1. 1 | Page
The Effect of Illegal Immigrants on
Domestic Employment Rates

2. 2 | Page
The Effect of Illegal Immigrants
on Domestic Employment Rates
A Linear Regression Analysis on the Economic Impact of Illegal Immigration
Conducted by Anna Palermini, Keegan Pronovost, and Waverly Walker
A common argument made by those in support of strict border control and deportation is that illegal
immigrants take jobs away from domestic, American citizens. Since illegal immigrants are willing to
work in worse conditions, with lower pay and longer hours, they make it effectively impossible for the
American citizen to compete. This, along with a general increases in the Labor Participation rate, causes
higher unemployment rates. Though we may question the validity of such claims, there does exist a
degree of intuitive logic to such a position. If a massive influx of workers increases the size of the labor
force (without the appropriate response in capital investment or economic expansion), it would seem
correct to assume that unemployment would rise. This is the issue we wish to analyze. Have the
increasing number of illegal immigrants had a negative effect on our economy, specifically as it pertains
to a rise in the unemployment rate? Clearly, the validity of such a correlation would have tremendous
implications for immigration policy, even outside of this sole issue (i.e. should we set a limit to the
amount of Syrian refugees we should take in?). In contrast, disproving it would encourage the
continuation of a free, open market and discredit many of the detrimental claims placed upon illegal
immigrants. Being such a controversial topic, we are not the first to attempt to uncover the presence or
significance of the correlation between illegal immigration and unemployment. In 2014, the Center for
Immigration Studies released an article in which they demonstrated the growing percentage of immigrants
(both legal and illegal) that are obtaining jobs within the US market. In contrast, they also showed that
domestic American workers own a decreasing percentage of the available labor. Specially, from 2000-
2014, the number of immigrants holding jobs rose by 5.7 million. The number of domestic workers
holding jobs fell by 127,000. Thus, though the domestic labor force grew considerably during this
fourteen year period, their employment numbers fell, leaving - as CIS estimates - about 17 million
domestic laborers out of work. The article argues that the high percentage of illegal immigrants should be
held responsible for this significant increase in domestic unemployment. Though there are certainly issues
with this study (it measures the combined impact of both legal and illegal immigrants, not just illegal) it
does provide a basis for our predictions within our estimated model. Namely, we expect that regions with
high percentages of illegal immigrants will tend to see higher rates of domestic unemployment.
In performing our analysis, we must recognize and account for the numerous other variables that impact
domestic unemployment, as not to skew our findings. Thus, we collected a myriad of variables that we
believe have tangible, real effects on US Unemployment rates (our Dependent Variable). In the end, we
arrived at a total of 7 variables. These variables are listed below with a short description of their expected
impacts:

3. 3 | Page
● Independent Variable #1: Percentage of Illegal Immigrants in the Labor Force
As our primary variable, we will use Regression Analysis to measure the impact of Illegal
Immigration on the unemployment rate. We anticipate that higher levels of Illegal Immigration
will have a positive impact on unemployment rates.
● Independent Variable #2: Average Annual GDP Growth from 2010-2013
A State’s average economic expansion/contraction will have a significant impact on its
employment levels. The time frame specified is chosen to capture the average growth in a period
which saw a dramatic spike in illegal immigration. We anticipate a high GDP Growth Rate over
this period will have a negative impact on unemployment rates.
● Independent Variable #3: Percentage of the State’s Population with College Degree
This will be measured as the percentage of college graduates per state. The more graduates, the
larger the number of skilled laborers in the State’s economy. We anticipate that a high percentage
of College Graduates will result in lower unemployment rates.
● Independent Variable #4: Minimum Wage per State
The higher a State’s Minimum Wage, the higher the unemployment rate.
● Independent Variable #5: Labor Participation Rate
The number of people in the Labor force as a percentage of the population. A high Labor
Participation rate indicates more unemployment.
● Independent Variable #6: Income Tax Rate per State
The income tax rate per state (taken from the highest tax bracket) will likely be higher if the State
is in debt. We expect that higher tax rates will lead to higher unemployment rates.
● Independent Variable #7: Income Tax Rate per State Squared
Variable #6 Squared. We have squared this variable as we expect Income Tax Rates to increase
unemployment at a decreasing rate.
In all, we combine these variables and compose the following Linear Regression Equation:
UNEMPLOYMENT=β0+β1ILLEGAL+β2GDP+β3GRAD+β4WAGE+β5PARTICIPATION+β6INCTAX+β7INCTAX2+ϵ1
In order to find the number of illegal immigrants per state, we will rely on reported data from the
Criminal Alien Program, National Illegal Alien Database, and recently released Pew Research. Though
there may exist controversy surrounding the “real” number of illegal immigrants per state, these statistics
will act as our basic threshold for measurement. The remainder of these variables can be found through a
variety of sites and reports, including the US Census, Bureau of Economic Analysis, the Federal Reserve,
the Department of Education, and the United States Department of Labor. In total, we will record 50
observations per variable - a record for each variable per state. Furthermore, this indicates that the
majority of our variables will be Cross Sectional. The one exception will be Variable #2 (Average Annual
GDP Growth), which is a time series data set from 2010-2013.
One of the major problems we encountered is the related nature of Labor Participation and
Unemployment. Since Labor Participation is mathematically related to Unemployment (Unemployment
Rate = Unemployed Laborers / Total Labor Participation), Labor Participation consistently attains the
highest level of significance and directly increases our R2
. In essence, it becomes a dominant variable.
Because of this, the remainder of our variables became skewed or had the incorrect sign. Thus, we
resolved to omit this variable.

4. 4 | Page
This omission is more than justified, as there exists a high correlation (r > .6) between the percentage of
College Graduates (Variable #3) and Labor Participation Rate (Variable #5). Thus, without skewing
results - caused by the addition of the Dominant Variable #5 - we can capture its relative effect.
A second issue we encountered was the relative insignificance of Variable #2 (GDP Growth 2010-2013).
Regardless of the regression or model run, we were unable to get Variable #2 to be significant, while
maintaining the correct sign. Thus, we resolved to omit this variable as well.
With these two omissions in mind, we arrived at what we believe to be the best available Regression
result. The result can be seen below:

5. 5 | Page
The results effectively capture many of the expected signs and impacts that we theorized before the
regression. According to the regression, a Dollar increase in the Minimum Wage causes unemployment to
rise by 0.412 percentage points (HAC*
; significant at 1.5%). A percentage increase in the ratio of College
Graduates to Total Labor Force decreases unemployment by 0.094 percentage points (HAC; significant at
1%). Our variable of interest was significant at about 8% and demonstrates that a percentage increase of
Illegal Immigrants within the Labor force increases unemployment rates by 0.086 percentage points,
holding all else constant. It should be noted that a percentage increase in the size of the Labor Force –
omitted variable due to regression Dominance – had a larger positive impact on unemployment rates, than
did Illegal Immigration. Since both variables effectively measure how the increased size of the labor force
impacts unemployment, it is interesting to note that Illegal Immigrants do not in fact always obtain
employment – in fact, a percentage increase in the presence of Illegal Immigrants seems to increase
unemployment slower than a domestic percentage increase in the size of the Labor Force. Lastly, Income
Tax and Income Tax2
seemed to be the least significant variables and impacted unemployment rates at a
decreasing rate. Specially, a percentage increase in the Income Tax increased unemployment rates by
0.134 percentage points (HAC; significant at 16%). However, Income Tax2
shows that at a certain point, a
percentage increase in Income Tax will have little to not impact on unemployment rates (-0.017 percentage
point change, HAC; significant at 9%).
Overall, the regression had an adjusted R2
of 0.260, meaning that about 26% of the variation in
unemployment is captured by our model. Though this percentage is relatively low, it was the highest R2
we could achieve without the inclusion of the dominant variable – which brought the R2
up to .600, but
caused error within our other variables. Furthermore, our F-statistic was significant at <1%, indicating
that our model was significant in explaining the variation in unemployment.
Perhaps the most significant flaw in our model is the issue of heteroskedasticity. Because our data is cross
sectional - as it is from the 50 states - it is hard to eliminate the differences that occur between small states
and large states. In essence, the impact of a dollar minimum wage increase will have a much larger impact
on California than it will Delaware. Furthermore, our variable of interest – illegal immigration – proved to
exhibit data collection problems. It is challenging to find specific, accurate, and trust-worthy data on the
specific number of illegals per state. Thus, illegals could be making a much larger/much smaller impact
than we actually predict, simply because we are not 100% certain of their actual number. For example, in
a handful of states, the best percentage of illegal population found was under 1.0%. Are we certain there
are really that few Illegals, or are there data collection problems?
Regardless, our results demonstrate that Illegal Immigrants do have a negative impact on employment
rates (positive impact on unemployment). Given the current political and social debate on this issue - and
the many policy implications that surround it - it’s important that we further analyze and recognize the
potential economic impact of this immigration phenomenon and continue to investigate what needs to be
done to resolve its.
*Holding All Else Constant

6. 6 | Page
Appendix A
Below are the Correlation Coefficients and Descriptive Statistics that we utilized in our Regression
Analysis.

7. 7 | Page
List of Sources
Center for Immigration Studies
Previous study on Illegal Immigration and Unemployment:
http://www.theblaze.com/blog/2014/06/27/shock-report-all-new-jobs-going-to-immigrants-not-u-s-born-
workers/
Illegal Immigrants per State
Pew Research Center:
http://www.pewhispanic.org/2011/02/01/iv-state-settlement-patterns/
Population per State
US Census: http://www.census.gov/
GDP Growth
Bureau of Economic Analysis: http://www.bea.gov/
Federal Reserve Bank of St. Louis: https://research.stlouisfed.org/fred2/
Percentage of the State’s Population with a College Degree
US Department of Education: http://www.ed.gov/news/press-releases/new-state-state-college-attainment-
numbers-show-progress-toward-2020-goal
Minimum Wage Requirement per State
The United States for Department of Labor: http://www.dol.gov/whd/minwage/america.htm
Labor Participation Rate
The United States Bureau of Labor Statistics: http://www.bls.gov/lau/ststdsadata.txt
Income Tax Rate per State
Tax Foundation: http://taxfoundation.org/article/state-individual-income-tax-rates-and-brackets-2016