KEO Discussion Paper No. 174

Immigration, imports, and (im)mutable Japanese labor markets

Akira Sasahara, Yumin Sui, and Emily Taguchi
February 2023

KEO Discussion Paper No. 174

Immigration, imports, and (im)mutable Japanese labor markets*
Akira Sasahara†
Keio University

Yumin Sui‡
Keio University

Emily Taguchi§
University of Cologne

February 2023

Abstract
This paper investigates the effects of globalization–measured by an increase in immigration and inimports from China–on labor market outcomes in Japan. We attempt to identify the causal links usinga shift-share instrument based on previous settlement patterns of migrants for immigration shocks,
and the one based on previous spatial allocation of sectoral employment for import shocks. The results suggest limited impact of these variables on wages, however, significant effects are found in thelate 1990s, suggesting their interactions with Japan’s Lost Decade.
Key Words: immigration, China trade shock, wages, JapanJEL codes: F16, F22, F66* The authors would like to thank Jaerim Choi, Masahiro Endoh, Jongkwan Lee, Lester Lusher, and Vasil Yasenov forhelpful comments and insightful discussions. They are also grateful to feedback from advisors and fellow classmates at theProfessional Career Program of Keio University. Akira Sasahara acknowledges financial support from the Japan Society ofPromotion of Science KAKENHI under grant numbers 21H00713, 21K1329, and 22H00063. The authors acknowledge paid
English language editing services by a private firm. All errors are the authors’ responsibility.
†(Corresponding author) Faculty of Economics, Keio University. E-mail: sasahara@keio.jp‡
Faculty of Economics, Keio University. E-mail: suiyumin0617@keio.jp§
Department of Economics, University of Cologne. E-mail: etaguchi@smail.uni-koeln.de1

1 Introduction
Globalization takes various forms, including an increase in the international flow of people and goods
and services. Increasing globalization often leads to public concern. For example, a recent article showsthat Japanese citizens hold greater opposition to refugees’ settlements (Horiuchi and Ono, 2018), potentially reflecting their apprehensions about depressing wages and deteriorating public safety.1 Previous
studies have shown adverse labor market effects of imports from China (e.g., Autor et al., 2013). Japanis also exposed to these globalization shocks, as it experienced a 1.4 percentage points (hereafter pp)
increase in foreign resident share and a 3.2 pp increase in imports from China as a share of GDP between
the early 1990s and the late 2010s, as shown in Figure 1.

Imports−to−GDP share, Foreign resident share, %
0
1
2
3
4

F IGURE 1: Foreign nationals living in Japan and imports from China

Imports from China/GDP

18

16

20

14

20

12

20

10

20

08

20

06

20

04

20

02

20

00

20

98

20

96

19

94

19

92

19

90

19

19

19

88

Foreign resident pop./Total pop.

Note: The data on foreign nationals living in Japan come from the Statistics on Foreign Residents (Zairyū Gaikokujin Tōkei), theMinistry of Justice and the population data come from the Statistics Bureau of Japan. We exclude Permanent Special Residents(Tokubetsu Eijūsha) from the category of “foreign nationals.” The import data come from UN Comtrade and the GDP data
come from the World Development Indicators.

The goal of this study is to investigate the labor market effects of these changes using Japanese dataand examine whether these public concerns are valid. To do so, we construct a panel dataset spanningnearly 30 years from 1989 to 2018, consisting of five stacked cross-sections. We then regress the wage
growth rates on two globalization variables: (1) immigration and (2) imports from China, and a rich1

Nishikido (2019) shows that individuals in prefectures with higher unemployment rates tend to oppose immigration, suggesting that their concerns come from their perceptions that immigration may deteriorate local economic conditions. Okubo(2021) also notes that immigration is often seen as a source of downward pressure on wages, which may cause conflicts andcrimes.

2

set of control variables. Further, we carefully deal with potential endogeneity by employing shift-share
instruments, utilizing lagged spatial allocation of immigrants of each ethnic group to identify the effects
of immigration shocks, and lagged spatial allocation of sectoral employment to identify the effects ofimport shocks.
We estimate a number of regressions to examine the effects of these shocks on wages by firm size,
age group, sector, and period. We find that the effects are mostly statistically insignificant. However, statistically significant and negative effects on wages are indeed found in the 1995–2000 period, suggestingthat globalization shocks adversely impacted the Japanese economy, which experienced lower productivity growth during what is known as Japan’s Lost Decade (see, for example, Hayashi and Prescott,2002).
There are several reasons for the insignificant effect. First, as documented by Genda et al. (2010),
Japan has had rigid wages for institutional reasons, making these less sensitive to globalization shocks.2
Second, the increase in the number of immigrants in Japan is still smaller than that in other developed
countries. For example, according to the World Development Indicators, foreign population as a shareof the total population increased by more than 10 pp between 1985 and 2015 in Singapore, Switzerland,
Norway, and Spain. By contrast, the increase was only 0.9 pp in Japan in the same period. This suggeststhat the recent increase in immigrant inflow in Japan may not have been large enough to trigger noticeable
labor market effects.
Third, the limited effects of immigration may be a product of its multiple offsetting effects. Whilean expansion of labor supply due to immigration leads to downward pressure on wages, it also increases
consumer demand, which works to increase labor demand and in turn wages (e.g., Bodvarsson et al.,2008).3 In addition, immigrants may not be a perfect substitute for Japanese natives and may havecomplementary and productivity-enhancing effects, as documented in the US context (e.g., Ottaviano
and Peri, 2012; Peri, 2012) and the South Korean context (Kim et al., 2022).
Regarding the limited labor market effects of imports from China, existing studies document thatwhile imported final goods from China have a negative effect on manufacturing employment in Japan,
imported inputs from China have a positive effect (see Taniguchi, 2019; Kiyota et al., 2021; Kainuma andSaito, 2022; Choi et al., 2023). While we do not estimate the effects of final goods and inputs separately,
we assume that the estimated null effects of imports from China are a combination of the two opposing
effects.
2

Genda et al. (2010) note that “the starting salaries for Japanese new graduates (conditional on educational background)have been downwardly rigid in Japan since the 1980s.”
3
For example, Bodvarsson et al. (2008) find a limited effect of immigration on wages in the Miami labor market in the 1980s.
They argue that the limited effect is explained by an increase in consumer demand for services generated by immigrants.

3

Our paper contributes to two strands of literature. First, it contributes to the literature on the effects
of immigration on wages. Some existing studies find negative effects (Borjas, 2003, the US; Dustmann etal., 2013, the UK; Edo, 2020, France; Malchow-Møller et al., 2012, Denmark) and others find null effects(Gandal et al., 2004, Israel; Bodvarsson et al., 2008, and Peri and Yasenov, 2019, the US) and positiveeffects (Kim et al., 2022, South Korea). In the context of the Japanese economy, Nakamura (2010) findsthat workers in regions with more immigrants tend to receive higher wages using individual-level data.4
Our paper differs from his because we use prefecture-level data and employ a shift-share instrument to
identify causal effects. In addition, we find that immigration effects on wages are mostly null.
Second, we contribute to the literature on the effects of the China trade shock. In the US context,
imports from China reduced manufacturing wages (Autor et al., 2013), but no such effect was observedin Germany (Dauth et al., 2014). Endoh (2018) finds, using establishment-level data on large firms inJapan, that import competition did not reduce the wages of low-skilled workers but increased the wagesof college graduates. Endoh (2021) extends his analysis to cover smaller firms, with similar results.
Our study differs from his because we use prefecture-level data, allowing for the inter-sectoral effects ofimport competition on non-manufacturing sectors within each local labor market.
The remainder of this paper is organized as follows. Section 2 outlines our regression model andidentification strategy. Section 3 summarizes the data sources and provides an overview of key variables.
Section 4 presents the regression results. Section 5 concludes. The Appendix presents additional analyses
and details.

2 Empirical approach
2.1

Baseline regression model
We estimate regressions following the literature, including Edo (2020) and Autor et al. (2013). The

baseline model is as follows:CHN∆ ln(wagei,t ) = βt + β1 ∆migi,t + β2 ∆ipwi,t+ Xit β3 + ui,t ,(1)

where ln(wagei,t ) = 100 × [ln(wagei,t+5 ) − ln(wagei,t )], log changes in prefecture i’s average hourly
wage from year t to year t+5. The variable wagei,t is computed as the total annual payments (12×monthly
payments + bonuses) divided by the total hours worked per year (12×monthly planned hours worked +4Nakamura (2010) finds that immigration had a positive effect on wages of native workers through reallocation of native
workers and capital across regions.

4

12×monthly overtime hours worked).5 Nominal hourly wages are deflated to the 2020 price level using
prefectural CPIs.
This equation includes two key explanatory variables. First, ∆migi,t measures the size of immigration shocks and is defined as follows:∆migi,t =M IGi,t+5 − M IGi,t,P opi,t /1000(2)

where M IGi,t indicates the stock of foreign residents in Japan; and P opi,t denotes the working age
population (ages 15–65), including Japanese and foreign residents, measuring the size of each local
labor market. The population variable is divided by 1,000. Thus, ∆mig represents the change in migrant
stock per 1,000 working age individuals from year t to year t + 5.6
Our definition of “immigrants” is foreign nationals living in Japan, excluding diplomats and shortterm visitors such as tourists and conference attendees. The original dataset considers Special Permanent
Residents (Tokubetsu Eijū Sha, hereafter SPRs), individuals who were born in Japan and maintain theirancestry nationalities for historical reasons, as foreign residents. However, we exclude the SPR population when constructing our immigration variables because we attempt to capture inflows of immigrants
during that period.7 Although the denominator is the working age population, as we are interested in
the overall effects of immigration including the channels through the goods market (e.g., an increase indemand for goods and services), foreign residents who are in education or training or dependents are also
included in ∆mig.8 Another reason why the “immigrants who are not in the labor force” is included in
the numerator is that only the overall number of immigrants–but not the number of “immigrants who arein the labor force”–is available at the prefecture-level for each ethnicity. As a result, we are unable toconstruct an appropriate shift-share instrument for an immigration variable using immigrants in the labor
force only.
CHN measures the degree of import penetration from China, and it is defined as folSecond, ∆ipwi,t5These variables are obtained from the Basic Survey of Wage Structure by the Ministry of Health, Labour and Welfare ofJapan. According to its website, each year, each firm reports the June monthly wage of the year and bonuses from last year.
Therefore, the data on bonuses are taken from the next year’s survey. The database covers firms with 10 or employees.
6
The database does not provide us with
we use the migration data for 1988 and construct migration data for 1989. Therefore,
the variable as follows: ∆migi,1989 = M IGi,1994 − M IGi,1988 × 65 / [P opi,1989 /1000]. We take the same approach for
one of the control variables, ∆ ln(P opN ative ).
7
The start year of the SPR data is 1992 because the Special Act on Immigration Control was enacted in 1991. We use
the 1992 SPR population data to construct the 1989 immigration variables. In addition, there are two groups of Korean SPR
population, the Republic of Korea (Kankoku) and Korea (Chōsen). The end year of the Chōsen SPR data is 2014 for an
unknown reason. Therefore, we impute the number of total Korean SPRs in 2018 using the 2014 share of the two groups of
SPRs, Chōsen/(Kankoku+Chōsen)= 0.10.
8
Appendix H reruns regressions using different migration variables: (1) the one excluding migrants who are not in the labor
force, and (2) the one using the total population in the denominator.

5

lows:
CHN∆ipwi,t=

CHN →JP N
CHN →JP N
X Li,s,t × ∆ims,t∆IMs,t
CHN →JP N
PP.
with ∆ims,t=s′ ∈S Li,s′ ,ti′ L ′s∈S(3)

i ,s,t

CHN →JP N = IM CHN →JP N −IM CHN →JP N denotes five-year changes in Japanese
The variable ∆IMs,t
s,t
s,t+5

imports from China’s sector s. The variable Li,s,t is the number of employees in sector s of prefecture i
CHN →JP N represents Japanese imports from China per sector s’s employee. Secin year t. Thus, ∆ims,t
CHN →JP N is used to find the weighted average of sectoral import shocks
toral import penetration ∆ims,twhere the weight is the sectoral employment Li,s,t .9 The set of sectors is indicated by S, which includes
63 manufacturing sectors, and non-manufacturing sectors.10
The period fixed effects are denoted by βt . Vector Xit includes two sets of control variables: (1) labormarket controls, variables from the same group of workers as the dependent variable, and (2) macroeconomic variables. The labor market controls include (1) the log of beginning-of-the-period annual income,(2) the log of beginning-of-the-period average tenure, (3) the log of beginning-of-the-period averageage, and (4) the beginning-of-the-period female share of that worker group. The macroeconomic controls include (1) log changes in native population, (2) beginning-of-the-period log of population density,(3) beginning-of-the-period male share, (4) beginning-of-the-period unemployment rate, (5) beginningof-the-period urban population share, (6) beginning-of-the-period share of the 60+ age population, (7)beginning-of-the-period share of workers in labor unions, and (8) sectoral value-added shares.11 The
error term is indicated by ui,t . All regressions are estimated using the initial populations as weights.

2.2

Instrumental variables for endogenous variables
The regression model suffers from endogeneity because immigrants’ settlement patterns and im-

ports from China are affected by local economic conditions, which may in turn affect the dependent
variable, wages. To deal with the endogeneity of immigration, following previous studies (e.g., Card,2009; Bianchi et al., 2012; and Peri, Shih, et al., 2015), we construct an instrument based on previous
9

As sectoral employment data are not available from every year, the gap between the survey year of the employment data
and year t differs across periods. When constructing the explanatory variable in equation (3), the sectoral employment datafrom 1991 is used for the 1989–1994 period, 1996 for the 1995–2000 period, 2001 for the 2001–2006 period, 2006 for the
2007-2012 period, and 2012 for the 2013–2018 period.
10
See Appendix A for a list of 63 manufacturing sectors. We include non-manufacturing sectors in set G because we also
analyze the effects on non-manufacturing wages.
11
It includes 10 sectoral value-added shares from (1) agriculture, (2) mining, (3) manufacturing, (4) construction, (5) utility,(6) wholesale/retail, (7) finance, (8) real estate, (9) transportation, and (10) public sector. It excludes hospitality, informationand telecommunication, education, healthcare and medical, and other unclassified service sectors.

6

immigrants’ settlement patterns. Specifically, we use the following shift-share instrument:
Bartik∆migi,t
=

where
\
∆M
IGi,t =

X
g∈G

\
∆M
IGi,t
,
P opi,t /1000(4)

M IGg,i,t−1
P
× (M IGg,t+5 − M IGg,t ),i M IGg,i,t−1

indicates the number of immigrants predicted by (1) the share of ethnic group g’s immigrants in prefecP
ture i in a year before year t, M IGg,i,t−1 / i M IGg,i,t−1 ,12 and (2) the overall change in the numberof ethnic group g’s immigrants throughout Japan, M IGg,t+5 − M IGg,t .13 The set of origin economies
is indicated by G, which includes 172 economies consistently available throughout the sample period.14
Bartik .15
We exclude all Korean SPRs when constructing ∆migi,tTo address the endogeneity of the import variable, following Autor et al. (2013), we use the instrument:
OT H∆ipwi,t=

CHN →OT H
CHN →OT H
X Li,s,t−5 × ∆ims,t∆IMs,t
CHN →OT H
PP,with ∆ims,t=s′ ∈S Li,s′ ,t−5i′ L ′s∈S(5)i ,s,t

CHN →OT H denotes the five-year changes in Chinese exports to eight developed countries:
where ∆IMs,tAustralia, Canada, Switzerland, Denmark, Germany, Spain, Finland, and New Zealand.16 The variable
CHN →OT H is meant to capture China’s supply shocks, increasing Chinese exports to Japan. The∆IMs,t
CHN →OT H is found using each prefecture’s sectoral employment five years
weighted average of ∆ims,tago as the weight.17
The validity of shift-share instruments has been discussed in several studies (e.g., Adão et al., 2019;
Goldsmith-Pinkham et al., 2020; Borusyak et al., 2022). According to them, for an estimate to be
12
Most existing studies fix the year for the share part at a lagged year. For example, Edo (2020) uses share data from 1954
to analyze the effects in 1962–1976. Malchow-Møller et al. (2012) use share data from 1987 for the 1993–2004 period. Peri,
Shih, et al. (2015) use share data from 1980 for the 1990–2010 period. These studies use share data from a 5-to-10 fixed lagged
year when constructing their instruments. However, in this study, fixing the share data at the initial level (or using the sharefrom every five years back) leads to low first-stage F -statistics probably because immigration to Japan is less strongly affectedby previous settlement patterns relative to other countries. Therefore, our shift-share instrument for immigration shocks uses
the share data from the previous year for each, following Bianchi et al. (2012).
13
We use the migration data from 1988 to impute the five-year change for the 1989–1994 period, (M IGg,1994 −M IGg,1988 ) × 5/6, because the migration data from the year 1989 are not available.
14
See Appendix B for a list of 172 origin economies.
15
In the sample period, almost all SPRs, 99%, are of Korean descent. The SPR population data for other ethnic groups are
not available consistently throughout the sample period. See Appendix C for the details about Korean SPRs.
16
Autor et al. (2013) use Chinese exports to the United States, instead of Canada. However, UN Comtrade does not provide
us with HS 92 data on Chinese exports to the US for 1989. Therefore, we replace the US with Canada when constructing the
instrument shown in equation (5).
17
As sectoral employment data are not available for every year, the number of lags slightly differ across periods. When
constructing the instrument in equation (5), the sectoral employment data from 1986 is used for the 1989–1994 period, from1991 for 1995–2000 period, 1996 for 2001–2006, 2001 for 2007–2012, and 2006 for 2013–2018.

7

consistent, one of the following two conditions must be satisfied: (1) the shares are uncorrelated with theerror term controlling for covariates or, (2) the macroeconomic changes are uncorrelated with the error
term controlling for covariates.18 We believe that both the conditions are satisfied for several reasons.
First, we introduce many control variables explaining the initial spatial distributions of immigrants andsectoral employment, which help reduce the correlation between each of the two globalization variables
and the error term. Second, Chinese exports to eight other developed countries must be considered
exogenous for Japan by construction. Third, national changes in the number of immigrants in eachethnic group must also be seen as exogenous because, for example, national immigration policies areabsorbed by period dummies. Therefore, after the inclusion of all controls, both the shares and changes
must be exogenous from each prefecture’s viewpoint.

3 Data3.1

Data sources and details
Our dataset comprised data from various sources. The data on the prefecture-level and origin-country-level numbers of foreign residents are obtained from the Statistics on Foreign Residents in Japan(Zairyū Gaikokujin Tōkei) of the Ministry of Justice, Japan. The import shock variables are constructedusing trade data from the UN Comtrade database. The unit of trade value is the nominal USD. Therefore,
we convert them into the JPY 2020 prices using the exchange rate from the Penn World Table 10.0 (Feenstra et al., 2015) and the CPI from the Statistics Bureau of Japan. We obtain employment data from theEstablishment and Enterprise Census of Japan (Jigyōsho Kigyō Tōkei Chōsa) and the Economic Census
for Business Activity of Japan (Keizai Sensasu Katsudō Chōsa).
The data on wages, work hours, and the number of employees come from the Basic Survey on Wage
Structure (Chingin Kozo Kihon Chōsa) by the Ministry of Health, Labour, and Welfare. We obtain
prefecture-level total population, population of the age 60+ group, urban population share, and population in the urban area with more than 4,000 people per square kilometer from the Japanese Census.19
The population density is determined by dividing the total population by habitable area, which is obtained from the Geospatial Information Authority of Japan, Ministry of Land, Infrastructure, Transport,
and Tourism. The prefecture-level unemployment rates come from the Japanese Census and the Labor
Force Survey of the Statistical Bureau of Japan.20 Data on the number of workers joining labor unions
18

This summary comes from Lu et al. (2020).
As the urban population share data are available every five years, the urban population data from 1985, 1990, 1995, 2000,
2005, and 2010 are used for the initial years of each sub-period, 1988, 1995, 2001, 2007, and 2013, respectively.
20
Neither of the two sources singly provides us with the data for the entire period of 1989-2018. Therefore, we use the198are obtained from the General Survey on Working Conditions. The prefecture-sector level value-added
data are obtained from the Cabinet Office of Japan.

3.2

Descriptions of the data

This section describes the key variables. Panel A of Figure 2 displays the geographical distributionPof migration shocks during the long-run period, 1989–2018. The displayed variable is t∈T ∆migi,twhere T = {1989, 1995, 2001, 2007, 2013} and ∆migi,t comes from equation (2). This shows that urban prefectures in the Kanto and Chubu areas are exposed to greater migration shocks, whereas ruralprefectures are less exposed to such shocks. Panel B of Figure 2 shows the geographical distribution ofP
CHN during 1989–2018. The result indicates that the distribution differs from the one for thet∈T ∆ipwi,tPPCHN ),
immigration variable. The correlation coefficient of the two variables, Corr( t∈T ∆migi,t , t∈T ∆ipwi,t
is 0.46.21 Table 1 summarizes the top five, median, and bottom five prefectures in terms of the size of
P
P
CHN .
t∈T ∆ipwi,tt∈T ∆migi,t andF IGURE 2: Japanese prefectures with immigration shocks and import shocks
Panel A: Immigration

Note:

Panel B: Imports from China

Panel A shows the sum of the migration shocks during 1989-2018,Pt∈T ∆migi,t ,where T=

{1989, 1995, 2001, 2007, 2013} and the unit is the change in the number of migrants per 1,000 population. Panel B shows theP
CHN
import penetration variables during 1989–2018, t∈T ∆ipwi,t, where the unit is 10,000 JPY.
Census unemployment rates for the years 1989, 1994, 2001, and 2006 and use the unemployment rates from the latter source
for the years, 2007, 2012, 2013, and 2018. As a result, the unemployment rates from 1990, 1995, 2000, and 2005 are used forthe years 1989, 1994, 2001, and 2006, respectively.
21
See Table D2 in Appendix D for the correlation between the two variables in each period.

9

TABLE 1: Summary of immigration shocks and import shocks

1
2
3
4
5

Migration
shocks
P
∆mig
i,t
t∈T
Tokyo 37.1
Aichi 36.4
Gunma 34.0
Mie 31.8
Gifu 30.1

P Import shocks
CHNt∈T ∆ipwi,tShiga 61.26
Gunma 45.61
Tochigi 35.30
Ibaraki 34.88
Shizuoka 32.33

23
24

Kagawa
Kyoto

15.4
15.4

OkayamaToyama

19.49
19.09

43
44
45
46
47

Kochi
Iwate
Wakayama
Aomori
Akita

6.7
5.8
5.0
4.2
3.3

Kochi
NagasakiKagoshima
Hokkaido
Okinawa

10.35
10.21
9.90
5.59
2.26

Note: The table shows (1) the sum of the migration shock variables during 1989–2018,Pt∈T ∆migi,t ; the unit is the changeP
CHN;t∈T ∆ipwi,tin the number of immigrants per 1,000 population, and (2) the import penetration shocks during 1989-2018,the unit is 10,000 JPY, for the top five prefectures, median prefectures, and bottom five prefectures, respectively.

The aggregate sectoral and cohort immigration patterns are described in Figure 3 and 4. The former
shows the sectoral distribution of employees in the construction, manufacturing, and financial sectors.
This shows a greater concentration and a smaller concentration of foreign employees than all employees
in the manufacturing and finance sectors. It shows a greater concentration and a smaller concentrationof foreign employees than all employees in the manufacturing and finance sectors, respectively. Figure 4
displays P opImmigrant/P opTg,total × 100 where P opImmigrant
denotes the number of immigrants (stock)g,tg,tof age cohort g in year t, and P opTg,total denotes the total population. This shows that the immigrant sharesin younger cohorts increased more rapidly than in other cohorts, with a 5 pp increase in the 20–24 year
cohort and a 4.2 pp increase in the 25–29 year cohort. A simple supply-demand analysis would imply agreater depressing effect on wages in younger cohorts in the manufacturing sector.
The average hourly manufacturing wages of all prefectures and the top five and bottom five prefectures in terms of each shock, listed in Table 1, are shown in Figure 5. 22 Part I is caused by migration
shocks and Part II, by import shocks. Panels A and C show that manufacturing firms in prefectureswith greater exposure to immigration and import shocks pay higher wages than those with lower exposure. However, Panels B and D show that prefectures with greater exposure to these shocks experienced
slightly lower wage growth paths in the late 1990s, the early 2000s, and then after 2010. This suggeststhat immigration and imports from China exerted a slight downward pressure on wages. We take an
22
We focus on manufacturing wages because (1) there is greater concentration of migrant workers in the manufacturingsector as shown in Figure 5, and (2) most imports from China are manufacturing goods.

10

20

Sectoral share of all employees
Sectoral share of foreign employees
29.2

17.2

10

Share, %30

F IGURE 3: Sectoral employment shares

6.9

5.6
2.8

0

0.6
Construction

Manufacturing

Finance

Note: The data are from Figure II-2 of Japan Institute for Labour Policy and Training (2020), Chapter 2, p. 7.

7

F IGURE 4: Share of immigrant population by age cohort

s

s

yr
+
70

60

−6
9

yr

s
50

−5
9

yr

s
40

−4
9

yr

s
30

−3
9

yr

s
yr
−2
9
25

−2
4
20

0−
19

yr

yr

s

s

0

Share of foreign residents, %
1
2
3
4
5
6

1988
1994
2000
2006
2012
2018

Note: Foreign resident population data come from Statistics on Foreign Residents (Zairyū Gaikokujin Tōkei) of the Ministry ofJustice. Native Japanese population data are obtained from the Statistics Bureau of Japan.

econometric approach to examine whether these differences are statistically significantly different fromzero.
The dataset is a stacked cross-section of 47 prefectures for five periods, 1989–1994, 1995–2000,
2001–2006, 2007–2012, and 2013–2018. Therefore, the baseline regressions include 47 × 5 = 235
observations. Table 2 presents summary statistics of the variables used in the baseline regressions. Ap-

11pendix D presents additional details.
F IGURE 5: Average hourly manufacturing wages

3

Part I: Top five and bottom five migration shock prefectures
Panel A: Average hourly wages
Panel B: Log deviation from 1988

.3

Bottom 5 migration shock prefectures

Top 5 migration shock prefectureslog deviation from 1988
.1
.2

Average hourly wage, 1,000 JPY
1.5
2
2.5

Average

Top 5 migration shock prefectures

0

Average1−.1

Bottom 5 migration shock prefectures

1990

2000

2010

2020

1990

2000

2010

2020

Part II: Top five and bottom five import shock prefectures
Panel C: Average hourly wages
Panel D: Log deviation from 1988

2.4

.3

Top 5 import shock prefectures
Bottom 5 import shock prefectureslog deviation from 1988
.1
.2

Average hourly wage, 1,000 JPY
1.8
2
2.2

Average

Top 5 import shock prefectures

1.6

0

Average−.1

1.4

Bottom 5 import shock prefectures

1990

2000

2010

2020

1990

2000

2010

2020

Note: Panels A and C plot the coefficients from regressing the average hourly manufacturing wages (1,000 JPY in 2020 prices)on year dummies. Panels B and D use log of wages as the dependent variable. The bands indicate 95% confidence intervals.
Each regression is based on (2018–1988+1)×47 = 1457 observations.

12TABLE 2: Summary statistics of variables
Mean

Std. dev.

Min

Max

Dependent variable∆ ln(annual wage) ×100

0.6

2.6

-6.5

6.9

Endogenous variable∆mig∆ipwCHN

3.2
4.2

4.0
4.7

-9.6
-7.5

18.5
20.8

Instruments∆mig Bartik∆ipwOT H

3.1
9.3

3.7
7.9

-8.1
0.3

22.1
39.4

Labor market controlsln(initial annual income)ln(initial tenure)ln(initial age)
Initial female labor share

8.4
2.4
3.7
34.1

0.1
0.1
0.0
4.5

8.1
2.0
3.6
24.4

8.8
2.6
3.8
45.8

Macroeconomic controls∆ ln(native population) ×100
Initial ln(population)
Initial ln(population density)
Initial unemployment rate
Initial urban population share
Initial share of age 60+ population
Initial share of labor union workers
Initial value-added share, agriculture
Initial value-added share, mining
Initial value-added share, manufacturing
Initial value-added share, construction
Initial value-added share, utility
Initial value-added share, wholesale retail
Initial value-added share, financeInitial value-added share, real estate
Initial value-added share, transportation
Initial value-added share, public

-0.8
14.5
2.3
3.8
49.9
26.3
11.8
2.5
0.2
24.3
7.9
3.8
11.8
4.5
11.0
6.9
5.9

2.2
0.7
0.7
1.1
18.6
6.2
3.1
1.9
0.2
8.9
2.8
2.0
3.3
1.3
2.1
1.5
2.0

-6.8
13.3
0.9
1.7
23.4
12.1
6.3
0.0
0.0
4.8
3.5
1.2
5.3
2.7
6.7
3.8
2.6

6.2
16.4
4.6
10.3
98.2
40.7
27.7
9.9
1.1
50.0
15.1
15.7
21.8
13.6
17.5
14.7
12.0

Note: The sample size is 235. The unit of ∆mig and its instrument is the number of immigrants per 1,000. The unit of∆ipwCHN is 1,000 JPY (in 2020 prices) per employee and the unit of its instrument is 100 USD (in current prices) per
employee. See the main text for the data sources.

3.3

First-stage results
We confirm the validity of our instruments for identifying the causal effects of migration and import

shocks. Figure 6 indicates that each of the two globalization variables is strongly correlated with the
instrument. As our baseline regression model includes the two endogenous variables simultaneously andthe control variables listed in Table 2, we run regressions with all explanatory variables.
Columns (1) and (3) of Table 3 separately regress each of the two endogenous variables on thecorresponding instrument, leading to large first-stage F -statistics of 47.09 and 147.79 for ∆mig and∆ipwCHN , respectively, well higher than the commonly used cutoff value of 10 (Staiger and Stock,13

1997). Column (2) regresses ∆mig on ∆mig Bartik and ∆ipwOT H , showing that the majority of thevariation in ∆mig is explained by ∆mig Bartik rather than ∆ipwOT H because the statistical significance of ∆ipwCHN is weaker than that of ∆mig Bartik and the coefficient is negative. We observe asimilar pattern in column (4) while regressing ∆ipwCHN on ∆mig Bartik and ∆ipwOT H : the statisticalsignificance of ∆mig Bartik is weaker and the coefficient of that variable is negative.23
F IGURE 6: First-stage fits
Panel B: Imports from China0t−stat = 4.46, R−sq. = 0.67
−10

0
10
delta.mig Bartik

−10

−10

0

delta.migdelta.ipw CHN
10

10

20

20

Panel A: Immigration

20

t−stat = 16.33, R−sq. = 0.85
0

10

20
delta.ipw OTH

30

40

Note: The sample size is 235 for each panel. The t-statistics and R-squared in the figure are based on regressions with period
dummies and standard errors clustered by prefecture. One outlier is dropped from Panel A for illustrative purposes.

TABLE 3: First-stage regression results∆mig∆mig Bartik(1)
0.78***(0.11)∆ipwOT H
R-squared
F -statistic for excluded instruments
p-value of F -statistic

0.84
47.09
0.000

(2)
0.81***(0.12)-0.09*(0.04)
0.84
24.26
0.000

∆ipwCHN(3)(4)-0.16*(0.08)
0.43*** 0.45***(0.04)(0.03)
0.89
0.90
147.79110.070.000
0.000

Note: The sample size is 235 for each regression. All regressions include labor market controls and macroeconomic controls as
regressors. All regressions are estimated using initial populations as weights. ***, **, and * indicate the statistical significanceat the 1%, 5%, and 10% level, respectively. Standard errors clustered by prefecture are in parentheses.
23
The negative coefficient of ∆ipwOT H in column (2) is presumably because immigrants had expected the arrival of China
trade shocks and chose locations less exposed to import shocks. The negative coefficient of ∆mig Bartik in column (4) ispresumably because prefectures with greater share of immigrants tend to be service-oriented and less exposed to import shocks.

14

4 Estimation results
4.1

Baseline results
Panel A of Table 4 presents baseline results for the full sample.24 The OLS results are presented incolumns (1)–(3), indicating that neither migration shocks nor import shocks have a significant effect on
wages, regardless of the set of control variables. To address the endogeneity of ∆mig and ∆ipwCHN ,columns (4)–(6) employ the instrumental variable approach. These columns report the Cragg-Donald
Wald F -statistics and Kleibergen-Paap rk Wald F -statistics testing for weak IVs. All columns in PanelA report sufficiently high F -statistics, indicating essentially no concern for weak IVs.
In column (4), the coefficient of ∆mig is 0.12 and statistically significant at the 5% level. A comparison of (1) and (4) suggests that the OLS estimates have downward bias. If there is an omitted variable x
with a positive coefficient βx with Corr(∆mig, x) < 0, there will be a downward bias. One candidate
for such omitted variables is, for example, regional growth policies targeting rural prefectures with fewer
immigrants. The downward bias could also be caused by the omission of variable x with a negative
coefficient βx with Corr(∆mig, x) > 0. A candidate variable is, for example, faster growth of firms
in urban prefectures, strengthening the market power of firms in the labor market and reducing the labor
share in the same prefectures.
The effects of import shocks are insignificant in all columns in Panel A. A comparison of each setof OLS and IV estimates ((1) and (4), (2) and (5), and (3) and (6)) suggests that there is a slight upwardbias with OLS. A possible cause is reverse causality: prefectures with higher wage growth increased
their purchasing power and increased imports from China. This generates a positive correlation between∆ ln(wage) and ∆ipwCHN , leading to upward bias.
While the first-stage F -statistics in Panel A suggest that IVs are working properly, a critique mayarise because the immigrant population decreased during the 2007–2012 period due to the 2008–09
Financial Crisis as shown in Figure 1. Jaeger et al. (2018) note that a shift-share instrument exploits thepull factors of immigration caused by previous settlements. Consequently, variations in the immigrant
population may not be suitable for constructing a shift-share instrument when immigrants are pushed
out of the country due to dire economic conditions. Therefore, Panel B of Table 4 reports the results
after excluding the 2007–2012 period, which decreases the estimated coefficient velues. Nevertheless,our central result reported in column (6), using the IV approach with the greatest number of controls,
indicates that neither immigration nor imports from China have a significant effect on wages.
24

Appendix G reruns the same sets of regressions using the wage growth rates based on different definitions of wages.
However, the results are essentially the same as the baseline results.

15

TABLE 4: Effects on the average hourly wage
Dep. var. = 100 × [ln(wagei,t+5 ) − ln(wagei,t )]
OLS
(1)
(2)
(3)
Panel A: Full sample (235 obs.)
∆mig
0.07
0.04
-0.02
(0.05) (0.05)(0.05)∆ipwCHN
-0.02
0.02
0.01
(0.05) (0.04)(0.05)
R-squared
0.54
0.56
0.66
C.-D. Wald F -statistic
K.-P. rk Wald F -statistic
Panel B: Dropping the 2007-2012 period (188 obs.)∆mig 0.11**
0.07-0.11**(0.06) (0.05)(0.04)∆ipwCHN -0.12* -0.04 -0.12**(0.06) (0.06)(0.06)
R-squared
0.55
0.58
0.71
C.-D. Wald F -statistic
K.-P. rk Wald F -statistic
Period dummies
X
X
X
Labor market controls
X
X
Macroeconomic controls
X

(4)

IV
(5)

(6)

0.12*
(0.07)-0.07(0.06)
0.53
107.6944.61

0.10(0.10)-0.01(0.07)
0.55
60.14
23.29

0.01
(0.08)-0.04(0.08)
0.65
63.02
33.56

0.20***(0.07)-0.16**(0.07)
0.54
74.45
41.13
X

0.14
(0.11)-0.07(0.08)
0.57
27.53
10.92
X
X

-0.14
(0.20)-0.13(0.09)
0.71
10.99
6.06
X
X
X

Note: The sample size is 235 for each regression. See the note for Table 3 for numbers in parentheses and significance symbols.
Standard errors are clustered by prefecture.

We find at least three reasons for the null wage effects of immigration. First, as Genda et al. (2010)
suggest, wages are rigid in Japanese labor markets for institutional reasons. Second, the increase in theshare of immigrants share in the total population is smaller in Japan compared with other developed
countries. The immigrant share increased by 0.9 pp in Japan between 1985 and 2015, when there wereseveral countries with over 10 pp increase. This may explain the limited effects of immigration on
wages. Third, while an immigrant supply shock generates downward pressure on wages, it also increases
consumer demand, leads to technology adoption, and has complementary effects on native workers,
which work to increase wages (e.g., Peri and Yasenov, 2019; Bodvarsson et al., 2008). The combinationof these conflicting wage effects may explain the null wage effects of immigration.
The null wage effects of imports from China are presumably caused by a combination of the negative
effects of imported final goods and the positive effects of imported inputs documented in the context ofimports from China on Japanese manufacturing employment (e.g., Taniguchi, 2019, and Choi et al.,2023). Endoh (2018) and Endoh (2021) also suggest worker-level heterogeneous wage reactions withineach firm, which may be canceled out within each prefecture in our analysis.

16

4.2

Results by firm size, age cohort, and sector
The dependent variable used in the previous section is the average wage of all workers, ignoring

the effects of endogenous changes in firm, sectoral characteristics, and worker characteristics. To takethese aspects into consideration, this section estimates the regressions by firm size, age group, and sector.
Previous studies have shown that import shocks affect wages differently, depending on firm size (e.g.,
Endoh, 2018 and Endoh, 2021). Therefore, we estimate the effects on wages by firm size: (1) small
firms, with 10–99 employees, (2) medium-sized firms, with 100–999 employees, and (3) large firms, with
1000+ employees. In addition, three sectors are consistently available throughout the sample period: (1)construction, (2) manufacturing, and (3) finance and real estate (hereafter finance).25
Figure 7 reports the results. Circled dots on the left indicate coefficients estimated using the specification in column (6) of Table 4, with 235 observations, and diamond-shaped dots on the right indicate
coefficients estimated by dropping the 2007–2012 period, with 188 observations. Bars in the figure illustrates first-stage Cragg-Donald Wald F -statistics, and show that our instruments are strong enough for
all regressions. The results indicate that, in either sample, none of the immigration and import effects on
wages are statistically significant.
Another important determinant of wages is workers’ age (see Kawaguchi and Mori, 2019, for theJapanese context), and we run separate regressions by age cohort. The sample size for the group of
employees aged 60+ in the finance sector is not large enough to run an IV model, and we are thus unable
to estimate the coefficients.
Panel A of Figure 8 summarizes the effects of immigration shocks, with circled dots and thicker
bars indicating results with the full sample, and diamond-shaped dots and thinner bars indicating results
without the 2007–2012 period. In the full sample, ∆mig has no statistically significant effect on wages.
The exclusion of the 2007–2012 period slightly increases the coefficients of ∆mig, and the effects on
the wages of the three groups–(1) the group of 40s in the manufacturing sector, (2) the group in their
20s in the finance sector, and (3) in their 30s in the finance sector–turn out to be positive and significant
at least at the 10% level. These results are consistent with the sectoral allocation of foreign workers,
as shown in Figure 3. As there is a smaller share of foreign workers in the finance sector, we expect asmaller downward pressure on wages in that sector. As a result, positive effects on wages through thegoods market and complementary effects dominate the negative effects of labor supply expansion.
Panel B of Figure 8 shows coefficients of ∆ipwCHN and the relevant statistics. There are no sizable
differences between with and without the 2007–2012 period. Import shocks have no impact on wages
25

The data from other sectors are not available for earlier years of our sample period, 1989–2018.

17

F IGURE 7: Results by sector and by firm size

4300

Full sample

Panel B: Imports from China

4300

Panel A: Immigration02
Coefficient of delta.ipw CHN

Cragg−Donald Wald F−statistic
100
150
200
250
0

Co
Co nstruns
tru ction
Co ction , sm
ns,tru med all
cti
on iumM, la
Ma anufrge
nu actu
facri
Ma turin ng, smnugfac , me all
tur
diu
ing
m
, la
r
g
e
Fi
Fin nanc
an e, scemFin , me allan diu
ce
m
, la
rge

−2

50

0
2
Coefficient of delta.mig−2

Co
Co nstruns
tru ction
Co ction , sm
ns,tru med all
cti
on iumM, la
Ma anufrge
nu actu
facri
Ma turin ng, smnugfac , me all
tur
diu
ing
m
, la
r
g
e
Fi
Fin nanc
an e, scemFin , me allan diu
ce
m
, la
rge

0

50

Cragg−Donald Wald F−statistic
100
150
200
250

Dropping 2007−2012 period

Note: The figure shows coefficients of ∆mig and ∆ipwCHN in Panels A and B, respectively, where the dependent variable is
100 × [ln(wagei,t+5 ) − ln(wagei,t )]. All the regressions employ the specification in column (6) of Table 4. The sample sizeis 235 for all point estimates. The dots represent point estimates. Bands represent 95 percent confidence intervals. The bars
indicate the first-stage Cragg-Donald Wald F -statistic. The thicker bars come from the full sample regressions and the thinnerbars come from the those without the 2007–2012 period.

in the construction sector. There are slight positive effects on wages of the group of the 40s in the
manufacturing sector and the 20s in the finance sector. We assume that the positive effects of imported
inputs are appear for these two groups of workers.
Figures E1 and E2 in Appendix E show the results for medium and large firms, respectively. Regarding the effects of immigration, statistically significant and negative effects are found in the “mediumsized firms in the manufacturing sector, age 60+ group” and significant positive effects are found fromthe “large firms in the manufacturing sector, age 30s group.” Regarding the effects of imports from China,
statistically significant and negative effects are found from the “medium-sized firms in the construction
sector, age 60+ group” and “large firms in the construction sector, 40s.”
As in the last analysis of this section, we run regressions period-by-period because there are potentially different effects of globalization shocks across periods. As there are three sectors, five age

18

F IGURE 8: Results by sector and by age cohort, small firms

200

Panel B: Imports from China

Full sample−101
Coefficient of delta.ipw CHN−3

2
Cragg−Donald Wald F−statistic
50
100
150
0

0
2
4
Coefficient of delta.mig−4CoConstru
Construction
Construction, 20
Co nstructio , 30s
ns ct n, 4 s
tru ion 0s
Ma
cti , 5on 0s
Manufa,60+cn
Ma ufa turincManufa turi g, 2
Ma nufacturing, 30s
nu ctu ng, 0s
fac rin 40
tur g, sing 50,6 s
Fin
0+Financea,
Fin nce 20
Finance, 30s
an , 4 s
ce 0s
,5
0s

Co
Constru
Construction
Construction, 20
Co nstructio , 30s
ns ct n, 4 s
tru ion 0s
Ma
cti , 5on 0s
Manufa,60+Manufacturi
Manufacturing, 2Ma nufacturing, 30s
nu ctu ng, 0s
fac rin 40
tur g, sing 50
,6 s
Fin
0+
a
Fin ncea,
Fin nce 20
Finance, 30s
an , 4 s
ce 0s
,5
0s

−2

−2

Cragg−Donald Wald F−statistic
50
100
150
0

Dropping 2007−2012 period

6200Panel A: Immigration

Note: The figure shows the results for small firms, defined as those with 10–99 employees. See the note in Figure 7.

categories, three firm sizes, and five periods, 225 combinations exist. Among the 225 samples, 194
include sufficiently large observations to run the IV model. Of the 194 coefficients of ∆mig, 95 coefficients are negative. Since the validity of these coefficients relies on the p-value of the coefficient andthe first-stage F -statistic, Panel A of Figure 9 plots the negative coefficients, taking the p-value on thevertical axis and the first-stage F -statistic on the horizontal axis.26 As our shift-share instrument for∆mig with the 2007–2012 period is less likely to be valid, the results from that period are indicated bydiamond-shaped dots. This shows that only 16 coefficients are in the range where the p-value is less than0.05, and the first-stage F -statistic is greater than 10.27 Panel B presents an enlarged view of this range.
Out of the 16 coefficients in the panel, nine are from the 2007–2012 period, meaning that only sevennegative coefficients are valid.
Table 5 lists the coefficients and other important statistics of the effects of ∆mig shown in Panel B
of Figure 9. All the coefficients come from the 1995–2000 period. The largest negative effect comes26

Figure E3 in Appendix E shows that same figure for positive coefficients of ∆mig.
Appendix F shows firs-stage regression results with different sets of controls to understand which control variables are
related with lower first-stage F -statistics.
27

19

from the 60+ age category of medium-sized firms in the construction sector. An increase in the share offoreign employees by one per 1,000 population reduces the wage growth rate by 4.13 pp. According to
the cross-sectional distribution of ∆mig during the 1995–2000 period, moving from the 25th percentile
prefecture to the 75th percentile prefecture raises the value of ∆mig by 4.28 Therefore, a move from the25th to 75th leads to 4 × 4.13 = 16.52 pp increase in the wage growth rate, which is about three quartersof the 25th-to-75th percentile move of the wage growth rate of the same group of observations, 23.6.29
According to Table 5, the negative coefficients come from either the construction or the finance sector, and either medium-sized or large firms. We assume that these results are related to the recession
during the 1990s, when productivity growth stagnated as documented by Hayashi and Prescott (2002).
Additionally, Shimpo (2005) shows that output prices in the finance sector decreased and stagnant productivity growth contributed to the decline during the 1990s. Griffin and Odaki (2009) find that TFP
growth rates were smaller for larger firms than for smaller firms during the 1988–1996 period. Thesefindings suggest greater adverse shocks to larger firms in these sectors. These are probably why immigration’s downward pressure on wages appears to be visible in these groups of observations.
F IGURE 9: Effects on hourly wages, negative coefficients of ∆mig

1

Panel A: All coefficients

Panel B: p-value < 0.05 and F -stat. > 10.015

F = 10.8

2007−2012 period

C30s, medium, 95−00

0

5
10
15
Cragg−Donald Wald F−statistic

20

10F30s, large, 95−00

F40s, large, 95−00
F20s, large, 95−0000p = 0.10
p = 0.05

C20s, medium, 95−00
C60+, medium, 95−00.2p−val. for delta.mig.4.6

p−val. for delta.mig
.005
.01

F30s, medium, 95−00

12
14
16
18
Cragg−Donald Wald F−statistic20

Note: The figure shows p-values and first-stage F -statistics from 194 regressions, three sectors (construction, manufacturing,and construction), five age categories (20s, 30s, 40s, 50s, and 60+), three firm sizes (small, medium, and large), and five periods(89–94, 95–00, 01–06, 07–12, and 13–18), with some missing coefficients due to insufficient observations.
28
29

See Panel A of Table D1 in Appendix D.
See Panel A of Table D3 in Appendix D.

20

TABLE 5: Negative immigration effects on wages
Sector, age, firm size, period
C60+, medium, 95-00
F30s, large, 95-00
F20s, large, 95-00
F40s, large, 95-00
C30s, medium, 95-00
C20s, medium, 95-00
F30s, medium, 95-00

Coef.
-4.13
-1.96
-1.20
-0.90
-0.70
-0.63
-0.58

Std. err.
1.03
0.60
0.29
0.32
0.28
0.23
0.23

p-value
0.000
0.001
0.000
0.005
0.012
0.006
0.011

C.D. F -stat.
10.06
11.17
13.25
12.02
12.33
12.06
17.63

K.-P. F -stat.
14.06
14.34
15.59
10.24
7.21
15.24
36.26

Sample size
41
47
47
47
46
46
47

Note: The table summarizes the coefficients, standard errors, p-values, Cragg-Donald Wald F -statistics, and Kleibergen-Paap
rk Wald F -statistics, shown in Panel B of Figure 9 with circled dots. C and F indicate the “construction” and “finance,”
respectively.

F IGURE 10: Effects on hourly wages, negative coefficients of ∆ipwCHN

1

Panel A: All coefficients

Panel B: p-value < 0.05 and F -stat. > 10

F = 10M60+, small, 95−00

.2

.01

p−val. for delta.ipw.4.6p−val. for delta.ipw
.02
.03

.8

.04

2007−2012 period00

p = 0.10
p = 0.05
05
1015
Cragg−Donald Wald F−statistic

20

11

12
13
14
Cragg−Donald Wald F−statistic

15

Note: See the note for Figure 9.

Regarding the effects of import shocks, of the 194 coefficients of ∆ipwCHN , 81 are negative. Figure
10 plots the 81 negative coefficients by taking the p-value and the first-stage F -statistic on the two axes.30
It shows that, out of the 81 negative coefficients, only five are in the valid range. Out of the five, four
are from the 2007–2012 period. As a result, only the estimate from the group of 60+ employees working
in small firms in the manufacturing sector in the 1995–2000 period is estimated with a sufficiently highfirst-stage F -statistic and statistical significance. The coefficient of this group is -6.77, meaning that a
one unit increase in ∆ipwCHN ; in other words, a 10,000 JPY increase in the exposure to imports from30

Figure E4 in Appendix E shows that same figure for positive coefficients of ∆ipwCHN . Table E1 lists all the valid positivecoefficients.

21

China, reduces the wage growth rate by 6.77 pp.
A move from the 25th percentile to the 75th percentile prefecture in the 1995-2000 period results
in an increase in ∆ipwCHN by 21,000 JPY, which reduces the wage growth rate by 14.22 pp.31 Thischange in the wage growth rate is greater than the 25th-to-75th move in the wage growth rate of thesame group of observations, 10.5.32 The negative effect on older workers in the manufacturing sector isconsistent with Autor et al. (2013)’s findings from the US.
TABLE 6: Effects on starting salaries
Dep. var. = 100 × [ln(salaryi,t+5 ) − ln(salaryi,t )]Male Female
Male
high
high
collegeschool school
(1)
(2)
(3)
Panel A: Full sample (235 obs.)
∆mig
0.05
-0.06
0.07
(0.07)(0.10)(0.07)∆ipwCHN
0.02
0.03
0.16*
(0.09)(0.14)(0.08)
R-squared
0.49
0.43
0.49
C.-D. Wald F -statistic 57.77
58.79
61.92
K.-P. rk Wald F -statistic 30.50
34.46
29.84
Panel B: Dropping the 2007-2012 period (188 obs.)
∆mig
-0.11
-0.11
0.16
(0.13)(0.24)(0.19)∆ipwCHN
0.01
-0.05
0.11
(0.11)(0.16)(0.10)
R-squared
0.52
0.48
0.54
C.-D. Wald F -statistic 16.68
17.16
17.67
K.-P. rk Wald F -statistic
6.81
6.92
7.07

Femalejuniorcollege(4)
0.21(0.14)
0.21(0.15)
0.46
64.04
32.40
0.42
(0.27)
0.25(0.17)
0.50
18.02
7.35

Note: The sample size is 235 for each regression. All regressions include macroeconomic variables and year dummies ascontrols. In addition, the share of female workers for the 20–24 age cohort is added as controls. All regressions are estimatedusing initial populations as weights. Standard errors clustered by prefecture are in parentheses. ***, **, and * indicate statisticalsignificance at the 1%, 5%, and 10% level, respectively.

4.3

Effects on starting salaries
We acknowledge that the individuals used to compute the average wages may differ across periods.

To fix the characteristics of workers in each period, we use the data on average starting monthly salariesof (1) male high school graduates, (2) female high school graduates, (3) male college graduates, and(4) female junior college graduates, in all sectors.33 These groups may compete more directly with new
immigrants. Our regressions here include macroeconomic control variables, year dummies, and the share
31

See Panel B of Table D1 in Appendix D.
See Panel B of Table D3 in Appendix D.
33
The data on starting salaries of female college graduates are not available for the entire period of 1989–2018. The ‘juniorcollege’ category includes ‘junior college (tanki daigaku)’ and ‘higher engineering school (koto senmon gakko).’32

22

of female workers in the 20–24 age cohort as controls to closely match the specification in the previous
section.
F IGURE 11: Effects on starting salaries by period, 94 obs. each−101Coefficient of delta.ipw CHN−2

2

Cragg−Donald Wald F−statistic
25
50
75
100
0

0
.5
1
Coefficient of delta.mig

1.5

125

Panel B: Imports from China

HS
HS, m
HS, male,HS, male, 89−, al 9 0
HS malee, 052−006, 0 −1
HS, feHS, female 7−028m,
HS fe ale, 89m00
Co , female,, 95−
ale 02−06
Colleg
12
College, m , 07−
College, male, −08elle , male 89
ge a , 9 −0Ju
, m le, 5− 0
ale 02 06
Junior,Junior, fem , 07−12n
Ju ior, female −08
nio fe al , 8r, f ma e, 99−0
em le, 5− 0
ale 02 06, 0 −1
7− 2
08

HS
HS, m
HS, male,HS, male, 89−, al 9 0
HS malee, 052−006, 0 −1
HS, feHS, female 7−028m,
HS fe ale, 89m00
Co , female,, 95−
ale 02−06
Colleg−,el
0
Co leg , m
7 12
College, male, −08
lle e, male 89
ge a , 9 −0Ju
, m le, 5− 0nale 02 06
Ju ior,nfi
Ju or, em , 07−12
Junior, female −08
nio fe al , 8r, f ma e, 99−0
em le, 5− 0
ale 02 06, 0 −1
7− 2
08

0

−.5

Cragg−Donald Wald F−statistic
25
50
75
100

125

Panel A: Immigration

Note: The dependent variable is 100 × [ln(salaryi,t+5 ) − ln(salaryi,t )]. All regressions include marcroeconomic controls,
year dummies, and the share of female workers in the age 20–24 cohort. The sample size for each regression is 94 unless there
are some missing observations. See the note in Figure 7 also.

Panel A of Table 6 reports the results for the full sample, showing that immigration shocks have nosignificant effects on the starting salaries of all four education groups. However, import shocks have asignificant positive effect on wages of male college graduates. A one unit increase in ∆ipwCHN –i.e., a
10,000 JPY increase in exposure to import shocks–raises the wage growth rate of that category by 0.16
pp. The median value of ∆ipwCHN is 2.49, which would lead to an increase in the wage growth rate by
0.42 pp, or three times higher than the median wage growth of male college graduates, 0.14. The positiveeffects on male college graduates are consistent with Endoh (2018), who reports that import competition
raised wages for college graduates using individual-level data. Panel B shows the results without the
2007-2012 period, which indicate that neither immigration shocks nor import shocks have a significant
effect on wages.

23

Figure 11 reports results from each of the two stacked cross-sections, with the effects of immigration
shocks in Panel A and those of import shocks in Panel B. We use two stacked cross-sections because
regressions with one cross-section in each period lead to low first-stage F -statistics.34 It shows thatnegative coefficients are entirely statistically significant. Further, greater coefficients (in absolute value)
tend to be associated with lower first-stage F -statistics, making the confidence intervals wider. Overall,
it is difficult to conclude that immigration and imports affected average starting salaries.

5 Concluding remarks
We have analyzed the labor market effects of globalization–(1) immigration and (2) imports from
China–in the context of the Japanese economy using data from 1989 to 2018 in an attempt to estimate
the causal effects of these shocks by using shift-share instruments. Our results suggest that neither
immigration nor import shocks are the primary determinants of wage growth paths for most worker
groups. We find statistically significant and negative effects on wages for only a limited number ofgroups, and these negative effects are found entirely during the 1995–2000 period, that is, during Japan’s
Lost Decade.
The limited labor market effects of globalization may be because the Japanese economy has rigid
labor markets, making wage structures less flexible, and immigration shocks have been relatively small
compared with other developed countries. In addition, globalization has multiple channels through which
it affects labor markets. While immigration exerts downward pressure on wages through an expanded
labor supply, it also increases consumption demands and stimulates the economy. Although imported
final goods have a pro-competitive effect, imported inputs have a complementary effect on production.
The identification of the effects of each channel is left to future research. Nevertheless, we hope that
our analysis provides us with important insights and helps us understand the interactions between labormarkets and globalization.

ReferencesAdão, Rodrigo, Michal Kolesàr, and Eduardo Morales (2019). “Shift-share designs: theory and inference”. In: Quarterly Journal of Economics 134.4, pp. 1949–2010.
34

Figure E5 in Appendix E reports results from each one of the stacked cross-sections, each of which consists of 47 observations; the result is one of the following: (1) the effects on starting salaries are essentially zero or (2) first-stage F -statistics are
low.

24

Autor, David H., David Dorn, and Gordon H. Hanson (2013). “The China syndrome: local labor market
effects of import competition in the United States”. In: American Economic Review 103.6, pp. 2121–
2168.
Bianchi, Milo, Paolo Buonanno, and Paolo Pinotti (2012). “Do immigrants cause crime?” In: Journal of
the European Economic Association 10.6, pp. 1318–1347.
Bodvarsson, Örn B., Hendrik F. Van den Berg, and Joshua J. Lewer (2008). “Measuring immigration’s effects on labor demand: a reexamination of the Mariel Boatlift”. In: Labour Economics 15.4, pp. 560–
574.
Borjas, George J. (2003). “The labor demand curve is downward sloping: reexamining the impact ofimmigration on the labor market”. In: Quarterly Journal of Economics 118.4, pp. 1335–1374.
Borusyak, Kirill, Peter Hull, and Xavier Jaravel (2022). “Quasi-experimental shift-share research designs”. In: Review of Economic Studies 89.1, pp. 181–213.
Card, David (2009). “Immigration and inequality”. In: American Economic Review Papers and Proceedings 99.2, pp. 1–21.
Choi, Jaerim, Masahiro Endoh, and Akira Sasahara (2023). “The China shock and supply chains: whenrising imports raise exports”. Unpublished manuscript, University of Hawai‘i at Manoa and Keio
University.
Dauth, Wolfgang, Sebastian Findeisen, and Jens Suedekum (2014). “The rise of the East and the Far East:
German labor markets and trade integration”. In: Journal of the European Economic Association
12.6, pp. 1643–1675.
Dustmann, Christian, Tommaso Frattini, and Ian P. Preston (2013). “The effect of immigration along the
distribution of wages”. In: Review of Economic Studies 80.1, pp. 145–173.
Edo, Anthony (2020). “The impact of immigration on wage dynamics: evidence from the Algerian independence war”. In: Journal of the European Economic Association 18.6, pp. 3210–3260.
Endoh, Masahiro (2018). “The effect of import competition on wages in the Japanese manufacturingsector”. In: Asian Economic Papers 17.1, pp. 46–67.
— (2021). “The effect of import competition on labor income inequality through firm and worker heterogeneity in the Japanese manufacturing sector”. In: Japan and the World Economy 59.September,p. 101076.
Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer (2015). “The next generation of the Penn
World Table”. In: American Economic Review 105.10, pp. 3150–3182.

25

Gandal, Neil, Gordon H. Hanson, and Matthew J. Slaughter (2004). “Technology, trade, and adjustment
to immigration in Israel”. In: European Economic Review 48.2, pp. 403–428.
Genda, Yuji, Ayako Kondo, and Souichi Ohta (2010). “Long-term effects of a recession at labor market
entry in Japan and the United States”. In: Journal of Human Resources 45.1, pp. 157–196.
Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift (2020). “Bartik instruments: what, when, why,
and how”. In: American Economic Review 110.8, pp. 2586–2624.
Griffin, Naomi N. and Kazuhiko Odaki (2009). “Reallocation and productivity growth in Japan: revisiting
the lost decade of the 1990s”. In: Journal of Productivity Analysis 31, pp. 125–136.
Hayashi, Fumio and Edward C. Prescott (2002). “The 1990s in Japan: a lost decade”. In: Review of
Economic Dynamics 5.1, pp. 206–235.
Horiuchi, Yusaku and Yoshikuni Ono (2018). “Public opposition to refugee resettlement: the case ofJapan”. RIETI Discussion Paper Series 18-E-050.
Jaeger, David A., Joakim Ruist, and Jan Stuhler (2018). “Shift-share instruments and the impact of immigration”. NBER Working Paper No. 24285.
Japan Institute for Labour Policy and Training (2020). “An analysis on the employment situation offoreign workers (Gaikoku jin rōdō sha no koyō jyōkyō ni kansuru bunseki)”. JILPT Shiryō Series,
No. 235, December 2022. Available at https://www.jil.go.jp/institute/siryo/2020/documents/235.pdf.
Kainuma, Shuhei and Yukiko U. Saito (2022). “China’s impact on regional employment: propagation
through supply chains and co-agglomeration patterns”. In: The World Economy 45.11, pp. 3559–
3601.
Kawaguchi, Daiji and Hiroaki Mori (2019). “The labor market in Japan, 2000–2018”. In: IZA World ofLabor 385v2.
Kim, Hyejin, Jongkwan Lee, and Giovanni Peri (2022). “Do low-skilled immigrants improve native productivity but worsen local amenities? learning from the South Korean experience”. NBER Working
Paper No. 30464.
Kiyota, Kozo, Sawako Maruyama, and Mina Taniguchi (2021). “The China syndrome: a cross-country
evidence”. In: The World Economy 44.9, pp. 2758–2792.
Lu, Yi, Yoichi Sugita, and Lianming Zhu (2020). “Wage markdowns and FDI liberalization”. Unpublished manuscript, Tsinghua University, Hitotsubashi University, and Osaka University.
Malchow-Møller, Nikolaj, Jakob R. Munch, and Jan Rose Skaksen (2012). “Do immigrants affect firmspecific wages?” In: Scandinavian Journal of Economics 114.4, pp. 1267–1295.

26

Nakamura, Jiro (2010). “Impacts of international migration on the labor market in Japan”. In: Japan
Labor Review 7.3, pp. 68–85.
Nishikido, Momoko (2019). “Welcoming change? an individual- and prefecture-level analysis of public
opinion towards immigration in Japan”. Master’s Programme in Economic Growth, Population, andDevelopment, Lund University.
Okubo, Toshihiro (2021). “Public preferences on immigration in Japan”. In: Japan and the World Economy 58.1, p. 101073.
Ottaviano, I. P. Gianmarco and Giovanni Peri (2012). “Rethinking the effect of immigration on wages”.
In: Journal of European Economic Association 10.1, pp. 152–197.
Peri, Giovanni (2012). “The effect of immigration on productivity: evidence from U.S. states”. In: Review
of Economics and Statistics 94.1, pp. 348–358.
Peri, Giovanni, Kevin Shih, and Chad Sparber (2015). “STEM workers, H-1B visas, and productivity in
US cities”. In: Journal of Labor Economics 33.S1 Part 2, S225–S255.
Peri, Giovanni and Vasil I. Yasenov (2019). “The labor market effects of a refugee wave: synthetic control
method meets the Mariel boatlift”. In: Journal of Human Resources 54.2, pp. 267–309.
Shimpo, Kazushige (2005). “Interindustry effects of productivity growth in Japan: 1960–2000”. In: Journal of the Japanese and International Economies 19.4, pp. 568–585.
Staiger, Douglas and James H. Stock (1997). “Instrumental variables regression with weak instruments”.
In: Econometrica 65.3, pp. 557–586.
Taniguchi, Mina (2019). “The effect of an increase in imports from China on local labor markets inJapan”. In: Journal of the Japanese and International Economies 51.March, pp. 1–18.

27

Appendix
A

List of manufacturing sectors
TABLE A1: Sectors used when constructing import shock variables
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

Description
Live stock food products
Seafood products
Vegetable, fruits, agricultural products
Beverages (non alcoholic)Alcoholic drinks
Teas and coffees
Fertilizers
Tobaccos
TextilesWooden products and construction materials
Paper and pulp
Chemical fertilizers
Inorganic chemicals
Organic chemicals
Fats and oils processed products
Medical products
Other chemical products
Refined oils and coke
Paving material
Plastic products
Tires and robber products
Leather and fur
Bags
Glass
CementClay for construction
Ceramics
Refractories
Carbon and graphite
Abrasive
Masonry materials
Steel

A1

No.
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63

Description
Nonferrous metals
Tin, aluminium, and other metals
HardwareAir conditioning apparatus
Architectural metal products
Bolts, nuts, and rivets
Engines and turbines motors
Industrial machine
Agricultural machinery
Construction and mining machinery
Textile machinery
Office machinery
Electrical measuring instrument
Medical apparatus and instruments
Optical instruments
Communication equipment
Industrial electrical machinery
Consumer electrical machinery
Electric lighting fixtures
Autos and their related products
Rail cars and their related products
Vessels and their related products
Aircrafts and their related products
Other transportation equipment
Ornaments
Watches and clocks
Musical instruments and records
Toys and sporting goods
Office supplies and equipment
Weapons
MiscellaneousB List of 172 origin economies for the migration variables
Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Australia, Austria, Bahamas, Bahrain, Bangladesh, Barbados, Belgium, Belize, Benin, Bhutan,
Bolivia, Botswana, Brazil, Brunei Darussalam, Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Cape Verde, Central African Republic, Chad, Chile, China,
Colombia, Comoros, Dem. Rep. Congo, Rep. Congo, Costa Rica, Cote d’Ivoire, Cuba,
Cyprus, Czech Republic and Slovakia, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Arab Rep. Egypt, El Salvador, Equatorial Guinea, Eswatini, Ethiopia andEritrea, Fiji, Finland, Former Soviet Union nations, Former Yugoslavian nations, France,
Gabon, The Gambia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, GuineaBissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia and Timor-Leste, Islamic Rep. Iran, Iraq, Ireland, Israel, Italy, Jamaica, Jordan, Kenya, Kiribati, Rep. of Korea,
Kuwait, Lao PDR, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius,
Mexico, Fed. Sts. of Micronesia, Monaco, Mongolia, Morocco, Mozambique, Myanmar,
Namibia, Nauru, Nepal, the Netherlands, New Zealand, Nicaragua, Niger, Nigeria, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, the Philippines,
Poland, Portugal, Qatar, Romania, Rwanda, Samoa, San Marino, Sao Tome and Principe,
Saudi Arabia, Senegal, Seychelles, Sierra Leone, Singapore, Slovak Republic, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, St. Kitts and Nevis, St. Lucia,
St. Vincent and the Grenadines, Sudan, Suriname, Sweden, Switzerland, Syrian Arab Republic, Taiwan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey,
Tuvalu, Uganda, United Arab Emirates, the United Kingdom, the United States, Uruguay,
Vanuatu, RB Venezuela, Vietnam, West Bank and Gaza, Rep. of Yemen, Zambia, and Zimbabwe
Country borders change over time. As a result, we have the following sets of economies: (1) Indonesia and Timor-Leste; (2) Czech Republic and Slovakia; (3) Ethiopia and Eritrea; (4) Former Yugoslavia
nations including Croatia, Bosnia-Herzegovina; Kosovo, Montenegro, North Macedonia, Serbia, Serbia and Montenegro, and Slovenia; and (5) Former Soviet Union nations including Armenia, Azerbaijan, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Moldova, Russia, Tajikistan,
Turkmenistan, Ukraine, and Uzbekistan.

A2

C

Korean descents

This section describes the data on Special Permanent Residents (hereafter SPRs). The data from theStatistics on Foreign Residents show that 99% of SPRs are of Korean descent. Therefore, we focus on
the data from Korean SPRs. The Special Act on Immigration Control was enacted in 1991 and the startyear of the SPR resident data is 1992.
Figure C1 shows a continuous decline in the Korean population in Japan since the early 1990s. It
also shows that the Korean population excluding SPRs is increasing except for the period of the 2008–2009 Financial Crisis and its aftermath. These suggest that most of the declines of the Korean population
including SPRs are caused by the naturalization of SPRs.

Korean pop. / Total pop. times 100
.1
.2
.3
.4
.5

.6

F IGURE C1: Korean resident population with and without SPRs

Korean population including Special Permanent Residents

0

Korean population excluding Special Permanent Residents

1990

2000

2010

2020

Note: The immigration data are from the Statistics on Foreign Residents (Zairyū Gaikokujin Tōkei) of the Ministry of Justice.

A3

D

Additional summary statistics
TABLE D1: Percentiles of key explanatory variables
Panel A: Immigration variable, ∆mig
1989- 1995- 2001- 20071994 2000 2006 2012
90th percentile
8.0
9.7
8.3
0.7
75th percentile
6.4
5.9
4.3
0.1
50th percentile
2.1
2.8
2.6
-0.5
25th percentile
1.3
1.9
1.3
-2.4
10th percentile
0.9
1.3
0.7
-6.4
The 75th − the 25th
5.1
4.0
3.0
2.5

20132018
12.3
8.6
5.7
3.7
2.4
4.9

Panel B: Import variable, ∆ipwCHN
1989- 1995- 2001- 20071994 2000 2006 2012
90th percentile
3.2
5.3
16.0
7.6
75th percentile
2.9
4.6
14.5
5.2
50th percentile
2.0
3.9
11.9
2.5
25th percentile
1.7
2.6
8.2
0.8
10th percentile
0.8
2.4
6.3
-0.1
The 75th − the 25th
1.1
2.1
6.3
4.4

20132018
4.2
2.1
0.1
-1.4
-2.2
3.4

Note: The sample size is 47 in each column of the two panels. The unit of ∆mig is the change in the number of immigrants per
1,000 people. The unit of ∆ipwCHN is 10,000 JPY per employee for the top five prefectures, median prefectures, and bottom
five prefectures, respectively. See the main text for data sources.

TABLE D2: Correlations between ∆mig and ∆ipwCHN

1989-1994
1995-2000
2001-2006
2007-20122013-2018

Corr.
-0.02
0.49
0.43
-0.60
0.30

Note: The sample size is 47 in each row. See the main text for data sources.

A4

TABLE D3: Percentiles of key dependent variables
Panel A: 100 × [ln(wagei,t+5 ) − ln(wagei,t )]of the age 60+ group, medium-sized firms, the construction sector
1989- 1995- 2001- 2007- 20131994 2000 2006 2012 2018
90th percentile
8.0
9.7
8.3
0.7
12.3
75th percentile
6.4
5.9
4.3
0.1
8.6
50th percentile
2.1
2.8
2.6
-0.5
5.7
25th percentile
1.3
1.9
1.3
-2.4
3.7
10th percentile
0.9
1.3
0.7
-6.4
2.4
The 75th − the 25th
5.1
4.0
3.0
2.5
4.9
Panel B: 100 × [ln(wagei,t+5 ) − ln(wagei,t )]of the age 60+ group, small firms, the manufacturing sector
1989- 1995- 2001- 2007- 20131994 2000 2006 2012 2018
90th percentile
3.2
5.3
16.0
7.6
4.2
75th percentile
2.9
4.6
14.5
5.2
2.1
50th percentile
2.0
3.9
11.9
2.5
0.1
25th percentile
1.7
2.6
8.2
0.8
-1.4
10th percentile
0.8
2.4
6.3
-0.1
-2.2
The 75th − the 25th
1.1
2.1
6.3
4.4
3.4
Note: The sample size is 47 in each column of the two panels. See the main text for data sources.

TABLE D4: Additional summary statistics of variables
Panel A: Hourly wage growth rates, all age groups, by firm sizeObs. Mean Std. dev.
Min
Construction, small firms 235
0.9
7.4 -22.7
Construction, medium-sized firms 233
0.2
11.0 -42.9
Construction, large firms 221-0.9
12.3 -52.1

Max
17.2
36.4
61.8

Manufacturing, small firms
Manufacturing, medium-sized firms
Manufacturing, large firms

235
235
234

0.3
1.1
1.6

3.6
4.9
6.5

-9.8
-13.5
-21.2

7.9
19.0
29.5

Finance, small firms
Finance, medium-sized firms
Finance, large firms

232
235
235

0.9
1.3
0.2

14.6
7.7
5.5

-38.3
-20.9
-16.4

46.4
25.6
19.0

A5

Panel B: Hourly wage growth rates, construction sector, all firms, by age group
Obs. Mean Std. dev.
Min Max
20s 235
0.3
7.2 -25.8 31.5
30s 235-0.3
7.5 -32.1 20.4
40s 235
0.4
7.7 -23.0 19.8
50s 235
1.0
9.2 -34.5 30.3
60+ 204-0.6
11.4 -33.8 30.8
Panel C: Hourly wage growth rates, manufacturing sector, all firms, by age group
Obs. Mean Std. dev.
Min Max
20s 235
1.0
3.5 -13.6 12.2
30s 235
0.6
3.4 -15.9 10.6
40s 235
1.0
4.3 -31.0 11.4
50s 235
1.2
4.9 -31.8 13.4
60+ 235
0.8
7.4 -21.1 18.7
Panel D: Hourly wage growth rates, finance sector, all firms, by age group
Obs. Mean Std. dev.
Min Max
20s 235
1.0
4.6 -14.3 15.4
30s 235
0.5
6.1 -23.3 18.1
40s 235
0.0
6.1 -23.1 15.1
50s 235-0.3
6.6 -17.5 20.9
60+ 171-0.6
16.9 -36.0 62.8
Panel E: Starting salary growth rates
Obs. Mean Std. dev.
Min
High school, male 235
0.3
2.8
-6.0
High school, female 235
0.7
3.6 -10.2
College, male 235
0.3
3.0
-7.8
Junior college, female 235
0.4
5.1 -29.7

A6

Max
10.3
29.6
8.6
18.4

E Additional figures
Figure E1 presents the results for medium-sized firms by age group and sector. This shows that theeffects of immigration and imports from China on wages were mostly statistically insignificant. However,significant negative effects come from the age 60+ group in the manufacturing sector, excluding the
2007–2012 period. Figure E2 shows the results for the large firms. Similarly, the effects of the twovariables were mostly insignificant. Nevertheless, immigration has a significant positive effect on wages
in the manufacturing sector of the age 30s group.
F IGURE E1: Results by sector and by age cohort, medium-sized firms

2

125

Full sample

Panel B: Imports from China
4125

Panel A: Immigration

Cragg−Donald Wald F−statistic
25
50
75
100

−4
−2
0
Coefficient of delta.ipw CHN−6Co
Constru
Construction
Construction, 20
Co nstructio , 30s
ns ct n, 4 s
tru ion 0s
Ma
cti , 5on 0s
Manufa,60+Manufacturi
Manufacturing, 2n
Ma nufacturi g, 30s
nu ctu ng, 0s
fac rin 40
tur g, sing 50
,6 s
Fin
0+
a
Fin ncea,
Fin nce 20
Finance, 30s
an , 4 s
ce 0s
,5
0s

−2
0
Coefficient of delta.mig

0

−4
−6

2

Cragg−Donald Wald F−statistic
25
50
75
100
Co
Constru
Construction
Construction, 20
Co nstructio , 30s
ns ct n, 4 s
tru ion 0s
Ma
cti , 5on 0s
Manufa
,6
0+
c
n
t
u
Ma fa uri
Manufacturing, 2n
Ma nufacturi g, 30s
nu ctu ng, 0s
fac rin 40
tur g, sing 50
,6 s
Fin
0+
a
Fin ncea,
Fin nce 20
Finance, 30s
an , 4 s
ce 0s
,5
0s

0

Dropping 2007−2012 period

Note: See the note for Figure 7.

A7

Note: See the note for Figure 7.

A8−2

Cragg−Donald Wald F−statistic
50
100
150

−1
0
1
Coefficient of delta.ipw CHN

−3

4

0

0
1
2
3
Coefficient of delta.mig

−1

−3

−2

Cragg−Donald Wald F−statistic
50
100
150

0

2

5

200

Panel A: Immigration
3

200

6

Full sample

Co
Constru
Construction
Construction, 20
Co nstructio , 30s
ns ct n, 4 s
tru ion 0s
Ma
cti , 5on 0s
Manufa,60+Manufacturi
Manufacturing, 2n
Ma nufacturi g, 30s
nu ctu ng, 0s
fac rin 40
tur g, sing 50
,6 s
Fin
0+
a
Fin ncea,
Fin nce 20
Finance, 30s
an , 4 s
ce 0s
,5
0s

Co
Constru
Construction
Construction, 20
ns cti , 3 s
tru on 0s
cti , 4
on 0s
Ma
,5
0s
n
u
Ma fa
c
n
t
u
Ma fa uri
Manufacturing, 2
nu ctu ng, 0s
fac rin 30
tur g, 4 s
ing 0s,50s
Fina
Fin nceFinance, 20
Finance, 30s
an , 4 s
ce 0s
,5
0s

F IGURE E2: Results by sector and by age cohort, large firms
Panel B: Imports from China

Dropping 2007−2012 period

Figure E3 plots positive coefficients of ∆mig when employing the wage growth rate as the dependent
variable, obtained from 194 regressions. It shows that none of the coefficients are in the range of p-value< 0.05 and first-stage F -statistic > 10. Figure E4 plots positive coefficients of ∆ipwCHN . It shows thatmost coefficients are outside of the valid range. However, seven coefficients are in the valid range andthree of them are not from the 2007–2012 period. Table E1 summarizes important statistics of the threecoefficients.
F IGURE E3: Effects on hourly wages, positive coefficients of ∆mig

2007−2012 period.2p−val. for delta.mig
.4
.6

.8

1

F = 100

p = 0.10
p = 0.05
05
1015
Cragg−Donald Wald F−statistic20

Note: See the note for Figure 9.

F IGURE E4: Effects on hourly wages, positive coefficients of ∆ipwCHN
Panel B: p-value < 0.05 and F -stat. > 10.05

Panel A: All coefficients2007−2012 period
F40s, large, 95−00

.8

.04

1

F = 10F30s, large, 95−000

p = 0.10
p = 0.05
05
1015
Cragg−Donald Wald F−statistic20

Note: See the note for Figure 9.

A9

0

.2

.01

p−val. for delta.ipw.02.03

p−val. for delta.ipw.4.6

C60+, small, 95−00

10

11
12
13
14
Cragg−Donald Wald F−statistic

15

TABLE E1: Positive import effects on wages
Sector, age, firm size, period
C60+, small, 95-00
F30s, large, 95-00
F40s, large, 95-00

Coef.
4.04
3.12
1.79

Std. err.
1.92
1.51
0.88

p-value
0.035
0.039
0.042

C.D. F -stat.
10.76
11.17
12.02

K.-P. F -stat.
12.09
14.34
10.24

Sample size
47
47
47

Note: See the note for Table 5.

Figure E5 presents the coefficients of ∆mig (in Panel A) and ∆ipwCHN (in Panel B) when thestarting salary growth rate is employed as the dependent variable. It plots coefficients from one period,
each of which consists of 47 observations, in contrast to Figure 10, which shows coefficients from two
periods, each of which consists of 94 observations. Figure E5 shows that first-stage F -statistics are
not sufficiently high to ensure strong IVs, presumably because of the small sample size. First-stage F statistics are particularly low in regressions for the 2002–2006 period. Appendix F discusses the sourcesof the low F -statistics.
F IGURE E5: Effects on starting salaries by period, 47 obs. each

50Coefficient of delta.ipw CHN

30
20

Note: See the note for Figure 11.

A10

−5

0
HS
HS, maHS, male,HS, male, 89−9
HS, male, 95−04, l 01 0
HS malee, 07−06
HS, fem , 13−12
HS, female −18
HS, female, 89
HS, female, 95−94, fe al , 0 −00
Coma e, 01−0
le, 7− 6
Colleg
13 12
College, m−1
8College, male,College, male, 89−
lle e, male 95 94
ge a , 0 −0
, m le, 1− 0
Ju
ale 07 06
Junior,−Junior, fem , 13−12n18fi
Ju or, emale,n
Ju ior, female, 89−
nio fe al 95 94
r, f ma e, 0 −0
em le, 1− 0
ale 07 06, 1 −13− 2
18

HSHS, maHS, male,HS, male, 89−9
HS, male, 95−04, l 01 0
HS malee, 07−06
HS, fem , 13−12
HS, female −18
HS, female, 89
HS, female, 95−94, fe al , 0 −00
Coma e, 01−0
le, 7− 6
Collegl
13 12
l
Co ege, m−18College, male,
e
a
l
8
l
,
Co eg m le, 9−elle , male 95 94
ge a , 0 −0
, m le, 1− 0
Ju
ale 07 06
Junior,−Junior, fem , 13−1218
Junior, female,
Junior, female, 89−
nio fe al 95 94
r, f ma e, 0 −0
em le, 1− 0
ale 07 06, 1 −1
3− 2
18

0

−20

10

Cragg−Donald Wald F−statistic20
0

Coefficient of delta.mig

30
20
10

Cragg−Donald Wald F−statistic

40

40

50

Panel B: Imports from China

40

50

Panel A: Immigration

F First-stage regression results
This section examines the control variables associated with lower first-stage F -statistics. Table F1
shows the results of regressing ∆mig on ∆mig Bartik for each period. The added controls are summarized at the bottom of the table. This shows that the effect of ∆mig Bartik on ∆mig remains statisticallysignificant at the 1% level in all periods except 2001–2006 shown in Panel C. In this period, the inclusion of the initial unemployment rate and the initial natural log of population density, or the inclusionof the sectoral value-added share, removes statistical significance. This means that these variables arecorrelated with ∆mig Bartik , which does not have a significant effect on ∆mig after controlling for thesevariables. Table F2 pesents the results of regressing ∆ipwCHN on ∆ipwOT H . This shows that, in all
periods, the effect of ∆ipwOT H on ∆ipwCHN remains statistically significant even after controlling for
all variables.
TABLE F1: First-stage regressing ∆mig on ∆mig Bartik each period

Panel A: 1989-1994∆mig Bartik
F -statistic for ∆mig Bartik
p-value of F -statistic
Panel B: 1995-2000∆mig Bartik
F -statistic for ∆mig Bartik
p-value of F -statistic
Panel C: 2001-2006∆mig Bartik
F -statistic for ∆mig Bartik
p-value of F -statistic
Panel D: 2007-2012∆mig Bartik
F -statistic for ∆mig Bartik
p-value of F -statistic
Panel E: 2013-2018∆mig Bartik
F -statistic for ∆mig Bartik
p-value of F -statistic
Controls
Initial unemployment rate
Initial ln(pop. density)
Sectoral value-added shares
All other controls

(1)

(2)

(3)

(4)

(5)

(6)

0.47***(0.14)
11.23
0.002

0.35
(0.25)
2.02
0.162

0.44***(0.14)
10.31
0.002

0.57***(0.13)
19.15
0.000

0.65***(0.12)
31.41
0.000

0.66***(0.14)
22.51
0.000

0.78***(0.17)
20.43
0.000

1.23***(0.22)
30.41
0.000

1.04***(0.22)
22.17
0.000

1.33***(0.21)
41.86
0.000

1.27***(0.19)
45.65
0.000

1.21***(0.23)
28.16
0.000

0.69***(0.21)
11.00
0.002

0.80***(0.29)
7.82
0.008

0.39
(0.33)
1.38
0.246

0.58
(0.38)
2.35
0.132

0.47
(0.38)
1.55
0.220

0.11
(0.38)
0.08
0.775

1.05***(0.10)
111.530.000

1.09***(0.08)
187.230.000

1.03***(0.08)
166.970.000

1.26***(0.17)
52.38
0.000

1.28***(0.17)
58.48
0.000

1.29***(0.15)
77.47
0.000

0.94***(0.14)
46.08
0.000

0.70***(0.10)
50.50
0.000

0.78***(0.13)
37.53
0.000

0.94***(0.08)
127.150.000

0.92***(0.09)
106.440.000

0.65***(0.15)
19.45
0.000

X

X
X
X

X
X
X

X
X
X
X

X

Note: The sample size is 47 for each regression. All regressions are estimated using initial populations as weights. ***, **, and* indicate the statistical significance at the 1%, 5%, and 10% level, respectively. Standard errors are in parentheses.

A11

TABLE F2: First-stage regressing ∆ipwCHN on ∆ipwOT H each period

Panel A: 1989-1994∆ipwOT H
F -statistic for ∆ipwOT H
p-value of F -statistic
Panel B: 1995-2000∆ipwOT H
F -statistic for ∆ipwOT H
p-value of F -statistic
Panel C: 2001-2006∆ipwOT H
F -statistic for ∆ipwOT H
p-value of F -statistic
Panel D: 2007-2012∆ipwOT H
F -statistic for ∆ipwOT H
p-value of F -statistic
Panel E: 2013-2018∆ipwOT H
F -statistic for ∆ipwOT H
p-value of F -statistic
Controls
Initial unemployment rate
Initial ln(pop. density)
Sectoral value-added shares
All other controls

(1)

(2)

(3)

(4)

(5)

(6)

0.66***(0.14)
22.35
0.000

0.72***(0.08)
74.41
0.000

0.78***(0.11)
54.09
0.000

1.08***(0.14)
60.32
0.000

1.06***(0.14)
58.64
0.000

1.00***(0.13)
57.89
0.000

0.83***(0.07)
156.440.000

0.90***(0.08)
145.450.000

0.87***(0.08)
123.000.000

0.84***(0.15)
33.36
0.000

0.81***(0.15)
27.94
0.000

0.94***(0.17)
29.26
0.000

0.53***(0.03)
261.100.000

0.53***(0.03)
307.540.000

0.53***(0.04)
228.070.000

0.40***(0.05)
54.76
0.000

0.38***(0.05)
62.57
0.000

0.33***(0.05)
37.88
0.000

0.71***(0.11)
41.86
0.000

0.62***(0.11)
33.33
0.000

0.72***(0.11)
42.13
0.000

0.94***(0.19)
25.59
0.000

0.89***(0.18)
24.43
0.000

1.04***(0.20)
26.81
0.000

0.32***(0.06)
27.38
0.000

0.30***(0.05)
37.73
0.000

0.32***(0.06)
24.96
0.000

0.31***(0.08)
14.32
0.000

0.29***(0.09)
10.43
0.002

0.32***(0.11)
8.37
0.006

X

X
X
X

X
X
X

X
X
X
X

X

Note: See the note for Table F1.

A12

G Different definitions of wages
This section re-estimates the baseline regressions in Table 4 using different wage definitions. The
baseline wage data are hourly wages constructed as follows:
total wages×12+bonuses• Baseline hourly wage = (planned monthly
hours worked+overtime hours worked)×12

where the “planned hours worked” and “overtime hours worked” are monthly hours worked. Therefore,
we multiply 12 to re-express them at an annual level. As a robustness check, we employ the following
wage definitions:
total wages×12• Hourly wage excluding bonuses = (planned hoursmonthly
worked+overtime hours worked)×12
planned wages×12• Hourly wage excluding overtime hours and extra pays = monthly
planned hours worked×12• Annual wage = monthly total wages ×12 + bonuses• Annual wage excluding bonuses = monthly total wages ×12• Annual wage excluding bonuses and extra pay = monthly planned wages ×12
where all wages are deflated using the prefecture-level CPI and expressed in 2020 prices. We rerun the
regression models used in Panel B of Table 4–dropping the 2007-12 observations– using each of thewage definitions. Panel A of Figure G1 shows the point estimates and associated confidence intervalsof ∆mig and Panel B shows those of ∆ipwCHN . The two panels show that there are no systematic orsizable differences across the six definitions of wages.

A13

F IGURE G1: Baseline regressions with different definitions of wages
Panel A: Immigration
Baseline hourly wage

−.3
0
.3
.6
Coefficient of delta.mig

Cragg−Donald Wald F−statistic
50
100
150

Hourly wage excluding bonuses
Hourly wage excluding overtime hours and extra pays
Annual wage
Annual wage excluding bonuses

ols

ls

ntr
ro
ac
IV,

IV,

+m

IV,

+la

pe

bo

rio

rc

co

on

dd

tro

um

.

ols
ntr
ro
ac
+m

OL

OL

S,

S,

OL

S,

+la

pe

bo

rio

rc

co

on

dd

tro

um

.

ls

0

−.6

Annual wage excluding bonuses and extra pays

Panel B: Imports from China.4

Baseline hourly wage

Cragg−Donald Wald F−statistic
50
100
150

Hourly wage excluding bonuses
Hourly wage excluding overtime hours and extra pays

−.2
0
.2
Coefficient of delta.ipw

Annual wage
Annual wage excluding bonuses

ols

s

ntr
ac
+m
IV,

IV,

+la

bo

ro

rc

co

on

dd
rio
pe
IV,

ac
OL

S,

+m

tro
l

um
.

ols
ntr
ro

rc
OL

S,

+la

bo

rio
pe
S,
OL

co

on

dd

tro
l

um
.

s

0

−.4

Annual wage excluding bonuses and extra pays

Note: The same specifications as Panel B of Table 4 are used. The sample size is 188 in each regression. See the note for Table
4 and Figure 7.

A14

H Different migration variables
This section reruns regressions by employing different definitions when constructing the migration
variables. The baseline model uses the following explanatory variable:a∆migi,t=otal − M IGT otal
M IGTi,t+5
i,torking
/1000
P opW
i,t

(H.1)

,

with the instrument:a,Bartik∆migi,t=X

otal
M IGTg,i,t−1
otalP× (M IGTg,t+5− M IGTg,total ),orkingT otal
M
IG
P opW
/1000
i
g,i,t−1
i,t
g∈G

1

(H.2)

where M IGTi,total denotes the number of immigrants including individuals who are in the labor force andorkingnot in the labor force; P opW
denotes the working age (ages 15-65) population including immii,tgrants and natives.
As a robustness check, we also estimate regressions using the following variables:b∆migi,t=orkingorkingM IGW− M IGWi,t+5i,torking
P opW
/1000
i,t

,

(H.3)

and
b,Bartik∆migi,t=X

otal
M IGTg,i,t−1
otalP× (M IGTg,t+5− M IGTg,total ).
orkingT otal
M
IG
P opW
/1000
i
g,i,t−1
i,t
g∈G

1

(H.4)

b (eq. (H.1)), immigrants who are under education or training and dependent immigrants are
In ∆migi,texcluded. As a result, the number of immigrants who are in the labor market–instead of all immigrants–isb (eq. (H.4)) is the same as ∆mig a (eq. (H.3))
used to construct the variable. On the other hand, ∆migi,ti,tbecause the data on the number of foreign residents by the purpose of stay (e.g., education training,family, or work) are not available at the prefecture-level for each ethnicity.
We also re-estimate regressions using the following variables:c∆migi,t=otal − M IGT otal
M IGTi,t+5
i,tP opTi,total /1000

,

(H.5)

and
c,Bartik∆migi,t=X

otal
M IGTg,i,t−1
otalP× (M IGTg,t+5− M IGTg,total ),otal
P opTi,total /1000 g∈G i M IGTg,i,t−1

1

(H.6)

orkingin equations (H.1) and (H.2) is replaced with P opTi,total in equations (H.5) and (H.6),
where P opW
i,trespectively.
We believe that equations (H.1) and (H.2) are the best to quantify the labor market effects of immigration in the Japanese context with our dataset. These variables measure the number of immigrants
who participate in economic activities–working and consuming–as a share of the size of the local labormarket measured by the working age population. Using the total population–instead of the working age
population–would distort the variables to some extent because rural prefectures have greater shares ofretired elderly individuals than urban prefectures in Japan.
Table H1 presents summary statistics of the three explanatory variables and the three instruments.
Table H2 shows correlation between the three explanatory variables, indicating that the three migration

A15

variables are highly positively correlated. Figure H1 presents the regression results with the baselinemigration variables and those with two additional sets of migration variables. All regressions exclude
the 2007-12 period, leading to a sample of 188 observations. The migration effects are presented inPanel A and the import effects are in Panel B. The estimated coefficients show that there are no sizable
differences across coefficients regardless of the definitions of the migration variables.
TABLE H1: Summary statistics of migration variables
Mean

Std. dev.

Min

Max

Endogenous variablea∆migi,t(baseline)b∆migi,tc∆migi,t

3.21
3.25
2.10

4.04
3.30
2.62

-9.57
-7.85
-6.15

18.50
12.95
12.35

Instruments
a,Bartik∆migi,t(baseline)b,Bartik∆migi,tc,Bartik∆migi,t

3.08
3.08
2.00

3.73
3.73
2.41

-8.09
-8.09
-5.20

22.11
22.11
14.45

Note: The sample size is 235.

TABLE H2: Correlations between the explanatory variables measuring migrationsa∆migi,tb∆migi,tc∆migi,ta∆migi,t
1
0.964
0.997

b
∆migi,tc∆migi,t

1
0.963

1

Note: The sample size is 235.

A16

F IGURE H1: Baseline regressions with different migration variables
Panel A: Immigration
Cragg−Donald Wald F−statistic
50
100
150

a. Including NILF in numerator, work age pop in denominator (baseline)

−.5
0
.5
Coefficient of delta.mig

b. Excluding NILF in numerator, work age pop in denominator

ols

ls

ntr
ro
ac
IV,

IV,

+m

IV,

+la

pe

bo

rio

rc

co

on

dd

tro

um

.

ols
ntr
ro
ac
+m

OL

OL

S,

S,

OL

S,

+la

pe

bo

rio

rc

co

on

dd

tro

um

.

ls

0

−1

c. Including NILF in numerator, total pop in denominator.2

Panel B: Imports from China
Cragg−Donald Wald F−statistic
50
100
150

a. Including NILF in numerator, work age pop in denominator (baseline)
b. Excluding NILF in numerator, work age pop in denominator

ols

s

ntr
ac
+m
IV,

IV,

+la

bo

ro

rc

co

on

dd
rio
pe
IV,

ac
OL

S,

+m

tro
l

um
.

ols
ntr
ro

rc
OL

S,

+la

bo

rio
pe
S,
OL

co

on

dd

tro
l

um
.

s

0

−.4

−.2
0
Coefficient of delta.ipw

c. Including NILF in numerator, total pop in denominator

Note: The same specifications as Panel B of Table 4 are used. The sample size is 188 in each regression. See the note for Table
4 and Figure 7.

A17