Drug-related crime and poverty in South Africa

Figures & data

Table

Drugs and crime relationship	Definition	Examples
Drug-related offences	These are offences in which drugs contribute; offences connected to drug distribution and by the end users need for income to support continued use	Violent behaviour resulting from drug effects or violence against rival drug dealers
Drug define offences	This includes offences such as violations of laws regulating or prohibiting the use, distribution and manufacture of illegal drugs	Use of marijuana or its cultivation. Sales of cocaine. Marijuana and heroin, etc.
Drug-using lifestyle	The likelihood and frequency of involvement in illegal activity are increased drug users tend not to participate in the mainstream economy and are exposed to situations that encourage crime.	Opportunities to offend resulting from illegal activities and illegal markets.

Table 1. Unit root test (augmented Dickey-Fuller test)

Variable	Intercept	Intercept & trend	None	Order of integration
∆y	−2.817658 (0.0598)*	−3.428410 (0.0540)**	−1.195859 (0.2106)	I(1)
∆Gini	−1.401584 (0.5784)	−4.084109 (0.0093)***	−1.132637 (0.2326)	I(0)
∆POV	−1.631107 (0.4628)	−3.330307 (0.0677)*	2.278697 (0.9944)	I(0)
∆GDP	−3.002652 (0.0383)**	−3.073429 (0.1188)*	−1.238839 (0.1966)	I(1)

Note: P values in parentheses.

Source: Authors calculations.

*Represent the rejection of the null hypothesis at 10%.

**Represent the rejection of the null hypothesis at 5%.

****Represent the rejection of the null hypothesis at 1%.

Figure 1. ARDL Hannan-Quinn criteria.

Table 2. ARDL long run form and Bounds Test.

Conditional error correction regression
Variable	Coefficient	Std. error	t-Statistic	Prob.
C	0.807609	0.192105	4.203990	0.0001***
LOG(Y(−1))*	−0.138140	0.033568	−4.115233	0.0001***
LOG(GINI(−1))	−0.022325	0.005465	−4.085260	0.0001***
LOG(GDP)**	0.054466	0.013031	4.179746	0.0001***
LOG(POV(−1))	0.107858	0.029834	3.615290	0.0006***
DLOG(Y(−1))	0.666969	0.085601	7.791621	0.0000***
DLOG(Y(−2))	0.118826	0.067926	1.749340	0.0846*
DLOG(Y(−3))	0.084441	0.068775	1.227781	0.2236
DLOG(Y(−4))	−0.695854	0.068949	−10.09224	0.0000***
DLOG(Y(−5))	0.461403	0.082853	5.568948	0.0000***
DLOG(GINI)	0.009307	0.013097	0.710599	0.4797
DLOG(POV)	0.342278	0.110112	3.108443	0.0027**
DLOG(POV(−1))	−0.221398	0.114356	−1.936039	0.0569*
DLOG(POV(−2))	−0.017168	0.108436	−0.158327	0.8747
DLOG(POV(−3))	0.003389	0.108500	0.031233	0.9752
DLOG(POV(−4))	−0.318684	0.119301	−2.671267	0.0094*
DLOG(POV(−5))	0.300514	0.096448	3.115804	0.0027**
@QUARTER = 2	0.001174	0.000967	1.213571	0.2290
@QUARTER = 3	0.000853	0.000954	0.893946	0.3744
@QUARTER = 4	0.000807	0.000954	0.845880	0.4005

*p value incompatible with t-Bounds distribution.

** Variable interpreted as Z = Z(−1) + D(Z).

Table 3. Long run regression results

Levels equation
Case 2: Restricted constant and no trend
Variable	Coefficient	Std. error	t-Statistic	Prob.
LOG(GINI)	−0.161613	0.017723	−9.118604	0.0000***
LOG(GDP)	0.394281	0.025596	15.40386	0.0000***
LOG(POV)	0.780783	0.060956	12.80902	0.0000**
C	5.846297	0.154428	37.85770	0.0000

Table 4. F bound tests

F-Bounds Test		Null hypothesis: no levels relationship
Test statistic	Value	Signif.	I(0)	I(1)
			Asymptotic: n = 1000
F-statistic	5.322987	10%	2.37	3.2
K	3	5%	2.79	3.67
		2.5%	3.15	4.08
		1%	3.65	4.66
Actual sample Size	90		Finite-sample: n = 80
		10%	2.474	3.312
		5%	2.92	3.838
		1%	3.908	5.044

Note, the null hypothesis is rejected if F > l(0)critical value, and accepted if F < l(0). For the t statistic, the null hypothesis is rejected if t > l(1),rejected if t < l(0).

Figure 2. CUSUM plots.

Table 5. Stability diagnostics checks

Test	Null hypothesis	Test statistic	p Value	Conclusions
Jarque–Bera	Residuals are normally distributed	1.56	0.30	Do not reject Ho as PV is greater than the L.O.S at 5%, therefore the residuals of the model are normally distributed
Ljung–Box Q	No autocorrelation	7.22	0.22	Do not reject Ho as PV is greater than the L.O.S at 5%, therefore the model does not suffer from an autocorrelation
Breusch–Pagan–Godfrey	No heteroscedasticity	18.43	0.39	Do not reject Ho as PV is greater than the L.O.S at 5%, therefore there is no heteroscedasticity in the model
Arch	No arch heteroscedasticity	0.29	0.55	Do not reject Ho as PV is greater than the L.O.S at 5%, therefore there is no heteroscedasticity in the model
White	No heteroscedasticity	15.41	0.56	Do not reject Ho as PV is greater than the L.O.S at 5%, therefore there is no heteroscedasticity in the model
Lagrange Multiplier test	No serial correlation	11.37	0.09	Do not reject Ho as PV is greater than the L.O.S at 5%, therefore there is no heteroscedasticity in the model

L.O.S is the level of significance.