INTRODUCTION
Crime is an offence against the values system of a society. The cost and effects of crime vary among the various segments of the population and touch almost everyone by some degree and in general as the economic growth and development of countries increase, it would be expected that crime level reduces. This may not necessarily be. The socioeconomic effects of crime have been well articulated in the literature (Odumosu, 1999; ESEC, 2008a, b; Akpotu and Jike, 2004; Egunjobi, 2007). The various costs of crime to victims and society or the economy include; loss of income, property losses, loss in community productivity, etc. Some other costs of crime, which are less tangible include psychological trauma on victims and their family and friends, pain and suffering and a lower quality of life. In all of these and other associated losses, the ultimate cost is loss of life.
In the philosophy of the social sciences, there exists no clearcut theory of crime in respect to human behaviour that is uniquely exemplified. However, an implicit reference that links society’s disfunctionality to criminal actions is the anomie theory. Anomie is knightly associated with the researches of Emile (1893, 1897) and Merton (1938). According to Emile (1897), anomie is a morally deregulated condition. A breakdown in either the rules of society or the amoral norms. As such, when there are no clear rules to guide members of the society, individuals find it difficult to adjust to the changing conditions of life. This in turn, leads to frustration, conflict, dissatisfaction and deviance (Odumosu, 1999). Though Merton (1938) anomie theory did not focus on criminality, it emphasizes the fact that the existence of inequality, due to the way society is structured, may make it anomic. Evidence exists about the several segments of society that are severely restricted from legitimate avenues to success. Thus, in a society where much emphasis is placed on achievements, especially wealth, without recourse to the sources and legitimacy; individuals, who are caught in anomic conditions will be faced with the strain of inability to reconcile their aspirations with their disadvantaged situations. In this wise, legitimate means do not necessarily become the most efficient way of gaining success. Other means, though deemphasised by society as perhaps illegitimate, become available and more efficient. By this, the Nigerian society may be adequately placed as being in a state of perpetual anomie (Odumosu, 1999).
One lesson that can be learnt from the theory of anomie, is that deprived persons may be led to take illegitimate actions (crime for instance), because of their relative deprivation and acute sense of want or greed. In this sense, the social environment surpasses the physical environment in the determination of crime. In a society like Nigeria where success in life is measured by a person’s wealth; corrupt practices and criminality would hold sway.
Some studies exist on crime and its attendant effects or costs and determinants.
Egunjobi (2007) on Nigeria, Odumosu (1999) and Akpotu and Jike (2004) also on
Nigeria. Andres (2002) on Span, Pyle and Deadman (1994) on Britain and Fougere
et al. (2006) on France. On the Nigerian scenario, Odumosu (1999) study
stressed the seriousness of poverty among the social problems that afflict Nigerians.
The study noted that poverty in Nigeria is mostly produced by increases in unemployment
and inflation and that the longer people remain unemployed, the more they are
tempted to commit crimes to satisfy their needs. The assertions of Odumodu’s
(1999) research though based on the descriptive behaviour of the data on unemployment,
inflation, poverty and crime rates, the study lacked the rigour of empirical
estimation that is expected in establishing functional relationships between
and among variables. Akpotu and Jike (2004) used primary data drawn from prison
inmates in 5 federal prisons located in Delta State, Nigeria via an administration
of questionnaire. The findings of the study support the view that there exists
a strong link between low levels of education and high crime rates and that
crime control is more expensive in monetary terms than education. One limitation
of the study is the spread of the prisons studied. The prisons were all located
in only one of the 36 states of Nigeria. In addition, the use of simple percentage
in empirical studies neither determines effects of variables in relationship,
nor establishes causation.
One study that may have overcome the identified gaps in Odumosu (1999) and Akpotu and Jike (2004) studies of Nigeria is Egunjobi (2007). Egunjobi (2007) study sought to establish determination and causation between unemployment and crime in Nigeria for the period 19811998. The method of analysis was the errorcorrection mechanism and the conventional Granger causality. The results of the study revealed that a positive longrun equilibrium relationship exists between unemployment and crime series. In addition, unemployment unidirectionaly Granger (1988) causes crime in Nigeria.
This study on Nigeria differs from Egunjobi (2007) in several ways. Firstly, it uses the errorcorrection based causality, which allows for the inclusion of the Granger lagged errorcorrection term derived from the cointegration equation as opposed to the conventional Granger causality method. By including, the lagged errorcorrection term, the longrun information lost through differencing is reduced in a statistically acceptable way (Odhiambo, 2007). In this wise, the application of the conventional Granger method (Egunjobi, 2007) on variables that are cointegrated, which by extension also implied incorporating differenced variable tantamount to missspecification unless the lagged errorcorrection is included (Granger, 1988).
Secondly, the present study, in addition to unemployment as in Egunjobi (2007), includes other socioeconomic factors such as inflation, population, literacy and income as determinants of crime in Nigeria. Thirdly, this study period is 19802005.
OVERVIEW OF PEACE INDICATORS
AND CRIME TREND IN NIGERIA
The Vision of Humanity (2008) is a collaborative enterprise, which brings together a group of initiatives that enjoys the support of philanthropists, business people, religious leaders and intellectuals. Since its establishment, the Vision of Humanity (2008) has also been involved in the measurement of global peace among countries.
Table 1 provides the Global Peace Index (GPI) rankings of Nigeria among 140 countries analysed in 2008 and the 121 countries analysed in 2007. Nigeria ranked 129 in 2008 and 117 in 2007 with a GPI score of 2.898 and 2.724 in 2007 and 2008, respectively. The peace index scores measured on a scale of 15, where rank 1 is most peaceful; implies that Nigeria is a fairly peaceful country and has enjoyed a marginal improvement in its peace efforts since 2007. More worrisome in the detailed information by the Vision of Humanity (2008) report is the qualitative assessment of the level of violent crime. On a ranking scale of 15 (very lowvery high), Nigeria’s rank using a level of violent crime is 5. Despite this predicament, Nigeria military deployments score to the United Nations’ peacekeeping missions worldwide (20062007) of 4.717 is very high on a scale of 15. The implication is that Nigeria is not only a peace loving country; Nigeria is equally a peace maker. In Nigeria, the main causes of death due to public violence are, in order of importance; accidents, crime, economic issues, political clashes and ethnoreligious fighting (Marc Antonine Perouse de Montclos, 2007). Crime, according to the data, accounted for the highest absolute number of deaths when compared with other 13 causes (Fig. 1).
At present, domestic and international data on crime in Nigeria is mute and where it exists, it is inadequate. Locally obtained information however, shows that acquisitive crime (including armed robberies, thefts/ stealing, burglaries and house/store breaking) and the offences of violence (including murders, assault and rape) constitute an average of 73.05% of all crimes reported to the police between 1994 and 1997. Acquisitive crimes during this period, which averaged 39.75% were higher than offences of violence with an average of 33.29% (UNODC, 2007). While between 2000 and 2005, an average of 171.901 cases of crime were recorded as against 93.981 cases reported between 1992 and 1999.
MODEL SPECIFICATION AND
ESTIMATION TECHNIQUE
In this study, the cointegration and errorcorrection model is used to examine the relationships between crime and its socioeconomic correlates. As opposed to the conventional Granger causality method, the errorcorrection based causality test is used to examine the direction of causality between crime and discomfort (unemployment plus inflation).
No standard economic theory exists in the specification of the relationship between crime and its socioeconomic correlates. However, for the particular aim of this study, it could be reasonably assumed that inflation, income, literacy level, unemployment and population level explain the crime rate in Nigeria. The variables entering the model can be specified as follows:
where:
CR_{t} 
= 
Crime Rate (CR_{t}) 
Icpi 
= 
Inflation 
Igdp 
= 
Income 
litsec 
= 
Literacy rate 
unemp 
= 
Unemployment rate 
lpop 
= 
Population 

Data sources: The data used in the study is obtained from publications of the Central Bank of Nigeria (2006) (various issues of its Statistical Bulletin) and the National Bureau of Statistics (Annual Abstract of Statistics, 2006).
Measurement of variables: All variables are in natural logarithm. The sample period (19812005) provided continuous time series data of the variables considered in the modelling process. Inflation is obtained from the country level Consumer Price Index (CPI 2000 = 100). Income is proxied by real Gross Domestic Price (GDP). Literacy level was captured by secondary school enrolment as supported by the literature. Unemployment variable is national employment rate; while population is Nigeria’s total population. Crime is proxied by expenditure on internal security which involves the Police. Although, some crime data exists, comparative analysis of crime rate statistics around the world remains complicated. Different definitions of what constitutes a crime make official crime statistics undependable (ESEC, 2008a). Consequent upon this, expenditure on internal security (including the Police) was used as a proxy for crime rather than the crime rate.
The conventional Granger causality relates to the lagged values of a variable, say Y, having an explanatory power on another variable X. In this wise, if Y Granger causes X, the prediction error of current X declines when lagged values of Y are used (Ciarreta and Zarraga, 2007). In order to test for linear Granger causality, for example, between crime and discomfort (inflation + unemployment), the estimation involves testing the null hypothesis that Crime (CR_{t}) does not cause discomfort (DCF_{t}) and vice versa, by simply running the following Eq. 2 and 3 regressions:
where:
CR_{t} 
= 
Crime Rate 
DCF_{t} 
= 
Discomfort index 
π_{1}, π_{2} 
= 
White noise error process 
m, n 
= 
Number of lagged variables 

The tests of causality can be conducted by testing whether some parameters of the lagged polynomials in Eq. 1 and 2 are jointly significant, for which a simple F test can be applied. This conventional approach suffers from 2 basic methodological flaws. First, such traditional tests do not examine the basic time series properties of the variables. Thus, if the series are nonstationary and are used in the tests, the results will be spurious (Sims et al., 1990; Toda and Phillips, 1993; Granger, 1988). Secondly, the conventional Granger causality tests inherently turn the series stationary mechanically, by differencing the variables. This, consequently, eliminates the longrun information in the original form of the series (Odhiambo, 2007).
To overcome the methodological deficiencies of the conventional Granger causality
as stated above, one alternative approach will be to apply an errorcorrection
based causality test that allows for the inclusion of the lagged errorcorrection
term derived from cointegration Eq. 2. In this sense, the
longrun information that would have been eliminated through differencing is
reintroduced into the estimated causality equations. In this current study,
the error correction model used is based on the following:
where:
Δ 
= 
The difference operator 
CR_{t} 
= 
Crime Rate 
DCF_{t} 
= 
Discomfort index 
ECM_{t1} 
= 
One period lagged Error Correction term obtained by the cointegration
equation 

From Eq. 4 and 5, the causal inference
is obtained through the significance of a_{3} and b_{3}. If
a_{3} is significantly different from 0, the null hypothesis that DCF_{t}
does not Grangercause CR_{t} is rejected. Conversely, the null hypothesis
that Cr_{t} does not Granger cause DCF_{t} is rejected if b_{3}
is significantly different from 0^{2}.
RESULTS AND DISCUSSION
Stationarity tests: In line with some other time series data, the variables for this study were tested for stationarity and cointegration before running the error correction model and causality tests. The Augmented DickeyFuller (ADF) test which is a parametric approach is applied in the test for unit roots. The results of the stationarity tests (Table 2) at levels show that all the variables are nonstationary at level. Due to their nonstationarity, the variables were differenced once and the tests were reperformed. The results of the stationarity tests on the differenced variables are also presented in Table 3.
The ADF tests applied to the first difference of the data series rejects the null hypothesis of nonstationarity for all the variables. It thus can be concluded that all the variables used in this study are integrated of order 1.
Cointegration analysis: Having confirmed that all the variables used
in the study are integrated of order one, the next step was to test for the
existence of cointegrated relationship among the variables (Litsec, Lgdp, Icpi,
Ipop, Isec and Iunemp). For this aim, the study applied the Johansen cointegration
test. If the variables are cointegrated, then there exists Granger causality
between the series in at least one direction.
Table 2: 
Stationarity tests of the variables 

Critical values: 1% = 3.6394; 5% = 2.9511; 10% = 2.6143 

The results of the JohansenJuselius cointegration tests are presented in
Table 3. The cointegration results indicate the existence
of a stable longrun relationship among the variables.
Empirical results: Confirming the existence of cointegration relationships among the variables provides evidence to proceed with the estimation of first, an ErrorCorrection Model by including error correction term (ECM_{t1}) variable lagged once in order to obtain a parsimonious long and short run results and secondly, an errorcorrection model which also includes a one lagged errorcorrection terms of the bivariate causality model. The parsimonious errorcorrection model of the shortand longrun relationship and that of the causality test based on errorcorrection model are reported in Table 4 and 5, respectively.
Dynamic error correction model: The cointegration results indicate only 1 cointegration equation at the 0.05 level, this is suggestive of at least one direction of causality between the variables. Cointegration of variables is inadequate for addressing whether insecurity or the rate of crime is responsive to inflation, income, literacy, unemployment and population. Therefore, we need to obtain a parsimonious estimate of the regressions to determine the influence of each category individually on crime.
From the parsimonious results, unemployment is properly signed and statistically
different from zero. This is according to the tstatistic for the coefficient
and the probability value. The results indicated further that crime is not significantly
responsive to population, literacy, inflation and income given that these variants
are statistically insignificant at 5% level. However, the speed of adjustment
is high as indicated by the errorcorrection term (0.64). It is symptomatic
that the determinants would adjust rapidly to handling crime. It can be stated
with some caution as implied by the signing of most of the determinants that
firstly, high unemployment rate induces crime in the long run but not in the
shortrun. Secondly, the relative large population of the Nigerian state is
not responsible for the crime rate. Thirdly, low literacy rate though weakly
significant, impels increasing crime rate. Fourthly, crime in Nigeria may not
be adduced to cost of living; but rather to a social disconnect as wealth is
not necessarily a function of hard work, honesty and legitimacy.
Table 3: 
JohansenJuselius cointegration tests series: Lcr Igdp Icpi
Ipop Isec Iuemp 

Trace statistic and the MaxEigen value indicate 1 cointegrating
equation at the 0.05 level; **MacKinnonHaugMichelis, pvalues 

Table 4: 
Parsimonious errorcorrection results: dependent variable
= Δlcr 

R^{2} = 0.642; DurbinWatson = 2.081; Fstatistic
= 3.364; Prob (Fstatistic) = 0.021 

Table 5: 
Causality test between dlitsee and dcomf 

Values in parentheses are the tstatistics 

This is further corroborated by the insignificant and wrong signing of the
income variable.
Causality test between Δlcr and Δdcf: As reported in Table 5, the error correction term in the Δlcr equation rejects the causality running from Δdcf to Δlcr. The errorcorrection term, though negative, it is statistically insignificant. However, the causality from Δlcr to Δdcf is accepted in the Δdcf equation. It can be concluded therefore, that for Nigeria, there is unidirectional causality running from Δlcr to Δdcf. This is partially in contrast with Egunjobi (2007) findings.
Although, the literature is not definite on the direction of causality between security and discomfort (inflation and unemployment), the unidirectional causality reported by this result has some policy implications for Nigeria. First, there is the implication that insecurity causes unemployment cum inflation. This may not be incontrovertible as some multinational corporations have left the shores of Nigeria basically because of insecurity.
Secondly, sequel to the first implication that insecurity creates enabling conditions for discomfort, threat to life and property reduces productivity and increases the cost of safety and production. This may further fuel inflation and overall discomfort as macroeconomic instability damages supplyside of the economy.
CONCLUSION
In this study, we estimated the dynamics of socioeconomic determinants of crime in Nigeria using population level, literacy, unemployment, inflation and income. The direction of longrun causality between security and discomfort was equally examined. Discomfort was proxied by the sum of unemployment and inflation; while security or crime was measured as expenditure on internal security including the Police. To achieve the set objectives of the study, a dynamic errorcorrection model was estimated in addition with an errorcorrection based Granger causality test.
The results in its parsimony indicate that unemployment in the longrun seems to be the most significant determinant of crime or insecurity. The errorcorrection Granger causality estimation found evidence of insecurity unidirectionally Granger causing discomfort. This is partially in contrast with some existing literature that used other models, while tending to establish causality between cointegrated variables of crime and unemployment.
• 
Cointegration guarantees the existence of Granger causality
between the series in at least one direction. However, if series are integrated
but not stationary, causality test may be implemented by estimating a Vector
Auto Regression (VAR) for the differenced series to achieve stationarity. 

• 
It is thus natural that we test the unit roots of the series,
and if they are integrated of the same order, then test for cointegration. 
