Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
Dynamic Effects of Unemployment and Crime Shocks on Internal
Security Budgets in Developing Economies: Evidence from
Nigeria
R. K. Ayeni
In view of the unfolding reality, coupled with the protracted debate on the issue of crime and
unemployment, this paper, which draws heavily from theoretical foundations, examines the
combined effect of unemployment and various categories of criminal activities classified as serious
crimes, minor crimes and minor offence on expenditures to maintain internal security in Nigeria. The
following research questions were specifically considered: What are the magnitudes of impacts of
unemployment and crime on internal security? Are the effects immediate or delayed? Do they
emerge slowly and then sustained? Are there initial effects that go away after a few periods or
distributed?
The study used data on unemployment rate, crime rates and expenditures on maintaining internal
security for the period 1970-2013. ARDL Bound Testing Cointegration method was used to
determine the long-run equilibrium relationship among the variables. The short-run dynamic
relationship and shock transmission among the variables were determined using the variance
decomposition and impulse distribution functions of the VAR. The findings of the tests provide
evidence of the existence of long-run equilibrium relationship between internal security and the set
of independent variables. There also short-run dynamic relationship inform of shock transmission
among internal security, unemployment and total crime cum subcategories of crime in Nigeria.
Unemployment rate and crime rate do cause unproductive expenditures on internal security thereby
hindering the growth of the economy. The paper suggested amongst others that the policy makers
should address adequately the problem of unemployment by creating and expanding job facilities,
equipping and fortifying crime preventing agencies and the direction of the resources of the country
to sector like agriculture where majority of the poor exist.
Keywords: Total Crime Rate, Unemployment Rate, Internal Security, ARDL, Impulse
Response
Field of Research: Economics
Introduction
The relationship between unemployment and crime seems obvious to most people. The
problem of unemployment, criminal and violent behavior has posed a great challenge to both
developed and developing countries. In spite of the fact that crime has resulted not only to
loss of property, lives, misery and also severe mental anguish, they also discouraged
entrepreneurial activities that causes growth. Both crime and unemployment has remained an
issue that continues to receive attention in developing countries, especially those in Africa
where high-level unemployment with stagnated growth. High levels of unemployment can
__________________________________________________________________________
AYENI, R. K. (PhD), Department of Economics, Ekiti State University, Ado Ekiti, E mail: raphayeni@gmail.com,
raphkolayeni@yahoo.com
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
dramatically increase crime. However, high rate of crime can undermine the security of
property rights and confidence in the rule of law.
Egunjobi (2006) quoting Becker’s economic theory of crime, opined that “unemployed people are
deprived of legal income and thus they tend to derive some income from illegal activities. Crime
includes not only some forms of corruption but also a wide range of other activities such as murder,
manslaughter, felony, armed robbery, extortion, burglary, larceny, rape, arson, perjury, drug
possession, gambling, tax evasion, breaching of peace and so on.
Daniel (2003) in Egunjobi (2006) finds that if unemployment rate goes up, the legitimate earning
opportunities decline and crime tends to increase, because the costs of crime go down for the
unemployed. The grave consequences of crime are no longer news. Intuitively, unemployment and
crime should have a negative impact on economic growth. This is because some models of crime
suggest that the unemployed and individuals with low wages face a strong urge to commit crime
(Egunjobi, 2006).
In view of the unfolding reality, coupled with the protracted debate on the issue of crime and
unemployment, this paper, which draws heavily from theoretical foundations, examines the
relationship between unemployment and various categories of criminal activities classified as serious
crimes, minor crimes and minor offence. This in a bid to ascertain the implications of the linkages for
the design of credible policy measures for economic growth and development with the particular
reference to Nigeria.
The main objective of this study is to analyze the dynamic effects of temporary and permanent shocks
to crime and unemployment on internal security. The design of the study is a causal design of the time
series model. That means the study considers not only how much effect unemployment and crime
have on internal security, but when they have the affects. This called for the dynamic analysis of the
short-run and long-run impacts of unemployment and crime rates on internal security. The following
research questions were specifically raised:
i.
What are the magnitudes of impacts of unemployment and crime on internal security?
ii.
Are the effects immediate or delayed?
iii.
Do they emerge slowly and then sustained?
iv.
Are there initial effects that go away after a few periods or distributed?
The rest of the paper is organized as follows: Following this introduction is section 2, which dwells on
the theoretical foundation and conceptual issues. Section 3 describes the descriptive statistics, while
section 4 explains the empirical results. Section 5 draws the summary and conclusions and gives the
recommendations.
Theoretical and Conceptual Issues
The theoretical foundations on the relationship among crime, unemployment and internal security
vary and are inconsistent. Some studies argue that there exists a positive relationship between crime
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
and unemployment while some studies found negative relationship, still, some revealed mixed
results, or insignificant relationship. Disparity in the possible explanations for cross country
differences may be adduced to the fact that definition of crime is whatever the state seems a crime to
be. As a result, what is considered a crime in one country is not necessarily considered a crime in
other cross-country data, time series, and panel, aggregate and regional empirical studies.
As for the relationship between unemployment and crime, many of the results are mixed. For
example, Papps and Winkelmann (1999) revealed some evidence of significant effects of
unemployment on crime both for total crime and for some subcategories of crime in their analysis
that covered sixteen region over the period 1984 to 1996 in New Zealand.
In a related study, Fajnzylber, et al (2002), examined a model of criminal gangs and suggest that there
is a substitution effect between property crime and violent crime. They further explained that
unemployment increases the relative attractiveness of large and less violent gangs engaging more in
property crime.
Also, Agell and Nilsson (2003), and Papps and Winkelmann (1999) are examples of studies which
found strong positive relationship between unemployment and crime, while Coomer, (2003)
reiterated that there is ambiguity in the empirical studies of crime economics regarding various
income variables used to proxy the expected net gains from crime and as a result empirical findings
are often mixed or contradictory to one another
In addition, Loto (2011) opined that the impact of national security on economic growth in Nigeria is
positive but not statistically significant. This implies that expenditure on national security does not
contribute meaningfully to economic growth in Nigeria. Also, Chiricos (1987) finds that unemployment
has a statistically significant positive effect on property crime in 40 per cent of the studies, while the
effect on violence is only statistically significant positive in 22 per cent of the study.
Furthermore, Thonberry and Christenson (1984) revealed that unemployment has significant
instantaneous effects on crime, while crime has significant but lagged effects on unemployment. Also,
Land et al (1995) found a lagged positive relationship between unemployment and crime in post-war
United States.
Gottfredson and Hirschi (1990) however, argued that the relationship between unemployment and
crime is very insignificant, while Pyle and Deadman (1994) thought that unemployment may be less
important to crime than other indicators of economic activity.
In a related study, Weatherbum et al (2001) found no relationship between unemployment and crime
in a study of the effect of an Australian recession on ‘break, enter and steal and motor vehicle theft’.
While there are significant number of studies linking unemployment to crime such as Allen (1996),
Gottfredson and Hirschi (1990) Field (1999), Chamlin and Cochram (2000), and so on, this paper would
attempt to trace crime to unemployment rate and the cumulative effects of the two on internal
security in Nigeria.
Data and Descriptive Analysis
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
The data for the aggregated crime and disaggregated subcategories of crime, for the corresponding
period (1970- 2013) was obtained from annual statistical publications of the National Bureau of
Statistics (NBS) and Central Bank of Nigeria (CBN). NBS classified total crimes (TCRM) into three:
Serious crime (SRCRM), minor crime (MNCRM) and minor offences (MNOFF). INTSEC is internal
security proxy by the budget expenditure on police affairs; Unemployment rate (UNEMPL) was
obtained from Statistical Bulletin of the Central Bank of Nigeria.
All crime variables are measured in number of cases, unemployment is measured in rate while
internal security is measured in naira. To standardize the variables for further analysis, all data were
transformed into natural logarithm.
The statistics of the main variables (Appendix I), Total crimes (comprises of serious crimes, minor
crimes and minor offences) averaged 245818.8 per cent, varying from 124752.0 per cent to 816603.2
per cent, with a standard deviation of 131851.1 per cent. The statistics further revealed that the level
of unemployment averaged 5.9769 per cent and varies between 1.8 per cent and 18.1 per cent, with a
standard deviation of 3.5985 per cent. Internal security also averaged 12571. The trend in the main
variable is shown in Figure 1.
FIGURE 1: TREND IN UEMP, INTSEC & TCRM
A
UEMP
20
18
16
14
12
10
8
6
4
2
0
UEMP
Linear (UEMP)
1 3 5 7 9 1113151719212325272931333537394143
A look at the trend lines in Figure 1 A, B and C indicates that the three variables follow the same
positive trend. Unemployment and internal security show upward trend; that is over time the high
rate of unemployment had increased expenditure on internal security. On the other hand the
downward trend in total crime rate suggests that total crime rate increases but at a diminishing rate
due to internal security measures. However this descriptive analysis is not conclusive.
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
B
INTSEC
70000
60000
50000
40000
30000
INTSEC
20000
Linear (INTSEC)
10000
0
-10000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43
-20000
C
TCRM
900000
800000
700000
600000
500000
TCRM
400000
Linear (TCRM)
300000
200000
100000
0
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43
Model Specification and Estimation
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
The study employed two estimation techniques ARDL and Variance Distribution Function of the VAR.
The first step in our empirical ARDL analysis is to determine the direction of causality among the
variables. This is done by conducting Granger Causality test on the variables. The result of this test
also determines the level of exogeneity of the variables thereby helping in the model specification.
A. Pairwise Granger Causality Test
The result of the pair wise Granger causality test is shown in Appendix II. The test is sensitive to the
lag length, the choice of lag was 2, this was made using the iterative method, that is gradually
increasing the lag length until there is no further improvement in the decision making (Gujarati, 2007).
The idea behind causality test is that, although regression analysis deals with the dependence of one
variable on the other variable, it does not necessarily imply causation. In other words the existence of
a relationship either the dynamic or equilibrium, between variables does not prove causality or the
direction of influence. The decision whether to reject or accept the null hypothesis is made based on
the probability of the F – statistics. Wherever the null hypothesis is rejected it implies the acceptance
of the alternative hypothesis. At 0.05 level of significance the following inferences are drawn from
the causality result.
(i)
There is a unidirectional causality running from unemployment rate (UNEMPL) to internal
security (INTSEC).
(ii)
There is a unidirectional causality running from minor crime rate (MNCRM) to internal
security (INTSEC).
The implication of this result is that unemployment and minor crime rate has weakened the strength
of internal security in Nigeria making the government to spend more on internal security. Also internal
security variable is identified as a strong exogenous variable.
The model for this study is specified as follows:
INTSECt   0  1UNEMPt   2 MNCRM t   3 MNOFFt   4 SRCRM t   5TCRM t  ut .....1
To test for stationarity of the variables in equation 1 the Augmented Dickey Fuller (ADF) and Philip
Peron (PP) unit root tests were conducted on each series. Accordingly the null hypothesis in each case
is that there is a unit root in each series that is each variable is non-stationary. The result is presented
in Table 1
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Proceedings of 30th International Business Research Conference
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B. Time Series Properties of Variables
Table 1: Unit Root Test
ADF STATISTIC
Level
Prob
1ST Diff.
Prob.
Integration
UEMPL
-2.76959
0.0725
-6.8033
0.0000
I(1)
INTSEC
1.205973
0.9976
-6.3133
0.0000
I(1)
MNCRM
-2.29369
0.1792
-6.18991
0.0000
I(1)
MNOFF
-2.20368
0.2085
-4.27441
0.0018
I(1)
SRCRM
-3.44833
0.0154
I(0)
TCRM
-3.22139
0.0266
I(0)
PHILIP PERON STATISTIC
Level
Prob
1ST Diff.
Prob.
Integration
UEMPL
-2.86677
0.059
-6.88413
0.0000
I(1)
INTSEC
1.520623
0.9991
-6.34168
0.0000
I(1)
MNCRM
-2.3448
0.164
-7.3444
0.0000
I(1)
MNOFF
-2.01144
0.2809
-4.27441
0.0018
I(1)
SRCRM
-3.49269
0.0138
-9.8949
0.0000
I(0)
TCRM
-3.22139
0.0266
-10.6039
0.0000
I(0)
The results of the two tests in Table 1 show that the variables are not integrated of the same order.
Four of the variables are integrated of order one while two are stationary at their levels. This implies
that the condition for cointegration using the Johannsen method is not met by the series. We resort
to the Bound Testing method of Cointegration using the ARDL approach.
The result of the Granger causality test suggests that INTSEC is more of an endogenous variable.
Based on this the equation 1 is re-specified for the ARDL analysis as follows:
 int sec t   0    j  int sec t 1    1 unempt  j    2 mncrmt  j    3 mnoff t  j  .......
......    4 srcrmt  j    5 tcrmt  j  0 int sec t 1  1unempt 1   2 mncrmt 1  ......
.......   3 mnoff t 1   4 srcrmt 1   5 tcrmt 1  et .........2
Equation 2 is the ARDL model, which is also known as the “unrestricted ECM”. The appropriate lag
length is determined using one or more of Akaike (AIC), Schwarts (SC) and Hannan-quin (HQ) criteria.
A key assumption in the ARDL/Bound Testing methodology of Peseran et al. (1999) is that the errors
of the equation must be serially independent. We use LM to test this hypothesis. The Bound test is
conducted on the unrestricted ECM to test by conducting the F statistics of the hypothesis,
H 0 :  0  1   2  0 against the alternative. As a check we perform a “Bounds” t-test” of H 0  0 , if
the t-statistic for int sec t 1 in equation 2 is greater than the “I(1) bounds” tabulated by Pesaran et al.,
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
this would support the conclusion that there is a long-run relationship between the variables. Pesaran
et al. supplied bounds on the critical values for the assymptotic of the F – statistics. If the computed F
– statistic falls below the lower bound we would conclude that the variables are I(0), so no
cointegration is possible, by definition. If the F- statistics exceeds the upper bound, we conclude that
we have cointegration. Finally if the F – statistics falls between the bounds, the test is inconclusive, we
may rely on the result of Granger causality and/or the short-run analysis.
C. Results of the ARDL Model
Equation 2 was estimated using a 1-equation Unrestricted VAR method. To implement the
information criteria for selecting the lag-length in a time-effect way the lag structure was estimated.
The result is presented Table 4.
Looking at the LR, FPE, SC and HQ values, a maximum of 1 lag was suggested for intsect-1, while the
AIC suggests 2 lags. But the LM test of serial correlation conducted showed the presence of serial
correlation at one lag. At maximum of two lags the LM test rejects the hypothesis of serial correlation
as shown in Table 2.
Table 2 :VAR Lag Order Selection Criteria
Endogenous variables: INTSEC
Exogenous variables: C UEMP MNCRM MNOFF SRCM SER01
Lag LogL
LR
0
-389.2886 NA
1
FPE
AIC
SC
HQ
3.79E+08
22.58792
22.85455
22.67996
-348.7793 64.81490* 39738561*
20.33024 20.64131*
2
-347.7355 1.610376
39748679 20.32774*
3
-346.9899 1.107744
40475664
20.34228
* indicates lag order selected by the criterion
20.68325
20.74223
20.43763*
20.45047
20.48034
Table 3: VAR Residual Serial Correlation LM Tests
Lags
LM-Stat
Prob
1 3.618723
0.0571
2 1.748629
0.0186
3 1.487509
0.2226
Therefore ARDL(2,1,1)was preferred. The result is presented in Table 4.
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Proceedings of 30th International Business Research Conference
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Table 4 : Unrestricted ECM, ARDL(2,1,1,1,1,1,)
Dependent Variable D(INTSEC)
Coefficient Std. Error
D(INTSEC(-1))
-0.03391
-0.15148
D(INTSEC(-2))
-0.162815
-0.15128
D(UEMP)
-984.5943*
-366.392
D(MNCRM)
0.011391
-0.08832
D(MNOFF)
-0.038842
-0.09929
D(SRCM)
-0.004005
-0.05573
D(TCRM)
0.004573
-0.05725
UEMP(-1)
749.0908**
-369.819
MNCRM(-1)
-0.075008
-0.0952
MNOFF(-1)
-0.039766
-0.08866
SRCM(-1)
0.035858
-0.08217
TCRM(-1)
-0.048897
-0.08421
INTSEC(-1)
t - Stat.
-0.22386
-1.07626
-2.68727
0.12897
-0.39120
-0.07187
0.07988
2.02556
-0.78789
-0.44853
0.43638
-0.19338*
-0.58064
-0.08469 -2.28338
9261.113
-4976.18 1.86109
C
R-squared
Adj. R-squared
F-statistic
Log likelihood
0.632172
0.448258
* significant at 5%; ** significant at 10%
3.437324
-401.624
The result in Table 4 portrays both the short-run and long-run relationship. Our interest is to test for
the presence of cointegration. To do this, the Bound test is conducted on the unrestricted ECM by
conducting the F statistics. The result of the Wald Test is presented in Table 5
Table 5 : Wald Bound Test for Cointegration
Test Statistic
Value
F-statistic
Df
(6,
3.945378 31)
Chi-square
23.67227
Peseran Critical Value
Probability
0.0048
6
I(0)
0.0006
I(1)
5%
2.32
3.50
1%
2.96
4.26
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Proceedings of 30th International Business Research Conference
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The value of our F-statistic is 3.9453, and we have (k+1) = 7 variables in our model. The critical lower
and upper bounds of the Pesaran et al. (2001) for the unrestricted intercept no trend is also presented
in Table 5. As the value of our F statistic of the restricted ECM exceeds the upper bound at 5%
significant level, we conclude that there is evidence of a cointegration relationship in our model. We
can therefore extract the long-run multiplier from the ARDL result. The long-run coefficient for the
respective variables is derived from the following equation:

  i
 0



Where  i represents the long run coefficient of ith variable in the ARDL model and  0 is the
coefficient of INTSEC(-1) in the ARDL model. The long-run multiplier for the individual coefficient is
shown as follows.
UEMP(-1)
INTSEC(-1)
1
3873.6725
MNCRM(-1)
MNOFF(-1)
-0.3878
-0.2056
SRCM(-1)
0.1854
TCRM(-1)
-0.2528
We therefore confirm a positive long-run multiplier effect of unemployment on internal security and
serious crime. An increase in unemployment rate leads to a more than proportional increase in
expenditure on internal security in the long - run. Also a unit increase in the rate of serious crime
leads to about 18% increase in expenditure on internal security in the long-run. The ARDL test also
established that there is a significant positive short-run and long-run relationship between UNEMP
(unemployment rate) and INTSEC (internal security). This is significant at 5% and 1% level of
significance respectively. This implies that high rate of unemployment tend to increase the
expenditure on internal security. The long-run coefficient of INTSEC is also significant implying a
feedback effect of poor security on the economy. The overall performance of the model is good. The
R2 is 0.63, signifying that even though all the marginal effect of individual variable are not significant,
about 63% of the changes in internal security can be explained by joint variations in unemployment
and crime variables in the model.
D. Short-run Shock Transmission Analysis
A major objective of this paper is to decompose the impact of crime rate and unemployment into
their disaggregated variables. The next analysis is the short-run shock transmission between the
variables. This analysis is done using the variance decomposition and impulse response which are
measures of short-run dynamics of the VAR. The shock transmission is discussed for the variables that
show short – run causality with the dependent variable. That is for the unidirectional causality the
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Proceedings of 30th International Business Research Conference
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magnitudes of the impact of the three variables; UNEMPL, MNCRM and INTSEC on one another are
discussed.
I.
Impulse Response Functions of UNEMPL, INTSEC and MNCRM
Table 6: Impulse Response Function
Response of UEMP:
Period
UEMP
INTSEC
MNCRM
MNOFF
SRCM
TCRM
1
3.237702
0
0
0
0
0
2
2.024135
0.899553
-0.03708
0.443592
0.321688
-0.44815
3
1.088328
0.559653
0.195773
0.255754
-0.16042
-0.71239
4
0.428719
0.366866
-0.08785
0.149965
-0.14499
-0.38397
5
-0.04984
0.267473
-0.14415
-0.06943
-0.31162
-0.22125
6
-0.10536
0.193164
-0.09027
-0.22715
-0.23296
-0.10559
7
-0.07527
0.255514
-0.10776
-0.34861
-0.18139
-0.06377
8
-0.00939
0.306131
-0.07539
-0.40934
-0.1238
-0.05763
9
0.068663
0.345028
-0.06312
-0.4237
-0.07502
-0.06998
10
0.116805
Response of
INTSEC:
0.371517
-0.07112
-0.40504
-0.04362
-0.08121
Period
UEMP
INTSEC
MNCRM
MNOFF
SRCM
TCRM
1
-1159.66
4112.937
0
0
0
0
2
1808.781
3243.931
707.8025
-1058.57
-966.335
-387.46
3
2245.199
4580.097
-1641.36
-1158.68
-714.44
-770.042
4
1693.912
5219.097
-2674.6
-1767.72
-1314.89
-1281.71
5
2044.497
5433.377
-3194.02
-2447.57
-1025.66
-1257.7
6
2389.658
5927.316
-3618.92
-3223.73
-843.354
-1235.94
7
2843.424
6255.267
-3578.37
-3859.04
-550.564
-1277.34
8
3293.077
6504.691
-3494.75
-4292.58
-256.642
-1362.69
9
3573.418
6633.412
-3386.11
-4506.8
-26.4721
-1434.3
10
3732.433
6597.972
-3244.02
-4538.32
149.8841
-1480.17
Response of MNCRM:
Period
UEMP
INTSEC
MNCRM
MNOFF
SRCM
TCRM
1
-622.085
-4111.71
16797.92
0
0
0
2
2371.531
-4754.01
13048.67
681.0509
-274.502
-717.515
3
-3952.53
-2877.21
7363.313
2256.16
-2751.46
-718.742
4
-6228.86
-4318.04
5985.926
2586.606
-3845.14
-587.345
5
-6219.94
-3940.5
3299.872
2185.287
-3478.89
306.7505
6
-5787.88
-2855.61
1959.673
1153.073
-3352.84
685.8516
7
-4296.83
-1812.92
1468.282
82.98355
-2690.85
654.9505
8
-2907.14
-499.161
834.3583
-829.657
-2115.81
426.4041
9
-1812.95
577.9417
437.3629
-1499
-1674.07
130.718
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Proceedings of 30th International Business Research Conference
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10
-898.75
1386.964
88.47311
-1936.66
-1291.22
-138.417
Impulse response function is a method of analyzing the short run dynamics of relationships among a
set of endogenous variables. Table 6 presents the results. Impulse Response Function traces the effect
of a one standard deviation shock to one of the innovations on current and future values of the
endogenous variables. For example a shock to ith variable say MNCRM, directly affects the variable,
and is also transmitted to all of the endogenous variables through the dynamic structure of the VAR.
It is another way of saying how a particular variable does responds to shocks in other variables.
Granger causality may not tell us the complete story about the interactions between the variables of a
system. In applied work it is often of interest to know the response of one variable to an impulse in
another variable in a system that involves a number of further variables.
Of course if there is a reaction of one variable to an impulse in another variable we may call the latter
causal for the former, this type of impulse response is called dynamic multiplier. Since the variables in
our study have different scales, we consider innovation of one standard deviation rather than unit
shocks. The impulse responses are zero if one of the variables does not Granger cause the other
variables taken as a group.
In Table 6 it is revealed that initially UNEMP, INTSEC and MNCRM show no response to the shocks in
other variables except their own lags. In the case of mutual shocks UNEMP responded positively (an
increase) to shocks in INTSEC. Internal security responded positively to unemployment, that a shock
in unemployment leads to extra expenditure to maintain internal security. Also minor crime rate
responds negatively to shocks in unemployment and internal security. That is increases in
unemployment rate worsen the rate of crime in Nigeria.
II.
Variance Decomposition of Shocks to UNEMP, IMTSEC and MNCRM
The variance decomposition in Table 7 analyses the decomposition of the shocks received by each
variable to its constituent sources. It is another way of describing causes and sources of variations or
shocks to the variables. The 36 years period under study is summarized into a quartile, a four year
period. Variance decomposition provides a different method of depicting the system dynamics.
Impulse response function traces the effects of a shock to each of the variables on one another. By
contrast variance decomposition decomposes variation in one variable into the component shocks to
each of the other variables.
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
Table 7: Variance Decomposition of UEMP
Period S.E.
UEMP
INTSEC
1 3.237702
100
0
2 3.986414 91.74612 5.092007
3 4.24572 87.45241 6.226559
4 4.30618 86.00512 6.778761
5 4.334613 84.89374 7.070889
6 4.354589 84.17518 7.202933
7 4.382181 83.14802 7.452491
8 4.414659 81.92959 7.824104
9 4.450506 80.63887 8.299591
10 4.487347 79.38798 8.849326
Variance Decomposition of INTSEC:
MNCRM
0
0.008652
0.220246
0.25572
0.362962
0.402615
0.458026
0.480472
0.492878
0.509941
MNOFF
SRCM
TCRM
0
1.238234
1.454467
1.535193
1.540772
1.79877
2.40905
3.233504
4.08799
4.83586
0
0.651185
0.716841
0.810214
1.316449
1.590594
1.741955
1.79506
1.794671
1.774772
0
1.2638
3.929475
4.614996
4.815189
4.82991
4.790455
4.737274
4.685995
4.642116
Period S.E.
UEMP
INTSEC
1 4273.298 7.364416 92.63558
2 5895.873 13.28059 78.93641
3 8119.082 14.65034 73.44812
4 10472.72 11.42142 68.97983
5 12735.99 10.29973 64.84193
6 15125.37 9.798716 61.33061
7 17482.05 9.980388 58.71261
8 19782.3 10.56541 56.66425
9 21953.27 11.22863 55.14141
10 23932.06 11.88088 54.00061
Variance Decomposition of MNCRM:
MNCRM
0
1.441212
4.846866
9.435386
12.66929
14.70729
15.19904
14.99079
14.55155
14.08208
MNOFF
SRCM
TCRM
0
3.223585
3.736513
5.094863
7.138172
9.603654
12.06168
14.12823
15.68653
16.79581
0
2.686327
2.190895
2.893162
2.604801
2.157725
1.714372
1.355693
1.100966
0.930351
0
0.431874
1.127269
2.175339
2.44608
2.401997
2.331907
2.295637
2.290911
2.310261
Period
1
2
3
4
5
6
7
8
9
10
MNCRM
94.22528
90.66714
85.82938
76.90469
70.27194
65.86374
63.73918
62.76407
62.16695
61.60887
MNOFF
SRCM
TCRM
0
0.09295
0.940883
1.735834
2.16134
2.168954
2.091685
2.134263
2.362567
2.753756
0
0.0151
1.295242
3.179886
4.385346
5.410684
6.041027
6.442891
6.690384
6.813567
0
0.10317
0.174726
0.195121
0.186734
0.229414
0.270037
0.285952
0.28503
0.284575
S.E.
17305.01
22338.53
24296.22
26559.41
28062.06
29086.34
29624.26
29872.07
30020.31
30156.18
UEMP
0.129228
1.204615
3.664809
8.567064
12.58699
15.67578
17.21545
17.87812
18.06669
17.99308
INTSEC
5.645497
7.917028
8.094964
9.417403
10.40766
10.65142
10.64262
10.4947
10.42837
10.54615
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
From Table 7 it is clear that the magnitude of shocks transmitted to UNEMP, INTSEC and MNCRM
from all the variable fluctuates over the periods. The greatest of the shocks came from feedbacks
from previous problems from each variable respectively. Apart from this the following are observed:




Internal security is the major source of shocks to unemployment. It contributed about 8% of
the problem of unemployment.
Minor offences and total crimes are products of unemployment rate
Shock to internal security come from high rate of unemployment and crime rate.
Also minor crime rate is increased when there are shocks to unemployment and internal
security.
Conclusion, Policy Implications and Recommendations
The main thrust of the study is to identify and examine the impact of unemployment and crime on
internal security in Nigeria. The results of the study revealed that unemployment promotes crimes in
Nigeria. In response to high crime rate government expenditure on internal security increases
tremendously. This evidence tends to confirm earlier conclusions reached by Small and Lewis (1996)
for New Zealand and McDowell and Webb (1995). Specifically, the following conclusions have been
drawn from the results: analyzed data shows that the total crime remains significantly affected by the
unemployment rate; unemployment and minor crime were found to have weakened the strength of
internal security in Nigeria making the government to spend more on internal security. The result of
causality affirmed the proposition that unemployment causes crim;. Papps and Winkelmann (1999)
reached similar conclusion.
Furthermore, this study indicates several possibilities for further research. In particular the
introduction of additional regression that may explain crime, for example income equality and sex
variable may alleviate any remaining omitted variable.
From the foregoing discussion, the following recommendations are suggested to prevent crimes and
reduce expenditure on security in Nigeria.
a. The policy makers should address adequately one of the major economic determinants of crime
such as unemployment by instituting palliative measures for the unemployed and creating and
expanding job facilities.
b. The planning strategy should focus on social and economic justice that will minimize excessive
spending on crime prevention and control.
c. Crime rate could be reduced if resources of the country are directed to those sectors where
majority of the poor exist like agriculture sector as well as empowering artisans.
d. Economic and Financial Crime Commission, Independent Corrupt Practices Commission and other
crime fighting agencies in the country should be well funded and fortified with modern hardware and
communication equipment to curb crime rate in the country .
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
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APPENDIX I : DESCRIPTIVE STATISTICS OF DATA
UEMP
INTSEC
MNCRM
Mean
5.976947 12571.56 38923.63
Median
5.977 2672.65 38923.63
Maximum
18.1 62396.9
86769
Minimum
1.8
90.08
9384
Std. Dev.
3.598521 20461.39 23043.51
Skewness
1.763245 1.517479 0.55578
Kurtosis
5.803464 3.655597 2.113493
Jarque-Bera
Probability
Sum
Sum Sq.
Dev.
Observations
MNOFF
29778.55
17534.5
108478
8114
27747.43
1.906909
5.209755
SRCM
173697.2
142125.5
747901
14457
129506
2.958746
12.54683
SER01
245818.8
214832
816603.2
124752
131851.1
2.726047
11.40739
32.13461 15.26457 3.200646 30.76135 199.7513 158.9818
0 0.000485 0.201831
0
0
0
227.124 477719.1
479.1262
38
1479098
1131585
6600494
9341114
1.55E+10 1.96E+10
2.85E+10
6.21E+11
6.43E+11
38
38
38
38
38
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Proceedings of 30th International Business Research Conference
20 - 22 April 2015, Flora Grand Hotel, Dubai, UAE, ISBN: 978-1-922069-74-0
APPENDIX II: Pairwise Granger Causality Tests
Null Hypothesis:
INTSEC does not Granger Cause UEMP
UEMP does not Granger Cause INTSEC
MNCRM does not Granger Cause UEMP
UEMP does not Granger Cause MNCRM
MNOFF does not Granger Cause UEMP
UEMP does not Granger Cause MNOFF
SRCM does not Granger Cause UEMP
UEMP does not Granger Cause SRCM
TCRM does not Granger Cause UEMP
UEMP does not Granger Cause TCRM
MNCRM does not Granger Cause INTSEC
INTSEC does not Granger Cause MNCRM
MNOFF does not Granger Cause INTSEC
INTSEC does not Granger Cause MNOFF
SRCM does not Granger Cause INTSEC
INTSEC does not Granger Cause SRCM
TCRM does not Granger Cause INTSEC
INTSEC does not Granger Cause TCRM
MNOFF does not Granger Cause MNCRM
MNCRM does not Granger Cause MNOFF
SRCM does not Granger Cause MNCRM
MNCRM does not Granger Cause SRCM
FObs Statistic
36
1.60656
4.77543*
36
0.01541
1.93357
36
0.16119
0.47484
36
0.31444
0.04619
36
0.4753
0.01068
36 4.51352*
0.92685
36
0.28156
0.65638
36
0.08877
0.44256
36
0.36245
0.3912
36
0.04648
0.0949
36
1.05135
0.30991
17
Prob.
0.2168
0.0156
0.9847
0.1617
0.8518
0.6264
0.7325
0.9549
0.6262
0.9894
0.019
0.4065
0.7565
0.5258
0.9153
0.6464
0.6989
0.6795
0.9547
0.9097
0.3616
0.7358
Decision
Accept
Reject
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Reject
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept
Accept