JAPANESE AMERICAN WAGES, 1940-1990
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
The Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Molly Malloy Cooper, M.A.
*****
The Ohio State University
2003
Dissertation Committee:
Approved by
Professor Richard H. Steckel, Adviser
Professor Carolyn Moehling
Professor Audrey Light
_________________________
Adviser
Department of Economics
ABSTRACT
This dissertation examines the evolution of Japanese American males’ wages relative
to those of their white, native-born counterparts. Using data from the Integrated Public
Use Microsamples of the U.S. Censuses, this ratio of their average wages, adjusted for
age and geographic distributions, was less than 65% in 1940 and had risen to just over
100% in 1990. Five main questions are addressed: (1) Why were the wages of Japanese
Americans so low in 1940? (2) How did their relative wages rise so rapidly in the
decades after World War II? (3) What role did the internment of the vast majority of
mainland Japanese Americans play in the wage gaps from 1950 to 1980? (4) Did
internment effects vary by internees’ age at the time of their incarceration? (5) How
unique is the Japanese experience in the U.S. labor market? Specifically, how do their
wage gaps over this era compare with those for Chinese and Mexican Americans?
This dissertation finds that large portions of the significant Japanese-white wage
gaps before 1980 cannot be explained by differences in measurable characteristics such
as years of schooling and experience. The large gaps immediately before and after World
War II were due, in large part, to intense discrimination directed at Japanese Americans.
As this discrimination abated over time, the wage gap narrowed. The long run effects of
internment, including the loss of human capital embodied in three years exclusion from
ii
the labor force, adversely affected wages of adolescent internees until at least 1960 and
adult internees as late as 1970, contributing to the aggregate wage gap.
The evolution of Japanese American wages is shown to be somewhat similar to that
of Chinese Americans but different from that of Mexican Americans. The Chinese-white
wage gap in 1940 was similar to that for Japanese Americans, also due to the impacts of
discriminatory laws and policies directed against them. Although a small wage gap
persists, it is attributable to a steady flow of recent immigrants who have not yet
assimilated into the U.S. labor market. Recently, U.S.-born Chinese men have had labor
market outcomes strikingly similar to those of Japanese men. The Mexican-white wage
gap widened throughout the period studied, but most of these gaps are explained by
differences in years of schooling.
iii
Dedicated to my husband, Tim
and our children, Patrick and Erin
iv
ACKNOWLEDGMENTS
I would like to thank my adviser, Richard Steckel, for helping me see the “big
picture” in constructing this thesis.
I am indebted to Carolyn Moehling for her extensive feedback on countless drafts
and for numerous lengthy discussions on just about every aspect of this thesis.
I also am grateful to Audrey Light for her detailed corrections on earlier drafts and
her econometric expertise.
Support for this dissertation was provided by the William Green Memorial
Fellowship.
v
VITA
September 5, 1972……………………. Born - New York City, New York
1994…………………………………… B.A. Economics and Japanese, University of
Notre Dame.
1998…………………………………… M.A. Economics, The Ohio State University
1997 - 2001……………………………..Graduate Teaching and Research Associate,
The Ohio State University
2001…………………………………… Instructor of Economics, Denison University
2002…………………………………… William Green Memorial Fellow
2002 - present………………………… Graduate Teaching Associate, The Ohio State
University
FIELDS OF STUDY
Major Field: Economics
vi
TABLE OF CONTENTS
Page
Abstract…………………………………………………………………………………ii
Dedication………………………………………………………………………….
iv
Acknowledgments…………….……………………………………………………
v
Vita…………………………………………………………………………………
vi
List of Tables…………………………………………………………………………. ix
Chapters:
1.
Introduction……………………………………………………………………… 1
2.
Immigration in the twentieth century: some general trends………………….…… 4
2.1 Literature review…………………………………………………………….... 5
2.2 Overview of Japanese immigration…………………………………………… 7
2.2.1 Japanese immigration until the Gentlemen’s Agreement of 1908….… 8
2.2.2 Japanese America, 1908-1942……………………………………….. 11
2.2.3 Japanese Americans and World War II……………………………… 15
2.2.4 Japanese immigration since World War II…………………………... 17
2.2.5 The evolution of Japanese American occupations, 1920-1990…….. 18
2.3 Brief history of Chinese immigration………………………………………. 24
2.4 Brief history of Mexican immigration……………………………………… 31
3.
Japanese American wages, 1940-1990………………………………………….. 38
3.1
3.2
3.3
3.4
3.5
4.
Literature review……………………………………………………………
Data……………………………..………………………………………..
Wage model and results………………………………………………….
Interpretation of the results..……………………………………………..
Conclusions…………………………………………………………………
Effects of internment on Japanese wages, 1950-1980………………………….
vii
42
44
55
60
67
69
4.1
4.2
4.3
4.4
5.
71
74
77
80
Japanese, Chinese, and Mexican Americans in California: a comparison……… 82
5.1
5.2
5.3
5.4
5.5
6.
Literature Review………………………………………………………….
Data………………………………………………………………………..
An analysis of the effect of internment on wages by lifecycle status…….
Conclusions……………………………………………………………….
Literature review…………………………………………………………… 88
Data………………………………………………………………………… 92
The evolution of Japanese, Chinese, and Mexican wages…….…………….103
Time effects for Japanese, Chinese, and Mexican wages………………... .. 111
Conclusions………………………………………………………………… 114
Conclusion……………………………………………………………………… 115
Data Appendix……………………………………………………………………… 118
A.1
A.2
A.3
A.4
A.5
A.6
1940………………………………………………………………………..
1950……………………………………………………………………….
1960……………………………………………………………………….
1970……………………………………………………………………….
1980………………………………………………………………………..
1990………………………………………………………………………..
120
121
121
122
124
124
Bibliography………………………………………………………………………… 126
viii
LIST OF TABLES
Table
Page
1.1
Ratio of Japanese, Chinese, and Mexican to native white
Californian male mean weekly wages………………………………………. 2
2.1
Japanese immigration to the United States………………………………….. 8
2.2
Japanese in the United States by gender, 1890-1990………………………. 10
2.3
Age distribution of the Japanese in the United States, 1910-1990……...…. 10
2.4
Japanese American educational attainment………………………………..... 14
2.5
Occupational distributions and dissimilarity indices
for Japanese and native whites, 1920-1990………………………………… 20
2.6
Average weekly wages for Japanese and
native white occupations, 1940-1990………………………………………. 22
2.7
Ratio of Japanese to native white male wages by occupation, 1940-1990….. 23
2.8
Transition matrix…………………………………………………………….. 23
2.9
Chinese in the United States by gender, 1850-1990………………………… 26
2.10
Age distribution of the Chinese in the United States, 1910-1990……..…
2.11
Occupational distribution of Chinese men
in the United States, 1920-1990…………………………………………….. 28
2.12
Chinese American educational attainment…………………………………. 30
2.13
Mexicans in the United States by gender, 1850-1990………………………. 32
2.14
Age distribution of Mexicans in the United States, 1930-1990…………….. 34
2.15
Mexican American educational attainment…………………………………. 35
ix
26
2.16
Occupational distribution of Mexican men
in the United States, 1950-1990……………………………………………. 36
3.1
Ratio of Japanese to native white mean weekly wages by birth cohort……... 40
3.2
Comparisons of Japanese American and native whites’ IPUMS samples…… 48
3.3
Sample means for Japanese and Whites, by birth
cohort……………………………………………………………...………… 53
3.4
Results of wage equation (1),,,,.……………………………………………. 59
3.5
Differences in average log wages for Japanese and White samples
for issei birth cohorts and the difference due to selected characteristics..….. 62
3.6
Differences in average log wages for Japanese and White samples
for nisei birth cohorts and the differences to due to selected characteristics… 63
3.7
Differences in average log wages for Japanese and white samples
post-1935 birth cohorts and differences due to differences in average
average schooling…………………………………………………………….. 64
3.8
Difference in estimated log weekly wages for issei cohorts by census
year, evaluated at S=8…………………………………………………………64
3.9
Differences in estimated weekly wages for nisei cohorts by census year,
evaluated at S=16………………………………………………………………66
3.10
Difference in estimated log weekly wages for Japanese cohorts born
After 1935 by census year, evaluated at S=16…………………………………66
4.1
Comparison of interned and non-interned Japanese
male IPUMS samples, 1950-1970……………………………..…………….. 75
4.2
Results of wage equations……………………………………………….
5.1
Ratio of Japanese to native white Californian male mean weekly wages…….. 84
5.2
Ratio of Chinese to native white Californian male mean weekly wages…….. 85
5.3
Ratio of Mexican to native white Californian male mean weekly wages…….. 85
5.4
Japanese, Chinese, and Mexicans in California………………………………. 93
5.5
Comparisons of Japanese, Chinese, Mexican, and white samples……………. 95
x
79
5.6
Sample means for Japanese, Chinese, Mexicans, and Whites, by
birth cohort………………………………………….……………………….. 100
5.7
Results of wage equation (1)…...…………………………………………….105
5.8
Differences in log wages of 1886-1905 birth cohorts
due to difference in marriage rates and average years schooling….…………. 108
5.9
Differences in log wages of post-1905 birth cohorts due to differences in
in years of schooling and to foreign birth…………………………………….109
5.10
Differences in estimated log weekly wages for Japanese cohorts born
1886-1935, by census year evaluated at S=12……….……………………. 111
5.11
Differences in estimated log weekly wages for Japanese cohorts
born 1936-1975, by census year evaluated at S=16…………………………. 111
5.12
Difference in estimated log weekly wages for Chinese cohorts born
1886-1935, by census year, evaluated at S=12………………………………..112
5.13
Differences in estimated log weekly wages for Chinese cohorts born
1936-1975, by census year, evaluated at S=16………………………………..112
5.14
Difference in estimated log weekly wages for Mexican cohorts born
1886-1935, by census year, evaluated at S=8…………………………………113
A.1
Sizes of Japanese and native-white samples drawn and
used in wage equations and percentages excluded from
wage equation estimations…………………………………………………...123
A.2
Sizes of Japanese and native-white subsamples and
Percentages excluded from wage equation estimations…………………….. 123
A.3
Sizes of Japanese, Chinese, Mexican, and native-white
Californian samples drawn and used in wage equations
and percentages excluded from wage equation estimation…………………. 124
xi
CHAPTER 1
INTRODUCTION
Immigrants from different nations have achieved varying levels of success in the
United States. Some groups have fared very well; they arrive in this country with
nothing, but achieve high levels of socioeconomic status within a few decades. Other
groups barely subsist as low-skill minimum wage workers for several generations. The
pace of immigrant groups’ assimilation into the U.S. labor market, as measured by the
disappearance of a gap between that ethnic groups’ wages and those of their white,
native-born counterparts, ranges from a few decades to several generations.
Characteristics which may affect the pace of assimilation of a given immigrant group
include its native language, cultural norms, average initial skill and education levels, their
human capital investments after arrival, and the presence of discrimination against them.
Today Japanese Americans are regarded as high achievers despite a language barrier, a
vastly different culture, and their subjection to legally sanctioned discrimination in the
recent past.
Table 1.1 compares the Japanese-white wage gaps for Californian men, aged 15-64
for 1950 to 1990 to those of Mexican and Chinese Californians. In 1950, the wages of
Japanese Californian men were almost 40% lower than those of their white, native-born
counterparts. Their wages were similar to those of Chinese men but lower than those of
1
Mexican men. Over the next four decades Japanese men reached parity with whites. A
small gap remained for Chinese men as of 1990, but the wage gap for Mexican men
persisted and widened over this era.
Census Year
1950
1960
1970
1980
1990
Japanese
Chinese
Mexicans
0.613
0.577
(.041)
(.041)
0.652
(.028)
0.878
0.792
0.670
(.032)
(.052)
(.009)
0.942
0.775
0.690
(.028)
(.032)
(.032)
0.945
0.939
0.637
(.012)
(.015)
(.015)
1.006
0.923
0.549
(.014)
(.011)
(.003)
Table 1.1: Ratios of Japanese, Chinese, and Mexican to
Native White Californian Male Mean Weekly Wages
Notes: Standard errors are in parentheses. Figures in bold
significantly different from 1 at the 5% level. Sample sizes in Table 5.5
These three groups have some similarities. They were all relative latecomers to the
United States versus Western Europeans; their first major immigration waves began in
the second half of the nineteenth century. They all initially settled in western United
States, especially California, and toiled in mines and salmon canneries and on farms and
railroad lines. They all faced a language barrier in their new land. They all have physical
features that distinguish them from whites of European descent. They were all targets of
racial discrimination. Why are there such divergent outcomes from such similar initial
conditions?
This dissertation examines the peculiar Japanese American labor market experience.
During the largest wave of Japanese immigration, which occurred in the first decade of
2
the twentieth century, the typical immigrant found employment as an agricultural laborer
and lived in barracks with dozens of his countrymen on large vegetable farms. The 1910s
saw the process of family formation as some immigrants became tenant farmers or small
business owners. By 1940, their children had attained very high levels of education,
some attending top universities, but for the most part discrimination barred them from
occupations commensurate with their education levels. Their situation worsened during
World War II as 87% of mainland Japanese Americans were incarcerated in internment
camps and those who were not were regarded as pariahs. By 1960, the younger members
of the second generation apparently enjoyed equal access to employment and the
occupational distribution shifted dramatically from farm and general laborers into
managers and professionals. By 1990, the typical Japanese American man had a college
education, owned a house in the suburbs, and worked as a manager or professional, the
typical American Dream.
The next chapter discusses the general performance of immigrants in the United
States’ economy and provides detailed histories of Japanese, Mexican, and Chinese
immigration history. Chapter 3 analyzes the evolution of the Japanese-native white wage
gap from 1940-1990. Chapter 4 examines the long-run effect on Japanese American
wages due to internment. Chapter 5 compares the evolution of the Japanese-native white
wage gap with that Chinese and Mexican Californians. Chapter 6 concludes.
3
CHAPTER 2
IMMIGRATION IN THE TWENTIETH CENTURY: SOME GENERAL TRENDS
This chapter describes the economic progress of immigrants to the United States in
general, and Japanese, Chinese, and Mexican immigrants in particular. Most of the
economic literature on the Great Migration, 1890-1924, focuses on the differences
between the “new” immigrants, primarily those from Central and Southern Europe, and
the “old” immigrants from Northern and Western Europe. This literature will be
reviewed in Section 2.1 in order to place the Japanese American experience in context.
This chapter also details the history of Japanese American immigration in order to
frame the analyses of their subsequent labor market outcomes presented in Chapters 3
through 5. The Japanese American experience has been shaped by government policies:
immigration restrictions, bans on the purchase of land by resident aliens, and, most
audaciously, their mass evacuation and incarceration during World War II. Section 2.2
documents these developments and examines the evolution of the Japanese American
occupational distribution over time.
Sections 2.3 and 2.4 briefly chronicle the Chinese and Mexican American
experiences. These histories provide the background information necessary for Chapter
5, in which the labor market outcomes of Japanese Californians are compared to those of
Chinese and Mexican Californians.
4
2.1. Literature Review
The literature on immigration in the late nineteenth and early twentieth century
evaluates the differences in economic outcomes for immigrants from different countries.
Higgs (1971), McGouldrick and Tannen (1977), and Chiswick (1992) analyze the
differential economic outcomes of immigrants from northern Europe versus southern and
central Europe. Higgs (1971) using data from the 1909 Immigration Commission Reports
argues that wage differentials across immigrant groups can be attributed to the lower skill
levels, in terms of English proficiency and literacy, of the then “new” immigrants from
Southern and Eastern Europe versus the “old” immigrants from Northeastern Europe.
Using the same data set, McGouldrick and Tannen (1977) counter that the Nativist
sentiment of the era caused employers to discriminate against the “new immigrants.”
Chiswick (1992) finds that Jewish immigrants earned higher wages than other
immigrants from Southern and Eastern Europe, although still earned less than natives.
Feliciano (2001) uses data from the same report to evaluate the wage differential
between Mexican and native workers. Similar to Higgs (1971) she finds a large wage
differential, most of which can be attributed to the much lower Mexican literacy rates.
Using a data set from Iowa in the 1890s, Eichengreen and Gemery (1986) find that
the wages of immigrants rise steeply with U.S. work experience. They find rather large
and persistent wage gaps between unskilled immigrants and natives but small gaps for
immigrants with prior training is skilled crafts.
Ferrie (1994) compares the wealth accumulation of Irish, British, German, and other
European immigrants in the mid-nineteenth century. Linking immigrants from passenger
5
ship lists in 1840s to the 1850 and 1860 federal census manuscripts, he finds British
immigrants, who had the fewest language and cultural barriers to assimilation, and
Germans, who on average possessed more wealth upon their arrival in this country, fared
the better than the poorer immigrants from Ireland and other countries. Ferrie does not
include a native-born control group in his study. But, the results of his regressions allow
for some conclusions about differences by country of origin in the shape of the wealth
profiles as immigrants time in the United States increases with age. The British
immigrants have profiles with higher intercepts and flatter slopes, whereas the German
immigrants have lower intercepts and steeper slopes. The immediate applicability of
British immigrants’ human capital to the U.S. labor market was higher than that of
Germans, resulting in their higher initial wealth, but lower returns to years in the United
States.
A reoccurring theme in the literature on the performance of participants in the Great
Migration is the adversity faced by the “new” immigrants due to the Nativists’ sentiments
of the time, their lack of skills, or a combination of these factors. The experiences of
newly arrived Japanese immigrants during the first decade of the twentieth century was
fairly similar; Higgs (1977) documents wage gaps for Japanese agricultural laborers in
1909 comparable to those found in other studies using the data in the 1909 Immigration
Commission Reports. These early Japanese immigrants did not possess good English
skills, but as will be documented in the next section, they had very high literacy rates.
The major difference in experiences between Japanese and the other new immigrants
during the first half of the twentieth century is the extraordinary legal and informal
barriers against which they ultimately persevered. Section 2.2 recounts the history of
6
Japanese American immigration. Sections 2.3 and 2.4 provide comparable accounts for
Chinese and Mexican Americans.
2.2: Overview of Japanese Immigration
Immigration from Japan can be divided into very distinct eras, summarized in Table
2.1. Under the feudalistic regime from 1638-1854, both immigration to and emigration
from Japan were illegal. In 1860 the first Japanese diplomats were dispatched to the
United States beginning the small trickle of Japanese immigration composed of
ambassadors, students and tourists from the gentrified classes. After labor emigration was
legalized in 1885, the number of immigrants rose steadily. After a slight decline from
1895-1897 caused by the Sino-Japanese War, Japanese immigration quickly climbed to
annual rates of over 10,000 until 1909 (Annual Reports of the Commissioner of
Immigration). The subsequent radical decline was caused by the enactment of the
Gentlemen’s Agreement of 1908.
The Gentlemen’s Agreement signaled a new era in Japanese immigration,
characterized by increasingly severe, legally sanctioned discrimination culminating in
their internment during World War II. This rest of this section is divided as follows.
Section 2.2.1 looks at immigration until the enactment of the Gentlemen’s Agreement in
1908. Section 2.2.2 summarizes the experiences of Japanese Americans between 1908
and the outbreak of World War II. Section 2.2.3 recounts the events during World War
II, especially internment. Section 2.2.4 explains changes in U.S. immigration policies
after World War II. Section 2.2.5 details the evolution of Japanese American
occupational distributions since 1920.
7
Era
Males
Females
Total
Annual Av
Policy in Effect
1861-70
218
21.8
Japan forbids labor emigration
1871-80
149
14.9
"
1881-85
112
22.4
"
431.6
Unrestricted Immigration
1886-90
2,158
1891-1900
25,942
2,594.2
"
22,468
126,655
15,831.9
"
5,331
2,665.5
Gentlemen's Agreement
1901-08
104,187
1909-10
3,425
1,906
1911-20
1921-24
1925-30
1931-40
1941-50
1951-52
1952-60
1961-64
1965-70
1971-80
1981-90
53,361
12,773
34,215
16,431
87,576
8,757.6
"
29,204
7,301.0
"
4,258
709.7
Immigration Act of 1924
1,948
194.8
"
1,555
155.5
"
198
4,742
4,940
2,470.0 McCarran-Walter Immigration Act 1952
6,200
40,594
46,794
5,849.3
(Also War Brides Act of 1945)
16,465
4,116.3
"
23,523
3,920.5
1965 Immigration Act
49,775
4,977.5
"
44,800
4,480.0
"
Table 2.1: Japanese Immigration to the United States
Source: Historical Statistics 105-7; for 1901-24 Resident Orientals 409; and for 1950-60 Asian America 307
2.2.1: Japanese Immigration until the Gentlemen’s Agreement of 1908
Around the turn of the century, the anti-Japanese agitation quickly spread. Since the
1850s Chinese immigrants had been victims of discrimination and racially motivated
violence culminating in the passage of the 1882 Chinese Exclusion Law barring Chinese
labor immigration (Chan 1991). At first, Japanese immigrants were welcomed as a new
source of cheap labor, preferable to the Chinese. Having learned from the experiences of
the Chinese immigrants, the new Japanese arrivals adapted Western dress immediately to
indicate their intention to assimilate (Chan 1991, Daniels 1988). But, the dramatic
increase in their numbers combined with the sudden ascendancy of their homeland to
world prominence in the wake of Japan’s victories over China and Russia made the
8
Japanese immigrants targets of the same type of hostility. The movement for Japanese
labor exclusion enjoyed increasing support during the first decade of the twentieth
century. Exclusionists’ arguments were both racially and economically based. American
laborers objected to their Japanese counterparts both because they underbid them and
because they were used as strike breakers (Ichihashi 1932, 229-33; Daniels 1988, 112-8).
Tensions ran particularly high in California, where the majority of Japanese
immigrants to the U.S. mainland settled. In October 1906 the San Francisco Board of
Education decided to segregate Japanese, Chinese, and Korean students into a separate
school. In an effort to quell the anti-Japanese agitation, to avoid complete exclusion of
Japanese immigration, and to end the school segregation, the Japanese government
entered into the Gentlemen’s Agreement with the United States. The agreement was
negotiated during 1907 and 1908. It limited Japanese immigration to individuals in four
categories:
1)
2)
3)
4)
Non-laborers, such as students or diplomats
Former United States residents
Parents, spouses, and children of current residents
Settled agriculturists (landowners seeking to claim their interests).
The most obvious effects of this agreement were the change in the number of and
demographics of Japanese immigrants. The annual number of Japanese immigrants
plummeted from 30,226 (an inflated number caused by anticipation of the agreement) in
1907, to 15,803 in 1908, and then to 3,111 in 1909 (Reports of Commissioner General of
Immigration). The annual number of Japanese immigrants remained below 10,000 until
1918, when World War I resulted in the closure of Europe to Japanese students seeking
an education abroad.
9
Prior to the Gentlemen’s Agreement, the Japanese population in the U.S. had been
predominately single prime-age males. In 1900, there were 23 men per each Japanese
woman (see Table 2.2). By 1910 this ratio fell to almost 7 to 1. Furthermore 88% of the
Japanese population was between the ages of 15 and 45 in 1910 (see Table 2.3).
Year
1890
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
Males
Females
Total
1,780
259
23,341
985
61,916
9,031
72,404
38,221
81,648
57,013
71,967
54,980
76,447
64,918
129,375
140,518
166,707
203,948
212,697
263,900
293,024
346,216
Table 2.2: Japanese in the United States* by Gender, 1890-1990
Source: U.S. Census Bureau Reports.
*Note: Figures exclude Hawaii
Year
Under 15
15-24
25-44
45-64
2,039
24,326
70,947
110,625
138,661
126,947
141,365
269,893
370,655
476,597
639,240
Over 65
1910
7.0%
22.0%
66.4%
4.5%
0.1%
1920
26.5%
12.6%
49.5%
11.2%
0.2%
1930
40.8%
9.6%
30.2%
18.9%
0.5%
1940
25.1%
28.5%
22.5%
21.5%
2.3%
1950
23.7%
18.0%
32.8%
18.0%
7.6%
1960
30.8%
11.8%
40.2%
11.3%
5.8%
1970
23.0%
15.8%
34.6%
18.6%
8.0%
1980
16.5%
18.1%
33.7%
26.4%
5.2%
1990
15.3%
13.4%
37.9%
21.7%
11.8%
Table 2.3: Age Distribution of the Japanese in the United States 1910-1990*
Source: U.S. Census Bureau Reports.
Note: Figures exclude Hawaii
10
2.2.2: Japanese America, 1908-1942
After the Gentlemen’s Agreement, the population first became more female as many
wives entered under the third category, then younger with a higher percent American
born as the couples started families. By 1920 there were fewer than 2 males to each
female. This ratio continued fall, and since 1970 females have outnumbered males.
Also, in 1930, over 40% of the Japanese American population was under age 15. The
children’s proportion of the population declined to about 25% and then followed the
trend of the rest of the United States with the Baby Boom most notably in the 1960 data
and the Baby Bust in 1980 and 1990.
In addition to its demographic effects, the anti-Japanese sentiment embodied in the
Gentlemen’s Agreement and the anti-oriental land ownership laws sparked a movement
of return emigration to Japan. Twelve states passed laws between 1913 and 1943
forbidding Japanese nationals from owning land (McGovney 1947, 7-8). The most
significant was Californian Alien Land Act of 1913. Under this law, aliens not eligible to
citizenship were prohibited from owning land or leasing land for agricultural pursuits for
more than three years (Hing 1993, 212).
The actual effects of this legislation are a debatable. Under the law, Japanese
farmers could still hold short-term leases on land. Market forces overshadowed the intent
of the legislation. World War I increased the demand for U.S. agricultural products and
simultaneously decreased the civilian male labor supply (Chan 1991). California’s
rapidly expanding population further fueled demand (Daniels 1988, 144). Many
researchers cite anecdotal evidence that the 1913 law was circumvented easily by
purchasing farms in the names of Japanese children born in the United States,
11
sympathetic citizen friends, or corporations in which U.S. citizens (usually lawyers hired
by the farmer) held a majority of the stock.
Even though the Japanese farmers persevered in spite of the 1913 Alien Land Law,
Daniels (1988), Ichioka (1988), and Suzuki (1994) discuss the negative effects of this
legislation on the Japanese American psyche. Suzuki and Ichioka both cite this law as a
motivation for a mass return migration of Japanese unskilled laborers. From 1909 to
1912 emigrant alien departures exceeded immigrant alien admissions to the mainland
United States and departures remained high throughout the decade (Annual Reports of
the Commissioner General). Furthermore, some of the Japanese who departed
immediately after the Gentlemen’s Agreement may have returned around 1912-1917, as
evidenced by an increasing number of entering laborers classified as “former residents”
(Japanese Association 1921, 4). Increases from the years 1914-1916 in the numbers of
non-laborers, primarily women and children, and of laborers classified as “parents,
spouses, and wives” lag behind that of “former residents.” These returning immigrants
may have sent for the families they started during their temporary return to their native
land after having reestablished themselves in the United States.
In the aftermath of World War I, the United States pursued a policy of isolationism
from the rest of the world. The Immigration Act of 1924 barred “aliens ineligible for
citizenship” from immigration. The Nationality Act of 1790 allowed only “free white
persons” to be naturalized. The Civil Rights Bill of 1870 extended the right of
naturalization to those of African decent. But, the U.S. courts continuously upheld that
Asian immigrants had no rights of citizenship until the passage of the McCarran-Walter
Act in 1952 (Hing 1993, 23-31; Daniels 1988, 149-51). The Immigration Act of 1924
12
superceded the Gentlemen’s Agreement and limited Japanese immigration to tourists,
business travelers, clergy, diplomats, college students, and their wives and unmarried
minor children (Ichihashi 1932, 302). The number of Japanese immigrants dropped
precipitously (see Table 2.1). The Great Depression and especially World War II worked
to reinforce the effects of this legislation.
In the 1920s and 1930s the second generation Japanese Americans, the native-born
sons and daughters, came of age. The Japanese American community and most of the
literature refers to this group as the nisei, the Japanese word for “second generation.”1
By 1940, the Japanese Americans were relatively well educated. 33.2% of Japanese
Americans over the age of 25 had at least a high school diploma, compared with 30.4%
of the white native-born population, and this difference widened considerably with each
passing decade (see Table 2.4). The native-born Japanese population over 25 in 1940 had
a staggering 12.2 median years of schooling (U.S. Bureau of the Census 1943a).
Obviously, there was a large gap in the educational attainment of the Issei and Nisei.
These gains in education status happened remarkably fast. According to the 1910 Census
reports, only 56% of Japanese children ages 6 to 17 attended school and only 34% of
those ages 15 to 17. By 1920 the figures had risen to 80% of children 7 to 17 and over
50% of 16 and 17 year olds remained in school. With the stability of family formation in
the 1910s came less reliance of households on their teenagers’ incomes and a greater
emphasis on education. Selective out-migration of less wealthy families may have
bolstered these trends.
1
Issei are the first generation, and Sansei are the third.
13
Census
Year
1940
1950
1960
1970
1980
1990
No High School Diploma
Whites
Japanese
High School Diploma
Whites
Japanese
Some College
Whites
Japanese
4+ years of college
Whites
Japanese
69.6%
66.8%
17.7%
23.5%
7.0%
4.5%
5.8%
5.1%
61.4%
42.3%
23.2%
40.6%
8.4%
9.5%
6.9%
7.7%
54.0%
32.2%
27.4%
41.8%
10.0%
13.7%
8.7%
12.3%
42.9%
26.5%
33.5%
38.7%
11.8%
16.0%
11.8%
18.8%
31.2%
13.8%
35.7%
35.0%
16.0%
21.4%
17.1%
29.8%
22.1%
9.4%
31.0%
24.2%
25.4%
27.8%
21.5%
38.6%
Table 2.4: Japanese American Educational Attainment
Source: Census Bureau Reports. Notes: Figures for 1940-1970 are for native whites,
figures 1980 and 1990 are for all whites. Figures for Japanese Americans exclude Hawaii
The benefits of this education were unrealized, however, for the Nisei in the interwar
period. Entrepreneurs and those trained in the professions were limited to the clientele
only within their ethnic community (Daniels 178-85, Chan 113-8). Edward Strong’s The
Second-Generation Japanese Problem (1934) addressed the frustrations of the
contemporary Nisei who worked very diligently in school only to be denied occupational
opportunities. He concludes, “As a group, the second generation have no right to expect
more than that a few will accomplish great things and the remainder will build upon the
foundations established by their fathers” (269). Roger Daniel’s illustrates this point with
an excerpt from an essay written by a Nisei entitled “The Protest of a Professional Carrot
Washer” (178).
Between the two World Wars, Japanese Americans struggled to overcome
discrimination. The first generation faced legal barriers to economic advancement in
agriculture based on their status as “ineligible for citizenship.” Their citizen children
acquired a very high level of education only to have their progress thwarted first by
employment discrimination and later by their mandatory internment during World War II.
14
2.2.3: Japanese Americans and World War II
The discrimination faced by the Japanese Americans, legally sanctioned or
otherwise, intensified greatly with the tensions leading up to World War II. Daniels
(1988) extensively documents the hostility received by the Japanese Americans and the
rift formed between the American born children and their immigrant parents. After the
December 7, 1941 bombing of Pearl Harbor, these tensions reached a boiling point.
Approximately 1,500 Issei, mostly business leaders, Japanese language school teachers,
and Buddhist priests were arrested by that evening (Daniels 1988, 202). The entire West
Coast Japanese American population would eventually join them in internment camps.
Beginning in March 1942 around 120,000 Japanese were evacuated from
Washington, Oregon, California, and Arizona. These evacuees were given 2 days notice
to gather only the personal effects they could carry and to make arrangements for their
pets and real and personal property. After being processed at temporary facilities, often
horse stalls at county fairgrounds, they were sent to 10 camps in very remote regions of
Arkansas, Colorado, Wyoming, Arizona, Utah, Idaho, and California (Daniels 1988,
217). The apartments at these camps were very sparse with no heating or cooling
mechanism to counter the effects of the often harsh climates nor running water, requiring
the inmates to use communal bathroom facilities and dining halls (Daniels 1988, 231).
Of the 120,313 individuals in the custody of the War Relocation Authority (WRA) for
some time between 1942 and 1946, about 70% were American born, including the 5,981
births in the camps (WRA 1946, 8 & 96).
About 4,300 college-aged Nisei were released from the camps for the start of the
1942 Autumn Semester, conditional on their acceptance at a school not on the West
15
Coast. And, throughout the evacuation, many agricultural workers were placed on
temporary leave as needed to assist the harvests during the severe labor shortage caused
by the war. Ironically, 2,355 young Nisei were released directly to the Armed Forces
(WRA 1946, 8), either as volunteers or as draftees. By 1944, the population of the camps
was down to about 80,000 as many had been relocated to the East or sent to college or to
the military (Daniels 1988, 241). By the end of the War in August 1945, there were
around 44,000 still incarcerated in the camps (Daniels 1988, 285).
Obviously, the interned Japanese bore terribly high costs. In 1954 the Federal
Reserve Bank of San Francisco estimated their property losses alone at $400,000,000
(Daniels 1988, 291). Daniels points out that in addition to the decay, theft, and
vandalism caused by the hasty and disorganized mandatory evacuation, West Coast
Japanese Americans were denied the opportunity to participate in the relatively
prosperous economy of 1942 to 1945 (292). Under the Japanese Americans Claims Act
of 1948, $38 million was allocated to settle documented claims for property loss directly
attributable to the evacuation. 23,000 claims for $131 million in damages were filed.
Few were settled, and most of the settlements were for pennies on the dollar (Daniels
297-9). In August 1988 Congress signed into law a bill to compensate each of the 60,000
remaining survivors of the relocation. A one-time payment of $20,000 was sent to each
remaining survivor, or his estate, beginning in 1990 with the eldest group. These
payments were spread over 10 years (Daniels 341).
16
2.2.4: Japanese Immigration since World War II
Labor immigration was restored under the McCarran-Walker Act in 1952 with a
small annual limit of 185. More significantly this legislation for the first time gave
Japanese American residents the same rights of naturalization as other immigrants. By
this time, however, because of the 28 years of exclusion, the Japanese-born population
had dwindled.
Since 1965 prospective immigrants from Japan and the rest of the world have been
subject to a fairly complicated preference system that gives priority to family
reunification and then those in occupations in which the United States has a shortage
(Kim 1996, 536-7). Many other Asian immigrant groups took advantage of the new
system to reunite families. Because the Japanese families were formed during the early
part of the century under the Gentlemen’s Agreement, and then further immigration was
banned under the Immigration Act of 1924, the preference system did not significantly
impact the Japanese American population as it did for other Asian groups, especially the
Indian and Chinese populations. Furthermore, the booming postwar Japanese economy
eliminated the “push factors” at least for males. Under the new system, the Japanese
Americans lost their status as the largest Asian American population. In the 1990 census,
they rank third after Chinese and Filipinos. Also, the female Japanese American
population percentage overtook the male percentage by 1970 (See Table 2.2). Prime age
female immigration surged after World War II and the U.S. occupation of Japan because
of war brides (see Table 2.1). The trend continued probably as more females applied
under the employment categories, seeking greater gender equality in the United States
(Hing 1993, 106-8).
17
2.2.5: The Evolution of Japanese American Occupations 1920-1990
The next three chapters use Japanese wages as the measure of economic success, this
section analyzes the evolution of another indicator, occupational status. While not as
quantitatively appealing as wages, occupational data has been enumerated separately by
race, including Japanese, back to the 1920 census, making it possible to evaluate the
economic progress of the issei. Also, some of the wage gap may be “explained” by
differences in occupational distribution, but it is impossible to infer to what extent the
distribution itself is influenced by discrimination. The Japanese may have received lower
wages on average than whites either because they chose or were given the opportunity to
work at only relatively lower paying jobs. In the case of Japanese immigrants in 1940
and 1950, laws prohibiting citizenship and landownership restricted occupational choice.
And, Daniels (1988) documents that entrepreneurs and those trained in the professions
were limited to the clientele only within Japanese community (178-85).
The analysis
that follows does not address to what extent the occupational distribution was influenced
by discrimination, it only attempts to assess the impact of the distribution on wages.
I follow the methodology of Gross (1968) and Goldin (1990). First, in order to
quantify the extent to which the Japanese occupational distribution differs from that of
native whites, I constructed an index of dissimilarity: D = Σji – wi / 2, where ji and wi
are the proportion of the Japanese and white samples engaged in each occupation i
respectively. This index measures the percentage of Japanese who would have to change
occupations in order for their distribution to be the same as native whites. If Japanese
and white men were completely segregated, no overlap in any occupation, D=1. If they
have the exact same occupational distribution, D=0.
18
My calculations are summarized in Table 2.5 show the occupational distributions for
Japanese and native whites from 1920 to 1990 Census population manuscripts and the
associated dissimilarity indices. I have weighted the proportion of the whites in each
occupation in each major geographic region by the proportion of Japanese in each
geographic region. The dissimilarity indices for Japanese versus native white males
hovered around 1/3 from 1920 to 1950. Between 1960 and 1980 it fell off dramatically
to under 1/5 and is only .125 for 1990. More significant is the change in occupational
distribution of Japanese males relative to white males. In 1920, 43% of Japanese men
were employed as farm or general laborers versus 22% of white men. By 1990, the
figures had fallen to 5.6% and 6.0%, respectively. Meanwhile, at the opposite end of the
spectrum, 1920 only 5% of Japanese men were classified as professionals or managers,
about half as many as their native-born white counterparts. By 1990, 54% fell into these
categories versus 44% of employed white males.
19
Professionals
Year
1920
1930
1940
1950
1960
1970
1980
1990
J
W
Farmers
J
W
Managers
J
W
Clerical & Sales
J
W
Craftsmen Operatives
J
W
J
W
Service Workers
J
W
Farm Laborers
J
W
Laborers
J
W
Dissim
Index
0.024
0.046 0.171 0.258 0.027 0.039
0.090
0.149 0.026 0.129
0.058 0.103
0.170
0.052
0.283
0.152 0.152 0.072 0.329
0.037
0.057 0.143 0.190 0.097 0.047
0.085
0.193 0.026 0.140
0.049 0.115
0.151
0.054
0.330
0.123 0.082 0.080 0.356
0.031
0.069 0.186 0.102 0.131 0.121
0.092
0.143 0.027 0.161
0.069 0.167
0.109
0.076
0.245
0.075 0.110 0.086 0.322
0.065
0.089 0.155 0.079 0.087 0.127
0.092
0.138 0.078 0.205
0.098 0.174
0.119
0.058
0.170
0.051 0.137 0.079 0.313
0.194
0.133 0.180 0.041 0.096 0.124
0.126
0.149 0.114 0.214
0.097 0.186
0.062
0.057
0.077
0.030 0.054 0.066 0.252
0.264
0.167 0.044 0.022 0.113 0.122
0.146
0.150 0.128 0.209
0.095 0.169
0.064
0.080
0.023
0.018 0.123 0.062 0.184
0.261
0.326
0.160 0.028 0.018 0.177 0.141
0.244
0.015 0.005 0.215 0.199
0.171
0.139
0.167 0.114 0.210
0.157
0.118 0.184
0.062 0.139
0.058
0.099
0.081
0.075
0.087
0.063
0.063
0.005
0.024 0.042 0.053 0.190
0.051 0.046 0.125
0.004
Table 2.5: Occupational Distributions and Dissimilarity Indices Japanese and Native White Occupations, 1920-1990
Notes: Figures for 1920 and 1960 are for native whites of native parents. Figures for 1940, 1950, and 1980 are for all whites.
Figures for 1990 only are calculated from the IPUMS; all others years calculated from Census Bureau Reports.
20
The next step is to evaluate the importance of occupational segregation to the gap in
Japanese-native white earnings. The Census manuscripts do not disaggregate wage
figures by occupation by region by race. I calculate the mean weekly wages of Japanese
and native white men within each occupational classification for my 1940 through 1990
samples in Table 2.6. Table 2.7 shows the ratios of these average wages. The wage gaps
closed considerably for all occupations over time, except for a very higher wage for
Japanese farmers in 1950 which seems to be small sample anomaly. As the relative share
of Japanese men employed in the lower paying labor occupations decreases, the relative
wages increase, actually exceeding 1, because the Japanese men in these categories are
relatively older and more experienced than the whites after 1970.
In Table 2.8, I construct a matrix with the occupational distributions over time on the
horizontal and the wage ratios on the vertical. The diagonal elements represent the
aggregate wage ratios using the population occupational distribution and the sample
wages when adjusted by region. The off-diagonal elements represent the hypothetical
ratio if for example, the 1940 occupation distribution remained given the 1990 within
occupation wages.
21
Y ear
1940
1950
1960
1970
22
1980
1990
P rofessionals
J
W
F arm ers
J
W
M anagers
J
W
W
C raftsm en
J
W
C leric al & S ales
J
O perativ es
J
W
S ervic e W orkers
J
W
W
Laborers
J
W
F arm Laborers
J
34.21
53.44
13.38
20.57
54.99
69.35
25.93
35.32
32.36
33.61
19.72
27.83
17.38
25.74
15.49
16.31
18.43
21.29
(12)
(172)
(5)
(38)
(13)
(198)
(26)
(483)
(6)
(496)
(18)
(403)
(35)
(165)
(63)
(157)
(18)
(256)
81.89
115.90
89.77
47.63
68.23
119.73
63.64
82.24
55.76
69.58
58.38
65.73
47.38
65.23
35.35
38.38
48.90
66.78
(7)
(137)
(11)
(40)
(8)
(175)
(7)
(206)
(18)
(354)
(8)
(196)
(15)
(70)
(8)
(31)
(25)
(97)
148.93
185.95
122.08
133.13
136.96
209.83
118.78
133.93
102.96
121.39
103.70
105.55
78.51
104.08
74.28
61.95
103.78
105.80
(104)
(902)
(94)
(86)
(44)
(744)
(77)
(815)
(84)
(1423)
(46)
(973)
(25)
(278)
(39)
(76)
(48)
(326)
274.80
303.23
156.01
167.90
267.34
314.26
192.89
216.25
180.81
186.32
157.60
167.83
135.66
160.81
143.90
106.13
147.50
162.04
(211)
(1537)
(27)
(64)
(97)
(1085)
(101)
(1203)
(138)
(1876)
(56)
(1145)
(29)
(415)
(21)
(60)
(86)
(275)
503.49
561.19
476.56
484.17
530.51
597.69
369.57
419.64
340.25
360.61
327.56
342.46
262.08
292.56
304.31
236.71
276.84
301.29
(1331)
(8832)
(146)
(315)
(776)
(7467)
(608)
(6687)
(683)
(8909)
(70)
(5503)
(250)
(2631)
(48)
(207)
(392)
(1846)
987.69 1054.26
808.54
740.92 1059.98 1113.21
666.45
791.20
661.98
617.03
528.74
548.50
527.84
535.03
680.66
440.00
514.37
489.90
(667)
(7232)
(578)
(8534)
(268)
(4511)
(355)
(2842)
(18)
(136)
(252)
(1903)
(1625)
(11666)
(72)
(238)
(1078)
(9553)
T able 2.6: A v erage W eekly W ages for Japanese and N ativ e W hite O ccupations, 1940-1990
N ote: F igure calculated using IP U M S sam ples. T he num ber of indiv iduals in each category is giv en in parentheses.
22
Profes-
Clerical
Service
Farm
Year sionals Farmers Managers & Sales Craftsmen Operatives Workers Laborers Laborers
0.640
0.650
0.793
0.734
0.963
0.709
0.675
0.950
0.866
1940
0.707
1.885
0.570
0.774
0.801
0.888
0.726
0.921
0.732
1950
0.801
0.917
0.653
0.887
0.848
0.982
0.754
1.199
0.981
1960
0.906
0.929
0.851
0.892
0.970
0.939
0.844
1.356
0.910
1970
0.897
0.984
0.888
0.881
0.944
0.956
0.896
1.286
0.919
1980
0.937
1.091
0.952
0.842
1.073
0.964
0.987
1.547
1.050
1990
Table 2.7: Ratio of Japanese to Native White Male Wages by Occupation, 1940-1990
Source: Calculated from IPUMS samples
Wages
1940
1950
1960
1970
1980
1990
1940
0.673
0.562
0.803
0.865
0.929
1.019
Distributions
1950
1960
1970
0.646
0.705
0.703
0.768
0.827
0.711
0.795
0.856
0.772
0.846
0.916
0.864
0.896
0.977
0.871
0.969
1.035
0.930
Table 2.8: Transition Matrix
1980
0.803
0.774
0.858
0.971
0.976
1.050
1990
0.772
0.737
0.822
0.937
0.942
1.000
Similar to Goldin’s (1990) results for men and women, the figures change less across
the columns and more down the rows. This indicates that the change in relative wages
within occupations rather than a redistribution across occupations caused most of the
narrowing wage ratios using the population occupational distribution and the sample
wages when adjusted by region. The sharp rise in the relative wages of cohorts who
entered the labor market after World War II is not explained by the shift out of lowerpaying occupations and into managerial and professional positions.
The changes in occupational distribution from 1920 until 1990 indicate tremendous
improvement in Japanese Americans’ socioeconomic status. By 1990, over half of
Japanese men in the American labor force were employed as managers and professions,
23
the categories with the highest average wages. In 1920, barely 5% of Japanese men had
attained this status. Despite these significant changes in occupational distribution, the
changes in relative wages within occupations have been more important to their
economic well being. After summarizing the history of Chinese and Mexican Americans,
I will rigorously analyze the evolution of Japanese American wages in Chapters 3
through 6.
2.3 Brief History of Chinese Immigration
Although a small number of Chinese immigrants were recorded as living in
Pennsylvania as early as 1785, their immigration to the U.S. mainland in significant
numbers coincided with the California Gold Rush of 1849. Between 1849 and the
passage of the 1882 Chinese Exclusion Act approximately 275,000 entered the
continental United States, almost all of them adult males who settled in California
(Daniels, 1988). They found work as miners and providing services in the booming
frontier, notably laundries. Chinese immigrants also built substantial portions of the
Central Pacific Railroad and worked as agricultural laborers in the rapidly expanding
Californian farming communities.
Despite the desperate need for the labor supplied by the Chinese immigrants, they
soon became targets of racially motivated hostility and violence. Leaders of the Nativist
movement of the era portrayed the Chinese immigrants as corrupt and exaggerated the
prevalence of prostitution and squalid living conditions which characterized the
Chinatowns no more so than other ethnic enclaves (Daniels 1988). In several Western
cities, including Los Angeles, Denver, and Seattle, angry white mobs killed dozens of
24
Chinese immigrants and destroyed several hundred thousand dollars worth of their
property (Chan, 1991; Daniels, 1988). Eventually, the Nativists successfully lobbied
Congress to enact the 1882 Chinese Exclusion Act which limited Chinese immigration to
tourists, students, teachers, and merchants. A series of amendments passed over the next
three decades closed loopholes in the original act, severely limiting further immigration
until its repeal in 1943.
The Chinese Exclusion Act almost completely halted the process of family formation
among Chinese immigrants. Although it is very common for young adult males to
immigrate first and for single women or new brides to follow, this pattern was precluded
by the immigration restrictions. As a result, the Chinese American population decreased,
remained male dominated, and the average age of Chinese American men increased
steadily until 1950 (see Tables 2.9 & 2.10). As U.S. citizens, native-born Chinese could
travel between the U.S. and China at will and bring any children they fathered into the
country. They were not allowed to bring their Chinese-born wives, however. A few
thousand Chinese women were admitted as wives of merchants between 1882 and 1943
(Daniels, 1988).
Most married Chinese men were forced to choose between leaving the
better economic opportunities in the United States and living an ocean away from their
wives and children. The 1930 Census Bureau reports show almost 24,000 married
Chinese men and only around 5,600 married Chinese women living in the U.S., in an era
where intermarriage was extremely rare, indicating that many opted to live away from
their families. Many others permanently returned to China as evidenced by the declining
population after 1890.
25
Year
1850
1860
1870
1880
1890
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
Males
Females
33,149
58,633
100,686
103,620
85,341
66,856
53,891
59,802
57,389
76,725
115,821
200,321
383,441
787,645
Total
1,784
4,566
4,779
3,868
4,522
4,675
7,748
15,152
20,115
40,415
82,066
178,679
372,821
792,247
758
34,933
63,199
105,465
107,488
89,863
71,531
61,639
74,954
77,504
117,140
197,887
379,000
756,262
1,579,892
Table 2.9: Chinese in the United States by Gender, 1850-1990
Source: U.S. Census Bureau Reports. Notes: 1850 figures for "born in China" and figures exclude Hawaii
Year
Under 15
15-24
25-44
45-64
Over 65
1910
6.0%
11.3%
37.1%
42.4%
3.3%
1920
12.1%
12.4%
33.6%
35.3%
6.6%
1930
20.6%
14.4%
39.5%
20.2%
5.3%
1940
21.2%
16.3%
35.9%
22.0%
4.6%
1950
23.3%
17.7%
33.7%
20.7%
4.6%
1960
32.9%
11.5%
32.0%
18.3%
5.3%
1970
26.4%
21.1%
29.8%
16.7%
6.0%
1980
21.4%
17.9%
36.5%
17.6%
6.6%
1990
19.4%
15.5%
40.2%
17.2%
7.6%
Table 2.10: Age Distribution of the Chinese in the United States 1910-1990
Source: U.S. Census Bureau Reports. Note: Figures exclude Hawaii
Between 1890 and 1920 the Chinese American population plummeted, and then it
remained fairly stagnant until 1940 (see Table 2.9). The small numbers of new
immigrants between 1882 and 1943 were primarily the young adult “sons” of American26
born Chinese men whose parents immigrated during the Gold Rush. Sons is in quotation
marks because falsified documents were used to bring many nephews and family friends,
called “paper sons” (Daniels 1988). Also, the San Francisco Earthquake of 1906
destroyed the birth records of many American-born Chinese, allowing many immigrants
to claim unverifiable U.S. citizenship and then bring their sons and “paper sons” into the
country. In most cases the daughters stayed behind in China with the inadmissible wives,
only exacerbating gender imbalance of the adult Chinese American population.
In 1920 over 40% of the Chinese American population was over age 45 (see Table
2.10). Because of the strong ties to families still in China, the social structures of the Old
World persisted and the assimilation process remained incomplete until almost 100 years
after the first immigration wave with most of the Chinese American population clustered
into Chinatowns in West Coast cities (Daniels, 1988).
Table 2.11 shows the occupational distribution of the Chinese Americans since 1920.
The proportion of Chinese men employed as professionals and managers in 1940, 2.4%
and 22.5% respectively, compares favorably with that of Japanese men, 3.1% and 13.1%
respectively as shown in Table 2.5. Both Japanese and Chinese professionals, however,
were limited to clientele within their own ethnic community. The relatively large number
of Chinese managers and business proprietors were concentrated primarily in the
operation of laundries and Chinese restaurants, businesses in which it was socially
acceptable for white customers to patronize Chinese-operated establishments, similar to
the Japanese produce markets (Chan, 1991; Daniels, 1988; Hing, 1993). Waiters in
Chinese restaurants and workers in Chinese laundries also account for the large
proportion of Chinese men in the “service workers” occupational category.
27
Year
1920
1940
1950
1960
1970
1980
1990
Professionals
Farmers
Managers
Craftsmen
Clerical & Sales
Operatives
Service Workers
Farm Laborers
0.012
0.027
0.027
0.172
0.012
0.024
0.544
0.024
0.014
0.214
0.101
0.013
0.221
0.368
0.064
0.014
0.225
0.114
0.034
0.166
0.347
0.201
0.008
0.166
0.134
0.050
0.140
0.282
0.299
0.004
0.111
0.133
0.057
0.106
0.257
0.312
0.002
0.148
0.151
0.074
0.060
0.226
0.320
0.001
0.153
0.176
0.074
0.062
0.188
Table 2.11: Occupational Distribution of Chinese Men in the United States 1920-1990
Source: U.S. Census Bureau Reports. Note: Figures exclude Hawaii
28
28
0.084
0.028
0.015
0.005
0.003
0.002
0.003
Laborers
0.101
0.017
0.020
0.015
0.031
0.023
0.023
China’s status as enemy-of-the-enemy in World War II, elevated Chinese
Americans’ social standing. In 1943, the Chinese Exclusion Act was repealed, and a very
small annual immigration quota of 105 was instituted. More importantly, the legislation
repealing the exclusion allowed the Chinese citizen wives of Chinese American men to
join their husbands in the United States in unlimited numbers. Between 1940 and 1950,
the number of Chinese women in the U.S. doubled (see Table 2.9). The long-delayed
process of family formation contributed to the Baby Boom as shown by the large
numbers of Chinese American children under the age of 15 in 1960 and 1970 (see Table
2.10).
Also during the 1940s, the educational attainment of the adult Chinese American
population rose dramatically as the oldest cohorts died. Table 2.12 shows the educational
attainment of those over the age of 25 whose race is classified as Chinese. Between 1940
and 1950, the percentage having least a high school diploma rose dramatically from
12.2% to 30.2%. By 1950 the percentage with a bachelor’s degree, 9.7%, exceeded even
that of Japanese Americans, 7.7%. Since 1950, the distribution of Chinese American
educational attainment has been stretched, with relatively high numbers at the opposite
ends of the spectrum. The percentage without a high school diploma has been greater
than that of native whites and nearly double that of the Japanese. Meanwhile, the
percentage of Chinese Americans with a college degree has exceeded that of Japanese
Americans and since 1960 has been double that of native whites (see Table 2.4).
29
Year
1940
1950
1960
1970
1980
1990
Some College
High School Diploma
87.8%
7.0%
2.1%
69.8%
14.3%
6.2%
57.6%
15.6%
9.6%
43.4%
18.8%
11.1%
29.0%
18.6%
15.0%
26.6%
14.0%
18.2%
Table 2.12: Chinese American Educational Attainment
No High School Diploma
4+ years of college
3.1%
9.7%
17.3%
26.7%
37.4%
41.2%
Source: Census Bureau Reports. Note: Figures excludes Hawaii.
Lagging the increase in Chinese Americans with college degrees, the percentage of
Chinese men classified as professionals tripled from 6.4% in 1950 to 20.1% in 1960 (See
Table 2.11). Since 1960 the percentages of Chinese professionals and managers have
been very similar to that of the Japanese, but there have been relatively more Chinese
service workers.
The stretching of both the educational attainment and occupational distributions
relative to whites and Japanese Americans results from U.S. immigration reforms after
1965 (Daniels, 1988; Hing, 1993). The Chinese in the college educated and professional
categories have been both the native born and those immigrating under the preferred
occupational categories created in 1965. Refugee immigrants tend to be less educated
and employed as laborers or service workers. The largest proportion of the newly-arrived
immigrants, the relatives of permanent residents, have educational and occupational
statuses somewhere between the preferred occupation admissions and refugees.
30
Like the Japanese Americans, Chinese American history has been shaped by U.S.
immigration policies. Whereas the Gentlemen’s Agreement applicable to Japanese labor
immigrants accelerated the family formation process by limiting further male and
encouraging female immigration, the Chinese Exclusion Act delayed Chinese American
family formation for 60 years. The Chinese American population consisted of aging
males until the 1940s with most employed in Chinese restaurants and laundries. Similar
to Japanese Americans, Chinese Americans have made great strides since World War II.
With family formation, the median years of education of Chinese Americans greatly
increased, which increased their occupational statuses. Immigration since 1965 has been
much more significant for Chinese Americans than Japanese Americans. Immigrants
under the refugee category and to some extent family members have pulled average
education and occupational statuses downward as those in the preferred occupations have
pulled them upward, stretching these distributions. Chapter 5 analyzes the effects of
these factors on Chinese wages and compares the evolution of their wages to Japanese
Americans.
2.4 Brief History of Mexican Immigration
Although adjacent to the United States, immigration from Mexico was very limited
until the turn of the last century. The 1850 U.S. Census enumerates only 13,317
individuals born in Mexico, compared with 147,711 from Canada, 961,719 from Ireland,
and 573,225 from Germany.
After the completion of the main lines of the Mexican
railroad in 1895, the number of Mexicans living in the U.S. increased rapidly (see Table
2.13). Increased labor demand during World War I and the 1917 waiver of literacy
31
requirements and a $8.00 per head tax resulted in an almost doubling of the Mexican
American population in the 1910s (Fogel, 1982). The onset of the Great Depression
signaled the end of the first Mexican immigration wave as demand for labor dissipated
and what few jobs there were went to the native born. Between 1931 and 1934 more than
350,000 Mexicans living in the U.S. repatriated (Annual Reports of the Commissioner of
Immigration). Table 2.13 shows a decline in Mexican Americans consistent with net
outflows, but some of the difference is due to the fact that the Census enumerators
counted those of Mexican descent in 1930, but not in 1940. The 1940 figures only
include Mexican natives and their children.
Year
Males
Females
Total
1850
13,317
1860
27,466
1870
42,435
1880
68,399
1890
77,853
1900
221,915
1910
382,761
1920
700,541
1930
758,674
663,859
1,422,533
1940
551,525
525,128
1,076,653
1950
694,705
648,965
1,343,670
1960
885,523
850,469
1,735,992
1970
2,245,323
2,287,112
4,532,435
1980
4,410,299
4,268,333
8,678,632
1990
6,949,673
6,443,535
13,393,208
Table 2.13: Mexicans in the United States by Gender, 1850-1990
Source: U.S. Census Bureau Reports. Notes: Figures prior to 1910 include only
those born in Mexico. Figures for 1910, 1920, and 1940-1960, include Mexican natives and
American-born children of Mexican natives.
32
The next immigration wave from Mexico began in 1942 in response to the great
labor shortage caused by the war effort. Congress formed the Bracero Program to bring
emergency contract laborers to work America’s fields (Calavita, 1992). From 1943 to
1950, an annual average of over 53,000 Mexican agricultural laborers entered the U.S.
under this program. Between 1951 to 1953, the Bracero Program averaged almost
200,000 participants annually. And, from 1954 to 1964 Congress again greatly expanded
the program to an annual average of around 330,000. From 1947 until 1964 the
Immigration Service curtailed its efforts recruiting laborers in Mexico and “paroled” into
the program any captured illegal immigrants, technically reducing illegal immigration.
The current massive immigration wave from Mexico has been characterized by those
entering legally primarily under the relative preference category since the 1965 and
probably at least an equally large number crossing the border as undocumented illegal
aliens.
Mexican immigration has never been subject to outright exclusion like Japanese and
Chinese immigration. Therefore, the gender ratio within the Mexican American
population has remained relatively balanced. In 1910 the Mexican born population was
over 38% female (Census Bureau), and by 1930 the Mexican American population was
almost 47% female (see Table 2.12).
The process of family formation proceeded
unaffected by U.S. immigration policy. Table 2.13 shows the age distribution of the
Mexican American population over time. The proportion under age 15 is fairly high,
especially in 1930, 1940, and 1970. The 1920s were apparently an era of early family
formation, the repatriation movement in the 1930s may have been disproportionately
comprised of single men and women, not families, and the large proportion of children
33
in1970 would reflect the Baby Boom population and an influx of immigrants of
childbearing years.
Year
1930
1940
1950
1960
1970
1980
1990
Under 15
15-24
25-44
45-64
Over 65
40.0%
20.0%
28.1%
9.9%
36.0%
19.9%
27.6%
13.1%
28.2%
22.0%
29.5%
15.9%
26.1%
17.0%
35.1%
16.6%
40.1%
19.7%
24.0%
12.1%
21.4%
17.9%
36.5%
17.6%
32.4%
20.1%
32.3%
11.3%
Table 2.14: Age Distribution of Mexicans in the United States 1930-1990
2.0%
3.3%
4.3%
5.2%
4.2%
6.6%
3.9%
Source: U.S. Census Bureau Reports. Note: Figures for 1940-1960 include only those born in Mexico
and the American-born children of Mexican natives
Unlike Japanese and Chinese Americans, Mexican American family formation in the
U.S. did not result in the narrowing of the education gap versus native whites. Table 2.14
shows educational attainment Mexican Americans since 1950. As of 1990, almost 56%
of Mexicans over the age of 25 living in the U.S. did not have a high school diploma,
compared with 27% of Chinese Americans (see Table 2.12), 9.4% of Japanese
Americans, and 22.1% of native whites (see Table 2.4).
34
Year
1950
1960
1970
1980
1990
Some College
High School Diploma
90.0%
6.9%
1.9%
84.7%
10.3%
3.2%
75.8%
16.8%
4.9%
61.9%
22.1%
11.6%
55.8%
20.5%
17.5%
Table 2.15: Mexican American Educational Attainment
No High School Diploma
4+ years of college
1.2%
1.8%
2.5%
4.4%
6.3%
Source: Census Bureau Reports. Note: Figures for 1950 & 1960 are for only those born in Mexico
and U.S. born children of Mexican natives.
Not surprisingly, the low levels of Mexican American educational attainment have
coincided with rather low occupational statuses. Table 2.16 gives the occupational
distribution for Mexican men since 1950. Between 1950 and 1990, the percentage of
Mexican men in the U.S. classified as either professionals or managers has increased only
from 5.7% to 11.3%. In the same interval, the Chinese American percentage of
professionals or managers increased from 28.9% to 45.7% (see Table 2.11), the Japanese
American percentage from 15.2% to 54.1%, and the native white percentage from 21.6%
to 44.3% (see Table 2.5). In 1990, 55% of Mexican American men were employed as
operatives, service workers, farm, and general laborers.
35
Year
1950
1960
1970
1980
1990
Professionals
Farmers
Managers
Clerical & Sales
Craftsmen
Operatives
Service Workers
Farm Laborers
0.019
0.030
0.038
0.059
0.125
0.197
0.060
0.035
0.015
0.036
0.073
0.161
0.248
0.069
0.053
0.006
0.040
0.090
0.210
0.270
0.134
0.055
0.006
0.051
0.105
0.219
0.238
0.122
0.060
0.007
0.053
0.123
0.207
0.197
0.154
Table 2.16: Occupational Distribution of Mexican Men in the United States 1950-1990
Laborers
0.267
0.198
0.092
0.086
0.094
Source: U.S. Census Bureau Reports. Note: Figures for 1950 and 1960 are for those born in Mexico and the U.S. born children of Mexican Immigrants
36
36
0.205
0.165
0.105
0.117
0.105
Similar to the history of Chinese and Japanese immigration, immigration from
Mexico can be divided into distinct eras. Significant immigration flows began at the turn
of the last century, and continued steadily fueled by demand for labor in the expanding
Western frontier and then by the World War I labor shortage. The Great Depression
halted and somewhat reversed immigration from Mexico and all other countries. Most of
the Mexican immigration from 1942 until 1965 was under the Bracero program. Since
1965 large numbers of Mexican immigrants have entered this country legally and perhaps
an even larger number illegally. Unlike Chinese and Japanese immigration, Mexican
immigration has never been subject to exclusion, so family formation was not shaped by
immigration policies. Recent immigration from Mexico has been much more significant
than from China and especially from Japan. Therefore, the persistent low levels of
average educational attainment and occupational status may result from a constant and
increasing influx of lower educated, lower skilled immigrants. This hypothesis will be
investigated in Chapter 5.
37
CHAPTER 3
JAPANESE AMERICAN WAGES, 1940-1990
This chapter uses decennial census data from 1940-1990 to examine the wages of
Japanese immigrants and their descendants relative to those of native-born whites. As
discussed in Section 2.2.2, the average educational attainment of Japanese men in 1940
was similar to that of their white counterparts. Every decade since 1940 relatively higher
percentages of Japanese men have completed college and relatively lower percentages
have dropped out of high school compared to white males born in the U.S. Average
wages of Japanese men, however, lagged behind those of their white, native-born
counterparts until the 1980s. This chapter addresses two main questions. Why were
Japanese wages so low in 1940 and 1950? And, why did the wage gap close so rapidly
after 1950?
Since the 1970s, Japanese Americans have been labeled a “model minority” because
of their high average levels of educational attainment and occupational status that
translated into their relatively high wages and because of the great relative progress they
had made in between the second and third generation in the United States. But, the
second-generation Japanese Americans, the nisei, had certainly invested in enough
education that they could have earned wages similar to their native-born, white
38
counterparts. This chapter looks at the peculiar evolution of the Japanese wage gap with
native whites both across time and across birth cohorts.
The initial wage gap could have been caused by the failure of the first-generation of
Japanese Americans, the issei, to assimilate into the U.S. labor market and then the rapid
convergence resulted as the issei aged out of the samples. The analysis in this chapter
evaluates the differences in wages by birth cohort to evaluate the assimilation effects.
The early wage gaps could also be due to differences in human capital, such as lower
years of schooling and marriage rates and higher employment in agriculture, and the
convergence due to relative improvements in Japanese human capital accumulation. As
shown in Chapter 2, the Japanese had fairly high rates of employment in agriculture at
least as late as 1960. Their lower initial wages could be due to their concentration in the
relatively lower paying agricultural sector and the rapid rise could be due to their
movement into other more highly paid sectors. The wage gap in 1940 and 1950 could
also have been caused by labor market discrimination against the Japanese because of
World War II, and the relative wages may have risen quickly once the discrimination
abated. The wage model and decompositions presented in Section 3.4 tests for the
changes in relative returns to human capital variables across birth cohorts and measures
the effects of differences in average quantities and returns to these variables on the
average wage gaps.
Table 3.1 below shows the evolution of Japanese wages relative to that of native
whites, by birth cohort for the exhaustive samples of Japanese Americans coded in the
Integrated Public Use Microdata Series (IPUMS). The underlying data in this table are
described in detail in Section 3.3; the white samples are constructed to have the same
39
geographic and age composition as the Japanese samples to provide meaningful estimates
of the wage ratios since Japanese Americans disproportionately live in West Coast cities.
Birth Cohorts
Census
Year
1940
1876-85 1886-95 1896-1905 1906-15 1916-25 1926-35 1936-45 1946-55 1956-65 1966-75
Total
0.519
0.598
0.672
0.758
0.830
0.647
(.107)
(.078)
(.133)
(.076)
(.071)
(.047)
1950
1960
1970
1980
0.741
0.583
0.738
0.724
1.045
0.722
(.263)
(.093)
(.102)
(.056)
(.056)
(.056)
0.596
0.869
0.872
0.831
0.767
0.832
(.044)
(.075)
(.043)
(.041)
(.050)
(.026)
1.017
0.826
0.989
0.928
0.978
0.927
(.189)
(.039)
(.046)
(.050)
(.078)
(.028)
0.824
0.948
0.988
0.948
1.018
0.937
(.021)
(.023)
(.026)
(.016)
(.026)
(.011)
1990
0.871
1.001
1.010
1.053
1.105
0.996
(.026)
(.025)
(.021)
(.019)
(.086)
(.012)
Table 3.1: Ratio of Japanese to Native White Mean Weekly Wages by Birth Cohort
Notes: Standard errors given in parentheses. Ratios in bold are statistically different from 1 at the
5% level. Sample sizes shown in Tables 3.2 and 3.2
Moving across the rows, Table 3.1 shows changes in the wage ratio for each
successive birth cohort. The wages of the oldest three cohorts in the sample, those
primarily Japanese-born prior to 1905 remain fairly large for all years they appear in the
samples. The wage gap of the next oldest cohort, mostly the older nisei born between
1906 and 1915, closes by 13 percentage points each decade during the 1950s and the
1960s. The wages of the younger nisei born between 1916 and 1925 improve greatly
relative to whites between 1950 and 1960, then remain stable in relative terms after 1960.
The wages of the youngest nisei and the sansei, born after 1925, are comparable to whites
after 1960.
40
Moving down the columns Table 3.1 shows how the wage ratios evolved over time.
Japanese wages overall relative to those of whites rise by around ten percentage points
each during the 1940s, 1950s, and 1960s. As documented in Chapter 2.2, in the years
before and immediately after World War II, Japanese Americans were limited in their
occupational choices. They could either choose jobs such as produce sellers in which it
was acceptable to for whites to patronize their businesses or professions with clientele
only within their own community (Strong, 1934; Broom and Reimer, 1949; Daniels,
1988). Because of these limitations, Japanese Americans likely did not reap returns to
their human capital investments in education and on-the-job training commensurate with
those of whites.
In this chapter, separate wage equations are estimated for each birth cohort to
evaluate the impact on wages of changes in relative human capital endowments across
cohorts and of changes in returns to human capital both across cohorts and across time.
The impacts of differences in marriage rates, years of schooling, and employment in
agriculture are analyzed for cohorts which have these differences. Also, the difference in
log weekly wages for each cohort, controlling for observable human capital, is calculated
for each census year. The changes in these differences within and across cohorts are
evaluated. Because of the limitations on Japanese immigration after 1908 (see Chapter
2.2) using these birth cohort effects, generation effects can be inferred.
Section 3.1 details the relevant literature on the wages of immigrants and their
descendants. Section 3.2 describes the census data sets used to evaluate Japanese
socioeconomic progress. Section 3.3 presents the wage model and results. Section 3.4
interprets these reults. Section 3.5 concludes.
41
3.1: Literature Review
The international migration literature has attempted to assess the achievements of
both immigrants and their descendants in the American economy, as measured by the
speed of the closing of their wage gap with natives. Using data from the 1970 Census,
Cheswick (1978, 1982, and 1986) and Boras (1985) test the effect of years since
migration on the wages of immigrants. They show that although the wages of white male
recent immigrants are lower than those of their native counterparts, their predicted
earnings increase rapidly, and after 10 to 15 years in the United States they earn more
than natives. Cheswick (1982) further finds that although non-English speakers have
lower wages than English-speakers immediately after immigration, their wages increase
even more rapidly, resulting in convergence with native wages within 20 years.
Carliner (1980), Chiswick (1977), and Borjas (1994) evaluate the economic outcome
of immigrants’ descendants. Using data from the 1970 Census, Carliner (1980) shows
that the wages of the second generation for eight different ethnic categories exceed that of
the first, holding other factors constant, and that the third generation does better than the
first, but not as well as the second. He theorizes that the regression towards population
means occurs in the third generation because the increases investment in human capital
made by the second generation parents are offset by decreased motivation of the third
generation (Furstenberg 1971). Also using the 1970 Census, Chiswick (1977) finds that
the sons of non-Hispanic white immigrants earn about 5% higher wages, holding other
variables constant, than sons of two native-born white parents.
42
Borjas (1994) looks at the intergenerational mobility of the participants in the Great
Migration (1890-1924). He calculates average wages in 1910 for immigrants from 20
different countries, using average wages of occupations at that time. He then calculates
the average wages of sons of immigrants from each of these countries in the 1940 Census
and average wages of native-born men of each ethnicity in the 1980 Census. Borjas
concludes that the wages of the first generation’s “sons” are highly correlated with that of
the “fathers” and that the pattern persists to a lesser extent to the third generation. He
also finds that the educational attainment of the second generation is correlated with the
literacy rates of the first generation.
The consensus in the literature is that the typical white, non-Hispanic newly-arrived
immigrant earns less than his native-born counterparts either due to discrimination or
difficulty transferring human capital acquired in his home country (Chiswick 1978).
Over the first few decades the immigrant’s wages rise sharply, eventually overtaking
those of his native counterparts (Borjas 1985, Chiswick 1978). His son will fare even
better in the U.S. labor market (Carliner 1980, Chiswick 1977), and his grandson will be
fully assimilated, and thus his wages will be distributed similar to the native-born
population in general.
The evolution of Japanese American relative wages differed from that of white
immigrant groups. As shown in Table 3.1, the first-generation immigrants’ wage gaps
remained several decades after migration. Their sons, however, made the kind of relative
progress throughout their careers observed by modern immigrants in Borjas’ and
Chiswick’s studies. The third generation Japanese Americans, most of the individuals in
43
the Japanese sample born in the 1940s and 1950s, progressed even further, earning as
much as their white, native-born counterparts in 1980 and 1990.
3.2: Data
The data used for the analyses in Sections 3.3 and 3.4 come from the Integrated
Public Use Microdata Series (IPUMS) constructed from the 1940 through 1990 U.S.
census data. Wage data were not collected prior to the 1940 Census. For 1940 through
1970 the IPUMS are 1 in 100 samples of the original population manuscripts. For 1980
and 1990, I use the available 5% samples. For each decennial census, I have extracted all
males in the IPUMS ages 15 to 64, not currently in school, whose race is classified as
Japanese. I exclude Hawaii because it is not included in the IPUMS prior to 1960,
because their labor markets are so different from the mainland, and because the Japanese
represent a significantly larger proportion of the population there than elsewhere.1 I also
dropped foreign-born men in the 1980 and 1990 samples who had been in the United
States less than five years, in order to eliminate businessmen temporarily assigned to the
U.S., a large proportion of modern Japanese-born U.S. residents (Hing 1993).
The IPUMS contain data on geographic location, usually within a multi-county area
(except in1960), age, marital status, birthplace, occupation, industry, educational
attainment, weeks worked, wages, and other income for the previous year. For the
dependent variable in my wage equations, I construct a variable “log of average total
1
In the 1960 IPUMS, individuals whose race was classified as Japanese comprised almost 27% of the male
Hawaiian population aged 15-64. This proportion fell to just under 22% by 1990. For comparison, the
Japanese proportion of the Californian population hovered around 1% from 1960 through 1990.
44
weekly income” which is the sum of annual wage, farm, and business income for the
previous year divided by weeks worked in the previous year.2 The weeks worked
variable in the 1960 and 1970 IPUMS is coded in intervals, 1-13 weeks, 14-26 weeks,
etc. I use the midpoint of each interval in my calculations
Following Mincer (1974), I construct “years of experience” equal to age minus
years of schooling minus six for individuals with nine or more years of school or equal to
age minus 15 for those with less schooling3.
Around 60% to 70% of the mainland Japanese population resided in California
throughout the time period considered, and only 30% lived in states not on the West
Coast. The white, native-born population is much more evenly distributed regionally4.
Wages tend to be higher in the West, especially as compared to the South. Furthermore,
as documented in the previous chapter, immigration restrictions affected the age
distribution of the Japanese American population. I adjust my control sample for both
geography and age in order to construct meaningful estimates of the wage gaps between
Japanese and white men such as those presented in Table 3.1.
2
Income values in the Census were top coded, or given a maximum value. For 1950-1980, I assign the
values calculated by Smith and Welch (1989) which reflect the means assuming the upper end of the
income distribution follows an exponential distribution. The top codes are given in parentheses with their
calculated values after the equal sign. 1940 ($5,001) = $8,900; 1950 ($10,000) = $22,500; 1960 ($25,000)
= $42,500; 1970 ($50,000) = $80,000; 1980 ($75,000) = $115,000. For 1990, the Census Bureau assigned
values equal to the mean of the actual values for top coded individuals. The values are different for wage,
business, and farm incomes. For wages ($139,000) = $195,516; for business income ($89,000) =
$123,515; for farm income ($50,000) = $90,905. In all cases, less than 2% of the samples were top coded
and top coding occurred more frequently in the white samples.
3
For 1990, the “highest grade of school” codes are in intervals for 1st through 4th and 5th through 8th grades,
so I use the midpoint of the intervals.
4
In randomly drawn, unmatched samples of white males ages 15 to 64 not in school, 7.7% lived on the
West Coast excluding Hawaii, 5.2% in California in 1940. These percentages increased steadily
throughout the period considered. In 1990, 13.2% lived in the Pacific states and 9.5% in California.
45
If I were to compare the random samples of Japanese men generated by the IPUMS
to random national subsamples of whites, then the wage gaps would be much smaller.
The portions of the decompositions explained by differences in characteristics such as
geographic region, urban residence, and experience would be larger. The main findings
of this type of exercise would be the large impact of Japanese wages due to their
concentration in large cities on the West Coast. By balancing the control sample for age
and geography, I can compare the relative outcomes for Japanese and whites in the same
labor markets.
For each decade, I constructed samples of white native-born men who are similarly
situated in terms of age and geographic area to the men in the Japanese samples. These
data sets are ten times as large as the corresponding Japanese sets for each census year.
Japanese Americans make up at most 10% of the population in some California counties,
and much less than that in other areas. For example, in the Japanese 1940 data set, there
are 38 observations from the greater San Francisco area and three of them are between
the ages of 15 and 24. The 1940 control sample contains 380 observations randomly
selected from the Bay Area, 30 between the ages 15 to 24. For 1960, the most specific
geographic designation is the state, but each household is identified as either living in the
central city, in a metropolitan area but not in the central city, not in a metropolitan area,
or status unknown. I have balanced the 1960 control sample to have proportionally the
same makeup by state and metropolitan status as the Japanese sample.
After the samples were balanced, I excluded individuals who worked less than 26
weeks per year or whose weekly total income equaled less than $1, since my analysis
46
uses the natural log of wages as the dependent variable. Table 3.2 compares the relevant
cross-sectional mean characteristics of the two populations.
After excluding individuals whose incomes or weeks worked failed to meet the
criteria described above, the white samples sizes do not necessarily equal ten times that
of the Japanese samples.5 A higher percentage was excluded from the Japanese samples
for 1940 and 1950 because agricultural workers more often reported very low or no
income. Therefore, the white sample sizes for these years shown in Table 3.2 exceed ten
times that of the Japanese samples. The percentages excluded are very close for 1960
and are equal in 1970. For 1980 and 1990, more the Japanese samples are more than
one-tenth the size of the white samples because a higher percentage of whites in the
original matched sample are unemployed or not in the labor force.
5
In 1940, 52.8% of the originally drawn Japanese sample and 42.9% of the white sample are excluded. For
1950, in which income questions were asked only of sample-line individuals, 82.7% of Japanese and 78.9%
of white individuals are excluded. The figures for 1960 are 11.0% and 10.7% respectively. In 1970, 9.9%
of both samples are excluded. For 1980, 9.0% of the Japanese sample and 16.2% of the white sample is
excluded. The figures for 1990 are 10.4% and 15.0% respectively. For a discussion of the differences in
proportion excluded, see the Data Appendix.
47
1940
Variables
% Urban
% Married
% employed in agriculture
Average Age
Standard Deviation
Average Years of Schooling
Standard Deviation
Japanese
66.8
40.8*
34.7*
41.9
1950
Whites Japanese
73.6
85.0
68.4
62.62*
8.2
17.8*
40.1
37.5
1960
Whites Japanese
79.9
85.2
78.4
69.7*
5.4
23.7*
37.0
38.8
Whites
84.3
81.6
2.9
38.4
(13.8)
(13.0)
(12.9)
(12.4)
(10.3)
(10.2)
9.2*
10.2
10.9
11.1
12.3*
11.7
(4.3)
(3.1)
(2.9)
(3.0)
(3.2)
(3.2)
Avg Yrs of Experience
24.9
23.2
20.0
19.4
20.1
Standard Deviation
Avg Experience Squared
Standard Deviation
(14.5)
(13.6)
(13.7)
(12.9)
(11.2)
(10.9)
831.4
720.2
586.4
541.2
531.2
528.5
(701.6)
(647.6)
(702.1)
(616.1)
(558.6)
(520.5)
1.5
66.8
45.4*
(9.1)
1.7
0
47.4
(6.5)
0.9
31.8
48.4
(5.0)
1.1
0
48.4
(5.6)
3.7
18.9
49.1
(4.5)
3.9
0
49.0
(4.7)
% living in the South
% foreign born
Avg weeks worked
Standard Deviation
Avg annual income
Standard Deviation
Avg weekly wages
Standard Deviation
N
$1,028.75*
(1116.8)
$ 22.1*
(21.8)
196
$ 1,651.77
$2,849.07*
(1450.5)
$ 34.17
(2384.7)
$ 58.31*
(28.3)
(46.4)
2,368
107
$ 3,947.24
(3842.1)
$ 80.73
(75.0)
1,306
$5,771.74*
(4094.2)
116.57*
(81.1)
561
20.3
$ 6,916.43
(6143.6)
$ 140.14
(122.8)
5,623
Table 3.2: Comparisons of Japanese American and Native Whites' IPUMS samples
(* indicates Japanese value different from Whites at 95% significance level)
(CONTINUED)
48
TABLE 3.2: CONTINUED
Variables
1970
Japanese
Whites
% Urban
% Married
% employed in agriculture
Average Age
Standard Deviation
Average Yrs of Schooling
Standard Deviation
1980
Japanese
Whites
N/A
73.9
6.3
40.4
N/A
80.0
1.6
39.6
(10.4)
13.3*
(3.0)
1990
Japanese
Whites
93.8
65.9
4.2
41.2
93.9
68.3
1.2
40.5
(10.9)
(12.9)
12.5
14.2*
(3.0)
(3.1)
93.6
64.1
1.8
40.8
91.5
63.1
0.8
40.3
(12.7)
(11.4)
(11.4)
13.5
14.7*
14.1
(2.9)
(2.8)
(2.8)
Avg Yrs of Exp
20.8
Standard Deviation
Avg Experience Sq
(11.3)
(11.4)
(13.6)
(13.1)
(11.7)
(11.4)
562.5
564.5
622.0
608.9
538.6
535.0
(507.1)
(512.0)
(621.3)
(605.4)
(553.4)
(530.1)
3.5
24.4
49.6
3.6
5.8
22.6
49.9
6.1
7.0
7.3
0
28.9
50.0
49.6
Standard Deviation
% living in the South
% foreign born
Avg weeks worked
Standard Deviation
Avg annual income
Standard Deviation
N
49.3
(4.2)
$ 11,258.98
(8371.1)
$
210.22
(164.5)
766
20.9
0
(3.7)
$ 10,501.64
Standard Deviation
Avg weekly wages
20.9
226.80
(5.8)
$ 22,145.23
(15657.8)
$
(182.5)
7,660
416.67
(313.0)
4,604
49
20.0
49.4
(4.9)
$ 20,892.33
(9195.9)
$
20.9
444.61
(370.8)
42,397
0
(4.9)
$ 41,855.43
(18802.8)
$
20.1
(5.5)
$ 41,916.34
(33028.9)
$
835.33
(665.9)
4,913
(36645.7)
$
838.45
(727.8)
46615
The percentages living in urban areas and in the South are similar because of the
sample matching process.6 The differences in percentage living in the South are not
statistically different from 0 for any year. In 1990, the difference in the percentages
living in urban areas is statistically different from 0, but fairly small. The difference in
1940 is almost statistically significant at the 95% level.
The average ages of the Japanese samples are higher those that of whites, and this
difference increases with time since students are not included in the drawn samples and
relatively more Japanese men have at least a bachelor’s degree after 1950. Mean years
of schooling are higher for white men in 1940 and 1950, but the difference is
insignificant for 1950. From 1960 onward, the men in the Japanese samples have about
one-half year more average schooling than their white counterparts. The increased ages
of the Japanese samples and the increased years of schooling for most years tend to
offset. The differences in average experience, constructed from age and years of
schooling, are not significant for any year.
The men in the white sample work more weeks per year than their Japanese
counterparts only in 1940. And, the average weeks worked for the Japanese samples are
significantly more than for whites for 1970 and thereafter. Whites earn more on average
per year and per week than Japanese men in the samples every year, but the difference is
not statistically significant in 1990. In unmatched national samples, not adjusted for
6
In the unmatched random sample of white natives, 51% lived in urban areas in 1940, 55% in 1950, over
72% in 1960 and 1980, and 62% in 1990.
50
region and urban residence, Japanese male weekly wages do not differ significantly from
those of whites in 1960 and are significantly more in subsequent decades.7
Table 3.3 presents relevant sample means by birth cohort, which clarifies the
differences in average marriage rates, years of schooling, and employment in agriculture
and the cross-sectional changes in the percentage of the Japanese samples who are
foreign born.
The percentages of the Japanese sample who are foreign born are very large for the
oldest three birth cohorts, those born between 1876 and 1905. These will be referred to
as the issei cohorts. The foreign-born percentage drops dramatically for the cohorts
born after 1905. The cohorts born between 1906 and 1935 will be considered the nisei
cohorts. For cohorts born after 1935, there is a modest rise in foreign born as new
immigrants entered the country after exclusion ended in 1952. These cohorts are made
up mostly of immigrants and third generation (sansei) or higher order generation
Americans and will be referred to as the post-1935 cohorts.
The Japanese men in the issei cohorts have much lower “married, spouse present”
rates. The issei were more likely than later birth cohorts to still have a wife living in
Japan or to remain bachelors due to the gender imbalance in the Japanese American
population and the stigma against intermarriage. For the nisei cohorts, there is no
statistically significant difference in marriage rates versus their native-born white
counterparts.
7
Average weekly wages for whites in the unmatched samples are as follows: 1940=$28.07; 1950=$70.83;
1960=$118.11; 1970=$190.64; 1980=$377.08; 1990=$651.82.
51
1876-1885
Variables
Urban
Married
Ag
Yrs School
South
Japanese
1886-1895
Whites
Japanese
N
Japanese
Whites
0.610
0.699
0.676
0.741
0.784
0.788
(0.49)
(0.46)
(0.62)
(0.44)
(0.41)
(0.41)
0.488
0.774
0.486
0.798
0.626
0.825
(0.51)
(0.42)
(0.47)
(0.40)
(0.49)
(0.38)
0.415
0.088
0.297
0.078
0.346
0.045
(0.50)
(0.28)
(0.46)
(0.27)
(0.48)
(0.21)
7.5
9.1
8.5
9.7
9.0
10.2
(4.6)
(3.3)
(4.1)
(3.3)
(4.3)
(3.4)
0
0.008
0
0.009
0.019
0.018
(0.10)
(0.14)
(0.13)
N/A
0.797
0.203
N/A
4.066
0.822
0.308
0.140
0.551
4.113
N/A
0.348
0.191
0.461
4.512
(0.09)
Foreign
Yr40
Yr50
Yr60
lnwage
1896-1905
Whites
1.000
1.000
N/A
N/A
2.716
1.000
N/A
N/A
3.358
0.959
0.770
0.230
N/A
3.616
(0.63)
(0.76)
(0.62)
(0.70)
(0.67)
41
376
74
843
107
Table 3.3: Sample means for Japanese and Whites, by birth cohort
Notes: Standard deviations given in parentheses.
Japanese figures in bold statistically different from white figures at 5% level
52
(0.71)
1,161
Table 3.3: CONTINUED
1906-1915
Variables
Urban
Japanese
1916-1925
Whites
Japanese
1926-1935
Whites
Japanese
Whites
0.627
0.628
0.781
0.766
0.837
(0.48)
(0.48)
(0.41)
(0.42)
(0.37)
(0.38)
Married
0.793
0.824
0.821
0.830
0.807
0.795
(0.41)
(0.38)
(0.38)
(0.38)
(0.39)
(0.40)
Ag
0.189
0.042
0.128
0.025
0.061
0.014
(0.39)
(0.20)
(0.33)
(0.16)
(0.24)
(0.12)
11.3
11.2
12.8
12.5
14.0
13.4
(3.6)
(3.1)
(2.9)
(3.2)
(3.0)
(3.3)
0.018
0.027
0.036
0.040
0.030
0.031
(0.13)
(0.16)
(0.19)
(0.20)
(0.17)
(0.17)
0.195
0.178
0.083
0.438
0.302
N/A
N/A
4.775
N/A
0.243
0.096
0.363
0.298
N/A
N/A
4.894
0.080
0.023
0.032
0.153
0.146
0.645
N/A
5.739
N/A
0.032
0.041
0.172
0.157
0.598
N/A
5.864
0.176
N/A
0.006
0.075
0.105
0.457
0.358
6.492
N/A
N/A
0.009
0.083
0.114
0.456
0.341
6.543
Yrs School
South
Foreign
Yr40
Yr50
Yr60
Yr70
Yr80
Yr90
lnwage
(0.82)
N
169
(0.72)
2,041
(0.70)
1,491
(0.76)
13,101
(0.72)
2,264
0.825
(0.74)
20,998
(CONTINUED)
53
Table 3.3: (CONTINUED)
1936-1945
Variables
Urban
Japanese
1946-1955
Whites
Japanese
1956-1955
Whites
Japanese
1966-1975
Whites
Japanese
Whites
0.858
0.849
0.911
0.897
0.926
0.902
0.927
(0.35)
(0.36)
(0.29)
(0.30)
(0.26)
(0.30)
(0.26)
(0.31)
Married
0.751
0.744
0.611
0.633
0.379
0.464
0.099
0.188
(0.43)
(0.44)
(0.49)
(0.48)
(0.49)
(0.50)
(0.30)
(0.39)
Ag
0.019
0.008
0.018
0.008
0.014
0.009
0.008
0.011
(0.14)
(0.09)
(0.13)
(0.09)
(0.12)
(0.10)
(0.09)
(0.10)
14.9
14.1
15.1
14.3
14.2
13.4
13.1
12.4
(2.9)
(3.0)
(2.7)
(2.7)
(2.5)
(2.4)
(2.0)
(1.9)
South
0.048
0.050
0.084
0.085
0.093
0.094
0.107
0.113
(0.21)
(0.22)
(0.28)
(0.28)
(0.29)
(0.29)
(0.31)
(0.32)
Foreign
Yr60
Yr70
Yr80
Yr90
lnwage
0.320
0.015
0.094
0.436
0.454
6.619
N/A
0.018
0.098
0.434
0.450
6.608
0.358
N/A
0.025
0.446
0.529
6.432
N/A
N/A
0.026
0.448
0.526
6.416
0.242
N/A
N/A
0.249
0.751
6.191
N/A
N/A
N/A
0.245
0.755
6.132
0.206
N/A
N/A
N/A
1.000
5.626
N/A
N/A
N/A
N/A
1.000
5.580
(0.69)
(0.72)
(0.67)
(0.71)
(0.66)
(0.67)
(0.64)
(0.62)
Yrs School
N
2,019
19,350
2,796
27,005
1,924
18,544
262
0.891
2,560
A difference in marriage rates reappears for the youngest two cohorts. But, these
men are not observed in the census over the age of 1935, so it likely is a result of a recent
trend in which Japanese American men marry later than their white counterparts, but are
not necessarily less likely to eventually marry.
Japanese men in the cohorts born before 1956 were more likely to be employed in
agriculture than their white counterparts. The differences are very large for the issei and
nisei cohorts. The percentages employed in agriculture for the post-1935 cohorts are
quite small.
The issei cohorts had relatively less schooling than their native-born white
counterparts. All but the youngest nisei had statistically significantly more schooling on
average than whites, and this trend continues for the post-1935 cohorts. The impact on
54
wages of differences in marriage rates for the issei cohorts, employment in agriculture for
the issei and nisei cohorts, and differences in years of schooling for all cohorts will be
quantified in section 3.4.
3.3 Wage model and results
The model in this section is estimated to show the improvement in Japanese
American wages within and across birth cohorts relative to their white, native-born
counterparts. Separate wage equations are estimated for the ten, 10-year birth cohorts.
Because of the small sample sizes for the early years and cohorts, the samples are pooled
across all relevant census years and for both races. Specific work history data is not
collected in the census, so experience is generally constructed in cross-sectional analysis
as a function of age and years schooling. In this cohort analysis, experience is controlled
for using census year dummies. Japanese is interacted with the parameters of interest,
Years of Schooling and Agriculture. An attempt was made to interact Japanese with
Married, but the coefficients on these interaction terms were very small for all years.
Formally, the model estimated for each birth cohort is:
ln wi ,n = a1,n + a2 ,n J i ,n + b1,nURBi ,n + b2 ,n MARi ,n + b3 ,n AGi ,n +
b4 ,b J i ,n AGi ,n + b5 ,n S i ,n + b6 ,n J i ,n S i ,n + b7 ,n SOUTH i ,n +
b8 ,n FORi ,n + cnYRi ,n + d n J i ,nYRi ,n + ui ,n
(1)
where wi,n is the weekly wage of individual i belonging to birth cohort n; Ji,n is dummy
variable=one if race is Japanese, zero otherwise; URBi,n is a dummy variable=one if the
55
individual lives in an urban locale, zero otherwise; MARi,n is a dummy variable=one if
the individual is “married, spouse present,” zero otherwise; AGi,n is a dummy
variable=one if the individual is employed in agriculture, zero otherwise; Si,n is the years
of schooling of individual belonging to birth cohort n; ; SOUTHi,n is a dummy
variable=one if the individual lives in the South, zero otherwise; FORi,n is a dummy
variable=one if a Japanese individual was born in Japan and zero otherwise; and YRi,n is a
vector of dummy variables for the census year in which the individual was observed, for
each birth cohort, the earliest census for which there are sample observations is used as
the omitted category.
Including urban and Southern residence controls for cost of living differences. A
dummy variable for “married spouse present” is included to control for the “marriage
premium” the finding that married men earn higher wages than their single counterparts
(Becker 1981 and 1985; Korenman and Neumark, 1991; Hill, 1979; and Keeley, 1977).
A control for foreign birth is included, but this variable is applicable only to the Japanese
individuals because the white sample extracted includes only natives.
Wages in the agricultural sector are generally much lower than those of others, but
the Japanese tended to specialize in high value pursuits such as orchards and vineyards
(Daniels, 1998) therefore Japanese average wages may have been lower on average
because a greater percentage were farmers, but this effect was offset possibly by
relatively higher agricultural wages.
Because separate wage equations are estimated for each birth cohort, year dummy
variables implicitly control for age and thus constructed experience within a ten year
interval. The interactions between Japanese and the Smith and Welch’s (1989) analysis
56
of changes in the wage gap between African American’s and whites from 1940 to 1980
divides the men in their samples into cohorts based upon their estimated years of labor
market entry from their constructed experience variables within five-year intervals. They
then estimate the effects of experience on wages separately for these labor market entry
cohorts by race and educational attainment status for each census year. The numbers of
Japanese men in the IPUMS samples are too small for intervals smaller than ten years,
and much too small for separate equations by educational attainment. An attempt was
made to interact Years Schooling with Japanese and the Year dummies, but the
coefficients were small and insignificant, probably due to the small samples. The results
of the wage equations are given in Table 3.4 below.
1876-1885
Variables
Urban
Married
Agriculture
Years school
South
foreign
Japanese
JYS
JAG
Yr50
Yr60
JYr50
JYr60
Constant
N
2
R
Coefficient
0.181
0.245
-0.629
0.060
-0.048
N/A
-0.052
-0.050
0.415
N/A
N/A
N/A
N/A
2.547
417
0.2507
t stat
(2.38)
(3.11)
(-4.88)
(5.54)
(-0.12)
(-0.19)
(-1.85)
(1.60)
(20.30)
1886-1895
Coefficient
0.191
0.206
-0.619
0.079
-0.186
N/A
0.067
-0.048
0.608
0.149
N/A
0.024
N/A
3.021
917
0.2985
t stat
1896-1905
Coefficient
(4.17)
(4.30)
(-7.91)
(12.33)
(-0.88)
(0.35)
(-2.56)
(3.52)
(2.94)
(0.14)
(37.23)
0.182
0.278
-0.549
0.058
-0.230
-0.188
0.141
-0.036
0.323
0.338
0.515
0.169
0.124
3.271
1,268
0.2874
t stat
(4.17)
(6.29)
(-6.22)
(10.83)
(-1.79)
(-1.22)
(0.61)
(-2.35)
(2.07)
(6.61)
(12.82)
(0.85)
(0.89)
(42.79)
Table 3.4: Results of Wage Equation ( 1)
Note: Dependent variable is log of weekly wages
(CONTINUED)
57
Table 3.4: CONTINUED
1906-1915
Variables
Urban
Married
Agriculture
Years school
South
foreign
Japanese
JYS
JAG
Yr50
Yr60
Yr70
Yr80
Yr90
JYr50
JYr60
JYr70
JYr80
JYr90
Constanat
N
2
R
Coefficient
0.115
0.266
-0.499
0.055
-0.123
-0.089
-0.242
0.005
0.071
0.464
0.762
0.991
N/A
N/A
-0.137
0.158
0.126
N/A
N/A
3.407
2,210
0.3838
t stat
(2.96)
(8.14)
(-7.67)
(13.35)
(-1.58)
(-0.78)
(-1.32)
(0.34)
(0.53)
(9.56)
(22.50)
(20.79)
(-0.70)
(1.21)
(0.91)
(51.09)
1916-1925
Coefficient
0.133
0.285
-0.273
0.067
-0.104
0.042
-0.183
0.0003
0.310
0.567
1.009
1.366
1.146
N/A
-0.145
0.018
0.050
-0.003
N/A
3.605
14,592
0.2675
t stat
(6.23)
(19.43)
(-7.35)
(37.62)
(-3.79)
(0.65)
(-1.32)
(0.05)
(4.87)
(13.18)
(28.32)
(35.77)
(33.62)
(-0.97)
(0.15)
(0.40)
(-0.03)
(89.76)
1926-1935
Coefficient
0.162
0.275
-0.225
0.071
-0.101
0.021
0.083
0.007
0.114
N/A
0.621
1.109
0.950
0.901
N/A
-0.402
-0.241
-0.268
-0.293
4.332
23,252
t stat
(8.50)
(25.27)
(-5.70)
(50.78)
(-4.01)
(0.56)
(0.42)
(1.39)
(1.60)
(12.00)
(21.01)
(19.04)
(18.01)
(-2.03)
(-1.23)
(-1.39)
(-1.52)
(81.51)
0.1833
(CONTINUED)
58
Table 3.4: CONTINUED
1936-1945
Variables Coefficient
Urban
Married
Ag
Yrs sch
South
foreign
Japanese
JYS
JAG
Yr70
Yr80
Yr90
JYr70
JYr80
JYr90
Constant
N
R2
t stat
1946-1955
Coefficient
t stat
1956-1965
Coefficient
t stat
0.166
(8.21)
0.134
(9.45)
0.127
(8.84)
0.254 (25.10)
0.290 (37.92)
0.262 (31.33)
-0.230 (-4.42) -0.240 (-5.60) -0.258 (-5.79)
0.073 (47.18)
0.075 (52.46)
0.075 (40.34)
-0.116 (-5.69) -0.112 (-8.49) -0.141 (-9.97)
0.004
(0.14) -0.022 (-0.85) -0.048 (-1.54)
-0.191 (-1.38)
0.142
(1.42)
0.087
(1.08)
-0.005 (-1.03) -0.015 (-3.29) -0.006 (-1.05)
0.071
(0.61)
0.071
(0.70)
0.113
(0.92)
0.836 (20.67)
N/A
N/A
0.803 (22.66)
0.279 (10.07)
N/A
0.898 (25.35)
0.544 (19.74)
0.350 (33.13)
0.091
(0.69)
N/A
N/A
0.209
(1.67)
0.038
(0.46)
N/A
0.260
(2.09)
0.082
(1.00)
0.041
(1.22)
4.418 (106.31)
4.647 (157.75)
4.644 (174.38)
21,369
29,801
20,468
0.1905
0.2099
0.2430
59
1966-1975
Coefficient
0.072
0.262
0.020
0.063
-0.111
-0.099
0.427
-0.029
-0.161
N/A
N/A
N/A
N/A
N/A
N/A
4.692
2,822
0.0627
t stat
(1.87)
(8.80)
(0.17)
(9.82)
(-3.05)
(-1.04)
(1.59)
(-1.47)
(-0.36)
(53.96)
Because there were no American-born Japanese men in the oldest birth cohort, and
only three of the 74 in the second oldest were born in the United States, Foreign was
dropped from the specification of the model for the 1876-1885 and the 1886-1895 birth
cohorts. The coefficient on the Foreign variable is not significant at the 5% level for any
birth cohort. A more refined analysis of the effect of foreign birth for more recent
immigrants is provided in Chapter 5.
As predicted the coefficient on the JAG term, the interaction of Japanese and
Agriculture, is positive for all but the youngest birth cohort, due to Japanese
specialization in high value crops. The next section quantifies the total effect of
employment in agriculture on the difference in average log wages. Calculations in the
next section also quantify the total effect of differences in returns to Years Schooling, as
captured by the coefficient on the JYS variable, and the coefficients on the Japanese
variable and its interactions with the Year dummy variables.
3.4 Interpretation of the Results
In this section the results of the wage model estimated in the last section will be used
to evaluate the effects of relative quantities of measurable human capital, assimilation
effects, and discrimination on wages. First the effects of differences in human capital on
the total wage gap for each birth cohort will be evaluated, specifically differences in
marriage rates for the issei cohorts, differences in the percentages employed in
agriculture for the issei and nisei cohorts, and differences in years of schooling for all
cohorts. Then, controlling for the human capital variables, the differences in estimated
60
log wages will be calculated for each cohort in each year and the plausibility of
assimilation versus discrimination will be discussed.
In order to parse the average wage gap for each birth cohort into portions explained
by differences in human capital, Oaxacan-type wage decompositions are performed using
the estimated parameters and sample averages. For the issei cohorts the differences in
marriage rates and employment in agriculture are fairly large and years of schooling are
relatively lower than for the native-whites. Table 3.5 reports the difference in the average
log wage gap,
Gn = ln ww ,n − ln w j ,n (2)
where
G n is the difference in average log wages between whites and Japanese within
cohort n.
Total Gap
Marriage
Agriculture
Schooling
1876-1885
0.642
0.070
0.033
0.099
1886-1895
0.450
0.064
-0.045
0.088
1896-1905
0.399
0.055
0.054
0.075
Table 3.5: Differences in average log wages for Japanse and White samples
for issei birth cohorts and the difference due to selected characteristics
The portion of the average log wage gap due to the differences in marriage rates is
calculated used the estimated coefficient on the MARRIED variable and the sample
means and is equal to
bˆ2 ,n ( MARw ,n − MAR j ,n )
(3)
61
These values are reported in Table 3.5 for the issei cohorts. There were no
significant differences in returns to marriage in results of wage equations run with
Japanese interacted with MARRIED..
The portion of the gap due to differences in employment in agriculture is the
difference in the percentages of Japanese employed in agriculture and the percentages for
native whites by the estimated AGRICULTURE coefficient offset by the JAG coefficient
times the percentage of Japanese employed in agriculture,
bˆ3,n ( AGw ,n − AG j ,n ) − bˆ4 ,n AG j ,n
(4)
The portion of the gap due to differences in average years of schooling, are also
reported in Table 3.5. This calculation is
bˆ5,n ( S w ,n − S j ,n )
(5)
The differences in returns to schooling, captured by the JYS coefficient are calculated
with the Japanese coefficient and the year interactions below.
The differences in these characteristics explain fairly small portions of the rather
large average log wage differentials for the issei cohorts. Furthermore, the difference due
to employment in agriculture is very small for the oldest cohort, favors the middle issei
cohort, and accounts for less than 1/5 of the overall average log wage gap of the youngest
issei cohort.
Table 3.6 shows the total gaps, and the differences due to schooling and employment
in agriculture for the nisei cohorts. The differences in marriage rates were insignificant
for these birth cohorts, so the effects are not calculated.
62
Total Gap
Agriculture
Schooling
1906-1915
0.119
0.060
-0.006
1916-1925
0.126
-0.012
-0.021
1926-1935
0.051
0.004
-0.042
Table 3.6: Differences in average log wages for Japanse and White samples
for nisei birth cohorts and the difference due to selected characteristics
The most remarkable feature of Table 3.6 is the difference in the overall average log
wage gaps for the nisei versus the issei cohorts shown in Table 3.5. The estimated gaps
by cohort and year will be further evaluated below. The differences in average log wages
due to differences in average years of schooling are small, favoring the Japanese, but
increasing with each successive nisei cohort as the total average gap declines. The
difference due to agriculture for the oldest nisei cohort is fairly large, almost 50% of the
total average gap. The farmers in this cohort and the youngest issei cohort, born between
1896-1905 would have been affected by property loss associated with internment at the
prime of their careers, perhaps causing this gap.
Table 3.6 shows the gaps for the post-1936 cohorts. The differences in average log
wages for these cohorts are negative, the figures for the Japanese samples are higher.
The higher levels of Japanese schooling predict a wage advantage of about 6% for these
cohorts. For the older two cohorts there is a small difference between the predicted gap
due to differences in schooling and the actual average log wage gap. For the youngest
two cohorts the gaps are almost equal to that predicted by their average schooling levels.
63
Total Gap
Schooling
1936-1945 1946-1955 1956-1965 1966-1975
-0.011
-0.016
-0.060
-0.046
-0.059
-0.059
-0.060
-0.047
Table 3.7: Difference in average log wages for Japanese and
white samples for post -1935 birth cohorts and differences
due to differences in average schooling
Overall, much of the large wage gaps for the issei cohorts and the smaller wage gaps
for the nisei cohorts are explained by differences in average human capital. The wage
advantage for the youngest Japanese cohorts are almost entirely explained by their higher
average schooling levels. The analysis that follows estimates the portion of estimated
wage log wage gaps, controlling for human capital variables.
Table 3.8 shows the difference in estimated log wages for the issei cohorts in each
census year, setting Years of Schooling equal to 8. Since these are the issei cohorts, the
calculation for the 1896-1905 cohort assumes Foreign=1, all other values are set to the
nodal value.
Year
1940
1950
1960
1876-1885
-0.449
N/A
N/A
1886-1895
-0.315
-0.291
N/A
1896-1905
-0.333
-0.164
-0.208
Table 3.8: Difference in estimated log weekly wages for issei cohorts by
census year, evaluated at S=8
The predicted gaps for the oldest two cohorts are rather large. The gap for the
youngest issei cohort is rather large in 1940 but falls quite a bit for 1950 and 1960.
Unfortunately, there is no wage data prior to 1940 to gauge how the wage gaps
progressed over time for these cohorts. The gaps for the oldest two cohorts are fairly
64
large considering that most of the men in these cohorts had been in the United States over
three decades and holding all human capital variables such as marriage and years of
school fixed. The men in these cohorts were educated in Japan. Differences in quality of
schools might account for differential returns to education, but the Japanese schools were
of fairly high quality by the end of the nineteenth century. Also, the narrowing of the gap
for the youngest issei cohort points to diminishing discrimination after 1940.
The differences in predicted log wages for the nisei cohorts are shown in Table 3.9.
These differences are evaluated at twelve years of schooling, which is about average for
these cohorts. For the older two nisei cohorts their predicted differential in log wages is
fairly large at about .18 in 1940, then grows to .32 in 1950, and then falls after that. The
widening gap between 1940 and 1950 for the cohorts born 1906-1925 is probably due to
the effects of career interruption caused by internment. This effects will be discussed in
the next chapter. The wage advantage for the youngest nisei cohort in 1950 seems to be a
small sample anomaly; there were only 13 Japanese individuals from this cohort in the
IPUMS sample. The falling wage gap for this cohort after 1960 points to decreased
discrimination.
65
Year
1940
1950
1960
1970
1980
1990
1906-1915
-0.187
-0.324
-0.029
-0.061
N/A
N/A
1916-1925
-0.180
-0.324
-0.161
-0.130
-0.183
N/A
1926-1935
N/A
0.167
-0.235
-0.074
-0.100
-0.126
Table 3.9: Difference in estimated log weekly wages for nisei cohorts by
census year, evaluated at S=12
The differences in predicted wages for the post-1935 cohorts are shown in table 3.10.
These differences are evaluated at 16 years of schooling, since the Japanese men in these
birth cohorts typically were college-educated. For the cohorts born between 1936-1955,
the predicted gaps are large when these cohorts enter to labor market, and then fall
rapidly to almost zero by 1990. The predicted gaps for the oldest two cohorts are very
small.
1960
1970
1980
1990
1936-1945
-0.277
-0.187
-0.069
-0.017
1946-1955
N/A
-0.106
-0.068
-0.023
1956-1965
N/A
N/A
-0.010
0.031
1966-1975
N/A
N/A
N/A
-0.043
Table 3.10: Difference in estimated log weekly wages for Japanese cohorts born
after 1935 by census year, evaluated at S=16
The results of the wage equations estimated in the previous section evaluated at the
sample means show that the wage differences due to human capital differences between
Japanese and native whites are very small. Specifically, the total differences due to
employment in agriculture are very small, except for the cohorts born 1896-1915. Some
of the large gaps for the issei cohorts are explained by lower levels of Japanese schooling
66
and marriage, but these portions are small relative to the size of the gap. Rather than
explain some of the gaps for the younger two nisei cohorts, the estimated effect of their
higher levels of Japanese schooling and employment in agriculture is a Japanese wage
advantage.
The evolution of the predicted log wage differential across and within cohorts
indicate a high level of discrimination in 1940 and 1950, internment effects in 1950 and
1960, and diminishing discrimination thereafter. The narrowing of wage differentials
within cohorts in particular points to eroding discrimination and attenuating interment
effects.
3.5 Conclusions
The analyses presented in the previous sections shows the initial wage gap in 1940
was due to relatively lower labor market outcomes for the first and second generation
Japanese men, differences in observable characteristics, including lower average years of
schooling and lower marriage rates.
Over the years the overall Japanese-native white wage gap diminished as the more
disadvantaged issei aged out of the samples and the wage differentials of the nisei
narrowed over time, and sansei and some new immigrants entered the U.S. labor market
on equal footing with their white, native-born counterparts. Also, observable
characteristics of the Japanese population became more similar to, and in the case of
years of schooling, exceeded that of whites after 1950.
The persisting wage differentials in 1970 could be due to what Arrow (1998) refers
to as the “residue of discrimination” or internment effects. The next chapter evaluates the
67
implications of Internment during World War II on Japanese wages from 1950-1970.
This analysis tests for differential effects by cohorts based on their constructed labor
market status at the time of internment to see if this residue stays only with the cohorts
who were already established in the labor market by 1942 or if younger cohorts’ wages
were also impacted by internment.
In Chapter 5, the evolution of Japanese Californians’ wages relative to whites is
compared with that of Chinese, and Mexicans. An analysis of the differential effects of
more recent immigration on the wages of all these groups from 1970 to 1990 is presented.
If discrimination is the primary explanation for the wage gap from 1940 to 1970,
then the erosion of discrimination is the plausible explanation for subsequent closing of
the wage gap after 1970. The next two chapters shed light on this theory. Chapter 4
evaluates differences in wages of Japanese Americans whose demographics make them
likely to have been interned by birth cohort. Chapter 5 compares the evolution of the
wages of Californians of different ethnicities and examines the role of more recent
immigration.
68
CHAPTER 4
EFFECTS OF INTERNMENT ON JAPANESE WAGES, 1950-1970
This chapter examines the impact of internment on Japanese wages. The analysis in
Chapter 3 shows fairly large and statistically significant wage gaps for Japanese men
born prior to 1936 versus their white, native-born counterparts. This chapter assesses
how large a role the three-year incarceration of a significant proportion of the mainland
Japanese population played in these gaps. Furthermore, this chapter evaluates the
differences in the magnitude of internment effects for cohorts who were at different
phases of life at the time of internment.
The forced evacuation and subsequent resettlement of most of the Japanese
American population resulted in occupational and geographic redistributions (see Chapter
2), which ultimately may have increased or decreased their earnings relative to what they
would have earned had they not been evacuated.
Adult internees, those in the cohorts
born before 1916, making them at least age 27 in 1942, were removed from their jobs and
for the most part denied the opportunity to accumulate the additional human capital that
they would have acquired over three years of work experience. The wage regression
analysis presented in this chapter attempts to quantify the costs to internees in terms of
their earnings differentials for the quarter century after their return versus their
counterparts who were not interned.
69
The impact on adolescent and young adult internees, the cohort born between 1916
and 1925, ages 17 to 26 in 1942, is difficult to predict. Many college students were
discharged from the camps to attend universities (as long as they were not on the West
Coast). While the psychic cost of temporary incarceration of these young men and of
having the rest of their families interned while they were away at college was probably
great, there may have not been a long-term impact on their earnings. Internment may
have also disrupted the educational plans of young Japanese men already attending or
planning to attend universities on the West Coast. Furthermore, incarceration likely
impacted the wages of those young men who had completed their education and should
otherwise have entered the job market, possibly to an even greater extent than men who
were more established in their careers. Chin (2002) hypothesizes that internment may
have been particularly damaging to new labor market entrants who otherwise would have
been engaging in the process of job churning, frequently changing jobs in order to find a
good job match. For these reasons, the analysis in this chapter tests for the differential
impact of internment on the 1916 to 1925 birth cohort.
There is no obvious reason why internment should have impacted the wages of child
internees, those born between 1926 and 1945. Japanese men in these birth cohorts would
have been under the age of twenty at the conclusion of World War II. The potential for
career or schooling interruptions due to internment was minimal. Primary and secondary
schools of adequate quality were established in the relocation centers (Daniels 1988).
The next section discusses the literature on career interruption in general, and
Japanese internment in particular. Section 4.2 describes the modifications to the census
data sets used in Chapter 3 and gives the relevant summary statistics for the interned and
70
non-interned subsamples. Section 4.3 presents the model and the results of the wage
equations with internment and lifecycle variables. Section 4.4 concludes.
4.1. Literature Review
The Japanese internment experience is a unique and disturbing episode in modern
United States’ history. Parallels between this event and more ordinary career
interruptions for child-raising, military service, or even criminal incarceration should be
made with great caution. In contrast to these types of career interruptions, the internment
of the West Coast Japanese population was completely unanticipated and involuntary.
Mincer and Ofek (1982) using longitudinal data find that the real wages of women
returning from a career interruption are initially lower than when they last worked,
indicating a depreciation of human capital during career interruptions for child rearing.
They find a rebound effect, however, wages of returnees to the labor market rise with
experience at a rate greater than that for women with uninterrupted careers. Japanese
internees likely incurred similar human capital depreciation, and their wages may have
risen more steeply with experience post-internment relative to non-internees as they made
greater investments to restore the lost human capital.
Light and Ureta (1995) present evidence that the penalty for career interruption is
slightly larger for men than women, after controlling for cumulative work history, and
that the growth in post-interruption wages is smaller. They propose that these gender
differences are caused by women being more likely to select careers with less erosion of
human capital during interruptions. Alternatively, men experience higher interruption
penalties and lower rebound effects because their reasons for interrupting their careers,
71
such poor health or just a desire to not work, are correlated with lower abilities versus the
more common scenario in which women leave the labor force for domestic
responsibilities. Neither of these selectivity issues applies to the case of Japanese
internees.
White males’ career interruptions tend to be either short and unanticipated, due to
health or involuntary job loss, or long and planned, usually to return to school or make
other human capital investments. Most men do not have the fairly long and unanticipated
career interruption experienced by the Japanese internees, with the possible exception of
those drafted for military service. Military training can also substitute for on-the-job
training, however, and the criteria necessary for selected into and completion of a
military tour of duty can be a signal of quality to employers (DeTray, 1982). Rather than
a penalty for career interruption, researchers had found a “veterans premium” (Rosen and
Taubman, 1982). Angrist and Krueger (1994) find no effect on the wages of World War
II veterans, however, after using instrumental variables to adjust for selection biases of
draftees, who tended to be healthier and more intelligent than the general population.
Berger and Hirsch (1983) find initially lower wages for returning Vietnam veterans, but
their wages rose steeply thereafter, leading them to predict no difference in their lifetime
earnings.
Criminal incarceration has been found to have a large negative impact on male
wages. The wage penalty of former prisoners includes the stigma of having been labeled
a criminal, and thus untrustworthy, in addition to lost labor market experience (Western,
Kling, and Weiman, 2001; Waldfogel, 1994). Japanese internment is not directly
72
comparable to incarceration because their incarceration was due to their race, not their
individual behavior.
Broom and Renier (1949) analyzed the immediate effects of internment from their
1947 survey of former internees living in the Los Angeles area. They note that although
there were limited employment opportunities in the internment camps, the wages paid
“provided inadequate incentive, so that many skills were lost to the communities” (34).
Broom and Renier also document that many Japanese entrepreneurs were unable to
reestablish their businesses in the years immediately following their resettlement and
often “professionals, such as dentists, found jobs as technicians or assistants” (37).
Chin (2002) uses data from the 1970 Census to estimate the long-run effects of
internment on Japanese wages. She estimates the difference between the wages of
Japanese Americans who were born in West Coast states and likely interned and those of
whites is 11% to 15% larger than the difference between Hawaiian Japanese, who were
not interned and whites. These findings apply to the cohort born between 1908 and 1919
(aged 23 to 34 in 1942). Chin finds no differential effect on the wages of the interned
cohort born between 1928 and 1939 (aged 3 to 14 in 1942).
As described in Chapter 2, the Japanese proportion of the Hawaiian population was
much greater, making it difficult to directly compare the Hawaiian and mainland
Japanese economic performance. The analysis presented in this chapter estimates the
variation in wages between those Japanese men in the census samples, excluding Hawaii,
who were likely interned and those who likely were not for 1950 to 1970. Similar to
Chin’s study, I evaluate separately the effects on cohorts who would have been adults and
those who would have been children at the time of the evacuation. The criterion I use to
73
construct these cohorts are less restrictive than Chin’s, however. I also evaluate the
effects on young adults whose labor market entry or college attendance decisions were
undoubtedly affected.
Unlike most of the career interruption literature, the analysis of the effects on
internment on wages presented in Section 4.3 does not compare the returns to experience
pre and post interruption to test for restoration of human capital. As shown in the
previous chapter, there were many changes in the wage structure of Japanese Americans
between 1940 and 1970 so it would be impossible to conclude whether returns to
experience increased due to more intensive investments in human capital to compensate
for the years of their careers lost during internment or decreased discrimination
increasing the returns to human capital, including years of work experience.
4.2. Data
The data sets used in this chapter are derived from those used in Chapter 3 and
described in Section 3.3. I excluded men born after 1945 from the samples. For the
analysis presented in this chapter, I classified individuals as INTERNED if they were
born in the evacuated states, California, Washington, Oregon, and Arizona. I also
classified men born in Japan but living in the evacuated states at the time of the census as
INTERNED. Because all of the Hawaiian-born Japanese men in my the 1940 sample
used in Chapter 3 were living in California, I classify all Hawaiian-born Japanese men
found living in California in subsequent censuses also as INTERNED. I also included
Japanese men born between 1942 and 1945 in Idaho, Utah, Arizona, Colorado, and
Arkansas. A disproportionate number of Japanese American births occurred during those
74
years in those states, most likely in the internment camps. Individuals not meeting these
criteria were assigned a value of “0” for INTERNED.
In order to differentiate effects by lifecycle status, I classified all the men in the
samples as ADULT, YOUNG ADULT, or CHILD. The men in the ADULT category
were born prior to 1915, over age 27 in 1942. YOUNG ADULTs are those in the 1916 to
1925 birth cohort, ages 17 to 26 in 1942. A CHILD are those born between 1926 and
1945, those ages 16 and under in 1942, including those individuals potentially born in the
internment camps. The relevant summary statistics for the interned and non-interned
individuals in the pooled 1950- 1970 sample are given in Table 4.1.
Variables
Interned
Married
Employed in Agriculture
Average Age
Standard Deviation
Average Years of Schooling
Standard Deviation
Non-Interned
73.4%
17.7%
40.9
74.9%
5.1%
38.8
(10.1)
(10.0)
12.6
13.3
(3.0)
(3.7)
Avg Yrs of Experience
22.1
19.2
Standard Deviation
Avg Experience Squared
Standard Deviation
CHILD
(11.0)
(11.3)
YOUNG ADULT
ADULT
Years since 1945
Weekly Wages
606.6
493.4
(537.9)
(554.5)
42.1%
62.0%
39.5%
26.0%
18.4%
12.0%
18.8
182.46 $
$
Standard Deviation
(155.9)
N
1,031
21.0
173.35
(102.9)
334
Table 4.1: Comparison of interned and non-interned Japanese
male IPUMS samples, 1950-1970. (Note: Interned figures in
bold different from non-interned figures at 95% significance level
75
The proportion of the interned sample employed in farming is much higher than that
of the non-interned because the Japanese concentration in farming was primarily a trend
among those on the West Coast. The remaining differences are all caused by the fact that
they younger birth cohorts were less likely that the older cohorts to be born and reside on
the West Coast, and thus less likely to be interned. These younger cohorts were
relatively more educated and less experienced. Since the non-interned cohort is younger,
they are also more likely to have been observed in the 1970 data set, causing the Years
since 1945 variable to be greater for the non-interned subsample.
In the pooled sample, 75.5% are classified as INTERNED. The proportion of the
Japanese sample classified as INTERNED may be a little low in 1950, given the fact that
87% of the mainland population would have been interned, extrapolating from the 1940
Census (WRA, 1946). A group that would be undercounted is those born in Japan, but
not living on the West Coast when observed in 1950, 1960, or 1970. Some of them likely
were living on the West Coast in 1942, interned, and then relocated to the east. This
group would have been in the internment camps for a shorter duration on average than
those resettled on the West Coast which was prohibited until 1945, so the impact on their
wages would be less anyway.
On the other hand, some of those classified as INTERNED because they were born
in the West Coast states may have moved east prior to the evacuation in 1942 and thus
were not subject to internment. In the 1940 sample used in Chapter 3, only 2 of the 72
Japanese men living outside California, Washington, Oregon and Arizona were born in
those states. Also, some of those classified as not being interned because they were born
in states not on the West Coast may have moved to the West Coast prior to 1942. Only 7
76
of the 343 men in the 1940 Japanese sample were living on the West Coast, but born in
states to the east. Therefore, these types of misclassification should be small.
4.3. Analysis of the Effects of Internment on Wages by Lifecycle Status
In order to evaluate the impact of internment on Japanese wages, a Mincerian (1974)
wage equation similar to the one in Section 3.4 is estimated for the pooled sample
described in the previous section. In this analysis the non-interned Japanese men are the
control group, and the interned men are the treatment group. The INTERNMENT
variable and its interactions with parameters of interest give the differential predicted
effect on wages for those who would have been interned relative to their non-interned
counterparts. Other, non-interned Japanese men used as the control group, rather than
whites or just a random sample because they would have been subject to similar degrees
of discrimination in the postwar years. To the extent that discrimination against the
Japanese may have been worse in the postwar years on the West Coast than in the interior
is impossible to control for and will be captured in the estimated coefficients on the
INTERNMENT variable and its interactions. The wage model estimated is:
ln w i = α 0 + α 1 INT i + β 0 MARRIED
i
+ β 1 AG + β 2 S i + β 3 X i + β 4 X i2 +
δ 1YA i + δ 2 ADULT i + δ 3 INT i * YA i + δ 4 INT i * ADULT + φ 1YS 1945 i
+ φ 2 INT i * YS 1945 i + ε i
(1)
where, wi is the weekly wages of individual i; INT is a dummy variable equal to one if
the individual was interned, zero otherwise; MARRIED is a dummy variable for
married, spouse present; Si is the years schooling for each individual; Xi is years of
experience for the individual; YAi is a dummy variable equal to one if the individual was
born between 1916-1925; ADULT is a dummy variable equal to one if the individual was
77
born prior to 1916; YS1945i is the difference between the census year in which the
individual was observed and 1945.
This specification gives the average predicted difference in the wages of internees
and the difference in wages for the ADULT internees, those ages 27 and over in 1942,
and YOUNG ADULT internees, those age 17 to 26 in 1942, versus their non-interned
cohorts. The estimated coefficient on the YS1945 variable measures the average growth
in real wages over time whereas the INTERNED*YS1945 measures the differential
growth in wages for internees. A positive coefficient on this interaction shows internees
wages growing at a faster rate than the non-interned as time since the end of internment
increases, a damping of the internment effect. A negative coefficient on this interaction
shows a widening of the differentials over time if internees wages fall further relative to
their non-interned counterparts with each successive year. An attempt was made to
interact interned with the experience variable, but because of the relative small sample
size and the fact that the ADULT and YOUNG ADULT variables control for age at the
time of internment and thus indirectly experience, the results were insignificant.1
The coefficients estimated using ordinary least squares regression are given in Table
4.2. The coefficient on the INTERNED variable is somewhat large and positive, but
insignificant. This coefficient measures the estimated effect for all internees, including
those interned as children. This coefficient’s magnitude probably results from
differences in cost of living components in wages, almost 80% of the interned cohort
1
Specifically the coefficient on the INTERNED*EXP variable was negative and fairly large, yet
insignificant.
78
resided in California at the time they were observed in the census, versus 25% of the non
interned.
Variables
MARRIED
AG
S
X
2
X
INTERNED
YA
ADULT
INTERNED*YA
INTERNED*ADULT
YS1945
INTERNED*YS1945
CONSTANT
N
2
ADJUSTED R
Coefficients
0.317
-0.211
0.053
0.048
-0.001
0.169
0.136
0.305
-0.203
-0.305
0.030
0.002
2.871
1,365
0.3544
t stat
(9.14)
(-4.84)
(8.95)
(7.72)
(-7.40)
(1.16)
(1.66)
(2.27)
(-2.48)
(-2.69)
(5.05)
(0.29)
(18.32)
Table 4.2: Results of wage equations
Note: dependent variable is log of weekly wages
The coefficients on the INTERNED*YA and INTERNED*ADULT variables are
large and negative, more than offsetting the positive INTERNED coefficient. The adult
internees, those born prior to 1916, had a wage differential of 13.6% relative to their noninterned counterparts summing the INTERNED and the INTERNED*ADULT
coefficients. The effect for those in the 1916-1925 birth cohort was smaller, 3.4%. The
coefficient on the INTERNED*YS1945 is very small and therefore insignificant. The
wages of the internees grew just slightly faster than those of their non-interned
counterparts, resulting in a .8% narrowing in 1950, 2.7% in 1960, and 4.5% in 1970. At
79
this rate the effect on wages of internment died out soon after 1960 for the young adult
internees, but a rather large impact persisted until 1970 for the adult internees.
The large wage differentials between Japanese internees born before 1926 and their
non-interned counterparts shows the internees paid a high price for this imposed career
interruption. The internment effect was higher for those men already established in their
careers at the time of their incarceration, but nontrivial for the YOUNG ADULT cohort
whose early careers or college educations were disrupted. Furthermore, the analysis
shows that this effect had not dampened for adult internees by 1970, a quarter century
after the most individuals had been released from the camps. Internees’ wages grew at
only a modest rate over that of non-internees.
4.4 Conclusions
This chapter evaluates the long run effect of the incarceration of most of the
mainland Japanese population for the better part of three years on their wages. The
results of the wage model presented show large effects on the birth cohorts who were
already working at the time of their incarceration up to 25 years after their release. The
effects on the 1916-1925 birth cohort, mostly college students and new labor market
entrants at the time of the internment, were also very large. The wages of child internees
compare favorably with their non-interned counterparts, although the large, but
statistically insignificant coefficient on the INTERNED variable is probably due to cost
of living differences for the primarily Californian internees.
The analysis in the last chapter showed that the men in the birth cohorts from 18961915 had wage differentials with their native-born white counterparts on the order of
80
about 18%. Around 82% of the men in the Japanese samples from these cohorts are
classified as INTERNED. Therefore, a wage penalty of about 13% due to internment for
such large proportions of these cohorts can explain a large portion of their wage gap
versus native-born whites. The same applies for the 14% Japanese-native white wage
gap for the 1916-1925 birth cohort. Their internment differential of 3.4% applies to 82%
of that cohort also.
Also large portions of the overall wage gaps for 1950, 1960, and 1970 due to
differences in returns to characteristics, especially returns to experience, are partially due
to the internment effects on cohorts born before 1926. This portion of the wage gap
diminishes as the ADULT cohort ages out of the samples.
81
CHAPTER 5
JAPANESE, CHINESE, AND MEXICAN AMERICANS IN CALIFORNIA:
A COMPARISON
This chapter attempts to put the Japanese American experience into context, by
comparing their socioeconomic performance with that of Chinese and Mexican
immigrants and their descendants. In some ways the Japanese immigrants are very
similar to those from China and Mexico. All of these groups come from non-Englishspeaking countries. These immigrant groups settled primarily in the American West.
They initially found jobs as primarily agricultural laborers, railroad workers, miners,
cannery laborers, or domestic servants. And, they have all been targets of racial
discrimination.
There are some potentially important differences in their histories in the United
States, however. Whereas Japanese labor immigration did not begin until the mid-1880s,
Chinese immigrants flooded into California during the 1849 gold rush. The first large
wave of Mexican immigration began at the same time as that of Japanese immigrants, the
turn of the twentieth century. Chinese immigration was abruptly halted in 1882 by the
Chinese Exclusion Act. Although Japanese labor immigration was prohibited under the
1908 “Gentlemen’s Agreement”, wives and children of residents were permitted to enter
82
the U.S. until the Immigration Act of 1924, allowing for the process of family formation
in the 1910s. The Chinese population remained male dominated until the 1950s.
Mexican immigration, although almost zero during the Great Depression, was never
subject to complete exclusion, like Chinese and Japanese immigration. Furthermore, the
Mexican American population did not experience the gender imbalance that the Chinese
and Japanese Americans did. Finally, the Japanese World War II and Internment
experience was very unique and particularly damaging, although Chinese Americans also
were regarded with suspicion during the Cold War and Red Scare of the 1950s.
This chapter compares the economic performance of Mexican and Chinese
Americans to that of Japanese Americans. In particular, this chapter tests whether the
clear pattern of relative improvement across birth cohorts found for the Japanese in
Chapter 3 is found in all these immigrant groups. Also, this chapter examines the
differences in returns to human capital across groups. An important difference between
the Japanese American and Chinese American experiences is the fact that the process of
family formation for the Chinese was adversely impacted by the Chinese Exclusion Act
in 1882. The Gentlemen’s Agreement negotiated by the Japanese, however, allowed
residents to bring their wives into the United States. This chapter evaluates the effect of
this difference by contrasting the effects of marriage on the Japanese and Chinese wage
gaps. The effects of differences in the average quantities of schooling across groups are
quantified. Finally, Chinese and Mexican American populations are comprised of
relatively more recent immigrants than the Japanese American population. The effect on
the mean wage ratio of new immigrants is examined. Japanese American wages may
83
have achieved parity with native-whites, while other ethnic groups have not, in part
because they have relatively few or relatively higher quality new immigrants.
In order to eliminate complicating regional variation, I limit my analysis to
California, where a large proportion of mainland immigrants from all three countries
settled. Similar to Chapter 3, I compare the average wage ratios of the Japanese,
Chinese, and Mexican samples by birth cohort to those of native whites. These ratios are
shown in Tables 5.1 through 5.3.
Birth Cohort
Census
Year
1950
1960
1970
1980
1886-95 1896-1905 1906-15 1916-25 1926-35 1936-45 1946-55 1956-65 1966-75
0.525
0.608
0.670
0.619
(.102)
(.096)
(.145)
(.068)
(.097)
0.661
0.830
0.895
0.874
1.004
0.878
(.046)
(.080)
(.055)
(.046)
(.179)
(.032)
0.812
0.838
0.893
0.957
0.911
0.942
(.073)
(.043)
(.048)
(.049)
(.080)
(.028)
0.777
0.866
0.966
0.942
(.022)
1990
Total
1.065
0.613
(.041)
1.049
0.945
(.022)
(.023)
(.018)
(.033)
0.829
0.997
1.035
1.065
1.142
1.006
(.012)
(.026)
(.028)
(.027)
(.022)
(.131)
(.014)
Table 5.1: Ratio of Japanese to Native White Californian Male Mean
Weekly Wages by Birth Cohort
Notes: Standard errors are given in parentheses. Ratios in bold are significantly different
from 1 at the 5% level. Sample sizes given in Tables 5.5 & 5.6
84
Birth Cohort
Census
Year
1950
1886-95 1896-1905 1906-15 1916-25 1926-35 1936-45 1946-55 1956-65 1966-75
0.540
(.126)
1960
0.498
0.487
0.664
0.894
(.047)
(.118)
(.078)
(.194)
0.786
0.679
0.724
0.838
(.181)
1970
0.577
(.041)
0.948
(.122)
(.051)
(.047)
(.071)
0.581
0.733
0.874
0.800
(.055)
(.078)
(.051)
1980
Total
0.792
(.052)
0.681
0.775
(.047)
(.071)
(.032)
0.783
0.780
0.910
1.008
1.117
0.939
(.040)
(.027)
(.027)
(.022)
(.046)
(.015)
0.721
0.865
0.930
0.993
1.173
0.923
(.029)
(.024)
(.016)
(.016)
(.069)
(.011)
1990
Table 5.2: Ratio of Chinese to Native White Californian Male Mean
Weekly Wages by Birth Cohort
Notes: Standard errors are given in parentheses. Ratios in bold are significantly different
from 1 at the 5% level. Sample sizes given in Tables 5.5 & 5.6
Birth Cohort
Census
Year
1950
1960
1970
1980
1886-95 1896-1905 1906-15 1916-25 1926-35 1936-45 1946-55 1956-65 1966-75
0.434
0.617
0.627
0.730
1.024
(.037)
(.083)
(.036)
(.033)
(.141)
0.599
0.620
0.664
0.701
0.974
0.670
(.022)
(.021)
(.017)
(.014)
(.034)
(.009)
0.656
0.614
0.646
0.764
1.020
0.690
(.028)
(.014)
(.012)
(.026)
(631)
(.032)
0.607
0.601
0.596
0.734
(.010)
1990
Total
0.652
(.028)
0.910
0.637
(.007)
(.006)
(.006)
(.010)
0.530
0.681
0.756
0.943
(.010)
0.549
(.015)
(.009)
(.006)
(.005)
(.005)
(.010)
(.003)
Table 5.3: Ratio of Mexican to Native White Californian Male Mean
Weekly Wages by Birth Cohort
Notes: Standard errors are given in parentheses. Ratios in bold are significantly different
from 1 at the 5% level. Sample sizes given in Tables 5.5 & 5.6
85
The gaps in wages between Californian Japanese men and native whites narrowed
significantly in the 1950s, and the wages of Japanese cohorts born after 1935 compare
favorably with those of their native white counterparts. Most of the narrowing in the
Chinese-white wage gap took place in the 1970s, and, similar to the Japanese sample, the
cohorts born after 1935 for the most part fared relatively well versus their white
counterparts. The overall ratio of Californian Mexicans’ wages to those of whites falls
after 1970, but cohorts born after 1955 seem to be closing the gap.1
In this chapter, I attempt to analyze how the different demographic, legal, and
historical factors affecting each population impacted the evolution of their wages over
time. As in Chapter 3, I divide calculate the average log wage gaps attributable to
differences in observable, measurable characteristics, such as years of schooling,
marriage rates, and percent foreign born. For example, as discussed in Chapter 2,
Japanese and Mexican family formation had taken place by 1950, but the Chinese
American population was still relatively single and older. Although the discrimination
against Chinese Americans was diminishing as a result of World War II, the Japanese
may have narrowed their wage gap more quickly because of the marriage premium.
Mexican immigration can be described as a fairly continuous flow throughout the
twentieth century whereas Japanese immigration was very restricted between 1908 and
1965. Chinese immigration between 1882 and 1965 was also very limited, but recent
Chinese immigration is much more significant than Japanese immigration. The wage
regression analysis will estimate the differential effect of old and new immigration.
1
Unlike the samples in Chapter 3, the samples used to construct Tables 5.1-5.3 are not balanced by age.
The large overall wage gap for Mexican men in 1990 is partially due to their relatively young age (see the
description of the data in Section 3.2).
86
Chapter 3 shows that the Japanese-native white wage gap fell from 1940-1970 due to
relative increases in the average quality of Japanese measurable characteristics, notably
years of schooling. After 1970, the wage gap was completely eliminated with the erosion
of discrimination. Chapter 4 concludes the long-run effects of internment had a
detrimental impact on the wages of adult internees until at least 1970 and a smaller
impact on the wages of adolescent internees until 1960.
This chapter will analyze whether the Japanese American experience is truly unique.
Was the discrimination faced by them particularly damaging and fueled by the hostilities
between the United States and their homeland, or were Mexican and Chinese Americans
subject to similar degrees of labor market discrimination? Was the apparent elimination
of labor market discrimination after 1970 due to the desire of Americans to heal the
wounds inflicted during the war and move on? Or did more equal treatment in the labor
market coincide with a generally more enlightened attitude about minorities and reforms
mandated by Title VII of the Civil Rights Act of 1964? Finally, this chapter will evaluate
whether Japanese wages have converged to those of whites while gaps for Mexicans and
Chinese remain, simply because large numbers of newly arrived immigrants in these
groups depress their average wages.
The next section summarizes the relevant literature on the performance of Mexican
and Chinese Americans. Section 5.2 describes the census data sets that I will use to
evaluate socioeconomic progress for the three groups. Section 5.3 examines the
evolution of Japanese, Chinese and Mexican versus native-born white Californian male
wages. Section 5.4 concludes.
87
5.1. Literature Review
Remarkably little has been written about the economic progress of Chinese
immigrants. Because of their fairly small numbers in the Integrated Public Use
Microsamples (IPUMS) prior to 1980, some comparative studies have grouped them with
Korean and Japanese immigrants (Schoeni, McCarthy, and Vernez, 1996) or with all
other Asians (Borjas, 1985; Chiswick, 1977). These studies are not helpful when
comparing their experience with that of Japanese Americans, but a paper by Borjas
(1994) offers some insight on the economic progress of Chinese immigrants across
generations. Studies by Chiswick (1982, 1986) and Carliner (1980) evaluate the
performance of Chinese immigrants since 1965.
Borjas (1994) investigates the correlation between average literacy and occupational
status of immigrants from a given country in 1910 and the average years of schooling and
wages of sons of immigrants from that country in 1940 and American-born men of that
ethnicity (the grandsons) in 1980. He finds that the educational attainment of Chinese
American children in 1940 was much lower than predicted given the nearly 85% literacy
rate for Chinese immigrants in 1910. Wages of the second generation are also very low
given those of the first generation. The opposite is true of the “grandchildren” in 1980;
they have much higher average educational attainment and wages than the trend.
Possible explanations for the intergenerational improvement of third-generation Chinese
men relative to their white counterparts include diminished discrimination and the
inability of the first generation to form families.
Using data from the 1970 Census, Chiswick (1982) shows that newly arrived
Chinese male immigrants earned 35% less than American-born Chinese men. Their
88
earnings do rise steeply with years in the U.S. Chiswick observes that a significant
proportion of modern Chinese immigrants are refugees. Relative to immigrants admitted
under the special professional classifications (see Chapter 2), refugees have lower levels
of educational attainment and are less likely to speak English. Therefore, Chiswick’s
finding of very low, but steeply rising wages as these immigrants learn English and get
better jobs is consistent with what is known about recent Chinese immigrants.
Further refining the analysis by arrival cohort, Chiswick (1986) shows that the gap
between recently arriving Chinese immigrants and native-born Chinese men narrows by 4
percentage points in the 1980 versus 1970 Census. Chiswick argues that this finding
indicates a slight improvement in Chinese immigrant labor market quality, perhaps as the
status of newly arriving immigrants shifts towards special professionals and away from
refugees.
Carliner (1980) uses data from the 1970 Census to illustrate that Chinese Americans
follow a similar pattern as other ethnic groups. Recent Chinese immigrants earn less than
earlier immigrants, but the children of immigrants fare better, and the third generation
does worse than the second generation, but better than immigrants.
The economic literature Chinese Americans who have been in the United States for
several generations tells a story similar to that of Japanese Americans. The recent high
educational and occupational attainments of American-born Chinese and Japanese men
has been celebrated in economics journals as well as the popular press (for example
“Asian-Americans: A ‘Model Minority’,” Newsweek, December 6, 1982). The story of
recent Chinese immigrants differs from that of Japanese Americans because they are such
a heterogeneous group in both educational attainment and immigration status.
89
Immigration from Mexico is very significant, comprising one fifth of the foreignborn population in 1990. Much of the recent economic literature on immigrants has
focused on this population. Feliciano (2001), and Borjas (1994) evaluate Mexican
immigration throughout the twentieth century. Chiswick (1977, 1978, 1982, 1986),
Borjas (1985), and Yuengert (1994) analyze the impact of the 1965 immigration reforms
on the quality of Mexican immigrants.
Feliciano (2001) looks at the link between the wages and educational attainment of
Mexican immigrants relative to native whites. She finds that the widening wage gap
from 1910 to 1990 coincides with a widening education gap. Borjas’ (1994)
investigation on the children and grandchildren of early 20th century immigrants shows
that the educational attainment and wages of second and higher generations failed to keep
pace with that of other immigrant groups, even accounting for very low initial Mexican
wages and literacy rates in 1910.
There is no consensus on the impact of the 1965 immigration reforms and increased
illegal immigration on the quality of the Mexican-born labor force in the U.S. Chiswick
(1986) finds no clear evidence on a decline between the 1970 and 1980 Census in wages
of newly arrived immigrants. Borjas (1985) argues that recent immigrants in 1970 had
relative wages over 12% higher than that of recent immigrants in 1980. Yuengert (1994)
disputes Borjas’ findings. Using non-linear earnings equations, he concludes that the
wage gap for recent Mexican immigrants is not widening.
Chiswick (1977, 1982) and Carliner (1980) use data from the 1970 Census to show
that second generation Mexican Americans have relatively higher wages than either
recent immigrants or higher-order generations. Chiswick observes than Mexican-born
90
men and their sons in his samples are more likely to live in cities than the sons of
American-born people of Mexican descent. Chiswick (1982) hypothesizes that
descendants of early Mexican settlers to the American West persist in rural ethnic
enclaves, settled over a hundred years ago, depressing wages and educational attainment
versus the modern urban Mexican immigrants and their children.
Shoeni, McCarthy, and Vernez (1996) compare the economic progress of immigrants
in the U.S. and in California using data from the 1970 through 1990 Censuses. They find
that the wages of Japanese, Chinese, and Korean immigrants, as a group, while initially
lower than natives, converge rapidly. The relative wage gap for Mexicans versus natives
does not converge significantly with years in the U.S. and the gap is even wider in
California. When adjusting for education, the gap for Asians gets wider, and the gap does
not narrow significantly for Mexicans. The authors attribute the wide gap for Mexicans
to differences in quality between the U.S. and Mexican educational systems.
In the wage equation analysis presented in Section 5.4, I compare the evolution of
the Japanese wage gap with native-born whites to that of Chinese and Mexican men. In
particular, I investigate the differential progress of these three groups across the decades
studied relative to native-born whites and the differential impact of more recent
immigration on the average wage gaps.
91
5.2. Data
The data I use come from the Integrated Public Use Microdata Series (IPUMS)
constructed from the 1950 through 1990 U.S. Censuses. Wage data was not collected
prior to the 1940 Census. And, the 1940 Census has very small numbers of Chinese
individuals and only asked parents’ birthplace for 1 in 20 individuals, making a Mexican
sample problematic. “Japanese” and “Chinese” are separate race classifications for all
censuses used. Mexican Americans have always been enumerated as “Whites”. For
1970-1990, there is a “Hispanic” category in which individuals of Mexican descent are
specifically identified. For 1950 and 1960, the Mexican samples consist only of Mexican
natives and the children of Mexican natives.
I have drawn the exhaustive set of males between the ages of 15 and 64, not in
school whose race is listed as Chinese or Japanese, living in California for 1950-1990.
For 1950 and 1960, I have drawn all individuals in the IPUMS who were born in Mexico
or who have at least one parent born in Mexico. And, for 1970 through 1990 I compiled
a sample of all individuals classified as Mexican. The native white samples are
comprised of individuals born in the 50 States and Washington, D.C., whose race is
classified as white, excluding Hispanics for 1970-1990.
As in Chapter 3, in order to eliminate the large number of recent Japanese
immigrants in the 1980 and 1990 Census who tend to be temporarily working for their
companies in the U.S., and therefore not really immigrants, I exclude those who
immigrated within the prior five years. For consistency, I do the same for the Mexican
and Chinese samples.
92
I limit my analysis to California in order to minimize local market variations. Table
5.4 gives the relevant population numbers and the percentage of each ethnic group in the
U.S. living in California. A substantial portion of all three populations live in California
after excluding Hawaii for Chinese and Japanese Americans.
Population
Percentage of U.S. population living in CA*
Japanese
Chinese
Mexicans
Japanese
Chinese
Mexicans
93,717
39,556
354,432
73.8%
51.0%
32.9%
84,956
58,191
481,014
59.9%
49.7%
35.8%
159,545
91,340
695,643
60.0%
46.2%
40.1%
213,277
170,419
1,857,267
57.5%
45.0%
41.0%
268,814
325,882
3,613,167
56.4%
43.1%
41.6%
320,730
713,423
6,070,637
51.8%
45.2%
45.3%
Table 5.4: Japanese, Chinese, and Mexicans in California
Source: U.S. Census Bureau. *Percentages for Japanese and Chinese exclude Hawaii
Year
1940
1950
1960
1970
1980
1990
Individuals with total weekly incomes less than $1 and those who worked less than
26 weeks are excluded from the sample. Income is defined as the sum of wages,
business, and farm incomes, any of which may be zero or negative.2 The percentages of
individuals in the original samples with positive income and working at least 26 weeks
per year are given in the Data Appendix. For 1960, 1980, and 1990, the Japanese have
the highest percentages, at least 89%. For these years, the Chinese and Whites’ samples
have percentages in the mid 80% range, and the Mexican samples are around 80%. Men
between the ages of 15 and 64 who have left school tend to be full-time participants in
2
Income values in the Census were top coded, or given a maximum value. For 1950-1980, I assign the
values calculated by Smith and Welch (1989) which reflect the means assuming the upper end of the
income distribution follows an exponential distribution. The top codes are given in parentheses with their
calculated values after the equal sign. 1950 ($5,000) = $8,900; 1960 ($10,000) = $22,500; 1970 ($25,000)
= $80,000; 1980 ($75,000) = $115,000. For 1990, the Census Bureau assigned values equal to the mean of
the actual values for top coded individuals. The values are different for wage, business, and farm incomes.
For wages ($139,000)= $195,516; for business income ($89,000) = $ 123,515; for farm income ($50,000) =
93
the labor market. The percentages for 1950 are affected by the fact that income questions
were asked only of sample-line individuals, around 1 in 5 respondents. And, the 1970
Form 1 samples do not code for individuals currently in school, which led to many more
part-time workers/students in the original sample draws, lowering the percentages,
especially for the Japanese and Chinese samples. The issues with the 1950 and 1970
samples are discussed in detail in the Data Appendix.
Table 5.5 on the next three pages gives the relevant cross sectional sample
characteristics for each group from 1950-1990. The trends are consistent with the
population histories detailed in Chapter 2. The Chinese men reside almost exclusively in
urban areas, whereas lower urban residence rates for Mexican men correspond to their
higher percentage employed in agriculture.
Individuals in the sample are identified as married if they are coded as “married,
spouse present”. The percentage of Chinese men in the sample who are married is very
low in 1950 nearly doubles by 1960, and since 1980 the Chinese men have the highest
marriage, and lowest divorce rates. Mexican marriage rates are relatively high, until
1990 when the mean age is at least 5.5 years younger than that of the other samples.
$90,905. In all cases, less than 2% of the samples were top coded and top coding occurred most frequently
in the white samples.
94
1950
Variables
1960
Japanese Chinese Mexican
Whites
Japanese
Chinese
Mexican
Whites
% Urban
% Married
91.0%
92.9%
71.7%
80.0%
92.1%
92.8%
85.3%
86.1%
61.2%
33.3%
66.8%
78.0%
69.9%
65.6%
73.2%
80.5%
% employed in ag.
11.9%
2.4%
23.0%
5.0%
30.6%
1.4%
19.5%
2.9%
37.3
41.5
36.4
38.4
38.4
41.9
37.0
39.3
Standard Deviation
(13.1)
(13.7)
(11.9)
(11.5)
(10.4)
(10.9)
(11.1)
(11.7)
Average Yrs Schooling
10.7
7.0
6.6
11.1
12.1
9.1
7.3
11.6
Standard Deviation
(3.2)
(5.4)
(4.0)
(3.0)
(3.2)
(5.5)
(4.2)
(3.1)
19.9
24.9
20.8
20.9
20.0
24.5
21.1
21.4
(13.9)
(14.5)
(12.2)
(11.9)
(11.5)
(12.3)
(11.5)
(12.3)
587.9
826.5
580.6
578.5
530.6
752.3
575.9
607.1
(581.0)
Average Age
Avg Yrs of Exp
Standard Deviation
Avg Experience Sq
(707.6)
(679.1)
(574.8)
(571.3)
(570.1)
(639.9)
(561.1)
% foreign born
Standard Deviation
32.8%
61.9%
53.7%
0%
20.1%
58.4%
43.2%
0%
% second generation
58.2%
26.2%
46.3%
21.7%
73.0%
25.3%
56.8%
22.1%
9.0%
11.9%
N/A
78.3%
6.9%
16.3%
N/A
77.9%
48.8
47.7
45.2
48.5
49.1
49.0
47.3
48.9
(5.5)
(8.2)
(9.0)
(6.8)
(4.5)
(4.7)
(6.0)
(4.8)
% 3rd genration+
Avg wks worked
Standard Deviation
Avg annual income
$ 2,469
Standard Deviation
$ 2,448
$ 4,015
(1420.4) (1061.1) (2020.2)
Avg weekly income
$ 50.40
Standard Deviation
N
$ 2,232
$ 47.48
$ 53.57
(3743.5)
$ 82.22
$
5,636
(4427.5)
$ 117.54
$
5,260
(5279.2)
$ 106.12
(27.4)
(21.7)
(39.8)
(74.3)
(86.4)
(103.1)
67
42
322
6052
418
221
$ 4,298
(2465.4)
$ 89.75
(48.6)
1,851
$
6,723
(5698.3)
$ 133.94
(98.3)
28,336
Table 5.5: Comparisons of Japanese, Chinese, Mexican, and White Samples
Figures in bold statistically different from white figures at 95% level
(CONTINUED)
95
TABLE 5.5: CONTINUED
1970
Variables
Japanese
% Urban
% Married
% employed in ag.
Average Age
Standard Deviation
% w/o HS diploma
Avg Yrs of Exp.
Standard Deviation
Avg Experience Sq
Standard Deviation
% native born
< 10 yrs USA
10-20 yrs USA
>20 yrs USA
Avg weeks worked
Standard Deviation
Avg annual income
Standard Deviation
Avg weekly income
Standard Deviation
N
$
1980
Chinese Mexican
Whites
Japanese
Chinese
Mexican
Whites
95.1%
98.3%
93.6%
93.0%
97.8%
98.8%
93.1%
91.9%
72.6%
6.4%
68.0%
0.2%
75.0%
9.5%
75.6%
2.0%
67.2%
4.2%
74.0%
70.0%
0.6%
7.8%
66.3%
1.4%
40.0
38.8
36.0
38.6
42.4
40.7
35.2
38.7
(11.1)
(12.3)
(11.5)
(12.8)
(12.6)
(11.2)
(11.4)
(12.6)
14.4%
20.7
33.0%
19.9
63.7%
19.4
25.8%
19.9
7.8%
22.3
16.7%
20.1
54.7%
18.2
12.6%
19.0
(12.1)
(13.4)
(12.0)
(13.2)
(13.5)
(12.6)
(12.0)
(12.8)
576.2
574.7
523.1
570.4
679.2
564.5
472.6
523.3
(548.2)
(591.1)
(543.9)
(585.7)
(630.8)
(577.1)
(536.2)
(571.0)
85.8%
6.8%
3.9%
3.5%
39.9%
25.2%
13.9%
21.0%
33.2% 100.0%
15.7%
N/A
10.0%
N/A
41.0%
N/A
81.9%
34.5%
54.6%
100.0%
4.5%
18.2%
18.0%
N/A
4.9%
25.7%
17.0%
N/A
8.7%
21.6%
10.4%
N/A
49.9
48.5
48.0
48.8
(3.2)
(5.4)
(5.6)
(4.9)
50.1
(4.6)
49.7
(5.2)
48.0
(6.8)
9,688
$ 7,833
$ 10,245
$ 6,860
(6360.2)
(6704.0)
(3978.4)
$193.43
$159.30
$141.65
(126.3)
(133.5)
486
409
$ 20,822
$ 20,543
(8533.7) (14756.3)
(16864.7)
$ 205.42
(80.1)
3,397
(155.6)
35,659
96
$ 413.89
(292.2)
3,030
$ 411.56
(337.3)
2,744
$ 13,496
(9199.1)
$ 278.93
(180.8)
49.1
(6.0)
$ 21,720
(18524.4)
$ 438.11
(367.8)
26,587 70,928
(CONTINUED)
Table 5.5: CONTINUED
1990
Variables
Japanese
% Urban
% Married
% employed in ag.
Average Age
Standard Deviation
Average Yrs Schooling
Standard Deviation
Avg Yrs of Exp.
Standard Deviation
Avg Experience Sq
Standard Deviation
% native born
5-10 YSM
10-20 YSM
>20 YSM
Avg weeks worked
Standard Deviation
Avg annual income
Standard Deviation
N
Whites
99.0%
73.1%
0.4%
40.6
94.0%
58.3%
7.0%
35.3
89.9%
64.2%
1.0%
40.3
(11.7)
(10.2)
(10.6)
(11.3)
14.6
14.3
9.5
14.0
(2.8)
(4.4)
(4.5)
(2.6)
20.8
19.8
18.1
20.2
(12.1)
(10.9)
(11.0)
(11.2)
580.3
512.5
448.6
533.4
(580.2)
(510.9)
(492.5)
(509.4)
75.8%
4.7%
8.5%
11.0%
50.1
22.2%
25.8%
31.7%
20.3%
49.8
43.1%
15.4%
27.5%
14.1%
48.2
100.0%
N/A
N/A
N/A
49.5
42,231
(5.2)
$
(31883.1)
$
Mexican
96.8%
63.2%
1.7%
41.5
(4.7)
$
Standard Deviation
Avg weekly income
Chinese
841.23
38,709
(6.7)
$
(33432.2)
$
(641.3)
3,206
97
772.17
(666.2)
5,943
22,356
(5.6)
$
(17522.4)
$
459.53
(350.9)
46,923
41,703
(35404.3)
$
836.31
(706.5)
69,590
The average years of schooling for these samples from California are consistent with
the national trends for each of these groups. After 1950 the men in the Japanese samples
have the highest average years of schooling. The Chinese men have average years of
schooling that since 1970 compare favorably with whites, but the standard deviations are
fairly high. These statistics are consistent with the national Chinese educational
attainment statistics (see Chapter 2) in which Chinese men are simultaneously more
likely to be high school dropouts or college graduates than whites whites. The Mexican
samples have relatively very low average years of schooling for all years.
The percentage of the Japanese sample who is foreign-born is about 33% in 1950
and is lower than that for all subsequent years. The foreign-born percentage increases
markedly for the Chinese samples. The foreign-born percentage for the Mexican samples
is between 40% and 60% for all years. In 1950 and 1960, there are more children of
immigrants than those who were native-born with native-born parents for both the
Japanese and Chinese samples. The Chinese and Mexican men are not only more likely
to be foreign-born than the men in the Japanese samples, but also they are more likely to
have immigrated more recently in the 1970 through 1990 samples.
The 1970 IPUMS samples were constructed from three separate forms which asked
slightly different questions (see the Data Appendix for a detailed description of all
samples). The nativity generations were coded only for the Form 2 samples, but the
Hispanic categories were only coded for the Form 1 sample, therefore the Form 1
samples were used and generation codes were unavailable. Generations also were not
coded in the 1980 or 1990 IPUMS.
98
Annual and weekly wage gaps between whites and both the Japanese and Chinese
men in the samples narrowed between 1950 and 1990. By 1990, the Californian Japanese
men had statistically insignificantly higher wages than whites. The wage gap between
whites and Chinese men remained statistically different from zero but smaller after 1980.
The wage gaps for Mexican men are large and very significant for all years. The gaps in
the average natural logs of weekly wages will be examined in detail in the next section.
For most years, the Japanese worked the most weeks per year, followed closely by the
whites and Chinese, with Mexicans working the fewest.
The sample means by birth cohort are given in Table 5.6. There are large differences
in marriage rates for the youngest two cohorts, especially for the Chinese Americans.
Some of the differences for later birth cohorts are statistically significant at the 5% level,
but really quite small in magnitude, so the effects of differences in marriage rates will
only be analyzed for the oldest two cohorts. The average years schooling for the men in
all the Mexican birth cohorts are much lower than native whites, and the other groups.
Similar to the Japanese, the Chinese had lower average schooling in earlier cohorts, but
the average schooling levels of more recent birth cohorts has exceeded that of native
whites. The effects of these differences in schooling on the log wage gap with whites
will be calculated for all groups and cohorts.
There are large differences in the percentage foreign born across ethnic groups. The
New Immigrant category is immigrants in the samples who would have immigrated after
1970. Old immigrants are the foreign born who would have immigrated prior to 1970.
The effects of immigration on the differences in log wages will be calculated for the
Chinese, Japanese, and Mexican samples.
99
1886-1895
Variables
Urban
Japanese
Chinese
1896-1905
Mexicans
Whites
Japanese
Chinese
Mexicans
Whites
0.900
1.000
0.586
0.788
0.860
0.930
0.858
(0.32)
(0)
(0.50)
(0.41)
(0.35)
(0.26)
(0.35)
(0.37)
Married
0.900
0.222
0.552
0.809
0.649
0.491
0.771
0.853
(0.32)
(0.44)
(0.51)
(0.39)
(0.48)
(0.50)
(0.42)
(0.35)
Ag
0.200
0.000
0.276
0.090
0.456
0.000
0.213
0.048
(0.42)
(0)
(0.45)
(0.29)
(0.50)
(0)
(0.41)
(0.21)
9.4
5.7
3.8
10.0
8.0
5.3
4.8
10.2
(3.1)
(6.3)
(3.9)
(3.5)
(4.0)
(5.6)
(4.0)
(3.4)
1.000
0.889
0.862 N/A
1.000
1.000
1.000
1.000
N/A
N/A
N/A
N/A
3.733
3.722
3.586
4.215
0.825
0.193
0.807
4.361
0.702
0.246
0.754
4.227
Yrs School
Foreign
Yr50
Yr60
lnwage
0.836
N
0.854 N/A
0.254
0.253
0.746
0.747
4.232
4.679
(0.55)
(.62)
(0.49)
(0.79)
(0.51)
(0.70)
(.62)
10
9
29
676
57
57
240
(0.65)
4,600
Table 5.6: Sample means for Japanese, Chinese, Mexican, and Whites, by birth cohort.
Notes: Figures in bold are significantly different from white figures at 5% level.
Standard Deviations are given in parentheses
(CONTINUED)
Table 5.6: CONTINUED
1906-1915
Variables
Urban
Japanese
Chinese
Mexicans
Whites
0.905
0.950
0.857
(0.29)
(0.22)
(0.35)
(0.33)
Married
0.876
0.720
0.815
0.872
(0.33)
(0.45)
(0.39)
(0.33)
Ag
0.229
0.010
0.189
0.033
(0.42)
(0.10)
(0.39)
(0.18)
11.2
8.2
6.5
11.3
(3.4)
(5.0)
(4.0)
(3.2)
0.200
0.067
0.476
0.457
4.933
0.700
0.050
0.430
0.520
4.614
(.64)
(0.68)
(.60)
105
100
615
Yrs School
Foreign
Yr50
Yr60
Yr70
lnwage
N
0.876
0.585 N/A
0.119
0.131
0.457
0.490
0.424
0.378
4.691
5.098
(.63)
12,845
(CONTINUED)
100
Table 5.6: CONTINUED
1916-1925
Variables
Urban
Japanese
Chinese
1926-1935
Mexicans
Whites
Japanese
Chinese
Mexicans
Whites
0.962
0.979
0.902
0.902
0.963
0.987
0.917
(0.19)
(0.14)
(0.30)
(0.30)
(0.12)
(0.11)
(0.28)
(0.29)
Married
0.853
0.857
0.825
0.865
0.804
0.850
0.794
0.815
(0.35)
(0.35)
(0.38)
(0.34)
(0.40)
(0.36)
(0.40)
(0.39)
Ag
0.123
0.014
0.132
0.025
0.060
0.007
0.115
0.018
(0.33)
(0.12)
(0.34)
(0.16)
(0.24)
(.08)
(0.32)
(0.13)
12.6
11.2
7.8
12.5
13.7
12.5
8.1
13.2
(2.7)
(5.1)
(4.3)
(3.1)
(2.8)
(5.0)
(4.6)
(3.1)
0.061
0.591
0.397 N/A
0.003
0.105
0.024 N/A
0.030
0.016
0.030
0.063
0.162
0.124
0.167
0.285
0.125
0.158
0.176
0.276
0.683
0.703
0.627
0.376
N/A
N/A
N/A
N/A
5.739
5.632
5.504
5.903
0.026
0.014
0.006
0.082
0.095
0.455
0.361
6.448
0.070
0.264
0.026
0.036
0.067
0.412
0.459
6.239
Yrs School
Old imm
New imm
Yr50
Yr60
Yr70
Yr80
Yr90
lnwage
(0.67)
N
1,001
(0.77)
565
(0.68)
3,225
(0.66)
28,699
(0.69)
1,625
(0.81)
1,609
0.905
0.056 N/A
0.079 N/A
0.007
0.019
0.072
0.183
0.107
0.210
0.472
0.344
0.342
0.245
6.076
6.498
(0.68)
8,628
(0.72)
38,226
(CONTINUED)
101
Table 5.6: CONTINUED
1936-1945
Variables
Urban
Japanese
Chinese
1946-1955
Mexicans
Whites
Japanese
Chinese
Mexicans
Whites
0.971
0.986
0.936
0.902
0.975
0.991
0.935
(0.17)
(0.12)
(0.24)
(0.30)
(0.16)
(.09)
(0.25)
(0.29)
Married
0.749
0.858
0.766
0.736
0.587
0.741
0.714
0.611
(0.43)
(0.35)
(0.42)
(0.44)
(0.49)
(0.44)
(0.45)
(0.49)
Ag
0.021
0.004
0.076
0.011
0.019
0.002
0.070
0.012
(0.14)
(.06)
(0.26)
(0.11)
(0.14)
(.05)
(0.25)
(0.11)
14.8
14.6
9.1
13.8
15.1
15.0
9.7
14.1
(2.7)
(4.5)
(4.7)
(2.9)
(2.6)
(3.8)
(4.5)
(2.6)
0.150
0.092
0.019
0.079
0.441
0.461
6.623
0.432
0.368
0.004
0.046
0.355
0.595
6.459
Yrs School
Old imm
New imm
Yr60
Yr70
Yr80
Yr90
lnwage
(0.67)
N
1,396
(0.80)
1,982
0.906
0.323 N/A
0.125
0.201
0.201 N/A
0.183 N/A
0.167
0.532
0.401 N/A
0.017
0.079 N/A
N/A
N/A
N/A
0.074
0.201
0.028
0.022
0.028
0.126
0.431
0.366
0.437
0.267
0.419
0.423
0.478
0.353
0.535
0.711
0.553
0.452
6.097
6.447
6.422
6.021
6.499
6.304
(0.68)
13,715
(0.75)
43,103
(0.66)
1,691
(0.72)
3,087
(0.66)
22,396
(0.79)
49,303
(CONTINUED)
Table 5.6: CONTINUED
1956-1965
Variables
Urban
Japanese
Chinese
1966-1975
Mexicans
Whites
Japanese
Chinese
Mexicans
Whites
0.973
0.987
0.942
0.917
0.994
0.990
0.948
(0.16)
(0.11)
(0.23)
(0.28)
(.08)
(0.10)
(0.22)
(0.26)
Married
0.341
0.484
0.514
0.424
0.084
0.067
0.221
0.193
(0.47)
(0.50)
(0.50)
(0.49)
(0.28)
(0.25)
(0.41)
(0.39)
Ag
0.013
0.003
0.067
0.010
0.000
0.005
0.057
0.005
(0.11)
(0.06)
(0.25)
(0.10)
(0)
(0.07)
(0.23)
(0.07)
14.4
14.5
10.0
13.2
13.2
13.3
10.3
12.5
(2.4)
(3.6)
(4.1)
(2.2)
(2.2)
(2.6)
(3.5)
(1.5)
0.088
0.091
0.215
0.785
6.264
0.113
0.562
0.088
0.912
6.263
(.63)
(.64)
Yrs School
Old imm
New imm
Yr80
Yr90
lnwage
N
1,168
1,792
0.930
0.270 N/A
0.071
0.051
0.025 N/A
0.498 N/A
0.084
0.641
0.474 N/A
0.227
0.385 N/A
N/A
N/A
N/A
0.773
0.615
1
1
1
1
5.833
5.729
5.523
6.073
5.669
5.602
(.63)
22,979
(0.69)
26,854
102
(0.67)
(0.66)
154
195
(0.56)
7,253
(.62)
6,259
5.3. The evolution of Japanese, Chinese, and Mexican Wages
Similar to the analysis in Section 3.3 this section examines the changes in relative
wages across and within birth cohorts for Japanese Californian males, and compares their
relative progress to that of Chinese and Mexican men. Again, the samples for all years
and races are pooled and separate wage equations are estimated for each birth cohort with
interactions on the relevant variables. This wage model is:
ln wi ,n = a1,n + a2 ,n J i ,n + a3,n Ci ,n + a4 ,n M i ,n + b1,nURBi ,n +
b2 ,n MARi ,n + b3,n AGi ,n + b4 ,n S i ,n + b5 ,n J i ,n S i ,n + b6 ,n Ci ,n S i ,n +
b7 ,n M i ,n S i ,n + c1,n OLDIM i ,n + c2 ,n NEWIM i ,n +
(1)
c3,n Ci ,n OLDIM i ,n + c4 ,n M i ,n OLDIM i ,n + c5 ,n Ci ,n NEWIM i ,n +
c6 ,n M i ,n NEWIM i ,n + d nYRi ,n + f n J i ,nYRi ,n + f n Ci ,nYRi ,n
f n M i ,nYRi ,n + ui ,n
where wi,n is the weekly wage of individual i belonging to birth cohort n; Ji,n is dummy
variable=one if race is Japanese, zero otherwise; ; Ci,n is dummy variable=one if race is
Chinese, zero otherwise; ; Mi,n is dummy variable=one if race is Mexican, zero otherwise
URBi,n is a dummy variable=one if the individual lives in an urban locale, zero otherwise;
MARi,n is a dummy variable=one if the individual is “married, spouse present,” zero
otherwise; AGi,n is a dummy variable=one if the individual is employed in agriculture,
zero otherwise; Si,n is the years of schooling of individual belonging to birth cohort n;
OLDIMi,n is a dummy variable=one if a Chinese, Japanese or Mexican individual
immigrated prior to 1970, zero otherwise; NEWIMi,n is a dummy variable=one if a
Chinese, Japanese or Mexican individual immigrated after 1970, zero otherwise; and
YRi,n is a vector of dummy variables for the census year in which the individual was
103
observed, for each birth cohort, the earliest census for which there are sample
observations is used as the omitted category.
Table 5.7 gives the results of these wage equations. These results will be interpreted
with calculations such as those in Chapter 3. Table 5.8 shows the total log wage
differentials and the portions due to differences in human capital, schooling and marriage
rates, for the oldest two birth cohorts. The portions are calculated by multiplying the
estimated coefficients by the differences in average human capital. These calculations
are completely analogous to those in Chapter 3. Differences in the human capital
variables considered explain a larger portion of the differences in log wages for the
Chinese and Mexican samples, although the results for the oldest cohort are based on
very tiny samples for the Chinese and Japanese. The effect of foreign birth is not
calculated for these cohorts, because so few of the Japanese, Chinese, and Mexican men
in these cohorts were born in the United States.
104
1886-1895
Variables
Urban
Married
Agriculture
Years school
Japanese
Chinese
Mexican
Foreign
JYS
CYS
MYS
Yr60
Yr70
JYr60
CYr60
MYr60
JYr70
CYr70
MYr70
Constant
N
2
Adj. R
Coefficient
0.029
0.271
-0.443
0.048
-0.325
0.133
-0.098
N/A
-0.012
-0.054
-0.020
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
3.536
724
0.1302
t stat
(0.41)
(3.95)
(-4.48)
(5.78)
(-0.42)
(0.38)
(-0.46)
(-0.15)
(-1.29)
(-0.56)
(30.77)
1896-1905
Coefficient
0.143
0.226
-0.369
0.050
0.108
-0.083
0.002
0.002
-0.033
-0.012
-0.031
0.206
N/A
0.155
-0.017
0.060
N/A
N/A
N/A
3.722
4,954
0.1653
t stat
(5.95)
(9.50)
(-9.59)
(18.92)
(0.43)
(-0.44)
(0.02)
(0.03)
(-1.59)
(-0.81)
(-3.07)
(10.03)
(0.75)
(-0.09)
(0.65)
(88.54)
1906-1915
Coefficient
0.130
0.302
-0.306
0.054
-0.684
-0.112
0.180
-0.048
0.011
-0.032
0.027
0.328
0.447
0.490
0.119
-0.102
0.448
0.051
-0.031
3.783
13,665
0.2047
t stat
(8.51)
(20.80)
(-11.64)
(34.62)
(-2.25)
(-0.41)
(2.06)
(-1.10)
(0.65)
(-2.71)
(-4.41)
(20.94)
(27.56)
(2.10)
(-1.59)
(-1.33)
(1.92)
(0.19)
(-0.39)
(134.57)
Table 5.7: Results of Wage equation (1)
Note: Dependent varible is log of weekly wages
(CONTINUED)
105
Table 5.7: CONTINUED
1916-1925
Variables
Urban
Married
Agriculture
Years school
Japanese
Chinese
Mexican
Oldimm
newimm
ChineseOI
MexicanOI
ChineseNI
MexicanNI
JYS
CYS
MYS
Yr60
Yr70
Yr80
Yr90
JYr60
CYr60
MYr60
JYr70
CYr70
MYr70
JYr80
CYr80
MYr80
JYr90
CYr90
MYr90
Constant
N
2
Adj. R
Coefficient
0.094
0.263
-0.208
0.060
-0.522
0.124
0.234
-0.017
-0.603
-0.332
-0.064
-0.028
0.359
0.006
-0.009
-0.028
0.447
0.672
0.567
N/A
0.338
0.012
-0.083
0.333
-0.067
-0.133
0.272
0.037
-0.068
N/A
N/A
N/A
4.313
33,490
0.2103
t stat
(8.13)
(27.49)
(-11.29)
(52.29)
(-3.74)
(0.59)
(3.37)
(-0.22)
(-1.73)
(-3.35)
(-0.78)
(-0.08)
(1.01)
(0.79)
(-1.68)
(-9.76)
(28.46)
(42.51)
(36.66)
(2.80)
(0.06)
(-1.21)
(2.69)
(-0.32)
(-1.94)
(2.39)
(0.18)
(-1.04)
(188.86)
1926-1935
Coefficient
0.121
0.259
-0.184
0.064
0.073
0.406
0.478
-0.287
0.124
0.207
0.118
-0.452
-0.384
0.012
-0.004
-0.022
0.649
0.993
1.006
0.950
-0.364
-0.471
-0.283
-0.349
-0.448
-0.364
-0.350
-0.562
-0.410
-0.353
-0.557
-0.397
4.422
50,088
0.2444
t stat
(11.75)
(34.99)
(-11.16)
(60.28)
(0.32)
(3.39)
(5.55)
(-2.72)
(0.92)
(1.49)
(1.06)
(-3.23)
(-2.80)
(2.13)
(-1.03)
(-11.49)
(25.68)
(39.37)
(40.42)
(37.76)
(-1.64)
(-3.54)
(-3.21)
(-1.58)
(-3.73)
(-4.19)
(-1.62)
(-4.72)
(-4.82)
(-1.63)
(-4.64)
(-4.65)
(159.11)
1936-1945
Coefficient
0.108
0.278
-0.203
0.066
-0.084
-0.146
0.461
-0.017
-0.059
-0.034
-0.075
-0.315
-0.258
0.004
0.006
-0.026
N/A
0.710
0.892
0.983
N/A
N/A
N/A
-0.069
-0.197
-0.221
-0.017
-0.030
-0.318
0.040
-0.015
-0.347
4.473
60,196
0.3112
t stat
(11.58)
(46.18)
(-11.91)
(60.98)
(-0.57)
(-0.60)
(9.83)
(-0.36)
(-1.00)
(-0.55)
(-1.52)
(-4.36)
(-4.18)
(0.57)
(1.63)
(-14.29)
(54.77)
(72.32)
(79.16)
(-0.50)
(-0.80)
(-4.68)
(-0.14)
(-0.13)
(-7.29)
(0.31)
(-0.06)
(-7.94)
(246.61)
(CONTINUED)
106
Table 5.7: CONTINUED
1946-1955
Variables
Urban
Married
Agriculture
Years school
Japanese
Chinese
Mexican
Oldimm
newimm
ChineseOI
MexicanOI
ChineseNI
MexicanNI
JYS
CYS
MYS
Yr80
Yr90
JYr80
CYr80
MYr80
JYr90
CYr90
MYr90
Constant
N
2
Adj. R
Coefficient
0.106
0.312
-0.149
0.069
0.111
-0.499
0.516
0.007
-0.102
0.005
-0.033
-0.065
-0.126
-0.013
0.011
-0.032
0.661
0.940
0.057
0.300
-0.171
0.109
0.283
-0.277
4.339
76477.000
0.3127
1956-1965
t stat
(12.41)
(63.37)
(-10.35)
(60.87)
(0.90)
(-5.62)
(15.66)
(0.14)
(-2.36)
(0.09)
(-0.67)
(-1.24)
(-2.82)
(-2.08)
(3.35)
(-19.44)
(69.62)
(97.84)
(0.59)
(3.68)
(-6.15)
(1.14)
(3.50)
-(9.98)
(244.58)
Coefficient
0.045
0.265
-0.112
0.072
0.217
0.240
0.496
-0.009
-0.029
-0.009
0.017
-0.121
-0.165
-0.012
-0.010
-0.042
N/A
0.408
N/A
N/A
N/A
0.011
-0.066
-0.104
4.714
52,793
0.2477
107
t stat
(4.34)
(50.06)
(-7.62)
(42.37)
(2.02)
(3.14)
(18.34)
(-0.15)
(-0.48)
(-0.12)
(0.28)
(-1.77)
(-2.70)
(-1.59)
(-2.27)
(-20.37)
(51.75)
(0.25)
(-1.29)
(-8.41)
(202.34)
1966-1975
Coefficient
0.005
0.279
-0.108
0.064
0.471
-0.246
0.496
-0.167
-0.047
0.413
0.208
0.052
0.035
-0.031
0.026
-0.042
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
4.742
13,861
0.0663
t stat
(0.23)
(23.01)
(-3.70)
(13.44)
(1.61)
(-1.05)
(7.50)
(-0.93)
(-0.28)
(1.55)
(1.12)
(0.27)
(0.20)
(-1.42)
(1.54)
(-7.97)
(75.84)
1886-1895
Japanese
Total Gap
Marriage
Schooling
0.482
-0.022
0.028
Chinese
0.493
0.144
0.206
1896-1905
Mexican
0.629
0.063
0.297
Japanese
Chinese
0.319
0.042
0.109
0.452
0.075
0.245
Mexican
0.448
0.017
0.269
Table 5.8: Differences in log wages of 1886-1905 birth cohorts
due to differences in marriage rates and average years schooling
Table 5.9 shows the calculations for the effect of differences in years of schooling
and of foreign birth on the difference in average log wages for the cohorts born after
1905. The effect on the log wage differential of new immigrants for the Chinese samples
is calculated as:
− 1 * (cˆ2 ,n + cˆ5 ,n ) * NEWIM c ,n
(2)
The effects for the Japanese samples are calculated with no interaction parameter,
and the calculations for the effects of “old immigrants” and for the Mexican samples are
analogous.
The 1906-1915 birth cohorts do not appear in the 1980 IPUMS samples, so there are
no “New Immigrants” for that year. Also, for that cohort the effect of foreign birth is not
estimated by race. The Japanese wage differentials become large wage advantages for
cohorts born after 1935. There are almost no wage differentials due to the immigration
variables for the Japanese. The percentages of immigrants are relatively low for Japanese
cohorts born after 1905 and the negative impact of foreign birth for the Japanese is small.
108
1906-1915
Japanese Chinese
Total Gap
Schooling
Old Imm
New Imm
0.165
0.006
0.010
1916-1925
Mexican Japanese Chinese
0.484
0.171
0.034
0.406 0.164
0.263 -0.005
0.028 0.001
0.002
0.271
0.080
0.207
0.066
1926-1935
Mexican Japanese Chinese
0.399 0.049
0.286 -0.026
0.032 0.008
0.006 -0.002
0.259
0.049
0.006
0.087
Mexican
0.422
0.328
0.009
0.021
Table 5.9: Differences in log wages of post-1905 birth cohorts due to differences in
years of schooling and to foreign birth
(CONTINUED)
Table 5.9: CONTINUED
1956-1965
Japanese
Total Gap
Schooling
Old Imm
New Imm
-0.192
-0.092
0.001
0.003
Chinese
1966-1975
Mexican
-0.190
-0.097
0.002
0.084
0.239
0.232
-0.002
0.097
Japanese
-0.066
-0.046
0.012
0.004
Chinese
-0.127
-0.053
-0.013
-0.003
Mexican
0.079
0.141
-0.001
0.006
The Japanese wage differentials become large wage advantages for cohorts born
after 1935. For the most part, these wage advantages are much larger than those due to
the higher level of Japanese schooling. The predicted wage differentials below control
for experience to see if these remaining wage differentials are explained by differences in
experience. Unlike the samples in Chapter 3, the Californian subsamples are not
balanced by age. There are almost no wage differentials due to the immigration variables
for the Japanese. The percentages of immigrants are relatively low for Japanese cohorts
born after 1905 and the negative impact of foreign birth for the Japanese is small.
Differences in years of schooling explains some of the fairly large Chinese wage
gaps for cohorts born before 1936, but large portions are not attributable to either
109
schooling or foreign birth in these years. For cohorts after 1945, the Chinese calculations
are similar to those of the Japanese, the average log wage differentials are greater than the
fairly large differential due to their higher returns to schooling. The Chinese wages
overall have been negatively impacted by more recent immigration, due to both the high
quantities of this immigration and the negative returns to foreign birth, especially for the
post 1970 immigrants. There is no immigration effect in the youngest birth cohort,
probably because this cohort is all recent labor market entrants for 1990. Since any
immigrants had to be in the country at least 5 years to be included in the sample, the
immigrants observed would have attended U.S. schools and been relatively assimilated.
For the Mexican samples, large wage differentials persist, but most of these
differentials can be attributed to the difference in average years schooling. Recent
immigration has also contributed to the wage gaps.
For cohorts born before 1936, differences in human capital explain very little of the
Japanese wage differential, slightly more of the Chinese wage differential, and most of
the Mexican wage differentials for all birth cohorts. As in Chapter 3, in the analysis that
follows, the results of the wage equations run for each cohort will be used to estimate
wage differentials for each cohort and year, controlling for the human capital variables.
Tables 5.10 and 5.11 give the predicted difference in log wages between Japanese
and native whites, controlling for years schooling and the other human capital variables.
For the cohorts born prior to 1936, schooling is set to 12 years. For cohorts born after
1935, the differences are evaluated using 16 years of schooling. Generally, the wage
gaps within birth cohorts improve greatly between 1950 and 1960 and then again after
1970.
110
Year
1950
1960
1970
1980
1990
1886-95 1896-05 1906-1915 1916-1925 1926-1935
-0.463
-0.287
-0.555
-0.452
0.220
-0.132
-0.064
-0.114
-0.144
-0.107
-0.119
-0.129
-0.052
-0.085
-0.133
Table 5.10: Difference in estimated log weekly wages for Japanese
cohorts born 1886-1935, by census year, evaluated at S=12
Year
1960
1970
1980
1990
1936-45 1946-55 1956-1965 1966-1975
-0.026
-0.094
-0.093
-0.043
-0.036
0.026
0.014
0.016
0.037 -0.02132
Table 5.11: Differences in estimated log weekly wages
for Japanese cohorts born 1936-1975, by census year
evaluated at S=16
The predicted differences for Chinese men versus native whites are shown in Tables
5.12 and 5.13. There is not a clear pattern. There are relatively large gaps for most birth
cohorts present in the 1970 Census. But, the wage differential does disappear for cohorts
born after 1945, except for the 1946-1955 cohort in 1970 when they had recently entered
the labor market.
111
Year
1950
1960
1970
1980
1990
1886-95 1896-05 1906-1915 1916-1925 1926-1935
-0.517
-0.227
-0.495
0.015
0.363
-0.309
-0.376
0.027
-0.109
-0.444
-0.052
-0.085
0.052
-0.200
-0.195
Table 5.12: Difference in estimated log weekly wages for Chinese
cohorts born 1886-1935, by census year, evaluated at S=12
Year
1960
1970
1980
1990
1936-45 1946-55 1956-1965 1966-1975
-0.058
-0.254
-0.322
-0.088
-0.022
0.084
-0.073
-0.039
0.018
0.163
Table 5.13: Differences in estimated log weekly wages
for Chinese cohorts born 1936-1975, by census year
evaluated at S=16
The differences for Mexicans are shown in Table 5.14. For all years, their wage
differentials are evaluated at S=8. Cohorts born after 1925 show a pattern of an initial
fairly large wage advantage and then the Mexican men rapidly lose ground relative to
their white, native born counterparts. The initial relatively high wages for Mexican men
are due to their relatively higher levels of experience in the youngest age cohorts for each
census year, because of their lower schooling levels. Once the native, white men
complete their education and accumulate experience, the wage differential widens within
cohorts because of the relative concentration of Mexican men in careers in which there is
little opportunity for advancement.
112
Year
1950
1960
1970
1980
1990
1886-95 1896-05 1906-1915 1916-1925 1926-1935
-0.261
-0.249
-0.071
0.010
0.303
-0.190
-0.140
-0.073
0.019
-0.069
-0.123
-0.062
-0.058
-0.107
-0.095
Table 5.14: Difference in estimated log weekly wages for Mexican
cohorts born 1886-1935, by census year, evaluated at S=8
(CONTINUED)
Table 5.14: CONTINUED
Year
1960
1970
1980
1990
1936-45 1946-55 1956-1965 1966-1975
0.256
0.035
0.263
-0.062
0.092
0.160
0.000
-0.277
0.056
0.160
The analysis of wage differentials within cohorts for Japanese, Chinese, and Mexican
men shows different patterns for the three groups. The Japanese relative wage improved
across cohorts, moving from issei to nisei, then within cohort as discrimination waned.
The Chinese relative improvements were generally across cohorts, mostly for cohorts
born after 1945 when the process of family formation could take place. The differences
in the predicted relative wages of Mexican Americans fall within cohorts over time,
therefore the relative improvements for the more recent cohorts are not an indication of
improvements across cohorts.
113
5.5. Conclusions
Chinese and Mexican immigration followed a somewhat similar pattern than that of
the Japanese. All three groups settled in the American West and were relative latecomers
versus European whites. The recent labor market outcomes have been very similar for
American-born Chinese and Japanese men, but very different for Chinese immigrants and
Mexican Americans, whether they were born in the United States or Mexico.
The Japanese experience with World War II and Internment was unique. The effects
of the war lingered at least until 1950 as shown in the large wage differentials.
Relative
to Mexican Americans and Chinese Americans, the Japanese American population was
comprised of a much lower percentage of foreign-born men after the 1965 immigration
reforms. Furthermore, the more recent Japanese immigrants have almost no wage
disadvantage versus their American-born counterparts.
The Chinese Americans as a group have been nearly as successful as the Japanese,
but they have experienced more recent large immigration wave comprised partially of
refugees which may account for a lingering, but still small overall wage gap. The
average wages and years of schooling of the foreign-born Chinese men in the sample
have lower means and higher variances, consistent with the educational attainment and
occupational distributions described in Chapter 2. Part of the persisting Chinese-native
white wage gap for the entire sample is attributable to the large influx of new immigrants.
The Mexican wage gap continues and has increased between1980 and 1990 as the
education gap has also widened, explaining most of the within-cohort wage gaps. The
Mexican-white wage gap remained very large as of 1990.
114
CHAPTER 6
CONCLUSION
This dissertation analyzes Japanese American economic performance since 1940. In
the half century following the outbreak of World War II, Japanese Americans have
dramatically improved their status: from “treacherous Jap” to “model minority.”
Simultaneously, their 45% wages gap with their native white counterparts has completely
closed, with much of this progress achieved by 1970. Their labor market quality,
especially their average educational attainment, has increased steadily, but they already
compared favorably with whites as early as 1950. The analysis presented in Chapter 3
shows that the closing of the wage had more to do with an equalizing of the returns to
characteristics rather than a relative improvement of average Japanese labor market
quality.
The Japanese American immigration story is one of the American Dream delayed.
The issei followed the traditional paths to success in the American labor market. They
worked very hard and made large investments in their children’s education. Their efforts
were mostly in vain, and as a final insult many of them spent three of their golden years
incarcerated, because of the actions of the land they had left behind thirty years prior.
The nisei eventually made modest gains, although lingering discrimination and the
long-effects of internment continued to diminish the returns to their large human capital
115
investments. Their children, the sansei finally have been able to participate in the United
States’ labor market on equal footing with whites.
An obvious question is why did the Japanese persevere despite such prolonged and
systematic hardship. The short answer is that endurance or gaman is a very highly
regarded trait in Japanese culture. Sociologists such as Peterson (1971), Kitano (1976),
and Jiobu (1988) have attributed the Japanese success to their Confucian work ethic and
especially their strong sense of group identity. In stark contrast to the American
celebration of the individual, the Japanese are defined by their group association, starting
with their family and extending to their schools, companies, and country. They are
motivated by a desire to avoid bring shame to their group.
To some extent, the Chinese American experience was similar to that of Japanese
Americans, possibly because of similar culture traits. Discrimination and legal barriers
that distorted the demographics of their population and prevented stabilizing family
formation also hampered their economic progress. Once the immigration restrictions
were lifted so that families could reunite, their wages rose along with their average levels
of education. Their persistent relatively small wage gap is due to the continuing influx of
recent immigrants. Overall, the recent labor market outcomes of American-born
Japanese and Chinese men have been remarkably similar.
The labor market outcomes of Mexican Americans differs dramatically from that of
the Japanese. Their rather large and persistent wage gaps are largely due to the persistent
gaps in their educational attainment. An interesting, yet not necessarily feasible
counterfactual is what would their outcome had been if they had made similar human
capital investments as Japanese immigrants.
116
Finally, the decision of a significant portion of the Japanese American population to
make large investments in education prior to 1940 remains perplexing. Certainly,
internment and the associate lost income could not have been anticipated. The hostile
economic client for Japanese Americans was well known to them at the time these
investment were made. Perhaps the nisei in addition to endurance also possessed a great
deal of optimism. This is an interesting issue for future research.
117
DATA APPENDIX
The data used in the analyses presented in Chapters 3, 4, and 5 come from the
Integrated Public Use Microdata Series created from the 1940 through 1990 United
States’ Census. For 1940 through 1970, the IPUMS samples are 1 in 100 samples. For
1980 and 1990, the data come from the available 1 in 20 samples. The Japanese samples
used in Chapter 3 are derived from the exhaustive set of men ages 15 to 64 living in the
United States (excluding Hawaii) whose race is classified as “Japanese” and who were
not currently attending school. As discussed in the Data section of Chapter 3, the white
samples were constructed by selecting at random ten men whose race was classified as
“white” and whose age was within a ten-year interval, who were not in school and who
lived in the same geographic area as each man in the Japanese sample. The white
samples are limited to those individuals born in the United States and those men not
currently enrolled in school. Table A.1 shows the sizes of the constructed samples, the
size of the samples used to estimate wage equations, and the percentages of the samples
excluded from the wage analyzes due to insufficient income or weeks worked. Specific
issues associated with data collection in each census year described below affect some of
these percentages.
118
The analysis in Chapter 4 uses the same sample for 1950 and 1960 and subsamples
of the 1970 and 1980 samples from Chapter 3. The relevant sizes of the 1970 and 1980
subsamples and percentages excluded are shown in Table A.2.
1940
1950
1960
415
618
630
Size of Japanese sample drawn
Size of constructed white sample
4,150
N for Japanese wage equations
196
6,180
107
6,300
561
1970
1980
850
5,057
1990
5,485
8,500
50,570
54,850
766
4,604
4,913
7,660
42,397
46,615
N for white wage equations
2,368
1,306
5,623
% of Japanese sample excluded
52.8%
82.7%
11.0%
9.9%
9.0%
10.4%
% of white sample excluded
42.9%
78.9%
10.7%
9.9%
16.2%
15.0%
Table A.1: Sizes of Japanese and native-white samples drawn and
used in wage equations and percentages excluded from wage equation estimations
1970
752
7,520
697
6,951
Size of Japanese subsamples
Size of native-white subsamples
N for Japanese wage equations
N for white wage equations
1980
2,652
30,970
2,422
25,755
% of Japanese sample excluded
7.3%
8.7%
% of white sample excluded
7.6%
16.8%
Table A.2: Sizes of Japanese and native-white
subsamples and percentages excluded from
wage equation estimations
As explained in Chapter 5, the samples used to compare Japanese Americans’ wages
to those of Chinese and Mexican Americans are restricted to California to simplify the
analysis. The Japanese and Chinese samples drawn are the exhaustive set of Californians
with those specific race classifications. The Mexican samples for 1970, 1980, and 1990
include all Californian men, born in the United States whose race is specified as
“Mexican” and all those born in Mexico. The 1950 and 1960 samples include individuals
who were born in Mexico or who had at least one parent born in Mexico. The native119
born white samples for 1950 and 1960 are all individuals classified as whites in the
IPUMS except those with a parent born in Mexico. The sample for 1970 includes all the
non-Hispanic whites in the IPUMS. The native-born white samples in 1980 and 1990
comprise 40% of non-Hispanic whites in the IPUMS. The sizes of the original samples,
the numbers included in the wage equations, and the percentages excluded are given in
Table A.3.
1950
1960
1970
1980
1990
Size of Japanese sample drawn
319
440
636
3,315
3,590
Size of Chinese sample drawn
262
265
596
3,117
6,925
Size of Mexican sample drawn
Size of white sample drawn
1,260
2,273
4,415
33,626
69,429
28,173
32,172
42,828
84,937
82,698
N for Japanese wage equations
67
418
486
3,030
3,206
N for Chinese wage equations
42
221
409
2,744
5,943
N for Mexican wage equations
N for white wage equations
322
1,851
3,397
26,587
46,923
6,052
28,336
35,659
70,928
69,590
% of Japanese sample excluded
79.0%
5.0%
23.6%
8.6%
10.7%
% of Chinese sample excluded
84.0%
16.6%
31.4%
12.0%
14.2%
% of Mexican sample excluded
74.4%
18.6%
23.1%
20.9%
32.4%
% of white sample excluded
78.5%
11.9%
16.7%
16.5%
15.9%
Table A.3: Sizes of Japanese, Chinese, Mexican, and native-white
Californian samples drawn and used in wage equations and percentages
excluded from wage equation estimation
The remained of this appendix will describe issues specific to each census year and
what effects these issues may have on the percentages of each sample excluded. All of
the information provided in this appendix comes from the IPUMS online documentation
(Ruggles, et. al., 2003).
120
A.1. 1940
Data from the 1940 IPUMS was used only for the analysis in Chapter 3. Selected
questions from the 1940 and 1950 Censuses were asked only of one in five individuals
who happened to fall on the sample line. Parents’ places of birth was asked only of
sample line individuals, making a Mexican sample problematic. Since Hispanic ethnicity
was not enumerated until 1970, a sample constructed of Mexican immigrants and the
children of Mexican immigrants, such as for 1950 and 1960, would significantly over
represent the foreign born. Also, the number of Chinese men in the 1940 Census was
very small, 161.
Unlike the later censuses, the 1940 Census only asked for wage and salary income,
not farm and business income leading to a high percentage excluded from both the
native-white and Japanese samples. Those employed in agriculture make up 44% of the
drawn Japanese sample and account for 53% of those excluded from the wage equations.
For the white sample, these figures are 15% and 23% respectively.
A.2. 1950
The analyses in Chapters 3, 4, and 5 use data from 1950. The samples used to
estimate all the 1950 wage equations are very small because income questions were only
asked of sample-line individuals. Data was collected on salary and wage income as well
as business and farm income so unlike for 1940, the percentage of excluded individuals
in agriculture roughly equals their percentage of the drawn sample.
For the Japanese and native-white samples used in Chapters 3 and 4 and described in
Table A.1, a slightly higher percentage of Japanese men were excluded from the wage
121
equations, consistent with their slightly lower labor force participation (not a sample line
variable) rate, 85.9 versus 86.4 for whites.
For the Californian samples, the relatively high exclusion rate for the Chinese sample
is consistent with their relatively low labor force participation rate of 74%. The average
age of a man in the Chinese sample is at least 3 years older than the other samples. The
relatively lower exclusion rate for the Mexican sample is due to the construction of the
sample. This sample includes all men meeting the basic criteria who were born in
Mexico or who had at least one Mexican-born parent. Parents’ places of birth only were
inquired of sample-line individuals. Therefore, all of the American-born Mexican men
would have been sample-line individuals and would have been asked income questions.
The exclusion rate for the American-born Mexicans is 32.3% and for the foreign-born is
83.4%, a high percentage similar to that for the Chinese sample.
A.3. 1960
The 1960 IPUMS samples do specify geographic location other than state of
residence. Data is available on metropolitan status: not in a metropolitan area, central
city, or in a metropolitan area not in the central city. Therefore, the white data set is
matched to the Japanese sample by state and metropolitan status. There are no specific
issues with this year affected the portion excluded of the samples. The portions excluded
for the national Japanese and native-white samples in Chapters 3 and 4 are very similar at
around 11% (see Table A.1). For the Californian subsamples there are large differences
in the percentage excluded (see Table A.3). The Japanese subsample has a very low
exclusion rate consistent with their very high labor force participation and low
122
unemployment rates. The Mexican sample has the highest exclusion and unemployment
rates. The Chinese exclusion rate is also fairly high due to the relatively higher
percentage of men not in the labor force, probably due to their higher average age.
A.4. 1970
The 1970 IPUMS samples were constructed from 2 separate forms in which
respondents were asked different questions. Also, three different samples were
constructed for 1970 based on different geographical criteria for each form. Different
samples were used for Chapters 3 and 4 and Chapter 5.
In order to get the most specific geographic region possible, the national samples in
Chapters 3 and 4 were drawn from the IPUMS “Metro” samples which were coded for a
multi-county area. The respondents in this IPUMS sample used were asked questions
from Form 2, which provides information on school attendance and place of birth and
parents’ birthplaces, but did not provide information on years since migration. Data on
metropolitan status also is not provided in the Metro samples. The percentages of the
samples excluded from the wage equation analysis were identical for Japanese and
native-whites at around 10%. For the wage equations in Chapter 4 the exclusion rates
were also similar at 7.3% for the Japanese subsample and 7.6% of the native-white
subsample (see Table A.2).
For the analysis in Chapter 5, it was preferable to be able to specifically identify
Mexican men, data available only in the Form 1 samples. Form 1 provides information
on years since migration, but not parent’s places of birth. Because the data did not have
to be balanced by specific geographic location as it was already restricted to California,
123
the State sample was used. Unlike the Metro samples, the State samples are coded for
metropolitan status.
Form 1 also did not include questions about current school attendance. Therefore,
the exclusion rates shown in Table A.3 for the Chinese and Japanese samples used in
Chapter 5 are fairly high. A large percentage of men under the age of 25 in these
samples were still in school and did not work enough weeks per year to meet the criteria
for the wage equations.
A.5. 1980
The 1980 samples do not need extensive explanation. The portion excluded from the
native-white sample shown in Table A.1 is higher than the prior two decades, consistent
with the decline in white men’s labor force participation since 1980. The gap between
the portions of the Japanese and white samples excluded widens for the truncated, older
subsamples as shown in Table A.2 because retirees comprise a slightly larger proportion
of the white subsample, but the older Japanese subsample has higher participation rates.
For the Californian subsamples, the Mexican exclusion rate is the highest because of
higher unemployment. The Japanese is the lowest, and the Chinese exclusion rate falls
between the Japanese and the native-white samples.
A.6. 1990
The trends that apply to the 1980 sample hold for the 1990s samples as well. The
only notable figure is in Table A.3 in which the Mexican sample’s exclusion rate is very
high, over 34%. This is due to not only their high unemployment, but also the high
concentration of recent Mexican immigrants in seasonal work such as farming and
124
construction. The widening wage gap for Mexicans would be even larger if these
workers were included.
125
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