Skip to main content
SpringerLink
Log in

The Impact of Private Precautions on Home Burglary and Robbery in Brazil

  • Original Paper
  • Published:
Journal of Quantitative Criminology Aims and scope Submit manuscript

Abstract

Objectives

This study evaluates the effects of precaution technologies (i.e., window bars, extra locks, alarms and electric fences, CCTV, private security, dogs and combinations of these tools) on household burglary and robbery in Brazil.

Methods

Using a sample of 121,042 unique households from the Brazilian National Household Sample Survey for 2009, this study estimates a Recursive Bivariate Probit to obtain the average treatment effect of these precaution technologies in the likelihood of home burglary and robbery victimization.

Results

This study demonstrates that technology does not reduce home burglaries, but certain combinations of technologies may reduce home robberies. The combination of electric fences and alarms with private security reduces the likelihood of home robbery by 9.5 %, and when combined with a dog, these technologies reduce the likelihood of home robbery by 86 %.

Conclusions

No precaution technology is able to prevent crimes when employed independently. Occupancy combined with a set of technologies can reduce the expected victimization in crimes against property.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Notes

  1. This utilitarian perspective initially introduced by Bentham (1789) is the basis of the Economics of Crime by Becker (1968), the Rational Choice Theory by Cornish and Clarke (1986) and the Theory of Crime as an Opportunity by Felson and Clarke (1998).

  2. This contribution from Cohen and Felson (1979) is known as the Routine Activities Theory.

  3. Taking such possessions is the main motivation encountered by Rengert and Wasilchick (2000) in interviews with residence thieves.

  4. For information about the effect of the justice system in deterring crime, see Eide (1994).

  5. From the social perspective, observable technologies may or may not improve social welfare. At first glance they have no effect when only displacement effects are considered. However, the more individuals who adopt these technologies, the lower the probability will be that a potential criminal can successfully commit a crime. The increase in expected costs from criminal activity leads to a reduction of criminals, which can improve social welfare (Shavell 1991; Ayres and Levitt 1998).

  6. See Wooldrige (2010) chapter 15.7.3 for further details.

  7. In this case, obtaining the necessary counterfactual analysis to assess the effect of the treatment is possible.

  8. The percentage of residential victims of burglary and robbery using precaution technology are reported in Table 9 of the appendix.

  9. In many cases people have dogs as pets and consider them a precaution technology, but the dogs may not necessarily serve security purposes.

  10. Only information from the person responsible for the residence is taken into account, which prevents counting instances of victimization multiple times. People between 16 and 100 years old are considered responsible for the residence. The sample includes 121,042 households.

  11. The PNAD questionnaire does not require the identification of family members. It is not possible to identify the family to which each individual belongs. The only information available about family composition is the number of components and the number of children under ten years of age living in the residence.

  12. The active participation of potential victims in the task of avoiding potential crimes is what Cornish and Clarke (1986) call the “situational control of crime”.

  13. All the models include controls for household and residence characteristics and Brazilian states to capture non observed regional characteristics regarding crime.

  14. A likelihood ratio (LR) test with the null hypothesis ρ = 0, where ρ is the coefficient of correlation between the residuals of the discrete bivariate model as indicated in Eq. (5). Monfardini and Radice (2008) shows that LR exogeneity test outperform other tests, such as Lagrange multiplier (LM) and conditional moment (CM), in the recursive bivariate probit context.

  15. Differently from the univariate case (i.e. Probit), which evolves only a standard normal distribution, the bivariate case evolves two bivariate conditional distributions that generates different probabilities which may be greater, equal or smaller than the other, consequently, the same sign for RBP coefficients and ATE is not guaranteed.

  16. Such an alarm system may be connected to a telephone line.

  17. Previous studies demonstrate that the high frequency of false alarms tends to decrease alarms’ effectiveness (Cook and MacDonald 2011).

  18. A private security service can take many forms, including the permanent presence of a guard or porter, alarm response services, and night watch services, among other more sophisticated forms, such as a team of armed guards.

  19. Dogs have a higher capacity to hear and smell than human beings.

  20. The PNAD data indicate that 42.87 % of apartment buildings in Brazil had some type of private security in 2009, whereas the same type of service was present in only 8.07 % of other types of residences.

  21. The appendix also presents the marginal effects of the variable that controls for the Brazilian States.

  22. The information contained in the PNAD 2009 indicates that mobile phones are taken by the criminals in 32.19 % of robberies and 14.74 % of burglaries.

  23. It is increasingly common to observe gangs specialized in burglarizing and robbing residences. There is a gain in the division of labor, which includes the tasks of monitoring, performance and escape, as well as in the need of a fence for the stolen objects.

References

  • Ayres I, Levitt S (1998) Measuring positive externalities from unobservable victim precaution: an empirical analysis of Lojack. Quart J Econ 113(1):43–77

    Article  Google Scholar 

  • Beato CC, Viegas M, Peixoto BT (2004) Crime, oportunidade e vitimização. Rev Brasileira Ciências Soc 19(55):73–89

    Google Scholar 

  • Becker GS (1968) Crime and punishment: an economic approach. J Polit Econ 76:169–217

    Article  Google Scholar 

  • Bentham J (1789) An introduction to the principles of morals and legislation. Methuen University Paperback, London

    Book  Google Scholar 

  • Bhattacharya J, Goldman D, Mccaffrey D (2006) Estimating Probit models with self- selected treatments. Stat Med 25(3):389–413

    Article  Google Scholar 

  • Brantingham PJ, Brantingham PL (1984) Patterns in crime. MacMillan, New York

    Google Scholar 

  • Budd T (1999) Burglary of domestic dwellings: findings from the British Crime survey. Home Office Statistical Bulletin, London, p 4/99

    Google Scholar 

  • Chiburis RC, Jishnu D, Lokshin M (2011) A practical comparison of the Bivariate probit and linear IV estimators (Policy Research Working Paper 5601). World Bank

  • Clarke RV (1983) Situational crime prevention: its theoretical basis and practical scope. Crime Justice 4:225–256

    Article  Google Scholar 

  • Clarke RV (1995) Situational crime prevention. In: Building a safer society: strategic approaches to crime prevention, Crime and justice, vol 19, pp 91–150

  • Clarke RV (1999) Hot products: understanding, anticipating and reducing demand for stolen goods. Police research series paper 112. Policing and reducing crime unit, research development and statistics directorate, Home Office, London

  • Clotfelter CT (1978) Private security and the public safety. Journal of Urban Economics 5:388–402

    Article  Google Scholar 

  • Cohen LE, Felson M (1979) Social changes e crime trends rate: a routine activity approach. Am Sociol Rev 44:588–605

    Article  Google Scholar 

  • Collett-schmitt KE (2007) Observable private precaution and its effect on crime: the case of burglar alarms. North Carolina State University, Mimeo

    Google Scholar 

  • Cook PJ (1986) The demand and supply of criminal opportunities. Crime and Justice 7:1–27

    Article  Google Scholar 

  • Cook PJ, Macdonald J (2011) Public Safety through Private Action: an economic assessment of BIDs, locks, and citizen cooperation. Econ J 121:445–462

    Article  Google Scholar 

  • Cornish DB, Clarke RV (1986) The reasoning criminal. Springer-Verlag, New York

    Book  Google Scholar 

  • Coupe T, Blake L (2006) Daylight and darkness targeting strategies and the risks of being seen at residential burglaries. Criminology 44(2006):431–464

    Article  Google Scholar 

  • Coupe T, Griffiths M (1996) Solving residential burglary. Crime detection and prevention series 77. Home Office Police Research Group, London

  • Cromwell PF, Olson JN, Avary DW (1991) Breaking and entering: An ethnographic analysis of burglary. Sage, Newbury Park

    Google Scholar 

  • Ehrlich I (2010) The market model of crime: a short review and new directions. In: Benson BL, Zimmerman PR (eds) Handbook on the economics of crime, vol 3. Elsevier, Northampton, MA, pp 3–23

  • Eide E (1994) Economics of crime: deterrence and the rational offender, contributions to economic analysis. North-Holland, Amsterdam, Oxford e Tokyo

    Book  Google Scholar 

  • Felson M, Clarke RV (1998) Opportunity makes the thief: practical theory for crime prevention (Police Research Series Paper 98). Home Office, London

    Google Scholar 

  • Greene W (1998) Gender economics courses in liberal arts colleges: further results. J Econ Edu 29(4):291–300

    Article  Google Scholar 

  • Hakim S, Blackstone E (1997) Securing home and business: a guide to the electronic security industry. Butterworth-Heinemann, Boston

    Google Scholar 

  • Heckman JJ (1978) Dummy endogenous variables in a simultaneous equation system. Econometrica 47:153–161

    Article  Google Scholar 

  • Jones A (2007) Applied econometrics for health economics, vol 2. Radclife Publishing Ltd, Oxford

    Google Scholar 

  • Klick J, Macdonald J, Stratmann T (2011) Mobile phones and crime deterrence: an underappreciated link. University of Pennsylvania, Mimeo

    Google Scholar 

  • Lee S (2008) The impact of home burglar alarm systems on residential burglary. Unpublished doctoral dissertation, The State University of New Jersey

  • Madalozzo R, Furtado GM (2011) Um estudo sobre a vitimização para a cidade de São Paulo. Rev Econ Política 31(1):160–180

    Article  Google Scholar 

  • Maddala GS (1983) Limited dependent and qualitative variables in econometrics. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Monfardini C, Radice R (2008) Testing exogeneity in the bivariate Probit model: a monte carlo study. Oxford Bull Econ Stat 70(2):271–282

    Article  Google Scholar 

  • Nichols A (2011) Causal inference for binary regression. In: Stata Conference Chicago (version June 14, 2011)

  • Peixoto BT, Andrade MV, Moro S (2007) Violência urbana: uma análise comparativa da vitimização em São Paulo, Rio de Janeiro, Recife e Vitória. UFMG/Cedeplar, Belo Horizonte

    Google Scholar 

  • Rengert GF, Wasilchick J (2000) Suburban burglary: a tale of two suburbs, 2nd edn. Charles C Thomas, Springfield, IL

    Google Scholar 

  • Reppetto TA (1974) Residential crime. Ballinger Publishing Company, Cambridge, MA

    Google Scholar 

  • Scarr HA (1973) Patterns in burglary, 2nd edn. US Department of Justice, Washington, DC

  • Shavell S (1991) Individual precautions to prevent theft: private versus socially optimal behavior. Int Rev Law Econ 11:123–132

    Article  Google Scholar 

  • Waller I, Okihiro N (1979) Burglary: the victim and the public. University of Toronto Press, Toronto

    Google Scholar 

  • Weisel DL (2002) Burglary of single-family houses. Department of Justice, Office of Community Oriented Policing Services, Washington, DC

  • Wilde J (2000) Identification of multiple equation probit models with endogenous dummy regressors. Economics Letters 69(3):309–312

    Article  Google Scholar 

  • Winchester S, Jackson S (1982) Residential burglary: the limits of prevention. HMSO, London

    Google Scholar 

  • Wooldridge JM (2010) Econometric analysis of cross section and panel data, 2nd edn. The MIT Press, Cambridge, MA

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Economics, Universidade Federal do Rio Grande, Italia Avenue, km 8, Rio Grande, Brazil

    Cristiano Aguiar de Oliveira

Authors
  1. Cristiano Aguiar de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristiano Aguiar de Oliveira.

Appendix

Appendix

See Tables 9, 10, 11, 12, 13 and 14.

Table 9 Percentage of victims and not victims of burglary and robbery by precaution technology and total
Full size table
Table 10 Recursive Bivariate Probit estimation results for burglary
Full size table
Table 11 Recursive Bivariate Probit estimation results for robbery
Full size table
Table 12 Estimated model log likelihoods
Full size table
Table 13 Marginal effects for Brazilian States
Full size table
Table 14 Dwellers under ten coefficients in RBP
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Oliveira, C.A. The Impact of Private Precautions on Home Burglary and Robbery in Brazil. J Quant Criminol 34, 111–137 (2018). https://doi.org/10.1007/s10940-016-9325-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-016-9325-6

Keywords

JEL classification

Search

Navigation