IDENTIFYING THE EFFECT OF UNEMPLOYMENT ON PROPERTY CRIMES: ANALYZING THE IMPACT OF THE 2007/2008 ECONOMIC RECESSION

A Thesis submitted to the Faculty of the

Graduate School of Arts and Sciences of Georgetown University

in partial fulfillment of the requirements for the degree of

Master of Public Policy in Public Policy

By

Christiana M. Hollis, B.A.

Washington, DC April 6, 2011

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Copyright 2011 by Christiana M. Hollis All Rights Reserved

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IDENTIFYING THE EFFECT OF UNEMPLOYMENT ON PROPERTY CRIMES:

ANALYZING THE IMPACT OF THE 2007/2008 ECONOMIC RECESSION

Christiana M. Hollis, B.A.

Thesis Advisor: Jumana Poonawala, Ph.D.

ABSTRACT

In this paper, I analyze the effect of the unemployment rate on property crimes.

Employing US state-level data from 2005 to 2009, I estimate the impact of the 2007/2008

economic recession on property crime, using the unemployment rate as a measure of economic

health. In the paper, state-level demographics and economic factors are controlled through the

use of control variables and a fixed effects regression model. Furthermore, the regression model

employed contains controls for both state effects and year effects. I find significantly negative

effects of unemployment on property crime rates. The regression results make it difficult to

support the assertion that increases in the unemployment rate result in increases in the number of

properties crimes committed.

.

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The research and writing of this thesis

is dedicated to everyone who helped along the way.

Many thanks, Christiana M. Hollis

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TABLE OF CONTENTS I. Introduction ............................................................................................................................................. 1

II. Background ............................................................................................................................... 2

A. Review of Literature ..............................................................................................................5 B. Conceptual Framework and Hypothesis..............................................................................11

III. Data and Methodology............................................................................................................ 13  

A. Data Sources ...................................................................................................................... 13  B. Analysis Plan .......................................................................................................................16

IV. Results Section ....................................................................................................................... 18

A. Summary of Descriptive Statistics.......................................................................................20  B. Ordinary Least Squares Regression Results ........................................................................20 C. Fixed Effects Regression Results ........................................................................................21 D. Robustness of Empirical Results .........................................................................................26

V. Discussion Section ................................................................................................................... 28 VI. Conclusion .............................................................................................................................. 30 VII. Appendices ............................................................................................................................ 32  

A. Appendix 1-Summary Statistics By Year........................................................................... 32 B. Appendix 2-Summary Descriptive Statistics...................................................................... 36 C. Appendix 3- Stata Output ................................................................................................... 37 D. Appendix 4- Stata Log........................................................................................................ 39

VIII. References............................................................................................................................ 42  

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I. INTRODUCTION In September of 2010, the National Bureau of Economic Research determined that the recession,

which began in December of 2007 and ended in June of 2009, is the longest economic recession

in post-World War II period1. With the unemployment rate in the double digits for the first time

since the recession of the 1980’s and more than 8.4 million jobs lost, many wonder if the impact

of the recession will ever cease 2. The unemployment rate in the United States has doubled from

a relative low 4.6% in 2007 to a staggering 10% by the end of 2009. In 2010, the unemployment

rate averaged around 9.7%, with many Americans wondering what the impact of such high rates

of unemployment will have on the crime rate3.

Controlling the crime rate and maintaining the rule of law are fundamental goals of any

administration. In 2008, at the heels of a period of increasing unemployment combined with

escalating drug violence at the Mexican border, policy-makers in the administration were asked

to delve into research that elucidates and isolates the impact that high levels of unemployment

have on crime. Administration officials attempted to answer the question “How much should we

expect crime to rise in the…recession?”4. In an economic recession, the rise in the

unemployment rate and the fall in real income create an untenable situation for thousands of

unemployed individuals, who frequently must turn to illicit means to support their families.

Several experts have studied the connection between the unemployment rate and crime over the

years. Many experts, including Steven Raphael and Rudolf Winter-Ebmer, have found 1 National Bureau of Economic Research 2 United States Labor Department 3 http://www.ncsl.org/?tabid=13307 4 Steven Raphael and Rudolf Winter-Ebmer, “Indentifying the Effect of Unemployment on Crime”, Journal of Law and Economics, April 2001, p. 260  

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“significantly positive effects of unemployment on property crime rates”5. However given the

recent nature of the 2007/2008 economic recession, no research has been conducted to determine

whether or not these findings remain true for the latest and arguably most devastating recession

since the Great Depression. As such, this paper attempts to address the question: how does the

unemployment rate affect property crimes in the 2007/2008 economic recession? By combing

three data sets, the Census Bureau’s American Community Survey, the Federal Bureau of

Investigation’s Uniform Crime Report, and the United States Bureau of Labor, Local Area

Unemployment Statistics, I created a forum with which to test my hypothesis that the rising

unemployment rate should result in a rise in the number of property crimes committed6.

However, the only way to isolate the effect of the unemployment rate on property crimes across

various states in America is by employing panel data along with a fixed effects model. The fixed

effects model would control for fixed state effects that vary over the duration of the panel, as

well as year effects.

II. BACKGROUND

Nearly one third of the states in the U.S. experienced the lowest level of unemployment in

history between 2005 and 2007. The range of unemployment rates among states was between 2.5

and 7.1 7. For several states, the latest economic recession, from 2008 until the present, has

induced the highest levels of unemployment experienced in history, with the range of

unemployment rates among states increasing from 3.7 to 13.68. Many economists,

5 Ibid, p.259 6 I combined the three data sets by hand and then merged them into STATA, in order to obtain a viable dataset with which to address my thesis question. 7 Bureau of Labor, http://www.bls.gov/web/laus/lauhsthl.htm 8 Ibid

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criminologists, and sociologists have conducted research to assess the impact of varying levels of

unemployment on crime. This paper attempts seeks to understand the causal relationship

between the 2007/2008 economic recession and property crimes9, from 2005 to 2009 using the

unemployment rate as a measure of economic health.

The assertion that high levels of unemployment result in an increase in property crimes

and criminal activity is based in the economic principal that people are rational creatures who

respond to incentives10. The labor-leisure model provides a general framework through which to

delve into this widely held assertion. The labor-leisure model expands the concept of a budget

constraint to deal with how one spends hours in a day, given the constraints on time. The model,

when applied to criminal activity, assumes that a “person converts non-market time into income

by engaging in either legitimate employment or income-generating criminal activity”11.

Moreover, the model operates on the principle of diminishing marginal returns stemming from

the principle of rational choice. In other words, people are rational creatures who respond to

incentives and as a consequence, “individuals first commit crimes with the highest expected

payoffs (lowest probability of getting caught and highest stakes) before exploring less lucrative

opportunities”12. This theory provides the reasoning behind why times of economic recession and

high unemployed generally lead to a spike in property crimes that are frequently classified as

misdemeanors and carry with them less jail time.

9 There are seven felony offenses: Burglary, Larceny, Auto Theft, Murder, Rape, Robbery and Assault. The first three crimes constitute property crimes in the study and the last four, violent crimes. 10 Steven Raphael and Rudolf Winter-Ember, “Identifying the Effect of Unemployment on Crime”, Journal of Law and Economics, p. 262 11 Ibid 12 Ibid

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People are rational creatures who respond to incentives. As such, times of economic

prosperity provides an increase in the opportunity to participate in legitimate employment, as an

increasing number of jobs are created. As a consequence, people are less likely to participate in

income-generating criminal activity. The Labor-Leisure model also provides an explanation for

why in times of economic prosperity, in this scenario 2005 to 2007, should result in a decline in

the number of property crimes committed. Understanding the economic incentives behind

property crimes provides a vital basis to exploring the relationship between the unemployment

rate and property crime during the 2007/2008 economic recession.

Property crimes are merely one of seven categories of crime. The Federal Bureau of

Investigation defines a property crime, as a combination of burglary, larceny and auto theft13. In

contrast to the relatively non-violent nature of property crimes, there is a category of violent

crime. The FBI similarly defines violent crime, as the additive calculation of murder, rape,

robbery and assault14. The labor-leisure model explained in detail above provides an economic

justification to commit crimes that carry with them less jail time, in this case property crimes.

The economic benefit of committing a violent crime, which is accompanied by a longer period of

incarceration, remains far more uncertain. This rationale provides another reason to explain why

this paper focuses on property crimes, in order to truly isolate the impact of economic factors on

crime.

This paper focuses on exploring the relationship between the unemployment rate and

property crimes, primarily as a result of the findings of other economists in the field. Several

13  Ibid, p.270  14  Ibid, p.270  

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experts including Steven Raphael and Rudolf Winter-Ebmer have found the relationship between

the unemployment rate and other forms of crime to be less evident, stating for example “the

evidence for violent crime is considerably weaker”15. In determining how economic factors

affect property crimes, one must seek to study the relationship in times of extreme economic

prosperity and decline.

Therefore, in order to properly evaluate the impact of unemployment on property crimes,

the most appropriate analytical method seeks to follow the state unemployment level from its

historical lows to historical highs. Analyzing the extreme levels of unemployment provides a

clearer picture, as to the precise relationship between the unemployment rate and property

crimes. While the 1980’s recession may have reached higher unemployment rates than the 2008

recession, the overall change in the unemployment rate was larger in the 2008 recession, given

the low rates of unemployment at the beginning of the 21st century. As such, I am going to

employ the Federal Bureau of Investigations, Uniform Crime Reports and Census Bureau’s,

American Community Surveys from 2005 until 2009, to assess the impact that the employment

rate has on property crimes, using the following variables: unemployment rate, number of arrests

for property crimes, number of police, race, income, and age.

A. REVIEW OF LITERATURE

Over the years, numerous economists, criminologists, and sociologists have spent countless

hours researching the relationship between unemployment and crime. While many articles have

been published on the subject, I found the following four to be the most helpful to test the impact

15  Steven Raphael and Rudolf Winter-Ebmer, “Indentifying the Effect of Unemployment on Crime”, Journal of Law and Economics, April 2001, p. 259  

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of the 2007/2008 economic recession on property crime rates. The articles are as follows:

“Identifying the Effect of Unemployment on Crime”16 by Steven Raphael and Rudolf Winter-

Ember, “Rates of Crime and Unemployment: An Analysis of Aggregate Research Evidence”17 by

Theodore G. Chiricos, “Crime and the Business Cycle”18 by Phillip J. Cook and Gary A. Zarkin,

and “Some Determinants of Property Crime: Economic Factors Influence Criminal Behavior but

Cannot Completely Explain the Syndrome”19 by Roy M. Howsen and Stephen B. Jarrell. In this

section of the paper, I will explore the intent, methodologies and findings of each article, as well

as how this thesis will expand on previous research.

In the article “Identifying the Effect of Unemployment on Crime” published in The

Journal of Law and Economics, Steven Raphael and Rudolf Winter-Ember analyze the

relationship between unemployment and crime. The study employs U.S. state data to determine

the extent to which the unemployment rate affects seven felony offenses: Burglary, Larceny,

Auto Theft, Murder, Rape, Robbery and Assault. The first three crimes constitute property

crimes in the study and the last four, violent crimes. The paper employs a state-level panel from

1971-1997 to estimate the effect of unemployment rates on all seven-crime rates20. The study

then runs a Two Stage Least Squares regression to estimate the effect of unemployment on the

16  Steven Raphael and Rudolf Winter-Ember, “Identifying the Effect of Unemployment on Crime”, Journal of Law and Economics, April 2001  17  Theodore Chiricos, “Rates of Crime and Unemployment: An Analysis of Aggregate Research Evidence”, Social Problems, 1987  18  Philip J. Cook and Gary A. Zarkin, “Crime and the Business Cycle”, The Journal of Legal Studies, 1985  19  Roy M. Howsen and Stephen B. Jarrell, “Some Determinants of Property Crime: Economic Factors Influence Criminal Behavior but Cannot Explain the Syndrome”, American Journal of Economics and Sociology 1987  20  Steven Raphael and Rudolf Winter-Ember, “Identifying the Effect of Unemployment on Crime”, Journal of Law and Economics, p. 261

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seven types of crime using the following variables: unemployment rate, prison population,

alcohol consumption, metropolitan, poor, black, income per worker, population age, military

spending and oil costs21. The variables military spending and oil costs were used as instruments,

in order to isolate the precise effect of the unemployment rate on crime. The authors believed

that too many other factors affect the unemployment rate, therefore they chose two instrumental

variables that satisfied the conditions of a valid instrument22. The instrument variable must be

correlated with the x variable of interest and not with crime rate. In the first stage of the equation,

the Raphael and Winter-Ember paper employs numerous variables including controls along with

one of the instruments. The aforementioned equation is then regressed on the unemployment

rate. In the second stage, the new estimated value of the unemployment rate using the

instruments replaces the original unemployment variable in the regression. A simple ordinary

least squares (OLS) regression was then conducted on crime with the variables mentioned above.

The researchers chose to include the aforementioned instrumental variables, as well as other

variables, to both extensively control for observable economic and demographic data, while also

exploiting the aspects of panel data by eliminating bias associated with fixed effects across

states. By creating state level panel data, the researchers were able to eliminate fixed biases

across states. In other words, characteristics that are not directly measured in the model, but are

fixed and vary across states can be eliminated using the model23. The study found that for

21 Ibid, p. 270 22 An instrumental variable must satisfy the following 2 conditions: 1) the instrument must be correlated with the x variable of interest, in this case unemployment rate, and 2) the instrument must not be correlated with the error term in the original equation. 23 A Fixed Effects Regression model eliminates fixed effect across the panel or characteristics that are unique to that state, which similarly does not vary over time. This allows for otherwise differing states to be compared to one another in a sound regression model.

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property crime rates, there is a clear relationship between unemployment and crime. As

unemployment rises, property crimes rise as well. This phenomenon is referred to as a

countercyclical effect. The study produces far less clear results with respect to the relationship

between unemployment and violent crimes. This paper provides the main model for my thesis,

which focuses on and builds on the relationship between unemployment and property crime. This

regression model will be adapted and expanded, in order to be tested on the newly published

2009 Census Data to delve further into the relationship between unemployment and property

crime in times of economic distress. In the article “Rates of Crime and Unemployment: An

Analysis of Aggregate Research Evidence” published in Social Programs, Theodore G. Chiricos

clarifies the relationship between unemployment and crime. He argues that an economic

recession provides the perfect test tube in which to thoroughly test and examine the

unemployment crime relationship24. Chiricos examined 63 studies published from 1960 to 1980

that measure some relationship between crime and unemployment25. He uses cross sectional data

to “indicate whether the net relationship between unemployment and specific crime rates is

positive (+) or negative (-), and whether the relationship between the two is statistically

significant”26. The author concludes that property crimes are more likely to produce both positive

and statistically significant results compared to violent crime. This research has underlined the

nature of the relationship between unemployment and crime, while also providing a justification

for assessing the relation during a time of economic recession.

24 Chircos outlines 14 types of crimes including: all crimes, property crimes, violent crimes, general crime, burglary, larceny, auto theft, general property, other property, murder, robbery, rape, assault, and general violent 25 Ibid, p.191 26 Ibid, p. 192

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In 1985, the Journal of Legal Studies published research conducted by the Philip J. Cook

and Gary A. Zarkin entitled “Crime and the Business Cycle”. The study asserts a countercyclical

relationship between crime rate and unemployment. The study goes on to argue that a recession

drives an increase in unemployment with a resulting increase in crime27. The paper focuses on

the relationship between unemployment and four types of crime including: criminal homicide,

robbery, burglary, and auto theft28. The research evaluates crime data from 1933 until 1981,

highlights nine business cycles for the purpose of assessing the impact of short-term fluctuations

in economic activity on the crime rate29. The paper uses an OLS regression to run the

unemployment rate and employment ratio on various types of crime, in an effort to determine

which variable does a better job predicting the relationship. The study is unclear about the

additional variables added to the model to control for unobserved factors that may be biasing the

relationships. The study uses fluctuations in the business cycle, in the form of the unemployment

rate and the employment ratio, as a measure of economic health. When the economy is

experiencing economic success, one would expect the business cycle to be favorable, as business

are growing and expanding. Furthermore, in these times, the unemployment rate should remain

low and the employment ratio should stay consistently high. The reverse should be true from

times of economic recession. The study concluded that the business cycle, in the form of changes

in the unemployment rate, affects the property crime rate. While the methodology of the research

remains inconclusive, the study provides further support for the extension of the model to

determine the impact of the current economic recession of 2007/2008 on property crime rates. 27 Philip J. Cook and Gary A. Zarkin, “Crime and the Business Cycle”, The Journal of Legal Studies 1985, p. 116 28Ibid, p. 124 29 Ibid, p. 120  

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The fourth article of interest is “Some Determinants of Property Crime: Economic

Factors Influence Criminal Behavior but Cannot Completely Explain the Syndrome” by Roy M.

Howsen and Stephen B. Jarrell. The study uses data from all 120 counties in Kentucky in 1980

and 1981 to evaluate what economic, sociological and other factors aid in explaining the

variation in the crime rate30. The model was based on the work of Becker (1968) and Elrich

(1973), but expanded to include more non-economic factors, including variables to control for

demographic factors and crime factors31. Three separate regressions were run to evaluate the

explanatory power of economic variables, as compared to other factors such as police density.

The first regression was a crime equation based on the following socioeconomic factors:

percentage of the population that is black, percentage of population which has a high school

education or less, percentage of the population between the ages of 15 and 24, percentage of the

population which is below 75 percent of the poverty line, degree of industrialization, percentage

of total households with low family ties, the level of tourism, public assistance to low income

families and the extent of urbanization32. The second regression run was a clearance equation,

which measured the number of property crimes that were resolved, with the following variables:

overall crime rate, overall crime rate lagged one period, and the arrest rate for all crimes33. The

final regression run was a police equation, which employed the following variable: the number

30  Roy M. Howsen and Stephen B. Jarrell, “Some Determinants of Property Crime: Economic Factors Influence Criminal Behavior but Cannot Explain the Syndrome”, American Journal of Economics and Sociology 1987, p. 446 31  Ibid  32  Ibid, p. 448  33  Ibid, p. 449  

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of police per county34. The specifications were run individually and then pooled to determine the

extent of the explanatory power for the individual equations, as well as the combined factors in

an OLS regression. The results were intuitive to a large extent. The law enforcement or police

equation confirmed that increased numbers of police deter criminal activity. The economic

equation corroborated previous studies that suggested that “illegal returns are a significant

attraction and cause property crimes to increase”35. The socioeconomic variables of poverty and

family ties again have a prominent effect on property crime rates, as one would expect.

B. CONCEPTUAL FRAMEWORK AND HYPOTHESIS

In this paper, I will test the following hypothesis: the 2007/2008 economic recession has resulted

in an increase in property crime. The choice in my hypothesis is based on the work of the

numerous researchers described in the Literature Review section of this paper. My thesis shall

take the work of previous researchers and apply their methodology to the most recent economic

recession. In order to address the hypothesis, I intend to use a regression model that is extremely

similar to the one outlined by Steven Raphael and Rudolf Winter Ember, including the following

variables:

34  Ibid, p. 451 35  Ibid, p. 452  

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The above diagram outlines the conceptual framework of my thesis. Property crime is the

dependent variable in the regression, with the main independent variable as the unemployment

rate. Income, age and race are used primarily to control for demographic variables that differ

across state and time. The number of police officer and arrests for property crimes are included

to control for the “Z” factor, or other factors that could be driving the regression results beside

the unemployment rate. Please see the Data and Methods section of the paper, for a detailed

description of how each variable is measured and defined. Below is the regression equation that

will be explained in further detail in the Data and Methods section.

Property  Crime  

Unemployment  Rate  

Income  

Age  

Police  officers    

Arrests  

Race  

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PropertyCrimeskt = a + β1UnemRtkt+ β2Policekt+ β3Incomekt+ β4Blackkt+ β5Asiankt+ β6 Other_Raceskt+ β7Pop_under15kt+ β8Pop_18to24kt+ β9Pop_25to34kt+β10Pop_35to44kt+β11Pop_45to54kt+β12Pop_55to64kt+β132006k+ β142007k+β152008k +β162009k + β16-66State Dummiest

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III. DATA AND METHODOLGY

A. DATA SOURCES

By combing the Census Bureau’s American Community Survey (ACS) and the Federal

Bureau of Investigation’s Uniform Crime Report (UCR), I was able to create a viable dataset to

identify how the economic recession, (unemployment rate), has impacted crime (property crime).

Table 1: Data Sources

Data Name Period Covered

Target Population

Frequency of Data Collection Short Description

American Community Survey

2005-2009

2.5% of the US Population Annually

The ACS issues a housing survey to the American people on annual basis. The data is then extrapolated and aggregated to form national and state level estimates.

Uniform Crime Report

2005-2009

All law enforcement agencies Monthly

The UCR is a nationwide, cooperative statistical effort created to allow law enforcement agencies to voluntarily report data on crimes.

Local Area Unemployment Statistics

2005-2009

State and National unemployment rate Quarterly

The LAUS is a relatively new initiative started to provide Americans with the national and state unemployment rates.

In a relatively recent move, the Census Bureau has begun collecting yearly data on

economic conditions and demographics to assess the quality of life of the average American

citizen. The American Community Survey (ACS) employs the Master Address File (MAF)

36  k= state, t= time period

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maintained and operated by the Census Bureau, to survey approximately two and a half percent

of the United States population a year. The result of the household survey on a micro-level are

then extrapolated and aggregated to form national and state level data37. The data are collected on

an annual basis. Since 2005, the ACS has conducted the survey of the United States population

through two separate samples: housing unit (HU) addresses and group quarters (GQ) facilities38.

The response rate to the survey ranges from year to year with a low of 97.3% in 2005 and high of

98% in 200939. The ACS sampling is determined by the MAF, which sorts each address into one

of five subcategories based on the size of the sampling entity40. No address can be sampled more

than once in a five-year period. In the second stage of sample selection, remaining viable

addresses are selected for sampling based upon predetermined figures to get the most accurate

estimate of the U.S. population. The chosen households then enter the data collection phase

either through mail, telephone or personal visit in the months after their selection41. Those

household surveys that are not completed in the allotted amount of time are referred to the

computer-assisted telephone interviewing (CATI)42. A similar procedure is followed for General

Quarters facilities, however the first stage stratum is only three subgroups instead of five43.

37  American Community Survey, “ACS Design and Methodology”, United States Census Bureau, 2009, p. 1  38  Ibid, p. 1  39  http://www.census.gov/acs/www/methodology/response_rates_data/  40  American Community Survey, “ACS Sample Design and Selection”, United States Census Bureau, 2009, p. 4  41  Ibid, p. 5  42  Ibid, p. 3  43  Ibid  

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Every year since 1930, the Federal Bureau of Investigation (FBI) has complied crime

statistics to assess and monitor the severity of crime throughout the country44. The FBI’s

Uniform Crime Report (UCR) program is “a nationwide, cooperative statistical effort of nearly

18,000 city, university and college, county, state, tribal, and federal law enforcement agencies

voluntarily reporting data on crimes brought to their attention”45. The UCR program in 2009

represented “more than 295 million United States inhabitants—96.3 percent of the total

population”46. To get a complete picture of the data from 2005 to 2009, I compiled relevant

statistics on each individual variable of interest from 2005 to 2009 including: property crimes,

unemployment rate, property crime arrests, average per capita income, race, and age. The

Uniform Crime Report program functions by asking each law enforcement agency across the

United States to submit a monthly report providing the details on the type and nature of the crime

committed. The crime data is then aggregated to an annual level, which is employed in my

analysis. While local agencies are frequently required to submit data on a monthly basis to the

FBI, occasionally agencies neglect to provide the data for the entire year. When this phenomenon

occurs, “the national UCR Program estimates for the missing data by following a standard

estimation procedure using the data provided by the agency… known crime figure of similar

areas within a state… population size covered by the agency… type of jurisdiction…and

geographic location”47. The specific variables of property crime, arrests and police by state are

44 Federal Bureau of Investigation, “Summary of the Uniform Crime Reporting (UCR) Program”, 2009, p.1 45 Ibid 46 Ibid 47 Federal Bureau of Investigation, “Methodology of Uniform Crime Reporting (UCR) Program”, 2009, p.6  

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figures that are collected or estimated on a national level to encompass the entire population of

the United States.

The United States Bureau of Labor, Local Area Unemployment Statistics, provided a

resource to obtain the unemployment rates by state. In order to combine these three data sources

together, I am individually compiling each year of data in an excel spreadsheet. The excel sheet

will be merged into STATA using both the year and state, as the defining identifying marker.

With the compiled dataset, there are numerous limitations. The majority of my analysis

will be based largely on estimates of both the economy and population. The availability of

published 2010 Census data would resolve this problem. However, the release of this census data

will not occur for at least another year. In contrast to the ACS data, the 2010 Census data is of

larger in scope, as the entire nation is surveyed, not merely a small fraction of American

families. Until the release of the 2010 Census data only estimates exist for the majority of

relevant variables.

B. ANALYSIS PLAN My thesis question is “how does economic change, in the form of the unemployment rate affect

crime in the 2007 economic recession.” I am combining statistics from the 2005 to 2009

American Community Survey conducted by the Census Bureau, the 2005 to 2009 Uniform

Crime Report Program published by the Federal Bureau of Investigation and the 2005 to 2009

Local Area Unemployment Statistics published by the Bureau of Labor. The population that I

plan to analyze is the entire United States population by state.

My dataset will be panel data for each year, 2005 to 2009, using a fixed effects model

and then ultimately running a pooled OLS regression on the data. The Fixed Effects Model

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allows for the bias associated with fixed characteristics across states to be eliminated from the

model. In running my fixed effects model, I plan to employ the dummy variable regression,

which estimates “an intercept for each i… to put in a dummy variable for each cross-sectional

observation, along with the explanatory variables”48. Dummy variables will also need to be

created for each year, minus the one reference year, in this case 2005. Similarly, I will create

dummy variables for each state. The dummy variable method is simply another way of obtaining

the fixed effects estimator model, without having to take the means and deviation of each

observation. In the article “Identifying the Effect of Unemployment on Crime: published in the

Journal of Law and Economics, Steven Raphael and Rudolf Winter-Ember also employ fixed

effects panel data to assess the very same phenomenon and then conduct a OLS regression on the

pooled data. My thesis will contribute by attempting to extend their model into the recent

economic recession. Since the panel of state data I am creating is more than two years long a first

difference model cannot be employed49.

In my specified model, the dependent variable is the number of property crimes

committed per capita50. The independent variables are as follows: unemployment rate, number of

police, total population, race, age by sex, poverty status in the past 12 months and per capita

income in the past 12 months. Each variable above was then transformed in a per capita estimate

using the total population variable. Moreover, all of the aforementioned variables with the

exception of number of police were included in the Raphael and Winter-Ember article and are 48 Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach, p. 485 49 After speaking with numerous experts in regression analysis from Eric Gardner to Michael Barker to Harry Holzer, the aforementioned model is the best method of running a regression with the aim of answering my specific thesis question. 50 After extensive discussions and consultations with Mike Barker, the regression analysis functions more succinctly if the variables are measured in per capita changes.

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consequently included in this model. The police variable measured by the number of police

officers by state and the number of arrests by state are added to control for the “Z” factor or the

presence of another variable that could be driving the regression results besides the

unemployment rate. Each variable is again analyzed on the five-year period of 2005 to 2009.

Below is a complete definition for each of the employed variables in the American

Community Survey. The total population variable is discrete, only taking on the values of whole

numbers, based on data submitted by the states and estimates created by the Census Bureau. The

race variable can take on the following 9 values: 1) white alone, 2) Black or African American

alone, 3) American Indian alone, 4) Alaska Native alone, 5) American Indian specified or

unspecified, 6) Asian alone, 7) Native Hawaiian and other Pacific Islander alone, 8) Some other

race alone, and 9) two or more major race groups51. Per capita income is top-coded at

$9,999,999 and income losses are bottom-coded to -$9,999. The sex by age variable is a

continuous variable that can take on any value, but is based off of estimates from the states and

weights devised but not specified by the aggregate state level data. The ACS data collects data

on the age of individuals, but separates the age variable by gender. More information is still

required regarding the weights associated with certain variables from the newly published ACS

codebook, however at this time the information does not appear to be readily available online.

IV. RESULTS SECTION

In this section, I present the main empirical results. First, I discuss the implications of the

summary statistics of the five-year panel, as well as the mean results. Next, I shall discuss the

OLS regression results and the shortcomings of the linear model. Lastly, I will present the results

51  American Community Survey: Codebook, p. 87  

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of the fixed effects model and discuss the implications of the results. The final section will deal

with overall shortcomings in the aforementioned model, as well as ways to improve this form of

analysis in the future.

Table 2: Regression Results Independent Variables OLS

Fixed Effects

Unemprt -0.0001*** -0.0018*** [.0004] [.003] totofficers -8.39*** -6.9724*** [1.1340] [.0005] income000 0.0002 -0.0017*** [.0002] [1.6731] Black 0.0436*** 0.0125 [.0103] [.0290] Asian -0.0373** -0.0949*** [.0150] [.0262] Other_Race 0.0222 -0.0019 [.0186] [.0282] Age Under 15 years old 0.6027*** 0.6635*** [.0906] [.0831] 15 to 17 years old -2.5404*** -2.7633*** [.4986] [.4834] 18 to 24 years old 0.1271 0.339*** [.0898] [.0858] 25 to 34 years old -0.1986** -0.2394** [.0999] [.0959] 35 to 44 years old 0.1788* 0.2326* [.0973] [.1313] 45 to 54 years old -0.1167 -0.2606* [.1281] [.1381] 55 to 64 years old 0.3663*** 0.3742*** [.0748] [.0815] Observations 250 250 R-squared 0.968 0.928

*Denotes significance at 10% level; ** Denotes significance at 5% level; *** Denotes significance at the 1% level.

20

[] Denote Standard Errors A. SUMMARY OF DESCRIPTIVE STATISTICS

The tables provided below in both Appendix 1 and 2 depict a clear picture of the trends that

occurred in both property crimes and the unemployment rate from 2005 to 2009. The

unemployment rate was lowest in 2007 with a mean rate of 4.34. Once the economic recession

commenced in December of 2007, one can visibly see an immediate impact in the

unemployment rate, as it rose from 4.34 in 2007 to 8.45 in 2009. While the unemployment rate

increased over the five-year panel, remarkably, the number of property crimes declined from

202,801 in 2005 to 196,291 in 2007 to 185,859 in 2009. The descriptive statistics show the

following relationship: the rising unemployment rate did not yield a rise in the number of

property crimes committed. The descriptive statistics run counter to the literature in the field,

including the work of Steven Raphael, Rudolf Winter-Ember, Theodore G. Chiricos, Phillip J.

Cook, Gary A. Zarkin, Roy M. Howsen and Stephen B. Jarrell.

B. ORDINARY LEAST SQUARES REGRESSION RESULTS

Table 2 presents regression results where the dependent variable is the per capita number of

property crimes committed. The first column provides the results from an Ordinary Least

Squares regression. In the OLS model, the effect of unemployment is negative and significant at

the 1 percent level of confidence. The magnitude of the relationship indicates that a 1 percentage

point increase in the unemployment rate results in a decrease of .0001 percentage points in the

number of property crime committed per capita. In the OLS regression results, eight of the

independent variables added to the model to control for demographic and other characteristics

are statistically significant. Income is controlled for inflation, thereby creating an estimate in real

21

dollars and not nominal dollars, and measured in thousands of dollars. However, income is not

statistically significant at any conventional levels.

The findings support the assertion that increases in the minority population result in an

increase in the number of property crimes committed per capita, as the coefficient on Black is

positive and statistically significant at the 1 percent level. The Asian demographic variable is

negative and statistically significant at the 5 percent level. These observed relationships again

support the findings of Steven Raphael and Rudolf Winter-Ember, Roy M. Howsen and Stephen

B. Jarrell in “Some Determinants of Property Crime: Economic Factors Influence Criminal

Behavior but Cannot Completely Explain the Syndrome”52. Lastly, five of the age variables are

statistically significant at the 10, 5, and 1 percent level. The results support the work of Steven

Raphael and Rudolf Winter-Ember, who found that “both property and violent crime rate are

higher in states with higher proportions of teenagers and young adults”53. Similarly, both my

results and their results observe a significant and unexplained positive effect by the group of 55

to 64 year olds and a subsequent, increase in property crimes.

C. FIXED EFFECTS REGRESSION RESULTS

The third and final column in Table 2 presents the results of the fixed effects model. The results

of this model clearly indicate findings that run counter to the literature in field, as previously

mentioned. In the fixed effects model, the effect of unemployment is negative and significant at

52  Roy M. Howsen and Stephen B. Jarrell, “Some Determinants of Property Crime: Economic Factors Influence Criminal Behavior but Cannot Explain the Syndrome”, American Journal of Economics and Sociology 1987  53  Steven Raphael and Rudolf Winter-Ember, “Identifying the Effect of Unemployment on Crime”, Journal of Law and Economics, April 2001, p. 272  

22

the 1 percent level of confidence. Moreover, the pvalue is .00. The magnitude of the relationship

indicates that a 1 percentage point increase in the unemployment rate is associated with a .0018

decrease in per capita property crimes.

Furthermore, the variable of total officers is statistically significant at the 1 percent level.

One would expect that as the number of police officers increases, the incentives to commit

property crimes would decrease, as the odd of being apprehended and receiving the expected

payoff decline54. The findings support the assertion, as one percentage point shift in the number

of police officers is associated with a 6.972 decrease in per capita property crimes. In other

words, as the number of police officers per capita increases, the number of property crimes per

capita decrease.

Similarly, income is statistically significant at the 1 percent level in Fixed Effects model.

A one-percentage point increase in the per capita income of an individual measured in thousands

of dollars is associated with a .0017 decrease in per capita property crimes. The results suggest

that as income rises, the number of property crimes committed per capita should decline. This

observation again remains consistent with the findings of work of Raphael and Winter-Ebmer

who suggest that the results remain in line with the legitimate labor market opportunities

discussed in the Labor Leisure model55. However, the findings do not show whether or not lower

income brackets felt the effects of the recession more and thereby, accounted for a larger number

of property crimes.

54 Ibid, p. 262  55 Ibid, p. 272

23

Similar to the OLS model, one of the race variables is statistically significant at the 1

percent level. A one-percentage point shift in population of Asian people is associated with .0949

decrease in property crimes per capita, as compared to whites. These findings remain consistent

with the work of economists in the field like Steven Raphael and Rudolf Winter-Ember, who

argue that race and the demographics of neighborhood have a positive observable impact on the

number of property crimes committed. Many assert that as the number of minorities [mainly

Hispanics and Blacks] in the neighborhood increases, the neighborhood reaches a tipping point at

which the white and middle class residents flee the neighborhood. This syndrome is referred to

as white flight and is proven to produce disastrous effects on minority neighborhoods throughout

the country.

Lastly, all seven age variables are statistically significant at various levels of significance.

The people in the under 15, 15 to 17, and 18 to 24 category are statistically significant at the 1

percent level. The model predicts that a one-percentage point shift in population from the under

15 age group is associated with a .664 decrease in per capita property crimes. This observation is

consistent with the findings of Raphael and Winter-Ebmer who argue that children below the age

of 15 do not experience enough freedom from the watchful eyes of parents to have adequate time

to perpetrate such crimes.

For the 15 to 17 age category, The model predicts that a one-percentage point shift in

population from the 15 to 17 age group is associated with a 2.763 decrease in per capita property

crimes. This observation runs counter to the literature in the field, as many experts such as

Raphael and Winter-Ebmer argue that a clear crime-age profile for property crimes emerges in

24

states with “higher proportions of teenagers and young adults”56. However, the observed

relationship could be a function of the economic recession. As more parents are fired from jobs

and spend greater amounts of time at home, perhaps the teenagers subsequently have less

freedom to commit crimes.

Similarly, the model predicts that a one percentage point shift in population from age18

to 24 is associated with a .339 increase in per capita property crimes, at the 1 percent level of

significance. In other words, the model suggest that as the number of people in the 18 to 24

category increase, one should observe a decline in the number of property crimes. These findings

again appear to be consistent with the findings of other scholars in the field, including Steven

Raphael and Rudolf Winter-Ember who suggest that higher numbers of teenagers and young

adults should result in an increase in the number of property crimes committed57.

The 25 to 34 age category is statistically significant at the 5 percent level. The model

predicts that a one-percentage point increase in population from age 25 to 34 is associated with a

.239 decrease in per capita property crimes. This observed relationship again remains consistent

with findings in the field, who suggest as people approach their mid twenties they are more

likely to seek gains from legal employment. This transformation is primarily the result of

maturity and the need to find legitimate means to support a family58.

The 35 to 44 age category is statistically significant at the 10 percent level. A one

percentage point shift in population from age 35-44 is associated with a .233 increase in per

capita property crimes. This observation runs counter to the literature in the field, as many

56 Ibid 57 Ibid 58 Ibid, p. 274

25

experts such as Raphael and Winter-Ebmer argue that people in this age bracket should still be

searching for legitimate means to support their families. However, in the latest economic

recession, perhaps more people in this age category lost their jobs as a result of cost and lack of

seniority. The loss of legitimate employment could have forced many to find illicit means to

continue to support their families.

The 45 to 54 age category is statistically significant at the 10 percent level. The model

predicts that a one percentage point change in population from age 45-54 is associated with a

.261 decrease in per capita property crimes. The finding remains consistent with the work of

Steven Raphael and Rudolf Winter-Ember, who argue that as people age the benefits reaped

from illicit criminal activities decline and the ability to engage in such activities decline as well

due to health effects of old age59.

Lastly, a positive effect and statistically significant relationship at the 1 percent level was

observed in the 55 to 64 year old group that corresponds with the OLS results. The model

predicts that a one percentage point change in population from age 55-64 is associated with a

.374 increase in per capita property crimes. This observation runs counter to the literature in the

field, as many experts such as Raphael and Winter-Ebmer argue that people in this age bracket

should not engage in large numbers of illicit criminal activities, due to the problems associated

with aging such as health effects60. However, this observation could be the result of could be

capturing the effect of some unobserved variable that has yet to be identified.

59 Ibid, p.272 60 Ibid

26

At the commencement of this thesis project, I anticipated that the increase in the

unemployment rate caused by 2007/2008 economic recession would result in an increase in the

number of property crimes committed, based on previous literature in the field. However, the

observed results differed from the finding of Roy M. Howsen and Stephen B. Jarrell, mainly in

the fact that the rising unemployment rate was not observed to produce an increase in the number

of property crimes. Existing literature in the field supports the observed relationship of several

variables including: officers, income, race variables, and several key age variables (under 15

category, 18 to 24 category, and 45 to 54 category). Given the methodology employed in the

model and the findings of the descriptive statistics, the relationship between the unemployment

rate and property crimes in the latest economic recession is one that cannot be ignored. The

regression results make it difficult to support the assertion that a rise in the unemployment rate

causes a rise in the number of property crimes committed. These findings have implications in

the realm of public policy, as analysts will need to understand the reasons for the decline in

crime, despite the onset of the worst economic recession in 70 years.

D. ROBUSTNESS OF EMIRICAL RESULTS

While the results provided above appear to run counter to a majority of the findings in the field.

The mechanisms employed in the regression analysis, mainly a fixed effects model based on

panel data, have been employed by countless experts in the field including Steven Raphael and

Rudolf Winter-Ember, “Identifying the Effect of Unemployment on Crime”. However, the model

presented above inherently carries with it numerous shortcomings, the first of which deal with

the source of the data. The American Community Survey was created by the Census Bureau to

27

provide researchers with yearly estimates of the economy and demographic changes. However,

the results of the survey are just that, merely estimations of what is taking place in the United

States. In order for proper conclusions to be drawn about the relationship between the

unemployment rate and property crimes in the latest economic recession, one would ideally want

to use the data from the 2010 census. The 2010 census would provide the most accurate and

complete picture of changes that the United States underwent through the 2007 to 2009

economic recession. Given the year of lag time for the publication of results from the 2010

census, one can only work with alternative data sets until the data becomes available in January

of 2011. The second way in which the results may have been improved is through the use of an

instrumental variable. Some economists in the field including Steven Raphael, Rudolf Winter-

Ember and Harry Holzer believe that a number of other factors affect the unemployment rate

beyond demographic characteristics61. As a result, they argue that an instrumental variable

should be used in order to properly isolate the effect of the unemployment rate on property

crime. Yet, the use of instrumental variables in published literature is a relatively new

phenomenon. A viable instrumental variable must satisfy two criteria: 1) the instrument must be

correlated with unemployment and 2) the instrument must not be correlate with the error term in

the original equation or property crimes62. However, it is extremely difficult to find an instrument

variable that adheres to second criteria, thereby remaining uncorrelated with the error term in the

original equation.

61 Jeffrey Wooldridge, Introductory Econometrics: A Modern Approach, p. 483 62 Ibid, p. 485

28

The observed results of the fixed effects model could also be attributed to any one of the

following unobserved three effects. First, perhaps the reason why the unemployment rate did not

produce an immediate increase in the number of property crimes could be due to a lagged effect.

The change in unemployment does not produce result in property crimes until a subsequent time

period. Second, a missing or omitted variable could also account for the observed negative effect

of the unemployment rate on property crimes. Perhaps there is some key variable or factor that is

unaccounted for in the regression model that is driving the observed relationship, such as alcohol

consumption or drug use. Lastly, the possibility exists that the impact of unemployment on

property crimes varies depending on income brackets. The regression model does not account for

this possibility, as it deals with aggregated state data.

V. DISCUSSION SECTION

Previous scholars in the field have asserted a simple fact: “higher unemployment unambiguously

increases property crime rates”63. However, the aforementioned relationship was not observed

using the regression construct employed. The findings indicate that perhaps unemployment is no

longer enough to secure a decrease in the number of property crimes. The results support two

assumptions: increasing both income and police officers should result in a decrease in property

crime. These findings should be kept in mind when pursuing policy to lessen the number of

property crimes committed per capita.

Economic and crime data produced by the 2007/2009 economic recession provided

plausible solutions to decreasing the prevalence of property crime in America. The results

63  Steven Raphael and Rudolf Winter-Ember, “Identifying the Effect of Unemployment on Crime”, Journal of Law and Economics, April 2001, p. 276  

29

suggest that the relationship between the unemployment rate and property crimes may not be as

pronounced as it was once believe to be. Instead, in the future policy solutions should be aimed

at increasing the real per capita income people living in the United States. By increasing real

income, people will no longer have a financial incentive to commit property crimes, as

diminishing marginal returns should dissuade theft64. In Income Inequality and crime in the

United States, Jongmok Choe found that “for property crime, burglary is greatly affected by

income inequality…[moreover] if we think of poverty rate as the absolute income inequality

measure, robbery and burglary are significantly influenced by it”65. In other words, as real

income rises, people will see decreasing benefits in committing crimes that carry with them

inherent risks of apprehension and little economic payoff66. However, in order to increase the

real income of millions of Americans, a major policy of redistribution of assets from the wealthy

to the impoverished would need to be undertaken. Given, the political realities of the United

States, such a policy would not be feasible. Therefore, policy remedies to eradicate or reduce the

number of properties crimes committed should be centered on the second finding of the data: the

relationship between police officers and property crimes.

The data indicates that increasing the number of police officers per capita should result in

a decline in the number of property crimes committed67. In The Effect of Police on Crime, James

Wilson and Barbara Boland conducted a study of the robbery rates of 35 major cities in the US

and found that “aggressiveness and a larger number of patrol units, separately and in

64  Jongmok  Choe,  “Income  Inequality  and  crime  in  the  United  States”,  Elsevier,  p.  31  65  Ibid,  p.  32  66  Steven Raphael and Rudolf Winter-Ember, “Identifying the Effect of Unemployment on Crime”, Journal of Law and Economics, April 2001, p. 272  67  See  Appendix  3,  Figure  3  

30

combination, will leaded to high arrest ratio for robbery, and this high ration, in turn, leads to

lower robbery crime rate”68. The reasoning behind the observation remains one filled with

contentious debate, as it relies heavily on the perceptions of public safety increasing as the

number of police on the street increase. In a similar study, David Weisburd and John Eck found

that the police tactics employed in the United States require more analytical and empirical

studies, before tactics can be suggested to and endorsed by policy makers69. While a policy

analyst can always propose policy remedies from conducted regression analysis, one should

always acknowledge the regression results are a product of the construct and model employed.

The results of the regression analysis coupled with research in the field allude to a different path

for future policy, with respect to reducing the number of property crimes. Policy should be aimed

at increasing the number of the police on the streets. The aforementioned findings provide

reasonable and attainable policy solutions to the problem of reducing the number of property

crimes in the United States.

VI. CONCLUSION

In reviewing the preliminary data available on the 2007/2009 economic recession, the

relationship between the unemployment rate and property crimes becomes less pronounced. In

Identifying the Effects of Unemployment on Crime, Steven Raphael and Rudolf Winter-Ember

observe a positive and significant relationship between the unemployment rate and crime70.

These findings remain consistent with the majority of literature in the field. However, the results 68  Ibid,  p.  380  69  David  Weisburd  and  John  E.  Eck,  “What  can  police  do  to  reduce  crime,  disorder  and  fear?”,  Annals  of  American  Academy  of  Political  and  Social  Science,  May  2004,  p.60  70 Steven Raphael and Rudolf Winter-Ember, “Identifying the Effect of Unemployment on Crime”, Journal of Law and Economics, April 2001, p. 273

31

presented in the paper above with respect to the latest economic recession run counter to those

observed by the majority of literature in the field. The results indicate that in the latest recession

the unemployment rate had a negative and significant impact on property crimes. Perhaps, the

observed findings were the result of an omitted variable, a lagged effect of unemployment rates

on property crimes, or inherent problems with the fixed effects model.

The results suggest a negative relationship between property crimes and the

unemployment rate. The results also elucidate a negative relationship between income and police

officers and property crimes, providing valuable insight into the future of public policy with

respect to property crimes. A one-percentage point shift in the number of police officers is

associated with an 8.069 decrease in per capita property crimes. Similarly, a one-percentage

point increase in the per capita income of an individual measure in thousands of dollars is

associated with a .002 decrease in per capita property crimes. I also observed a race impact on

property crimes, as well as age impacts varying by age subgroup. The aforementioned results

cast doubt on the both the economic and behavioral interpretation for why people commit

property crimes in times of economic strife.

The magnitude of the crime-unemployment effect presented in the paper provides policy

makers with a tool, with which to analyze previous findings in the area and aid in preparation of

new policies aimed at decreasing the number of property crimes committed. Given, the political

realities of the United States and the unpopular nature of redistribution plans, the future of policy

interventions for property crimes should be aimed at increasing the number of police on the

streets.

32

VII. APPENDICES

A. APPENDIX 1- SUMMARY STATISTICS BY YEAR

Descriptive Statistics: 2005 Averages among States Variable Name Mean Std. Dev. Min Max Property Crimes 202,800.50 231,854.10 12,595 1,200,531 Unemployment Rate 4.91 1.07 2.8 7.8 Property Crime Arrests 25,842.86 32,405.15 82 173,561 Total Police Offices 13,198.90 15,358.50 1,158 72,853 Average Income per Capita (in $) 24,550.92 3,870.59 17,971 37,569 Population of one race 5,545,509 6,249,004 485,678 3.42E+07 White Only 4,222,223 4,297,127 166,815 2.15E+07 Black Only 685,541 820,692 2,932 2,858,062 Asian Alone 244,545.40 631,879 3,148 4,365,548 Some other Race 339,188.30 895,781.20 960 5,784,073 Two Races 101,327.10 152,264.10 7,109 1,027,251 Three or More Races 7,110.12 13,440.47 168 73,690 Population under 15 1,187,836 1,419,574 92,098 8,043,927 Population 15 to 17 246,118.50 285,557.60 15,508 1,611,215 Population 18 to 24 515,601.80 590,357.70 31,647 3,324,585 Population 25 to 34 760,499.50 890,079.40 61,118 5,010,503 Population 35 to 44 847,796 970,127.40 66,131 5,384,615 Population 45 to 54 824,418.80 896,695.50 69,704 4,871,349 Population 55 to 64 590,623.70 630,239.20 55,587 3,331,470

33

Descriptive Statistics: 2006 Averages among States Variable Name Mean Std. Dev. Min Max Property Crimes 199,130.10 226,363.30 12,664 1,156,017 Unemployment Rate 4.42 1.02 2.50 6.90 Property Crime Arrests 25,587.84 31,739.00 59.00 166,417.00 Total Police Offices 13,399.92 15,729.74 1,163.00 75,483.00 Average Income per Capita (in $) 24,789.04 3,863.24 18,165.00 37,043.00 Population of one race 5,750,703.00 6,478,106.00 505,576.00 35,200,000.00 White Only 4,339,833.00 4,387,047.00 200,395.00 21,800,000.00 Black Only 726,499.70 871,463.30 3,686.00 2,990,260.00 Asian Alone 256,864.60 650,674.20 4,348.00 4,483,252.00 Some other Race 372,688.80 986,541.50 1,876.00 6,296,602.00 Two Races 111,441.10 168,438.40 7,583.00 1,132,628.00 Three or More Races 7,809.28 14,195.14 142.00 74,316.00 Population under 15 1,119,408.00 1,408,403.00 95,392.00 7,887,770.00 Population 15 to 17 253,967.50 292,731.40 19,438.00 1,643,276.00 Population 18 to 24 582,363.10 673,634.40 54,676.00 3,793,329.00 Population 25 to 34 782,462.70 926,366.20 65,653.00 5,232,260.00 Population 35 to 44 860,639.80 996,320.90 65,430.00 5,525,036.00 Population 45 to 54 849,516.90 927,940.60 75,160.00 5,017,599.00 Population 55 to 64 619,965.30 656,054.50 59,849.00 3,430,449.00

Descriptive Statistics: 2007 Averages among States Variable Name Mean Std. Dev. Min Max Property Crimes 196291.5 224536.7 12088 1108660 Unemployment Rate 4.34 0.98 2.7 7.1 Property Crime Arrests 27702 33005.92 91 165192 Total Police Offices 13722.55 16052.48 1107 78561 Average Income per Capita (in $) 26,238.53 4,167.47 19,365 40,379 Population of one race 5,786,513 6,505,402 508,151 3.53E+07 White Only 4,372,657 4,426,706 210,332 2.20E+07 Black Only 732,050.40 878,228.90 3,297 3,010,970 Asian Alone 259,476.20 654,763 2,939 4,511,407 Some other Race 367,427.10 958,626.50 1,319 6,096,927 Two Races 118,559.90 175,637.80 8,453 1,179,969 Three or More Races 8,430.69 155,532.37 267 85,747 Population under 15 1,194,062 1,396,651 94,057 7,772,053 Population 15 to 17 255,114.40 295,024.20 19,517 1,661,568 Population 18 to 24 584,721.90 679,215.50 55,168 3,837,832 Population 25 to 34 784,059.90 924,750.10 68,502 5,205,540 Population 35 to 44 851,184.60 984,856.50 65,175 5,449,923 Population 45 to 54 861,297.10 943,755.40 76,006 5,114,104 Population 55 to 64 641,747.20 679,020.40 60,986 3,561,246

34

Descriptive Statistics: 2008 Averages among States Variable Name Mean Std. Dev. Min Max Property Crimes 194,754.10 222,421.70 12,152.00 1,080,747.00 Unemployment Rate 5.32 1.22 3.10 8.30 Property Crime Arrests 28,530.92 34,792.52 104.00 170,546.00 Total Police Offices 13,893.51 16,380.96 0.00 81,286.00 Average Income per Capita (in $) 27,226.51 4,364.45 20,228.00 42,069.00 Population of one race 5,824,429.00 6,547,841.00 517,898.00 35,400,000.00 White Only 4,474,165.00 4,598,595.00 221,911.00 23,000,000.00 Black Only 736,981.40 887,940.80 4,650.00 3,101,231.00 Asian Alone 263,019.10 661,387.90 3,732.00 4,548,741.00 Some other Race 293,964.50 795,962.00 2,340.00 5,228,278.00 Two Races 127,103.80 180,852.70 8,185.00 1,206,497.00 Three or More Races 9,652.33 17,066.21 121.00 93,389.00 Population under 15 1,198,163.00 1,401,762.00 92,797.00 7,729,753.00 Population 15 to 17 251,285.80 290,918.00 19,174.00 1,632,944.00 Population 18 to 24 590,349.30 685,764.60 55,311.00 3,865,493.00 Population 25 to 34 790,577.40 929,980.80 70,425.00 5,217,280.00 Population 35 to 44 838,129.30 972,458.60 63,982.00 537,665.00 Population 45 to 54 871,287.30 955,278.70 76,629.00 5,154,622.00 Population 55 to 64 661,138.70 698,926.00 63,189.00 3,668,085.00

35

Descriptive Statistics: 2009 Averages among States Variable Name Mean Std. Dev. Min Max Property Crimes 185,859.30 214,231.00 12,502.00 1,009,614.00 Unemployment Rate 8.45 2.01 4.30 13.60 Property Crime Arrests 29,699.39 34,644.75 143.00 164,744.00 Total Police Offices 13,860.51 16,338.17 1,072.00 80,321.00 Average Income per Capita (in $) 26,165.57 4,310.57 19,232.00 40,797.00 Population of one race 5,872,576.00 6,591,607.00 530,338.00 35,600,000.00 White Only 4,505,356.00 4,641,290.00 232,247.00 23,200,000.00 Black Only 746,935.80 896,788.10 2,944.00 3,068,887.00 Asian Alone 270,090.40 671,574.10 3,669.00 4,618,747.00 Some other Race 293,105.20 776,974.70 2,061.00 5,097,436.00 Two Races 136,174.60 195,230.60 100,92.00 1,303,836.00 Three or More Races 101,48.86 18,365.99 150.00 94,946.00 Population under 15 1,212,759.00 1,423,742.00 94,848.00 7,827,554.00 Population 15 to 17 247,966.10 287,413.00 18,862.00 1,607,548.00 Population 18 to 24 599,160.60 674,994.60 58,776.00 3,744,453.00 Population 25 to 34 811,246.66 958,482.30 68,806.00 5,374,632.00 Population 35 to 44 817,141.40 947,354.60 65,935.00 5,224,573.00 Population 45 to 54 874,456.20 965,309.70 78,364.00 5,204,985.00 Population 55 to 64 682,366.20 724,677.00 66,655.00 3,834,688.00

36

B. APPENDIX 2- SUMMARY DESCRIPTIVE STATISTICS71

Number of Observations Mean

Standard Deviation

Minimum Value

Maximum Value

Dependent Variable Property Crimes 250 0.031 0.007 0.016 0.049 Independent Variable Unemployment Rate 255 5.49 2.01 2.5 13.6 Control Variables: Total Police Offices 255 0.002 0.0008 0 0.008

Average Income per Capita (in thousands of $) 255 25.800 4.206 17.971 42.069

White Only 255 0.756 0.143 0.248 0.965 Black Only 255 0.106 0.109 0.003 0.536 Asian Alone 255 0.033 0.055 0.004 0.418 Other_Race 255 0.056 0.041 0.013 0.239 Population under 15 255 0.193 0.016 0.152 0.259 Population 15 to 17 255 0.041 0.003 0.028 0.051 Population 18 to 24 255 0.096 0.01 0.058 0.132 Population 25 to 34 255 0.127 0.012 0.108 0.193 Population 35 to 44 255 0.136 0.01 0.109 0.165 Population 45 to 54 255 0.143 0.012 0.109 0.172 Population 55 to 64 255 0.109 0.011 0.073 0.138

71  See State Results Section- Figure 1

37

C. APPENDIX 3- STATA OUTPUT

38

. reg propcrimes unemprt income000 Officers Black Asian Other_Race newpop_under15 newpop_15 > _17 newpop_18_24 newpop_25_34 newpop_35_44 newpop_45_54 newpop_55_64 Source | SS df MS Number of obs = 250 -------------+------------------------------ F( 13, 236) = 541.16 Model | .697695265 13 .053668867 Prob > F = 0.0000 Residual | .023405119 236 .000099174 R-squared = 0.9675 -------------+------------------------------ Adj R-squared = 0.9658 Total | .721100384 249 .002895985 Root MSE = .00996 ------------------------------------------------------------------------------ propcrimes | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- unemprt | -.001013 .000353 -2.87 0.004 -.0017084 -.0003176 income000 | .0002009 .0002115 0.95 0.343 -.0002158 .0006177 Officers | -8.385728 1.133961 -7.40 0.000 -10.61971 -6.151748 Black | .0436148 .0102927 4.24 0.000 .0233375 .0638921 Asian | -.0373315 .014994 -2.49 0.013 -.0668707 -.0077923 Other_Race | .0221756 .0186107 1.19 0.235 -.0144886 .0588399 newpop_un~15 | .602729 .0905739 6.65 0.000 .4242924 .7811657 newpop_15_17 | -2.540415 .4985967 -5.10 0.000 -3.522684 -1.558146 newpop_18_24 | .1270578 .0897698 1.42 0.158 -.0497946 .3039103 newpop_25_34 | -.1986414 .0998834 -1.99 0.048 -.3954183 -.0018644 newpop_35_44 | .1788302 .0972508 1.84 0.067 -.0127603 .3704207 newpop_45_54 | -.1167041 .1281363 -0.91 0.363 -.3691413 .135733 newpop_55_64 | .3663235 .0747751 4.90 0.000 .2190116 .5136353 _cons | -.0022032 .005774 -0.38 0.703 -.0135783 .0091719 . xtreg propcrimes unemprt income000 Officers Black Asian Other_Race newpop_under15 newpop_ > 15_17 newpop_18_24 newpop_25_34 newpop_35_44 newpop_45_54 newpop_55_64, fe Fixed-effects (within) regression Number of obs = 250 Group variable: staten Number of groups = 50 R-sq: within = 0.9902 Obs per group: min = 5 between = 0.7003 avg = 5.0 overall = 0.9285 max = 5 F(13,187) = 1450.14 corr(u_i, Xb) = -0.1477 Prob > F = 0.0000 ------------------------------------------------------------------------------ propcrimes | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- unemprt | -.001763 .000348 -5.07 0.000 -.0024496 -.0010765 income000 | -.0016934 .0005254 -3.22 0.001 -.0027299 -.000657 Officers | -6.972442 1.673142 -4.17 0.000 -10.2731 -3.671783 Black | .0125086 .0289886 0.43 0.667 -.0446781 .0696954 Asian | -.0949488 .0261973 -3.62 0.000 -.146629 -.0432686 Other_Race | -.0018939 .0282428 -0.07 0.947 -.0576094 .0538216 newpop_un~15 | .6634648 .0831985 7.97 0.000 .4993365 .8275931 newpop_15_17 | -2.763322 .4834207 -5.72 0.000 -3.716981 -1.809663 newpop_18_24 | .3390479 .0857939 3.95 0.000 .1697997 .5082961 newpop_25_34 | -.2393606 .0959359 -2.50 0.013 -.4286163 -.0501048 newpop_35_44 | .2326379 .1313039 1.77 0.078 -.0263895 .4916652 newpop_45_54 | -.2606111 .1380727 -1.89 0.061 -.5329913 .0117691 newpop_55_64 | .3741944 .081495 4.59 0.000 .2134267 .5349621 _cons | .0483979 .0137241 3.53 0.001 .021324 .0754718 -------------+---------------------------------------------------------------- sigma_u | .01382749 sigma_e | .0055269 rho | .86224524 (fraction of variance due to u_i) F test that all u_i=0: F(49, 187) = 11.82 Prob > F = 0.0000

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D. APPENDIX 4- STATA LOG

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