1. Vincent Shields
Final Paper
Econ 42
Chiswick and Americanization
Quick Summary
This study attempts to capture the “Americanization effect” that Barry Chiswick
so famously showed, with expansions to ethnicity and other variables. Overall, Black
migrant workers take the longest time for their earnings to match those of the native
workers, and other/mixed race workers are very close behind. Interestingly, it appears
that Asian migrant workers’ earnings rise more quickly than White migrant workers.
Also, the effect of having a toddler was examined. The effect was stronger for native
born workers, because native born workers with at least one child under five should
expect to earn more that than native born workers without at least one child under five by
about five percent, whereas foreign born workers with at least one child under five should
expect to earn about five percent less than native born workers with no children under
five
Introduction:
In the 1970’s, economist Barry Chiswick showed that, “Upon arrival [to the United
States], immigrants earn on the average substantially less than the native born with
similar characteristics. As earnings rise more sharply with post-immigration experience,
the earnings gap narrows. Other things the same, 5 years after immigration foreign-born
white men have weekly earnings 10 percent lower than the native born, but earnings are
approximately equal after 13 years and are 6 percent higher after 20 years” (Chiswick).
Chiswick used multiple regression analysis on subsamples of foreign and native born
men, while controlling for many variable such as region of residence, marriage status, but
most importantly: years since migration. The data used in this study is from the American
Community Survey, which surveys about one percent of the U.S. population annually and
asks questions about several demographic variables. Chiswick, on the other hand, uses
data from the United Census Bureau from 1970. This sample includes females and all
races rather than just white males. Because Chiswick already measured the effect of
years since migration on an immigrants earnings, I would like to measure this same

2. effect, as well as how this effect might differ if the immigrant has at least one child under
five.
Hypothysis(e)
Since this analysis uses data from 2013 rather than 1970, one could expect that due to the
rise of the internet and the exponential increase in globalization, the return to years in the
united states will be steeper in this data set. Immigrants in 2013 likely have more
opportunities for online training, which could speed up the “assimilation” process. Also,
foreign born white males with at least one child under five will assimilate more quickly
than foreign born white males without at least one child under five. It may be interesting
to expand this analysis to questions of ethnicity, because different ethnicities have
different cultural practices that translate into different attitudes towards children. It is
impossible to capture the affect of culture in regression analysis, but it may be interesting
to speculate and see if some cultural stereotypes line up well with the data. Asians are
thought to have a stricter attitude towards their children, emphasizing discipline, hard
work, and respect to their elders. In contrast, many say that Hispanics’ main reason for
migration to the U.S. is that they cannot support their many young children with wages
from their country of origin. However, it is worth noting here that the in ACS survey,
race and Hispanic origin are different questions, so someone of Hispanic origin can be of
any race. Anyways, Hispanics with at least one child under five may have higher
incentive to earn than Asian migrants with at least one child under five, thus causing
Hispanic migrants to assimilate more quickly than Asian migrants
Methods/Variables
This paper contains one table. Table 1 starts with the base specification model in the first
column, displaying the binary variable forborn equal to unity if the worker is foreign born
and zero if native born. The second column adds ethnicity variables (equal to 1 if Asian,
Black, and other/mixed, and zero if “White”) and gender, equal to one if the person is
female and zero if the person is male, denoted by the “female” variable. Regression 3
adds the variables that Chiswick used to measure the length of the assimilation process.
“Exper” was created by subtracting the person’s age by their years of schooling and then
subtracting that by five. “Educ_Years” is that person’s highest year of education, and the
variable “forsym” is the product of the variable “forborn” and the “ysm”, which

3. represents the amount of years since their migration. Regression 4 and 5 attempt to
measure the “toddler effect”. “toddler” is a binary variable equal to 1 if the person has at
least one child under five, otherwise it is equal to zero. The not married spouse present
variable (nmspp) was included as a control because a single parent requires a lot more
effort than a parent who is able to share his or her duties with their respective spouse. I
also checked these two variables for issues of imperfect multicolinearity (because some
migrants leave their country of origin to send remittances back to their family and that
may cause them to mark not married spouse present in the survey when in reality their
spouse is in their home country) by regressing the toddler variable against the nmspp
variable and observing the R^2 figure, which was 0.035. The details can be found in the
detailed method/code section at the end of this essay. The last regression includes an
interaction term between forborn and toddler, attempting to allow the effect of being
foreign born to depend on the toddler variable. The dependent variable in Table 1 is the
natural log of the workers’ Wage and salary income earned over the previous year, in $.
Observations
Without controlling for any other variables, foreign born persons earn 9% less that native
born persons.
You can see by the boxplot above that native born earnings do not appear that much
higher than foreign born earnings without controlling for other variables. However, when

4. you control for ethnicity and gender, the effect of being foreign born appears less
negative, increasing to about 0.0866(as noted by regression 2). Moreover the coefficient
on forborn went from being statistically significant at the 1 percent level to not being
significant at any conventional significance levels, while all the other coefficients in
regression 2 are significant. Next, when we add the Chiswick variables, all the other
coefficients on the ethnicity variables appear less negative. This implies that Ethnicity
does not have as strong of an effect as long as that person has work experience and
education. The coefficients on the race variables in regression two where likely picking
up on their negative correlations with education and experience, because experience and
education appear positively correlated with the dependent variable lnearn. When we
control for experience and education and years since migration in the United States, the
coefficient on forborn becomes statistically significant at the one percent significant
level, but decreases to -0.3146(as noted by regression 3). The coefficient on the Asian
variable is not statistically significant at any conventional significance levels while all the
other coefficients in regression 3 are statistically significant at the one percent level.
Regression 4 adds the toddler variable that I created, along with a control for marital
status. Adding the aforementioned variables does not change the coefficient on forborn.
However, when adding the toddler variable, the coefficient on Asian increases from
0.0387 to 0.0472 and becomes significant at the 10 percent significance level. Likewise,
the coefficient on Black increases from -0.2358 to -0.1801, and the coefficient on
Hispanic increases from -0.0828 to -0.0593. The Other/mixed variable also increases
from -0.1999 to -0.1725. Foreign born individuals with at least one child under five, all
else equal, should expect to earn 0.017 percent less than foreign born individuals with no
children under five(found by the difference between the coefficient on toddler and the
coefficient on forborn:toddler in regression 5). Additionally, someone who is native born
and has a toddler should expect to earn 5.4 percent more than a foreign born person
without a toddler, which is represented by the toddler coefficient in regression 5. The
coefficient on the toddler variables is statistically significant at the one, five, and ten
percent significance levels. Moreover, a foreign born worker with at least one child under
five should expect to earn 0.055 less than a native born worker without at least one child
under five. After fifteen years in the United States, all else equal, foreign born white men

5. earn about 0.6 percent less than native born white men, on average. However, all else the
same, after 15 years in the united states, Asian foreign born males earn 4.1 percent more
than native born white males. So this implies that, on average, foreign born Asian males’
wages catch up to native born white males’ wages faster than foreign born white males,
other things the same. After 15 years in the United States, all else equal, foreign born
black males still earn 18.6 percent less that native born white males. After 15 years in the
U.S., Foreign born Hispanic males earn only 6.5 percent less than native born white
males on average. Lastly, on average, after 15 years in the U.S., mixed race foreign born
people earn 17.8 percent less than native born white males, all else the same. So, it takes
about 15-16 years in the United States for a foreign born white male to “Americanize”,
whereas it takes only about 13 years in the United States for an Asian migrant to undergo
this earnings assimilation process, all else constant. It is important to note that the
coefficient on the Asian variable in regression five is significant at the ten percent
significance level, but not at the five or one percent significance level. Foreign born black
workers are not so fortunate. It appears that their wages will not catch up to native born
white males’ until about 32-33 years in the United States, and about thirty years in the
united states for other or mixed race workers. However, another important note to make
is to point out the shadiness of the “mixed-race/other” variable. There is no helpful
definition of this variable on the ACS website, so many of the people included in this
variable could be racially Black. To show this, I ran a joint hypothesis test on the null
that: Black-Other/mixed=0. It turns out that we cannot reject the null at any
conventional significance levels. More information on the Entire F- test is included in the
last section of this essay.
Table 1
======================================================================================================
Dependent variable:
----------------------------------------------------------------------------------
lnearn
Base All Males/Females All Males/Females All Males/Females All Males/Females
(1) (2) (3) (4) (5)
------------------------------------------------------------------------------------------------------
forborn -0.0902*** -0.0066 -0.3146*** -0.3346*** -0.3210***
(0.0150) (0.0188) (0.0432) (0.0433) (0.0446)
racenewAsian 0.1114*** 0.0387 0.0472* 0.0471*
(0.0298) (0.0276) (0.0275) (0.0275)
racenewBlack -0.3460*** -0.2358*** -0.1801*** -0.1799***
(0.0198) (0.0185) (0.0184) (0.0185)

6. racenewHispanic -0.3769*** -0.0828*** -0.0593*** -0.0595***
(0.0191) (0.0186) (0.0186) (0.0186)
racenewOther/mixed -0.2946*** -0.1999*** -0.1725*** -0.1723***
(0.0371) (0.0353) (0.0350) (0.0350)
female -0.3881*** -0.4383*** -0.4278*** -0.4280***
(0.0111) (0.0105) (0.0105) (0.0105)
educ_years 0.1320*** 0.1268*** 0.1269***
(0.0022) (0.0022) (0.0022)
exper 0.0462*** 0.0413*** 0.0414***
(0.0021) (0.0021) (0.0021)
I(exper2) -0.0007*** -0.0006*** -0.0006***
(0.00004) (0.00004) (0.00004)
forysm 0.0268*** 0.0257*** 0.0255***
(0.0035) (0.0035) (0.0036)
I(forysm2) -0.0004*** -0.0003*** -0.0003***
(0.0001) (0.0001) (0.0001)
notmspp -0.2305*** -0.2303***
(0.0115) (0.0115)
toddler 0.0420** 0.0539***
(0.0166) (0.0186)
forborn:toddler -0.0550
(0.0369)
Constant 10.3953*** 10.6539*** 8.1733*** 8.3928*** 8.3883***
(0.0063) (0.0085) (0.0431) (0.0446) (0.0447)
------------------------------------------------------------------------------------------------------
Observations 35,936 35,936 35,936 35,936 35,936
R2 0.0010 0.0539 0.1751 0.1859 0.1859
Adjusted R2 0.0009 0.0538 0.1748 0.1856 0.1856
Residual Std. Error 1.0810 1.0521 0.9825 0.9761 0.9760
F Statistic 34.6096*** 341.3053*** 693.0508*** 630.8440*** 585.9422***
======================================================================================================
Note: *p<0.1; **p<0.05; ***p<0.01
>
Limitations
As far as internal validity goes, I checked suspected variables for issues of
multicolinearity and tried to control for all the variables I could, but there may be sample
selection bias or other forms of variable bias. It seems quite evident that there will be
errors-in variable bias, because the measure of that person’s wage may not accurately
represent that persons real income. Also, there is potential for functional form error with
the interection term, because the coefficient on the interaction term in regression five is
not statistically signicicant. As far as external validity, the figures were based on broad
averages that were only focused on men, but the same conclusions will not hold for
women.

7. Conclusion
This study expands on some of the claims made in a paper by the economist Barry
Chiswick about the “Americanization” of migrant workers. Chiswick compared foreign
born white men to native born white men, so I thought it would be interesting to expand
that to questions of race. Black migrant workers take the longest time for their earnings to
match those of the native workers, and other/mixed race workers are very close behind.
However, the coefficients on Black and Other may be capturing the same effect, as noted
by the F-test. Interestingly, it appears that Asian migrant workers’ earnings rise more
quickly than White migrant workers. Also, the effect of having a toddler was examined.
The effect was stronger for native born workers, because native born workers with at
least one child under five should expect to earn more that than native born workers
without at least one child under five by about five percent, whereas foreign born workers
with at least one child under five should expect to earn about five percent less than native
born workers with no children under five. Migrants who are racially Black may have the
lowest earnings due to their average areas of residence (many live in violent areas),
access to transportation (same reasoning) and a number of socio-economic factors. It
does not imply anything causal about being racially Black
Detailed Methods/Code
Testing for Imperfect Multicolinearity
===============================================
Dependent variable:
---------------------------
toddler
Base
-----------------------------------------------
notmspp -0.1287***
(0.0031)
Constant 0.1755***
(0.0026)
-----------------------------------------------
Observations 35,936
R2 0.0364
Adjusted R2 0.0364

8. Residual Std. Error 0.3240
F Statistic 1,358.2550***
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
Linear hypothesis test
Hypothesis:
racenewBlack - racenewOther/mixed = 0
Model 1: restricted model
Model 2: lnearn ~ forborn + racenew + female + educ_years +
exper + I(exper^2) +
forysm + I(forysm^2) + notmspp + toddler
Note: Coefficient covariance matrix supplied.
Res.Df Df F Pr(>F)
1 35923
2 35922 1 0.0394 0.8427
#===========================================================
===================
# Final Data Assignment :(
#===========================================================
===================
# <Vincent Shields>
# <10/26/2015>
# Description:Our last project.
#===========================================================
===================
# 1. Settings, packages, and options (run these every R
session)
#===========================================================
===================

9. # Clear the working space
rm(list = ls())
# Set working directory
# Copy the complete "path" to your ECON 42 folder
# Example: mine is "/Users/wsundstrom/Dropbox/econ_42"
setwd("~/Desktop/ECON 42:Rstudio/files42 (1)")
# Load the packages (these must have been installed once:
see Chapter 2 of the Guide)
library(AER)
library(sandwich)
library(lmtest)
library(car)
library(stargazer)
library(ggplot2)
library(openintro)
library(OIdata)
library(WDI)
library(gdata)
library(doBy)
library(XML)
library(countrycode)
library(erer)
library(plyr)
library(plotly)
# turn off scientific notation except for big numbers
options(scipen = 9)
# set larger font size for qplot (default is 12)
theme_set(theme_gray(base_size = 18))
# function to calculate corrected SEs for regression
cse = function(reg) {
rob = sqrt(diag(vcovHC(reg, type = "HC1")))
return(rob)
}
Sys.setenv("plotly_username"="vlshields")
Sys.setenv("plotly_api_key"="p9npq82vkc")

10. #===========================================================
===================
# 2. Data section
#===========================================================
===================
### Read the data
acs = read.csv("acs_2013_data.csv", header = TRUE, sep =
",")
## make new variables/subsets
acs$lnearn = log(acs$incwage)
acs$exper = acs$age -acs$educ_years - 5
acs$master = acs$educ_mastersplus==1
acs$forysm = ifelse(acs$forborn==1, acs$years_usa, 0)
acs$foredu = ifelse(acs$forborn==1, acs$educ_years, 0)
acs$forbornn = acs$forborn==1
acs$southeq = acs$south==1
acs$child = relevel(acs$nchild,ref = "0 children present")
acs$toddler = acs$nchlt5=="1 child under age 5" |
acs$nchlt5=="2" | acs$nchlt5=="3" | acs$nchlt5=="4"
acs$racenew = relevel(acs$ethnicity,ref = "White")
acs$notmspp = acs$marstatus=="Never married" |
acs$marstatus=="Separated" | acs$marstatus=="Divorced" |
acs$marstatus=="Married spouse absent" |
acs$marstatus=="Widowed"
### Describe the data
summary(acs$nchlt5)
#===========================================================
===================
# 3. Analysis section
#===========================================================
===================
## Regressions
r1 = lm(lnearn ~ forborn, data = acs)
r2 = lm(lnearn ~ forborn + racenew + female, data = acs)
r3 = lm(lnearn ~ forborn + racenew + female + educ_years +
exper + I(exper^2) + forysm + I(forysm^2), data = acs )

11. r4 = lm(lnearn ~ forborn + racenew+female + educ_years +
exper + I(exper^2) + forysm + I(forysm^2) + notmspp +
toddler , data = acs)
r5 = lm(lnearn ~ forborn + racenew+female + educ_years +
exper + I(exper^2) + forysm + I(forysm^2) + notmspp+ toddler
+ forborn:toddler, data = acs)
###testing for imperfect multicolinearity
u= lm(toddler~notmspp,data = acs)
## Table(s)
stargazer(u,
se=list(cse(u)),
title="Testing for Imperfect Multicolinearity",
type="text",column.labels=c("Base", "All Males/Females","All
Males/Females","All Males/Females", "Females", "Males"),
df=FALSE, digits=4)
stargazer(r1,r2,r3,r4,r5,
se=list(cse(r1),cse(r2),cse(r3),cse(r4),cse(r5)),
title="Table 1",
type="text",column.labels=c("Base", "All Males/Females","All
Males/Females","All Males/Females", "All Males/Females"),
df=FALSE, digits=4)
#This is a cool little plot but i cant figure out how to put
it in my document.
# it is just for fun and is not informative
plot_ly(subset(acs,forborn==1&female==1&educ_mastersplus==1)
, x = exper, y = lnearn, text = paste("race:", racenew),
mode = "markers", color = age)
## natives with out toddler
#Black and mixed variables are probably the same. here is an
F-test
lht(r4,c("racenewBlack = racenewOther/mixed"), white.adjust
= "hc1")
#boxplot
acs$fbox = factor(acs$forborn==1 , labels = c("Native Born",
"Foreign Born"))
qplot(fbox, lnearn, data=acs, geom=c("boxplot"),

12. main="Earnings Gap",
xlab="employed workers", ylab="log of earnings")