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1
Illegal Immigration Affects Crime
Rate: Racist or True?
A Regression Analysis on the Factors Affecting
Crime Rate in the United States
An Empirical Study Presented to the
Faculty of the School of Economics Department
De La Salle University – Manila
In partial fulfillment
Of the requirements in
ECONMET (Econometrics)
1st Term AY 2015 – 2016
Submitted to:
Dr. Cesar C. Rufino
School of Economics
De La Salle University – Manila
Submitted by:
Vladimir C. Santos
11211512
BS in Applied Economics major in Industrial Economics and
BS in Commerce major in Management of Financial Institutions
ECONMET V25
December 4, 2015
2
TABLE OF CONTENTS
I. Introduction……………………………………………………………………………3
A. Background of the Study………………………………………………………….3
B. Objectives…………………………………………………………………………4
C. Statement of the Problem………………………………………………………….4
D. Scope and Limitations……………………………………………………………..4
II. Related Literature……………………………………………………………………...5
A. Population Density and Crime Rate……………………………………………….5
B. Poverty Rate and Crime Rate……………………………………………………...5
C. Illegal Immigration and Crime Rate………………………………………………6
D. Undocumented Mexican Immigrants……………………………………………...6
E. Violent Crimes and Index Crime Rates…………………………………………...7
F. Unemployment and Crime Rate…………………………………………………...8
G. Full Time Police Employees and Crime…………………………………………..8
III. Theoretical Framework………………………………………………………………..9
A. Marginal Cost – Marginal Benefit Model…………………………………………9
B. Rational Choice Theory…………………………………………………………...9
IV. Operational Framework……………………………………………………………...10
A. Description of Variables Used…………………………………………………...10
B. Hypothesized Relationship Between the Explained and Explanatory Variables...11
C. Presentation of Econometric Model……………………………………………...13
V. Methodology…………………………………………………………………………15
VI. Initial Regression…………………………………………………………………….16
VII. Tests for the Model…………………………………………………………………..18
A. Test for Heteroscedasticity………………………………………………………18
B. Test for Multicollinearity………………………………………………………..20
C. Test for Misspecification………………………………………………………...21
VIII. Final Model and Interpretation………………………………………………………22
IX. Bibliography…………………………………………………………………………24
X. Appendix…………………………………………………………………………….26
3
I. INTRODUCTION
A. BACKGROUND OF THE STUDY
“When Mexico sends it people, they’re not sending their best. They’re not sending you
(points to crowd above). They’re not sending you (points to another person in the crowd
above). They’re sending people that have lots of problems, and they’re bringing those
problems with us. They’re bringing drugs. They’re bringing crime. They’re rapists, and some,
I assume, are good people.” On June 16, 2015, at Trump Tower, New York, USA, history was
made when real estate mogul Donald Trump officially declared that he is running for President
of the United States under the Republican Party. Two minutes into his speech, he said those
exact words above which made him controversial instantaneously, being criticized for his
seemingly “racist” comments, generalizing Mexicans to be criminals, drug dealers, and rapists,
which he later defended his stance on, and became a big part of his platform.
Despite all the criticism, Donald Trump remains steadfast in his position that illegal
Mexicans can bring rampant crime to the U.S. (Carroll, 2015). Apparently, his statement that
there are “hundreds of thousands” of illegal Mexicans have been questioned. In 2014,
Immigration and Customs Enforcement have deported just under 178,000 illegal immigrants.
Scholars also claim that involving local police in federal immigration enforcement does not
seem to offer measurable public safety benefits. While Republicans would consider it true,
Democrats would consider it purely racist to correlate the two. Regardless of the media buzz,
a formal regression analysis on the factors that affect crime in the U.S. including a new
variable, illegal immigration, should put all doubts to rest.
4
B. OBJECTIVES
The objective of this paper is to be able to determine the extent of the relationship between
the dependent variable, total crime rate by state, and independent variables chosen to come up
with a regression analysis using ordinary least squares (OLS). This paper uses mostly
socioeconomic variables such as population density, poverty rate, number of police employees,
and number of illegal immigrants. By identifying which variables contribute mostly to the
amount of crime, policymakers will be able to develop policies that can target a certain problem
to ensure maximum effectiveness and efficiency given limited resources.
C. STATEMENT OF THE PROBLEM
With the increase of crime yearly, this study aims to determine the factors that grossly
affect the crime rate in order to aid policymakers such as the government in achieving their
goal of crime eradication.
D. SCOPE AND LIMITATION
This study is focused on the marginal effects of population density, poverty rate,
unemployment rate, police employees, and number of illegal immigrants living in the US. The
data were gathered from multiple sources such as United States Bureau of Labor Statistics,
Statista, Federal Bureau of Investigation, United States Census Bureau, and Pew Research
Center. The study uses cross-section data on all states in the US for the year 2012.
5
II. REVIEW OF RELATED LITERATURE
A. POPULATION DENSITY AND CRIME RATE
Population Density refers to the number of inhabits per square mile. One of the most classic
arguments for correlating population density and crime rate is that the increase in population
density increases the opportunities for crime due to the vast number of properties per square mile
associated with it, therefore enticing thieves. However, another argument is that the increase in
population density also offers a larger natural surveillance for people living within the area by
people living within the area, which may increase reported crimes (Harries, 2006).
B. POVERTY RATE AND CRIME RATE
By definition, poverty rate refers to the ratio of number of people falling below the poverty
line against the total population (OECD, 2015). Poverty status is then determined by comparing
annual income to a certain dollar value standard, that of which varies depending on the number of
people within a household, number of children as well as their ages. If the family's income before
tax fall below that threshold, then that family, as well as everyone in it, are included within the
poverty count. Crime exists everywhere in the United States, and it is one of the biggest problems
in the urban areas, where poverty is high. “There is a higher rate of mental illness in the poor than
in the rich” (Brill 40), and this can lead to higher rate of crime due to the additional level of stress
that poverty can bring to individuals, which would incentivize them to commit theft, robbery, and
other forms of economic crimes.
6
C. ILLEGAL IMMIGRATION AND CRIME RATE
By definition, "immigrant", a.k.a. "foreign born", refers to the people unable to acquire U.S.
citizenship upon birth. This includes the naturalized, lawful permanent residents, refugees, persons
on temporary visas, and the generally unauthorized. According to the Migration Policy Institute
(MPI), in 2013, approximately 41.3 million immigrants live in the U.S., 13% of the total population
in the US, the highest record so far (Zong & Batalova, 2015). US alone houses 20% of the world’s
total number of immigrants. As a matter of fact, Mexicans account for 28% (approximately 11.6
million) of the 41.3 million immigrants, with Indians coming at second place. 46% of immigrants
(approximately 19 million people) are of Hispanic origin. According to DHS’ Office of
Immigration Statistics (OIS), an estimated 11.4 million unauthorized immigrants live in the US
back in 2012.
D. UNDOCUMENTED MEXICAN IMMIGRANTS
According to the estimates of MPI, between 2008-2012, 71% (8.1 million) of undocumented
immigrants are Mexicans, and Mexicans account for the largest apprehensions in 2013 (MPI).
Over the period 1976-1995, it was estimated that a 10% decrease in real wages in Mexico leads to
a 7.5-8.5% increase in border apprehensions (Hanson & Spilimbergo, 1996). Economic instability
in Mexico seems to be the biggest factor in attempting to cross the border. There is a strong
negative correlation between Mexican real wage and border apprehension, and there is a positive
correlation between U.S. real wage and border apprehension. Unlike their documented counterpart,
Undocumented Mexican Immigrants (UMIs) are frequently exploited, which makes them highly
vulnerable. They experience severe physical, mental, and emotional hardships, lower wages,
uncertain wages, higher unemployment, fewer social networks, less English proficiency, less
7
education in general, and generally poorer housing, not to mention less health coverage, less access
to healthcare, less health quality, fear of deportation, and the stress of living a secret life, constantly
on the run from the authorities. Because of their lack of lack of US documentation and the current
US immigration policies, bad employers use the UMI’s vulnerability to their advantage, knowing
that these UMIs are desperate. As such, UMIs have less tendency to speak up against injustices,
because reporting a case of injustice requires going to the proper authorities, the same people that
they are hiding from. This opens them up to blackmail and being forced to do odd jobs at a very
low wage, some of which are strictly speaking, not legal. This creates the image that UMIs
contribute to the increase in crime rate within the US (Sullivan & Rebm, 2005).
E. VIOLENT CRIMES AND INDEX CRIME RATE
Index Crime Rate refers to the number of crimes committed and reported per 100,000
inhabitants. By definition, violent crime refers to the grave offenses such as murder, rape, robbery,
and aggravated assault. Murder refers to the act of taking away the life of a person. Rape, with its
revised definition, refers to the “penetration, no matter how slight, of the vagina or anus with any
body part or object, or oral penetration by a sex organ of another person, without the consent of
the victim.” (UCR, 2011) Robbery refers to grand theft of property. Aggravated assault refers to
the physical contact done by a person to another with the intent of gross harm. The sum of all these
is violent crime. There are cases when a White American would commit a crime against a
Hispanic/Latino/Mexican or vice-versa, and this may fall into the category of “hate crime”. Not
only may the categorization be subjective at times, but it also does not affect the outcome whether
a crime is considered violent or not, because violent crime has its own operational definition and
qualifications.
8
F. UNEMPLOYMENT AND CRIME RATE
Unemployment rate is the ratio among the labor force who are unable to find work. People
who are unemployed rely on wealth for their daily autonomous consumption. The lack of income
can push people to commit crime. Of course, this is not without criticism. It has been said that the
relationship between unemployment rate and crime rate is “weak”, “insignificant”, and
“inconsistent” (Chiciros, 1987), and then there are some that claim that “cities may create greater
returns to crime because criminals may have greater access to the wealthy and face a greater
density of victims in urban areas” (Glaeser & Sacerdote, 1999).
G. FULL TIME POLICE EMPLOYEES AND CRIME
The FBI (2009) confirms that indeed, either an increased spending in local law enforcement or
the increase in full time police employees can lessen the instances of crime. Full time police
employees refer to the collective sum of both law enforcement employees working in the field and
in their respective headquarters. Logistics and other forms of administration work contribute just
as much in terms of coordinating operations and making their work more efficient. Whenever
crime is getting out of hand within a state in the US, policymakers would always lobby for the
increase in government budget to be allocated to the law enforcement agencies. There are some
that would argue that an increase in police spending is not necessary, that the extra budget would
only fall in the wrong hands, in the pockets of certain law enforcement personnel, while some
would argue that the increase in police budget is necessary and can actually deter police corruption.
9
III. THEORETICAL FRAMEWORK
A. MARGINAL COST – MARGINAL BENEFIT MODEL
In 1968, Gary Becker formulated the marginal cost – marginal benefit model. In this case,
the supposed criminal determines whether a crime is worth doing or not by analyzing the
personal benefit of a successful crime, where successful means that the authorities were not
able to stop the criminal. If the potential cost of being apprehended by the authorities is more
than the potential benefit of doing the crime, then the criminal will choose not to do the crime.
This model is done in the form of a weighing scale, and crime itself is identified as a “type of
work”. Therefore, the objective of the criminal is to look for crimes whose marginal benefit is
higher than its marginal cost, whereas the authorities must enforce stricter rules and harsher
punishments and improve apprehension abilities in order to increase the marginal cost of doing
crime, which will then discourage criminals from committing crime in the first place.
B. RATIONAL CHOICE THEORY
The Rational Choice Theory illustrates three “actors”, namely: rational, predestined, and
victimized. Some examples include people with psychopathic tendencies. Contrary to popular
belief, psychopaths are actually “completely rational”. The Rational Actor commits crime as a
choice. Severe punishments work best against these actors. Predestined Actors cannot control their
urge to commit crime. This urge is the result of the influence of their environment. Such an
example is a person living within a poverty-stricken area. The Victimized Actor commits crime as
a result of a severely unequal society. Proper legislation to minimize inequality is said to be able
to fix these problems (Regis University, 2015).
10
IV. OPERATIONAL FRAMEWORK
A. DESCRIPTION OF VARIABLES USED
Below is a list of the dependent and independent variables to be used in the study. A
detailed description of each variable is also present.
Dependent Variable
Total Violent Crime (crime)
Total violent crime is simply the sum of the four major crimes: murder, rape, robbery, and
aggravated assault. Mathematically,
Total Crime = Murder + Rape + Robbery + Aggravated Assault
Data was taken from the Federal Bureau of Investigation (FBI).
Independent Variables
1. Population Density (density)
Population Density refers to the number of residents per certain area. In this case, population
density refers to the number of residents per square mile. Data was taken from Statista.
2. Poverty Rate (poverty)
Poverty rate refers to the ratio of number of people falling below the poverty line against the total
population (OECD, 2015). Data was taken from the Index Mundi.
11
3. Unemployment Rate (unemployment)
Unemployment refers to the percentage of people within the labor force looking for work but are
unable to find work. Data was taken from the United States Bureau of Labor Statistics.
4. Full Time Police Employees (police)
Full time police employees include all people working for the law enforcement agencies: those
working in the field and those working at the headquarters handling administrative matters. Data
was taken from the Federal Bureau of Investigation (FBI).
5. Unauthorized Immigrants (immigrants)
Unauthorized immigrants refer to the non-U.S. born people who reside in the U.S. without a legal
basis. Data was taken from Pew Research Center.
B. HYPOTHESIZED RELATIONSHIP BETWEEN THE
EXPLAINED AND EXPLANATORY VARIABLES
Table 1 (see below) represents the a-priori expectations dependent on relevant economic theory
and common sense of the regressors with the regressand.
Table 1: A-priori Expectations
Variable Variable
Name
Algebraic
Sign
Intuition
Dependent Variable
12
Total Violent
Crime
crime N/A
(regressand)
Total Violent Crime depends on a number of
socioeconomic factors. It is the ratio of criminals
arrested by the police per 100,000 inhabitants
Independent Variables
Population
Density
density +/- Population density refers to the number of people per
square mile. As stated before, conflicting theoretical
frameworks make the a-priori sign ambiguous. On one
hand, the increase in population density may lead to
the increase in total violent crime due to the increase
of properties and property value per fixed area,
enticing criminals to commit crime. On the other hand,
population density may be inversely related to total
violent crime; as the number of people within a certain
area increase, there is also an increase in natural
surveillance that can discourage criminals from
committing crime within the area.
Poverty Rate poverty + Poverty rate refers to the ratio of people living below
the poverty line. Poverty can cause stress to people
that can motivate them to commit crime as an act of
desperation.
Unemployment
Rate
unemployment + Unemployment refers to the amount of people within
the labor force looking for work but are not able to.
Unemployment rids the person of income; therefore, it
13
may incentivize people to commit crime to compensate
for the lack of income.
Full Time
Police
Employees
police - The increase in police employees would discourage
criminals from committing crime due to the larger area
coverage that additional police on the ground can
provide.
Illegal
Immigrants
immigrants + Illegal immigrants (in thousands) lack the protection of
the law which leaves them vulnerable to bad
employers, forcing them to work in harsh conditions
for a lesser pay, and can be blackmailed into doing
illegal activities.
C. PRESENTATION OF ECONOMETRIC MODEL
While adding many variables can ideally explain all the changes within a regressand, it is
no doubt laborious to say the least, not to mention costly. Due to the impracticality, other factors
that affect a regressand but cannot be included are compiled into a stochastic disturbance term (u)
added to the regression model. This is used to capture all other factors that explain the changes in
the regressand but are not included as part of the known regressors (Gujarati & Porter, 2009). Other
important reasons include:
a. Vagueness of theory
b. Unavailability of data
14
c. Core variables versus peripheral variables
d. Intrinsic randomness in human behavior
e. Poor proxy variables
f. Principle of parsimony
g. Wrong functional form
The model below was built with several economic theories as the foundation. The model
is a linear-linear (lin-lin) model, which means that an absolute change in one of the regressors will
result in an absolute change in the regressand.
crime = β1 + β2 density + β3 poverty + β4 unemployment – β5 police + β6 immigrants + ui
where:
β1 = intercept
β2 = parameter for Population Density
β3 = parameter for Poverty Rate
β4 = parameter for Unemployment Rate
β5 = parameter for Full Time Police Employees
β6 = parameter for Illegal Immigrants
ui = stochastic disturbance term
15
V. METHODOLOGY
The data on Appendix A was gathered from data sources such as United States Bureau of Labor
Statistics, Statista, Federal Bureau of Investigation, United States Census Bureau, and Pew
Research Center. The study uses cross-section data on all 50 + 1 states in the US for the year 2012.
The task is to estimate the Population Regression Function (PRF) using a Sample Regression
Function (SRF). The regression method used is Ordinary Least Squares (OLS). This method is
much celebrated because it has “attractive statistical properties that have made it one of the most
powerful and popular methods of regression analysis” (Gujarati & Porter, 2009). Under OLS,
certain assumptions must be held under the Classical Linear Regression Model (CLRM):
1. The regression model is linear in parameters
2. X values are independent of the error term, or in mathematical form:
𝑐𝑜𝑣 (𝑋𝑖,𝑢𝑖) =0
3. Zero Mean Value of Disturbance 𝑢𝑖, or in mathematical form:
𝐸 (𝑢𝑖) =0
4. Homoscedasticity which means that the variance of the error term is a constant, or in
mathematical form:
𝑣𝑎𝑟 (𝑢𝑖) =𝜎2
5. No autocorrelation which means that the covariance of the error terms is equal to 0, or in
mathematical form:
𝑐𝑜𝑣 (𝑢𝑖𝑢𝑗) =0
16
6. The number of observations must be greater than the number of parameters.
7. The X’s in a given sample must not all be the same.
8. No severe multicollinearity which means that the regressors must not be related
9. The regression model is correctly specified
Immediately after the initial regression, several tests must be done in order to verify that the
assumptions of CLRM still hold true for the model; in other words, it must be the Best Linear
Unbiased Estimator (BLUE). Stata 13 is the software used for the initial regression and other tests
using a 5% level of significance.
VI. INITIAL REGRESSION
Using Stata, the following was generated:
. reg crime density poverty unemployment police immigrants
Source | SS df MS Number of obs = 51
-------------+------------------------------ F( 5, 45) = 12.82
Model | 937647.199 5 187529.44 Prob > F = 0.0000
Residual | 658294.785 45 14628.773 R-squared = 0.5875
-------------+------------------------------ Adj R-squared = 0.5417
Total | 1595941.98 50 31918.8397 Root MSE = 120.95
------------------------------------------------------------------------------
crime | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
density | .0786414 .0119292 6.59 0.000 .0546147 .1026681
poverty | 13.46387 5.945246 2.26 0.028 1.489526 25.43821
unemployment | 9.996606 12.08451 0.83 0.412 -14.34285 34.33606
police | .0018611 .0021344 0.87 0.388 -.0024378 .0061599
17
immigrants | -.0687429 .1094068 -0.63 0.533 -.2890996 .1516138
_cons | 47.51009 95.72345 0.50 0.622 -145.2868 240.307
------------------------------------------------------------------------------
The next step is to plug in the coefficient results of the initial regression into our model.
Interpretations will come later in the final model (if necessary).
crime = 47.51009 + 0.0786414 density + 13.46387 poverty + 9.996606
unemployment + 0.0018611 police – 0.0687429 immigrants + ui
Given that the null hypothesis 𝐻𝑜 : 𝛽𝑖 = 0 , independent variables whose p-values are less than 0.05
implies that there is sufficient evidence to reject it, and we may do so consider it statistically
significant. Readers may observe that the results of the parameters of police and immigrants
contradict the a-priori expectation. Do not mind the significance level for now. Recall that the a-
priori expectation for police is negative; results show otherwise. Recall that the a-priori expectation
for immigrants is positive; results show otherwise. Keep it in mind as we will get back to the
interpretations of the model later on.
18
VII. TESTS FOR THE MODEL
A. TEST FOR HETEROSCEDASTICITY
Recall that one of the assumptions of CLRM is homoscedasticity, which means that the variance
of the stochastic disturbance term must be constant; otherwise, the model is said to be
heteroscedasticity, the absence of homoscedasticity. Mathematically, the assumption is as follows:
𝑣𝑎𝑟 𝑢𝑖 = 𝜎𝑖2 ; 𝑖 = 1 , 2 , 3 , … , 𝑛 .
A model that suffers from heteroscedasticity suffers certain consequences. Gujarati and Porter
(2009) says:
1. Heteroscedasticity does not destroy the unbiasedness and consistency properties of OLS
estimators.
2. But these estimators are no longer minimum variance or efficient. That is, they are not
BLUE.
3. In the presence of heteroscedasticity, the variances of OLS estimators are not provided
by the usual OLS formulas. But if we persist in using the usual OLS formulas, the t and
F tests based on them can be highly misleading, resulting in erroneous conclusions.
While there are two approaches (informal and formal) to identify heteroscedasticity, the formal
approach will be used. While there are many other tests for heteroscedasticty such as Park’s Test
and Glejser Test, only Breusch-Pagan Test and the White’s Test will be used because the former
tests have been criticized by Goldfeld and Quandt.
19
1. Breusch-Pagan Test
While the Goldfeld-Quandt Test is more superior than the Park’s Test and the Glejser Test, the
Breusch-Pagan Test is more superior than the Goldfeld-Quandt test because the Breusch-Pagan
Test is not hindered by first having to depend on c and identify the correct variable X. Because H0
= constant variance, getting a p-value of less than 0.05 means that the model suffers from
heteroscedasticity (Gujarati & Porter, 2009).
. hettest
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
Ho: Constant variance
Variables: fitted values of crime
chi2(1) = 0.69
Prob > chi2 = 0.4065
The results of the test show that the p-value = 0.4065 > 0.05. Therefore, we can say that the
model does not suffer from heteroscedasticity at 5% level of significance.
2. White’s Test
Without considering the Breusch-Pagan as significantly inferior than the White’s Test, the
White’s Test is easy to use and is not sensitive to the normality assumption. Given 𝑛 ∙ 𝑅2 ~ 𝑎𝑠𝑦𝑋2𝑑𝑓,
if the chi-squared value obtained exceeds the level of significance given by 5%, then the model is
said to be homoscedastic, otherwise it is suffering from heteroscedasticity (Gujarati & Porter,
2009).
. estat imtest , white
White's test for Ho: homoskedasticity
20
against Ha: unrestricted heteroskedasticity
chi2(20) = 28.72
Prob > chi2 = 0.0934
Cameron & Trivedi's decomposition of IM-test
---------------------------------------------------
Source | chi2 df p
---------------------+-----------------------------
Heteroskedasticity | 28.72 20 0.0934
Skewness | 12.65 5 0.0269
Kurtosis | 0.77 1 0.3801
---------------------+-----------------------------
Total | 42.14 26 0.0237
---------------------------------------------------
The results show a p-value of 0.0934 > 0.05. Therefore, the model does not suffer from
heteroscedasticity.
B. TEST FOR MULTICOLLINEARITY
Another assumption of CLRM is that there must be no multicollinearity, but it is a well-
known fact that in non-experimental data, some variables have a distinct relationship with another,
such as wealth and income. There are two types of multicollinearity: perfect and imperfect. In
other words, it is not a question of whether multicollinearity is present in the model of not but
rather a question of its degree. If the multicollinearity is severe, the model suffers from high
variances, making confidence intervals wide and thus inference may be insignificant (Gujarati &
Porter, 2009).
21
One of the tests for multicollinearity is the Variance Inflating Factor (VIF). Mathematically:
𝑉𝐼𝐹 = 1 / 1 − 𝑅𝑗2
The test is simple. The level of tolerance is between 1 and 10. Anything higher than
means that the model suffers from severe multicollinearity; anything less is tolerable.
. vif
Variable | VIF 1/VIF
-------------+----------------------
police | 7.86 0.127214
immigrants | 7.39 0.135368
unemployment | 1.46 0.683673
poverty | 1.21 0.828959
density | 1.06 0.940240
-------------+----------------------
Mean VIF | 3.80
Results show that Mean VIF = 3.80 < 10. Therefore, we have a tolerable level of
multicollinearity. The model does not violate the multicollinearity assumption of CLRM.
C. TEST FOR MISSPECIFICATION
A critical assumption of CLRM is that the model does not suffer from model specification
bias or simply misspecification. According to Gujarati and Porter (2009), there are several kinds
of specification errors: omission of relevant variable(s), inclusion of unnecessary variable(s),
adoption of wrong functional form, errors of measurement, incorrect specification of the stochastic
error term, and assumption that the error term is normally distributed. If the p-value is less than
0.05, then the model suffers from Omitted Variable Bias (OVB).
22
. ovtest
Ramsey RESET test using powers of the fitted values of crime
Ho: model has no omitted variables
F(3, 42) = 1.21
Prob > F = 0.3183
Since the p-value = 0.3183 > 0.05, accept H0. The model has no omitted variables.
VIII. FINAL MODEL AND INTERPRETATION
A summary of the statistics is shown below:
. summarize crime density poverty unemployment police immigrants
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
crime | 51 371.2745 178.6584 122.7 1243.7
density | 51 400.051 1478.726 1.3 10588.8
poverty | 51 14.76863 3.159968 8.7 22.7
unemployment | 51 7.372549 1.71185 3.1 11.2
police | 51 18751.41 22469.01 1677 117268
-------------+--------------------------------------------------------
immigrants | 51 220.1961 424.9294 5 2450
In each variable, there are 51 observations (to see the list of states of US, visit Appendix
A). The average unemployment rate for all states is 7.37%. There is an average of 18751 fully
employed police employees per state. Approximately 14.77% of the people live below the poverty
line. There are about 400 people per square mile, and 371 incidences of crime per 100,000 people.
There are at least 123 crimes per 100000 inhabitants per day, and a maximum of 1244 per 100000
inhabitants.
23
crime = 47.51009 + 0.0786414 density + 13.46387 poverty + 9.996606
unemployment + 0.0018611 police – 0.0687429 immigrants + ui
For every increase of one person per square mile, violent crimes increase by 0.0786 per
100000 inhabitants. For every one-point increase in poverty rate, violent crimes increase by
13.4639 per 100000 inhabitants. For every one-point increase in unemployment, violent crimes
increase by 9.9966 per 100,000 inhabitants. For every increase of full time police employee,
violent crimes increase by 0.0019. Note that while this may contradict a-priori expectation, recall
that these are arrest crimes. It is also possible that the increase in law enforcement officers can lead
to the increase in arrests due to the wider coverage per area. For every one-person increase in
illegal immigrants in the US, violent crimes decrease by 0.0687 per 100000 inhabitants. While
contradictory to a-priori, this is also plausible when (1) the increase in population increases the
denominator, especially if none of the illegal immigrants are committing crime, or (2) illegal
immigrants may be doing crime but not the violent type. Recall that even though illegal immigrants
may not be doing violent crimes, they are still included in the population count.
In this model, the p-values of population density and poverty rate are less than 0.05.
Therefore, these variables are significant. As with the prescribed model and data set, it seems that
the increase in violent crime rate has nothing to do with the increase in illegal immigration.
Nevertheless, illegal immigration is still a big problem for both “sending” and “receiving” country.
This research output should be used by policymakers to acknowledge the lack of evidence linking
illegal immigration and violent crime rate in our times. As a matter of fact, policymakers should
try a different approach and try to be more understanding of illegal immigrants’ physical,
emotional, and psychological aspects, get rid of racial prejudice, and foster good relations with
each other. In the end, no immigrant wants to go to another country to simply commit crime.
24
IX. BIBLIOGRAPHY
Becker, G.S. (1968). Crime and Punishment: An Economic Approach. Journal of Political
Economy, Vol. 76, No. 2, 169-217. Retrieved from http://www.jstor.org/stable/1830482
Brill, Norman Q (1993). America’s Psychic Malignancy. Springfield, IL: Charles C Thomas
Publisher, 1993.
Chiricos, T. (1987, April 1). Rates of crime and unemployment: an analysis of aggregate research
evidence. Oxford University Press Journals, volume 34(2). http://dx.doi.org/10.2307/800715
Federal Bureau of Investigation. (2009). Variables affecting crime. Crime in the United States.
Retrieved from https://www2.fbi.gov/ucr/cius2009/about/variables_affecting_crime.html
Federal Bureau of Investigation. (2012). Crime in the United States by State, 2012. Retrieved
from https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2012/crime-in-the-u.s.-
2012/tables/5tabledatadecpdf/table_5_crime_in_the_united_states_by_state_2012.xls
Federal Bureau of Investigation. (2012). Full-time Law Enforcement Employees by State, 2012.
Retrieved from https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2012/crime-in-the-u.s.-
2012/tables/77tabledatadecpdf/table_77_full_time_law_enforcement_employess_by_state_2012.xls
Glaeser, E & Sacerdote, B. (1999). Why is there more crime in cities? Journal of Political
Economy, 107(6). Retrieved from http://www.jstor.org/stable/10.1086/250109
Hanson, G. & Spilimbergo, A. (1996). Illegal Immigration, Border Enforcement, and Relative
Wages: Evidence from Apprehensions at the U.S.-Mexico Border. NBER Working Paper No. 5592.
Retrieved from http://www.nber.org/papers/w5592
Harries, K. (July 2006). Property crimes and violence in united states: an analysis of the
influence of population density. International Journal of Criminal Justice Sciences, volume 1 (2).
Retrieved from http://www.sascv.org/ijcjs/harries.html
Index Mundi. (2012). United States – Poverty Rate by State. Retrieved from
http://www.indexmundi.com/facts/united-states/quick-facts/all-states/percent-of-people-of-all-ages-
in-poverty#map
Mueller, R. (2011). UCR Program Changes Definition of Rape. Retrieved from
https://www.fbi.gov/about-us/cjis/cjis-link/march-2012/ucr-program-changes-definition-of-rape
Pew Research Center. (2014, November 18). Estimates of Unauthorized Immigrants in the Total
Population, Labor Force and Foreign-Born Population, by State, 2012. In Unauthorized Immigrant Totals
Rise in 7 States, Fall in 14 pages 30-31. Retrieved from
http://www.pewhispanic.org/files/2014/11/2014-11-18_unauthorized-immigration.pdf
25
Poverty Rate. (2015). In OECDiLibrary. Retrieved from http://www.oecd-
ilibrary.org/sites/factbook-2010-en/11/02/02/index.html?itemId=/content/chapter/factbook-2010-89-
en
Regis University (2015). Varying Theories on Crime. Retrieved from
http://criminology.regis.edu/criminology-programs/resources/crim-articles/varying-theories-on-crime
Statista. (2012). Population density in the U.S. by federal states including the District of
Columbia in 2012. Retrieved from http://www.statista.com/statistics/183588/population-density-in-the-
federal-states-of-the-us/
Sullivan, M., and R. Rehm. 2005. Mental health of undocumented Mexican immigrants: a review
of the literature. Advances in Nursing Science 28(3):240-51.
Taylor, B. (2006). Poverty and crime. Retrieved from
http://economics.fundamentalfinance.com/povertycrime.php
Trump, Donald. [Donald Trump]. (2015, June 16). Donald Trump FULL SPEECH: 2016 Presidential
Campaign Announcement June 16 at Trump Tower, New York. Retrieved from
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Tryger, E., Chalfin, A., & Loeffler, C. (2014). Evidence from the secure communities program.
Criminology & Public Policy, Volume 13, Issue 2, pages 285-322. doi: 10.1111/1745-9133.12085
Zong, J. & Batalova, J. (2015, February 26). Frequently Requested Statistics on Immigrants and
Immigration in the United States. Retrieved from http://www.migrationpolicy.org/article/frequently-
requested-statistics-immigrants-and-immigration-united-states
26
X. APPENDIX
U.S. States crime density poverty unemployment police immigrants
Alabama 449.9 95.4 18.6 8 12745 65
Alaska 603.2 1.3 9.9 7.1 1968 15
Arizona 428.9 58.3 17.9 8.4 22999 300
Arkansas 469.1 56.9 19.2 7.6 9148 80
California 423.1 246.1 15.9 10.4 117268 2,450
Colorado 308.9 50.8 13.2 7.8 17270 180
Connecticut 283 742.6 10.2 8.3 10271 130
Delaware 547.4 475.1 11.7 7.2 3151 20
District of
Columbia 1244 10589 18.6 9 4976 20
Florida 487.1 364.6 16.3 8.5 65683 925
Georgia 378.9 173.7 18.2 9.2 34769 400
Hawaii 239.2 218.6 11.2 6 3720 35
Idaho 207.9 19.5 15.5 7.2 4265 50
Illinois 414.8 232 14.1 9 45505 475
Indiana 345.7 183.4 15.4 8.3 12032 85
Iowa 263.9 55.3 12.4 5 7375 40
Kansas 354.6 35.4 13.7 5.8 9675 75
Kentucky 222.6 11.3 18.8 8.2 9728 35
Louisiana 496.9 107.1 19.1 7.1 19364 55
Maine 122.7 43.1 13.6 7.5 2826 5
Maryland 476.8 610.8 9.8 7 17956 250
Massachusetts 405.5 858 11.4 6.7 19282 150
Michigan 454.5 175 16.8 9.1 23165 120
Minnesota 230.9 68.1 11.5 5.6 13476 95
Mississippi 260.8 63.7 22.7 9 5662 25
Missouri 450.9 87.9 15.5 7 19487 65
Montana 272.2 7 15.2 6 2405 5
Nebraska 259.4 24.3 12.8 4 4943 55
Nevada 607.6 25.4 15 11.2 9447 210
New Hampshire 187.9 147.8 8.7 5.5 3436 10
New Jersey 290.2 1210.1 10.4 9.3 37881 525
New Mexico 559.1 17.2 20.4 7.1 6023 70
New York 406.8 417 15.3 8.5 79358 750
North
Carolina 353.4 202.6 17.5 9.2 33353 350
North Dakota 244.7 10.5 11.9 3.1 1968 5
27
Ohio 299.7 283.2 15.8 7.4 19288 95
Oklahoma 469.3 56.1 16.9 5.3 12445 100
Oregon 247.6 40.9 16.2 8.8 9918 120
Pennsylvania 348.7 285.5 13.3 7.9 30203 170
Rhode Island 252.4 1017.1 13.6 10.4 3045 35
South
Carolina 558.8 158.8 18.1 9.2 15135 95
South Dakota 321.8 11.1 14.1 4.3 2820 5
Tennessee 643.6 157.5 17.6 7.8 26268 130
Texas 408.6 101.2 17.6 6.7 72877 1,650
Utah 205.8 35.3 12.7 5.4 7042 100
Vermont 142.6 68 11.8 5 1677 5
Virginia 190.1 209.2 11.3 6 23625 275
Washington 295.6 104.9 13.4 8.1 14212 230
West Virginia 316.3 77.1 17.9 7.5 4475 5
Wisconsin 280.5 106 13 7 18638 85
Wyoming 201.4 6 11.5 5.3 2074 5
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