Property Crime Dissertation - Ben Lindsey

Word Count: 10,997
Ben P Lindsey
Police Recorded Property Crime and Unemployment
in the Regions of England between 2005 and 2012
Dissertation submitted in partial fulfilment of the
award of degree.
BSc Economics with Banking
“The Devil makes work for idle hands”

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Table of Contents
Abstract …………………………………………………………………………………………………………………………………………… 4
List of Tables and Figures …………………………………………………………………..…………………………………………..… 5
Chapter 1: Introduction …………………………………………………………………………………………….……………… 6-7
1.1: Research Question …………………………………………………………………………………….…………………. 6
1.2: Research Aim …………………………………………………………………………………………….…………………. 6
1.3: Research Objectives ………………………………………………………………………………….………………..… 6
1.4: Research Motivation ……………………………..………………………………………………….…………….… 6-7
1.5: Dissertation Layout ……….………………………………………………………………………….………………..… 7
Chapter 2: Literature Review …………………………………………………………………………………….….……….… 8-13
2.1: Rational Choice Theory ….………………………………………………………………………….…………....… 8-9
2.1.1: Limitations of Rational Choice Theory …………………………………..……….……………… 9
2.2: Routine Activity Theory ……………………………………………………………………………..…………..… 9-10
2.2.1: Limitations of Routine Activity Theory ………………………………………………………… 10
2.3: Empirical Research of Crime Determinants …………………………………………….………..……. 10-13
2.4: Methodological Review of Empirical Research …………………………………………………………... 13
Chapter 3: Methodology …………………………………………………………………………………….……………….… 14-20
3.1: Research Design ……………………………………………………………………………………………………….… 14
3.2: Data Collection …………………………………………………………………………………………………………… 14
3.3: Variable Analysis ………………………………………………………………………………………………….… 14-16
3.3.1: Dependent Variable – Property Crime …….……………………………………………… 14-15
3.3.2: Independent Variables …………………………………………………………………………… 15-16
3.4: Limitations of Data …..……………………………………………………………………………………………. 16-17
3.5: Preliminary Data Analysis …………………………………………………………………………………….… 17-19
3.6: Hypotheses ………………………………………………………………………………………………………………… 19
3.7: Econometric Approach ….………………………………………………………………………………………. 19-20
Chapter 4: Model Specification, Diagnostic Tests and Findings …..………………….………………………. 21-32
4.1: Diagnostic Tests …………………………………………………………………………………………………..… 21-22
4.2: Model Specification ……………………………………………………………………………………………..… 22-27
4.3: Further Diagnostic Testing ………………………………………………………………………………………..… 27
4.4. The Final Model - Mixed Effects Model ………………………………………………………….…………... 28
4.5: Variable Analysis – Findings …………………………………………………..………………………………. 28-31
4.6: Concluding Remarks ………………………………………………………………………………………….…… 31-32
4.7: Research Study Limitations ………….……………………………………………………………………………... 32
Chapter 5: Conclusion and Recommendations …….…………………………………….………………………….. 33-34
5.1: Conclusion …………………………………………………….……………………………………………..…………..… 33
5.2: Contribution ……………………………………………………………………………………………………………..… 34
5.3: Further Research and Policy Recommendations ………………………………………………………..… 34
References ……………………………………………………………………………………………………………………………….. 35-39
Appendix ………………………………………………………………………………………………………………………………..… 40-41
Stata Output Screenshots ………………………………………………………………………………………………………… 41- 62

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Acknowledgements
“Shoot for the moon, and if you miss, you will still be among the stars”.
Les Brown.

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Abstract
This paper extends the idea first introduced by Becker (1968) that criminals are economic rational
actors by investigating the possible influences of police recorded property crime within the 9 regions
of England between 2005 and 2012 through reflecting on the empirical research and the theoretical
impetus of criminal activity. The explanatory variables include unemployment rates, gross value added
per head, median gross annual income, education, and government public order and safety
expenditure and unemployment benefit expenditure. The inclusion of female unemployment rates in
this study is intended to extend previous research which has predominantly excluded females.
Government unemployment benefit expenditure was included to extend Fougere’s (2009) study, as
again it is a predominantly excluded variable in empirical research. To study the explanatory variable’s
influence on police recorded property crime, a panel data analysis was utilized controlling for omitted
variables. A mixed-effects model that includes fixed and random effects was chosen as the efficient
model due to contradictions amongst the model specification tests and less stringent assumptions in
comparison to a GLS. This paper found that male and total unemployment rates exhibit a significant
positive relationship on recorded property crime, but education and median gross annual income
exhibited stronger statistically significant negative effects on recorded property crime, providing an
explanation for the overall decreasing trend seen in recorded property crime within this period.
Female unemployment rates and gross value added per head were both insignificant in this study.
Keywords: Property crime, unemployment, panel data, mixed-effects model.

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List of Tables and Figures
Table 1.1: Variable Data Sources ……………………………………………………………………………….…………………... 17
Table 1.2: Summary of Descriptive Statistics ………………………..……………………………………….….…….………. 19
Table 1.3: Summary of Hypotheses ………………………………………………………………………………………………... 19
Table 1.4: Description of Equation Components………………………………………………………………….………….. 20
Table 1.5: Summary of Estimators for Pooled OLS, Fixed, Random and Mixed Effects Models….…….. 25
Table 1.6: Breusch-Pagan Lagrange Multiplier Test Results…………………………………….…………….…………. 26
Table 1.7: Hausman Test Results………………………………………………………………………………………….……….… 26
Table 1.8: Mixed Effects Overall Goodness of Fit ………………………………………………………………….…………. 28
Table 1.9: Restricted Random-Effects Model in Mixed Effects Model …………………………………..……….… 28
Table 2.0: Multi-Level Mixed Effects Model …………………………………………………………………………..……….. 29
Table 2.1: Summary of Regressor Outcomes …………………………………………………………………………………… 29
Figure 1.1: The Effect of Unemployment on Time Allocation between legal and illegal activities …….. 9
Figure 1.2: Percentage of CSEW Incidents Reported to the Police in 2002/03 - 2011/12 …………………. 16
Figure 1.3: Total CSEW Reported Property Crime and Unemployment Rate Trends from 1993 - 2013/14
…………………………………………………………………………………………………………………………………………………..….. 17
Figure 1.4: Total Recorded Property Crime and Male and Female Unemployment Rate Trends in
England from 2002 - 2012 ………………………………………………………………………………………………………….…… 18
Figure 1.5: Education Attainment and Median Gross Annual Earnings in England in 2005 - 2012 ……. 18
Figure 1.6: Differences between Stationary and Non-stationary Time Series ……………………………........ 21
Appendix
Table 2.2: Notifiable Offences Categories included under Property Crime in this study
Table 2.3: Summary of the Regions of England Included in this Analysis
Stata Output Screenshots
A1: Summary of Data and Variables
A2.1 - A4: Pooled OLS, Fixed and Random Effects Models
A5: Final Mixed Effects Model
A6: Estimates Tables
A7 – A9: Chow, Breusch-Pagan and Hausman Tests
A10: Diagnostic Tests

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Chapter 1: Introduction
In this chapter I will briefly introduce the dissertation topic with my research question, aims and
objectives and conclude with my research motivation regarding why I chose to analyse this topic.
1.1. Research Question
1. What factors have influenced police recorded property crime in the regions of England between
2005 and 2012?
2. What influence have unemployment rates had on police recorded property crime in the regions
of England between 2005 and 2012?
1.2. Research Aim
The aim of this dissertation is to evaluate a range of variables that influence crime participation
following the analysis of previous literature and analyse their impact on recorded property crime in
the regions of England.
1.3. Research Objectives:
 Establish the theoretical reasoning behind why individuals might commit crime.
 Evaluate the empirical research and conclude what variables could influence an individual to
commit crime, specifically property crime.
 Gather regional police recorded property crime and a variety of economic data to perform a
panel data analysis.
 Critically analyse the relationships between recorded property crime and chosen variables and
make policy recommendations regarding future crime prevention policies.
1.4. Research Motivation
Crime has perpetually been detrimental within society, damaging businesses, harming individuals and
destabilizing communities. A study conducted by the Home Office (2000) found the highest costs from
criminal activity were attributed to property crimes, which are crimes involving the theft or damage
of property, and in 2013/14 they accounted for 70% of police recorded crime. The same study found
that the costs per incident for theft of a vehicle were £4,700, burglaries cost on average £2,300 and
robberies on average cost £5,000. The total cost of crime for 1999/2000 was estimated to be nearly
£60bn, categorized into 3 key areas. Firstly there are costs attributed to the anticipation of the crime,
such as personal or business security expenditure or insurance, followed by the direct consequences
of crime such as stolen or damaged property as well as the long term health impacts on victims, and
lastly there are the costs of responding to and dealing with the crime such as expenditure on law
enforcement.
Recorded property crime rates have grown on average by 5% annually throughout the 20th
century,
and because of the consequently high costs derived from criminal activity, criminologists have long
studied the reasons why an individual participates in criminal activity, with the hope that they can
successfully develop effective crime prevention policies.
Theories attempting to explain criminal behaviour are vast according to Newburn (2013), from genetic
and biological-social interactions to maternal deprivation and learning theories. But since the mid-20th

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century, economics has started to play a larger part in explaining why individuals commit crime by
describing offenders as rational economic actors who are influenced by costs and benefits instead of
irrational psychologically disturbed individuals. If this is true, then crime prevention policies can be
correctly adjusted to further their efficacy, targeting areas of possible influence through economic
policies and budget changes, concomitant to traditional law enforcement and deterrence policies. The
implementation of economics within the study of crime argued by Albertson and Fox (2012) is
advantageous as findings of correlations between variables can be incorporated into macroeconomic
crime prevention policies.
Due to the evidently high costs of property crime, this paper will evaluate the possible influences on
police recorded property crime within England, drawing from previous empirical research. A panel
data analysis will be utilized emulating methodology in empirical research, as it controls for omitted
variables and allows time and cross-sectional flexibility (Gujarati, 2011). Due to the variability of
econometric models used in empirical research, several models including OLS, fixed, random and
mixed effects will be fitted and compared to determine the efficient model.
1.5. Dissertation Layout
The next chapter will evaluate two noteworthy theories which explain the possible reasons that cause
individual’s to commit crimes, followed by an analysis of the empirical research studying crime
determinants. Chapter 3 will outline the methodological approach of this analysis. Chapter 4 will
describe the steps taken to identify the efficient econometric model, fitting a variety of models for
comparison, implementing necessary diagnostic tests with a description of the final findings with
comparisons to previous literature. The final conclusion will be in chapter 5, evaluating the research
aim, answering the research question and providing future research and policy recommendations.

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Chapter 2: Literature Review
The literature review will be split into two sections:
1. Overview of Rational Choice and Routine Activity Theory with limitations of each.
2. Analysis of the empirical research studying the influences of crime.
2.1. Rational Choice Theory
Becker’s (1968) rational choice and economic theory of crime was the first to describe criminals as
economically rational actors, linked to the concept of optimal choice where individuals participate in
activities only if the utility acquired from them exceeds the utility gained from using their time and
resources elsewhere. Becker (1968) argued that individuals become criminals not because their
motivation differs from others, but because their benefits and costs differ.
The number of offences committed during a particular period described by Becker (1968) is a function
of the probability of conviction and the punishment per offence, and increases in either would
decrease the overall utility gained from participating in crime, thus reducing the number of offences
committed. Becker (1968) also considered other influencing variables such as income or education, as
a change in potential returns from legal opportunities will reduce the incentive to participate in crime
through increasing the opportunity costs of criminal activity.
Ehrlich (1973) extended Becker’s (1968) model arguing that criminals respond to incentives and
allocate their time optimally between legal and illegal activities. In their models, deterrence is
exogenously determined, but Sah (1991) proposed that the probability of punishment is instead
endogenously determined, and an individual’s past experiences are more prominent in determining
their perception of the probability of punishment. The individual’s estimated probability of
punishment will alter their propensity to commit crime due to changes in perceived risk, which would
mean invasive crime deterrent initiatives imposed by policy makers would be inadequate. This could
be extended to potential returns from legal and illegal activities, which if also endogenously
determined, could strongly influence an individual’s choice between crime and legal opportunities,
regardless of whether the perceptions of realistic. Sah (1991) argued that an individual’s optimal
choice in a period will be to commit crime if the relative payoff from crime is more than the perceived
probability of punishment. An individual’s characteristics could also alter the propensity to commit
crime according to Sah (1991), but the channels which they work through are unelaborated.
An extension of Ehrlich’s (1973) time allocation model by Raphael and Winter-Ebmer (2001) is shown
in Figure 1.1.
With 𝐵𝐶𝐸 as the illegal activities budget constraint, individuals allocate their time to illegal activities
until returns equalize at point 𝐶, where the income for legal opportunities outweigh returns from
illegal activities, shown by the steeper 𝐵𝐶𝐷 budget constraint. To the right of point 𝐶 is time allocated
to crime, and to the left of 𝑡0 is time allocated to both legal and illegal activities, depending on
individual preferences. For individuals with a low potential income where returns to crime exceed
potential returns from legal activities, Raphael and Winter-Ebmer (2001) state unemployment for
example will cause the budget constraint to change from 𝐴𝐵𝐶𝐷 to 𝐴𝐵𝐶𝐸, and the utility function 𝑈1
crosses the budget constraint at a flatter slope, symbolizing differences in marginal substitution

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between non market time and income in comparison to point 𝐶. For those involved in both legal and
illegal activities, unemployment will increase time allocated to illegal activities.
In conclusion, the rational choice theory proposes that individual’s compare the costs and benefits of
criminal activity in comparison to legal activities, responding to incentives and deterrents. The income
generated from one activity is explained by Altindag (2011) to reflect the cost of participating in the
other. The choice to commit crime is therefore dependent on the associated costs and benefits from
legal and illegal activities, and the optimal choice will reflect the most attractive and fruitful activity.
2.1.1. Limitations of Rational Choice
In Becker (1968) and Sah’s (1991) optimal choice functions, the duration of the offending period is
unspecified, similarly with the amount of allocated time available in Raphael and Winter-Ebmer’s
(2001) time allocation model. Therefore it is unclear whether the incorporated components have short
or long term effects on an individual’s crime propensity.
This theory also argues that there are no barriers or costs moving between legal and illegal activities.
Individuals could continue committing crime regardless of the potential income from legal activities
according to Mocan et al (2004), because of changes in human capital that represents skills and
knowledge that affects potential income. Individual’s legal human capital could depreciate due to a
decline in reputation or loss of income, making it difficult to re-engage in legal activity.
This is supported by Bayer et al’s (2003) study in juvenile prisons who found that in prisons, criminal
knowledge can be transferred down to new offenders via experienced offenders, increasing criminal
human capital and the potential returns from criminal activity. Therefore, if an individual participates
in criminal activities for long periods of time, re-engaging in legal activities could be difficult due to
the lack of necessary skills, experience or employment barriers from possessing a criminal record. This
argument can be related to gang culture, because violent threats and reputation according to Decker
and Van Winkle (1996) can make it difficult for individual’s to leave gangs, summarizing the
perspective of a gang member stating “…there are only two ways to leave a gang, move or be killed”
p264.
2.2. Routine Activity Theory
Routine activity theory, a criminological theory developed by Cohen and Felson (1979), argues that
focus should be placed on the situation which crime occurs instead of on the criminal and that changes

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in the number of targets and capable guardians of targets creates changes in crime trends. Cohen and
Felson (1979) suggest changes in “routine activity” influence recorded crime when three elements
assemble. These are:
1. Motivated Offenders
2. Suitable Targets
3. Absence of Capable Guardians
Guardians can include law enforcement, CCTV or other factors which provide increased protection of
targets (targets can include individuals or property). This is a concept Cornish and Clarke (2003) call
“target hardening”, incorporating components that increase deterrence or make targets more crime
resistance. Farrell et al (2010) argues this could involve improved forensic technology or online
security as not all crimes are committed in person.
Motivated offenders are a necessary component of crime, but if one of these 3 elements is lacking,
Newburn (2013) argues it is sufficient enough to prevent the completion of a criminal offence. This
theory argues that in addition to motivated offenders, factors that change the opportunity of crime
can influence the prevalence or supply of crime, a concept supported by Albertson and Fox (2012)
who suggest consumerism has increased suitable targets through supplementary cars and electronic
goods.
With unemployment as an example, Cantor and Land (1985) argued that higher unemployment could
lead to increased levels of “guardianship”, as unemployed workers will spend more time at home and
less time travelling to work, decreasing the risk of becoming a victim and having their home targeted,
thus lowering suitable targets and decreasing crime rates.
In conclusion, routine activity theory explains how changes in opportunities and situations can alter
crime prevalence, and although motivated offenders are essential, crime will be unsuccessful without
suitable targets with a lack of capable guardians.
2.2.1. Limitations of Routine Activity Theory
Increased security measures may deter offenders in the short run by decreasing opportunities, but in
the long run Newburn (2013) proposes that a displacement of crime will occur and offenders will
search for new targets until they’re successful, possibly moving between locations or committing
different crimes. Furthermore, increased guardianship could increase the severity of crimes as
offenders begin using more violent or invasive tactics to achieve their aims. Therefore, the
effectiveness of policies involving situational crime prevention will vary depending on the strength of
the individual’s cost-benefit analysis and potential returns.
2.3. Empirical Research of Crime Determinants
Crime throughout this study will hereby refer to property crime (excluding in the unemployment-
crime term, hereby referred to as U-C) unless stated otherwise.
Unemployment
According to Ehrlich’s (1973) time allocation model, unemployment is expected to increase recorded
crime due to lower legal income. Reviewing the findings of 63 U-C studies, Chiricos (1987) found that
the U-C relationship is three times more likely to be positive than negative, and property crime is more
likely to produce significant and positive results than violent crime. This is supported by a US panel
data study by Raphael and Winter-Ebmer (2001) who found the unemployment rate exerted positive

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and significant effects on total crime rates with coefficients between 1.6 – 5.0 in a two-stage least
squares model, but found an insignificant U-C relationship with violent crime. Offenders are seeking
some kind of monetary gain according to Melick (2003), and so a relationship between unemployment
and violent crime is unlikely as there is no evident monetary benefit. Therefore it is hard to consider
violent offenders as Becker’s (1968) rational economic actors. Because of the insufficient theoretical
rationale and empirical evidence, violent crime will be excluded within this analysis.
In a US U-C time series study, Cantor and Land (1985) postulated that unemployment can influence
crime rates through opportunity changes and motivational effects discussed by Becker (1968) and
Cohen and Felson (1979), but crime rates change depending on the strength of each effect.
Motivational effects from unemployment were found to be strongest for crimes including a property
component, but their models were shown to suffer from autocorrelation following a Durbin-Watson
test, demonstrating correlation between errors across time periods (Wooldridge, 2006) and so their
results could be spurious. National data analyses may also suffer from aggregation bias Chiricos (1987)
argues, as the U-C relationship on lower levels could be eliminated, supported by Janko and Popli
(2013) in a Canadian panel data analysis who found an insignificant U-C relationship on a national
level, but a significant U-C relationship on a regional level, demonstrating the need for a regional
analysis within this study.
However, positive U-C relationships were found in country-level panel data studies by Altindag (2011)
and Buonanno et al (2014), who analysed the U-C relationship simultaneously across multiple
countries in Europe and North America. Altindag (2011) found on average a 1% increase in the
unemployment rate caused a 2% increase in crime rates, which is further supported by Swedish and
Greek panel data analyses by Edmark (2005) and Saridakis and Spengler (2012), who both found
unemployment rates significantly impacted crime rates. These studies strengthen the positive U-C
relationship on an international perspective, but also demonstrate that panel data is the most efficient
analytical approach for evaluating the determinants of crime.
The importance of unemployment as a crime determinant is validated by the Kirkholt crime prevention
project (1988), which involved interviewing offenders convicted of burglary in a dwelling in the
Rochdale area where crime rates were extremely high. They found that 70% of offenders were
unemployed at the time of the offense, whilst 88% of offenders felt that unemployment was linked to
their incentive to commit crime, supporting Cantor and Land’s (1985) findings of the motivational
effect from unemployment, which Box (1987) suggests could be because unemployment creates
financial and social tension within families.
Unemployment has been shown to have a significant impact on crime rates, with panel data being
predominantly exploited to mitigate omitted variable bias as argued by Raphael and Winter-Ebmer
(2001), and so it seems indispensable to utilize it within this study. A possible drawback within this
review is the possible variations in unemployment definitions. Slight nuances in the definitions of an
unemployed individual could bias the interpretation of results and definitions are not always explicitly
specified in the empirical research.
Gender
Crime is largely a male activity according to Hale et al (2009), and males predominantly commit more
crime than females. An Office for National Statistics (ONS) report evaluating crime found in 97% of
incidents the offenders were male, 2% were female and 1% were mixed, suggesting there is a gender-
gap in crime. In a regional analysis of Britain, Carmichael and Ward (2001) found a positive and
significant relationship between male unemployment and economic crimes, supporting this concept.
But whilst Edmark (2005) and Buonanno et al (2014) both included young males as demographic

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variables, they failed to specify the reasons why females were excluded, as Walklate (2004) found
both genders shared similar reasons to commit crime. Albertson and Fox (2012) argue this “gender-
gap” could be due to differences in social expectations or “sex roles”, but a study reviewing gender
differences by Croson and Gneezy (2009) concluded women are simply more risk averse then men.
Therefore, this provides room to analyse the impacts of both male and female unemployment rates
on property crime as an extension on previous literature which has predominantly excluded females,
but males in this analysis are expected to influence crime rates more than women reflecting on
previous findings.
Education
Education is predicted by economic theory to inhibit criminal behaviour by improving earnings and
knowledge, a concept supported by Lochner (1999) who found high school graduation substantially
lowered crime rates by nearly 60%. Altindag (2011) further supports this finding that the
unemployment rate for low educated individuals had a significant impact on crime within the EU, but
the underlying reasons why are still debated. Education could improve potential income from legal
activities according to Lochner and Moretti (2001), raising the opportunity costs of committing crime,
but Hjalmarsson and Lochner (2012) argue education could make individuals more risk averse as
individuals learn the risks of illegal activities. However, the short and long term impacts are
undiscussed, but education customarily improves long-term lifetime earnings, which in Raphael and
Winter-Ebmer’s (2001) time-allocation model would shift the legal income budget constraint, but as
the time-period in rational choice models is unspecified, it perplexes the degree of influence education
would have on criminal participation. Nonetheless, the inclusion of education is necessary as it has
shown to decrease crime rates.
Deterrence
Deterrence in theory is predicted to decrease the returns to crime through increased risk of
punishment, demonstrated in a US cross-sectional study by Ehrlich (1973), who found strong negative
relationships between criminal justice variables and 10 out of 14 crime categories, but in a 73 year
study of England, Wolpin (1978) found that the deterrence variable’s coefficients were higher and
more significant for property crimes than violent crimes. In support of this, Edmark (2005) found a 1%
increase in police clear up rates (the rate which crimes are solved) caused a 0.38% decrease in
aggregate crime. However, the variable used to accurately depict deterrence is difficult, as a surge in
crime could overwhelm law enforcement services and in Edmark’s (2005) study this would depress
clear up rates without representing a real change in deterrence. Schnelle et al (1978) found
introducing a patrol helicopter in a high crime area significantly reduced burglary levels, so empirical
research has shown deterrence has a negative impact on crime rates and the inclusion of deterrence
in this analysis is necessary, but the appropriate choice of a proxy is important.
Income
Legal income according to economic theory is expected to decrease crime rates by reflecting better
legal opportunities and increasing the opportunity costs of committing crime. But, higher income
argued by Raphael and Winter-Ebmer (2001) could be associated with consumerism with growing
purchases of consumer goods, creating more targets and opportunities for offenders, supportive of
Cohen and Felson’s (1979) routine activity theory. This is substantiated by Edmark (2005) who argues
that higher regional wealth can widen aggregate demand for crime by “supplying the booty” and
increasing income to illegal activities. But Papps and Winkelmann (2000) found the level of income
had an overall negative effect on crime rates, and argued the opportunity effect was stronger for legal
activities in comparison to illegal activities. The effects of income inequality however have been largely
avoided by empirical research, which Nilsson (2004) found positively affected crime prevalence in

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Sweden, which could explain the disparities amongst findings. Because of the theoretical ambiguity,
the impacts of regional and legal income will be analysed within this study.
Unemployment Benefit
Unemployment might not have an immediate effect on individuals because Cantor and Land (1985)
argue that they would be cushioned by financial aid from family and government unemployment
benefits. In Becker’s model (1968), benefits would increase the opportunity costs of crime, as it
temporarily fills the income void created by unemployment. This is supported by a youth
unemployment and crime study in France by Fougere (2009), who found benefits decreased the
incentives to commit economic crimes through decreasing expected returns. Unemployment benefits
are vastly excluded from previous literature, therefore providing an opportunity to make a significant
contribution to existing research.
2.4. Methodological Review of Empirical Research
Factors included for the analysis of crime are explained by Edmark (2005) to be motivated by economic
theory, such as concepts argued by Becker (1968) and Cantor and Land (1985). However, Edmark
(2005) also states that the inclusion of other demographic and social variables reduces the risk of
distorted results.
Panel data analysis is predominantly utilized in the previous literature due to the complex and broad
nature of crime analysis, as it allows for flexibility to test and control for different cross-sectional
influences. Previous models used include OLS, fixed, random and mixed effects models. Melick (2003)
utilized a least squares dummy variable model using US state dummy variables to capture the unique
heterogeneity within each state. Edmark (2005) argues an OLS might underestimate the U-C
relationship, and so incorporated a fixed effects model with estimated region and time specific effects
to control for county-specific effects. To control for the economic bias arising from reported crimes
misrepresenting the true unobserved number of crimes committed, Buonanno et al (2014) also
included geographical and time fixed effects
But, using fixed effects models consumes multiple degrees of freedom and causes higher standard
errors, and Papps and Winkelmann (2000) argue these could be retained if time and region fixed
effects were modelled as random effects, involving the assumption that explanatory variables are
uncorrelated with the region and time effects. A balance between over specification and the inclusion
of important variables is necessary which could be achieved by utilizing a mixed effects model
mimicking Papps and Winkelmann (1998) methodology which incorporates fixed and random effects
and might prove more efficient. Chiricos (1987) argues time series analysis may include too few
variables for an accurate analysis such as in Janko and Popli’s (2013) study which yielded insignificant
findings, and so won’t be adopted.
The methodology of empirical research has varied, suggesting multiple models should be fitted and
compared to determine the efficient model including OLS, fixed, random and mixed effects, which will
be discussed in Chapter 4. The empirical research has suggested several important explanatory
variables including unemployment, education, deterrence and income which will all be adapted within
this analysis of crime influences utilizing panel data in regions of England between 2005 and 2012 to
avoid aggregation bias. Due to being previously excluded in the empirical research, this study will
incorporate the female unemployment rate and unemployment benefits which presents an
opportunity to contribute to existing literature.

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Chapter 3: Methodology
In this chapter the methodological framework will be overviewed, outlining the choice of variables,
data collection and the econometric approach adopted within this study.
3.1. Research Design
A deductive approach will be implemented to study recorded property crime, evaluating the validity
of the previously discussed theories and empirical research regarding the influences of crime through
data collection and hypothesis testing. However, due to the complexity of this issue, inductive
reasoning will be implemented if the findings cannot be explained through previous research.
A quantitative rather than a qualitative analysis is more suitable in this study as Albertson and Fox
(2012) states it enables us to quantify the parameters within the predicted relationships, where we
can define and measure the influences of variables on recorded crime. The necessary analyses will be
accomplished using the statistical software package Stata 13.
3.2. Data Collection
This study will examine the influence of 6 variables on recorded property crime following an analysis
of empirical research. Secondary quantitative annual data will be collected from the Home Office, the
Department of Education, HM Treasury and the Office for National Statistics (ONS) for all 9 regions of
England in 2005 – 2012 (the regions included are outlined in the appendix). Secondary data is data
already collected by someone else but utilized for a different purpose. This is advantageous as it
incorporates historical data which I would be unable to collect, but as I had no control over the
methods of data collection, measurement bias may be present within the data.
Panel data will be utilized because Gujarati (2011) explains that it advantageously incorporates both
time series and cross-sectional observations, providing more informative data whilst incorporating
omitted variables which Albertson and Fox (2012) state’s is important because many factors are
simultaneously changing in economic relationships.
3.3. Variable Analysis
3.3.1. Dependent Variable - Property Crime
Property crime is chosen because rational choice theory describes offenders as rational economic
actors, and Melick (2003) argues that property crimes involve some kind of monetary gain to
compensate for the lack of legitimate legal opportunities or income. This is theoretically less evident
for violent crime (Raphael and Winter-Ebmer, 2001). Although categorized separately due to the
inclusion of violence, robbery will be included within this study as it includes an element of theft and
therefore monetary gain.
Property crime is defined as “incidents where individuals, households or corporate bodies are
deprived of their property by illegal means or where their property is damaged” (ONS, Property Crime
Overview 2014). The offences that are categorized within property crime are listed in Table 2.2
Appendix A.
3.3.2. Independent Variables
The explanatory variables chosen for analysis are the unemployment rate (male, female and total),
gross value added (GVA) per head, education, unemployment benefit expenditure and public order
and safety expenditure.

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Unemployment Rate
The International Labour Organization definition used by the ONS (2011) describes the unemployed
as those who are:
“…without a job, want a job, have actively sought work in the last 4 weeks and are available to start
work in the next 2 weeks, or are out of work, have found a job and are waiting to start in the next 2
weeks” p1.
The analysis of male and female unemployment rates will fill the gender exclusion seen in previous
research including individuals aged 16-64, and is calculated using the following formula:
Total Number of People who are Unemploymed
Total Number of People in the Labour Force
× 100
The unemployment rate described by Melick (2003) is used to represent the amount of legal
opportunities available to individuals, with a higher rate representing fewer opportunities and
therefore lowering the opportunity cost of committing crime (Becker, 1968) and motivating
individuals towards criminal activity. A positive relationship with recorded crime is predicted, but
because the variable fails to account for frictional unemployment or immigration influxes, it might not
truly represent available opportunities.
Gross Value Added per head
GVA is “the contribution to the economy of each individual producer, industry or sector in the United
Kingdom” (ONS Website), and is a key indicator of regional economic performance. This will reflect
the “supply of booty” described by Edmark (2005), creating targets and increasing aggregate demand
of crime, represented by regional GVA per head which is GVA divided by the population. Following the
routine activity theory, a positive relationship with recorded property crime is predicted through
increasing suitable targets.
Education
Education has demonstrated to significantly decrease crime rates by Lochner (1999) and Altindag
(2011), but the explanations differentiate between increasing potential legal income (Lochner and
Moretti, 2001) to making individual’s more risk adverse (Hjalmarsson and Lochner 2012). Education
will be represented by the percentage of pupils achieving at GCSE or equivalents 5+ A*-C grades
including maths and English, as this represents the indispensable educational benchmark that
provides access to future educational and employment opportunities. Drawing on empirical research,
a negative relationship with recorded property crime is predicted.
Median Gross Annual Earnings
Median gross annual earnings collected from the ONS Patterns of Pay release will reflect potential full-
time legal earnings of employees working more than 30 paid hours a week, or more than 25 hours for
teaching professions. Higher potential legal income according to Becker (1968) increases the
opportunity costs of committing crime, making crime less attractive. If an individual has better current
and future opportunities in the legal market, Altindag (2011) agrees they are less likely to commit
crime. Drawing on rational choice theory, a negative relationship with recorded property crime is
predicted.
Unemployment Benefit Expenditure

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Cantor and Land (1985) argued that unemployment benefits financially support unemployed
individuals during recessions, so individuals may not immediately choose crime, supported by Fougere
(2009). UK government job seekers allowance expenditure will act as a proxy for the amount of
unemployment benefits received, as higher expenditure either represents more individuals claiming
benefits or higher payments to unemployed individuals. This study will extend previous research by
including unemployment benefits as an explanatory variable. A negative relationship is predicted with
recorded property crime by financial cushioning individuals, deterring them from seeking income in
crime.
Public Order Expenditure
In this study, public order and safety expenditure will be a proxy for deterrence within regions, as
increased expenditure will create additional well-equipped law enforcement and improved public
security agencies, and the subsequent effect will be evaluated. This proxy could be bias due to
measurement inaccuracies as expenditure figures are consistently reviewed, and an alternative such
as police officer numbers might have been more appropriate. This variable will reflect the deterrence
level in rational choice theory, but could represent capable guardians in routine activity theory. In
both theories, deterrence and guardians exert negative effects on crime, so a negative relationship
with recorded property crime is predicted.
3.4. Limitations of Data
Crimes recorded by the British Transport Police are excluded as they incorporate crimes committed
throughout Great Britain, and cannot be attributed to specific regions. Additionally, police recorded
crime won’t account for unreported or online crimes, and therefore might not fully reflect the “true”
number of crimes committed. Figure 1.2 demonstrates the number of incidents recorded by the Crime
Survey for England and Wales (CSEW) reported to the police. Reported bicycle, other household and
vehicle-related theft incidents have all decreased within the last decade, demonstrating that changes
in reporting habits could have real implications on recorded crimes reflecting the true number.
20
25
30
35
40
45
50
55
60
65
70
2002/03 2003/04 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12
Percentage(%)
Year
Figure 1.2: Percentage of CSEW Incidents Reported to the
Police in 2002/03 - 2011/12
Burglary Vehicle-related theft Bicycle theft
Other household theft Theft from the person Other theft of personal property
Source: Office for National Statistics - Crime Statistics (2012)

16 | P a g e
Additionally GCSE education attainment data only covers achievements from public and not private
schools, but considering fewer children attend private schools, this isn’t expected to have sizeable
implications. These limitations have been identified as possible analytical drawbacks, and could
inaccurately portray the broader relationship between crime and the explanatory variables. Table 1.1
summarizes the data sources for all the variables.
Table 1.1 – Variable Data Sources
Variable Data Source
Recorded Property Crime Home Office Crime Statistics- Historical Crime Data (2014)
Unemployment Rates (Male, Female and Total) ONS Labour Market Statistics (2015)
Regional Gross Value Added ONS Regional Economic Statistics (2014)
Education Department of Education (2014)
Median Gross Annual Earnings ONS Patterns of Pay Release (2014)
Unemployment Benefit Expenditure Department for Work and Pensions (2015)
Public Order Expenditure HM Treasury Public Expenditure Statistical Analyses (2010 and 14)
3.5. Preliminary Data Analysis
Using statistics from the CSEW (as police recorded crime before and after 2002 is uncomparable due
to methodological changes) and ONS labour market statistics, I plotted a scatter graph between the
unemployment rate and crime offences in Figure 1.3 to analyse the preliminary relationship. A positive
correlation is seen from 1993 with both variables declining simultaneously, but this trend begins to
reverse with a divergence around 2005. One explanation could be there are disparities between male
and female unemployment rates.
Figure 1.4 shows both male and female unemployment rates exhibit similar trends, so the relationship
isn’t gender isolated. But what could be causing the divergence between the unemployment rates and
recorded property crime witnessed in the previous decade?
Source: ONS - Property Crime Review (2014) and UK Labour Market Statistics (2014)
2
3
4
5
6
7
8
9
10
11
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
UnemploymentRate(%)
Date
NumberofOffences(000's)
Figure 1.3 - Total CSEW Reported Property Crime and
Unemployment Rate Trends from 1993 - 2013/14
CSEW Recorded Property crime England & Wales Average Unemployment Rate (%)
England Unemployment Rate (%)

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Since 2005, median gross annual earnings and percentage of pupils earning 5+ A*-C GCSE’s have both
increased, demonstrated in Figure 1.5 which could provide one explanation why trends in recorded
crime have decreased in the presence of higher unemployment rates, but further analysis is needed.
A summary of descriptive statistics for the sample data collected is shown in Table 1.2.
2
3
4
5
6
7
8
9
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
UnemploymentRate(%)
Year
RecordedNo.ofOffences
Figure 1.4 - Total Police Recorded Property Crime and Male and
Female Unemployment Rate Trends in England in 2002 - 2012
Recorded Property Crime England England Male Unemployment Rate (%)
England Female Unemployment Rate (%)
Source: ONS - Property Crime Review (2014) and UK Labour Market Statistics (2014)
Source: Department of Education (Revised GCSE and Equivalent Results in England 2013/14 release) and ONS Patterns of Pay Survey 2014
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
20,000
21,000
22,000
23,000
24,000
25,000
26,000
27,000
2005 2006 2007 2008 2009 2010 2011 2012
EducationAttainment(%)
MedianGrossAnnualEarnings(£)
Year
Figure 1.5 - Education Attainment and Median Gross Annual
Earnings in England in 2005 - 2012
Median Gross Annual Earnings (£)
Percentage of Pupils at the end of KS4 achieving at GCSE and equivalents 5 A*-C including Maths and English (%)

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3.6. Hypotheses
The following predicted hypotheses that will be tested in this study are shown in Table 1.3, reflecting
on empirical research and previously evaluated theories.
Table 1.3. – Summary of Hypotheses
Null
Hypothesis
Description Alternative
Hypothesis
Description
H0 There is no relationship between
unemployment rates (Male,
Female and Total) and total
recorded property crime.
H1 There is a positive relationship between
unemployment rates (Male, Female and
Total) and total recorded property crime.
Gross value added per head and
total recorded property crime.
H1 There is a positive relationship between
Gross value added per head and total
recorded property crime.
median gross annual earnings and
total recorded property crimes.
H1 There is a negative relationship between
median gross annual earnings and total
recorded property crimes.
education and total recorded
property crimes
H1 There is a negative relationship between level
of education and total recorded property
crimes.
fiscal public order and safety
expenditure and total recorded
property crime.
fiscal public order and safety expenditure and
fiscal unemployment benefit
expenditure and total recorded
property crime.
fiscal unemployment benefit expenditure and
3.7. Econometric Approach
A multiple regression analysis will be utilized to measure the relationships between property crime
and the explanatory variables. This is advantageous because Wooldridge (2006) argues it allows for
control over a multitude of factors that simultaneously affect the dependent variable, allowing us to
highlight the explanatory variable’s impact on police recorded crime between 2005 and 2012.
Table 1.2 - Summary of Descriptive Statistics
Variable Observations Mean Median Min Max Std. Dev Skewness Kurtosis
𝐥𝐧 𝑪𝒓𝒊𝒎𝒆 72 12.276 12.311 10.879 13.256 0.527 -0.779 3.566
𝒖𝒏𝒆𝒎𝒑_𝒎𝒂𝒍𝒆𝒕−𝟏 63 7.086 6.9 3.7 11.6 2.130 0.392 2.274
𝒖𝒏𝒆𝒎𝒑_𝒇𝒆𝒎𝒂𝒍𝒆𝒕−𝟏 63 5.908 5.6 3.4 9.9 1.592 0.609 2.707
𝒖𝒏𝒆𝒎𝒑_𝒂𝒍𝒍 𝒕−𝟏 63 6.548 6.3 3.6 10.8 1.826 0.357 2.186
𝐥𝐧 𝑮𝑽𝑨𝒑𝒉 72 9.913 9.839 9.619 10.576 0.231 1.627 4.834
𝐥𝐧 𝒎𝒈𝒂𝒆 72 10.108 10.087 9.917 10.460 0.124 1.307 4.364
𝑬𝒅𝒖 72 52.536 53.3 40.3 65.1 6.459 -0.185 1.853
𝐥𝐧 𝒑𝒐_𝒆𝒙𝒑 72 6.121 6.083 5.799 6.744 0.238 1.132 3.671
𝐥𝐧 𝒖𝒏𝒆𝒎𝒑_𝒆𝒙𝒑 72 5.729 5.710 4.710 6.641 0.501 -0.146 2.163

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A regional analysis will be conducted following recommendations from Chiricos (1987) and the
methodological approaches of the empirical research. This will reduce the likelihood of aggregation
bias and the risk of bias estimates stemming from “criminal mobility”, which Edmark (2005) explains
is when criminals might commit crimes outside of their residing region. At lower levels of analysis
(such as city analyses), mobility will be high and crime can differentiate within districts, so a regional
analysis can capture this mobility. Simultaneity bias risk will also be mitigated by conducting a regional
analysis, which occurs when crime influences the unemployment rate within a region (Edmark, 2005).
To represent the motivational effect argued by Cantor and Land (1985) the unemployment rates will
be lagged by 1 period as due to financial safety nets, Melick (2003) explains unemployment is unlikely
to have an immediate impact on crime rates.
Because recorded crimes underestimate their true value, this could create bias econometric results
when estimating the explanatory variable’s effects on crime. To address this problem, Buonanno et al
(2014) recommends a log-log model specification which gives all of the estimated coefficients the
interpretation of an elasticity. Cantor and Land (1985) state that log-transforming the model also deals
with the trends in the variability of the time series. All of the variables are in log form excluding the
unemployment rates and education, as these are already expressed in percentages.
Two equations will be compiled within this study. Model 1 incorporates male and female
unemployment rates simultaneously, and Model 2 includes the total unemployment rate. Equations
for both models are shown below:
𝑀𝑜𝑑𝑒𝑙 1: ln 𝐶𝑖𝑡 = 𝐵1 + 𝐵2 𝑈𝑛𝑒𝑚𝑝_𝑚𝑎𝑙𝑒𝑖𝑡−1 + 𝐵3 𝑈𝑛𝑒𝑚𝑝_𝑓𝑒𝑚𝑎𝑙𝑒𝑖𝑡−1 + 𝐵4ln 𝐺𝑉𝐴𝑝ℎ𝑖𝑡
+ 𝐵5 ln 𝑚𝑔𝑎𝑒𝑖𝑡 + 𝐵6 𝐸𝑑𝑢𝑖𝑡 + 𝐵7 ln 𝑝𝑜_𝑒𝑥𝑝𝑖𝑡 + 𝐵8 ln 𝑢𝑛𝑒𝑚𝑝_𝑒𝑥𝑝𝑖𝑡 + 𝑒𝑖𝑡
𝑀𝑜𝑑𝑒𝑙 2: ln 𝐶𝑖𝑡 = 𝐵1 + 𝐵2 𝑈𝑛𝑒𝑚𝑝_𝑎𝑙𝑙𝑖𝑡−1 + 𝐵3 ln 𝐺𝑉𝐴𝑝ℎ𝑖𝑡 + 𝐵4 ln 𝑚𝑔𝑎𝑒𝑖𝑡 + 𝐵5 𝐸𝑑𝑢𝑖𝑡
+ 𝐵6 ln 𝑝𝑜_𝑒𝑥𝑝𝑖𝑡 + 𝐵7 ln 𝑢𝑛𝑒𝑚𝑝_𝑒𝑥𝑝𝑖𝑡 + 𝑒𝑖𝑡
Table 1.4 provides a summary of the equation components, and the estimated coefficients of the
explanatory variables, extending Table 1.3. Because the 9 regions are observed over the same number
of time periods (8 years), Greene (2008) states this is a balanced panel data.
Table 1.4. – Description of Equation Components
Variable Definition Expected Coefficient Sign
𝑪 𝒕 Total police recorded property crime n/a
𝑼𝒏𝒆𝒎𝒑_𝒂𝒍𝒍 𝒕−𝟏 Total unemployment rate (%) +
𝑼𝒏𝒆𝒎𝒑_𝒎𝒂𝒍𝒆 𝒕−𝟏 Male unemployment rate (%) +
𝑼𝒏𝒆𝒎𝒑_𝒇𝒆𝒎𝒂𝒍𝒆 𝒕−𝟏 Female unemployment rate (%) +
𝑮𝑽𝑨𝒑𝒉 Gross value added per head (£) +
𝒎𝒈𝒂𝒆 Median Gross Annual Earnings (£) -
𝑬𝒅𝒖 Percentage of pupils at the end of key stage 4 achieving at
GCSE and equivalents 5 A*-C including maths and English
GCSE's. (%)
-
𝒑𝒐_𝒆𝒙𝒑 Public Order and Safety Government Expenditure (£ per head) -
𝒖𝒏𝒆𝒎𝒑_𝒆𝒙𝒑 Government Unemployment Benefit Expenditure (£mill) -
𝑩 𝟏 Intercept of the model n/a
𝑩 𝟐 − 𝑩 𝟖 Regression coefficients n/a
𝒆 Error term n/a

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Chapter 4: Model Specification, Diagnostic Tests and Findings
This chapter will compare the pooled OLS, fixed, random and mixed effects models and outline
necessary diagnostic tests to mitigate any estimation problems arising from multicollinearity,
stationarity, heteroscedasticity and autocorrelation. The efficient model and final results will be
presented at the end.
4.1. Diagnostic Tests
Stationarity
A variable is stationary when its mean and covariance are constant, and is indispensable before a
regression analysis because Hill et al (2008) argues non-stationary variables could yield spurious
relationships when estimates are significant but unreliable. Figure 1.6 illustrates the differences
between stationary and non-stationary variables.
The Harris-Tsavalis (HT) stationarity test was used to identify non-stationary variables and is applicable
to balanced panel data. Stata Corp (2013) outlines the assumptions and null hypothesis of the test:
𝑁 → ∞, 𝑇 = Fixed
𝑁 = No. of Panels 𝑇 = No. of Time Periods
H0: Panels Contain Unit Roots
HA: Panels are Stationary
This is suitable because crime statistics are incomparable pre and post-2002, so enlarging the sample
size would require more panels instead of time periods.
The HT test was insignificant for most variables (P>0.10), indicating non-stationarity. To resolve this,
Cantor and Land (1985) recommends taking the first differences which sufficiently eliminates any
linear secular trends, illustrated as:
∆𝐶 = 𝐶𝑡 − 𝐶𝑡−1
But, this removes one observation per variable which could weaken the strength of this analysis.
After first-differencing, subsequent HT tests were significant at 1% and 10% (P<0.01 and P<0.10),
rejecting the null hypothesis that the panels contain unit roots integrated at order 1 (Gujarati, 2011).

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Multicollinearity
Another classical linear regression model (CLRM) assumption is there are no exact linear relationships
between variables, which if violated can cause OLS best linear unbiased estimators (BLUE) to have
large variances and covariances, altering the coefficient values of other variables (Gujarati 2011). A
partial correlation matrix can be used to identify any correlation amongst variables, with values above
0.5 indicating multicollinearity. Two cases of multicollinearity were identified between:
1. Male and female lagged unemployment rates
2. GVA per head and unemployment expenditure
However, they are not perfectly collinear and Gujarati (2011) argues pairwise correlations are
unreliable because they don’t hold other variables constant. Greene (2008) argues that doing nothing
is the best practical advice due to lack of control over data, and if a variable that belongs in the model
is omitted, the remaining coefficients could be bias. Therefore these will be left and are identified as
possible limitations to this analysis.
4.2. Model Specification
For succinctness and comparative purposes, the estimators and models are formulated from model 1,
but the same models and specification tests (e.g. Hausman, Chow and Breusch-Pagan) were
implemented for model 2 with similar results.
Pooled OLS
We can illustrate the equations for two regions as:
𝑎) ln 𝐶𝐿𝑂𝑁𝐷𝑂𝑁,𝑡 = 𝐵1 + 𝐵2 𝑈𝑛𝑒𝑚𝑝_𝑚𝑎𝑙𝑒𝑡−1 + 𝐵3 𝑈𝑛𝑒𝑚𝑝_𝑓𝑒𝑚𝑎𝑙𝑒𝑡−1 + 𝐵4ln 𝐺𝑉𝐴𝑝ℎ 𝑡
+ 𝑒𝑡 𝑡 = 1, … . , 8
b) ln 𝐶𝑆. 𝐸𝐴𝑆𝑇,𝑡 = 𝐵1 + 𝐵2 𝑈𝑛𝑒𝑚𝑝_𝑚𝑎𝑙𝑒𝑡−1 + 𝐵3 𝑈𝑛𝑒𝑚𝑝_𝑓𝑒𝑚𝑎𝑙𝑒𝑡−1 + 𝐵4ln 𝐺𝑉𝐴𝑝ℎ 𝑡
+ 𝑒𝑡 𝑡 = 1, … . , 8
The pooled OLS model assumes the coefficients within the model are constant over time and cross-
section (Gujarati, 2011), so Papps and Winkelmann (2000) argue the parameters would be the sole
determinants of recorded crime regardless of region or time period. Identical parameters in both a)
and b) means we can treat both models as a single pooled regression (Hill et al, 2008), rewritten as:
ln 𝐶𝑖𝑡 = 𝐵1 + 𝐵2 𝑈𝑛𝑒𝑚𝑝_𝑚𝑎𝑙𝑒𝑖𝑡−1 + 𝐵3 𝑈𝑛𝑒𝑚𝑝_𝑓𝑒𝑚𝑎𝑙𝑒𝑖𝑡−1 + 𝐵4ln 𝐺𝑉𝐴𝑝ℎ𝑖𝑡 + 𝑒𝑖𝑡 𝑡 = 1, … . , 8
𝑖 = 1 (𝐿𝑜𝑛𝑑𝑜𝑛) 𝑎𝑛𝑑 𝑖 = 2 (𝑆. 𝐸𝑎𝑠𝑡)
If this specification is incorrect, Gujarati (2011) argues it could be because regional heterogeneity
across time is within the error term, illustrated by Papps and Winkelmann (2000) as:
𝑒𝑖𝑡 = 𝛼𝑖 + 𝛽𝑖
𝛼𝑖 + 𝛽𝑖 = Region Specific Fixed Effects and Time Specific Effects
Papps and Winkelmann (2000) state the region and time specific effects can be estimated parameters
in a fixed effect model if they’re in the pooled OLS error term. If the error term is correlated with the

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regressors, Wooldridge (2006) argues the pooled OLS estimators will be bias. Raphael and Winter-
Ember (2001) found the OLS specification was inefficient, supported by Edmark (2005) who argued
the OLS model could underestimate the U-C relationship as Raphael and Winter-Ebmer’s (2001)
instrumental variable model yielded higher coefficients in comparison.
The pooled OLS model was statistically significant at 1% (P<0.05) for models 1 and 2, but as it’s not
predominantly used in empirical research methodology, further confirmation is required via the Chow
test.
Fixed Effects
The fixed effects model (FEM) assumes omitted effects are correlated with independent variables
(Greene, 2008), allowing unobserved effects to be correlated with variables within each time period
but are fixed over time (Wooldridge, 2006). A least squares dummy variable (LSDV) model was
developed following Melick’s (2003) methodology who used US state dummy variables to capture
unobserved effects and unique US state characteristics. The LSDV model is written as:
ln 𝐶𝑟𝑖𝑚𝑒𝑡 = 𝐵1 + 𝐵2 𝑈𝑛𝑒𝑚𝑝 𝑚𝑎𝑙𝑒 𝑖𝑡−1
+ 𝐵3 𝑈𝑛𝑒𝑚𝑝 𝑓𝑒𝑚𝑎𝑙𝑒 𝑖𝑡−1
+ 𝐵4 ln 𝐺𝑉𝐴𝑝ℎ𝑖𝑡 + 𝐵5 ln 𝑚𝑔𝑎𝑒𝑖𝑡
+ 𝐵6 𝐸𝑑𝑢𝑖𝑡 + 𝐵7 ln 𝑝𝑜_𝑒𝑥𝑝𝑖𝑡 + 𝐵8 ln 𝑢𝑛𝑒𝑚𝑝_𝑒𝑥𝑝𝑖𝑡 + 𝐵9 𝐼𝐷2 + 𝐵10 𝐼𝐷3
+ 𝐵11 𝐼𝐷4 + 𝐵12 𝐼𝐷5 + 𝐵13 𝐼𝐷6 + 𝐵14 𝐼𝐷7 + 𝐵15 𝐼𝐷8 + 𝐵16 𝐼𝐷9 + 𝑒𝑖𝑡
8 dummy variables (𝐼𝐷2 - 𝐼𝐷9) are created for each region, with the constant as the reference category
representing the region without a dummy (id=1) to avoid the dummy variable trap. The coefficient for
each ID Melick (2003) explains reflects the standard deviation from the constant.
The model is significant at 5% (P<0.05), with an R-squared of 0.4721, which measures the overall
goodness of fit of the estimated regression line (Gujarati 2011). The Adj R-squared incorporates the
number of variables and degrees of freedom, and a value of 0.2637 demonstrates the model has low
explanatory power in comparison to Melick (2003) whose LSDV Adj R-squared was 0.745. It explains
that on average the variation in the independent variables account for 26% of the variation in the
dependent variable. One disadvantage of the LSDV is that it consumes 8 degrees of freedom because
of the additional dummy variables, which leaves fewer observations for meaningful analysis (Gujarati,
2011).
The entity FEM was also fitted using a within regression estimator which is more efficient than the
LSDV and Gujarati (2011) describes that it expresses all variables as deviations from their group mean
values. The overall model is statistically significant at 1% (P>0.01). The F-test at the bottom of the
entity FEM summarized by Adkins and Carter-Hill (2011) tests the null hypothesis of no significant
differences between individual intercepts are equal to zero:
H0: 𝐵1 = 𝐵9 = ⋯ = 𝐵16
H1: 𝐼𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡𝑠 𝑎𝑟𝑒 𝑛𝑜𝑡 𝑎𝑙𝑙 𝑒𝑞𝑢𝑎𝑙
With an insignificant test of 0.7253 at 10% (P>0.10), we cannot reject the null hypothesis that there
are individual differences, so an FEM might not be the right specification.
Random Effects
The random effects model (REM) contrary to the FEM assumes the unobserved effects are
uncorrelated with the independent variables in each time period in addition to the standard FEM
assumptions (Wooldridge, 2013). An REM was generated following Papps and Winkelmann’s (2000)

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recommendations to conserve degrees of freedom and to compare with the FEM. The REM is written
as:
ln 𝐶𝑟𝑖𝑚𝑒𝑖𝑡 = 𝐵1 + 𝐵2 𝑈𝑛𝑒𝑚𝑝 𝑚𝑎𝑙𝑒 𝑖𝑡−1
+ 𝐵3 𝑈𝑛𝑒𝑚𝑝 𝑓𝑒𝑚𝑎𝑙𝑒 𝑖𝑡−1
+ 𝐵4 ln 𝐺𝑉𝐴𝑝ℎ𝑖𝑡 + 𝐵5 ln 𝑚𝑔𝑎𝑒𝑖𝑡
+ 𝐵6 𝐸𝑑𝑢𝑖𝑡 + 𝐵7 ln 𝑝𝑜_𝑒𝑥𝑝𝑖𝑡 + 𝐵8 ln 𝑢𝑛𝑒𝑚𝑝_𝑒𝑥𝑝𝑖𝑡 + 𝑒𝑖𝑡
Gujarati (2011) explains the error term is made of 2 components:
𝑒𝑖𝑡 = 𝜀𝑖 + 𝜇𝑖𝑡
𝜀𝑖 = Individual Specific Errors
𝜇𝑖𝑡 = Cross − sectional error component
Gujarati (2011) says the individual specific errors here aren’t correlated with each other and the error
term 𝑒𝑖𝑡 is uncorrelated with any other variables, but if 𝑒𝑖𝑡 is correlated with some of the regressors,
the REM will cause inconsistent regression coefficient estimators. A Hausman test will be conducted
to compare the FEM and REM coefficients.
Mixed Effects Model
The mixed effects model (MEM) explained by Allerhand (2010) includes a mixture of fixed and random
effects. Following the application of longitudinal models, this implies the use of multi-level models.
Consider the following example:
A. ln 𝐶𝑟𝑖𝑚𝑒𝑡 = 𝐵1 + 𝐵2 𝑈𝑛𝑒𝑚𝑝 𝑚𝑎𝑙𝑒 𝑡−1
B. 𝐵1 = 𝛿0 + 𝛾1
C. 𝐵2 = 𝛿1 + 𝛾2
In a simple linear regression model A, the parameters are assumed to remain constant, but Allerhand
(2010) explains that in an MEM, some or all of the parameters are allowed to be randomized and can
change at any level (Wooldridge, 2002). In an MEM, Gutierrez (2008) says that you estimate the
between-group variance as the original parameters represents the mean throughout all groups. The
random parameters in the level 1 model (A) are then modelled into “level 2” models (B and C). The
level 2 parameters 𝛿0 and 𝛿1 described by Allerhand (2010) represent the population average slope
and intercept, whilst 𝛾1 and 𝛾2 are the level 2 residuals. Consolidating the terms, we get equation B:
B. ln 𝐶𝑟𝑖𝑚𝑒𝑡 = 𝛿0 + 𝛿1 𝑈𝑛𝑒𝑚𝑝 𝑚𝑎𝑙𝑒 𝑡−1
+ (𝛾1 + 𝛾2 𝑈𝑛𝑒𝑚𝑝 𝑚𝑎𝑙𝑒 𝑡−1
+ 𝑒)
"𝛿0 + 𝛿1 𝑈𝑛𝑒𝑚𝑝 𝑚𝑎𝑙𝑒 𝑡−1
" represents the fixed effects, whilst "(𝛾1 + 𝛾2 𝑈𝑛𝑒𝑚𝑝 𝑚𝑎𝑙𝑒 𝑡−1
+ 𝑒)”
represents the random effects. This equation can be extended in multiple regression analyses with
further parameters. An MEM was fitted following Papps and Winkelmann’s (1998) methodology who
utilized an MEM with random region and fixed time effects as their final efficient model. This study
will utilize a level 1 model instead of a hierarchical 2 level model.

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The restricted random-effects model is shown at the bottom of the MEM output. Because there is no
p-value, we can test its significance by dividing the estimate by the standard errors. If the sum is more
than 2, Gujarati (2011) says it is significant and demonstrates random effects are present. Table 1.5
summarizes the coefficients and goodness of fit indicators for all fitted models.
Chow Test
Wooldridge (2006) says the chow test evaluates whether the coefficients estimated in one group in
the data are equal to the coefficients estimated in another, determining whether a multiple regression
function varies between two groups and tests whether the data is poolable.
The null hypothesis in the example equation is:
𝐶𝑡 = 𝐵0 + 𝛿1 𝑋 + 𝑒𝑡 = Group 1
𝐶𝑡 = 𝐵1 + 𝛿2 𝑋 + 𝑒𝑡 = Group 2
H0: 𝐵0 = 𝐵1 𝑎𝑛𝑑 𝛿1 = 𝛿2
If the null hypothesis is true, it means the same model can be used to explain both groups (Wooldridge,
2013). To compute the Chow test for two periods, a dummy variable representing one of the two
groups can be interacted with the independent variables. Subsequently, the dummy variables and all
Table 1.5. – Summary of Estimators for Pooled OLS, Fixed, Random and Mixed Effects Models
Dependent Variable
= 𝐥𝐧 𝑪𝒓𝒊𝒎𝒆
Pooled OLS Entity Fixed
Effects
Least Squares
Dummy Variable
Random
Effects
Multi-Level
Mixed Effects
𝑼𝒏𝒆𝒎𝒑_𝒎𝒂𝒍𝒆 𝒕−𝟏 0.02096*** 0.01902* 0.01902** 0.02131*** 0.02097***
𝑼𝒏𝒆𝒎𝒑_𝒇𝒆𝒎𝒂𝒍𝒆 𝒕−𝟏 -0.00452 -0.00133 -0.00133 -0.00448 -0.00452
𝐥𝐧 𝑮𝑽𝑨𝒑𝒉 0.40280 0.13184 0.13184 0.37209 0.40280
𝐥𝐧 𝒎𝒈𝒂𝒆 -1.07438*** -1.27608** -1.27608** -1.06765*** -1.07438***
𝐥𝐧 𝒖𝒏𝒆𝒎𝒑_𝒆𝒙𝒑 0.09319** 0.05124 0.05124 0.09100** 0.093189**
𝑬𝒅𝒖 -0.02036*** -0.02333*** -0.02333*** -0.02042*** -0.02036***
𝐥𝐧 𝒑𝒐_𝒆𝒙𝒑 0.05969** 0.33769 0.33769 0.06058*** 0.05969***
Constant -0.38809*** -2.07349 -2.10778 -0.39293*** -0.38809***
R-squared 0.3991 - 0.4721 - -
Adj. R-squared 0.3076 - 0.2637 - -
R-squared within - 0.4301 - 0.4096 -
R-squared between - 0.2563 - 0.2887 -
R-squared overall - 0.1417 - 0.3990 -
Prob > F test 0.0009 0.0000 0.0211 - -
Prob > chi2 - - - 0.0000 0.0000
Root MSE 0.03999 - 0.04124 - -
Sigma_u - 0.06789 - 0.00842 -
Sigma_e - 0.04124 - 0.04124 -
Rho - 0.73049 - 0.04001 -
No. of Observations 54 54 54 54 54
*** = Significant at 1 percent. ** = Significant at 5 percent. * = Significant at 10 percent.

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of the interacted variables are tested for joint significance (Wooldridge, 2006). The Chow test shown
in A7 for both models was inconclusive, so further analysis is required.
Breusch-Pagan Lagrange Multiplier Test
The Breusch Pagan LM tests for the presence of random effects. The null hypothesis is that the
variance in cross-sectional components are equal to zero, illustrated by Greene (2008) as:
H0: 𝜎 𝑢
2
= 0
. H1: 𝜎 𝑢
2
≠ 0
With insignificant chi-bar squared p-value’s (P>0.05) in both models, we fail to reject the null
hypothesis, which Adkins and Carter-Hill (2011) state means there are no random effects in the
sample, demonstrating a pooled OLS model is sufficient. Table 1.6 summarizes the results below.
Hausman Test
The Hausman test is a specification test that examines the differences between the fixed and random
effects coefficients, analysing any correlation between the error component and the regressors in an
REM expressed by Gujarati (2011) as:
(𝑏 𝑅𝐸 − 𝑏 𝐹𝐸)2
Adkins and Carter-Hill (2011) explains that both FEM and REM are consistent if the error term is
uncorrelated with the independent variables, and should merge towards the true parameter values.
If the error term is correlated with the explanatory variables, the random effects estimates are
inconsistent, but the fixed effects estimator remains consistent. The null hypothesis summarized by
Papps and Winkelmann (1998) is the independent variables are uncorrelated with the error terms,
and the coefficients estimated in both models are consistent but the REM is efficient.
Finding insignificant p-values (p>0.05) for both models, we fail to reject the null hypothesis, suggesting
both FEM and REM are consistent but the REM is efficient because 𝑒𝑖 is not correlated with any
independent variables. The REM is preferred as it considers the random sampling process of the data
and incorporates how changes in crime could influence the different coefficients in each region (Hill
et al, 2008). Table 1.7 summarizes the results for both models.
Table 1.6 – Breusch-Pagan Lagrange Multiplier Test Results
Test: Var(u) = 0 Model 1 Model 2
𝐥𝐧 𝑪𝒓𝒊𝒎𝒆 E [Region, t] U [Region] 𝐥𝐧 𝑪𝒓𝒊𝒎𝒆 E [Region, t] U [Region]
Var 0.00230955 0.0017005 0.0000709 0.0023095 0.0017061 0
SD = Sqrt (Var) 0.0480571 0.0412371 0.0084184 0.0480571 0.0413048 0
Chi-Bar Squared 1.21 0.00
Prob > chibar2 0.1356 1.0000
Table 1.7 - Hausman Test Results
Model 1 Model 2
Chi-Squared 2.18 3.85
Prob > chi2 0.9490 0.6964

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Table 1.5 demonstrates all models have significant overall p-values, rejecting the null hypothesis that
none of the explanatory variables have any effect on police recorded crime (Wooldridge, 2013). But,
there is a contradiction between the Breusch-Pagan and Hausman test regarding the use of REM, as
both tests were insignificant.
Although specified to use the REM by the Hausman test, to avoid contradiction the multi-level mixed
effects model will be implemented following Papps and Winkelmann’s (1998) methodology. A Pooled
OLS specification is not clear and could lead to biased estimated coefficients if incorrect (Gujarati,
2011) or underestimate the relationships between the variables (Edmark, 2005). The Chow test was
inconclusive and some regions when tested individually in Pooled OLS models were both significant
and insignificant overall, suggesting that the model’s significance varies between regions due to
possible regional disparities, which Papps and Winkelmann (2000) argues could be regional disparities
in urbanization, ethnic groups or age profiles.
The generalized least squares (GLS) model is an extension of the OLS model relaxing the assumptions
of homoscedastic and uncorrelated errors, but an MEM was used instead because it has less stringent
assumptions compared to the GLS and allows for nested random effects which can be estimated.
4.3. Further Diagnostic Testing
Serial correlation
Serial correlation occurs when current error residuals are effected or correlated with past error
residual values, violating the CLRM assumption that error terms are uncorrelated and preceding error
terms don’t affect future error terms (Gujarati 2011). Although the OLS estimators are consistent and
unbiased, Gujarati (2011) explains that they won’t be efficient and will produce unreliable significant
variables. A test conducted by Wooldridge (2002) can be implemented to test for autocorrelation
specifically in panel data using the xtserial command in Stata. The test is supported by Drukker (2003),
who explains the test has power properties in relatively sized samples. With a null hypothesis of no
first order correlation, we fail to reject the null hypothesis finding insignificant p-values, concluding
there are no signs of autocorrelation and the model’s estimators are BLUE.
Heteroscedasticity
Heteroscedasticity occurs when there is unequal variance in the error term, which can be caused from
the presence of outliers or incorrect functional form of the regression model (Gujarati 2011). A CLRM
assumption explained by Wooldridge (2006) is that the unobservable error term is constant, and when
this is violated, Gujarati (2011) says the estimators are no longer BLUE, and because the estimators of
the variances are biased without homoscedasticity, the standard errors argued by Wooldridge (2006)
are invalid for constructing t statistics and confidence intervals. Gutierrez (2008) suggests a visual test
for mixed models by plotting the residuals and predicted values on a scatter graph, showing the
variations coming from between groups.
Within both models there appears to be a degree of heteroscedasticity. To correct this, Gujarati (2012)
recommends the use of robust standard errors which will be implemented to both models.

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4.4. The Final Model - Mixed Effects Model
The final efficient model is the multi-level MEM. To analyse the overall significance of the model, the
model’s f-test p-value is first evaluated which Gujarati (2011) explains tests the null hypothesis that
all the slope coefficients are equal to zero, or that none of the explanatory variables have any impact
on police recorded crime.
If the P-value is equal or less than the tested significance level such as 5% (P<0.05), than Wooldridge
(2013) states the estimated coefficients are jointly significant and not equal to zero, concluding that
the explanatory variables are significant determinants of police recorded crime.
The chi-square p-values are significant at 1% (P<0.01) in both models shown in Table 1.8, controlling
for heteroscedasticity, demonstrating that the explanatory variables are significant determinants of
police recorded crime.
I will also evaluate the individual variable z-tests, with Z>1.96 demonstrating significance and the
significance of the corresponding p-values at 1%, 5% and 10%.
In the restricted REM in the MEM, we can calculate the z-test by dividing the estimate by the standard
errors. In both cases the residuals are significant ([P>|z|] > 1.96) shown in Table 1.9, therefore I can
make interpretations the same way as if it was separately tested from an FEM. However, there is a
possibility of nested fixed effects affecting the explanatory variables stemming from unique regional
characteristics or period specific effects. Within the MEM, the standard errors are almost elastic within
groups, and we can deduct from Table 1.9 that since the random effects predominate,
homoscedasticity across groups sustain, whereas this can’t be said about the fixed effects of the
model.
4.5. Variable Analysis - Findings
Table 2.0 below summarizes the outcomes of the MEM coefficients, the z tests and p-values for each
explanatory variable for both models. Table 2.1 shows the actual direction of the coefficients in
comparison to the predicted direction of the coefficients.
Table 1.8 – Mixed Effects Overall Goodness of Fit
Model 1 Model 2
Wald chi2 49.54 36.09
Prob > chi2 0.0000 0.0000
Table 1.9 – Restricted Random-Effects Model in Mixed Effects Model
Model 1 Model 2
Estimate 0.0369069 0.0377889
Robust Standard Errors 0.0031117 0.0033601
Z Test 11.8606871 11.2463617

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Unemployment
The lagged male unemployment rate is statistically significant at 1%, with a z-test of 3.01 and a p-value
of 0.003. Therefore, we can reject our null hypothesis that the male unemployment rate does not have
a positive relationship with recorded property crime. To analyse the specific effect, we must interpret
the coefficient. With a coefficient of 0.02, this means that on average a 1 % increase in the male
unemployment rate from the previous year causes a 0.02% increase in the differences of recorded
property crime between the current and previous year. A 95% confidence interval is automatically
provided by Stata, which provide the range of values that has a 95% chance of including the “true”
value. The lagged male unemployment rate coefficient of 0.0209657 falls into its confidence interval
(0.0063669 – 0.0355646), indicating it is a good estimator.
The lagged female unemployment is statistically insignificant at 10% with a z-test of -0.36 and a p-
value of 0.716. Therefore, we cannot reject the null hypothesis that the female unemployment rate
doesn’t have a positive relationship with recorded property crime.
As females are largely excluded in previous studies, it is difficult to draw a comparative conclusion.
The insignificant relationship between female unemployment rates and recorded property crime
could be because of the differences in sex roles described by Walklate (2004), as men are usually
considered as the providers in a relationship or family, and so their financial responsibility is more
laden than women’s, causing them to turn to crime during financial pressures suggested by Box (1987).
Table 2.0. - Multi-Level Mixed Effects Model
Regressand = ∆𝐥𝐧 𝑪𝒓𝒊𝒎𝒆
Model 1 (1) Model 2 (2)
Variable Coefficient Z-test P>|z| Coefficient Z-test P>|z|
∆𝑼𝒏𝒆𝒎𝒑_𝒎𝒂𝒍𝒆 𝒕−𝟏 0.020965 3.01 0.003 - - -
∆𝑼𝒏𝒆𝒎𝒑_𝒇𝒆𝒎𝒂𝒍𝒆𝒕−𝟏 -0.004520 -0.36 0.716 - - -
∆𝑼𝒏𝒆𝒎𝒑_𝒂𝒍𝒍 𝒕−𝟏 - - - 0.022478 2.07 0.039
∆𝐥𝐧 𝑮𝑽𝑨𝒑𝒉 0.402797 1.16 0.247 0.445798 1.18 0.238
∆𝐥𝐧 𝒎𝒈𝒂𝒆 -1.074383 -2.99 0.003 -1.089265 -2.99 0.003
∆𝑬𝒅𝒖 -0.020357 -4.03 0.000 -0.017318 -3.06 0.002
𝐥𝐧 𝒑𝒐_𝒆𝒙𝒑 0.059693 2.74 0.006 0.054006 2.42 0.016
∆𝐥𝐧 𝒖𝒏𝒆𝒎𝒑_𝒆𝒙𝒑 0.0931891 2.20 0.028 0.094569 2.14 0.032
𝑪𝒐𝒏𝒔𝒕𝒂𝒏𝒕 -0.388073 -2.94 0.003 -0.364297 -2.72 0.007
Table 2.1: Summary of Regressor Outcomes
Variable Predicted Direction of Impact
on Recorded Property Crime
Actual Direction of Impact
on Recorded Property
Crime
Hypothesis Result
𝑼𝒏𝒆𝒎𝒑_𝒎𝒂𝒍𝒆 𝒕−𝟏 Positive (+) Positive (+) Reject H0
𝑼𝒏𝒆𝒎𝒑_𝒇𝒆𝒎𝒂𝒍𝒆 𝒕−𝟏 Positive (+) Negative (-) Cannot reject H0
𝑼𝒏𝒆𝒎𝒑_𝒂𝒍𝒍 𝒕−𝟏 Positive (+) Positive (+) Reject H0
𝑮𝑽𝑨𝒑𝒉 Positive (+) Positive (+) Cannot reject H0
𝒎𝒈𝒂𝒆 Negative (-) Negative (-) Reject H0
𝑬𝒅𝒖 Negative (-) Negative (-) Reject H0
𝒑𝒐_𝒆𝒙𝒑 Negative (-) Positive (+) Reject H0
𝒖𝒏𝒆𝒎𝒑_𝒆𝒙𝒑 Negative (-) Positive (+) Reject H0

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Or it could be because women as described by Croson and Gneezy (2009) are more risk averse than
men, mitigating the likelihood that they will seek income through criminal activity. Additionally the
findings confirm the truism stated by Hale et al (2009) that males are more inclined to commit crime
than females.
In model 2, the total unemployment rate is statistically significant at 5% with a z-test of 2.07 and a p-
value of 0.039. Within the model, on average a 1% increase in the total unemployment rate in the
previous year causes a 0.022% increase in the differences of recorded property crime between the
current and previous year. Therefore we can reject the null hypothesis that the total unemployment
rate doesn’t have any relationship with recorded property crime.
The direction of the U-C relationship is in line with previous findings by Altindag (2011) and Edmark
(2005), but the size of the influence is substantially smaller in comparison to Edmark (2005) who found
a 1% increase in unemployment caused a 0.11% increase in aggregate crime, and Buonanno et al
(2014) who found a 1% increase in unemployment caused a 1.3% increase in total crime. Additionally
the evidence supports the theoretical concept argued by Cantor and Land (1985) that the
unemployment rate has a lagged motivational effect.
Gross Value Added per head
GVA per head was statistically insignificant in both models with z tests of 1.16 (1) and 1.18 (2), below
the significant value of 1.96. With p-values of 0.247 (1) and 0.238 (2), we cannot reject the null
hypothesis stating there is no relationship between GVA per head and recorded property crime. This
is contradictory with findings by Altindag (2011) who found GDP per capita was positively associated
with property crime, but consistent with Buonanno et al (2014) who found real GDP and GDP growth
rate was insignificant in influencing aggregate crime. This could be because of heightened
guardianship as explained by Cantor and Land (1985) or Cornish and Clarke’s (2003) target hardening,
such as improvements of technology, CCTV and home security in the last decade. Nonetheless, GVA
per head doesn’t impact recorded crime through target creation or Edmark’s (2005) wealth effects.
Median Gross Annual Earnings
The z-test of median gross annual earnings is strongly significant in both models at -2.99 with p-values
of 0.003, significant at 1% (P<0.01). Therefore we can strongly reject the null hypothesis and accept
the alternative hypothesis that there is a negative relationship between median gross annual earnings
and total recorded crime. The coefficient explains that on average a 1% increase in median gross
annual earnings causes a -1.07% decrease in the differences of recorded property crime between the
current and previous year. This is supportive of Becker’s (1968) rational choice model, as median gross
annual earnings will reflect the potential income in legal activities, and thus will increase the
opportunity costs of participating in crime when they increase, which is evident from the negative
coefficient. The findings are consistent with Raphael and Winter-Ebmer (2001) who found income per
worker was statistically significant and negatively affected crime rates. However, Edmark (2005) found
mean income to be statistically insignificant in all models, and these disparities could be explained by
variations in income inequality discussed by Nilsson (2004), which could have differed inconspicuously
throughout the empirical research.
Unemployment Expenditure
The impact of unemployment expenditure is significant at 5% with a p-value of 0.028 and a z-test of
2.20. However, the direction of the coefficient is the opposite to what was predicted though still a
very small effect, as on average a 1% increase in unemployment benefits expenditure causes a 0.09%

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increase in the differences of recorded property crime between the current and previous year in both
models. This is inconsistent with Cantor and Land’s (1985) theoretical deduction that unemployment
benefits would fill the income gap of unemployed individuals and decrease the incentives to commit
crime as supported by Fougere (2009). However, as unemployment benefits are fixed and relatively
small, this supplementary income might not be sufficient to offset the lure of potential returns from
criminal activity which could be reflected in Raphael and Winter-Ebmer’s (2001) time allocation
model, and both could theoretically be earned simultaneously.
Education
Education is very significant at 1% in both models with high z-test values of -4.03 (1) and -3.06 (2) with
p-values of 0.000 (1) and 0.002 (2, rejecting the null hypothesis that education doesn’t have any
relationship with recorded property crime. Interpreting the coefficients, on average a 1% increase in
education caused a 0.02% (1) / 0.017% (2) decrease in the differences of recorded property crime
between the current and previous year. Educated individuals are therefore less influenced to commit
crime, supported by findings from Altindag (2011) and Lochner (1999) who found a 1% increase in
graduation rates decreased the overall property crime index by 1.6%. Education therefore exhibits a
strong influence on recorded crimes, however the explanations why and the long term impacts are
still ambiguous as Hjalmarsson and Lochner (2012) believe it increases risk aversion whilst Lochner
and Moretti (2001) argues it increases legal earnings, but this could be explained by Mocan et al’s
(2004) human capital concept, as education would increase legal human capital, increasing potential
legal income and lowering potential returns from crime.
Public Order and Safety Expenditure
Deterrence in this model represented by government public order and safety expenditure is
statistically significant in both models at 1% in model 1 with a p-value of 0.006 and a z-test of 2.20,
and at 5% in model 2 with a p-value of 0.016 and a z-test of 2.14, rejecting the null hypothesis that
there is no relationship. However, the direction of the coefficients are opposite to what was expected,
as it explains that a 1% increase in public order and safety expenditure causes a 0.05% increase in
recorded property crimes in both models. In the traditional rational choice models by Becker (1968)
and Sah (1991), deterrence as represented by the probability and cost of punishment decreases the
incentive to commit crime. One reason to explain the coefficient’s direction could be public order and
safety expenditure is an insufficient representative of deterrence, as it includes fire departments and
emergency medical services, so higher expenditure might not be specifically targeted at law
enforcement. This could also relate to Sah’s (1991) theory that the probability of punishment is
endogenously determined, and so increases in public order might not be effective in deterring
individuals if they believe the levels of deterrence haven’t changed. This could be related to
Hjalmarsson and Lochner’s (2012) concept of risk, who argue that education causes individuals to be
more risk averse, thus suggesting that offenders are naturally risk takers.
4.6. Concluding Remarks
It is clear from the findings that unemployment has a significant positive impact on recorded property
crime. However, if we refer back to figure 1.3, we can see that the unemployment rate has risen whilst
police recorded property crime has lowered. To truly understand why this has occurred, we can refer
to figure 1.5 and the strong coefficients from median gross annual earnings and education, which have
both increased within this same period. Therefore we can deduct that whilst unemployment rates
exhibit a motivational effect as described by Cantor and Land (1985), the influences of education and

31 | P a g e
legal income are stronger in deterring individuals to commit crime deducted from their larger negative
coefficients.
4.7. Research Study Limitations
Because the possible influences of criminal activity are vast, it is possible this study has several omitted
explanatory variables. Demographic factors such as race, marital status, population density and a
more detailed age breakdown were excluded due to data limitations, and possibly could further
explain the changes in property crime trends and alter the size of the coefficients. Omitted variables
could also explain the ambiguity seen in the previous literature regarding variables such as income,
and the unanticipated direction of government public order and safety and unemployment benefits
expenditure coefficients. Further cross-sectional analysis of individual’s would therefore provide a
more accurate and efficient analysis of crime influences.
A further limitation is the dependability of the regressand. As stated in chapter 3, as these are police
recorded crimes, they won’t fully reflect the true number of offenses committed, as many crimes are
left unreported demonstrated by Figure 1.2. Improvements in technology might also affect crime
rates, as offenders steal credit cards, bank details and commit online crimes which are harder to
quantify. This could explain the downtrend seen in Figure 1.2, reflecting Newburn’s (2013)
displacement concept from physical to online crimes. Therefore, the impacts of the explanatory
variables in this model are limited only to the observable crimes, and are unlikely to represent the
true degree of influence they have over all individuals currently and considering to commit crime.

32 | P a g e
Chapter 5: Conclusions and Recommendations
This chapter will provide a conclusion with regards to the findings and research question, and provide
future research and policy recommendations.
5.1. Conclusion
The aim of this study was to evaluate possible crime influencing explanatory variables from the
existing theoretical and empirical literature, and analyse their impact on police recorded property
crime within the 9 regions of England between 2005 and 2012. The explanatory variables which were
chosen were the unemployment rate (male, female and total), GVA per head, median gross annual
income, education, public order and safety expenditure and unemployment benefit expenditure. A
panel data analysis was utilized to take into account both time and cross-sectional elements, and due
to contradictions with the Hausman and Breusch-Pagan lagrange multiplier test, a mixed effects model
was used to assess the influences on police recorded property crime, correcting for heteroscedasticity
The model incorporates both nested fixed and random effects, though the significant restricted
random-effects model suggests random effects predominate.
In the mixed effects model, 75% of the regressors were statistically significant at 1% and 5%, with
significant z-tests and p-values, demonstrating the chosen variables significantly influenced property
crime in England. Reflecting on the research aim, male and total unemployment rate, education and
median gross annual income were found to significantly influence recorded property crime within the
9 regions of England from 2005 – 2012 supported by Lochner (1999), Chiricos (1987) and Becker
(1968). An interesting find is that income and education exhibited a stronger influence on property
crime in this period than unemployment, explaining the downtrend in Figure 1.2, but the underlying
reasons why education influences individuals requires further analysis.
Intriguingly, the male unemployment rate was statistically significant, directly affecting recorded
property crime, but the female unemployment rate was statistically insignificant. The influence of
male unemployment rates is supportive of Carmichael and Ward’s (2001) findings, and could be due
to either differences in “sex roles” described by Albertson and Fox (2012) or differences in risk-taking
behaviour (Croson and Gneezy, 2009). This study was able to confirm the importance of males in
criminal activity suggested by Hale et al (2009), but was unable to explain the role of female’s
involvement in crime.
GVA per head was statistically insignificant within this model, and it is possible that regional GVA
doesn’t affect individuals as directly as education or income, and doesn’t supply opportunities for
offenders in Cohen and Felson’s (1979) routine activity theory or “supply the booty” (Edmark, 2005).
The substantial negative impact of median gross annual income is supportive of Becker’s (1968)
rational choice theory, concluding that legal income exhibits a negative influence on individuals
committing crime, supporting the idea that criminals are economic rational actors.
For government public order and safety and unemployment benefits expenditure, the direction of the
final coefficients contradicted the predicted hypotheses. Both explanatory variables had significantly
positive impacts on recorded property crime, and contradict the findings of Fougere (2009) and Sah
(1991). The deterrence proxy therefore could be unsuitable, and other factors might need to be
distinguished such as endogenous and exogenous perception. For unemployment benefits, further
analysis incorporating other demographic variables might provide a more accurate explanation
considering the coefficient direction was unpredicted.

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5.2. Contribution
This dissertation has made some noteworthy contributions to the study of crime determinants. It has
confirmed the significance of variables incorporated in previous literature such as education,
unemployment rate and legal income. Unemployment benefits was also included extending the study
by Fougere (2009) as it has predominantly been excluded in the previous literature, which surprisingly
was significant in this study. Furthermore, female unemployment rate was included in this study due
to a large exclusion by empirical research to further investigate if females are equally influenced as
men to commit crime. However, due to its insignificance, it is possible females are not influenced to
the same degree as men to commit crime, but this remains nebulous.
5.3. Further Research and Policy Recommendations
Further Research
Because the direction of the unemployment benefit expenditure variable was unanticipated, a
reanalysis of its impact on recorded property crime would be beneficial to determine whether the
social cushioning it provides described by Cantor and Land (1985) truly does influence crime.
Additionally, public order and safety expenditure should be re-examined as a suitable deterrence
proxy to represent the probability of punishment expressed by Sah (1991).
Although traditionally crimes motivated by monetary gain have been modelled in the empirical
literature as property crimes such as theft or burglary, one concept which hasn’t been largely
considered is drug crimes, which also provides illegal income through smuggling or distribution. In this
circumstance, an individual could be presented with two choices within illegal activity. If drug crimes
provide higher potential returns than property crime, individuals will participate in drug crimes over
property crimes in accordance to Becker (1968). Therefore, a reduction in property crimes might be
simultaneously met by an increase in drug related crimes, creating a displacement of types of crime
described by Newburn (2013) rather than influencing an individual to return to legal activities.
One last area for further research opportunities could be comparisons within the UK between
Scotland, Northern Ireland and Wales, to analyse whether the same patterns persist throughout other
regions.
Policy Recommendations
As unemployment rates and median gross annual income both significantly influence recorded
property crime, it is important for fiscal economic policies to ameliorate economic conditions during
crises to avoid spikes in crime. In the current climate, perhaps the current austerity measures are
myopic and detrimental as it is negatively impacting financial conditions of families, especially in the
lower classes.
Additionally, as education exhibits significant negative impacts on police recorded property crimes,
encouraging young adults to achieve a good education in school is imperative because GCSE’s are
foundation qualifications which are necessary for further education and will likely raise future
potential earnings as argued by Lochner and Moretti (2001).

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