Determinants of crime control in public security policy management

Figueiredo, Sabrina Oliveira de; Sincorá, Larissa Alves; Leite, Maria Clara de Oliveira; Brandão, Marcelo Moll

doi:10.1590/0034-761220200058

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Revista de Administração Pública

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Article • Rev. Adm. Pública 55 (2) • Mar-Apr 2021 • https://doi.org/10.1590/0034-761220200058 copy

Determinants of crime control in public security policy management

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The topic related to public security in Brazil is central in discussions regarding crime rates. This study approaches the theme from the economic theory of rational choice and sociological theories that seek to explain the causes of crime. The study’s objective was to identify determining factors for controlling crime rates based on data from the state of Espírito Santo. This is a study with a quantitative approach. Secondary data were used, based on the proposed theoretical model. The linear regression model was also adopted as a hypothesis testing method, with the aid of the SPSS Statistics software version 25. The results show that the element related to the population’s employability directly and significantly influences the control of crime rates, intentional homicides, and crimes against property. Furthermore, combined with employment (formal labor activities), the population’s income and education positively influence the control of crimes against property.

Keywords:
determining factors; control of crime; willful murders; crimes against property; public security

MODEL SUMMARY
Model	R	R²		Adjusted R²	Standard error of estimate
1	0,176ª	0,031		0,028	24,98786877
ANOVAª
Model		Sum of Square	df	Medium Square	Z	Sig.
1	Regression	23178,986	3	7726,329	12,374	0,000
	Residue	728042,921	1166	624,394
	Total	751221,907	1169
COEFFICIENTS
Model		Non standarlized coefficients		Standalized coefficients	T	Sig
Model		B	Standard Error	Beta
1	(Constant)	16,497	7,304		2,259	0,024
	OCCU	0,472	0,120	0,195	3,932	0,000
	AFI	-0,004	0,005	-0,39	-0,803	0,422
	EDU	0,070	0,090	0,024	0,774	0,439
CORRELATION
		HOM	OCCU	AFI	EDU
Pearson’s Correlation	HOM	1,000	0,173	0,125	0,085
	OCCU	0,173	1,000	0,803	0,379
	AFI	0,125	0,803	1,000	0,319
	EDU	0,085	0,379	0,319	1,000

MODEL SUMMARY
Model	R	R²		Adjusted R²	Standard error of estimate
1	0,534ª	0,285		0,282	175,8189799
ANOVAª
Model		Sum of Square	Df	Medium Square	Z	Sig.
1	Regression	9553752,222	3	3184584,074	103,020	0,000
	Residue	23987955,42	776	30912,314
	Total	33541707,64	779
COEFFICIENTS
Model		Non standarlized coefficients		Standalized coefficients	T	Sig
Model		B	Standard Error	Beta
1	(Constant)	-291,961	73,333		-3,981	0,000
	OCCU	4,923	1,185	0,245	4,156	0,000
	AFI	0,164	0,042	0,225	3,910	0,000
	EDU	4,319	0,898	0,159	4,813	0,000
CORRELATION
		PROP	OCCU	AFI	EDU
Pearson’s Correlation	PROP	1,000	0,500	0,486	0,332
	OCCU	0,500	1,000	0,849	0,398
	AFI	0,486	0,849	1,000	0,334
	EDU	0,332	0,398	0,334	1,000

Fundação Getulio Vargas Fundaçãoo Getulio Vargas, Rua Jornalista Orlando Dantas, 30, CEP: 22231-010 / Rio de Janeiro-RJ Brasil, Tel.: +55 (21) 3083-2731 - Rio de Janeiro - RJ - Brazil
E-mail: rap@fgv.br

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[1] Sabrina Oliveira de Figueiredo -Master in Business Administration; Doctoral student in the Graduate Program in Business Administration at the Federal University of Espírito Santo. E-mail: sab.figueiredo@gmail.com