Discussion Paper No. 116
International and Interprefectural Migrationin Japan:
Implications for the Spatial Assimilation Theory
Koji Murayama and Jun Nagayasu

July, 2020

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANInternational and Interprefectural Migration in Japan:
Implications for the Spatial Assimilation Theory
Koji Murayama†,

Jun Nagayasu‡

Abstract

We study the spatial assimilation of foreign residents in Japan by analyzing the concentration tendency among foreigners who arrive from overseas and relocate within
Japan. Using spatial models, we find that immigrants from overseas tend to moveto ethnically concentrated prefectures in Japan; this finding aligns with the spatial assimilation theory. In contrast, this trend weakens substantially with theirsubsequent domestic relocation in Japan, and it differs by national group. Slow
assimilation is found among nationals from countries characterized as being lowincome or culturally dissimilar from Japan.
Keywords: spatial assimilation; social integration; spatial interaction model; migration; JapanJEL classification:†

Tohoku University, Graduate School of Economics and Management. Address: 27-1 Kawauchi, Aobaku, Sendai, Miyagi 980-8576 JAPAN. Email: koji.murayama.t1@dc.tohoku.ac.jp, Tel.: +81 22 795 6265,
Fax: +81 22 795 6270.
‡
Tohoku University, Graduate School of Economics and Management. Address: 27-1 Kawauchi, Aobaku, Sendai, Miyagi 980-8576 JAPAN. Email: jun.nagayasu.d8@tohoku.ac.jp, Tel.: +81 22 795 6265, Fax:
+81 22 795 6270.

1

Introduction

In 2018 the Japanese government announced the Basic Policy on Economic and Fiscal
Management and Reform, which sets out a new immigration policy.1 This policy recognizes that Japan’s labor shortage is becoming very serious, and that foreign workers
are needed to fill job vacancies. Unlike immigration policies in other advanced countries,
Japan’s new policy is not limited to foreign personnel in traditional specialized and technical fields: it also includes those who have more basic expertise and skill levels. As aresult, the number of foreigners immigrating to Japan has rapidly increased in recent
years, and the majority of them are from China or Korea.
According to the Ministry of Health, Labor and Welfare (MHLW), foreign workers
in Japan can be categorized as per the following employment status typology: thosewho (1) are eligible to work, (2) stay based on their status, (3) participate in technicalintern-training programs, (4) conduct designated activities, and (5) conduct nonqualified
activities.2 In October 2017, the distribution of foreign workers among the five categories
was approximately 19%, 36%, 20%, 2%, and 23%, respectively. Many foreigners fromthe United States, the United Kingdom, and South Korea—most of whom are relatively
wealthy—are employed in professional and technical fields. Filipino, Brazilian, and Peruvian individuals tend to be permanent or long-term residents. Many Japanese descendants
in Brazil and Peru come to work in Japan and hold permanent resident status, and many
Filipinos have obtained permanent residency through intermarriage with Japanese.
It is generally believed that foreigners do not assimilate well into Japanese society,and that friction between Japanese and foreigners will be conspicuous in the near future (Iguchi, 2011). The excessive concentration of foreigners in certain areas has been
considered a failure of integration policy and has prevented foreigners’ integration andparticipation in the destination countries (Andersson, 1998; Bolt and van Kempen, 2003;
Wright and Ellis, 2000). For immigrants’ children, growing up in an ethnically concentrated environment can have adverse economic effects; those effects can persist acrossgenerations, resulting in slow integration and difficulty in obtaining employment (Asselin
1

While the government claims that this is not an immigration policy, the Basic Policy does relax entry
requirements for foreigners who wish to work in Japan, and so here it is considered an immigration policy.
2
Under the status of “designated” activities, foreign nationals are permitted to work only in activities
designated by the Ministry of Justice.

2

et al., 2006). Therefore, the spatial distribution of immigrants (i.e., spatial assimilation) is
considered a key integration indicator (Alba and Foner, 2015). Japan’s current situation
is similar to Europe’s past immigration experiences, in that it lacks a social integration
policy that would eliminate discord between foreigners and local individuals and prevent
foreigners from feeling isolated from society.
Against this background, we study the adaptability of foreigners in Japan by analyzing the relationship between foreign residents’ backgrounds and their residential location.
In particular, based on the spatial assimilation theory (SAT)—which we describe in thenext section—we compare the concentration tendency of international migrants (i.e., immigrants from other countries to Japan) with that of foreign migrants relocating within
Japan (i.e., migrants moving from one Japanese prefecture to another). To the best of
our knowledge, the current study is the first comprehensive attempt to apply the SAT inthis way to the Japanese context.

2

Spatial assimilation theory

The geographic concentration of immigrants has been used to evaluate the SAT, which was
first put forward in the early 20th century to study the integration of European immigrants
with Americans in the United States (Park and Burgess, 1921). According to the SAT,
immigrants will concentrate in the same ethnic city, not only to overcome disadvantages
associated with language barriers, information uncertainty, and low socioeconomic status,
but also to make the best use of employment and welfare conditions attributable tobeing in a community of immigrants. However, they can improve their local language
skills, cultural fitness, and socioeconomic status over time, and eventually move about
the country freely—even to suburban areas where few immigrants reside (Massey and
Denton, 1985). In this way, immigrants eventually make more contact with local people
and become assimilated into society.
Such assimilation is said to continue and progress across generations (Alba and Nee,2003); however, the time required for spatial assimilation will be comparatively shorter if
there are similarities (rather than dissimilarities) between immigrants and the local peopleof the target destination. Similarities among people can be measured in terms of their3

level of human capital, language proficiency, and religion. Immigrants who have culturaland linguistic characteristics similar to those of the local people are, from the very start,less likely to settle in ethnically concentrated areas (Zorlu and Mulder, 2010). It follows
that assimilation patterns may differ according to the immigrants’ backgrounds.
While the SAT has a strong sociological aspect, economists recognize that multiple changes in the residential location of immigrants stem from information uncertainty(Bodvarsson et al., 2015). A moderate concentration of immigrants creates a positive
externality through cooperation among themselves, but excessive concentration is known
to create for immigrants an uncontrollable externality. There is a considerable body ofresearch on the impact of the geographical concentration of immigrants on society and on
immigrants themselves. Ethnic segregation—whereby foreigners are geographically isolated from the local people—leads to a failure to educate immigrants, and can increase
immigrants’ reliance on social welfare as well as higher unemployment rates and higher
poverty and crime levels (Andersson, 1998; Bolt and van Kempen, 2003). In fact, violence
among foreigners has emerged in several European cities, including London, Paris, andAmsterdam (Zorlu and Mulder, 2008). Moreover, higher geographical concentrations offoreigners are also known to increase tension and concerns among the local residents of
those immigrants’ target destinations (Wright and Ellis, 2000).
Many US studies that analyze immigration concentration support the SAT. For example, ethnic groups who have had a long history of living in the United States tend to
be more spatially distributed (Jones, 2003). Moreover, residential location and socioeconomic status are found to strongly correlate. Higher-income African–Americans are more
likely to move from the center of a large city to the suburbs (Clark, 2007). Similarly,
Hispanic immigrants with a higher socioeconomic status and higher English proficiency
are more likely to move from ethnically concentrated areas (South et al., 2006).3
Similarly, the experience of Europe frequently supports the SAT. Zorlu and Mulder(2008) show that asylum-seekers and non-Western immigrants are most likely to moveto ethnically concentrated areas in the Netherlands. In contrast, many immigrants from
Western countries—which is to say, countries associated with high levels of human capitaland socioeconomic status, and proximate the Dutch in terms of language and religion—
3

Many other studies have shown the sluggish rate at which African–Americans in the United States
spatially integrate (Crowder, 1999; Denton and Massey, 1989).

4

enter the country mainly as workers and with their families, and do not reside in ethnicallyconcentrated places. Similarly, Chinese immigrants reportedly relocate, on a repeatedbasis, to ethnically concentrated areas of the United Kingdom (Simpson and Finney,2009). Tammaru and Kontuly (2011) show that in Estonia, Estonian-language proficiency
among immigrants and the possession of Estonian citizenship promote the movement fromethnically concentrated areas to nonconcentrated regions. However, other studies showthat immigrants remain undispersed, on account of discrimination and their preference
for their own ethnic community (Bolt and van Kempen, 2010).
Traditionally, SAT-related studies have focused on demographic movements to thesuburbs from a city, in the latter of which immigrants tend to concentrate. However,in recent years, geologists and sociologists have argued that the topic of spatial integration among immigrants should be expanded so as to focus on interregional movements(Goodwin-White, 2007). In Japan, prefecture-level relocation data are available, making it possible to focus on the movement of foreigners among prefectures (i.e., regionalmigration) in addition to that of immigration from overseas.4

3

Econometrics for relocation analysis

We use gravity models to study the residential relocation of international and interprefectural migrants; these models have been previously used to analyze international trade.
However, unlike the gravity models of trade, the random utility maximization model
serves as a theoretical basis for migration analysis (McFadden, 1974), where migrants
will pay the cost for the move as an investment in their own human capital in order tomaximize future utility. This premise is called the human capital investment theory of
migration (Bodvarsson et al., 2015). Until the mid-1980s, there was the misconceptionthat international and domestic migration are fundamentally distinct phenomena. Today,
however, the idea that migration is a form of human capital investment is a fundamental
unifying theme of migration theory in economics (Bodvarsson et al., 2015). Eq. (1) shows
the standard gravity model.
4

A prefecture is the most broadly defined subdivision of Japan.

5

Yod = K

Xoβ1 Xdβ2
distβod3(1)

The subscript o denotes origin, and d destination. Yod is the amount of migration from o
to d. K is a constant, and Xo and Xd represent the unique characteristics of the origin
and destination, respectively. distod is the geographical distance between o and d, and β1 ,
β2 , and β3 are parameters. In empirical analysis, Eq. (1) is examined in the natural log
form.

ln Yod = ln K + β1 ln Xo + β2 ln Xd − β3 ln distod(2)

Both international and regional migration models use Xo and Xd , which include unemployment rates, per-capita gross domestic product (GDP), and population. Xd also
includes the concentration rate of foreigners, as per the SAT. Unemployment rates andincome are economic factors that are thought to strongly relate to migration (Sjaastad,1962). The population of a destination is used to proxy the size of the labor market, witha large destination population encouraging emigration from elsewhere (Bodvarsson et al.,2015). The distance is a proxy for the transaction costs of migration.
We extend the standard spatial model to capture spatial autocorrelation between
migration flows; we do so by using a spatial weight matrix devised by LeSage and Pace
(2008).5
Yod = ρo Wo Yod + ρd Wd Yod + ια + Xo βo + Xd βd + distod γ + ε(3)This is known as the spatial interaction model, and it has been used for example ininterstate migration analysis (Beenstock and Felsenstein, 2016) and interregional traveler
analysis (Marrocu and Paci, 2013). In (3), ι is a unitary term, ε is a residual, and ρo ,ρd , α, βo , βd , and γ are parameters. Wo and Wd are origin and destination-based spatial
weight matrices, respectively.
Figure 1 depicts the theoretical implications of spatial weights on migration flows(Yod ). The origin-based matrix (Wo ) assigns greater weight to “flows to d from areas close
5

LeSage and Pace (2008) introduced another spatial weight matrix called Ww . However, when origins
and destinations are configured in different regions, Ww cannot be created with the Kronecker product.
Therefore, Ww is omitted from analyses of international transfers (Beenstock and Felsenstein, 2016). For
comparison purposes, we omit Ww from domestic migration analysis.

6to o” than to “flows to d from areas remote from o.” Therefore, Wo Yod is a variable that
captures the stronger spatial correlation between flows that originate from nearby areas.
Similarly, the destination-based matrix (Wd ) assigns greater weight to “flows to areas neard” than to “flows to areas distant from d.” Thus, Wd Yod is a variable that captures thestronger spatial correlation between flows to nearby destinations.
[Figure 1]
More precisely, in our international migration analysis featuring k origin countries(k = 10) and 47 destination prefectures, Wo can be constructed using the following
fo .
specification of W

0 W12 W13

W21 0 W23

fo = 
W
W31 W32 0

 ..
..
..
 .
.
.

Wk1 Wk2 Wk3· · · W1k
···
···
..
.
···





W2k 


W3k 

.. 
. 

0,

where 0 is a 47 × 47 nil matrix. The diagonal elements of W12 are an inverse of thesquared distance between capitals of origin countries 1 and 2, and other elements are 0.
fo by the row
Wo is a 470 × 470 matrix that can be obtained by dividing all elements of W
fo .
sum of W
Similarly, Wd is a 470 × 470 matrix comprising W .

W 0 0 ··· 0 0 W 0 ··· 0 
Wd =  0 0 W · · · 0 


 ..
..
.. . .
.. 
.
.
.
.
.


0 0 0 ··· W
The elements of W are
dist−2ij
 P47, if i 6= j
−2
dist
ij
j=1
wij =


0,
otherwise.

7(4)

wij is a 47 × 47 that is constructed using the distance between prefectures i and j (distij ).
Because it is normalized to the row, the row sum of Wd is 1. Since we have 10 origin
countries and 47 destination prefectures for Yod in (3), Wd is also a 470 × 470 matrix.
In our analysis of interprefectural migration, the origins and destinations involve 47
N
N
Japanese prefectures. Therefore, Wo = W
I and Wd = I
W , where W is a 47 × 47
matrix with diagonal elements equal to wij in (4) and other elements 0, I is a 47 × 47
Nunitary matrix, andis the Kronecker product. There are 2209 combinations of o andd for Yod in (3), and so Wo and Wd are both 2209 × 2209 matrices.
Finally, as the spatial lag variables (Wo Yod , Wd Yod ) may be endogenous, to estimate
Eq. (3), we use spatial two-stage least squares (S2SLS) to eliminate any endogeneitybias. We follow Badinger and Egger (2011), who set all exogenous explanatory variables
to Z, and the instrumental variables for the endogenous variables Wo Yod and Wd Yod to
H = (Z, Wo Z, Wd Z, Wo2 Z, Wd2 Z, Wo Wd Z). Here, all explanatory variables, other thanspatial lag variables, are used as Z.

4

Data

We obtained migration data from the Statistics Center of the Ministry of Internal Affairs
and Communications. The National Census 2010 (Kokuseichosa) captured the numberof foreign migrants (aged 15–64) who have settled in 47 Japanese prefectures, and thatof foreign nationals in Japan who moved to other prefectures; all data are the October
2005–October 2010 period. The top 10 countries of origin (in descending order of numberof immigrants) are China, (North and South) Korea, Vietnam, the Philippines, Brazil,
Indonesia, the United States, Thailand, Peru, and the United Kingdom;6 their proportions
by employment type are shown in Figure 2. As mentioned in the Introduction, there are
significant differences in employment status by nationality.
[Figure 2]
In terms of international migration analysis, Xo consists of unemployment rates, thenatural log of population, and per-capita GDP in US dollars. (All data were drawn fromthe International Monetary Fund’s (IMF’s) World Economic Outlook.)7 Similarly, per6

Information with respect to other popular origin countries of origin, such as India and Nepal, was
not available on a national basis.
7
For Korea, we use only information pertaining to South Korea, as there are no official diplomatic

8

capita GDP in Xd was converted to US dollars using the average yen-to-US dollar exchange
rate published by the IMF. The distance is proxied by the natural log of geographicaldistance between the capital of each country and the government of each prefecture. The
concentration rate equals the number of foreign nationals as a percentage of the total
population in each prefecture. Data pertaining to these explanatory variables are from2005.
In our interprefectural migration study, no single nationality on its own had enoughpeople migrating to Japan’s many prefectures to provide a sufficiently large sample.
Therefore, we classify foreign nationals as being in the East Asian Group (Korea andChina), the Southeast Asian Group (Vietnam, the Philippines, Indonesia, and Thailand),the South American Group (Brazil and Peru), and the Western Group (the United Kingdom and the United States). This immigrant typology is based on intragroup similarities
in terms of culture, location, and economic conditions.8 Yod is the flow of interprefectural
migrants, and it equals 0 if o = d. Xo contains unemployment rates, the natural log of
per-capita GDP (in Japanese yen), and the number of foreigners belonging to each of
the aforementioned groups if o 6= d; it equals 0 if o = d. Xd contains the natural log of
per-capita GDP of d, unemployment rates, the natural log of the total population, andthe concentration rate of foreigners if o 6= d, and it equals 0 if o = d. The distance variable
is the natural log of the interprefectural distance when o 6= d, and it equals 0 when o = d.
Data pertaining to these explanatory variables are also from 2005 (Table 1). As one cansee, in that year, foreign nationals accounted for a small percentage (0.85%) of Japan’s
population.
[Table 1]
Table 2 shows the results of Moran’s I test from the spatial weight matrices. In allcases, the p-value of the test was less than 0.0001, and the null hypothesis of no spatial
correlation was rejected. Therefore, there is evidence of spatial correlation in migration.
relations between Japan and North Korea.
8
For example, in the East Asian Group, China and Korea are adjacent nations and their cultural
spheres have historically used the same alphabet (Kanji ); they also share the traditional practice of
Mahayana Buddhism. Vietnamese, Filipinos, Indonesians, and Thais (who comprise the Southeast AsianGroup) have different religions and languages, but their home countries are in close proximity and theirincome levels are relatively similar. Brazilians and Peruvians (the South American Group), despite
speaking different languages, have similarities in terms of distance to home countries, and most of theircitizens are Catholic. Americans and British nationals (the Western Group) have commonalities (e.g.,
speak English and are Christian) and their countries’ per-capita GDP is higher than that of Japan.

9Moreover, with respect to international immigration, Wd has a larger Moran I statistic
and a smaller standard deviation than Wo . Therefore, a country has a strong spatial
autocorrelation based on destination (Figure 1): immigrants tend to move from overseas
to prefectures near certain destinations. Morin’s I statistic for interprefectural migration is
at most 0.33, and less than the statistic associated with Wd for international immigrants.
Moreover, there are variations in the spatial weights for interprefectural migration bygroup; the Moran I statistic of East Asians and of Southeast Asians is larger than that ofWestern countries and of Southeast Americans.
[Table 2]

5

Empirical results

5.1

International immigration

Table 3 shows the results of analyzing migration from overseas to 47 Japanese prefectures. Column (A) shows the ordinary least squares (OLS) results, which imply that
economic motivations are driving forces of international migration. Low income and large
population in o (origin countries) increase immigration to Japan, particularly to highly
populated prefectures. Moreover, the concentration rate of foreigners is significantly positive; foreigners find particular prefectures containing many foreign nationals attractive.
These results align with those of many previous studies (Backhaus et al., 2015; Belotand Ederveen, 2012; Clark et al., 2007; Pedersen et al., 2008). The unemployment rates
in o are significantly negative; as such, unemployment does not serve as an adequate
explanation for economic motivation pertaining to immigration. Nonetheless, this result
is consistent with that of DeWaard et al. (2012) and suggests that many people cannot
afford to migrate internationally from countries with a high unemployment rate: after all,in the context of the current study, the majority of foreigners are originally from China
and South Korea, each of which has low-unemployment conditions.
[Table 3]
The results from the spatial model—which fits the data better than the OLS modelaccording to the Akaike information criterion (AIC)—are similar to those from the OLS
model (Column B). Interestingly, the spatial lag variable Wd Yod is significantly positive,
10

but Wo Yod is not found to be significant. The latter suggests that migration flows are notstrongly affected by migration flows from various other countries: the decision to leave
one’s home country must in itself be a momentous decision for immigrants to Japan. On
the other hand, the significantly positive parameter for Wd Yod implies that immigrants
from the same home country tend to go to prefectures that are geographically close toeach other.
Given heterogeneity among the 10 types of foreign nationals studied, Column (C)speaks to their preferences in terms of the concentration of certain groups of immigrants
who share many similarities. For this analysis, we create concentration rates for the fourgroups (i.e., East Asian, Southeast Asian, South American, and Western). Then, we
confirm different preferences among the groups in terms of residential location in Japan.
The concentration rates of the East Asian and Southeast Asian Groups are found to besignificantly positive; the other groups do not show such strong preferences that manifest
as concentrations in particular Japanese prefectures. These findings are in line with thoseof Simpson and Finney (2009), who report that Chinese immigrants prefer to stay inparticular regions in the United Kingdom. Interestingly, the groups with positive andsignificant concentration rate estimates share some common features. For example, theper-capita GDP of their home country is lower than that of Japan, and this phenomenon
is consistent with the results of Zorlu and Mulder (2008) and predictions derived from the
SAT. The SAT also explains the Southeast Asia Group’s high concentration rate: it differsfrom Japanese society in terms of culture, language, and religion. Koreans and Chinese(i.e., the East Asian Group), on the other hand, have a stronger affinity with Japanesesociety in these respects, but the per-capita GDP of their home country is lower than thatof Japan, and many Chinese technical interns live in Japan. It seems that these interns
attempt to compensate for their relatively low human capital and weak socioeconomic
positions by cooperating with their ethnic community.
Immigrants within the South American and Western Groups appear to be spatiallydispersed. The concentration rate of the South American Group is insignificant at the 5%
level (p = 0.072). Although they do not possess strong socioeconomic status, many Brazilians and Peruvians living in Japan are descendants of Japanese immigrants. Therefore,
their affinity with Japanese society at the time of entry is higher than that of foreigners

11belonging to either the East Asian or Southeast Asian Group, and their need to concentrate upon entry is relatively small. Both Americans and British (i.e., the WesternGroup) come from countries with higher per-capita GDP values, and in many cases workin specialized fields. It is thought that the concentration rate of the Western Group isinsignificant because its members feel no need to concentrate upon entering the country,
given their high socioeconomic status.
Finally, the impact of distance (dist) between origin country and Japanese prefecture
on immigration is, on many occasions, uncertain. This finding runs counter to the resultsof previous studies on international immigration, where the expected sign significantly
negative. According to Ramos and Surinach (2017), adjoining borders have the effectof significantly increasing immigrant inflows, and because Japan is an island country
with tight immigration control, it is presumed that short distance does not act among
immigrants as a pull factor.

5.2Interprefectural movements

Tables 4 and 5 show the results of analyzing foreigners’ interprefectural migration. Table
4 presents the OLS results, while Table 5 summarizes the S2SLS results with the additionof the spatial lag variable. These tables provide evidence that spatial assimilation has
occurred in Japan, but that the speed of assimilation differs significantly by nationality.
In both tables, the concentration rate of foreigners is insignificant in analyzing all
foreign nationals (i.e., overall) and the East Asian Group, but it remains significant andpositive for the other foreigner groups. The insignificant concentration rate for membersof the East Asian Group derives from their proximity to Japanese culture, and the overall
result is affected by the results of East Asian Group, since the majority of immigrants
in Japan are Chinese or Korean. Like Japanese, Chinese and Koreans (the East AsianGroup) have a long history of Mahayana Buddhism and have linguistic, cultural, andreligious characteristics relatively similar to Japan’s. Moreover, they belong to a cultural area that has since ancient times used Chinese letters (kanji) and had a historyof immigrating to Japan before World War II. The insignificant concentration rate offoreigners for interprefectural migration is consistent with the findings of previous studies (Jones, 2003; White and Glick, 1999; Zorlu and Mulder, 2008) and the predictions
12

deriving from the SAT. Given our finding of a high concentration tendency in their international movements, a low concentration tendency in interprefectural analysis for the
East Asian Group suggests that Chinese and Koreans have improved their socioeconomic
status in Japan, and that their social integration is progressing successfully. Their assimilation into Japanese culture sharply contrasts with their behavior in the United Kingdom,
where Chinese tend to congregate over a protracted period (Simpson and Finney, 2009).
We hereby confirm the conventional implication of the SAT, that immigrants find it easier
to assimilate themselves into societies that resemble their own.
[Tables 4 and 5]
While the spatial assimilation of foreign nationals in Japan is generally in progress,
the concentration rates of the Southeast Asian, South American, and Western Groups are
significantly positive. Immigrants in the first two groups earn relatively low incomes intheir home countries, are distant linguistically and religiously from Japanese, and have aweak socioeconomic position in Japan. This result is consistent with the behavior of nonWestern immigrants in the Netherlands (Zorlu and Muder, 2008). These immigrants—
who are both linguistically and religiously distant from the Dutch—choose ethnicallyconcentrated areas, regardless of whether their migration is international or domestic.
Moreover, our results indicate that foreigners from the Southeast Asian Group, whose
concentration rate was also significant during international migration (Table 3), fail to
improve their socioeconomic status and carry out cultural transformation within Japan;
consequently, they have experienced significant difficulties in integrating themselves into
Japanese society. The South American Group seems to start residing close to each otherupon arriving in Japan, since their concentration rate was insignificant for internationalmigration (Table 3) but significant for interprefectural migration. Finally, it appears
that members of the Western Group change their preference of residential location upon
arriving in Japan: at the time of entry, the spatial location is not an important factor for
immigration, but they prefer to stay together when making relocation decisions in Japan.
Besides a tendency for immigrants to concentrate, there are some differences between
the empirical results of international and interprefectural migration analyses.9 For example, while there was a strong tendency for workers to relocate to highly populated
9

Based on the AIC, the spatial model is more appropriate for all foreigners and the East Asian Group(Table 5), while the OLS model is better for the other groups (Table 4).

13

prefectures during domestic migration, such a tendency was very weak in the spatial
analysis of international migration. Moreover, while economic motivations remain important with respect to domestic migration, job opportunities (rather than higher income)tend to be the motivation behind relocation. In the case of international immigration,
increases in unemployment rates in origin countries reduce workers’ economic gains andemigration, but in the case of interprefectural migration, unemployment rates (o) are often significantly positive. Foreigners living in Japan already have the economic power tomove between nations, and domestic travel costs are significantly lower than international
travel costs; these facts facilitate foreign nationals’ movement to other prefectures thatoffer better employment opportunities.
Additionally, the distance functions more consistently with theoretical predictions ofinterprefectural movement than with those of international immigration. Unlike the caseof migrating from outside the country, in the interprefectural analysis, the distance variable is correctly signed and both significant and negative. Compared to cases of international movement where there is no adjacent foreign country, prefectural borders (which
are more likely to be adjacent to each other in domestic movement) are associated witha cost of movement (i.e., between neighboring prefectures) that is significantly lower andmore affordable. Therefore, we obtain the predicted result that the distance between
prefectures negatively correlates with the number of migrants.

6

Conclusion

Based on the spatial assimilation theory (SAT), we analyzed the concentration tendency
of immigrants to Japan and that of foreigners relocating within Japan. According to the
SAT, when first entering a country, immigrants tend to flow into areas where their own
ethnic group concentrates, but subsequently relocate to areas with low ethnic intensity.
Therefore, spatial assimilation—the theme of this study—acts as an indicator of social
integration, and we show that spatial assimilation among foreigners in Japan is in progress.
Analysis of international migration data showed that the effect of the prefecture-level
concentration of foreigners on immigration was significantly positive for the East Asianand Southeast Asian Groups, both of which come from regions whose per-capita GDP

14

is lower than that of Japan. In contrast, interprefectural migration analysis showed that
the East Asian Group (Chinese and Korean)—which is characterized by similarities withJapan in terms of language, culture, and religion—had a weaker tendency to concentrate during domestic migration; this finding aligns with SAT predictions. However, foreach of the Southeast Asian, Western, and South American Groups—which are relatively
different from Japan in terms of culture, language, and religion—the concentration rate
significantly and positively correlated with migration. This finding suggests that the integration of Chinese and Korean immigrants into Japanese society is progressing at a fasterrate than that of other immigrant groups.
In Japan—a country that aims to increase its volume of foreign labor—it is urgently
necessary to prepare a social integration policy for foreigners. European countries—
which have had a comparatively longer history of hosting foreign workers and refugees—
have invested considerably in social integration policies so as to prevent isolation among
foreigners and stabilize society. Given that many immigrants’ children do not attend
school in Japan, investment in education on Japanese culture and history is needed toestablish an immigration policy that will have positive impacts on Japanese society.

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Table 1: Descriptive statistics

Immigration of foreigners to prefectures (100people)
Unemployment rates of foreign countries
Real GDP per capita of foreign countries
No. of population of foreign countries
Inter-prefectural migration of foreigners (100 people)
Unemployment rates of prefectures
Real GDP per capita of prefectures
No. of population of prefectures
Foreigners’ rates of prefectures
Distance between foreign countries and prefectures
Distance between prefectures

Mean
6.34
6.86
9.22
4.78
0.37
5.94
10.1
14.5
0.85
8.52
5.98

SD
16.7
3.45
0.85
1.06
1.88
1.34
0.16
0.74
0.50
0.933
0.80

Min
0
1.86
8.04
3.33
0
4.24
9.83
13.32
0.26
6.29
2.35

Max
180
11.4
10.7
7.18
42.2
11.9
10.8
16.3
1.83
9.84
7.72

Note: Standard deviation (SD). All variables are in natural log except number of
migrants that includes 0, and unemployment rates and foreigners’ rates that are expressed in terms of percentage.

Table 2: Moran’s I tests for migration flows
Moran’s I
statistic
Wo (Immigration to Japanese prefectures)
0.203
Wd (Immigration to Japanese prefectures)
0.738
Wo (Inter-prefectural migration all foreigners)
0.334
Wd (Inter-prefectural migration all foreigners)
0.294
Wo (Inter-prefectural migration East Asians)
0.306
Wd (Inter-prefectural migration East Asians)
0.259
Wo (Inter-prefectural migration Southeast Asians)
0.317
Wd (Inter-prefectural migration Southeast Asians)
0.321
Wo (Inter-prefectural migration Americans and British)
0.193
Wd (Inter-prefectural migration Americans and British)
0.171
Wo (Inter-prefectural migration South Americans)
0.183
0.225
Wd (Inter-prefectural migration South Americans)
Spatial weight matrix

ExpectationSD

p-value-0.002-0.002-0.000-0.000-0.000-0.000-0.000-0.000-0.000-0.000-0.000-0.000

0.036
0.025
0.011
0.011
0.011
0.011
0.012
0.012
0.011
0.011
0.012
0.012

0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000

Note: Expectation and SD express the expectation value and standard deviation of the null distribution in Moran’s I test. Wo and Wd are origin- and destination-based spatial matrices, respectively.

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Table 3: Results of immigration models
Dependent variable:

Immigration to prefectures from abroad
(A)
(B)
(C)
Intercept−169.789−118.835∗−113.013∗(93.595)(51.818)(54.819)
Unemployment rate o−0.936∗∗∗ −0.474∗∗−0.585∗∗∗(0.187)(0.159)(0.172)
Real GDP per capita o−4.370∗∗∗ −2.888∗∗−2.933∗∗(0.732)(0.921)(0.886)6.397∗∗∗
2.781∗∗∗
3.066∗∗∗
No. of population o(0.874)(0.723)(0.833)−0.0040.446
0.502
Unemployment rate d(0.635)(0.535)(0.548)
Real GDP per capita d
11.5688.468
7.454(9.565)(5.666)(5.805)5.048∗∗∗
1.925
1.580
No. of population d(1.040)(1.485)(1.415)
Foreigners’ rate d36.197∗∗∗15.999∗∗(9.441)(5.399)19.261∗
East Asians d(9.628)42.820∗∗Southeast Asians d(16.223)
Americans and British d−12.875(31.529)
10.886South Americans d(6.039)Distance od−0.1062.127
3.110∗(0.581)(1.294)(1.465)
Wo Yod0.233
0.275(0.173)(0.172)
Wd Yod0.570∗∗∗
0.563∗∗∗(0.076)(0.079)Sample size
470
470
470
AIC
3654.615
3448.405
3455.068
Note: ***p<0.001, **p<0.01, *p<0.05. Subscripts ’o’ and ’d’ represent origin
and destination regions, respectively.

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Table 4: OLS results for inter-prefectural migration models
All
East
Southeast
AmericansSouthforeigners
AsiansAsiansand British Americans
Intercept−0.079∗∗−0.066∗∗−0.002−0.0010.006∗∗(0.028)(0.021)(0.002)(0.001)(0.002)0.068
0.063∗
0.006∗−0.0010.007∗∗
Unemployment rate o(0.035)(0.025)(0.002)(0.002)(0.002)−0.945−0.435−0.111−0.013−0.023
Real GDP per capita o(0.898)(0.562)(0.065)(0.031)(0.024)
No. of foreigners (same group) o
0.440∗∗∗
0.281∗∗∗
0.060∗∗∗
0.020∗∗∗
0.030∗∗∗(0.098)(0.063)(0.010)(0.005)(0.003)
0.055
0.050
0.007
−0.002
0.000
Unemployment rate d(0.043)(0.034)(0.004)(0.002)(0.003)
Real GDP per capita d−0.057−0.2130.015−0.008−0.053(0.889)(0.577)(0.065)(0.037)(0.030)0.533∗∗∗
0.333∗∗∗
0.050∗∗∗
0.016∗∗
0.057∗∗∗
No. of population d(0.097)(0.074)(0.008)(0.005)(0.010)0.053
0.179
0.253∗∗∗
0.591∗∗
0.232∗∗∗
Foreigners’ rate (same group) d(0.122)(0.121)(0.055)(0.215)(0.045)Distance od−0.720∗∗∗ −0.445∗∗∗−0.079∗∗∗−0.026∗∗∗ −0.075∗∗∗(0.153)(0.112)(0.014)(0.007)(0.010)Sample size
2209
2209
2209
2209
2209
AIC
8584.404
7160.988−1295.597−4448.364
35.530Note: ***p<0.001, **p<0.01, *p<0.05. Subscripts ’o’ and ’d’ represent origin and destination regions, respectively.

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Table 5: S2SLS results for inter-prefectural migration models

Intercept
Unemployment rate oReal GDP per capita o
No. of foreigners o
Unemployment rate dReal GDP per capita dNo. of population dForeigners’ rate (same group) dDistance od
Wo YodWd YodSample size
AIC

All
East
Southeast
AmericansSouthforeigners
AsiansAsiansand British Americans−0.042−0.023−0.0000.033
0.050(0.308)(0.215)(0.034)(0.022)(0.044)0.100∗∗
0.090∗∗∗
0.005−0.0020.007∗∗(0.035)(0.026)(0.004)(0.002)(0.002)−0.499−0.176−0.121∗∗−0.025−0.010(0.570)(0.320)(0.047)(0.015)(0.030)0.466∗∗∗
0.320∗∗
0.060∗∗∗
0.025∗∗
0.027∗∗∗(0.128)(0.102)(0.013)(0.010)(0.004)0.032
0.025
0.008∗−0.003−0.002(0.029)(0.019)(0.003)(0.002)(0.003)−0.430−0.4560.022−0.009−0.106(0.602)(0.380)(0.036)(0.016)(0.055)0.470∗∗
0.305∗∗
0.051∗∗∗
0.022∗
0.085∗∗∗(0.163)(0.118)(0.014)(0.009)(0.021)0.068
0.052
0.243∗∗∗
0.742∗
0.335∗∗∗(0.109)(0.223)(0.058)(0.334)(0.101)−0.720∗∗∗ −0.458∗∗−0.080∗∗∗−0.033∗∗−0.083∗∗∗(0.181)(0.140)(0.018)(0.012)(0.017)0.238
0.269−0.006−0.264−0.496(0.316)(0.334)(0.040)(0.514)(0.351)−0.239−0.2840.005−0.1040.291(0.288)(0.315)(0.037)(0.496)(0.184)
2209
2209
2209
2209
2209
8577.211
7156.315−1277.948−4153.134
169.017

Note: ***p<0.001, **p<0.01, *p<0.05. Subscripts ’o’ and ’d’ represent origin and destination regions, respectively.

22

Figure 1
Origin-based dependence (left) and Destination-based dependence (right) of migration

Figure 2
The percentage of foreign residents in Japan by residential type (October, 2018)

Note: The data source is the report on employment status of foreigners produced by
Ministry of Health, Labor and Welfare

23