QMSS 5999 - Thesis - AVaughn

RUNNING HEAD: CRIME RATES AND CONCERN FOR CRIME TIME SERIES ANALYSIS

A Time Series Analysis of Crime Rates and Concern for Crime in the United States: 1973-‐2010

ABSTRACT: Real crime rates may not be the only source of information people use to assess their fear of crime. The present study conducts time series analysis to explore if society does or does not incorporate other information factors into their concern for crime. Using data from the FBI’s Uniform Crime Reports and the General Social Survey, I explore the relationship of concern for crime and real crime rates across domains that include covariates of demographic information, national priorities and opinions, and societal values for the years 1973-‐2010. The study finds support for the argument that people use violent crime rates to logically determine their concern for crime as opposed to using competing sources of information.

Alexandra Vaughn Columbia University QMSS 5999 Thesis

Spring 2012

CRIME RATES AND CONCERN FOR CRIME TIME SERIES ANALYSIS

i.

Table of Contents

1. Introduction 1

1.2. Hypothesis 2 2. Literature Review 4

2.1. Crime and Public Perception 4 2.2. Crime and Demographics 8 2.3. Crime, National Priorities, and Political Opinion 10 2.4. Crime and Social Values 11

3. Data & Methodology 12 3.1. Data 12 3.2. Variables 17 3.3. Tests 21

4. Results 27

4.1. Descriptive Statistics 27 4.2. Crime and Demographics 38 4.3. Crime, National Priorities, and Political Opinion 42 4.4. Crime and Social Values 45

5. Discussion 48

Appendix 53

References 58


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1. Introduction

After peaking in the 1990s, crime rates have steadily declined for two decades. Public

concern for crime has also decreased in recent years, although people continue to believe

crime is out of control. The symmetry of trends in crime rates and concern for crime over time

and the paradox of people’s belief about the state of crime raise the question: do people

logically use true crime rates to derive their concern for crime or do they use competing

sources of less relevant information. Understanding how crime rates may or may not influence

people’s beliefs about their relative safety and the state of crime has implications for decision-‐

makers and policy advocates. Better knowledge of the relationship can focus alignment of

crime prevention more closely with public opinion so that the public accurately perceives crime

prevention as having a positive effect. This paper aims to address the question: if the public’s

concern for crime and actual crime rates trend together over time, do people appear to

logically refer to the crime rate, and what societal characteristics and concerns may help

explain this relationship.

I use a collection of variables from the General Social Survey (GSS) and FBI Uniform

Crime Reports (FBI UCR) to investigate the relationship between public concern for crime and

crime rates. Additionally, I include the several demographic, policy, and societal concerns that

may influence the relationship between concern for crime and crime rates. By including

demographic information and public opinion questions, I will also be able to examine if these

variables may account for people’s concern for crime although they may be irrelevant sources

of information compared to true crime rates. Criminal and forensics research has established

that crime rates may not the only source of information people use when determining their


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fear of crime (Bakhan & Cohn, 2005). However, contrary research shows that crime rates have

been falling for the last two decades after years of steep increases and fear of crime appears to

be following similar trends across time (McDowall & Loftin, 2009). Since I am concerned with

the relationship between concern for crime and crime rates over time, I progress through a

series of regression and time series analysis that allow me to understand what occurs over

several years rather than from a cross-‐sectional analysis at a single point in time. I conduct

traditional Ordinary Least Squares (OLS) regression, First Differences, and Prais-‐Winston

Feasible Least Squares (FLS) regressions for time series to explore the relationship between

concern for crime and actual crime rates and expand upon previous literature that has explored

these trends over time, nationally, and with the addition of control variables.

1.2. Hypothesis

I explore the question do people logically use the true rate of violent crime in the United

States to determine their concern for crime for the years 1973-‐2010 and what does the

relationship look like over time?

Within the context of this larger objective, I seek to explore the following: if people do

not logically use real crime rates, what other sources of information do people use to form their

opinions of and concern for crime:

- Specifically, do demographic trends correlate with concern for crime along with real

crime rates across time?

- Do preferences for other national priorities and political opinions correlate with people’s

concern for crime across time?


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- And, do measures of social values correlate with concern for crime and crime rates

across time?

I will answer these questions by first looking at the summary characteristics of the GSS

respondents who answered a question about national crime. The initial, summary analysis will

be done at the individual level and will allow me to examine if there are any underlying

characteristics and demographic trends that distinguish those who have “high concern” for

crime versus those who are “neutral” or have “low concern” for crime across demographics and

opinions.

Additional analysis will take place at the aggregate level after the data have been

collapsed by year to examine trends of concern for crime across time rather than just across

individuals. I will consider people’s concern for crime across three domains: concern for crime

as it relates to demographics, concern for crime within the space of national priorities and

politics, and concern for crime as it relates to people’s social values. Within the three domains

under consideration I will conduct initial, simple Ordinary Least Squares (OLS) regressions,

include a year trend in OLS, run First Differences, and finally, explore time series analysis using a

Prais-‐Winston Feasible Lease Squares (FLS) regression. Time series analysis will be

complimented by heteroscedasticity and serial correlation tests to check the integrity of the

data. Durbin’s alternative h-‐statistic test will check for serial correlation in the errors of the time

series regression model. The h-‐statistic statistic is a useful tool to check for autocorrelation

because it also works with models whose explanatory variable are not strictly exogenous

(Wooldridge, 2009). A heteroscedasticity check will confirm whether or not the models have


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any left out variables that are creating bias because they have an effect on the dependent

variable.

2. Literature Review

2.1. Crime and Public Perception

The changing picture of crime rates and public attitudes towards crime over time is a

complex relationship that may not be fully explained by univariate analysis of crime rates on

people’s concern for crime. Criminologists, law enforcement agencies, and researchers from

many academic disciplines have noticed that the public has maintained the belief that violent

crime is out of control despite the fact that crime rates have been steadily declining for years. A

2008 study confirms that despite dramatically decreased crime rates in recent years, the public

continues to believe that violent crime rates are out of control (Duffy, Wake, Burrows, &

Bremner, 2008). As long as this belief persists, the public tends to blame the government for

failing to properly address their beliefs about crime rates and for neglecting to meet their

personal safety needs (Duffy, et al. 2008). Researchers, policy makers, and law enforcement

officials in the U.S. benefit from awareness of the public’s varying relationship with true crime

rates. If we can better understand if and when people are making logical decisions about their

concern for crime relative to crime rates, we can address how to improve instances of

irrationality when people use competing sources of information to learn about crime.

Public perceptions of crime may be swayed by several contributing factors. Felson

(2002) contributes a theory for predicting people’s concern for crime and attempts to explain

why concern for crime and falling crime rates do not always align. Felson’s random crime fallacy

argues that people believe crime is random and unpredictable, while the opposite is more likely


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true – that crime events are actually predictable. The random crime fallacy is helpful to begin a

conversation about crime rates and fear of crime. However, I am interested in explaining what,

if any, other variables and opinions are used as competing sources when people form their

concern for crime.

One source of information that people may use to is the media. According to Duffy et al.

(2008), the public’s exposure to television may significantly influence the publics’ perception of

the state of crime. Several studies have looked at time spent watching TV crime shows and the

news as predictors of attitudes towards crime (Buijzen, Walma van der Molen, & Sondji, 2007;

Gerbner, & Gross, 1976; Gilliam, & Iyengar, 2000; Gilliam, Iyengar, Simon, & Wright, 1996).

Heath and Gilbert (1996) review the literature on media and fear of crime in order to

understand what is the prevailing opinion. They find many contradictions in researchers’

conclusions, especially given the disparate survey methods, data collection, variable sources,

and populations used. For instance, they find that television portrays society as suffering from

much more crime than is true in reality, but when Heath and Gilbert (1996) examine studies

that measure if TV with intensified and extremely violent crime influences viewers to develop

an unrealistic opinion of crime the link between media and fear of crime fails to be significant.

The weak relationship is maintained when they review studies that look at TV viewing with

other independent and control variables included. From their review, they conclude that when

TV viewing and crime content on TV are examined independently, TV and media influence fear

of crime. Essentially, they infer that some television seems correlated with crime for some

viewers. However, when control and demographic variables are included the relationship

deteriorates (Heath & Gilbert, 1996). Despite contradictions, crime perception and media have


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done much for the literature and our understanding of how people may obtain crime

information from this source of communication. I choose to include it as a control variable, but

focus on studying people’s concern for crime outside of this relationship. I include many other

control variables, as well, in order to get a better understanding if any additional factors shape

Americans’ attitude toward crime rather than actual crime rates.

Across a variety of fields, including criminology, anthropology, sociology, and other

disciplines, crime research uses different sources to measure crime rates and of fear of crime.

One widely used source of data is the GSS. It includes a variable, natcrime, that asks about

people’s opinion on the government’s efforts and spending to halt crime; it is often used in

crime studies as a measure of public sentiment about crime. Using the GSS, Frost and Clear

(2009) argue that decreases in fear of crime are not due to better punitive measures, but rather

decreasing crime rates. They demonstrate that as of 2010, fear of crime does not even rank on

citizens’ list as one of America’s most pressing issues, whereas in the 1980s and early 1990s

when crime rates were at their peak, it consistently ranked in the top five on Gallup poll surveys

(Frost & Clear, 2009). As recently as early the 2000s, Smith (2011) also takes note of Americans’

decreasing opinion that crime should be addressed as a top national priority, while it ranked for

many years as a top concern. Despite people’s decrease in fear of crime, 60% of Americans in

2010 say that the government is not spending enough to combat crime (National Opinion

Research Center, 2011).

The literature suggests that U.S crime rates, American’s concern for crime, and their

calls for more measures to fight crime do not align. As a result, research tries to explain the

disconnect between them. Attempts to do so have begun to question if competing source of


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information and other control variables may uncover more information about the relationship.

Yet, the gap between actual fear of crime and concern for crime leaves room for further

research and exploration into the dynamics that make up people’s concern for crime.

Furthermore, the majority of this research has been done with cross-‐sectional analysis of

society. It is not to be unexpected that an aggregate study that looks at yearly trends might

point to the fact that media’s role is diminished across time.

Research on crime rates is conducted at various geographical levels of analysis. In

particular researchers and criminologists have been interested in whether the decline in crime

rates seen since the 1990s persists at the national-‐level, as well as at the city-‐level. A significant

body of research that looks at crime trends examines the trend at the national level rather than

locally (Blumstein, 2006; Rennison & Planty, 2006; Rosenfeld, 2002). Many studies that use

national level crime rates and fear of crime find that national annualized data for many years

rather than cross-‐sectional data is easier to procure than city-‐level crime across the U.S. While

there are studies that examine crime rates in cities, states, and smaller regions of analysis their

methods, variables, and surveys are not always consistent enough to compare across different

locations. Using national data enables consistent variables, McDowall & Loftin (2009) conduct

one of few studies that has explicitly examined national versus city crime rates for patterns.

They use annualized panel data of the Uniform Crime Rates from 130 U.S cities for the years

1960 to 2004 to compare crime rates of the nation’s major urban areas against national crime

rates to measure the degree to which U.S crime rates follow a national trend. They determine

that a consistent pattern exists at both levels by demonstrating that increases in the crime rate

in one city occur during crime increases across other U.S cities. Their work supports the claim


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that crime follows a national trend; the city-‐level data they use represents a similar trend in

crime rates. There is support for looking at national-‐level crime trends. This study is consistent

with previous studies that look at annual and time series crime trends that look the trend at the

national level.

2.2. Crime and Demographics

Individual demographics and personal characteristics may play a role in people’s

interpretation of information about crime. For example, beliefs about demographic factors and

racial composition of criminal offenders influence attitudes towards criminal punishment and

fear of crime (Chiricos, Welch, Gertz, 2004). People may approach information about criminal

events not only from the context of their own race, but also from pre-‐conceived beliefs about

the nature of criminal actors (Barkan & Cohn, 2005). Barkan and Cohn (2005) find a positive link

between racial prejudice and increased concern for crime using the GSS natcrime variable to

investigate race and opinions on crime. They find that whites, especially those with more racial

prejudice, are more likely want more money spent on fighting crime. Conversely, Baulmer

(1979) does not find any patterns, although he suggests that racial concentrations in

neighborhoods and familiarity with surroundings mitigate fear of crime. There does not appear

to be a consensus in the literature on the role of race in people’s concern for crime, which

highlights the need for exploration into the role it may.

In addition to race, other demographic factors contribute to the public’s and concern for

crime. Fear of crime displays consistent gender divergence (Baulmer, 1979); women tend to be

more afraid than men (LaGrange & Ferraro, 1989). Older respondents, as an age group, tend to

be more afraid of crime than other age groups (Baulmer, 1979). Franklin and Franklin (2008)


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confirm previous research that, in general, women and elderly citizens are more fearful of

crime. Interestingly, they also find that as women age their fear of crime reduces, however this

is not the case for men. Gender and age appear to play a role in predisposing people towards

fear for crime, but there may be interactions or mitigating factors that can reduce the risk of

fear of crime in these subpopulations. Marvell and Moody (1991) demonstrate that variations

over time in the age structure of the population do not mirror crime trends as expected. They

reviewed a large number of studies in the late 1980s and early 1990s and find that a decrease in

the proportion of teenagers and young adults in the population did not, in fact, precede a dip in

crime levels as they expected. This finding minimized the role that the number of younger

members in society previously played in crime trend theorizations. For age, race, and gender it

appears that researchers do not agree on these demographics’ relationship with concern for

crime.

Marriage status appears to play a role in people’s fear of crime (Toseland, 1982). Those

who are married have been found to display more fear of crime than unmarried people.

Although Toseland (1982) uses GSS data for his analysis he limits his study to one survey year,

so it is unknown if these trends persist with time. Studies have not specifically looked at

whether presence of kids under 18 in the household appears to play a role in people’s concern

for crime, but the more persons in the household under respondents care has been shown to

increase concern for crime (Toseland, 1982). I include number of kids in the household because

it may show an additional contingency towards concern for crime. It does appear that both

marriage and number of household members increase people’s fear of crime, which makes

them interesting variables to include in multivariate analysis with violent crime rates.


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Although demographic factors have received much attention in fear of crime research,

they have not been consistently compared with actual crime rates’ relationship on concern for

crime, nor has a consensus been reached on what direction their relationship is with concern

for crime and violent crime rates.

2.3. Crime, National Priorities, law, and Political Opinion

Literature has explored the public’s concern for crime and its link to societal attitudes

towards national priorities and the law, such as the death penalty (Johnson, 2009) and handgun

ownership (Holbert, Shah, & Kwak, 2004; Ludwig, Cook, & Smith, 1998; Moody & Marvell,

2005), and political opinion (LaFree, 1999). There is scare literature on the relationship between

fear of crime and people’s opinions on other national issues. Smith (2011) shows through

analysis of GSS surveys from 1974-‐2010 that crime was consistently ranked as a top national

priority through the 1990s. This increasing trend also coincided with peaks in crime rates. When

crime began to fall in the 1990s, Smith (2011) identifies the point in time when fighting crime as

a national priority begins to loose favor. Despite our understanding of the synchronicity of

crime rates and fighting crime as a top priority, I do not find any research that has measured if

changes in top national priorities influence people’s attention to concern for crime.

Changes in policy, politics, and policing strategies may drive relationships between

concern for crime and what the public believes to be the state of crime. In this domain I include

a measure of hours spent watching TV per week. It fits with this domain as an opinion variable,

because it research suggests that people who watch TV may be influenced by crime reports and

fictional crime on TV (Buijzen, Walma van der Molen, & Sondji, 2007; Gerbner, & Gross, 1976;

Gilliam, & Iyengar, 2000; Gilliam, Iyengar, Simon, & Wright, 1996). Since media and crime


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research is inconsistent in its conclusion of how strong the influence of media is on people’s

concern for crime (Heath & Gilbert, 1996), its inclusion here will add to this discussion. Thus,

the second domain examines what other sources of opinion and beliefs may explain the

relationship between crime rates and concern for crime it is appropriate to include.

2.4. Crime and Social Values

Evidence suggests that values, personality characteristics, and emotional states may

contribute to people’s fear of crime and victimization risk. LaFree (1999) found social

institutions and values to be instrumental in explaining his theory of crime rate explosions in

the 1960s and 1970s. For those decades, American society was filled with growing political

distrust and social disintegration, whereas a process of stabilization of traditional social values

and institutions in the 1990s may account for the decrease of crime (LaFree, 1999). Using a

measure of people’s trust for society may tap into a boarder measurement of trust than

political distrust. It can shed light on how overall levels of trust inform people’s concern for

crime. Trust as a social value may be useful in representing some portion of the morals and

values that people use when they approach issues of crime. It has been found to be a more

significant in predicting fear of crime than age, isolation from society, and income (Mullen &

Donnermeyer, 1989; Toseland, 1982). However, Mullen & Donnermeyer’s (1985) sample is

located in a rural proportion of the country and does not represent a national sample, so

extending their conclusion outside a rural population is tenuous. However, a measure of trust in

society may yield insight into whether high or low trust plays a role in concern for crime given

crime rates. Johnson (2009) suggests that anger about crime is a positive predictor of attitudes

about criminal punishment, when racial prejudice, fear of crime, attributions for criminal


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behavior, and political ideology are controlled for. The GSS does not have an emotional

question that measures anger. Instead I propose using a happiness measure from the GSS as an

emotional variable in analysis. Overall, studies that explore the role of values and emotional

content in fear of crime research are few (Toseland, 1982), but there is preliminary evidence

that in cross-‐sectional studies these values and emotions may influence fear of crime.

3. Data & Methodology

3.1. Data

I hypothesize that people logically use real crime rates to determine their concern for

crime. If logic appears to fail, I am interested in seeing it they incorporate competing social,

demographic, and other informational cues to derive a sense of what they believe are

reasonable expectations for their concern of crime.

I analyze the relationship between the U.S. public’s concern for crime and real crime

rates across the years 1973-‐2010. I incorporate two sources of data into my analysis. In order to

get a measure of the rate of crime, I use violent crime rates from the FBI UCR (Federal Bureau

of Investigation). As a measure of the public’s concern for crime, I choose a question from the

GSS (National Opinion Research Center) as my source. The GSS is particularly useful because it

has asked a set of time-‐invariant questions to a representative sample of the United States

population since 1972. It asks opinions about demographic characteristics, socio-‐economic

factors, social values traits, and opinions of national priorities that have been asked every

survey year. In addition to using the GSS for the concern for crime variable, it is also the source

of the additional independent variables I use to examine the relationship between crime rates

and concern for crime change across time.


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For the purposes of this study, I focus on crime rates that come from institutional

records, namely from crime reports. Thus, the source of crime data in this project will be the

FBI’s UCR. A benefit of this dataset is the collection institution – the FBI – is a national law

enforcement agency, collection procedures have been standardized since 1930, and it is

nationally representative (FBI, 2011).

3.1.1. The FBI Uniform Crime Reports

I choose to operationalize the measurement of crime rates in the U.S. using the FBI’s

UCR. Collection of the UCR been standardized since 1930 when the UCR was first established as

a source of reliable, uniform crime statistics for the country. Individual, independent law

enforcement boroughs, offices, and states report police-‐collected measures of crime to the FBI

on a monthly basis. Nearly 17,000 law enforcement agencies across the United States report

crimes to be collected, analyzed, and published in the UCR and FBI’s annual Crime in the Unites

States (FBI, 2011). An estimated that 97% of the U.S. population is represented in the FBI UCR,

and the highest participation rates are within metropolitan cities (Mosher, et al. 2002).

Accordingly, coverage of reported crimes can be assumed relatively complete across the

country.

I source the crime rate data from the FBI’s UCR online data section. I use 1973 as the

first year of analysis because it is when the GSS started to include a measure for concern for

crime – the dependent variable in my analyses. Crime in the UCR are recorded in two parts:

Part I violent crimes, which includes murder, robbery, rape, and assault, and Part II – lesser

degree crimes such like drug offenses, fraud, public order offenses, and weapons violations.

Part II crimes are only recorded if someone is arrested and often not reported to the police,


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which makes these crimes difficult to estimate and measure (Block & Maxfield, 2011). Even

with the two categories, the Uniform Crime Reports do not include an exhaustive list of all

types of crime. The largest source of under reported crimes are personal crimes that do not

involve injury and property crime such small monetary losses because they are least likely to be

reported to the police (Block & Maxfield, 2011). Because Part I is considered the more reliable

collection of crime incidents based on the fact that crimes of this nature are reported to the

police and their collection is more consistent, I use total violent crime. Including crime rates

across many years will enable a comparison of crime rates as they change in time.

3.1.2. Limitations of the FBI UCR

A major consideration in criminal research is the issue of measuring crime and

understanding the characteristics of the data and measurement methodologies. Crime is

measured from a variety of perspectives; many sources, surveys, and institutes collect counts of

crime events for a variety of reasons with a range of tools, goals, and measures. Crime can be

examined from the perspective of institutional records, victimization studies, first-‐person self-‐

reports and accounts of crimes committed, and recollections of crimes witnessed or

experienced via victims have all been used as sources of crime research (Mosher, Miethe, &

Phillips, 2002).

Inaccuracies in reporting lead to potential confounding in police reports. Outside

influences can exert pressure on crime record keepers so that records reflect numbers that

politicians, chiefs of police, and other decision-‐makers would like to see because the doctored

reports are professionally flattering rather than accurate accounts of the crime taking place

under their watch. External pressure may influence crime record keepers to inflate or deflate


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actual numbers. In instances when numbers are inflated, competition for monetary support

and funding for departments are the incentives (Mosher, et al. 2002). Conversely, reports may

undercount instances of offenses in order not to receive more negative attention than wanted

for high levels of offenses. From year-‐to-‐year or city-‐to-‐city if there are significant changes in

the numbers, investigators cannot exclude the influence that altering the number of offenses,

unintentionally or intentionally, may have on data accuracy. The UCR, however, does test if

annually reported rates are inconsistent year-‐to-‐year and corrects for inaccuracies (See

Appendix, pg. 48 for more).

3.1.3. The General Social Survey

The literature reviewed above includes studies that use a variety of sources and surveys

to measure public biases towards crime as opposed to a nationally consistent source across all

studies and years. Most of the studies also use cross-‐sectional analysis rather than time series,

which is easier to implement, but does not provide information about variables over time.

Cross-‐sectional analysis enables researchers to look at society at one point in time only and

limits their ability to extrapolate beyond the population sample used or beyond the time used.

Time series analysis overcomes the inability to extend analysis beyond the timeframe of cross-‐

sectional analysis because it uses multiple years. The results inform researchers about trends in

the data that occur over time.

The GSS is a useful source of data in comparison to what has previously been used in

some studies because it includes many valuable covariates, is time-‐invariant in structure, and is

nationally representative. It is particularly useful because of its time consistent format. The

flexibility in data it provides allows me test alternative explanations for the connection between


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concern for crime and crime rates. Furthermore, the GSS asks a set of questions that are

deliberately constant across time, which allows for time series analysis of a large range of

issues. Given the advantages of the GSS, I use it as the source of my dependent variable and

covariates.

Rasinski (1989) has completed work on the flexibility of the wording on the GSS

“government spending” questions. He shows that multiple versions of the same question give

people the opportunity to flexibly interpret the question. In his study, using GSS data to

measure fear of crime, Toseland (1982) argues that measures of concern for crime and fear of

crime can be inferred from survey questions and applies these definitions to his work.

Furthermore, Warr (1995) notes that public opinion studies on concern for crime are few, and

that researchers are often forced to compromises on the available items to include in their

work. These studies conclude that fear of crime and the GSS variable natcrime are both

decreasing over time, which indicates the two measures might be accessing some amount of

“concern for crime.” In light of these studies, I argue that it is reasonable to consider natcrime

as one permutation of people’s concern for crime and use it as such in this paper.

The GSS is nationally representative survey that has sampled members of the U.S.

population since 1972. Most of the data is collected via face-‐to-‐face interviews; a minority

interviews are conducted over the phone or with the aid of computer assisted personal

interviews (National Opinion Research Center, 2011). Covariates on the GSS include a core set

of demographics, attitudinal questions, life topics, opinions, and several sets of special interest

topics that appear in rotation. I use survey years from 1973-‐2010. Missing survey years are

1979, 1981, 1992, 1995, 1997, 1999, 2001, 2003, 2005, 2007, and 2009; the majority of missing


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years can be explained from the introduction of biennial surveying in 1995. I take these gaps

into account and perform transformations and imputation on the data to overcome missing

years; I explain this process below. I source the cumulative GSS dataset from the NORC online

distribution page (NORC, 2011).

3.2. Variables

3.2.1. Dependent Variable

The dependent variable is concern for crime. It comes from a question from the GSS –

natcrime1 – that asks individuals how they rate the government on spending enough to halt

rising crime rates. The GSS does not include a variable that directly asks participants their

opinion of the level of crime or how safe they feel, nor is there a national-‐level study or survey

that uses a similar dependent variable and includes as many useful control variables. However,

previous research has established that natcrime follows the same trends across time as actual

crime rates and may be tapping into some measure of “concern for crime” (Frost & Clear,

2009). As a result I am confident that this study follows the arguments previous research has

established. For ease of interpretation, I recode natcrime scale to read from low to high

concern for crime, instead of in its original form from high to low concern for crime. Responses

are valued at 1 = little concern for crime, 2 = neutral concern for crime, and 3 = a lot of concern

for crime. I interpret natcrime to indicate that people are likely to report the need for more

national spending on crime when they believe not enough is being done to combat the crime.

This side of the scale represents “much” or high concern for crime. The other side of the

1 The GSS prompt for national crime states. “68. We are faced with many problems in this country, none of which can be solved easily or inexpensively. I'm going to name some of these problems, and for each one I'd like you to tell me whether you think we're spending too much money on it, too little money, or about the right amount. e. Halting the rising crime rate.”


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natcrime scale to signifies that people are more likely to report that too much is being spent on

battling crime, when they have “a little” or low concern for crime. For ease, for the remainder

of the study I refer to natcrime as concern for crime.

3.2.2. Independent Variables

The FBI UCR measures the violent crime rate by aggregating murder, robbery,

aggravated assault, and forcible rape counts per 100,000 in habitants. The UCR counts these

crimes as Part I offenses. Although, these four types of crime also exist as a unique count on

the UCR, I choose to use the rate of total violent crimes, which is the aggregate of the four

unique types of crime. Warr (2005) finds that people use an overall count of crime rather than

specific types of crime to assess the state of crime in society and to determine how safe they

feel. MacDonald (2002) & Sampson (1987) suggest that violent crime is the most reliable

measure of crime. I choose crimes that rank higher in seriousness under the logic that public

perception of crime trends will be similar for crimes of similar seriousness. Furthermore using

the crime rate per 100,000 inhabitants instead of the pure count of incidents takes into account

the fact that the number of crimes committed tends to increase with time and that states,

cities, and precincts vary in population size. As a result of this standardization comparison of

crime rates nationally and across years is possible.

Of the types of violent crime recorded by the FBI, aggravated assault is consistently the

most prevalent crime and accounts for the majority of that make up the total violent crime rate

across all years (Table 1A in Appendix). All types of crime reach their peak in the early 1990s.

Recent years have shown a return to total violent crime levels not seen since the early 1970s, as

evidenced by minimum rates crime in 1972 and 2010 (Figure 1).


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Figure 1.

I tested if violent crime is the appropriate crime rate to use rather than the individual

types of crime. Separately, I regressed the four individual types of crime rates on people’s

concern for crime in OLS. I also regressed the total violent crime rate on people’s concern for

crime in its own regression. The specific types of crime are not statistically significantly related

to how people rate their concern for crime. A more detailed discussion is contained below in

the results (Table 3, below).

In addition to the FBI UCR violent crime rate, I use attitudinal questions and

demographic variables from the GSS as independent variables. The GSS attitudinal and opinion

variables are used to test if their addition to the models helps predict changes in concern for

crime in accordance or divergence from crime rates. All GSS variables included in the study are

asked for the survey years 1973-‐2010.


20

I group the GSS variables into three domains for analysis. The first domain is

demographics. This includes the variables gender, age, race, married, kids, and income. Gender

is a binary variable with the values 1 = male or 0= female. Age is a continuous variable of

respondents’ aged 18 years and above. Race is also coded as binary with 1 = white and 0 =

other. Although the United States is a deeply multicultural country, in the GSS race question

continues to be asked in the same manner as it has been since it was first asked in 1972.

Otherwise, had changes been made to the structure of the question, researchers would not be

able to compare responses and societal changes over time. We are constrained by the original

question design, even though more diverse racial data would lead to richer analysis. Married

has been created from the original GSS marital. It is valued 1 = married and 0 = other. Marital

includes several categorical responses on the GSS but recoding married to be a binary variable

simplifies interpretation. Kids is a measure of whether there are children under 18 in the

household, 1 = yes, 2 = 0. Income is a self-‐reported, continuous variable measured in real

dollars.

The second domain is includes measures that capture opinions of national priorities and

politics. Political views have been rescaled to create a binary variable that values liberal = 1 and

not liberal (i.e. conservative) = 0. National Priorities is a measure that captures people’s

attention to other national priorities and government spending issues. It represents a scale of

concern for government spending on other national issues that were: national defense, the

drug problem in the U.S., national welfare programs, national healthcare, and the education

system. Chronbach’s alpha is 0.45. During interpretation, I am interested to see how its changes

on a yearly basis predict concern for crime. It serves as a measure of how concerned society is


21

as a whole with a variety of other government concern. TV hours is a continuous, self-‐report

variable of respondents’ reported hours of TV viewing per week.

The third domain consists of measures attitudinal and trust with the U.S. population

that I refer to as social values. Trust asks can people be trusted; fair asks if people are

inherently fair or try to take advantage of others. Both are binary such that 1 = yes, 0 = no.

Happy is a measure of general, individual happiness of GSS respondents. It is valued on a three

point scale: 1 = “not too happy”, 2 = “pretty happy”, and 3 = “very happy”.

3.3. Tests

In order to evaluate the relationship between concern for crime and crime rates across

time I conduct a series of analytical tests. I begin by examining if there are demographic and

other differences in opinion differences for those who rate their concern for crime as high

versus low concern for crime. After establishing if any trends exist among the people who have

“a lot” of versus “a little” or neutral concern for crime, I test basic regression assumptions of

concern for crime and crime rates across three domains using multivariate OLS. The OLS model

across all three domains will take the same format:

𝑌! = 𝛼 + 𝑏!𝑋!! + 𝑏!𝑋!! +⋯+ 𝑏!𝑋!" + 𝜀!

Where, 𝑏! represents the expected change in value of 𝑌! given 𝑋!", and 𝜀! is the value of the

deviation by 𝑌! from the mean distribution value. It represents the effects on 𝑌 not accounted

for in the model (Berry & Feldman, 1985).

Due to variations in each variable and possible non-‐stationary across time, general OLS

assumptions may signify that OLS may not be the appropriate test for this study. Spurious

relationships may result, and the interpretation gleaned from OLS analysis does not accurately


22

describe the relationships that exist with the type of data I use. OLS assumes six basic

assumptions: that the mean of the error terms is zero, the variance of the error term is

constant, the error terms are uncorrelated, the independent variables are uncorrelated with

the error term, 𝜖, there is no perfect collinearity between independent variables, and 𝜀! is

normally distributed for each set of k independent variables (Berry, 1993).

Additionally, since values in time may be more closely related to values from other years

or close time periods, I am not only interested in the random distribution of the values of

concern for crime and crime rates, but also the order in which values occur across all years

(Ostrom, Jr., 1990). As a result, I also take advantage of First Differences analysis and traditional

time series studies applying Prais-‐Winsten regressions on the variables for all domains. The First

Differences model will take the form:

∆𝑦! = 𝑎∗ + 𝑏!∆𝑋! + 𝑏!∆𝑋! +⋯+ 𝑏!∆𝑋! + ∆𝑒!

Where, ∆ denotes the change from t =1 to t = 2. The unobserved effect will “differenced away”,

such that 𝑎∗ = 0 (Ostrom, 1990). The idiosyncratic error term ∆𝑒! at each time 𝑡 is assumed

uncorrelated with the independent variables, 𝑏!𝑋! in all time periods (Wooldridge, 2009). First

Differences measures variations of ∆𝑥! across 𝑖 in order to predict changes in ∆𝑦! over time. It is

often employed to deal with autocorrelation (Ostrom, 1990). The alternative Durbin statistic,

Durbin’s h-‐statistic, is an appropriate test to check for AR(1) serial correlation time series

analysis (Wooldridge, 2009). It works by running an OLS regression to obtain the residuals, 𝑢!

and then runs a regression on the residual of:

𝑢!𝑜𝑛 𝑥!!, 𝑥!!,… , 𝑥!" ,𝑢!!! for all 𝑡 = 2,… ,𝑛


23

which produces the coefficient 𝜌 and the t-‐statistic, 𝑡𝜌. 𝑡𝜌 tests H0 = 0 against H1 ≠ 0, which

allow for usual tests of the hypothesis that there is no autocorrelation against the alternative

hypothesis of positive, first-‐order autocorrelation (Ostrom, 1990).

Prais-‐Winsten estimation is a time series regression used to overcome AR(1) serial

correlation of one lag in linear models (Ostrom, 1990). Prais-‐Winsten takes the form:

𝑌! − 𝑝!"𝑌!!! = 𝑎 1− 𝑝!" + 𝑏 𝑋! − 𝑝!"𝑋!!! + 𝑣!

Prais-‐Winsten regression obtains initial OLS estimates and calculates residuals, minimizes 𝑝 and

replaces it with 𝑝!", and applied OLS to the equation above. The iterations are repeated until

convergence (Ostrom, 1990).

It is important to note that I compute summary statistics and initial analysis at the level

of individuals. In order to assess people’s variation in concern for crime across these domains

comparison must be person-‐to-‐person. However, the order of analysis across time must be

done at the level of year. Each variable is collapsed by mean so mean values per year are

compared across time. This is necessary since individuals and incidents of crime are not the

same every year and because FBI UCR crime rates are not individual-‐level measures, but rather

annual, national counts. Mean values of the variables can be examined for trends that appear

over time. Completing analysis at two levels will enable exploration into whether the data used

supports previous research that people do not derive their concern for crime directly from

falling crime levels over time (Duffy, et al. 2008; Warr, 2005). However, the most important

level of analysis for this project is by year, because I examine trends over time rather than using

a cross-‐sectional analysis, which has been the method of most previous research into crime and

fear of crime.


24

In order to compare trends in concern for crime and total violent crime rates over time

the data must be quantified within the same unit of time (Cromwell, Hannan, Labys, & Terraza,

1994). I explore summary statistics while the GSS data are at the individual level. I must position

the GSS variables on the same collection-‐level as the FBI UCR crime rates so that they are at the

year unit of time and nationally representative, I condense the GSS variables into their means

values by year because analysis will compare their mean values over time. Thus, I can explore

the nature of linear or non-‐linearity of the trend of crime rates and concern for crime using OLS

with the year trend included, First Differences, Durbin’s alternative statistic, and Prais-‐Winsten

Feasible Least Squares.

For the OLS models I perform imputation and compute two year averages of variables as

two methods of dealing with missing time data. In effect, for every year of comparison – 1973-‐

2010, each variable is condensed around its mean values that are compared against other

years. Averages by two-‐year increments for the years 1973 through 2010 are computed to

create 19 observations. Creating two-‐year averages ensures that year gaps when the GSS was

not asked are taken into account.

For analysis using First Differences, the Durbin’s alternative statistic test, and Prais-‐

Winsten I use imputation to estimate missing year values. Transformations may be necessary

with time series data when there are missing values in order to properly incorporate

stationarity into time series analysis (Cromwell, et al. 1994). Multiple interpolation works by

substituting or estimating multiple missing data points within the range of known values. It

produces consistent estimates, can be used with the data and models of this study (Allison,

2002). However, if too many missing values need to be interpolated its usefulness is at risk


25

because the transformed can start driving that data. Creating two-‐year averages across the

survey years the effect of the two methods is very similar, although performed via a slightly

different method and results in a smaller number of observations. Figure 2 and Figure 3 display

the mean trends of concern for crime over time for comparison. Figure 2 is the two year

averaged method with 19 observations. Figure 3 is the interpolated version with 38

observations. They follow similar trends; the trend line in Figure 3 is a bit more smoothed

because there are more data points from the estimated missing values.

Tables 1-‐2 compare OLS regressions with the year trend included using the two methods

to account for missing year values. In both regressions, violent crime is a statistically significant,

positive predictor of people’s attitude of concern for crime. Additionally, inclusion of the year

trend reveals a decreasing trend in people’s concern for crime outside of any other factors’

influence (β = -‐0.007 in Table 1; β = -‐0.003 in Table 2) The results support my use of the two-‐

year averages of time for all analysis and results, because imputation and interpolation of

variables run the risk of interpolating too much information, which can start driving the data in

a spurious manner. Since almost a third of survey years are missing, I consider estimating that

many years too great a risk to the data. I have demonstrated that using the two year averages

produces similar results to when the data is imputed in order to justify using the two-‐year

averages method to account for missing years. Tables 2A-‐4A(Appendix) provide further

evidence that two-‐year averages and imputated years produce similar output for these data;

the tables are OLS regressions with the year trend across all three domains.


26

Table 1. OLS Regression – Time in Two Year Averages Coefficient t p Violent Crime 0.0003 4.14 0.001 Year -‐0.007 -‐5.12 0.000 N = 19 Adj. R2 = 0.738

Table 2. OLS regression – Missing Years Imputed

Coefficient t p Violent Crime 0.0003 7.15 0.000 Year -‐0.003 -‐7.71 0.000 N = 38 Adj. R2 = 0.773

The preceding outline introduces how I intend to test the hypothesis if people use real

crime rates to rationally decide their concern for crime rather than competing sources of

information. Instead, if people are not rational they may incorporate relevant social,

demographic, and other informational cues to derive a sense of what they think are reasonable

expectations for concern for crime.

Figure 2. Figure 3.


27

4. Results

4.1 Descriptive Statistics

The total number of people who respond to concern for crime on the GSS is 30,821 for

all years. For the years 1973-‐2010, people’s concern for crime has a mean of 2.62 and a

standard deviation of 0.60 (Appendix Table 5A). The sample skews negatively towards little

concern for crime. The mean for the violent crime rate is 549.97 (per 100,000 people) with a

standard deviation of 99.73 and a range (401, 758.2). The year with the highest rate of reported

violent crime was 1991; the lowest rate was in 2010 (401 versus 417.4 in 1972). For summary

statistics on the individual types of violent crime refer to the appendix (Appendix Table 1A).

The majority of violent crimes are incidences of assault and robbery. The individual

types of crime do not strongly predict a relationship with concern for crime compared to the

total rate of violent crime. I establish this through a comparison of simple OLS regressions for

the unique crime types’ rates and the total violent crime rate. As a result, I use total violent

crime rate as the variable of investigation with concern for crime going forward, as opposed to

the individual crimes, which I explain in more detail above.

An OL S regression on concern for crime using the four unique types of crime on concern

for crime does not show significant trends across the crimes (Table 3, Model 1). When I do the

same for violent crime, the results do reach significance, suggesting that the total violent crime

rate rather than individual rates is the source of information that people use for crime rates, as

previous research has suggested (Table 4). I also include a regression of the individual crime

rates with year trend included (Table 3, Model 2). None of the crime variables’ coefficients

succeeding in reaching statistical significance. Adding the year trend does not improve the


28

value of adjusted R2, or it seems the model with individual crimes. However with the year trend

included the direction of the coefficients changes, which suggests the year trend is accounting

for some degree of spurious or omitted variables in Model 1 (Table 3).

In the OLS model 1 (Table 4) using total violent crime as the independent variable, an

increase in one incident of violent crime (per 100,000 population) predicts a less than one

percent increase in people’s concern about crime, at significance p = 0.004 (F[1,17] = 10.84, R2 =

0.38, Adjusted R2 = 0.35). The value of adjusted R2 suggests that 38% of the variance in the

model is explained by the effect of violent crime on concern for crime. Given the low value of

adjusted-‐R2, other sources of information may help people make the decision of how

concerned they are about crime. When a year trend is included in the OLS model with violent

crime, every 1 increase in the violent crime rate (per 100,000 inhabitants) predicts a less than

1% increase in people’s concern for crime (Table 4, Model 2). There is also a statistically

significant year trend that signifies, net of all, other factors, concern for crime seems to be

decreasing in the population each year as some sort of artifact or time (p < 0.001) (Table 4,

Model 2). Model 2 has a higher adjusted-‐R2 value which indicated including the year trend may

account for some spuriousness that was present in Model 1 (Table 4). Thus if I were to stop

analysis at a univariate OLS regression, I could miss important and real trends. Indeed, I remedy

this risk by incorporating the variables of the three domains into analysis for multivariate

analysis of concern for crime.

Accordingly, the results compared in Tables 3 and 4 support using the total violent crime

rate for analysis and are consistent with previous literature. The individual crime rates’

coefficients show non-‐significance in predicting people’s concern for crime whereas, the total


29

violent crime rate may influence how much concern for crime people have. People may look at

the overall number of events rather than rates for specific types of crime to determine how

much crime is happening and how safe they deem society to be, which is supported by previous

research (Warr, 2005). Since I establish here that the total violent crime rate is the valid

measure of crime to use in this analysis, it will be the only variable of crime in further analysis.

Table 3. OLS Regression with Individual Crime Rates Averaged

OLS no year trenda (1)

OLS with year trendb (2)

Murder 0.006 -‐0.008 Rape -‐0.004 -‐0.004 Robbery 0.001 0.001 Assault 0.0001 0.0002 Year -‐ -‐ -‐0.003 aAdj. R2 = 0.737

bAdj. R2 = 0.728 -‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01

Table 4. OLS Regression with Violent Crime Rates

Averaged

OLS no year trenda (1)

OLS with year trendb (2)

Violent Crime 0.0004*** 0.0003* Year -‐ -‐ -‐0.007* aAdj. R2 = 0.353

bAdj. R2 = 739 -‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01

Summary statistics reveal insightful patterns for people’s concern for crime, when

examined across gender, age, race, marriage, and kids in household (Table 5). In all three of the

domain’s summary statistic tables, the rows reveal respondents’ concern for crime as a

percentage of the total for each variable. The total across rows will be 100% for the variable


30

listed at left. For example, less than 5% of females responded with low concern for crime,

whereas almost 7.5% of males have low concern for crime. Both males and females tended to

rate their concern for high rather than low or neutral (64% of males and 71% of females have

high concern for crime), but more females than males to have “much concern for crime.” Age is

relatively equally distributed across the scale of concern for crime with almost equal standard

deviations. By race, white respondents and respondents of other races are more likely to have

concern for crime, but non-‐white respondents’ rate high concern for crime with a greater

percentage, 73% versus 67%, respectively. There is little to no difference in the percentage

distribution of concern for crime for respondents who are married versus not married. Using

just summary statistics for marriage and concern for crime it does not look like the state of

marriage mediates people’s concern for crime. Likewise there is little difference across the scale

of concern for crime by those who have kids under 18 years in the household versus those who

do not. Having kids in the house does not seem to mediate concern for crime. Those with a lot

concern for crime is made of females in the middle of years age (i.e. mid-‐life) who are not

white, and are married with kids in the house. Non-‐white respondents show they are more

likely to have a lot of concern for crime rather than little for crime compared to white

respondents’ distribution across concern for crime. Across all of the demographic variables the

majority of individuals have much concern for crime; consistently, we above 60% of

respondents for all control variables with much concern for crime. Knowing that crime rates

have steadily declined since the early 1990s, these results lend themselves to previous claims

that people may not be rationally assessing their concern for crime given true crime rates.


31

In the national priorities and political opinions domain, there are similarly few patterns

to discern, but again across all control variables a majority of people respond that they have

high concern for crime (Table 6). More people who have high concern for other national

priorities also have high concern for crime than low concern for crime. People who consider

Table 5. Descriptive Statistics of Domain Two Variables and Concern for Crime Concern for Crime Much Neutral Low Sex (%) Female Male Age (years) *** Age 18-‐25 (%) Age 26-‐35 (%) Age 36-‐45 (%) Age 46-‐55 (%) Age 56-‐65 (%) Age 66-‐75 (%) Age 76-‐85 (%) Race (%) White Other Married (%) Yes No Kids (%) Yes No

71.02 64.11 48.02a

(17.87)a

66.77 68.12 66.90 67.83 70.12 68.07 67.33 66.74 73.41 68.44 67.20 68.97 67.18

24.24 28.43 44.26a

(17.21)a

28.50 26.56 27.48 26.08 23.24 24.69 24.37 27.32 20.49 25.73 26.65 26.11 26.35

4.74 7.46 45.30a

(17.36)a

4.72 5.31 5.61 6.09 6.63 7.25 8.30 5.94 6.10 5.83 6.15 4.93 6.47

-‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01 -‐ ***a Values represent means and standard deviations


32

themselves liberal are concentrated in slightly lower percentages towards high concern for

crime. 71% of conservative respondents have high concern for crime, whereas 65% of liberal

respondents have high concern for crime. However, liberal respondents are concentrated in

greater percentages around neutral concern for crime (29% versus 22%).

In the third domain, the percentage of respondents who have much concern for crime is

higher across all variables (Table 7). Almost 70% of those who rate themselves are pretty happy

have much concern for crime, whereas only 8% of very happy people have little concern for

crime. For those who are pretty happy and not too happy, the pattern is similarly clustered

around people responding that they have high concern for crime. People who are trusting and

rate society as fair respond that they have much concern for crime with a slightly lower

percentage than those who are not trusting and do not believe society will treat them fairly

(trusting and high concern for crime = 63.95%, fair and high concern for crime = 65.49% versus

not trusting and high concern = 70.20% and society is not fair and high concern for crime =

Table 6. Descriptive Statistics of Domain Two Variables and Concern for Crime Concern for Crime Much Neutral Low National Priorities (%) Health Welfare Drugs Education

72.97 74.50 81.40 78.86

22.72 20.71 15.82 23.43

4.31 4.79 2.78 4.70

Political Views (%) Liberal Conservative

65.38 66.53

28.86 26.58

5.94 6.90

-‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01 -‐ b Values represent means and standard deviations


33

71.41%). The summary information of these variables suggests further investigation is

warranted to understand their possible relationship with people’s concern for crime.

Across all domains, covariates, and demographic characteristics summary statistics point

towards skewness in the data towards low concern for crime, such that people are

concentrated around high concern for crime. Thus, it is worth investigating if these variables

inform people of or help to explain the relationship between concern for crime and true crime

rates, especially since crime rates have been falling since the early 1990s and people’s

persistent high concern for crime does not seem to rationally align with crime rates given this

information (Table 7).

Table 7. Descriptive Statistics of Domain Three Variables and Concern for Crime Concern for Crime Much Neutral Low Happy (%) Very happy Pretty happy Not too happy Trust (%) Yes No Fair (%) Yes No

68.36 68.14 69.75 63.93 70.20 65.49 71.41

26.05 26.18 22.07 30.19 23.52 28.88 21.57

5.60 5.68 8.18 5.88 6.28 5.62 7.02

-‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01 -‐ b Values represent means and standard deviations

The final descriptive transformation I perform on the data is to explore the nature of

possible splines. Splines are a useful tool for modeling simple non-‐linear time trends (Marsh &

Cormier, 2002). Splines can often model non-‐linear trajectories that appear to change direction

after a particular period in time (Marsh & Cormier, 2002). For example, in the 1990s concern for


34

crime appears to begin dropping (Figure 4), and splines are able to capture this abrupt drop and

model multiple linear trends within the data. In essence splines work to join two or more

regression lines that may exist in the data across time by fitting and smoothing turns and kinks

in time (Marsh & Cormier, 2002). An alternative to using splines is polynomial regression, which

I tested by including the multinomial version of time. This did not reveal any significant trends

and suggests that splines’ success in modeling multiple linear trends in the data is a better fit. It

also runs the risk of creating mulitcollinearity with each additional term and fails to capture

sudden changes in slope (Marsh & Cormier, 2002). Given Figure 4, I argue a sudden change in

slope does occur in the 1990s in the concern for crime variable.

Figure 4.


35

Figure 5.

I determine that there should be a knot at year 1991 in the data based on the possible

trends depicted in Figure 5 (Marsh & Cormier, 2002). The first spline is 1973-‐1991 and the

second spline is 1991-‐2010. The regression output for this regression models new linear trends

taking 1991 as the knot-‐year as the place where the linear trend changes. The regression

output can be seen in Table 6A in the appendix. The first spline indicates that there is a positive

trend for people’s concern for crime for each increasing year until 1991. A 0.002 point increase

in people’s concern for crime is predicted for each additional year until 1991 (N = 30, 821, β =

0.002 p = 0.001, adjusted-‐R2 = 0.0062). After 1991, there is a statistically significant decrease in

concern for crime predicted. Each additional year predicts a 1% decrease in concern for crime

(β = 0.01 p < 0.001). The low adjusted-‐R2 value suggests the spline regression model is not

modeling all the effects that determine people’s concern for crime. A test for heteroscedasticity

(p = 0.944) and a Durbin alternative statistic (p = 0.0246) confirm that neither are present in the

linear spline model. Thus, splines are useful to understand the basic changes in the linearity of

concern for crime over time, but there is a lot of variation unexplained, which is why I continue


36

to higher-‐leveling modeling and attempt to capture more of what determines the relationship

between people’s concern for crime and violent crime rates. I explore this relationship father by

analyzing the relationship across the three domains of variables using different time series

modeling techniques.

As a final check on the variables before heading into analysis of concern for crime and

crime rates across the three domains, I explore the stationarity of the two variables: concern

for crime and the violent crime rate (per 100,000 inhabitants). Time series analysis assumes a

constant error values, means, and standard deviations across time. By checking these

characteristics I can confirm whether I should include analysis that checks for heteroscedasticity

and serial correlation in my analysis. Figure 4 and Figure 6 show the trend of the mean of

concern for crime over time. They show that the values are non-‐stationary across time. The

mean of concern for crime appears to increase without a clear trend until the 1990s and then

continuously decreases (Figure 6). Figure 5 and Figure 7 show the trend of the mean and

standard deviation of the violent crime rate for all years. The violent crime rate, like concern for

crime, increases until the 1990s (Figure 7). After 1991 the rate of violent crime appears to

decrease over time, while concern for crime appears much more variable until the early 1990s

when the trend decreases.


37

In light of these non-‐stationary trends, standard OLS could produce spuriousness in the

results. The present study collapses variables around mean values to compare by year and

includes tests that check for risk factors in the data. I perform basic checks on the data from the

OLS regression in Table 4 (above) to confirm whether the results are being spuriously driven by

other factors and require more analysis. I check normality and fail to reject the null hypothesis

that the sample comes from a normally distributed population, and conclude the data when

Figure 4. Figure 5.

Figure 6. Figure 7.

2.5

2.55

2.6

2.65

2.7

Mea

n

1970 1980 1990 2000 2010Year

Mean of Concern for Crime by Year40

050

060

070

080

0M

ean

1970 1980 1990 2000 2010Year

Mean of the Violent Crime Rate, by Year

0.0

2.0

4.0

6St

anda

rd D

evia

tion

0 5 10 15 20Year (in Two Year Averages)

Standard Deviation of Concern for Crime


38

collapsed by two year averages come from a normally distributed population (p = 0.211). I

check for heteroscedasticity and fail to reject the null hypothesis that there is no

heteroscedasticity (p = 0.445). I check up to three lags for autocorrelation using Durbin’s

alternative h-‐statistic and fail to reject the null hypothesis that no autocorrelation is present

(one lag: p = 0.742; two lags: p = 0.568; three lags: p = 0.687). These tests suggest the data are

free from these risks, however there is a low number of observations and I choose to include a

year trend in further analysis to ensure that analysis is picking up any omitted variables that

relate to both crime rates and concern for crime. Thus my analysis across the three domains

includes checks of heteroscedasticity and autocorrelation. Including First Differences and Prais-‐

Winsten feasible least squares regressions will correct for these risks if they arise in my analysis

and will enable me to investigate the relationship of concern for crime and violent crime rates

over time with properly controlled data.

4.2 Crime and Demographics

A standard OLS regression on concern for crime using the rate of violent crime and

demographic variables does not reveal many statically significant patterns (Table 8). When the

violent crime rate increases, so too is concern for crime predicted to increase (N = 19, β =

0.0004, p < 0.01, R2 = 0.74), holding all other factors constant. Although the other variables in

the OLS model do not show significance, they do operate in the expected direction for reach

relationship. Males are predicted to have a lower concern for crime than females; each

additional year of age predicts an increase in concern for crime; those who are married are

expected to have higher concern for crime, as are those who have children under 18 in the

household. Inclusion of the year trend picks up on potential omitted variables that may drive


39

part of the relationship between concern for crime and crime rates. Although the adjusted-‐R2

value is high for this model, I chose to interpret it with caution because in OLS it may be a result

over over-‐fitting with so many variables included in the model. I fail to reject the null

hypothesis of no autocorrelation with one lag and conclude there is no autocorrelation in the

model (1) (Chi2 = 1.361, p = 0.243). Furthermore there is no serial correlation in the model (p =

0.195).

Table 8. Regression Models for Demographics Domain OLS (1) OLS with year (2) Prais-‐Winston (3) Violent Crime 0.0004***

(0.0001) 0.0004*** (0.0001)

0.0004* (0.0001)

Male -‐0.845 (0.799)

-‐0.347 (0.991)

-‐0.578 (0.898)

Age 0.0001 (0.012)

0.007 (0.015)

0.012 (0.013)

Race -‐0.425 (0.214)

-‐0.412 (0.217)

-‐0.561** (0.194)

Married 0.462 (0.330)

0.277 (0.397)

0.303 (0.3)

Kids 0.135 (0.133)

0.064 (0.157)

0.07 (0.127)

Year -‐-‐ -‐0.006 (0.007)

-‐0.007 (0.006)

Adj. Durbin-‐Watson 1.361 2.24 2.36 Rho Adj. R2

-‐-‐ 0.746

-‐-‐ 0.740

-‐0.52 0.995

-‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01 -‐ Standard errors in parenthesis

Including a time year with the year variable does not change the significance of the

independent variable’s coefficients in OLS by much, the relationships between the variables and

concern for crime can be interpreted net of time (Table 8, Model 2). For each subsequent year

of the survey, concern for crime is predicted to decrease. This trend is seen in Figure 2, which


40

depicts the average rating of concern for crime across time. However, the OLS model with a

time trend does not take into account that each year is important in its specific place in time. I

fail to reject the null hypothesis of no autocorrelation with one lag and conclude there is no

autocorrelation in the model (2) (Chi2 = 2.24, p = 0.135). Furthermore there is no serial

correlation in the model (p = 0.195).

Following the OLS model with a year trend included I ran First Differences on the

demographics domain variables with the year variable included to capture the year trend (Table

9). I interpret the coefficient on the rate of violent crime to show that a 1% increase, a 0.03%

increase is expected in people’s average concern for crime given a three point concern for

crime rating scale, net of all other differences at any point in time. However, this result is not

statistically significant, nor do any other coefficients in the model reach statistical significance.

The overall adjusted-‐R2 = 0.2 value for the model is quiet low, although not unexpected for

aggregate level data, and suggests that only about 20% of the variation of the relationship

between the variables is explained by the model. First differences takes care of

heteroscedasticity (p = 0.313). There is marginal serial correlation of the first-‐order in the model

as evidenced by a Durbin alternative test (Chi2 = 4.094, p = 0.043).


41

Table 9. First Differences Model for Demographics Domain First

Differences d.Violent Crime 0.0003

(0.0003) D.Male 0.476

(1.147) D.Age 0.003

(0.-‐23) D.Race -‐0.340

(0.259) D.Married 0.243

(0.578) D.Kids 0.138

(0.224) D.Year -‐0.0008

(0.003) Adj. Durbin-‐Watson 0.043 Adj. R2 0.2 -‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01 -‐ Standard errors in parenthesis

The final model I ran on the first domain is Prais-‐Winsten estimation (Table 8, Model 3).

The rate of violent crime and race are the only statistically significant coefficients on concern

for crime. For each percent increase in the rate of violent crime, people’s average concern for

crime is expected to increase by 0.04% (N = 19, β = 0.0004, α < 0.001). A 1% increase in the

proportion of white respondents predicts a -‐0.561 decrease in concern for crime, net of all

other factors and at any point in time (β = -‐0.561, α < 0.05). Across the four tests, violent crime

is a statistically significant predictor of crime in the demographics domain. In the Prais-‐Winsten

model the proportion of white versus non-‐white respondents seems to play a role in

determining concern for crime at a marginally statistically significant level. Although the


42

demographic variables fail to reach statistical significance, analysis suggests the variable do

trend in the expected direction for these variables.

4.3 Crime, National Priorities, and Political Opinion

The second domain of exploration is national priorities, political views, and media. To

begin I ran a basic OLS regression on concern for crime with the independent variables of the

rate of violent crime and the national priorities variables (Table 10, Model 1). This test was to

determine if as people increase their focus on other national priorities their attention to

concern for crime changes in a significant way. The results of the simple OLS indicated that an

increase in concern for other national priorities predicts a decrease in concern for crime at a

non-‐significant level . Political views and hours spent watching TV create positive change in the

level of people’s concern for crime, but neither were significant. Violent crime does predict a

statistically significant increase in people’s concern for crime – across Models 1, 2, and 3.

Table 10. Regression Models for National Priorities and Politics Domain OLS (1) OLS with year (2) Prais-‐Winsten (3) Violent Crime 0.0004*

(0.0001) 0.0003*** (0.0001)

0.0003*** (0.0001)

Political Views 0.184 (0.475)

0.137 (0.387)

0.072 (0.370)

National Priorities -‐0.213 (0.118)

0.048 (0.135)

-‐0.008 (0.126)

TV Hours 0.118 0.135

0.008** (0.117)

0.082 (0.119)

Year -‐ -‐ -‐0.007** (0.002)

-‐0.006** (0.002)

Adj. Durbin-‐Watson 0.066 0.111 2.05 Rho -‐ -‐ -‐ -‐ -‐0.243 Adj. R2 0.517 0.68 0.962



43

Contrary to expectations in Model 1, when people’s concern for other national issues

increased, it predicted a decrease in people’s concern for concern for crime, although the result

is not statistically significant. Also, for each 1% increase of the population responding as having

liberal political views, there is a predicted 18% increase in people’s concern for crime; however

this results fails to reach significance. It should be noted the low number of observation may

contribute to the amount of non-‐significance seen in my analysis. In Model results may also be

driven by spurious factors, which is why a year trend is included in Models 2, 3, and 4 to try to

account for variables that may be driving the relationship but are not included. For the OLS

model without year trend (Table 10, Model 2), a heteroscedasticity test fails to reject the null

hypothesis that there is homoscedasticity in the data (Chi2 = 2.40, p = 0.122). A Durbin

alternative test fails to reject the null hypothesis that there is no serial correlation in the

residuals in the OLS model with year trend (Chi2 = 0.066, p = 0.8). Including two and three lags

in the model also creates situations in which the adjusted DW test fails to reject the null

hypothesis. In the OLS Model (2) with a year trend included, violent crime and the year trend

significantly predict changes in people’s concern for crime in the expected directions – positive

for the crime rate and negative for the year trend. A heteroscedasticity test for Model 2 fails to

reject the null hypothesis that there is no heteroscedasticity (Chi2 = 1.01, p = 0.315). A Durbin

alternative test fails to reject the null hypothesis that there is no serial correlation in the

residuals in the OLS model with year trend (Chi2 = 0.111, p = 0.739). Including two and three

lags in the model also fails to reject the null hypothesis that there is serial correlation (two lags:

p = 0.751; three lags: p = 0.893).


44

After running the OLS models, I ran First Differences on the second domain of variables

with the year variable included (Table 11). I interpret the coefficient on the rate of violent crime

to show that a 1% increase, a 0.03% increase is expected in people’s average concern for crime,

net of all other differences and at any point in time. However, none of the coefficients in the

model reach statistical significance. The direction of the relationship for political views is the

same direction as predicted by the OLS Model (1) and by Model (3) such that someone who is

liberal is predicted to have a decreased level of concern for crime (Table 10). In OLS model (2)

the relationship is positive. This may indicate some sort of deficiency in the variable to measure

a relationship with concern for crime, since Chronbach’s alpha was moderately low (α = 0.45).

In the First Differences Model (Table 11), the adjusted-‐R2 = 0.085 value for the model is low and

suggests that the model might not be fitting the correct trend to the data. The model does not

have any first-‐order autocorrelation (Chi2 = 0.633, p = 0.426) nor does it have any

heteroscedasticity (Chi2 = 0.06, p = 0.808).

Table 11. First Differences Model for National Priorities and Politics Domain First Differences Violent Crime 0.0003

(0.0003) Political Views 0.222

(0.44) National Priorities 0.244

(0.196) TV Hours -‐0.122

(0.109) Year 0.0007

(0.003) Adj. Durbin-‐Watson 0.663 Adj. R2 -‐0.088 -‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01 -‐ Standard errors in parenthesis


45

The final model I ran on the national priorities domain is a Prais-‐Winsten test. The rate

of violent crime is the only statistically significant coefficient on concern for crime. For each

percent increase in the rate of violent crime, people’s average concern for crime is expected to

increase by 0.03% (N = 18, β = 0.0003, α < 0.01). Also, the test is successful because the Durbin-‐

Watson statistic approaches 2, which suggests no autocorrelation in the model (Durbin-‐Watson

= 2.05).

4.4. Crime and Social Values

The third domain of independent variables is social values measures. An OLS regression

on concern for crime using social values and emotions variables highlighted the ways in which

people’s judgments about society’s “goodness” or their emotional state predict changes in

concern for crime (Table 12, Model 1). When people believe that society is trustworthy, there is

an increase in people’s concern for crime. Likewise, when people judge society inherently fair

versus not fair, it is predicted that there is a positive change in people’s concern for crime.

Oddly, when people increased their happiness by a unit on the three point GSS scale, they are

predicted to have stronger feelings of concern for crime. Intuitively one might expect that

positive rating on social values would predict decreases in concern for crime. However, it may

be that getting people to make value statements about society’s positive qualities might draw

their attention to its failures – in this case the state of crime. Also, it is important to note that

none of these variables is significant. Violent crime does statistically significantly continue to

positively predict people’s concern for crime (N = 18, β = 0.0003, p < 0.01, R2 = 0.62).

Model 2 (Table 12) is an OLS regression on concern for crime with the year trend

included. The direction of the variables’ relationship with concern for crime for all three of the


46

domain variables flips when the year trend is included. Although, none are significant the

relationship interpretation is what is expected compared to Model 1. Here the trend points to

people being trusting have less concern for crime, which is supported by the literature (Mullen

& Donnermeyer, 1989). The trend for those who rate society as inherently fair and not trying to

take advantage of people suggests that they will have decreased concern for crime. Finally, for

those who respond that they are happiest (on the three point scale) the trend suggests they will

also have decreased concern for crime. There is not any heteroscedasticity (p = 0.424), not does

Durbin’s h-‐statistic show that there is no first-‐order autocorrelation in the model (Chi2 = 0.843,

p = 0.359). The addition of two and three lags to the test does not change the state of no

autocorrelation in the model. All three variables’ relationships with concern for crime are

logical and expected, and perhaps if there were more observations to add robustness to the

models statistically significant results would emerge.

Table 12. Regression Models for Social Values Domain OLS (1) OLS with year (2) Prais-‐Winsten (3) Violent Crime 0.0003**

(0.0001) 0.0003** (0.0001)

0.0003* (0.0001)

Trust 0.439 (0.349)

-‐0.122 (0.400)

-‐0.152 (0.411)

Fair 0.316 (0.303)

-‐0.190 (0.354)

-‐0.146 (0.314)

Happy 0.198 (0.412)

-‐0.222 (0.411)

0.496 (0.377)

Year -‐ -‐ -‐0.009** (0.004)

-‐0.010** (0.004)

Adj. Durbin-‐Watson 0.891 0.843 2.107 Rho -‐ -‐ -‐ -‐ -‐0.384 Adj. R2 0.616 0.702 0.994



47

Following the OLS analysis, I ran First Differences on the societal values and emotion

domain of the variables with the year variable included (Table 13). I interpret the coefficient on

the rate of violent crime to show that a 1% increase in the crime rate predicts a less than 1%

increase in people’s average concern for crime, net of all other differences at any point in time.

However, none of the coefficients in the model reach statistical significance. The value of the

coefficients on trust, fairness, and happiness continue to change model to model. The

extremely low adjusted-‐R2 = -‐0.0798 value for the model is low and negative and suggests that

the first difference model is having trouble using differences to determine the true state of the

relationship between the variables. Perhaps, people’s trust in society, rating of society’s

fairness, and people’s personal level of happiness may not have strong bearing on their

determination of concern for crime. Durbin’s alternative h-‐statistic fails to reject the null

hypothesis that there is first-‐order serial correlation (Chi2 = 2.659, p = 0103).

Table 13. First Differences Model for Social Values and Emotions Domain First Differences Violent Crime 0.0003

(0.0003) Trust -‐0.116

(0.413) Fair -‐0.541

(0.446) Happy 0.663

(0.545) Year 0.0016

(0.003) Adj. Durbin-‐Watson 0.103 Adj. R2 -‐0.08



48

The last test is a Prais-‐Winsten estimation (Table 12, Model 3). The rate of violent crime

is the only statistically significant coefficient on concern for crime. A 1% increase in the rate of

violent crime leads to a 0.03% increase in people’s average concern for crime (N = 18, β =

0.0003, α < 0.000). Also, the model is free of autocorrelation because the Durbin-‐Watson

statistic approaches 2 (Durbin-‐Watson = 2.107).

5. Discussion

In this study, I examined if people’s concern for crime and true violent crime rates trend

together over time. I was interested in studying if people logically use crime rates rather than

other sources of information to assess their concern for crime. Previous literature has tried to

explain the dynamics that contribute to people’s concern for crime. Warr (2005) argues that the

way in which people derive their concern for crime is a complex process that is not only related

to their knowledge of crime rates. I find that crime rates appear to be the main source of

information people use to establish their concern for crime across time.

In order to test this relationship and explore other contributing factors, I included

several independent covariates from the GSS to examine if additional variables across three

domains predicted p concern for crime when information about true crime rates was included

in analysis. I included demographic variables to understand which sub-‐groups and portions of

U.S. society are likely to be more or less concerned with the state of crime. National priorities,

political opinions, and media habits were included to see how concern for crime will change as

people focus on other national priorities and public opinion sources over time. I also included

social values and emotions variables from the GSS to assess if changes people’s responses to

these questions moved in conjunction with or opposition to concern for crime across the years


49

1973-‐2010. None of these variables appeared to be significant factors in determining people’s

concern for crime across all years. Given analysis of several control variables, it appears that

people respond rationally to information about crime rates and use them to develop their

levels of concern for crime.

I ran initial analysis using ordinary least squares regressions across three domains of

investigation. I followed with First Differences and Prais-‐Winsten feasible least squares

regression. The latter two tests are designed to control for possible risks in the data in the form

of heteroscedasticity and serial correlation. OLS regression, even with time trends included,

may have spurious results because it assumes certain patterns in the data that may not hold

with time series analysis. In the present study, consistent checks for heteroscedasticity and

serial correlation show that there is none present when I use two year averages to collapse the

data by year for analysis. Conversely, First Differences is recognized as being capable of

correcting time series autocorrelation – a common analytical complication of time series

(Ostrom, Jr., 1990), and it is easy to run and interpret. Finally, because First Differences deals

with serial correlation further time series tests are unnecessary for the purposes of the study

beyond the tests I have included. However, First Differences is a very parsimonious test. Across

all three domains, the variables fail to produce any significant results.

A large body of previous research has explored the influence that media exposure has

on people’s fear of crime. I find that at the aggregate level of analysis rather than using a cross-‐

sectional design, TV hours was only a marginally significant predictor of people’s concern for

crime in one model – OLS with a year trend included. It also did not to alter the relationship

between actual crime rates and concern for crime. That TV hours had a slight relationship with


50

concern for crime is not surprising given the extensive literature about media sensationalization

about crime, nor is it a surprise that it does not drive the overall concern for crime relationship.

The study intended to analyze if people use crime rates to get information about their concern

for crime, and even with the inclusion of the media variable the relationship holds.

Readers should not overlook potential limitations in the data. In all models observations

are low – to the point that all models have less than 30 observations. The small number of

observations creates possible bias because we cannot assume the observations are

independent and individually distributed under the assumptions of the central limit theorem

(Berry & Feldman, 1985). I confirm that the concern for crime variable is normally distributed

after the data transformation. Yet, in all models there are a maximum of 19 observations.

Although many of the models do not show statistical significance, given the constraint of

observation numbers, interpretation of the results does show that the variables tend to predict

the expected relationship with concern for crime. Thus, I am reassured that the relationship

between concern for crime and total crime rates does exist. In all models expect for First

Differences violent crime rates predict increases in concern for crime, indicating that people

logically use crime rates to determine their concern. Future studies will benefit from more

years of observations to take into account, and their examination will hopefully show statistical

significance where we expect relationships to exist between violent crime and concern for

crime.

Other limitations include the datasets used. Given prior knowledge that the FBI UCR

report are known to have inaccuracies or be lacking full participation of law enforcement

agencies around the country, it is possible that missing data have taken a toll on accurate


51

exploration into the possible relationship between concern for crime and actual crime rates

over time. In order to understand if this is a major concern for researchers, further studies

could explore the reliability uncertainty concerns that have been raised about the FBI UCR.

Although the models contain variables of non-‐significance, these results are not

necessarily a disappointment nor should it be a deterrent from interpretation. The rate of

violent crime is a consistently positive predictor of people’s concern for crime with statistical

significance. When the rate of violent crime rises or falls, people’s concern for crime is expected

to move in the same direction. The results suggest that people do, in fact, use information

about crime rates to accurately perceive their concern for crime. Previous literature has studied

a wealth of covariates to better understand if anything mediates the relationship between

concern for crime and violent crime rates. I find here that none of the covariates from three

domains are useful predictors of concern for crime when violent crime rates are also

considered. The trends still exist, and I expect that with the availability of more years of data

the full nature of these relationships will be revealed.

In the present study I explore the relationship of people’s concern for crime and crime

rates for the years 1973-‐2010. I conclude from this information that people appear to logically

refer to crime rates when determining their concern for crime across time rather than using

other competing sources of information. Demographic, national priorities, opinions, and values

and emotions may play a role by informing some people some of the time about the state of

crime. However, across time and at the aggregate level these variables do not drive the

relationship of concern for crime and crime rates. Recognizing how crime rates influences

people’s beliefs about their safety and crime has implications for decision-‐makers and policy


52

advocates. Increased knowledge of the relationship can focus alignment of crime prevention

more closely with public opinion so that the public accurately perceives crime prevention as

having a positive effect. In conclusion, it does appear that for the years studied, across a

national sample, people’s concern for crime has been decreasing over the years and that the

main source of information that people use to determine their concern for crime is crime rates.


53

APPENDIX

FBI UCR Data Limitations

However, the UCR is not without its constraints, controversies and inadequacies. Thus,

readers should be aware of the weaknesses of UCR crime data collection. The unique system

that goes into keeping police records and further compilation and storage by the FBI creates

opportunities for error and manipulation (Milkakovich & Weis, 1975). Effects that influence that

reported numbers of crime within the UCR include the crime coding process, adherence to the

UCR Reporting Handbook, and ambiguities in crime definitions (Mosher, et al. 2002). Although

the UCR is subject to inaccuracies and poor accounting each year, it is not the only measure of

crime to face these risks. Despite the recognized issues with the UCR, I use it as the source of

crime rates because of its long existence, attempts at standardization and record-‐checking,

several levels of analysis of crime and the fact that it is the most comprehensive, single

directory of crimes for the U.S. Additionally, as a whole the UCR data collection tries its best to

get accurate counts of crime and corrects for flaws when identified.

The FBI UCR records crimes using the Hierarchy Rule. Under this classification system

each event of crime can only be reported to the UCR based on its highest ranking offence,

regardless of whether more than one type of crime occur in each event2. Since only one type of

crime can be recorded per event, the rest are not captured in UCR reporting (Mosher, et al.

2002). This can lead to an undercount of levels of crime, in addition to other recording

malpractices that may occur. Two major compromises may result from using this method. The

2 Under the Hierarchy Rule, if a reporting law enforcement agency records a crime that includes an assault and robbery, the incident would only be captured once in the UCR as a robbery not as an assault. Although both crimes are types of violent crime, robbery ranks higher according to hierarchy rule standards.


54

first is that not all offenses are recorded when multiple offenses occur within the same

incident, as explained above. The second is the assumption that reporting agencies will

truthfully ignore multiple offenses rather than including them as separate offenses in effect

meaning they report these accounts as separate crime. The latter issue is an important

consideration since it means that agencies have to go through crime report to standardize their

crime counts for the UCR separately from their own accounting methods. While this is a risk,

the consistent methodology and standardization of reporting measures that has been in place

since 1930 is more important and ensures consistent methodology year to year.

FBI UCR Crimes

TABLE 1A. Crime Rates Summary Statistics, 1973-‐2010 Crime

Minimum (rate per 100,000)

Maximum (rate per 100,000)

Mean (Std. Deviation)

Murder Rape Assault Robbery Total Violent Crime

4.8 (2010) 24.5 (1973) 200.5 (1973) 119.1 (2010) 403.6 (2010)

10.2 (1980) 42.3 (1991) 440.5 (1993) 272.7 (1991) 758.2 (1991)

7.68 (1.67) 7.68 (1.67) 315.33 (67.66) 193.24 (41.32) 554.149 (99.73)

All crime rates given per 100,000 citizens Year in parentheses

There are four unique offenses measured as crime rates for murder, rape, robbery, and

assault. Table 1A displays their summary statistics. Crimes peak in the early 1990s except for murder, which spikes in 1980. Minimum rates are reached in 2010. Imputation I tested the two-‐year averages model with 19 time observations versus output from the imputed model with 27 time observations. (Table 2A-‐4A).


55

Table 2A. Demographics Domain – Comparison of Time Collapsing Methods Averaged

OLS with year trend

Time Series Fill

OLS with year trend

Violent Crime 0.004** Violent Crime 0.0004* Male -‐1.482 Male -‐0.723 Age 0.017 Age 0.009 Race -‐0.534** Race -‐0.359 Kids 0.171 Kids 0.099 Income 7.73e-‐06 Income 3.61e-‐06

Year -‐0.009 Year -‐0.003 N = 19 Adj. R2 0.75

N = 27 Adj. R2 0.72

-‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01

Table 3A. National Priorities & Politics Domain – Imputation Comparison Time as Two-‐Year Averages

OLS with year trend

Time as Imputed

OLS with year trend

Violent Crime 0.0003** Violent Crime 0.0003* Nat’l Priorities 0.053 Nat’l Priorities 0.192 Political Views 0.152 Political Views 0.055 Year -‐0.007** Year -‐0.003* N = 19 N = 26 Adj. R2 0.71 Adj. R2 0.71 -‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01

Table 4A. Social Values & Emotion Domain – Imputation Comparison Time as Two-‐Year Averages

OLS with year trend

Time as Imputed OLS with year trend

Violent Crime 0.0003** Violent Crime 0.0004* Trust -‐0.122 Trust 0.042 Fair -‐0.190 Fair -‐0.097 Happy -‐0.222 Happy -‐0.261 Year -‐0.009** Year -‐0.004*** N = 18 N = 23 Adj. R2 0.70 Adj. R2 0.72 -‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01


56

Summary Statistics

Table 5A. Descriptive Table of Concern over Crime

A little Neutral

A lot

Total

N 20,927 8,055 1,839 30,821 Mean (St dev.)

-‐ -‐ -‐ -‐

-‐ -‐ -‐ -‐

-‐ -‐ -‐ -‐

2.62 (0.60)

Table 6A. Splines Regressions

Concern for Crime a Spline Regression Spline 1973-‐1991 0.002*

(0.001) Spline 1991-‐2010 -‐0.01*

(0.001) Durbin-‐Watson Statistic 1.344 F(2,24) 23.66 Adj. R2 0.0062 -‐ * Difference is significant at p < 0.001 -‐ **Difference is significant at p < 0.05 -‐ *** Difference is significant at p < 0.01 -‐ Standard Errors in parentheses

Significance Tests

Table 7A. Significance tests for Concern for Crime across National Priority Variables Concern for Crime A Little A lot

Drugs

1.84 (0.86)

2.70 (0.55)

Education

2.25 (0.84)

2.63 (0.58)

Arms

1.83 (0.80)

1.93 (0.58)

Child Care

2.56 (0.77)

2.56 (0.60)


57

Table 8A. Significance tests for Concern for Crime across Social Values Domain Concern for Crime A Little A Lot

Happy

2.14 (0.67)

2.20 (0.64)

Trust

0.39 (0.49)

0.39 (0.49)

Fair

0.56 (0.5)

(0.59) 0.49

References

Allison, P.D. (2002). Missing data. Sage University Papers Series on Quantitative Applications in the Social Sciences, 1-‐85. Thousand Oaks, CA: Sage Publications.

Berry, W.D., & Feldman, S. (1985). Multiple regression in Practice. Sage University Papers Series

on Quantitative Applications in the Social Sciences, 9-‐89. Thousand Oaks, CA: Sage Publications.

Berry, W.D. (1993). Understanding regression assumptions. Sage University Papers Series on

Quantitative Applications in the Social Sciences, 3-‐12. Thousand Oaks, CA: Sage Publications.

Barkan, S.E., & Cohn, S.F. (2005). Why whites favor spending more money to fight crime: The

role of racial prejudice. Social Problems, 52(2), 300-‐314. Baulmer, T.L. (1979). Research on the fear of crime in the United States. Victimology, 3(3/4),

254-‐264. Block, S., & Maxfield, M.G. (2011). The U.S Uniform Crime Reports and the National Crime

Victimization Survey. In Mangai Natarajan (Ed), International Crime and Justice. New York, NY: Cambridge University Press.

Blumstein, A. (2006). Disaggregating the violence trends. In Blumstein, A., Wallman, J. (Eds). The

crime drop in America. Cambridge University Press, New York, 13–44. Buijzen, M., Walma van der Molen, J.H., & Sondiji, P. (2007). Parental Mediation of Children's

Emotional Responses to a Violent News Event. Communication Research, 34(2),. Chiricos, T.E.D., Welch, K., & Gertz, M. (2004). Racial typification of crime and support for

punitive measures. Criminology. 42(2), 358-‐390. Cromwell, J.B, Hannan, M.J., Labys, W.C., & Terraza, M. (1994). Multivariate tests for time series

models. Sage University Papers Series on Quantitative Applications in the Social Sciences, 1-‐86. Thousand Oaks, CA: Sage Publications.

Duffy, B., Wake R., Burrows, T., & Bremner, P. (2008). Closing the gaps – crime and public

perceptions. International Review of Law & Technology, 22, 17-‐44. Federal Bureau of Investigation. (2011). FBI uniform crime reports (1973-‐2010). [machine-‐

readable data file]. Retrieved from http://www.ucrdatatool.gov/ Felson, M. (2002). Crime and everyday life: Insight and implications for society. Thousand Oaks,

CA: Sage Publications.


59

Franklin, C.A., & Franklin, T.W. (2008). Predicting fear of crime: Considering differences across

gender. Feminist Criminology, 4, 83-‐106. Frost, N.A., & Clear, T.R. (2009). Understanding mass incarceration as a grand social

experiment. In A. Sarat (1st Ed.), Studies in Law, Politics, and Society (Vol. 47, 177-‐183). Bingley, UK: Emerald Group Publishing.

Gerbner, G., & Gross, L. (1976). Living with television: The violence profile. Journal of

Communication, 26, 173-‐199 Gilliam, F. & Iyengar, S. (2000). Prime suspects: The influence of local television news on the

viewing public. American Journal of Political Science, 44, 560-‐573. Gilliam, F., Iyengar, S., Simon, A., & Wright, O. (1996). Crime in black and white: The violent

scary world of local news. Harvard International Journal of Press-‐Politics, 1, 6-‐23. Heath, L. & Gilbert, K. (1996). Mass media and fear of crime. American Behavioral Scientist,

39(4), 379-‐386. Holbert, R.L., Shah, D.V., & Kwak, N. (2004). Fear, authority, and justice: Crime-‐related TV

viewing and endorsements of capital punishment and gun ownership. Journalism & Mass Communication Quarterly, 81, 343-‐363.

Johnson, D. (2009). Anger about crime and support for punitive criminal justice policies.

Punishment & Society, 11(1), 51-‐66. LaFree, G. (1999). Losing legitimacy: Street crime and the decline of social institutions in

America. Boulder, CO: Westview Press. LaGrange, R.L., & Ferraro, K.F. (1989). Assessing age and gender differences in perceived risk

and fear of crime. Criminology, 27(4), 697-‐720. Ludwig, J., Cook, P.J., & Smith, T.W. (1998). The gender gap in reporting household gun

ownership. American Journal of Public Health, 88, 1715–8. MacDonald, J.M. (2002). The effectiveness of community policing in reducing urban violence.

Crime & Delinquency, 48(4), 591-‐618. Marsh, L.C., & Cormier, D.R. (2002). Spline regression models. Sage University Papers Series on

Quantitative Applications in the Social Sciences, 1-‐62. Thousand Oaks, CA: Sage Publications.


60

Marvell, T. B., & Moody, C.E. (1991). Age structure and crime rates: The conflicting evidence. Journal of Quantitative Criminology, 7(3), 237-‐273.

McDowall, D., & Loftin, C. (2009). Do US city crime rates follow a national trend? The influence

of nationwide conditions on local crime patterns. Journal of Quantitative Criminology, 25(3), 307-‐324.

Milakovich, M.E., & Weis, K. (1975). Politics and measures of success in the war on crime. Crime

& Delinquency, 21(1), 1-‐10. Moody, C.E., & Marvell, T.B. (2005). Guns and crime. Southern Economic Journal, 71, 720–736. Mosher, C.J., Miethe, T.D., & Phillips, D.M. (2002). The mismeasure of crime. Thousand Oaks,

CA: Sage Publications. Mullen, R.E., & Donnermeyer, J.F. (1989). Age, trust, and perceived safety from crime in rural

areas. The Gerontological Society of America, 25(3), 237-‐242. National Opinion Research Center (2011). General social surveys, 1972-‐2010 [machine-‐readable

data file]. Retrieved from www3.norc.org/GSS+Website/Download/ Ostrom, Jr., C.W. (1990). Time series analysis: Regression techniques (2nd Ed.). Sage University

Papers Series on Quantitative Applications in the Social Sciences, 6-‐90. Thousand Oaks, CA: Sage Publications.

Rasinski, K.A. (1989). The effect of question wording on public support for government

spending. The Public Opinion Quarterly, 53(2), 388-‐394. Rennison, C.M., & Planty, M. (2006). Reassessing Who Contributed Most to the Decline in

Violence During the 1990s: A Reminder That Size Does Matter. Violence and Victims, 21(1), 23-‐47.

Rosenfeld, R. (2002). The crime decline in context. Contexts 1, 25-‐34. Sampson, R.J. (1987). Urban black violence: The effect of male joblessness and family

disruption. American Journal of Sociology, 93, 348-‐382. Smith, T.W. (2011). Trends in national spending priorities, 1973-‐2010. General Social Survey:

National Opinion Research Center. Retrieved from www.norc.org/PDF/publications/SpendingPrioritiesRpt_08March2011.pdf

Toseland, R.W. (1982). Fear of crime: Who is the most vulnerable? Journal of Criminal Justice,

10, 199-‐209.


61

Valkenburg, P.M, & Patiwael, M. (1998). Does Watching Court TV `Cultivate' People's Perceptions of Crime? International Communication Gazette, 60(3), 227-‐238.

Warr, M. (1995). Poll trends: Public opinion on crime and punishment. The Public Opinion

Quarterly, 59(2), 296-‐310. Wooldridge, J.M. (2009). Introductory Econometrics: A modern approach (4th Ed). Newbury

Park, CA: Sage Publications.

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