Crime an offence against ethics system of humanity

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There is no country known where criminality is not present. No person exists whose morality is not daily infringed upon. We must therefore declare that crime cannot be nonexistent.

Crime is an offence against the ethics system of humanity. It also refers to the commission of an act or act of omission that breaks the law and is punishable by the state. It is presented as a complicated historical and social phenomenon which always exists in a person's life but it keeps pace with social and technological evolution. Theorists admit that the root of this problem is unknown. But, however, Economics of Crime has become a new field for economic investigation particularly due to the fact that over the same period of time there has been a rise in criminal activities in many countries, as confirmed by several empirical studies. Economics of crime are mostly related with factors such as poverty, income inequality, social exclusion, cultural characteristics, age, sex, education level and family background (Buonanno, 2003).

A crime is an illegal behavior classified by the Sate as a felony or misdemeanor. It is an offence against a public law. This word, in its most general sense, includes all offences, but in its more limited sense is confined to felony.

According to the Central Statistics Office of Mauritius, crimes are defined as 'offences that are punishable by penal servitude and fine which exceed five thousand rupees'. Misdemeanours, on the other hand, are defined as 'offences that are punishable by imprisonment for a term exceeding ten days and fine exceeding five thousand rupees'.

Criminality, however, is the process of criminal behaviour and action that constitutes intervention by official and formal agencies of social control. Perpetrators are those who benefit from the criminal acts they commit. For example, the satisfaction criminals derive from hurting another person. In this case this will be a benefit for the criminal and a cost for the adversary.

Given the high level of crime, it is obvious that crime prevention is a major economic activity. In fact, crime is a negative externality with enormous social costs, hence it appears to be relevant that feasible and effective social and economic policies in tackling crime will be pursued and undertaken.

2.2 Economists views on criminality

Fleisher (1963-1966) was the first who worked on criminality from an economic point of view, but, yet, Gary Becker's model (1968) which deals with the relationship between economic variables and criminality in his seminal paper gained much more importance and became a major breakpoint. Fleisher, in 1963 published a paper titled "The Effect of Unemployment on Juvenile Delinquency" in which he tried to explore the relationships existing between unemployment and youth crime. Fleisher's work was merely an empirical study aiming at detecting economic determinant of individual's criminal behaviour. But it's Becker's paper to represent a milestone for economic disciplines. In fact Becker stressed that "crime is an economically important activity or 'industry' . . . almost totally neglected by economists.

In his Nobel lecture, Becker emphasizes that while "in the 1950s and 1960s, intellectual discussions of crime were dominated by the opinion that criminal behaviour was caused by mental illness and social oppressions, and that criminals were helpless victims" the economics approach implie(s) that some individuals become criminals because of the financial and other rewards from crime compared to legal work, taking account of the likelihood of apprehension and conviction, and the severity of punishment. Other researchers have based their economic model on crime depending on Becker's model by bringing some minor change.

Becker emphasizes that criminals, being economic individuals, are rational maximisers of expected utility. They allocate their time between legitimate and illegitimate activities taking into account their cost-benefit analysis, the probability of conviction and the punishment if convicted. His study lays emphasis on: 'some individuals become criminals because of the financial and other rewards from crime compared to legal work taking account of the likelihood of apprehension and conviction and the severity of punishment'.

Regarding the cost side, criminals are being sanctioned by the society. Becker's rationale weighs the expected cost against the benefits and if the costs are lower than the benefits, the individuals commit the crime. According to Becker, the society can reduce the level of crimes by simply reducing the benefits derived from crime or by increasing the cost of indulging in crimes or they may adopt policies for both reducing the benefits an increasing the costs. He also emphasized that there should be an increase in apprehension and increasing the severity of punishment for those who are caught.

Becker's model was further extended by the theorist Erhlich who introduced some other variables into the criminal's sensitivity to punishment. He laid emphasis on severe punishment and made sure that the criminals are punished. He also takes into account the relative value of the benefits from crime and the next best alternative foregone- legal wages.

Stephen Jones said that: 'The idea of a cause of violence must be treated with care'. However, he did not give a fixed definition of crime. According to Jones, crime is a function of the society and it varies within societies. He claimed that long ago people defined 'violence' as being a psychological damage but, with time, it is defined as the infliction of physical injury by force. Moreover, racism or sexism should also be considered as a form of violence.

Box on his part argues that the relationship between unemployment and crime is complicated. He also identifies that not only the deprived people are prone to commit crimes but also the rich people are to be blamed in periods of high crime; he claims that the rich as well may have the incentive to be involved in crimes. For example, in times of depression, they have to deal with an increased struggle for scarce resources. According to Box, White collar crimes, are often sorted out within the boundaries of the business due to high prosecution costs and therefore they are under-reported. Note that white collar crimes are crimes committed by a person of respectability and high social status in the course of his occupation" (Sutherland, 1939).

2.3 Theories of crime

Criminality has been defined differently by different theorists but our main objective is to focus on the possible explanations on the occurrence of crime. This is illustrated as follows:

Economic inequality and crime

Conflict and Marxist theory

Anomie and Strain theory

Theories of crime

Control theory

Figure 1: Theories of Crime

2.3.1 Economic inequality and Violence

Generally, there has been a rise in national wealth over the whole world, but, this wealth is distributed unequally among people. Many studies have been done with the view of examining the correlation between crime and inequality. When we talk of economic inequality, we are referring to income inequality. Pablo Fajnzylber, Daniel lederman and Norman Loayza investigated the 'robustness and causality of the link between income inequality and crime across countries. They examined the link between the Gini index (measure of income inequality) and the rate of homicides and thefts both within and between countries. This means that the higher the level of income inequality the higher the rates of crimes. In addition, their study showed that the flow of causality is from inequality to the rate of crime.

2.3.2 Conflict and Marxist theories of crime

According to the conflict theory, crimes are motivated by the 'social and economic forces' operating in the society. The theorists arrogate that societies consist of groups with different values and cultures and the government cater only for the affluent (that is those who are very powerful, have lots of money and prestige in the society).

Hence, as a consequence, there is discord between the government and the powerless. The government tries to keep the powerless under control, but, the latter try to impose themselves into a position of greater control. Marxist theory is the same as the conflict theory but here the conflict comes from the power which is exerted by capitalism. Many theorists, after Marx, elaborated on what he said.

Wilhem Bonger, who conducted his study after Marx, emphasized on the planned system as being responsible for the occurrence of crime as it encourages people to be self-centered and self indulged. This means that, the planned system causes people to become more and more selfish thus distancing them from the norms and values of the society. Greed and selfishness are considered to the drivers of crime to certain extent. However, deprivation can lead to the unattainability of certain factors like progress, basic needs for one's family, enjoying a good and sound standard of living. The government must ensure that all the citizen of a country can access the necessary resources and treated equally else they will feel deprived thus tempting them to commit crimes.

2.3.3 Anomie and Strain Theory

Emile Durkheim, a famous sociologist, talk of strain as being one factor contributing to crime in a study done to establish a relationship between strain and economic inequality. The strain theory considers people to be essentially good but they tend to commit crimes due to stresses and complications in their lives.

This is possible in two ways:

Anomie or

Relative deprivation

The word Anomie was used to describe suicide which was caused by changes in economic conditions either for the better or worse by Durkheim. This takes place when people become anxious and depressive and they blame themselves for all their anxieties. As a result there is a disbalance in their way of living and they tend to demand more money. Due to this, they fall on drug addiction or they may even commit suicide.

A situation where there is an increase in income inequality is referred to as relative deprivation. This occurs when people are jealous of others and when they see other people enjoying a better and higher standard of living they feel envied as these things are impossible for them. As a result, they tend to commit crime in order to satisfy their needs.

2.3.4 Control theory

The strain theory is limited and this lead to the emergence of the control theory. Hirschi and other control theorists claimed that people find it easier to access their desires illegally than by adopting legal means. A lot of emphasis is laid on the unemployment-crime relationship by control theorist. Those who are more personal, demotivated and not holding the right beliefs, are more prone to commit crimes.

2.4 Empirical Review

An analysis of empirical studies of the impact of various variables on criminality is discussed below. Since the beginning of 80s, Becker's paper opens the door to a new field of empirical research whose main purpose is to verify and study the economic variables that determine criminal choices and behaviours of agents.

2.4.1 Crime and Risk

There are some empirical findings on criminals' behavior that are inconsistent with expected utility theory. Eide (1995-2008) refer to studies where criminals overestimate the probability of apprehension and as a result a low probability of punishment with a deterrent effect. According to Block and Gerety (1995) individuals are generally risk averse but criminals are more prone to modifications in the probability of punishment whereas non criminals are more prone to changes in the monetary penalties than criminals.

These facts support the view that crime can be considered as a gamble (financial or property crimes). It is astounding that risk averse people (those who avoid losses) tend to commit crimes more persistently which is beyond expectation. Garoupa (1998) reveals that the maximal fine result holds when the rate at which social planner is just willing to substitute probability for fine is always larger than the rate at which the social planner can change the probability for fine. When the individuals are risk averse, a less-than-maximal-fine may be optimal because individuals are vigilant about the expected fine and a risk premium.

2.4.2 Crime and Unemployment

The crime-unemployment relationship has been ambiguous in most studies, leading to different approaches. The first one indicates a positive relationship (Reilly & Witt, 1992; Papps & Winkelmann, 2000; Raphael & Ebmer, 2001; Edmark, 2005), known as "motivation effect", where a rise in unemployment rates leads to economic problems and increases the motivation to engage in criminal acts. The second one comes from the work of Cantor & Land (1985), who found a negative correlation known as "opportunity effect" (Britt, 1994; Melick, 2004) and indicates that, during economic depression a rise in unemployment rates leads to decrease in median family income and discourages a person from the decision to commit a crime.

According to Machin and Meghir (2000), crime rates are higher where and when wages at the bottom of the wage distribution are lower, indicating poor market opportunities for labourers, where the probability of being caught is lower, where crime rates are already higher and where the potential returns to crime are high. Increased wages lead to fall in crimes, while an increase in the economic returns from crime increases criminal activity.

According to Scorcu and Cellini(1998) and Narayan and Smith(2004) an increase in unemployment rate will lead people to commit more crimes since this is the only way for them to increase their revenues and they are provided with more opportunities by engaging in illegal activities. Witt et al(1998) endorses that changes in the rate of unemployment are positively correlated with changes in the crime rate.

Box (1987) noted that 33 of the 50 studies examined gave a positive correlation, while the remaining was characterized by an irrelevant and negative correlation mix. In that order, the time series studies search of Chiricos (1987) showed that 46 studies supported positive correlations and 22 negative correlations, revealing that less than half of them were statistically significant. Beside the cases where the crime-unemployment relationship is proved to be insignificant (Timbrell, 1990, Young, 1993), there are researchers that argue even for the existence of such a causal relationship between the two variables. For example, Field (1990) and Pyle & Deadman (1994) highlighted that unemployment might not be so important in order to study the crime rates variations in Great Britain.

Fleisher comes to the conclusion that "an examination of delinquency rate and other variables by age and through time suggests that the effect of unemployment on juvenile delinquency is positive and significant. However, this statement is easier to support when it refers to individual who are over sixteen years of age.

2.4.3 Crime and Income

Several studies show that changes in income can affect crime in three ways: first, a fall in income encourages people to commit crimes. This is known as "motivation effect" (Grogger, 1998; Machin & Meguir, 2000; Gould et al. 2002). Second, an income increase sets the opportunities for criminal offences, due to the large amount of stolen goods, known as "opportunity effect" (Levitt, 1999). Finally, the third way is known as "routine-activity effect" (Beki et al. 1999), indicating that an increase in income leads to outdoor activities, thus increasing the likelihood of potential crime victims.

There is ambiguity in the studies of crime concerning various income variables used to proxy the net expected benefits from crime. Earlier studies on income inequality and crime have typically used total income and total earnings. It is changes in permanent income that affects crime rates rather than changes in transitory income. As a result, it is necessary to separate the two effects. An increase in permanent income inequality yields a positive and significant effect on total crimes and different property crimes compared to an increase in inequality in transitory income that has no significant effect on any type of crime (Dahlberg and Gustavsson (2005). Chisholm and Choe (2005) provide a theoretical argument that relates the net expected gains from crime to a measure of income inequality (Gini) and the mean income of a society.

A number of empirical studies has set the question how the authorities and the prevention policies can better combat crime. Different variables have been tested, such as the growth of police force (De Oliveira, 2003), the money spent for the appropriate equipment (Imrohoroglu et al. 2000), people who have been arrested (Corman et al. 1987, Corman & Mocan, 2000), convicted (Pudney et al. 2000; Funk & Kugler, 2003) or sentenced to imprisonment (Levitt, 1996). The results are still ambiguous, but it seems that the possibility of sentence and conviction are more effective ways for crime prevention than the others. That is because, in most cases, criminal actions are not always connected with arrests, and arrests do not always lead to convictions and imprisonments.

However, many support the view that, different methodology approaches used for empirical analysis can lead to ambiguous results. In the following section, we discussed about two cases whereby studies were carried out on criminality mainly in Nigeria and Greece.

2.4.4 Study in Nigeria

In the Nigerian situation, Odumasu (1999) study emphasised on the seriousness of poverty among the social problems that trouble Nigerians. The study observed that increases in unemployment and inflation are the main factors that cause poverty in Nigeria. The more people are unemployed, the more they are tempted to commit crimes to satisfy their needs.

The affirmation of Odumodu's (1999) research though established on the descriptive behavior of the data on unemployment, inflation, poverty and crime rates, the study missed the accuracy of empirical estimation that is expected in establishing functional relationships between and among variables.

Akpotu and Jike (2004) used primary data obtained from prison inmates in five federal prisons located in Delta State, Nigeria by issuing questionnaire. The results show that there exist a strong relationship between low education level and high rate of crimes. However, the drawback of the study is the distribution of the prisons considered. They were all located in only one part of Nigeria. Egunjobi (2007) study has overcome the identified loop holes in Odumosu (1999) and Akpotu and Jike (2004) studies. Eunjobi (2007) sought to find determination and causation between unemployment and crime in Nigeria for the period 1981-1998. The method of analysis was the Error Correction Model (ECM) and the Granger causality. The findings revealed that there is a positive long run equilibrium link between unemployment and crime. Moreover, unemployment unidirectionaly causes crime in Nigeria.

This analysis on Nigeria differs from Egunjobi (2007) in many ways. Initially, it uses the error-correction based causality which always allows for the addition of the Granger lagged error-correction term derived from the cointegration equation as opposed to the traditional Granger causality method. By including, the lagged error-correction term, the long run information lost through differencing is reduced in a statistically acceptable way (Odhiambo, 2007)

Subsequently, the current analysis, in addition to unemployment as in Egunjobi (2007), includes other socio-economic factors namely inflation, population, literacy and income as determinants of crime in Nigeria.

In the study based on Nigeria, the cointegration and error-correction model is used to study the relationships between crime and its socio-economic associates. No standard economic theory exists to explain the relationship between crime and its socio-economic correlates. Though, for the specific purpose of the study, inflation, income, literacy level, unemployment and population level were assumed to explain the crime rate in Nigeria. The variables entering the model were given as follows:


CRt = Crime Rate

Icpi = Inflation

Igdp = Income

Litsec = literacy rate

Unemp = Unemployment Rate

Lpop = Log of Population

Note thet income was proxied by Gross Domestic Price (GDP). Literacy rate was measured by Secondary school enrolment and unemployment is national unemployment. Crime is proxied by expenditure on internal security which involves the Police. To be able to test for linear Granger causality, for example between crime and discomfort (inflation + unemployment), the null hypothesis that crime (CRt) does not cause discomfort (DCFt) and vice versa were tested by running the following equation 2 and equation 3 :


CRt = Crime Rate

DCFt = Discomfort Index

Π1, Π2 = White Noise Error Process

m, n = Number of lagged variables

A simple F-test was carried out to check whether equation 1 and 2 are jointly significant. Moreover, to overcome the methodological deficiencies such as spurious results and elimination of long run information of the Granger causality, the error-correction causality test was used. The johansen cointegration test was further applied to test for the existence of cointegration among the variables.

To conclude, the dynamics of socio-economic determinants of crime in Nigeria were population level, literacy, unemployment, inflation and income. The findings indicate that unemployment was the most significant determinant of criminality. Also, cointegration guarantees the presence of Granger causality in at least one direction between the series.

2.4.3 Case for Greece

Over the last 30 years criminality in Greece is on the rise, especially in thefts, violent attacks and the so-called economic crimes, in which digital, electronic and tax scams are included. According to official data tables, during the last decades there is a continuous rise of illegal acts to developed countries and countries of the Western World, including Greece.

The model is built on Becker's (1968) model but some amendments have been made in the study. For example, the justice system function was more preferred than the total force of the authorities. Furthermore, the variable that refers to migration has been inserted into the model. The study used time-series data and the model used in the economic analysis of crime rates is as follows:

CRt = f( UNt,RCt,CONVt ,MIGt) (1)


CR is the number of total offences per 100.000 people known by the Greek police and refers to Greek Penal Code and Special Penal Law infringements

UN is the number of unemployed per 100.000 people who belong to the work force

RC shows the real compensation per employee adjusted with GDP deflator and 2000 is used as a base year (2000=100)

CONV is the total number of people who have been convicted by the Greek Courts of Law per 100.000 people, and

MIG is the net migration per 100.000 people, consisting of the algebraic sum of immigrations and emigrations.

Note that the variables represent annual time series, covering the time period 1971- 2006. In the current research, the unit root was tested by the Augmented Dickey-Fuller (1979) and Phillips- Perron (1988) tests and the findings according to the calculated ADF and PP statistics depict that all the explanatory variables are integrated of order one Ι (1).

Given that it has been found that crime and the other socioeconomic variables were integrated I (1), the cointegration techniques of Johansen (1988) and Johansen-Juselius (1990,1992) were carried out. Furthermore, they provided a combined structure for the estimation of the cointegration relations within the Vector Error Correction Model (VECM).

Before the application of the Johansen technique, a sufficient lag length is required for the VAR model estimation, so a procedure based on Likelihood Ratio tests (Sims, 1980) was first applied. The findings depicted that a lag p=2 was the best condition. So, the order of the model is VAR (2). The results of the cointegration tests proved the presence of one cointegrating relation that describes the long run crime rates in Greece. The numbers in brackets are the t-statistics.

LCR = 4.003 + 0.774LRC + 0.094LUN - 0.055LCONV + 0.026LMIG

[4.999] [-5.771] [3.983] [1.064] [0.796]

where the coefficients estimated in the above relation show a significant elasticity of crime rates in unemployment rates and real compensations, an insignificant elasticity in net migration flows, and an insignificant inelasticity in conviction rates.

After determining the cointegrating vector among the model variables, the residuals was used as an error correction term in the VEC model, whichresulted from the long-run equilibrium relationship and is expressed as:

Δ LCRt = lagged (Δ LCRt , Δ LRCt, Δ LUNt, Δ LCONVt, Δ LMIGt ) + λ ut-1 + Vt (2)

Where Δ denotes the first differences of the variables

Ut-1 are the estimated residuals of the cointegrating regression (long-run relationship) and represents the deviation from the equilibrium state, during a time period t

0< λ<-1 is the short-run convergence coefficient, which represents the dependent variable's reaction from the equilibrium state in the beginning of each time period t

Vt is the 5X1 vector of white noise errors.

The results indicate that the error correction term should be negative and statistically significant as well. In particular, a short-run rise in both the macroeconomic variables (unemployment and real compensations) can actually affect the decision to engage in illegitimate activities, while a rise in sentenced persons and in migrant flows has a small affluence in crime rates, due to their insignificant coefficients. Finally, the error correction term coefficient and its t-ratio present a normal convergence to the long run equilibrium state.