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

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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

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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.

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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.

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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.

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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

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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.

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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.

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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).

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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.

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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

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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

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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

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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

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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.

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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.

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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

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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

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Tryger, E., Chalfin, A., & Loeffler, C. (2014). Evidence from the secure communities program.

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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