CHAPTER ONE: INTRODUCTION

1.0. Background Information

Crime or criminal and violent behaviour has emerged as one of the major concern in the recent years across the world, and it has arguably gained significant popularity in term of the number of studies being performed and the study results being debated (Gillani, Rehman & Gill 2009). According to Baharom and Habibullah (2009), crime rates differ hugely across regions and countries. There have been a great number of studies recently, quantitative investigations in comparative criminology, in order to explore the influences of societal developments and crime trends. Crime literatures originally proposed by Ehrlich and Becker are arguably regarded as the most significant work in the rejuvenation of interest in crime studies (Gillani, Rehman & Gill 2009). The norms promoting fairness such as equality and inequality are considered sometimes to be closely linked to a level of criminal activities.

For a very long time, poverty has been suspected to be the major cause of criminal activity, but hard and valid evidence of this causality is extremely difficult to come by (Gillani, Rehman & Gill 2009). According to Baron (2006), there are various reasons for this state of play, all having to do with the joint causality concerning poverty and crime. First, individuals highly predisposed to crimes are more than likely to have unobservable traits, such as lack of discipline that make them unemployable ones, and, therefore, would make them poorer even if they do not resort to criminal activity (Baharom & Habibullah 2009). Secondly, high crime neighbourhoods might also lure high profile criminals. This is because such criminals are likely to find it relatively easier to escape detection or because such areas create focal points for customers of illegal services or goods, such as gambling, drug trade, or prostitution (Gillani, Rehman & Gill 2009; Demombynes & Ozler 2005). Lastly, the pervasiveness of criminal activities in such areas discourages business, which, in its turn, contributes to poverty. For all the reasons, the analyses of the association between poverty and crime are frequently regarded with scepticism (Rufrancos et al. 2013).

The fact that the deprived neighbourhoods, by some means, are not respectable can be found in the history of the European society during the inception of Calvinism (Umaru et al. 2013; Fajnzylber, Lederman & Loayza 2002). As Max Weber reported, they hit upon the notion that blessings from God might be revealed via achievement in a worldly profession or calling as Calvin’s followers pursued the indication from God that they were amongst His foreordained elite. On the contrary, the poor could not be regarded as members of the predestined elite. According to Tang (2009) and Umaru et al. (2013), the notion that the poor individuals were very lazy and refused to “pick themselves using their own bootstraps” became principle over the initial viewpoint that the poor were closer to God. Governments worldwide attempt trying to control the level of crime, and if imaginable eradicate crime (Gillani, Rehman & Gill 2009). Likewise, unemployment, being an economic problem, is also another issue, which governments are trying to eradicate (Baharom & Habibullah 2009). However, it is very interesting to note that dearth and joblessness exist at a sophisticated rate where the tendency for crime is high (Baharom & Habibullah 2009).

Crime is the primary problem in major cities. For instance, in New York City, which is comparable to London, particularly in Bronx Borough, there about 247 crime complaints reported for a single week (Baron 2006). In the entire New York City, there are more than 36,000 crime cases being reported during a year. Bronx has a poverty rate of about 37 per cent (Baron 2008). Studies have revealed that every criminal activity costs the society approximately US $ 5,700 annually because of the lost productivity (Baron 2008). Crime victims suffer a burden of approximately US $ 472 billion annually, including physical and mental suffering (Demombynes & Ozler 2005). These figures vary depending on prevalence of criminal activities, which implies that London might exhibit relatively less figures of low poverty rates.

According to Baharom and Habibullah (2009), crime is apparently a considerable problem, which needs to be confronted. Rufrancos et al. (2013) argued that the efficacy of rehabilitation programs do not have an impact on the comprehensive levels of crime. Nevertheless, it also costs more to detain criminals in prison. Building new prisons to accommodate the increasing number of criminals, because of increased prevalence of poverty, costs even more (Rufrancos et al. 2013). From an economic perspective, detaining every criminal is greater burden to the society than a crime itself. Ultimately, the objective of the most government initiatives is reducing crime to such an extent that the cost of crime remains less than the total cost of crime under status quo (Baharom & Habibullah 2009).

On the one hand, the current theories on criminology can be utilized in backing up poverty as a causal factor in explaining several crimes; however, it is directly connected to crimes (Baharom & Habibullah 2009). Conversely, this does not suggest that a cause-and-effect association subsists. This is probably because there are significant numbers of poverty-stricken citizens who have not resorted to crime (Baharom & Habibullah 2009). Very contrary, some rich individuals have resorted to criminal activities. Criminology theorists often consider the other social disorganization elements, which can be found where dearth is evident, i.e., such as poor housing, unemployment, low-quality schools, single family home, absence of social and community controls or lack of discipline (Nolan, Conti & McDevitt 2004). According to Gillani, Rehman and Gill (2009), it is hard to separate the variables and appraise the effect of each one of them solely.

Regardless of the recent decrease in crimes, the United Kingdom continues having an extremely high level of crimes relative to other industrialized countries (Baharom & Habibullah 2009). In addition, the rates of crimes differ significantly across the UK, with some cities and neighbourhoods having remarkably high prevalence rates of rape, aggravated assault, murder or robbery. According to Umaru et al. (2013), the high rates of crime often have noticeable levels of socioeconomic disadvantage, and they seem to be disproportionately poor citizens. Most researchers have argued that the high rates of crimes are a reflection of the high poverty levels (Baharom & Habibullah 2009). However, other researchers have noted that whereas there is a strong link between crimes and socioeconomic status for the entire population, the link is essentially similar to a certain race (Rufrancos et al. 2013; Nolan, Conti & McDevitt 2004).

The association between poverty and criminal activity is that of interest in various disciplines, such as economics, sociology, epidemiology and psychology (Gillani, Rehman & Gill 2009). All these disciplines broadly agree that there is a connexion concerning crime and poverty regardless of the theoretical explanations, which this study attempts to explore. From a sociological perspective, theories of relative deprivation argue that income inequality increases feelings of unfairness and dispossession (Baharom & Habibullah 2009). From a psychological perspective, crime is a result of status competition because individuals at the bottom of the inverted pyramid are very sensitive to inequalities. Quite contrary to the psychosocial viewpoints, economic theorists have traditionally typified criminal activities as occupational choice that arises from low risks of being caught (Umaru et al. 2013). According to economic theories, the impacts of deterrence have been shown to modify the price of crime via imprisonment. This viewpoint considers inequality as a red light for the incentives to crimes (Rodriguez & Belshaw 2010).

1.1. Problem Statement

As pointed out earlier, poverty has been suspected to increase crime rates. In addition, the variation of poverty from one region to another is likely to have a significant impact on the prevalence of crime. The magnitude of the crime levels witnessed in the United Kingdom can be attributed to poverty.

1.2. Research Objectives

The key objective of the following research is to explore how poverty influences the delinquency prevalence in the United Kingdom. With this purpose, the research will use the following specific objectives:

1. To establish the relationship between income inequality and crime prevalence;

2. To explore the linkages between unemployment and crime prevalence;

3. To use the General Strain Theory (GST) in explaining link between crime and poverty;

4. To explore the connexion between crime prevalence and ecological characteristics of neighbourhood.

1.3. Overview of the Research

This study comprises four chapters that include introduction, methodology, results presented in a critical literature review format and conclusion. Chapter 1, Introduction, offers the background information on the research, as well as the aims, problem statement and objectives of the study. Chapter 2, Methodology, discusses the research methodology utilized, including the research methods and design of data collection applied in the study. Chapter 3, Results, presents the study findings of the literature search in relation to the objectives of the following research. Chapter 4, Conclusion, summarizes the findings of the study and offers the recommendations for future studies.

CHAPTER TWO: METHODOLOGY

2.0. Research Approach

According to McBurney and White (2009) research approach refers to the critical methodological aspect that should be considered while undertaking forms of the research. The research approach linked to the manner in which this research will be designed is qualitative research approach. According to McBurney and White (2009), the qualitative research approach was utilized in the research as the study started by collecting qualitative data. Those data were followed by reflection and the critical analysis of the theoretical themes. McBurney and White (2009) pointed out that the qualitative research approach is mainly concerned with the critical appraisal of texts, instead of using statistics and numbers to present the findings. On the contrary, quantitative research approach uses numbers and statistics to quantify data represented as results (McBurney & White 2009). This research is rather explorative, and not descriptive. As such, the qualitative research approach was regarded as more suitable. Moreover, the research was aimed at presenting the information on the crucial topic of interest, which is the connexion concerning poverty and crime, and making informed assertions relating to income inequality, employment and developed and under-developing countries. In addition, the study depended on the utilization of the crucial approach and adopted a narrative style that required the use of the qualitative research approach. As such, the justification that determined the selection of the qualitative research draws upon the fact that the present research is not descriptive but explorative. McBurney and White (2009) cited that exploratory study is utilized in circumstances typified by an undefined and unstructured research problem. Therefore, exploratory studies seek to distinguish new information that is consistent with the nature of this research.

2.1. Research Methods

This research is explorative in nature. McBurney and White (2009) pointed out that exploratory studies have the key aim of studying what is happening, asking questions and assessing a phenomenon from new viewpoints. Moreover, scholars have affirmed that explorative studies must be adequately flexible so as to cater for changes in direction of the research in the event that new data requires that the researcher change the research (McBurney & White 2009). Flexibility that is intrinsic in explorative investigations does not imply that such studies cannot guide the process of inquiry (McBurney & White 2009). This research used the literature-based research methodology. According to McBurney and White (2009), the literature-based methodology allows the research to gather and assess data from the present literature. Two forms of the literature-based methodology exist and they include the traditional work review, which is occasionally referred to as narrative or comprehensive one, and systematic work review, which is occasionally referred to as systematic overview (McBurney & White 2009). This investigation utilized the systematic literature-based research method, which involves searching for necessary data, performing critical analysis of the gathered data, and then synthesizing and combining data. McBurney and White (2009) pointed out that the literature-based methodology essentially involves reassessing the previous research findings in relation to the study objectives. Moreover, in this research, the literature-based methodology enabled the spotting, selection and synthesis of the literature relevant to the study under investigation.

The literature-based method utilized by this study was essentially based on the appraisal of both the past and present literature found on citation indices and electronic databases, including Emerald, Pubget, Science Direct, Ebscohost, JSTOR, Google Scholar and Springer Link. The study used the literature-based methodology because it is less likely to produce biased results when the criteria for selection are drawn upon well-developed procedures, which averts the omission of vital results. In addition, this methodology involves assessing the procedures that integrates the use of explicit inclusion and exclusion criteria. Another advantage is that the literature-based methodology consumes less time compared to such methods as data survey.

2.2. Data Collection

This study is the secondary study, which means that the literature is utilized as the main source of data to be analysed. In relation sampling strategy, the criteria for selection were utilized to ensure that the sources of data applied in the review satisfied the established standards in order to guarantee reliability and validity of the data collected. Firstly, this investigation made sure that the data analysing was recent enough, within the period of 2000-2014. This ensured that the findings reflected the present situation. Secondly, the sources of data used had to be purely peer-reviewed journals. Lastly, the sources had to be pertinent to the aspects and ideas of the investigation, such as the connexion concerning criminal activities and income inequality. Any source data outside the set period were not considered for review.

In relation to the search strategy, the above-mentioned citation indices and electronic databases were searched for information. The search terms or key words were utilized in finding any pertinent information related to the objective of the study. Examples of the search terms and key words included “poverty prevalence and criminal activities”, “the influence of joblessness on delinquent activities”, and “social factors contributing to increase crime prevalence in the UK” among others. These electronic searches produced at least 25 peer-reviewed journals devoted to the relationship between poverty and crime. A matching World Wide Web was also performed in order to increase the sample of the sources of data. This produced additional papers in the form of conference ones, published these and concept papers. Peer-reviewed source of data are preferable to books since the specialized journals offer recent information in the fields of economic crimes (McBurney & White 2009). In addition, the appropriateness of peer-reviewed journals can be attributed to the comparative ease in assessing them compared to accessing books. While depending on several electronic journals as a source of data, it is important to ascertain the level of influence and authoritativeness of the content.

CHAPTER THREE: RESULTS

3.0. Relationship between Crime Prevalence and Income Inequalities

Fajnzylber, Lederman and Loayza (2002) examined the association and robustness of the association between violent crimes and income inequality across countries. First, they premeditated the connexion concerning homicide and robbery rates and the Gini Index between and within countries. According to Fajnzylber, Lederman and Loayza (2002), Gini index or Gini ration refers to the measure of statistical dispersion intended to represent the distribution of income of a country’s citizens. Secondly, they assess the partial link by considering other determinants of crime. Thirdly, they provided control for endogenous character of the inequality by isolating its exogenous effect on these crimes. Fourthly, they provided control for the extent of error in the rates of crime by showing it as equally random noise and unobserved country. Fajnzylber, Lederman and Loayza (2002) finally examined the strength of the partial connexion to different measures of disparity. Their panel data comprised 5-year non-overlapping averages for 39 nations during the 1965-1994 for homicide crimes, and 37 nations during 1970-94 for robberies.

Fajnzylber, Lederman and Loayza (2002) found out that income inequality and rates of crime are positively associated, particularly, between them and in countries, and this correlation shows the connexion from difference in the rates of crime, even after controlling other factors influencing crime. These findings made them to conclude that inequalities in income, measured using the Gini ratio, has a considerable impact on crime prevalence (Fajnzylber, Lederman & Loayza 2002). According to Fajnzylber, Lederman and Loayza (2002), this outcome is tough to fluctuations in the rates of crimes when it is utilized as a depended variable (whether robbery or homicide), the sample of nations and periods, alternative income inequality measures, the set of extra variables that explain the rates of crimes, and the methods of econometric estimation. According to Fajnzylber, Lederman and Loayza (2002), these research outcomes, particularly perseveres while utilizing instrumental variable techniques that capitalize on the changing properties of time series and cross-country to control for both error measurement in the crime data and the combined endogenous effect of the descriptive variables. In the course of arriving at their deduction, Fajnzylber, Lederman and Loayza (2002) found out three important results. The first one is that the rates of violent crimes tend to decline when economic growth increases (Fajnzylber, Lederman & Loayza 2002). Because violent delinquency is determined jointly by income distribution pattern and by the change rate of national income, it is justified to determine that faster reduction of poverty results in decrease of national crime rates. The second finding was that violent crime incidence has a high notch of inactivity, which also explains early mediation to avert waves of crimes (Fajnzylber, Lederman & Loayza 2002). The last finding was the mean income level, the average attainment of education, and the notch of suburbanization in a country that are not associated to rates of crime in a substantial or reliable way.

Neumayer (2005) sets out to examine the association between violent property crime and income inequality. Using a linear model, Neumayer (2005) took 3-year averages of the dependent and all independent variables for period between 1980 and 1997 to reduce the effect of essentially low or high rates in one single year. Unlike Fajnzylber, Lederman and Loayza (2002), Neumayer (2005) argued that the association between violent property crime and income inequality might be spurious. In contrast, Fajnzylber, Lederman and Loayza (2002) provided seemingly robust and strong evidence that income inequality causes higher rates of both robbery and homicide crimes, even after controlling for country-specific fixed effects. According to Neumayer (2005), no matter whether inequalities in income are measured using the Gini ratio or by co-efficient of the top to the bottom quintiles, it is not significant in fixed-effects and dynamic estimation. However, Neumayer (2005) agrees that income inequalities are only significant in the estimation of random-effects, and unless the sample of the nations is constrained to contain any other country. These study results suggest that if one allows for more representative sample and control for specific fixed effects, then the inequalities in incomes are not statistically significant in determining violent crimes (Neumayer 2005). From these results, Neumayer (2005) concluded that the association between violent crimes and income inequality is far less robust as Fajnzylber, Lederman and Loayza (2002) seem to suggest.

Baharom and Habibullah (2009) set out to examine the relationship between crime and inequalities in incomes in Malaysia for a period between 1973 and 2003. They used the bounds of Autoregressive Distributed Lag (ARDL) to examine the causality. The ARDL procedure of testing is utilized in analysing the effect of inequalities in incomes on various classes of criminal activities, as well as to appraise the effect of various cases of criminal activities on inequalities in incomes (Baharom & Habibullah 2009). These results interestingly concurred with those of Neumayer (2005), and differed from those of Fajnzylber, Lederman and Loayza (2002), by indicating that inequalities in incomes have no meaningful causality with any of the different class of crime selected, such as violent crime, total crime, burglary, theft, and property crime. Despite the incorporation of an advanced technique (ARDL), Baharom and Habibullah (2009) still failed to find any meaningful causality between criminal activities and inequalities in incomes. The study results suggested that all the chosen variables are non-stationary and achieved as stationary after the first differencing (Baharom & Habibullah 2009). Baharom and Habibullah (2009) used the cointegration analysis to indicate that none of the criminal activities selected are coinitegrated with income inequality. The impulse response function and variance decomposition further supported the strength of the results (Baharom & Habibullah 2009).

Although these results are contradicting with the previous findings of Fajnzylber, Lederman and Loayza (2002), who found meaningful causality between crimes and income inequality, there are several other studies that could not find meaningful causality between crime and income inequality, such as Neumayer (2005). Although Baharom and Habibullah’s (2009) research finding has no robust association among various categories of crimes, such as violent crimes, property crimes, total crime, burglary and theft, it is still a significant finding that can help in explaining the London 2011 riots. Though many contemporary sociologists believe that the riots were because of economic deprivation, it can be concluded that income inequalities did not contribute to the riots (Neumayer 2005).

Rufrancos et al. (2013) set out to analyse the time-series evidence of the impacts of changing income inequality on crime for several countries and types of crime. Rufrancos et al. (2013) analysed 17 peer-reviewed papers that analysed the relationship using time-series evidence. Rufrancos et al. (2013) classified the findings of every paper such as offering evidence of No Significant Associations, Significant Negative Associations, or Significant Positive Association. The analyses of these paper showed that only property crime increased with increases in income inequality. On the one hand, unlike Neumayer (2005) and Baharom and Habibullah (2009), Rufrancos et al. (2013) as well as Fajnzylber, Lederman and Loayza (2002) found out that specific measures of violent crimes, including robbery and violence, also show some sensitivity to income inequality over time. However, on the other hand, like Neumayer (2005) and Baharom and Habibullah (2009), Rufrancos et al. (2013) found out that aggregated non-specific measures of violent crime do not show such sensitivity.

Rufrancos et al. (2013) attempts to explains why Neumayer (2005) and Baharom and Habibullah (2009)’results differed from those of Fajnzylber, Lederman and Loayza (2002) by claiming that there is found no significant association between income inequalities and crime prevalence because of differences in crime reporting. The results indicate considerable variations in viewpoints among researchers. According to Rufrancos et al. (2013) and Fajnzylber, Lederman and Loayza (2002), the review of literature is likely to suggest that property crime is associated very strongly to the changing income inequality. This is consistent with the sociological and economic theory, and it is shown to be case in several countries and international comparisons (Rufrancos et al. 2013). This is the reason why many contemporary sociologists believed that the London riots in 2011 were caused by income inequalities and economic deprivation. Rufrancos et al. (2013) time-series evidence on the causality between violent crime and inequality is considerably more mixed. North American and international analyses validate this relation, whereas European data is much less conclusive (Nolan, Conti & McDevitt 2004). The disparity between the data might be attributed to various levels of reporting for different forms of crimes (Nolan, Conti & McDevitt 2004).

3.1. Relationship between Crime Prevalence and Unemployment

Tang (2009) sets out to study the association among unemployment, inflation and rates of crimes in Malaysia. The sample period covered the yearly data between 1970 and 2006. He used the Bartlett trace test because it was appropriate for the small sample. The trace test reveals the macroeconomic variables (Tang 2009). The corrected trace test affirmed the presence of long-run equilibrium relationship between the degree of crimes and its determinants (Tang 2009). With the cointegrating factor, Tang (2009) found out that unemployment and inflation were combining with rate of crimes to achieve their steady state equilibrium in the end, although deviation might occur in the short run. In Tang’s (2009) study, the normalised co-efficient of unemployment and inflation rate was positively linked to the degree of crimes in Malaysia over the period between 1970 and 2006. However, inflation seemed not significant in the short run according to Tang (2009). This means that unemployment is an important criminal motivation factor in Malaysia (Tang 2009). The empirical evidence found by Tang (2009) suggested that the causality direction runs from inflation and unemployment to crime, but there is not any evidence of reverse causality. In addition, the practical evidence means that the degree of crime is Granger Causality unemployment.

Gillani, Rehman and Gill (2009) set out to investigate the association between numerous economic pointers such as inflation and joblessness, and crime. Their research covered the period between 1975 and 2007. They used the Augmented Dickey-Fuller (ADF) test to examine the time series data stationary features. They also applied the Granger Causality and Johansen Maximum Likelihood Cointegration tests in order to find the long-term association along with the connexion among the variables. Gillani, Rehman and Gill (2009) study revealed these important findings. Similar to Tang (2009), Gillani, Rehman and Gill (2009) found out that unemployment in Pakistan causes crime. According to Gillani, Rehman and Gill (2009), this is because rate of unemployment in a nation is a matching pointer of wages opportunities at the legitimate labour market. Gillani, Rehman and Gill (2009) explain this finding by claiming that when the degree of unemployment increases, income-earning opportunities decrease. The decrease in incomes instigates persons to commit crime. From these findings, one can conclude that poverty causes crime (Gillani, Rehman & Gill 2009). Poor households have limited resources and incomes to satisfy their wishes and desires. In London, the economic deprivation statistics show dreary picture besides the swelling income disparity (Gillani, Rehman & Gill 2009). Low income implies low saving capacity, which leads to low living standards. According to Gillani, Rehman and Gill (2009) and Tang (2009), the low income with regard to price increases (inflation) has an effect of instigating crime by reducing the moral threshold of an individual. It can be concluded that persons living in economic deprivation are tempted to commit crimes (Gillani, Rehman & Gill 2009).

Umaru et al. (2013) researched the connection concerning the level of crime, rate of poverty, corruption level, rate of joblessness and inflation rate in Nigeria between 1980 and 2009. Similar to Gillani, Rehman and Gill’s (2009) research, Umaru et al.’s (2013) study utilizes the Augmented Dickey-Fuller (ADF). The properties of the time series variables were assessed via the use of the ADF in assessing the unit root features of series and Granger causativeness test of relationship concerning the variables. The OLS outcomes showed that poverty, unemployment rate and corruption levels had a negative effect on the level of crime, whereas the degree of inflation passively affects the level of crime in Nigeria (Umaru et al. 2013). Unlike Tang (2009) and Gillani, Rehman and Gill (2009), Umaru et al.’s (2013) research result of causality indicated no association concerning crime rate and unemployment level. However, similar to Tang (2009) and Gillani, Rehman and Gill (2009), the research found a one-way connexion concerning crime and poverty rate. In addition, the study found a two-way causation between crime-level and corruption, and two-way causation between inflation and corruption (Umaru et al. 2013). Corruption and unemployment had a one-way causation. The study indicated that a relationship among inflation, poverty rate, corruption level and unemployment subsists (Umaru et al. 2013). However, even if individuals were poor, unemployed, and corrupt ones, the level of crime might not be as high as claimed. The level of crime increases when the living cost that is determined by inflation becomes high (Umaru et al. 2013).

3.2. The General Strain Theory (GST)

According to Baron (2008), the general strain theory (GST) suggests that stressors or strains upsurge the probability of negative emotions, such as frustration and anger. According to this theory, such emotions create pressure for corrective action, and a crime is one of the possible responses (Baron 2008; Baron 2006). Essentially, crimes such as stealing, seeking revenge, or alleviating negative emotions through illegal drugs, are a method for decreasing stressors (Baron 2008; Rufrancos et al. 2013). GST improves or builds on the previous strain theories in various ways. Most remarkably, this theory points out several new categories of strain, such as the loss of positive stimuli that might be caused by death of a friend; new categories of goal blockage which might be caused by failure to achieve justice; and the presentation of negative stimuli that might be caused by verbal insults and physical assaults (Baron 2008; Baharom & Habibullah 2009).

Recent studies indicate several of the specific strains falling under these categories that are linked to crime and delinquency (Baron 2008; Gillani, Rehman & Gill 2009). Researchers have encouraged more intention to be paid to the numerous variables concerning unemployment to crime. It has been particularly suggested that the individuals’ understanding of the labour market state plays a significant role in modelling their reaction to it. By using the general strain theory, Baron (2008) and investigated the role played by unemployment in the criminal behaviour of 400 homeless street youths. The way the youths understand their labour market capabilities, and how such understandings and experience impact criminal behaviour, is of particular interest (Baron 2008). The findings of this study revealed that the impact of it on unemployment is facilitated and moderated majorly by other variables. According to Baron (2008), unemployment is particularly conditioned by the external unpremeditated attributions leading to anger over joblessness that, in its turn, leads to crime. The direct impact of unemployment is moderated by minimal employment searches and monetary dissatisfaction (Baron 2008; Tang 2009). Anger over joblessness is also because of the negative and idiosyncratic understandings of the economic states and a continued connection to the labour market (Tang 2009). According to these researchers, the negative subjective viewpoints, the lack of state support, a drop in social control and prolonged homelessness resulted in involvement in criminal activities. Baron’s (2008) study also revealed that criminal participation is also invigorated by deviant values and dearth of fear of punishment.

Baron (2006) sets out to assess the classic strain theory by integrating the measures of strain neglected in the past research and applying them to marginal population. By utilizing a sample of homeless youth, Baron (2006) again studied more complete model of the classic strain perspective whereby comparative deprivation, monetary goals, objective structural factors and monetary dissatisfaction result in crime. Baron (2006) also investigated the interactions between these factors and the conditioning impacts of beliefs, peers and attributions. The study outcome indicated that comparative deprivation, monetary goals, unemployment, homelessness and monetary dissatisfaction were linked to crime (Baron 2006). This research concurred with his previous research. In addition, monetary dissatisfaction and comparative deprivation were conditioned by an objective economic situation in the relationship with several illegal behaviours and interactions between monetary expectation and monetary goals and achievements that were linked with crime (Baron 2006). These findings also borrowed some support for the measure of strain where inconsistency between monetary expectations and goals for financial ones was linked to associated criminal behaviour. According to Baron (2006), the discrepancies between monetary goals and actual achievement have an association with criminal behaviour although these cannot be generalizable across all offenses.

These findings have supported the arguments of Baron’s (2006) study. However, the findings that revealed the causality between perceptions of achievement and goals were less supportive since it is not predictive of crime (Baron 2006). In comparison to other forms of causalities, back up for the effect of strain when conditioned by external provenances, deviant values, and deviant peers was more limited. The study found some evidence of those who perceived comparative deprivation and who interacted with large numbers of deviant members who were more likely to participate in crimes as there were those who were monetarily dissatisfied and blamed others for their situation.

Rodriguez and Belshaw (2010) investigate how general strain theory (GST) might help in explaining the racial differences in offending. The investigations particularly compared the measures of White Juvenile delinquency and general strain theory to that of Latino juvenile delinquency (Rodriguez & Belshaw 2010). In their research, the analysis of secondary data was conducted by means of data taken from the National Survey of Adolescents. The data were gathered during a 6-month period from January to June in 1995. The study involved a sample size of 31,336 of which 87.6 were White youths and 12.4 per cent were Latinos (Rodriguez & Belshaw 2010). The outcomes indicated that even though Latino youth suffer from strain and might deal with strain differently (Rodriguez & Belshaw 2010). Latino youths are not likely to commit delinquent acts because of strain (Rodriguez & Belshaw 2010). On the other hand, the results indicated that White youths have a likelihood of committing more serious acts of crime because of strain than Latino youth (Rodriguez & Belshaw 2010). Caution must be taken while interpreting the outcomes because of the limited initial preliminary nature of the findings. Majority of the present literature, including Baron (2008), on the general strain theory recognizes that the theory is applicable t all segments of the society, including the homeless, delinquent peers, African Americans, college students and urban youth. According to Rodriguez and Belshaw (2010), this can be concluded about Latinos and GST. Nevertheless, a caution exists concerning the strength and degree of the association between the delinquency behaviour of Latinos and the general strain theory (Rodriguez & Belshaw 2010). Based on these findings, Rodriguez and Belshaw (2010) concluded that outcomes and predictors of the general strain theory differ by ethnicity.

3.3. The Relationship between Environmental Neighbourhood Characteristics and Violence Crime Prevalence

Demombynes and Ozler (2005) examine the impacts of economics on violent crime in South Africa. Both empirical and theoretical studies in criminology literature have called for an assessment of crime at relatively smaller levels of geographical disaggregation than states, nations or large metropolitan areas. According to Demombynes and Ozler (2005), when the region of analysis is large, there is loss of information concerning relative welfare levels across neighbourhood (Demombynes & Ozler 2005). In addition, while assessing the effect of economic development on large areas, the fact that individuals might travel to engage in criminal activities is ignored. As a result, Demombynes and Ozler (2005) used data on welfare and crime in all police precincts in South Africa to analyse the impact of local economic inequality or difference on crime. In relation to property crimes, Demombynes and Ozler (2005) found out that economic differences in the area of study is highly linked to both vehicle and burglary theft. Property crime prevalence is strongly linked to mean expenditure in the precinct, which indicates that returns from a criminal activity are the main determinants of property crime (Demombynes & Ozler 2005; Baron 2008).

Economically developed areas attract criminals, whereas economically deprived regions seem to scare criminals. If wealthier precincts have an efficient protection from crime, the elasticity of residential burglaries in relation to mean expenditure, controlling for protection, might be even higher (Demombynes & Ozler 2005; Fajnzylber, Lederman & Loayza 2002). The local economically successful precincts are targets for residential burglary (Demombynes & Ozler 2005). This is consistent with economic theories that relate economic deprivation to property crime, and with sociological theories, which means that inequalities in economic development results in crime (Demombynes & Ozler 2005; Rufrancos et al. 2013). According to their study, the rates of burglary ranged between 25 and 43 per cent higher in wealthiest places regardless of the constant presence of the police (Demombynes & Ozler 2005). This implies that criminals travel to neighbourhood with the highest returns from burglary (Baharom & Habibullah 2009; Gillani, Rehman & Gill 2009). These findings imply that the ecological characteristics do not have a strong association with property crimes since such criminals are motivated by the high returns.

Morenoff, Sampson and Raudenbush (2001) set out to examine the relationship between collective efficacy, neighbourhood inequalities and the spatial dynamics of urban violence. By highlighting resource inequality and longitudinal interdependence, Morenoff, Sampson and Raudenbush (2001) combine the structural characteristics from 1990 census with a survey of 8872 residents of Chicago in 1995 in order to predict the variations of homicide rates between 1996 and 1998 across 343 neighbourhoods. Unlike Demombynes and Ozler (2005), Morenoff, Sampson and Raudenbush (2001) found out that spatial proximity to homicide is robustly linked to increased rates of homicide. The low collective efficacy and concentrated disadvantage, which is defined as the association between cohesion and social control, also autonomously predict increased rates of homicide. According to Morenoff, Sampson and Raudenbush (2001), the high returns linked to wealthiest precincts do not have a strong association with crime. These results suggest that spatial embedding, social organization and internal structural characteristics are significant in comprehending the variations in the rates of violent crimes at neighbourhood level (Morenoff, Sampson & Raudenbush 2001).

Nolan, Conti and McDevitt (2004) posit that neighbourhoods regress, pass or get stuck in recognisable stages of development as they move toward higher levels of collective efficacy. Over the last 20 years, the Broken Windows theory, a version of the social disorganization theory, has had a significant effect in the US. Contemporary sociologists, such as Morenoff, Sampson and Raudenbush (2001), have indicated that collective efficacy, as neighbourhood level, is more significant predictor of violence crime than social and physical disorder. Collective efficacy is regarded as an evolving neighbourhood property. Considering both the stages of neighbourhood development and level of neighbourhood crime and disorder, Nolan, Conti and McDevitt (2004) constructed four neighbourhood types: Vulnerable, Anomic, Responsive and Strong. They then introduce the concept of situational analysis as a way of addressing the occurrence of crime and disorder and the development of collective efficacy in every type of neighbourhood.

CHAPTER FOUR: CONCUSIONS AND RECOMMENDATIONS

4.0. Key Findings

Crime is apparently a considerable problem, which needs to be confronted. The effectiveness of incapacitation and rehabilitation programs does not have any impact on the comprehensive levels of crime. This research has investigated the impact of poverty on crime levels, especially concerning the relationship between crime prevalence and income inequalities, the relationship between crime prevalence and unemployment, general strain theory (GST), and the relationship between environmental neighbourhood characteristics and violence crime prevalence.

Various literatures reviewed by this research have shown varying conclusions concerning the relationship between income inequalities and the prevalence of crime. These conclusions are contradictory in nature. Most researches have put the strength of the association between crime prevalence and income inequalities. On the one hand, the study found out that income inequalities and rates of crimes are positively associated, particularly, between and within nations, and this parallel shows the causality from disparity in rates of crimes. Inequalities in income, measured using the Gini ratio, have a considerable impact on crime prevalence. On the other hand, the paper found out that inequalities in incomes have no meaningful causality with any of the different class of crime selected, such as violent crime, total crime, theft and burglary, and property crime. No matter whether inequalities in incomes are measured using the Gini ratio or by co-efficient of the top to the bottom quintiles, it is not significant in fixed-effects and dynamic estimation. These results indicate considerable variations in viewpoints among researchers. Based on these findings, majority of literatures have concluded that there is not any meaningful association between income inequalities and crime prevalence. As a result, in relation to this specific objective, this research concludes that income inequalities are not the major reason for the increasing levels of crimes in the United Kingdom. Because violent delinquency is determined jointly by income distribution pattern and by the change rate of national income, it is justified to determine that faster reduction of poverty results in decrease of national crime rates.

In relation to impact of unemployment on crime prevalence, all the researchers agree that the causation between these two ones is robust. As a result, this research approves this relationship by asserting that unemployment increases the prevalence of crime. Unemployment and inflation were combining with rate of crimes to achieve their steady state equilibrium in the end although deviation might occur in the short run. The causality direction runs from inflation and unemployment to crime, but there is not any evidence of reverse causality. Inflation seems to have less substantial consequence on the prevalence of crime in the short run. Unemployment rate is a complementary indicator of income possibilities at the legal labour market. Low income implies low saving capacity, which leads to low living standards. Poor households have limited resources and income to satisfy their wishes and desires. Poverty, unemployment and corruption have a negative effect on the level of crime, whereas the degree of inflation passively affects the level of crime in Nigeria.

In relation to the general strain theory, stressors or strains increase the likelihood negative emotions, such as frustration and anger. The negative subjective viewpoints, decrease in social control, the lack of state support and prolonged homelessness result in involvement in criminal activities. Comparative deprivation, monetary goals, unemployment, homelessness and monetary dissatisfaction are linked to crimes. The negative subjective viewpoints, such as decrease in social control, the lack of state support and prolonged homelessness resulted in involvement in criminal activities. However, the findings that revealed the causality between perceptions of achievement and goals were less supportive since it is not predictive of crime. In comparison to other forms of causalities, support for the effect of strain when conditioned by external attributions, deviant values and deviant peers was more limited. Based on these findings and in regard to this research objective, this paper concludes that ecological environment affects crime rate to a certain level.

Lastly, concerning relationship between environmental neighbourhood characteristics and violence crime prevalence, past and present literature has varying opinions. Some previous literature has affirmed that ecological characteristics do not have a strong association with property crimes since such criminals are motivated by the high returns linked to precinct. The study has found out that burglary and property crimes are common in wealthiest neighbourhoods despite the constant resent of law enforcement officers. On the other hand, the study revealed that spatial proximity to homicide is robustly linked to increased rates of homicide. These results suggest that spatial embedding, social organization and internal structural characteristics are significant in comprehending the variations in the rates of violent crimes at neighbourhood level. Besides wealthy precincts attracting criminals, high crime neighbourhoods lure high profile criminals. This is because such criminals are likely to find it relatively easier to escape detection or because such areas create focal points for customers of illegal services or goods, such as gambling, drug trade, or prostitution.

4.1. Recommendations

This research has only focused on how poverty affects criminal behaviour, especially in relation to income, unemployment, general strain theory and environmental neighbourhood characteristics. In addition, the research restricted by the general knowledge and literature of other researchers who focused on developing countries. The factors influencing crime in underdeveloped countries have not been explored that is a potential future research.

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