firms. These firms are linked with the Domestic
Affiliates Database to create information about groups of firms. We are able toidentify 49 groups that cover 147 firms (see table 9). We also use the Japan Company Handbook to get information about each firm’s capital, year of establishment
and employees. We only keep observations of which investments were made after
1990 but keep all possible investments before 1990 to calculate the initial state ofeach industry, group and firm agglomeration.
To construct city connectivity, we use Japanese firms in four sectors: Financeand Insurance, Service Activities, Transportation and Communications, Wholesale and Retail Trade. To create the connectivity index, we only focus on firmsthat have more than two or three affiliates in the EU (see table 10)4 . Specifically, we use 45 finance and insurance firms, 101 services firms, 53 transportationand communication firms, and 119 wholesale and retail firms. We convert sector
classifications by the Toyo Keizai Database to SNA sectors (see table 11).
For regional characteristics and region maps, we use data from EuroStat. We
use this map to extract information of what regions firms belong to with calculated
coordinates. Other data includes population density, land, GDP, value added per
employee and human capital as people with tertiary education and/or employedin science and technology as a percentage of total population.
Figure 1 shows locations of affiliates from firms that have more than one affiliate
in the EU before and after 1990. Manufacturing and Headquarters seem to beconcentrated in certain regions such as the UK, Belgium, Netherlands and somewestern countries while wholesale affiliates are more scattered over many regions.
As mentioned earlier, the choice of a location may differ based on region size.
Because we have detailed coordinates of affiliates, we can assign different administrative levels including country, group of states, states and group of districts.
EuroStat provides statistics for different levels of Nomenclature of Units for Ter3
We use the geonames.org database to search for coordinates from postal codes as well as
cities. We also check results manually to make sure they are correct.
4
The criteria to choose these firms is the top 25 percentile of the distribution of the number
of affiliates of each firm within each sectors. This means we only focus on firms that can createconnection among cities.

4

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(a) 1980(b) 1990

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(c) 2000(d) 2010

Figure 1: Accumulated Japanese Affiliates in EU in different years
Manufacture(Blue), Wholesale(Red), Headquarters(Green), Services(Purple)5

ritorial Statistics (NUTS), we estimate for four levels from NUTS0 (country) to
NUTS3 (group of districts) level. In another estimation, we focus on NUTS2 to examine affiliates’ distances from region centers to compare our results with Defever(2012).

3 Empirical models
Following Defever (2012) and Mayer et al. (2010), we assume that firm f choose
to open affiliates of sector s in region r at time t if πfs rt > πfs r0 t for r, r0 ∈ R
where πfs rt is the expected profit function of the firm. This profit function depends
on region r characteristics Xrt and four components that reflect within-industry,
within-group, within-firm agglomeration effects and connection index.
πfs rt = Xrt β s + δ1s Log IndN etsi(f )r(t−1) + δ2s Dummy IndN etsi(f )r(t−1) +γ1s Log GroupN etsf r(t−1) + γ2s Dummy GroupN etsf r(t−1) +η s F irmN etsir(t−1) +X

τS SectorConnectsSr(t−1) + +S

ζ s GroupConnectf r(t−1) + sirt(1)

The within-industry indices have two variables: a dummy showing an existing
affiliate from the same industry with firm f in region r at time t and a log ofaccumulated affiliates count plus 1. Separating the dummy and log count allows
us to see the effect of the first affiliate and the effect of any additional affiliates ofthe same industry. We should remind readers that we exclude those affiliates thatbelong to the same group here. In the baseline specification, we count affiliatesof all sectors. To consider the effect of prior affiliates of different sectors on thelocation choice of a specific sector, we calculate the dummy and log count variables
for each separate affiliate sector (we call this the cross-sector effect).
The group level indices also have a dummy showing an existing affiliate belonging to the same financial group as firm i in region r at time t and an index
that is calculated as following:
Log GroupN etsf r(t−1) =Xssharea(g(k))) Dg(k)r(t−1)(2)g(k),k6=f

Dfs r(t−1) is a dummy that equals 1 if firm i has an affiliate of sector s in region
r at time t − 1 and sharea(g(k)) = capgk ∗ capka is the product of ownership ratio of6

group g in firm k with the ownership ratio of firm k in affiliate a. By weighting theownership ratio instead of just counting the number of affiliates of group g, we take
into account the inter-relationship in terms of “ownership control” among different
affiliates a that belongs to group g. Similar to within-industry indices, we sum theabove index Log GroupN etsf r(t−1) over all sectors in the baseline specification andseparate each sector to examine the cross-sector effects in a different specification.
We exclude affiliates that belong to firm f in both cases.
The firm level index is a dummy indicating the firm’s prior investment in regionr. To take into account cross-sector effects, we have a list of dummies for different
sector s that is equal 1 if there is at least one prior investment in sector s of firm
f in region r at time t.
As we have mentioned earlier these three components are proxies for industry,group and firm level agglomeration effects. We expect these coefficients to havepositive signs.
The connectivity index consists of two components. The first one is calculated
as the log count of affiliates of firms that have at least two affiliates in more than
two regions5 . This first components are further divided into four categories in accordance with four sectors of the firms: Finance and Insurance, Service Activities,
Transportation and Communications, Wholesale and Retail Trade. Higher valueof the index indicates more services from Japanese firms being available in theregion. The second component is the log of ownership-weighted-count of affiliatesfrom non-manufacturing members from the same financial group of the firm6 . We
do not include these firms in the first components to avoid correlation between the
two. Similarly, higher value of the index indicates more stronger presence of thefirm’s group in the region. We expect these indices have positive signs.
Xrt includes land (in square kilometers) which is a proxy for resources, population density (inhabitants per square kilometers) which reflects market size, humancapital (people with tertiary education and/or employed in science and technology as a percentage of total population) and average value added for employees
in industries (one thousand Euros per person). Since we only have firms from one
country, we do not include other gravity-like controls such as common language,
distance, etc.
5“Connectivity” is used loosely in this case. We assume that cities with many affiliates of wellconnected firms are more likely to be more connected to each other. If we consider the firms andcities in a two-mode network, what we calculate here is the log of in-degree of cities, which is
one simple measure of centrality in network analysis (for example, Borgatti and Everett (1997)).
In the context of global city as in Taylor (2001), if we consider the value of firm to be 1, thenwhat we calculate here is the log of city nodal connection.
6
This is different from the Group Level index because these are non-manufacturing firms while
the Group Level index just count affiliates of manufacturing firms.

7

4 Estimation Method
We assume that profits coming from different sectors do not correlate with eachother so they can be estimated independently. We further assume that profits are
independent among periods. However, there may be unobserved characteristics offirms that make error terms correlated across regions. One way to address this issue
is to assume that coefficients β are random variables that follow some distribution
with mean µ and standard deviation σ. Here, we assume these coefficients follow
normal distributions. The model can be estimated using simulated maximum
likelihood (Train, 2002). For comparison, we also report results for conditional
logit model, however, unobserved firms characteristics, if any, are not taken into
account.7
To be more specific, we are interested in estimating parameters (β, θ(γ, δ, η, τ , ζ))given the choice yf rt of firm f to region r in time t conditional on region characteristics Xrt and industry, group, firm level agglomeration indices and connectivity
indices Af rt .8 Assuming error terms follow type I extreme distribution, the choiceprobability for yrf t = 1 can be calculated as in equation 3.
Z
exp(Xrt β + Af rt θ)P
f (β|µ, σ)dβ
(3)
Pf rt (yf rt = 1) =f qt exp(Xqt β + Af qt θ)Here, we assume normal density function f (β|µ, σ). Since we assume independent choice between periods, we can write the likelihood function taking into
account all periods for each firm f as in equation 4.
Sf =Tf R Z Y
Y
t=1 r=1

exp(Xrt β + Af rt θ)P
f qt exp(Xqt β + Af qt θ)yf rtf (β|µ, σ)dβ(4)Since it is not feasible to integrate out β in most cases, we can use the simulation approach described in (Train, 2002) to calculate the simulated log-likelihoodand estimate mean µ and stand deviation σ of random parameters β and fixedparameter θ. If the estimated variance σ 2 is tested to be zero, mixed logit will
collapse to simple logit.
Because variances of error terms may be different for estimations with different
NUTS levels, we should not compare the absolute value of coefficients. We can7

Another way to model preference heterogeneity is to allow scale of the idiosyncratic error
term to be different among consumers as in Fiebig et al. (2010). These authors develop theso-called generalized multinomial logit model where the random coefficient can be written asβi = σi β + [γ + σi (1 − γ)]ηi . σi is the individual-specific scale of the idiosyncratic error. We run
the baseline regression with this method and find no differences in results so we do not reporthere.
8Sector subscripts are omitted for brevity.

8

use the ratio among coefficients instead. For example, the ratio δ2 /γ2 for two
dummies of Industry and Firm Level Agglomeration shows how big/small theeffects of having prior industry presence on probability of new affiliate compared
to prior firm’s existing affiliates.

5 Empirical Results
5.1 Basic Specifications
In the basic specification, we estimate four equations for four sectors of affiliates
and all agglomeration indices are the sum of all sectors of prior investments. The
result is in tables 1, 2 and 3 where each sector has four columns. The first twocolumns report results without connectivity indices while the last two add these.
We also report results from conditional logit and the mixed logit for each specification.
Table 1 shows results for Manufacturing affiliates. The region-specific variables
including density, land and value added per employee do not show significant results while Human Capital is significantly positive in all specifications. This suggests that the type of manufacturing is not very skill-intensive. The connectivity
indices for four sectors do not show any significance but connectivity among nonmanufacturing members of the firm’s group is significantly positive. The manufacturing member dummy (Group level dummy) is also significantly positive. Thisshows that manufacturing subsidiaries tend to be located in regions with strong
presence of financial groups, but not necessarily Japanese firms in general. The
significantly positive sign of Firm level dummy shows that an existing affiliate of
the firm increases the probability of a new investment. But the log count of industry level affiliates with significantly negative sign indicates that costs causedby congestion may have dominated the benefits from agglomeration.
Table 2 shows results for Wholesale affiliates. Density as proxy for market size,
Human Capital and Land are all significantly positive as expected. Similarly, Industry level dummy and Firm dummy and Group Connectivity are all significantly
positive. However, Group level dummy is no longer significant and Connectivity
in Wholesale sector is significantly negative. We might expect firms to spread out
their wholesale networks to reach more customers given the fixed costs to open
new wholesale affiliate is often not as high as in manufacturing. In many cases,a small representative office would do the job. This finding is similar to Defever(2012).
Table 3 shows results for Headquarters and Services. In the case of Services,since the mixed logit model does not converge, we can only report the conditional logit. Human Capital is significantly positive for Headquarters, which often
9requires high-skill workers. Similar to Manufacturing, while having one affiliate
from the same industry or group increases the probability of having a new affiliate
in the region, more than one industry level affiliate will reduce the probability.
Only the Connectivity of the wholesale sector is positive at 5 percent while othersectors such as services, finance or transportation do not show significant results.
In the last two columns for Services, only Industry level dummy shows significant results. We have expected a stronger results for these two sectors because
Headquarters and Services are more likely to benefit from the connectivity of theregion.
In all specifications, the coefficients for agglomeration effects are higher thatthose of connectivity indices. For example, in table 1, the ratio between Group
Connectivity and Indstry level log count is about 0.6. Since the latter is negative,
it suggests the effect of having less than one affiliate of the same industry is equal
to increasing the Connectivity Index of the region by 0.6 percent.
In the mixed logit models, most standard values for random coefficients are notsignificant. It may be the case that mixed logit has not captured the unobserved
heterogeneity.99We have tried different approaches as in Fiebig et al. (2010) but the results are almost thesame, including the insignificance of standard deviation of random parameters. However, theτ in the scale parameter σi = exp σ̄ + τ ∨i is significant. This means there are heterogeneous
preferences by mean of different error variances of each individual.

10Table 1: Location choice at NUTS Level 2

main
Density
Human Cap.
Land
VA/emp
IndNet(log)GroupNet(log)
IndNet(dum.)GroupNet(dum.)
FirmNetManuf.C1

Manuf.M1

Manuf.C2

Manuf.M2-0.044(0.102)-0.773∗∗(0.271)
0.047(0.101)-0.122(0.124)-1.093∗∗∗(0.253)-0.418(1.690)5.360∗∗∗(0.310)2.829∗∗(0.967)7.660∗∗∗(1.100)-0.050(0.102)-0.700∗(0.293)0.044(0.101)-0.127(0.124)-1.121∗∗∗(0.258)-0.547(1.686)5.392∗∗∗(0.316)2.841∗∗(0.974)7.487∗∗∗(1.114)-0.069(0.107)-0.798∗∗(0.276)0.033(0.104)-0.097(0.126)-0.970∗∗∗(0.259)0.743(1.620)5.185∗∗∗(0.317)2.173∗(0.944)7.571∗∗∗(1.110)0.066(0.146)-0.066(0.141)-0.148(0.107)-0.053(0.045)0.531∗∗(0.167)-0.077(0.108)-0.695∗(0.301)
0.029(0.105)-0.104(0.126)-0.978∗∗∗(0.262)1.020(1.687)5.204∗∗∗(0.322)2.043∗(0.966)7.422∗∗∗(1.125)0.057(0.147)-0.077(0.145)-0.148(0.107)-0.060(0.045)0.556∗∗∗(0.166)

48427

0.001
(0.171)0.618(0.562)0.001(0.178)-0.006(0.234)45441

SecCon.Fin
SecCon.Ser
SecCon.Tra
SecCon.Who
GroupConnectSD
Density
Human Cap.
Land
VA/empObservations

48427

0.001
(0.171)0.501(0.655)0.001(0.179)-0.007(0.238)45441
11Standard errors in parentheses
C: Conditional Logit, M: Mixed Logit∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Table 2: Location choice at NUTS Level 2

main
Density
Human Cap.
Land
VA/emp
IndNet(log)GroupNet(log)
IndNet(dum.)GroupNet(dum.)
FirmNetWhole.C1

Whole.M1

Whole.C2

Whole.M2

0.612∗∗∗(0.113)0.761∗(0.316)
0.338∗∗∗(0.102)0.081(0.180)0.072(0.176)
4.140(3.778)4.678∗∗∗(0.256)
3.487(2.280)7.448∗∗∗(1.215)0.577∗∗∗(0.115)0.705∗(0.324)0.326∗∗(0.103)0.573∗(0.271)
0.007(0.186)
4.460(4.351)4.764∗∗∗(0.274)3.363(2.617)7.298∗∗∗(1.229)0.672∗∗∗(0.123)0.925∗∗(0.325)0.373∗∗∗(0.107)0.041(0.180)0.234(0.181)
5.739(4.260)4.540∗∗∗(0.262)2.369(2.481)7.560∗∗∗(1.228)0.040(0.146)-0.085(0.103)-0.129(0.081)-0.092∗∗(0.031)0.448∗(0.222)0.653∗∗∗(0.126)
0.868∗(0.346)0.370∗∗∗(0.109)0.529(0.271)
0.168(0.192)
5.782(4.777)4.648∗∗∗(0.280)2.385(2.802)7.501∗∗∗(1.251)
0.043(0.150)-0.078(0.104)-0.169∗(0.083)-0.092∗∗(0.032)
0.462∗(0.229)

55019

0.056
(0.183)0.372(1.651)-0.033(0.218)0.955∗∗∗(0.205)49800

SecCon.Fin
SecCon.Ser
SecCon.Tra
SecCon.Who
GroupConnectSD
Density
Human Cap.
Land
VA/empObservations

55019

0.045
(0.178)0.143(1.798)-0.046(0.252)0.958∗∗∗(0.206)49800
12Standard errors in parentheses
C: Conditional Logit, M: Mixed Logit∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Table 3: Location choice at NUTS Level 2
Head.C1
main
Density0.217(0.217)
Human Cap.
2.187∗∗(0.714)Land-0.177(0.204)
VA/emp0.463(0.395)
IndNet(log)-0.815∗(0.389)GroupNet(log)-3.359(3.773)
IndNet(dum.)
5.166∗∗∗(0.532)
GroupNet(dum.)
3.162(2.405)
FirmNet21.815(1230.236)
SecCon.Fin

Head.M1

Head.C2

Head.M2

Serv.C1

Serv.M1

0.222(0.217)2.263∗∗(0.721)-0.168(0.204)0.446(0.395)-0.803∗(0.391)-3.535(3.778)5.180∗∗∗(0.537)3.210(2.411)31.865(183608.933)-0.105(0.233)
1.498∗(0.742)-0.412(0.216)0.621(0.409)-1.352∗∗(0.467)-3.917(3.439)5.436∗∗∗(0.607)3.551(2.312)
20.232(622.388)-0.694(0.494)-0.011(0.147)
0.039(0.127)0.102∗(0.042)0.512(0.309)-0.106(0.234)
1.583∗(0.746)-0.409(0.218)0.591(0.409)-1.342∗∗(0.470)-4.117(3.465)5.451∗∗∗(0.612)3.644(2.319)23.351(2904.922)-0.715(0.497)-0.009(0.147)0.030(0.129)0.103∗(0.042)0.520(0.309)0.453(0.350)0.598(1.110)0.502(0.322)0.503(0.667)-0.379(0.513)26.816(14.604)
5.876∗∗∗(0.718)-7.094(5.567)12.686(1070.574)

0.238(0.387)0.361(1.137)0.448(0.353)0.654(0.680)-0.988(0.615)30.876(18.465)
6.571∗∗∗(0.814)-8.020(6.803)14.960(2250.846)-0.428(0.883)0.476(0.295)-0.231(0.336)0.138(0.088)-0.316(0.817)12080-0.004(0.316)0.014(1.556)-0.001(0.237)0.003(0.519)
11929

6795

6795

SecCon.Ser
SecCon.Tra
SecCon.Who
GroupConnectSD
Density
Human Cap.
Land
VA/empObservations

12080

Standard errors in parentheses
C: Conditional Logit, M: Mixed Logit∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001-0.003(0.271)0.016(1.774)-0.001(0.193)0.004(0.511)11929
13

5.2 Results at different levels of NUTS
From this section on, we focus on Manufacturing and Wholesale since Headquarters
and Services do not always converge. We estimate the same specifications as inprevious section but at different levels of NUTS. In the smaller level NUTS3,
we do not have data for Human Capital and Value Added per Employee so wejust use Density and Land. Results are reported in tables 4 and 5. Each table
has seven columns. The first four show results without Connectivity Indices, andthe last three add them10 . The four columns correspond to four administrative
levels11 . In a nutshell, there have been changes in signs of some regional specificcharacteristics. For our indices, even though magnitudes change, the signs are thesame for industry and firm level indices.
Table 4 shows the results for Manufacturing. Human Capital becomes insignificant at country and group of state level. Land becomes significantly positive atgroup of district level. In terms of significance, agglomeration indices at industry
and firm level are almost the same for all specifications except for GroupN et(log).
It becomes significantly negative at group of district level while IndN et(log) becomes insignificant at country level. In terms of magnitude, we can look at theratio between the industry dummy and firm dummy, which are 1.2, 1.3, 1.4 and
1.3 for country, group of states, states, and group of district levels. Regarding connectivity index, GroupConnect becomes insignificant at country and state levels.
Table 5 shows the results for Wholesale. Density, Land and Value Added per
employee remain significantly positive. However, as in the case of Manufacturing,
Human Capital becomes insignificant at country and group of state level. In
terms of significance, industry and firm level indices remain significant. The ratio
between firm level and industry level dummy is less than one for country level whileothers are 1.7, 1.5 and 1.7 consecutively. Group level dummy is only significant atcountry level but become insignificant for smaller levels. This highlights the resultsof Mayer et al. (2010) who also studies this effect at country level. Similar to
Manufacturing, GroupConnect becomes insignificant at country and state levels.
There may be a few reasons why we observe this feature at Wholesale but not
Manufacturing. The latter may need more fixed costs to set up and often clusterwith industry while wholesale is less costly to set up and can cover a wide area toreach customers and/or collect market information.

10
11

There is no Connectivity for Group of District level because there are collinearity.
The four levels are 0 (country), 1 (Group of States), 2 (States), 3 (Group of Districts).

14

Table 4: Mixed Logit at Different NUTS levels for Manufacturing

Mean
IndNet(log)GroupNet(log)
IndNet(dum.)GroupNet(dum.)
FirmNetDensity
Human Cap.
Land
VA/emp

Manuf.0a

Manuf.0b

Manuf.1a

Manuf.1b

Manuf.2a

Manuf.2b

Manuf.3-0.323(0.190)1.938(1.065)4.204∗∗∗(0.328)1.735(0.897)5.037∗∗∗(0.648)-0.005(0.175)0.270(0.517)0.134(0.093)-0.400∗(0.162)-0.260(0.197)1.970(1.095)4.170∗∗∗(0.332)1.731(0.931)5.110∗∗∗(0.682)0.114(0.200)0.525(0.549)0.167(0.099)-0.450∗∗(0.166)-0.027(0.037)0.039(0.036)-0.041(0.035)-0.018(0.019)-0.043(0.097)-0.833∗∗∗(0.211)0.125(1.578)5.017∗∗∗(0.290)2.080∗(0.877)
6.671∗∗∗(1.113)-0.102(0.124)-0.576(0.345)0.027(0.110)-0.108(0.124)-0.848∗∗∗(0.218)0.422(1.658)5.047∗∗∗(0.296)1.816∗(0.926)6.637∗∗∗(1.118)-0.123(0.135)-0.640(0.359)0.019(0.115)-0.082(0.128)0.013(0.071)0.034(0.070)-0.109(0.061)0.011(0.027)0.103(0.131)-1.121∗∗∗(0.258)-0.547(1.686)5.392∗∗∗(0.316)2.841∗∗(0.974)7.487∗∗∗(1.114)-0.050(0.102)-0.700∗(0.293)0.044(0.101)-0.127(0.124)-0.978∗∗∗(0.262)1.020(1.687)5.204∗∗∗(0.322)2.043∗(0.966)7.422∗∗∗(1.125)-0.077(0.108)-0.695∗(0.301)
0.029(0.105)-0.104(0.126)0.057(0.147)-0.077(0.145)-0.148(0.107)-0.060(0.045)0.556∗∗∗(0.166)-1.228∗∗(0.411)-6.365∗∗(2.403)5.267∗∗∗(0.415)6.185∗∗∗(1.129)6.845∗∗∗(0.884)-0.035(0.081)

0.214(0.534)1.162(0.628)-0.002(0.173)-0.000(0.226)6852

0.279(0.427)1.243∗(0.610)-0.001(0.164)0.000(0.224)
6852
15

0.000
(0.269)0.751(0.655)-0.003(0.174)-0.003(0.210)
21828

0.000
(0.243)0.734(0.678)-0.003(0.165)-0.004(0.220)
21828

0.001
(0.171)0.501(0.655)0.001(0.179)-0.007(0.238)
45441

0.001
(0.171)0.618(0.562)0.001(0.178)-0.006(0.234)
45441

0.003
(0.191)

SecCon.Fin
SecCon.Ser
SecCon.Tra
SecCon.Who
GroupConnectSD
Density
Human Cap.
Land
VA/empObservations

Standard errors in parentheses
Manuf: Manufacturing, Who: Wholesale. Numbers indicate NUTS level.
∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

0.195∗(0.081)0.240∗(0.104)95148

Table 5: Mixed Logit at Different NUTS levels for Wholesale

Mean
IndNet(log)GroupNet(log)
IndNet(dum.)GroupNet(dum.)
FirmNetDensity
Human Cap.
Land
VA/empWhole.0a

Whole.0b

Whole.1a

Whole.1b

Whole.2a

Whole.2b

Whole.3-0.191(0.245)-0.325(1.265)4.959∗∗∗(0.455)4.098∗∗(1.315)3.748∗∗∗(0.677)0.463∗(0.221)0.863(0.680)0.398∗∗(0.121)0.716∗(0.343)-0.152(0.254)0.356(1.460)4.902∗∗∗(0.458)3.685∗∗(1.358)4.019∗∗∗(0.759)0.575∗(0.248)0.950(0.704)0.440∗∗∗(0.126)0.618(0.350)-0.032(0.033)0.026(0.034)-0.011(0.030)0.003(0.020)-0.182(0.149)0.188(0.196)5.796(3.747)4.475∗∗∗(0.297)1.614(2.114)7.804∗∗∗(1.361)0.505∗∗∗(0.143)0.591(0.388)0.430∗∗∗(0.126)
0.778∗∗(0.274)0.223(0.197)5.790(3.714)4.462∗∗∗(0.300)1.619(2.082)
7.847∗∗∗(1.374)0.624∗∗∗(0.168)0.798(0.413)0.500∗∗∗(0.137)0.682∗(0.279)0.010(0.077)-0.021(0.066)-0.035(0.052)-0.028(0.025)0.017(0.179)0.007(0.186)
4.460(4.351)4.764∗∗∗(0.274)3.363(2.617)7.298∗∗∗(1.229)0.577∗∗∗(0.115)0.705∗(0.324)0.326∗∗(0.103)0.573∗(0.271)

0.168(0.192)
5.782(4.777)4.648∗∗∗(0.280)2.385(2.802)7.501∗∗∗(1.251)0.653∗∗∗(0.126)
0.868∗(0.346)0.370∗∗∗(0.109)0.529(0.271)
0.043(0.150)-0.078(0.104)-0.169∗(0.083)-0.092∗∗(0.032)
0.462∗(0.229)-0.208(0.229)5.188(3.622)4.950∗∗∗(0.281)
3.138(2.129)8.497∗∗∗(1.106)
0.611∗∗∗(0.079)0.326(0.352)2.081∗∗(0.802)-0.083(0.162)0.884∗∗∗(0.242)7626

0.384(0.325)
1.861∗(0.835)-0.090(0.166)0.840∗∗∗(0.242)
7626
16

0.228
(0.150)0.095(1.247)0.007(0.285)0.959∗∗∗(0.222)
24330

0.232
(0.155)0.200(1.597)0.005(0.241)0.931∗∗∗(0.222)
24330

0.045
(0.178)0.143(1.798)-0.046(0.252)0.958∗∗∗(0.206)
49800

0.056
(0.183)0.372(1.651)-0.033(0.218)0.955∗∗∗(0.205)49800

SecCon.Fin
SecCon.Ser
SecCon.Tra
SecCon.Who
GroupConnectSD
Density
Human Cap.
Land
VA/empObservations

Standard errors in parentheses
Manuf: Manufacturing, Who: Wholesale. Numbers indicate NUTS levels.
∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

0.304∗∗∗(0.071)0.001(0.120)-0.014(0.391)1061105.3 Cross-sector effects
In this section, we replace the indices for all sectors with sector-specific indicesto examine the cross sector effects. Results are in table 6. Some of the variables
are dropped due to collinearity. The table has eight columns for Manufacturingand Wholesale at four administrative levels. We omit results for Connectivity here
since they are almost the same and our focus is agglomeration indices. The first
four columns show the results for Manufacturing. In all specifications, Industry
dummy and log count and Group dummy of the same sector (ie. Manufacturing)show significant effects while Firm indices show no significance. In terms of signs,
dummies are positive but log count shows negative. Wholesale indices have nosignificant effects on Manufacturing affiliates. The last four columns show resultsfor Wholesale affiliate’s location choice. The first three indices of log count are
dropped due to collinearity. Industry level indices of both Manufacturing andWholesale are significantly positive while only the Group level index of Wholesale
is significantly positive. The standard errors of firm level indices are very high aswell as some other variables. The reason for that may come from some outliersin the data set. However, when we drop the supposed outliers, the cross sector
indices also drop due to collinearity.
Nevertheless, we can see that the industry and group level of a sector significantly increase the probability of new affiliates of the same sector at all administrative levels. For cross-sector, wholesale affiliates seem to be located near othermanufacturing affiliates but manufacturing affiliates do not tend to be located nearother wholesale affiliates. This is somewhat different from Defever (2012) where
manufacturing plants tend to be located near other manufacturing and services
plants but sales units do not show the same patterns.

17

Table 6: Cross sector effect of Manufacturing and Wholesale for different NUTS levels
Manuf.0
choiceDensityLand
IndNet(log)Man
IndNet(log)Who
GroupNet(log)ManGroupNet(log)Who
IndNet(dum.)Man
IndNet(dum.)Who
GroupNet(dum.)Man

Manuf.1

Manuf.2

0.331∗(0.134)0.185∗(0.084)-0.552∗∗(0.208)-47.873(66695.431)
0.914(1.264)33.158(126131.598)
4.931∗∗∗(0.335)32.316(46229.750)
1.762∗(0.836)

0.276∗
0.179(0.123)(0.103)0.263∗
0.163(0.111)(0.101)-0.924∗∗-1.526∗∗(0.343)(0.558)-3.224-0.055(35291.640) (2976.323)-5.415∗-4.532(2.338)(5.640)-4.041(401852.348)
5.363∗∗∗
6.226∗∗∗(0.374)(0.529)-12.043-9.709(28981.152) (3025.792)
3.837∗∗
5.237∗∗∗(1.215)(1.570)28.193(65285.973)
25.294(260035.679)
7212

21.004(1708.489)
53.232(1.132e+08)
23052

Manuf.3
0.111(0.080)0.234∗∗(0.076)-0.487(0.906)-5.716(23653.125)-7.402(6.591)5.515∗∗∗(0.812)-6.234(16859.954)
7.168∗∗∗(1.698)GroupNet(dum.)Who
FirmNetMan
FirmNetWho
Observations

20.345(961.250)
254.473(.)
51432

218.508(34550709.300)-8.202(790.035)
100602Whole.0

Whole.1

Whole.2

0.384∗
0.458∗∗∗
0.593∗∗∗(0.169)(0.135)(0.115)
∗
∗∗
0.251
0.305
0.308∗∗(0.098)(0.118)(0.097)-0.074(1.725)-0.031(0.166)-1.521(2036875.964)
2.044(4.153)2.082
3.541∗∗
4.040∗∗∗(3.064)(1.112)(1.105)
∗∗∗
∗∗∗
4.773
4.888
5.055∗∗∗(0.329)(0.180)(0.164)-17.493-21.736-12.178(1520377.340) (2799236.436) (2024.330)
3.034
6.578∗∗∗
20.378(2.589)(1.236)(719.338)-7.488-1.306-1.691(11490270.479) (10187.907) (29806.193)
20.21317.43619.141(3254.224)(561.958)(1015.051)
8242
26434
59028

Standard errors in parentheses
Manuf: Manufacturing, Who: Wholesale. Numbers indicate NUTS level. Blank values indicate being obmitted due to collinearity.
∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Whole.3
0.640∗∗∗(0.078)0.375∗∗∗(0.072)5.032∗∗∗(1.223)5.116∗∗∗(0.149)-12.615(846.558)
21.842(1180.068)-1.385(25041.374)
19.904(759.847)
115499

To further examine our results, we follow Defever (2012) to replace the F irmN et
dummy with a dummy that equals 1 if there has been an affiliate of the same firmin d km vicinity of the region. However, instead of using geographical centroids, we
use the centroid of the most populated cities of that region to calculate the vicinity.
In the baseline case, d = 76km. To put this in perspective, the minimum distanceof an affiliate to a region’s centroid is 0.41km and the furthest is 244km. The
results are in tables 7 and 8. Again we omit results of Connectivity here to focus
on the cross-sector effects. Table 7 shows the results for manufacturing affiliates.
At industry level, existing wholesale affiliates do not seems to affect manufacturinglocation choice. However, wholesale location choice is affected by the existence ofboth Manufacturing and Wholesale. At group level, existing manufacturing groupmembers’ affiliates do increase the likelihood of a new manufacturing affiliate.
The results for firm level are different with Defever (2012) in four points. First,
the effects of an existing wholesale unit on setting up a manufacturing subsidiary
are only significantly positive at short distances, specifically shorter than 51km
(In his analysis the effects are still significant up to 120km). Second, the effect ofan existing manufacturing plant on setting up a manufacturing subsidiary has two
peaks at 51km and 126km (In his analysis, the effects die down until 360km andrise to 530km before dying down again.). Third, the effects of existing wholesale on
opening a new wholesale subsidiary are significantly negative for longer distances(In his analysis the coefficients are mostly insignificant.). Fourth, an existing
manufacturing affiliate has a positive effect on wholesale only at 76km away.
There are several reasons why our results are different from Defever (2012).
First, we only use Japanese firms while they use firms from many countries including Japan. In fact, country samples seem to affect the estimation of FDI inmany cases (for example, see Blonigen et al. (2007)). Furthermore, it is possiblethat we have not been able to address properly the source of heterogeneity infirms’ preferences12 . Second, our model controls for sector-specific industry-leveland group-level effects that are not present in his model. It is possible that his
cross-sector dummy absorbs somewhat this type of effects because firms may want
to locate near their group’s affiliates too. Indeed, in table 6, we can see that thedummy for cross-sector industry and group index are significantly positive for all
administrative levels in the case of manufacturing location choice.

12

A fixed effect mixed logit may perhaps address the problem.

19

Table 7: Cross sector effect of prior presence in vicinity for Manufacturing Location
26km
choiceDensity

51km

76km

101km

126km

151km

176km

0.228∗
0.228∗
0.231∗
0.234∗
0.240∗
0.237∗
0.236∗(0.104)(0.104)(0.104)(0.105)(0.105)(0.105)(0.104)
Human Cap.
-0.339-0.319-0.321-0.316-0.311-0.323-0.331(0.271)(0.271)(0.271)(0.271)(0.272)(0.271)(0.271)
Land
0.203
0.207
0.211∗
0.215∗
0.223∗
0.219∗
0.213∗(0.106)(0.106)(0.106)(0.106)(0.106)(0.106)(0.106)
VA/emp-0.076-0.083-0.081-0.084-0.088-0.083-0.076(0.120)(0.120)(0.120)(0.120)(0.120)(0.120)(0.120)
IndNet(log)Man-1.881∗∗-1.890∗∗-1.867∗∗-1.878∗∗-1.900∗∗-1.861∗∗-1.824∗∗(0.575)(0.576)(0.575)(0.576)(0.578)(0.576)(0.573)
IndNet(log)Who
0.153
0.155
0.173
0.170
0.165
-0.151-0.047(1966.673) (2208.308) (1698.640) (3180.200) (2183.337) (1454.987) (1521.899)
GroupNet(log)Man-4.970-4.969-4.973-4.981-5.038-5.017-4.974(5.705)(5.700)(5.696)(5.695)(5.680)(5.680)(5.685)
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
∗∗∗
IndNet(dum.)Man
6.536
6.537
6.519
6.528
6.533
6.507
6.479∗∗∗(0.539)(0.540)(0.539)(0.540)(0.542)(0.540)(0.538)
IndNet(dum.)Who-8.966-9.223-8.750-9.995-9.238-8.486-8.572(2039.937) (2287.023) (1752.852) (3279.630) (2250.177) (1591.007) (1630.802)
GroupNet(dum.)Man
5.295∗∗∗
5.304∗∗∗
5.305∗∗∗
5.316∗∗∗
5.370∗∗∗
5.345∗∗∗
5.309∗∗∗(1.562)(1.561)(1.561)(1.561)(1.558)(1.560)(1.562)Man in 26km
0.905
(0.727)
Who in 26km
3.472∗∗(1.158)Man in 51km
1.085∗(0.512)
Who in 51km
2.829∗(1.108)Man in 76km
0.652
(0.493)
Who in 76km
2.030
(1.059)Man in 101km
0.720(0.421)
Who in 101km
1.719(1.060)Man in 126km
0.910∗∗(0.347)
Who in 126km
1.464(1.066)Man in 151km
0.641(0.343)
Who in 151km
1.207(1.062)Man in 176km
0.208(0.342)
Who in 176km
0.867(1.060)Observations
48427
48427
48427
48427
48427
48427
48427
Standard errors in parentheses
Man: Manufacturing, Who: Wholesale.
∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

Table 8: Cross sector effect of prior presence in vicinity for Wholesale Location
26km
choiceDensity

0.588∗∗∗(0.116)
Human Cap.
0.831∗∗(0.307)Land
0.312∗∗(0.106)
VA/emp0.106(0.169)IndNet(dum.)Man
3.960∗∗∗(1.103)
IndNet(dum.)Who
4.980∗∗∗(0.174)
GroupNet(dum.)Man-11.886(2654.165)
GroupNet(dum.)Who
19.606(622.615)
Man in 26km
-11.740(1817.164)
Who in 26km
-0.277(0.466)Man in 51km
Who in 51km

51km

76km

101km

126km

151km

176km

0.588∗∗∗(0.116)0.828∗∗(0.307)0.305∗∗(0.107)0.112(0.169)3.967∗∗∗(1.103)5.005∗∗∗(0.175)-11.634(2363.099)
19.300(531.068)

0.586∗∗∗(0.116)
0.841∗∗(0.307)0.305∗∗(0.107)0.109(0.169)3.976∗∗∗(1.103)5.002∗∗∗(0.174)-11.686(2455.512)
19.310(534.857)

0.585∗∗∗(0.116)0.834∗∗(0.307)0.302∗∗(0.107)0.113(0.169)3.968∗∗∗(1.103)5.004∗∗∗(0.175)-11.678(2457.418)
19.300(531.804)

0.580∗∗∗(0.116)0.826∗∗(0.307)0.295∗∗(0.107)0.119(0.169)3.959∗∗∗(1.104)5.010∗∗∗(0.175)-11.672(2459.421)
19.283(526.105)

0.576∗∗∗(0.116)0.818∗∗(0.307)0.289∗∗(0.107)0.127(0.169)3.886∗∗∗(1.106)5.019∗∗∗(0.175)-11.624(2494.262)
19.267(520.526)

0.576∗∗∗(0.116)0.814∗∗(0.307)0.279∗∗(0.107)0.135(0.169)3.888∗∗∗(1.106)5.035∗∗∗(0.176)-11.621(2504.175)
19.226(508.155)-11.476(1203.112)-0.773(0.416)2.337∗(1.166)-0.706(0.393)Man in 76km
Who in 76km
Man in 101km

1.868(1.200)-0.828∗(0.388)Who in 101km
Man in 126km

1.664(1.212)-0.975∗(0.385)

Who in 126km
Man in 151km

1.350(1.207)-1.068∗∗(0.366)

Who in 151km
Man in 176km
Who in 176km
Observations

55019

Standard errors in parentheses
Man: Manufacturing, Who: Wholesale.
∗p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

55019

55019

55019

55019

55019

1.077
(1.212)-1.045∗∗(0.327)55019

6 Conclusion
We have examined the effects of within-firm agglomeration and cross-city connectivity on the probability of having a new affiliate in the region. Using data about
Japanese firms investing in the EU, we confirm the within industry agglomeration
force described in the literature. Furthermore, we separate the effect of affiliatesof group companies from that of the firm only. It is possible that having an affiliate of group companies in a region that is far from the firm’s prior investment
will increase the possibility of the firm investing in that region. In fact, we findboth group and firm level indices increase the possibility. In terms of magnitude,
the effect of the firm-level index is higher than that of the group-level index for
Manufacturing and Wholesale.
We also examine how the role of cities in a “city network” formed by interaction
among Japanese firms in different sectors as well as non-manufacturing membersof firms’ financial group. Cities with more existing non-manufacturing members
are more likely to attract wholesale and manufacturing through the capacity toprovide more advanced services to the firm’s new affiliate.
To take into account the concerns of agglomeration effects being sensitive togeographical boundaries, we have estimated the model at country as well as groupof districts levels. In most cases, we do not see any qualitative changes in industry
level and firm level dummy coefficients but group level coefficients become insignificant at country and group of state levels. The Connectivity through Group offirms is also insignificant at these levels.
Finally, we have examined the presence of cross-sector affiliates on the probability of having a new affiliate from a separate sector. Here, our results departfrom previous literature where we do not see the significance of such effects. This
could be because underlying firm distribution of our data is different from others’.
We will address this issue in our future research.

References
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23

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24

Appendix
Table 9: Data cleaning
1
2
3
4
5
6
7
8
9

dataRange
firmNo affiNoall4966 29083
Europe
1119
3212
FDI
1035
1862
with year1035
1862with geocode
1000
1763
parent manuf only
688
1233
invest before 1990 only
161
223
invest before and after 1990
143604
invest after 1990 only
384
605

Table 10: Numbers of alliliates of each non-manufacturing big firm in the EU
1
2
3
4
5
6
7
8
9
10

firmsnasector
Agriculture, Forestry and Fishing
Construction
Electricity, gas and water supply
Finance and insurance
Manufacturing
MiningReal estate
Service activities
Transport and communications
Wholesale and retail trade

25

Nfirm
5
14
10
45
761
6
5
101
53
119

min max Q25 Q50 Q75
1
6 1.002
4
1
6 1.0012
1
7 1.253
3
1
13 1.00
2
3
1
46 1.00
1
3
1
2 1.252
2
1
3 1.001
2
1
15 1.00
1
2
1
22 1.00
1
3
1
69 1.00
1
2

Table 11: List of Industries and Sectors
Manufacturing
WholesaleChemistry
Chemical wholesale
Electric equipment wholesale
DrugElectric equipment
Fiber clothes wholesale
General Wholesale
Fiber clothes
Glass soil and stone
Glass soil and stone wholesale
Grocery wholesale
Grocery
Iron and steel
Machinery wholesale
Other wholesale
MachinePetroleum fuel wholesale
Metal products
Non-ferrous metal
Pharmaceutical wholesale
Precision equipment wholesale
Other manufacturing industry
Petroleum coal
Steel & Metal Wholesale
Precision mechanical equipment Transportation equipment wholesale
Pulp paper
Rubber productTransport equipment
Services
Finance
Advertisement
Bank
Commodity futures
Architectural Design
Building management security
Investment Management
Communication broadcasting
Investment Services , etc.
Consulting
Lease
Life insurance
Hotel
Information system software
Money Lending Credit Card
Leisure entertainment
Other financial
Machinery repair
Property and casualty insurance
Newspaper publishing
Securities
Other services
Trust bank
Real estate
Temporary staffing business contract
TravelVideo and Music
Note: These industries are classified by Toyo Keizai Database. Sectors are inconcordance with SNA sectors except for Wholesale because SNA does not separate
Wholesale and Retail. Headquarters only contain Headquarters.

26