Tsukuba Economics Working Papers
No. 2014-006

The Effect of Inflow of Unskilled Immigrants on Natives' Human Capital
Accumulation: Difference of Differences Estimation Using Policy Changes
in JapanbyHisahiro Naito
September 2014

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPANThe Effect of Inflow of Unskilled Immigrants on
Natives’ Human Capital Accumulation: Differenceof Differences Estimation Using Policy Changes inJapanHisahiro Naito ∗Department of Economics
Gradutate School of Humanities and Social Sciences
University of Tsukuba
Current version, September 2014

Abstract
Utilizing changes of Japanese immigration law in 1990s, I study the effectof the inflow of unskilled immigrants on native’s human capital accumulation
by applying the difference of differences estimation method. Using 10 percent
sample of the Japanese census data, I show that for individual aged 19-20 one
percentage point increase of the immigrant-native ratio increased the probabilityof going to college by 1.3 percentage point for males and 0.8 percentage point
for females. Consistently, one percentage point increase of the immigrant-native
ratio reduced the probability work by 1.2 percentage point for males and 1.0
percentage point for females. For unemployment, an increase of immigrant ratio
did not affect the probability of unemployment for males and decreased it for
females. The regression results are robust with respect to several specifications.
The results suggest that the inflow of unskilled immigrant increases the humancapital accumulation of young natives.

∗

email: naito@dpipe.tsukuba.ac.jp Address: The author is responsible for all remaining errors of
this paper.

1

Introduction

When a young individual faces a large inflow of unskilled immigrants in the local labormarket, there are several choices for a native high school student. One choice would
be to go to labor market after graduation and compete with unskilled immigrants.
A more natural choice would be that he/she proceeds to college, accumulates morehuman capital and attempts not to compete with those unskilled immigrants. If
a large numbers of native high school graduates decide to accumulate more humancapital, then the change of relative wage between the unskilled labor and the skill laborcaused by the inflow of unskilled immigrants can be mitigated. Thus,examining how
a native young individual responde to the inflow of unskilled immigrant is importantfrom the point of policy.
In addition, studying the effect of the inflow of immigrants on young individuals
is useful from the following reason. In many developed countries, the unemployment
rate of the youth is usually higher that the unemployment of the old or middle aged
individuals. This is partly due to the fact that young individuals have less experiencein jobs and thus it is not essential to continue the firm’s operation. As a result, young
workers are the one who are fired first when firms are hit by the negative shocks. Thisimplies that young individual can be affected more strongly than the middled or old
aged individual by the inflow of immigrants. The purpose of this paper is to examine
whether such a behavioral response exists in the data using substantia policy change
on immigration law in 1990s in Japan.
Before and just after the world war II, many Japanese families immigrated to theSouth America especially Brazil and Peru. This is primarily due to the poor economic
condition in the rural area of Japan. However, after 1960s, Japanese economy started
to recover from the worst state after the world war II. In the mid 1980s, Japanese
economy kept growing and was started to experience a shortage of labor. On the other1

hand, Japanese government consistently closed the door of accepting immigration1 . In
1990, as a compromise, the Japanese government allowed unskilled Brazilian and Peru
immigrants to work in Japan if his/her father, grand-father or grand-grander father
was Japanese. Thus at 1990, the Japanese immigrant policy changed from a practically zero immigration to a limited acceptance of immigration. In 1993, Japanese
government made another exception. Japanese government allowed unskilled workers
to work in Japan if his or her purpose of work is to get knowledge of Japanese factory system and they go back to their home country after working for three years for
immigrants from specific countries. Those two changes of immigration law increased
the number of immigrant in 1990s. Those two changes of the law increased the shareof foreigners from 0.3 percent to 1 percent. As in many other countries, the ratio of
immigrant to native did not increase evenly in Japan. Immigrant tended to live inparticular cities. This implies that in some cities, the ratio of immigrant to native
become bigger than ten percent. The presence of such cities provide us useful naturalexperiments. We take advantage of those cases as natural experiments.
In the economics literature, many papers analyzed the effect on wages, employment, labor supply. Regarding the effect of inflow on unskilled immigrant on natives’
human capital accumulation, there are two strand of the literature that analyzes theeffect of inflow of immigrants on native’s human capital accumulation. The first standof the literature analyzes the effect of immigrant inflow on human capital accumulation through a change of school quality. When a immigrant arrives at the US, many
children of immigrant have difficulty of speaking and understanding English. Teacher
often need to pay more attends to children of immigrants, resulting in a lower over-all
quality of the class. Recently, the second strand of the literature
1

This was partly due to the concern that once the inflow of unskilled immigrants are allowed, thea huge volume of Chinese immigrant will overflow to Japanese airport and harbor since in 1980s the
Chinese economic development is not started and the population size of China is almost ten times
as the size of Japan.

2

2

Data

I use two data sets for our analysis. The first data set is the Japanese National Census
of 1980, 1985, 1990, 1995, 2000. The census of 1980, 1990, 2000 is the lange national
census and those Census sets ask very detailed questions such as residence of five years
ago. The sensus 1985, 1995 is the small census and ask only important questions suchas current employment and student status.
The second data set is the current labor force survey, which is similar to CPS in the
US. There are advantage and disadvantage of using two data sets. The advantage ofusing the census is its large sample size. Since we focus on the schooling informationof aged 19-21, it is critical to have a large sample size. The second advantage ofusing the census is the availability of information regarding the residence of five years
ago. When a student graduates from high school and decide to go to college, someof them commute from home and others leave their home and live near college. Thisimplies that we need to college the residential information whey they were high school
students to connect the ration of immigrant in an area and tendency to go to college.
The availability of information on the residence t five year ago allow us to do this.
However, there are several disadvantages of using the national Census too. The
first disadvantage of using the national Census is the lack of the detailed economics
information compared with labor force survey. Although the national census college
whether a persian is working or not and largely industry category, other informationsuch as income and wage rate are not available. This lack of economic information
make us difficult to control household income when we analyze the children’s schoolingdecision. To overcome the disadvantage of using the national census, we use the labor
force survey which has economic information such as the parent’s wage and income.
However, the labor force surrey has several disadvantages. First, the sample size
is relatively small compared with the national census if we focus on the schooling3behavior of those aged 19-21. The second, due to the sampling structure, not all
cities are sampled. This implies that it is difficult to make the pseudo panel data
since the same cities are often not sampled in two surveys. This force us to use thelabor force surrey only as a cross sectional data rather than multi-year cross sectional
data. Thus, both the national census and the labor force surrey has advantage anddisadvantage. As a result, we use two data sets to complement each other.

3

Empirical Strategy

To examine the effect of inflow of unskilled immigrant on native schooling decision,consider an young individual iaged from 15-18, who live in economic area c, at theperiod t. Let
yt+5,c,i = Dt + Dc + β1 immigrant ratiot+5,c,i + β2 Xc,t + εictwhere Dt is the year dummy and Dc is economic area dummy in which a young
individual live at time t . yt+t,c,i is the dummy variable of indicating whether anindividual i who live in c at time t is attending university or community college attime t+5. immigrant ratioict is the hypothetical immigrant ratio that individual iwould face in year t + 5 if he or she keeps living in the same city c that he or she lived
in five years ago. In Japan, most of high school student decide to go to collage or workat the age of 16 or 17 due to difficulty of entrance examination and the time needed toprepare for the college entrance examination. Thus, this model specification assumes
that , a young individual of aged 16-17 at the time t decide to go to collage or towork after graduation by expecting the labor market condition t + 5 in a communitythat he lives. When a young person goes to university, often they leave the homeand live an area which is near the college. Thus, it is misleading to regress college
attendance dummy at time t + t on the immigrant ratio at time t + 5 of the area inwhich this person is living. In order to avoid this endogeniety, I use immigrant ratio4

in period t at the place in which this person lived. When individuals are 14-16 years
old, for the most of cases they live with their parents in Japan. Thus, the endogenietyof residential place is not likely to occur. The parameter of our interest is β1 and it
shows that how the predicted immigrant ratio affect school decision of person i. Xc
is the vector of the economic condition of the economic area c at time t.
Although equation (1) is useful at the starting point, there are several concern.
Since we pick up all individuals whether they live with parent or not, we cannot
collect information on parents. Thus, with the above regression, it is impossible tocontrol parent education and parent income.Parent income is only controlled throughaverage education level and average income level of the economic area. Due to thedifficulty of controlling parent education level and parent income, we use labor force
survey to control parent income and parent education. However, using labor force
survey has its own problem. The sample size of the labor force survey is relatively
small compared with the 10 percent national census. It is so particularly if we restrict
our sample to those aged between 19-21. This implies that it is difficult to construct
area level panel by combining labor force surveys in several years. This means that
we use only the labor force survey in a single year. In addition, to use the information
on parent income and parent education, we need to restrict the individual of aged
19-21 who are living with parent at the time of the survey. This implies that wehave selection problem when we use the labor force survey. In principle, if we canfind some exogenous variables that affect the co-residence with parents but does notaffect the decision to go to college, we can control this selection problem by using
an appropriate econometrics technique. However, it is quite difficult to find such avariable. As a result, in this paper, we restrict our sample in area where there aremany university and public transportation such as rail-line are fully available. In suchan area, a person i is more likely to commute from home either for school or work.

5However, it is still possible that my control is not sufficient, thus I use this information
only as circumstantial evidences.

4

ResultTable 2 to table 8 are my main results. In all tables, ratio1 is the ratio of immigrant
to natives. In the ratio 1, the immigrant includes Koreans. Including Koreans islikely to overestimate the effect of immigrants since most of Korean in Japan live
from the world war II from the historical reason. Thus, I also calculate the ratio of
immigrant to natives where Korean are not included in the number of immigrant.
This is represented by ratio2. ratio1 at old and ratio2 at old the ratio of immigrant tonative at the place where the sampled individuals lived five years ago. Predictedratio1
and Predicted ratio2 is the ratio of immigrants to native that an individual face atthe period t if he or she kept living in the same place that he or she lived in at theperiod t − 5. The coefficients of Predicted ratio1 and Predicted ratio2 are our maininterests.
In Table 2 column(1) and (2)show how the immigrant ratio in five year ago affects
the current probability to attend the college for individual who are aged 19-20. Thisimplies that they are aged 14-15. Thus, the at the immigrant ratio that they faced at
the age of 14-15 does not affect the probability of going to college. Column (3) andcolumn (4) examine how the immigrant ratio that an individual, who is aged 14-15 at
the period t − 5, predicted for the period t affect the school attendance choice at theperiod t. The column (3) and column (4) shows that when an individual predicts that
immigrant ratio would increase five years later, then they decide to go to college at theage of 14-15. More specifically, column (3) shows that one percentage point increaseof immigrant ratio, which include Korean, will increase the probability of going tocollege by 1.4 percentage point for male young individual aged 19-20. In column

6

(4), it shows that one percentage point increase of the immigrant ratio that does notinclude Korean will increase the probability of going to college by 1.3 percentage point
for male young individual aged 19-20. Column (5)-(8) show the results for female.
Table 4 looks at the result regarding having jobs. Column (3) and (4) show thatone percentage point increase of the immigrant ratio will decrease the probability thatan young male individual, aged 19-20, mainly are working, not attending a school
Column (7) and (8) also show that the effect of an increase of the immigrant ratioon the probability of having job for young female individual is negative, but it is not
significant.
Table 5 looks at the probability of unemployment. Column (3) and (4) show thatthe an increase of immigrant native ratio does not affect the probability of becoming
unemployed for male individuals and column (7) and (8) shows that one percentage
point increase of the immigrant ratio will decrease the probability of unemployment
by 0.2 percentage point.
Table 6 examines how an increase of immigrant ratio affects the probability of going
to college for male individuals aged 21-22. For individual aged 21-22, the age at thefive years ago correspond to the age 16-17, at which age a typical Japanese high-school
determine their career. As predicted, the immigrant-native ratio that youn individual
face at the age of 16-17 started to affect the probability that they go to college five
years later. Also, the predicted immigrant ratio affected the probability of going tocollege. The column (3) and column (4) show that one percentage point increase of
immigrant ratio increase the probability of going to college by 1.4 percentage point for
male and 0.56 percent for female. Table 7 look at the effect of increasing immigrant
ratio on the probability of working. It shows that one percentage point increase of
immigrant ratio will reduce the probability of working by 0.74 percent for male and0.7 percent for female.

7

Table 8 show the robustness checks. At the age of 23, it shows that past immigrant
ratio and the current immigrant ratio does not affect the probability of work, neither
probability of going to college. It seems to suggest that at the age of 23, individual
graduate college and work where job is available. As a result, the immigrant ratio at
the place that an young individual lives is not correlated with the works status.
Table 9 show the same result for schooling for individuals aged 23. At the age
23, the current or the past immigrant ratio does not have any explanatory power toexplain the probability of work or schooling.

5

Conclusion

This paper analyzes the effect of the inflow of unskilled immigrants on native’s decision
to go to college. As predicted by the economic theory, young native decide to go tocollege when they expect an increase of the share of unskilled immigrant. Using 10
percent sample of the census data, I show one percent point increase of immigrantnative ratio will increase the probability of going to college by 1.3 percentage point
and 0.8 percentage point for female of those aged 19-20. Consistently, one percentage
point increase of immigrant-native ratio will reduce of work by 1.2 percentage point
for male and 1.0 percentage point for female. For unemployment, an increase of
immigrant ratio did not affect the probability of unemployment for male and decrease
it for female. The result is robust regarding several specifications.

8

Table 2 The effect of foreign ratio on career of young: decision to go to school: (mea-based)
estimation method: fixed effectaged 19-20
dependent var=attending 4 or 2 year college
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
ratio1_at_old
0.168-0.330(0.24)(0.37)
ratio2_at_old
0.219-0.226(0.35)(0.29)
predicted_ratio1_at_old
1.429
0.880
(3.70)**(1.94)
predicted_ratio2_at_old
1.310
0.833
(3.62)**(2.11)*
locaiton unitmea
mea meamea
mea
mea meamea
sample age
19-20
19-20 19-20 19-20
19-20 19-20 19-20 19-20
sample gendermale
male male
male
female female female female
33178
32208 32208
N. of obs
331786331786 331786 322088
3220886
8
8R-squared
0.04
0.04 0.04
0.04
0.05
0.05 0.05
0.05
Clustering robust t statistics in parentheses* significant at 5%; ** significant at 1%

9

Table 3: The effect of foreign ratio on young career: having some job: fixed effect (mea-based)(1)
ratio1_at_old 0.479(0.62)
ratio2_at_old_ _predicted_ratio1_at_old

(2)

0.406
(0.60)

aged 19-20
dependent var=having some job dummy
(3)
(4)
(5)
(6)
0.187
(0.22)
0.124(0.16)-0.979-0.711(2.49)*(1.53)

predicted_ratio2_at_old
Constant(7)-0.456-0.453-0.445(22 80)**(22.80)**(22 09)**(22.09)**(21 33)**(21.33)**location unit mea
meamea
sample age 19-20
19-20
19-20
sample
male
male
male
genderN. of obs
331786331786331786R-squared 0.04
0.04
0.04
Clustering robust t statistics in parentheses* significant at 5%; ** significant at 1%-0.885(8)-0.608(2.51)*-0.450(21 47)**(21.47)**
mea
19-20

-0.735(20 89)**(20.89)**
mea
19-20

-0.734(20 98)**(20.98)**
mea
19-20

-0.728(20 75)**(20.75)**
mea
19-20

-1.47
-0.732(20 91)**(20.91)**
mea
19-20

male

femalefemalefemalefemale3317860.04

3220880.05

3220880.05

3220880.05

3220880.05

10

Table 4: The effect of foreign ratio on young career: decision to work mainly: fixed effect(1)
ratio1_at_old -0.494(1.16)
ratio2_at_old
predicted_ratio1_at_old(2)-0.592((1.52))

aged 19-20
dependent var= mainly workingdummy
(3)
(4)
(5)
(6)
-0.715(1.48)-0.777((1.75))(7)-1.200-1.035(4.22)**(3.29)**

predicted_ratio2_at_old
locaiton unit mea
meamea
sample age 19-20
19-20
19-20
sample
male
male
male
genderN. of obs
331786331786331786R-squared 0.07
0.07
0.07
Clustering robust t statistics in parentheses* significant at 5%; ** significant at 1%-1.218(8)-1.006(4.24)**
mea
19-20

mea
19-20

mea
19-20

mea
19-20

(3.18)**
mea
19-20

male

femalefemalefemalefemale3317860.07

3220880.083220880.083220880.083220880.08

11

Table 5: The effect of foreign ration on career: being unemployed: fixed effect(mea-based)(1)
ratio1_at_old -0.541(1.97)*
ratio2_at_old
predicted_ratio1_at_old
predicted_ratio2_at_old(2)

aged 19-20
dependent var=job search dummy
(3)
(4)
(5)
-0.161(1.30)-0.458((1.84)
)

(6)

(7)

(8)

-0.155((1.21))-0.105-0.205(0.75)(2.14)*-0.044

sample unit mea
meamea
sample age 19-20
19-20
19-20
sample
male
male
male
genderN. of obs
331786331786331786R-squared 0.01
0.01
0.01
Clustering robust t statistics in parentheses* significant at 5%; ** significant at 1%-0.194(0.37)
mea
19-20

mea
19-20

mea
19-20

mea
19-20

(2.01)*
mea
19-20

male

femalefemalefemalefemale3317860.01

3220880.01

3220880.01

3220880.01

3220880.01

12

Table 6: The effect of foreign ratio on career: going to school: fixed effect(mea-based)(1)
ratio1_at_old 1.302(3.78)**
ratio2_at_old
predicted_ratio1_at_old

(2)

1.421
(
(4.24)**)

aged 21-22
dependent var=going to school dummy
(3)
(4)
(5)
(6)
0.665
(2.92)**
0.695((3.14)**
)

(7)

1.545

0.506

(5.90)**(2.49)*

predicted_ratio2_at_old
location unit meameamea
samle age 21-22
21-22
21-22
sample
male
male
male
genderN. of obs
333271333271333271R-squared 0.05
0.05
0.05
Clustering robust t statistics in parentheses* significant at 5%; ** significant at 1%

1.537

(8)

0.564

(5.64)**
mea
21-22

mea
21-22

mea
21-22

mea
21-22

(3.34)**
mea
21-22

male

femalefemalefemalefemale3332710.05

3285380.04

3285380.04

3285380.04

3285380.04

13

Table7: The effect of foreign ratio on career: decision to work mainly: fixed effect( mea_based)(1)
ratio1_at_old -1.442(3.15)**
ratio2_at_old
predicted_ratio1_at_old(2)-1.523((3.40)**)

aged 21-22
dependent var= mainly working dummy
(3)
(4)
(5)
(6)
-1.196(2.84)**-1.206((2.99)**)(7)-0.657-0.640(1.82)(2.07)*

predicted_ratio2_at_old
location unit mea
meamea
sample age 21-22
21-22
21-22
sample
male
male
male
genderN. of obs
333271333271333271R-squared 0.07
0.07
0.07
Clustering robust t statistics in parentheses* significant at 5%; ** significant at 1%-0.748(8)-0.697(2.40)*
mea
21-22

mea
21-22

mea
21-22

mea
21-22

(2.58)**
mea
21-22

male

femalefemalefemalefemale3332710.07

3285380.05

3285380.05

3285380.05

3285380.05

14

Table 8: The effect of foreign ratio on career: decision to work mainly (mea-based) fixed effect(1)
ratio1_at_old -0.304(0.43)
ratio2_at_old
predicted_ratio1_at_old(2)-0.220(0.34)

age 23dependent var= mainly working dummy
(3)
(4)
(5)
(6)
0.184
(0.47)
0.385(0.91)

(7)

0.668

0.310

(1.49)(0.86)

predicted_ratio2_at_old
location unit mea
meamea
sample age 23
23
23
sample
male
male
male
genderN. of obs
169333
169333
169333R-squared 0.03
0.03
0.03
Clustering robust t statistics in parentheses* significant at 5%; ** significant at 1%

0.628

(8)

0.533

(1.67)
mea
23

mea
23

mea
23

mea
23

(1.36)
mea
23

male

femalefemalefemalefemale1693330.03

1672290.01

1672290.01

1672290.01

1672290.01

15

Table 9: The effect of foreign ratio on career: going to school : fixed effect(mea-based)(1)
ratio1_at_old 0.154(0.51)
ratio2_at_old
predicted_ratio1_at_old
predicted_ratio2_at_old

(2)

0.120
(0.39)

aged 23
dependent var=going to school dummy
(3)
(4)
(5)
(6)
0.055
(0.34)
0.058(0.37)(7)

0.124-0.112(0.51)(0.91)
0.135(8)-0.093(0.58)
mea(0.83)
location unit mea
mea
mea
mea
mea
mea
mea
sampleno condition no condition no condition no condition no condition no condition no condition no condition
gakurekisample age 23
23
23
23
23
23
23
23
sample
male
male
male
male
femalefemalefemalefemalegenderObservation 169333
169333
169333
169333167229167229167229167229R-squared 0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
Clustering robust t statistics in parentheses* significant at 5%; ** significant at 1%

16

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