AN ECONOMETRIC ANALYSIS OF THE INCOME DISPARITY
BETWEEN EAST ASIAN AND SOUTH AMERICAN IMMIGRANT
GROUPS IN THE UNITED STATES
Deepak Shrivastava
SUBMITTED TO PROF. BARRETO AND PROF. HOWLAND
IN PARTIAL COMPLETION FOR THE REQUIREMENTS FOR ECO 31
APRIL 17, 2000
ABSTRACT
This paper deals with the income disparity between East Asian immigrants and South
American immigrants in the United States. Using data from the March 1999 Current Population
Survey, it is shown that East Asian immigrants on the average have a higher personal income
than that of South American immigrants. However, this income differential is not due to a
difference in the degree of American society’s susceptibility to one immigrant group over
another. Though immigrants may be discriminated against in the job market when competing
against natives, there is no evidence of any type of favoritism or discrimination toward one
immigrant group over another. The notion of the Asian “model minority” is shown to not have
any effect on personal income of East Asian immigrants. Furthermore, reason for the income
differential is given by exploring the disparity in educational attainment between East Asian and
South American immigrant groups.
2
Table of Contents
I.
Introduction
4
II.
Literature Review
6
III.
Theoretical Analysis
8
IV.
Empirical Results
10
A. Data
10
B. Presentation and Interpretation of Empirical Analyses 13
V.
Conclusions
26
Works Cited
28
3
I.
Introduction
Numerous studies have been conducted to measure the economic performance of various
immigrant groups to the United States. Economic performance is usually measured by analyzing
the total personal income of an immigrant. Several factors influence this level of personal
income such as the level of educational attainment, whether or not the immigrant has received
citizenship, or the amount of years spent in the United States, among others. Many studies show
that the earnings of immigrants are significantly lower than that of natives. Though this income
inequality between natives and immigrants can be understandable, due to reasons such as
discrimination, racism, and overall resentment of foreigners “stealing” jobs from the Americanborn population, it is interesting to compare the economic performance between two different
immigrant groups from two different vast regions of the world. The question as to whether there
is any income disparity between East Asian immigrants and South American immigrants and if
so, why this inequality exists, is the main focus of this paper.
The reason for the supposed inequality can be due to the degree of American society’s
susceptibility to one immigrant group over another. Are East Asian immigrants groups more
“welcome” to the United States, so to say, over South American immigrants? If so, many factors
can play into this degree of susceptibility. Stereotypes rising in American society alleging East
Asians to be keen in mathematics, or having a stronger work ethic, can very well allow East
Asian immigrants to have an edge over other immigrant groups in the US job market. I will
attempt to distinguish whether such discrimination exists.
Certainly other factors, besides discrimination, contribute to the income disparity.
Education of the immigrant groups plays a large role in determining the average personal
income. Years spent in the Unites States, citizenship status, age, marriage and sex will all be
4
other be variables controlled for in order to determine whether or not East Asians are more
“welcomed” by American society over South American immigrants, as measured by their
economic success.
5
II. Literature Review
Immigrant income has been the subject of writing for many economists. Moreover, most
of the papers I came across were comparing immigrant income to native income. It was difficult
to find many studies that solely focused on comparing immigrant groups to each other without
regarding the native population. However, the methodology in which these papers conducted
their empirical analyses aided me greatly in conducting my own analysis.
Since my paper focuses on East Asians immigrants and their economics performance
versus South American immigrants, I found it very helpful to read “Education, Occupational
Prestige, and Income of Asian Americans” by Herbert Barringer, David Takeuchi, and Peter
Xenos. Going by the notion of Asian Americans being labeled as the “model minority”, they
attribute the success of Asian Americans (measured by income) over other ethnic groups to the
high levels of education in the Asian American community (which includes immigrants). If in
fact, Asians are looked upon as the “model minority”, this stereotype should be in the favor of
East Asian immigrants. US employers could favor East Asians as opposed to other immigrant
groups. This paper also helped me to incorporate the “Assimilation Factor” in order to account
for the time spent in the United States- the longer an immigrant stays in the US, the higher
his/her income.
“Minority Concentration and Earnings Inequality: Blacks, Hispanics, and Asians
Compared” by Marta Tienda and Ding-Tzann Lii focused on the ethnic groups, but not
immigrants. This paper did however, compared the incomes of the groups, and concluded that
the inequality was due to the varying educational levels between the groups. This provided me
with a good idea as to importance of education playing a major role in the income disparity
between the immigrant groups in my study.
6
From papers that concentrated on earnings in general (not necessarily those of
immigrants), I attained the notion of using Age and Age^2 to take into account as some of the
variables in my regression analysis. Age has a directly proportional effect on personal income.
However, after higher ages, the effect is diminished and has decreasing effect on personal
income (thus the Age^2 component).
Along the same logic, I assumed that Years in US^2 would be a necessary variable in my
analysis. The effect of remaining in the US when one has stayed for many years decreases. For
example, there might be a difference between an immigrant who as been in the US for 2 years,
and an immigrant who has been in the US for 8 years. However, there is not much difference (in
their effect on personal income) of an immigrant who has been here for 32 years as opposed to
38 years. The effect of Years in the US decreases has one spends more and more time in the US.
My paper will concentrate on the income differential between East Asian immigrants and
South American immigrants. If the claims above are true, there should be a real statistical
difference in not only the incomes, but also the educational levels between the two groups.
Furthermore, the “model minority” notion will be analyzed to see whether this special status
exists, benefiting the East Asian immigrant community, and partially accounting for the income
differential.
7
III.
Theoretical Analysis
In measuring the economic performance of an immigrant group, numerous variables
come into consideration. Economic performance, demonstrated by the average personal income
of an immigrant group, depends on several factors. The ability of an immigrant to attain a job
relies on the amount of education attained, years in the United States, citizenship status, sex,
marital status, age, and if their residency is in a metropolitan area.
After reading several papers on immigrant economic performance and earnings (mostly
comparing those to natives’ earnings), I came up with the above dynamics that would be factored
into my regression equation as independent variables. When dealing with the predicting of
income, age, marital status and being male all are directly proportional to personal income.
When further analyzing immigrant income, citizenship status, years in the US, and urban
residency are considered to be main contributing factors (all directly proportional) to personal
income.
My main focus of this paper is to observe the disparity in income among two different
immigrant groups. Thus, I included a dummy variable East Asian. Those with a 1 for the
variable were immigrants from China, Japan, S. /N. Korea, Taiwan, and Hong Kong. Those with
0 for the variable were immigrants from Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador,
Guyana, Peru, Uruguay, and Venezuela.
Aside from the East Asian variable, education will have a significant impact on personal
income. It is possible that the average amount of education attained by the two immigrants is a
real statistical difference, and not due to chance. This could play an important role as to why an
income disparity between the two immigrant groups exists.
8
In order to determine whether being East Asian or South American effects the personal
income of immigrants, it is vital to perform a regression analysis with the general formula given
below:
Y = 0 +  1 * X1 +  2 * X2 +  3 * X3 + … … … … +  n * Xn + 
where Y is the dependent variable, in this case personal income, and Xn signifies the independent
variables. The Betas are the true values of the coefficients to the independent values, while the
epsilon represents the error term (taking into account omitted variables, measurement error, and
the chance process from my sample).
In order to model the error term to some precision, I utilized the Standard Econometric
Gaussian Error Box Model. Certain assumptions concerning the box need to be stated:
1 – the average of the box is zero
2 – the error terms are identically distributed
3 – the errors are independent of each other
4 – the errors are not correlated with any of the independent variables
Providing these hold true, and that I use an Ordinary Least Squares method for my regression
analysis, my regression analysis will be the best linear unbiased estimator. Using JMP to
perform all the regression analysis and applying other statistical tools provided by JMP will
further aid me in analyzing the income disparity between East Asian and South American
immigrants to the United States.
9
IV.
Empirical Results
A. The Data
All data attained for my empirical results are from the March 1999 Supplement of the
Current Population Survey (CPS). The CPS is a national survey administered monthly to 50,000
households across the United States. Conducted by the Bureau of the Census in conjunction with
the Bureau of Labor Statistics, this survey has been carried out for the past 50 years. The sample
is scientifically selected to wholly represent the Unites States civilian non-institutional
population and is regarded as the primary source of labor force characteristics. Respondents are
interviewed and data concerning the employment status of each member of the household over
15 years of age is obtained. It is important to note that this is the best data available, which is
closest to a simple, random, survey.
The dependent variable in my analysis will be Personal Income. This is comprised of a
person’s total income for the previous year. This excludes taxes, or any other deductions for the
year 1998. Note, however, that my whole sample is only comprised of East Asian and South
American immigrants to the United States. In my original sample there was a significant amount
of zeroes (0) of personal income. Because of the fact that many immigrants might have very
recently immigrated, the possibility lies that they have not had sufficient time to attain a job, but
still might be qualified for one and most likely get one within a short period of time. As a result,
out of my original sample of 1791 observations, I discarded 263 of whom had “0” as he/she’s
personal income.
The independent variables included in my analysis are East Asian, Age, United States
Citizen, Education, Ever Married, Female, Years in the United States, and Immigrated to
Urban Area.
10
A table describing all variables is given below:
VARIABLE
DESCRIPTION
Personal Total Income
This is the dependent variable, showing the dollar
amount of an observation’s total income for the year
1998, before taxes or any other deductions.
A dummy variable signifying if one is an East Asian
immigrant, or a South American immigrant.
East Asian
Age
United States Citizen
1 = immigrated from China, Japan, South/North Korea,
Taiwan, or Hong Kong
0 = immigrated from Argentina, Bolivia, Brazil, Chile,
Colombia, Ecuador, Guyana, Peru, Uruguay, or
Venezuela
Age at the time of when the survey was taken (1999)
Ages range from 15 to 90.
Another dummy variable, signifying whether the
immigrant is a naturalized citizen, or still retains
citizenship from the country of origin.
1 = Naturalized foreign-born citizen
0 = Not a citizen
Education
Re-coded from the original CPS codes (see JMP file),
years of education attainment.
Another dummy variable representing whether or not
one has been married before.
Ever Married
1= has been married before, includes those who are
separated, divorced, etc.
0 = never been married
11
Another dummy variable representing sex.
Female
1 = female
0 = male
Years in the United
States
Immigrated to Urban
Area
Years spent in the United States since immigration.
Another dummy variable. Represents whether a
respondent immigrated to an urban area.
1 = urban
0 = other
Description of Variables
The summary statistics of all the variables obtained from the MS Excel spreadsheet are shown
below:
Variable
Mean
SD
Min
Max
AGE
42.8560209
15.0584177
15
90
EDUCATION
13.4751309
3.51077068
1
21
YRS IN US
17.8334424
11.114112
3
50
CITIZEN
0.43
0.50
0
1
MARRIED
0.79
0.41
0
1
FEMALE
0.52
0.50
0
1
EAST ASIAN
0.47
0.50
0
1
URBAN
0.96
0.19
0
1
PERS
INCOME
$
27,222.49
$ 36,584.26
1
434730
12
n
1528
1528
1528
1528
1528
1528
1528
1528
1528
B. Presentation and Interpretation of Empirical Analyses
In order to answer the question as to whether there exists a difference between the incomes
of East Asian and South American Immigrants, we cannot rely on our single Guassian error box
model. We must extend our notions of the single box model to a two-box model. Instead of
testing a hypothesis about a single population, we are more interested in a comparison of two
populations.
In this case, the two populations will be East Asians, with the dummy variable East Asian
equal to one (1), and South Americans, with the dummy variable East Asian equal to zero (0).
Below is pictured the statistics on Personal Income broken down by East Asian and South
American:
East Asian
0
1
Personal Income
Total
Average
$
25,518.26
StdDev
$
34,361.83
StdError
$
879.05
Max
$ 434,730.00
Min
$
1.00
Average
$
29,145.08
StdDev
$
38,874.11
StdError
$
994.49
Max
$ 371,156.00
Min
$
1.00
Total Average of PERS INCOME
$
27,222.49
Total StdDev of PERS INCOME
$
36,584.26
13
Total Max of PERS INCOME
$ 434,730.00
Total Min of PERS INCOME
$
1.00
The average personal income for East Asian immigrants from my sample was $ 29,145.08
give or take $ 994.49. The average personal income for South American immigrants from my
sample was $ 25,518.26 give or take $ 879.05. Though the means of personal income differ
by approximately $3600 between East Asian and South American immigrant groups in my
sample, this is not conclusive evidence to state that this income disparity exists for the whole
population. With the large standard deviations (SD’s) and the possibility of chance influencing
my sample results, I cannot claim that East Asian immigrants, on the average, have a higher
personal income than South American immigrants do.
Here is where the two-box model is utilized. It is first necessary to set-up a hypothesis test
with both a null and an alternative:
Do East Asian immigrants to the US earn more than do South American immigrants to
the US?
Null Hypothesis: The average personal income of East Asian immigrants is equal to the
average personal income of South American immigrants.
Alternative Hypothesis: The average personal income of East Asian immigrants is higher
than that of South American immigrants.
In order to test such hypothesis, a Standard Error (SE) of the Difference is needed to obtain a zstatistic. A p-value is then obtained to determine whether the difference is real or not.
14
By bootstrapping the sample SD for both our populations we can calculate the SE of each
population’s sample average income. The formula for the SE of the sample difference is a
function of the SE’s of both the populations’ sample average, shown below as:
SE of the sample difference =
2
2
SEEastAsian

SE
1
EastAsian 0
The SE of the sample difference attained was 1327.30. Next, we need to calculate the
z-statistic using the following formula:
z=
ObservedDifference  Hypothesiz edDifference
SEofTheDifference
With our observed average income differential in our sample being $3626.82 and the SE of the
difference being 1327.30, the z-statistic obtained is 2.7324. Using a normal distribution the
probability value of attaining such results is 0.345%. This means that, assuming that there is no
income differential between East Asian and South American immigrants, the probability of us
obtaining (from our sample) a mean income differential of $3626.82, is 0.345%. This p-value is
extremely low, allowing us to reject the Null Hypothesis.
Asserting that our Null Hypothesis is not true, we then safely claim the Alternative
Hypothesis to be true. The difference between average personal income of East Asian
immigrants to the United States as compared to the average personal income of South American
immigrants to the United States is real. East Asian immigrants to the United States have a
higher average personal income than South American immigrants to the United States do.
15
The second part of my analysis explores whether being an East Asian immigrant or South
American immigrant influences one’s personal income. We have already seen above that there
exists a real difference in the average personal incomes of East Asian and South American
immigrants. However, we need to control for confounding variables in order to determine
whether being East Asian or South American has a significant impact on the amount of earnings
an immigrant makes in the United States.
The general form of my regression equation to analyze the effect my independent
variables have on my dependent variable of personal income is given below:
Personal Income =  0 +  1 * Age +  2 * Citizen +  3 * EastAsian +  4 * Education +
 5 * EverMarr +  6 * Female +  7 * YearsInUS +  8 * YearsInUS2 +  9 * Urban +
 10 * Age2 +  11 * (Female * EverMarr) + 
Providing that:
 i = Coefficients of the parameters of the independent variables
 = The error term, taking into account omitted variables, measurement error, and the chance
process
It is again necessary to state that the Standard Econometric Gaussian Box Model is being
applied. In order for the model to be valid, I am working under certain assumptions. The
following are needed to be true in my model in order for the Gaussian Error Box Model to hold
and for my results to be feasible:
1 – the average of the box is zero
16
2 – the error terms are identically distributed
3 – the errors are independent of each other
4 – the errors are not correlated with any of the independent variables
Providing that the above assumptions are all true, the regression I will run will be the Ordinary
Least Squares (OLS) estimator. With the Gaussian Error Box model, the OLS estimator is the
Best Linear Unbiased Estimator (BLUE). This means that the estimator will have its sampling
distribution centered on the true value (In accordance with the Gauss-Markov Theorem,
OLS will also have the smallest Standard Error when compared to all other linear unbiased
estimators.
However, because I have attained all of my data from the March 1999 Current Population
Survey, I have theoretical reasons to believe that I will encounter heteroscedasticity in my crosssectional data. Heteroscedasticity is present when the error terms for the independent variable
are not identically distributed, a violation of the 2nd assumption of the Gaussian Error Box Model
(as explained above). If indeed, there is heteroscedasticity present, the plot of the residuals
versus the independent variable should have a definite shape (a wedge, football, etc.), as the
expected formless cloud will not be present (as in the case of homoscedasticity).
When looking at Personal Income as a function of Education, and then plotting the
residual, we have the following graph:
17
Eyeballing the plot, we see that the residuals (which are good estimations of the errors) seem to
be unevenly distributed. As the amount of education increases, the spread of errors increases.
Thus we have reason to believe that there is heteroscedasticity present with our
independent variable Education. In order to accurately detect heteroscedasticity (as opposed to
simply eyeballing), we need to refer to the Goldfeld-Quandt test. This test-statistic will allow us
to determine how likely it is that the difference in the size of the residuals of the two groups (in
this case, immigrants with “high” educational attainment and immigrants with “low” educational
attainment) is due to chance. In my sample, I organized the lower third of the sample (sorted by
educational attainment) as the low-dispersion group. The high-dispersion group was the upper
third of my sample with higher educational attainment. Applying my regression model to both
groups, we can obtain the Goldfeld-Quandt test statistic with the following formula:
G-Q test Statistic =
RSS 2 (n 2  k )
RSS 1 (n1  k )
18
Where:
RSS = Residual Sum of Squares from Group 1 (the low-dispersion group) and Group 2 (the
high-dispersion group).
n1 = the number of observations in the supposedly low dispersion group
n 2 = the number of observations in the supposedly high-dispersion group
Under the Null hypothesis, that there is no heteroscedasticity present, the G-Q test statistic
should be a little more than 1, under an F-distribution. Regarding Education as the independent
variable in question, the GQ statistic I obtained was 8.1479 with a p-value virtually 0 (obtained
from JMP). Hence we can reject the null that there is no heteroscedasticity present, and proceed
to take steps to correct the unevenly distributed error terms.
Since there is heteroscedasticity present, any estimation of the Beta-coefficients obtained
from running the OLS regression is not feasible. The estimations themselves will be unbiased,
but the standard errors will be imprecise. In order for us to revert back to the Gaussian Error
Box Model, we need a weighing term to multiply our whole regression model to account for the
unevenly distributed errors. Using the trial and error method, I finally came up with a weighing
term of:
1
0.6
Education
, used as the weighing variable when running the regression in JMP.
The GQ-statistic obtained after using the weight was 1.0798 with a p-value of 0.1959. We
cannot reject the null hypothesis here because of our favorable, relatively large (over 5%) pvalue. Hence, we can conclude that we have transformed the regression equation successfully to
take into account the heteroscedasticity present in education.
19
Again, it is important to note that because of the nature of cross-sectional data,
heteroscedasticity will violate the assumptions of the Gaussian Error Box Model. Education
was not the only variable in which heteroscedasticity was present.
By eyeballing, the residual plot of Age seems to be unevenly distributed:
Looking at the residual plot of Years in the United States, it seems as though heteroscedasticity
might be present here as well:
20
I applied the same technique to Age and Years in the United States in calculating the GQ-test
statistic in order to determine if indeed heteroscedasticity is present. After testing positive to
heteroscedasticity, I then used the trial and error method to obtain a weight which would give
GQ-test statistics close to one (1) and p-values over 5%. The table below displays the GQ –test
statistic and p-values (after and before the weight was applied to the whole regression) and the
actual weight utilized:
Variable
GQ
p-value
Weight
(before weighted)
(before weighted)
Education
8.1479
<.0001
Age
3.674
<.0001
GQ
(after
weighted)
1
0 .6
edu
1.0798
1
0.5
age
1.09
p-value
(after weighted)
.1959
.1557
1
Yrs in US
3.69
<.0001
0.75
yrsInUs
1.0381
.3385
It is important to note that I obtained GQ-test statistic and p-value for Age after I had accounted
for the heteroscedasticity in Education. I weighted my whole regression equation for education;
only then did I proceed to transform that weighted equation to account for the heteroscedasticity
in Age. The same was done for Yrs in US. I found the GQ-test statistic and the weight for Yrs
in US after I had transformed the regression equation to account for Age (and Education).
21
Also, I used JMP to analyze my transformed equations. JMP thinks of the weights not in
terms of SD’s, but rather in terms of variance (SD^2). Thus the “true weight” which the whole
regression equation is multiplied by is the square root of the weighing terms I have tabled above.
Since, I have now accounted for heteroscedasticity, using the appropriate weighing
variables, I must note that I am no longer using OLS as my estimator. Rather, I am using
Generalized Least Squares (GLS) as my estimator, with my Gaussian Box model back in effect
since the 2nd assumption is no longer violated (the errors are not unevenly distributed anymore).
Now it possible to proceed to model my whole regression equation with the proper
weights to allow the Gaussian Error Box Model to hold true. Again, my regression equation is
given below:
Personal Income =  0 +  1 * Age +  2 * Citizen +  3 * EastAsian +  4 * Education +
 5 * EverMarr +  6 * Female +  7 * YearsInUS +  8 * YearsInUS2 +  9 * Urban +
 10 * Age2 +  11 * (Female * EverMarr) + 
Providing that:
 i = Coefficients of the parameters of the independent variables
 = The error term, taking into account omitted variables, measurement error, and the chance
process
Running the regression in JMP, gives us the following estimates to the coefficient of the
independent variables:
22
PERSONAL INCOME
Independent
Coefficient
Variable
estimate
SE
t-ratio
p-value
Intercept
-34793.4
4362.077
-7.98
<.0001
Age
1290.95
185.28
6.97
<.0001
US Citizen
1948.22
1446.82
1.35
.1783
East Asian
1200.82
1006.00
1.19
.2382
Education
1327.81
100.27
13.24
<.0001
Ever Married
9314.12
1553.99
5.99
<.0001
Female
-1402.09
1492.41
-.94
.3476
Years in US
742.33
199.30
3.72
.0002
Years in US^2
-12.73
6.23
-2.04
.0410
Urban
3214.45
2516.70
1.28
.2017
Age^2
-13.37
2.19
-6.11
<.0001
-9874.18
1944.99
-5.08
<.0001
Female*Ever Marr
To validate the above regression, I first obtained the f-statistic. For the whole model test,
my f-statistic was –41.63 with a p-value of virtually 0. Thus, we can reject the null that the
coefficients of my independent variables are all equal to 0.
From the above b-values, the estimates of the true Beta coefficients, I am focusing on the
dummy variable East Asian. The b obtained is 1200.82. This means that ceteris paribus, if an
immigrants is East Asian, he/she has earns $1200.82 give or take $1006.00 more than a South
23
American immigrant. However, we are not sure if this is significant until we perform a t-test,
complete with a null and an alternative hypothesis:
Null Hypothesis:  3=0; providing all other independent variables remain constant,
being East Asian does not effect Personal Income
Alternative Hypothesis:  3 does not equal 0; there is a statistical significance in how
being East Asian effects Personal Income
The t-statistic reported by JMP is 1.19 with a p-value of 0.2382. This means that if the
null hypothesis were true, the probability of getting such a result is 23.82%. This is by no
means enough to reject the null. Therefore, being East Asian does not effect the personal
income of the two immigrant groups in my study.
Though the race of the immigrant (Hispanic versus Asian) does not seem to effect
the economic performance as measured by personal income, there must be some other
statistically significant variable to account for the real difference in average personal
income between the two immigrant groups. The independent variable Education seemed
to be the logical explanation to how there might be such a statistically significant
difference in average income. Looking at the distribution of educational attainment
(years of education completed on the y-axis) for South Americans (East Asian =0):
10
20
24
Looking at the distribution of educational attainment of East Asians (East Asian=1):
10
East Asian
0
1
20
EDUCATION
Total
Average
12.9617
StdDev
3.1841
StdError
0.1119
Average
14.053
StdDev
3.7652
StdError
0.1405
To check if the variation in the average education attained between East Asian immigrants and
South American immigrants is statistically significant, I applied our two-box model again to see
if the difference is real. When comparing the two populations, I attained an SE of the difference
of 0.1796. The z-statistic comes out to be –6.075. The p-value for the z is virtually 0. This is
enough to reject the null claiming that there is no difference between the average educational
attainment between East Asian immigrants and South American immigrants. Thus, it is safe to
assert that there exists a statistically significant difference in the level of educational attainment
between East Asian immigrants and South American immigrants.
25
V.
Conclusions
There is a definite income inequality between East Asian immigrants and South
American immigrants to the United States. As shown by the comparison of the two populations
using the two-box model, the difference of approximately $3600 was real and statistically
significant. As to reasons why this disparity exists, this idea was further explored with the notion
of possible discrimination against Hispanics immigrating from South America or possible
favoritism beneficial to East Asian immigrants (the “model minority” status).
The dummy variable in question, East Asian, does not seem to have any statistically
significant effect on the personal income of an immigrant. Though the estimated coefficient for
the Beta value of East Asian came out to be positive as expected, there was not enough evidence
to reject the null that claims that there being East Asian effects personal income. This suggests
that it is not necessarily favorable to be in one immigrant group over another when looking at
race/region of origin alone. East Asians do on the average, have a higher personal income than
do South American immigrants, but there is no evidence of any favoritism or discrimination
(when the immigrant are compared to each other – this does not mean that there is no
discrimination present when compared to natives).
However, I further analyzed other possible avenues as to how the income differential
might exist between the two immigrant groups. Looking at the distribution of educational
attainment of both immigrant groups and comparing the two populations to see if there is real
difference was my next step. I came to the conclusion that there does in fact exists a statistically
significant difference in the education attained between the two immigrant groups. East Asians
immigrants on the average have 1.09 more years of education give or take 0.18 years. With the
b-value estimate obtained for our Education variable positive, a one-year increase in the amount
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of education attained increases personal income by about $1327.81 give or take $100.27. The
income inequality between East Asian immigrants and South American immigrants seems to be
caused by a disparity of educational attainment, and not of any presence of discrimination of
favoritism of any one immigrant group over another.
Further study might be interesting in evaluating the Education variable even more.
Many of the respondents of the survey were educated abroad in their country of origin. The
weight a degree holds from Japan, might differ from the weight a degree from Uruguay holds. It
is possible that this could be one of the determining factors of personal income, especially if US
employers look upon Japanese college degree with more respect than a degree from Uruguay. If
this were true, the income disparity due to educational attainment might be even more than what
my results have shown. It would be interesting to analyze the significance this difference has on
personal income.
27
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2. Borjas, George J., “The Economics of Immigration”, Journal of Economic Literature,
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3. Borjas, George J., “Self-Selection and the Earnings of Immigrants”, The American Economic
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4. Daneshvary, Nasser, Herzog, Henry W., Hofler, Richard A., Schlottmann, Alan M., “Job
Search and Immigrant Assimilation: An Earnings Frontier Approach”, The Review Of
Economics and Statistics”, Volume 74, No. 3, August 1992, pp.482-492.
5. Greenwood, Michael J., McDowell, John M., “Differential Economic Opportunity,
Transferability of Skills, and Immigration to the United States and Canada”, The Review of
Economics and Statistics, Volume 73, No. 4, November 1991, pp.612-623.
6. Tienda, Maria, Lii, Ding-Tzann, “Minority Concentration and Earnings Inequality: Blacks,
Hispanics, and Asians Compared”, American Journal of Sociology, Volume 93, No. 1, July
1987, pp.141-165.
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