Exploration of macroeconomic effects on criminality in Indonesia

Figures & data

Figure 1. The number of criminal cases in Indonesia from 2000–2020.

Note: Source: Indonesia criminal statistics (BPS, 2021) [Author’s work].

Figure 2. Indonesia’s macroeconomic development trends from 2000–2019.

Note: (a) Inequality. (b) Income per capita. (c) Inflation. (d) Unemployment.

Source: World Development Indicator (WDI, 2021) [Author’s work].

Figure 3. Frame Macroeconomics on crime.

Table 1. Definition of variables used in study over the period 2000–2019

Variable	Symbol	Definition	Measure	Source
Criminality	Crime	Number of crime according to police territorial jurisdiction	Cases (unit)	Indonesian Central Bureau of Statistics (BPS)^×
Inequality	Gini	Distribution of income among individuals or households within an economy by World Bank estimate		WDI World Bank^××
Income	Gpdc	GDP constant (2015) devide by population at the end of the year	USD	WDI World Bank^××
Inflation	Infla	Consumer price index	Index	WDI World Bank^××
Unemployment	Unem	Percent of total labor force according to International Labour Organization estimate	Percent	WDI World Bank^××

^{Note: ×}BPS access: https://www.bps.go.id/

^××WDI access: https://data.worldbank.org/country/ID

Figure 4. Framework Research.

Table 2. Descriptive statistics

	Crime	Gini	Gdpc	Infla	Unem
Mean	290,938	35.74	2737.45	97.49	5.68
Min.	172,532	28.60	1867.54	44.01	3.59
Max.	357,197	40.00	3877.38	151.17	8.06
Std. dev	64,459	3.62	645.65	64.53	1.46
Skewness	−0.75	−0.53	0.25	0.01	0.15
Kurtosis	2.00	2.14	1.78	1.73	1.65
JB	2.75	1.56	1.44	1.34	1.58
Prob. JB	0.252	0.457	0.458	0.511	0.452

Note: Source: Author’s calculations.

Table 3. Correlation matrix

Variable	Crime	Gini	Gdpc	Infla	Unem
Crime	1
Gini	0.873* (0.000)	1
Gdpc	0.663* (0.000)	0.850* (0.000)	1
Infla	0.728* (0.000)	0.883* (0.000)	0.994* (0.000)	1
Unem	−0.396*** (0.083)	−0.669* (0.001)	−0.832* (0.000)	−0.800* (0.000)	1

Note: Source: Author’s calculations. () is the p-value. *,*** represents a significant level of 1%, 10%

Table 4. Unit Root Test

Variable	KPSS		Decision
	Level	First Difference (∆)
Crime	0.169**	0.077	I(1)
Gini	0.175**	0.112	I(1)
Gdpc	0.130	-	I(0)
Infla	0.068	-	I(0)
Unem	0.101	-	I(0)

Note: Critical value 5% = 0.146. ** represents significant level of 5%.

Table 5. Optimal Lag result

Model	AIC	Lag Model
104	−3.369*	(1, 2, 0, 1, 1)
23	−3.334	(2, 2, 0, 1, 1)
95	−3.325	(1, 2, 1, 1, 1)
103	−3.297	(1, 2, 0, 1, 2)
101	−2.741	(1, 2, 0, 2, 1)

Note: Source: Author’s calculations. *mean best model selection. Optimal lag determined by Akaike Information Criterion with set 2 maximum lag

Table 6. Cointegration Bound Test

Model	F-statistic	Critical Value (Narayan, 2005)	Lower Band	Upper Band
Crime = Gini, Gdpc, infla, unem	8.632*	10%	2.52	3.56
		5%	3.05	4.22
		1%	4.28	5.84

Note: Source: Author’s calculations. * represents a significant level of 1%

Table 7. Long-term and short-term estimation results

Variable	Coefficient	Std. error	t-stat	P-value
Long-run
Gini	5.003*	0.744	6.716	0.000
Gdpc	−5.08**	2.082	−2.439	0.040
Infla	0.030***	0.013	2.276	0.051
Unem	0.130*	0.028	4.672	0.001
Short-run
∆Gini	1.889*	0.541	3.490	0.008
∆Gdpc	−2.972**	1.236	−2.401	0.043
∆Infla	0.001	0.007	0.232	0.822
∆Unem	0.032	0.021	1.519	0.167
ECM(−1)	−0.584*	0.145	−4.020	0.004
R-squared	0.893
Adj. R-squared	0.773
Normality	0.340 (0.843)	CUSUM	Stable
Autocorrelation	1.905 (0.228)	CUSUMQ	Stable
Heteroscedasticity	2.033 (0.165)
RESET Ramsey	1.774 (0.224)

Note: Source: Author’s calculations. *, **, *** represents significant levels of 1%, 5%, 10%.

Figure 5. Cusum.

Figure 6. Cusumq.

Table 8. Robustness Model (FMOLS & CCR)

Variable	FMOLS		CCR
	Coefficient	P-value	Coefficient	P-value
Gini	2.822*	0.000	2.975*	0.000
Gdpc	−3.434***	0.094	−4.186***	0.074
Infla	0.023***	0.078	0.027***	0.063
Unem	0.066**	0.018	0.053***	0.052

Note: Source: Author’s calculations. *, **, *** represent significant levels of 1%, 5%, 10%.

Central Bureau of Statistics (BPS). (2021). Criminal Statistics 2021. https://www.bps.go.id/publication/2021/12/15/8d1bc84d2055e99feed39986/statistik-kriminal-2021.html Accessed 1 November.

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Narayan, P. K. (2005). The saving and investment nexus for China: Evidence from cointegration tests. Applied Economics, 37(17), 1979–1990. https://doi.org/10.1080/00036840500278103

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