Journal of Quantitative Criminology

, Volume 29, Issue 4, pp 477–541 | Cite as

Estimating the Causal Effect of Gun Prevalence on Homicide Rates: A Local Average Treatment Effect Approach

  • Tomislav Kovandzic
  • Mark E. Schaffer
  • Gary Kleck
Original Paper

Abstract

Objective

This paper uses a “local average treatment effect” (LATE) framework in an attempt to disentangle the separate effects of criminal and noncriminal gun prevalence on violence rates. We first show that a number of previous studies have failed to properly address the problems of endogeneity, proxy validity, and heterogeneity in criminality. We demonstrate that the time series proxy problem is severe; previous panel data studies have used proxies that are essentially uncorrelated in time series with direct measures of gun relevance.

Methods

We adopt instead a cross-section approach: we use US county-level data for 1990, and we proxy gun prevalence levels by the percent of suicides committed with guns, which recent research indicates is the best measure of gun levels for crosssectional research. We instrument gun levels with three plausibly exogenous instruments: subscriptions to outdoor sports magazines, voting preferences in the 1988 Presidential election, and numbers of military veterans. In our LATE framework, the estimated impact of gun prevalence is a weighted average of a possibly negative impact of noncriminal gun prevalence on homicide and a presumed positive impact of criminal gun prevalence.

Results

We find evidence of a significant negative impact, and interpret it as primarily “local to noncriminals”, i.e., primarily determined by a negative deterrent effect of noncriminal gun prevalence. We also demonstrate that an ATE for gun prevalence that is positive, negative, or approximately zero are all entirely plausible and consistent with our estimates of a significant negative impact of noncriminal gun prevalence.

Conclusions

The policy implications of our findings are perhaps best understood in the context of two hypothetical gun ban scenarios, the first more optimistic, the second more pessimistic and realistic. First, gun prohibition might reduce gun ownership equiproportionately among criminals and noncriminals, and the traditional ATE interpretation therefore applies. Our results above suggest that plausible estimates of the causal impact of an average reduction in gun prevalence include positive, nil, and negative effects on gun homicide rates, and hence no strong evidence in favor of or against such a measure. But it is highly unlikely that criminals would comply with gun prohibition to the same extent as noncriminals; indeed, it is virtually a tautology that criminals would violate a gun ban at a higher rate than noncriminals. Thus, under the more likely scenario that gun bans reduced gun levels more among noncriminals than criminals, the LATE interpretation of our results moves the range of possible impacts towards an increase in gun homicide rates because the decline in gun levels would primarily occur among those whose gun possession has predominantly negative effects on homicide.

Keywords

Crime Homicide Gun levels Endogeneity 

References

  1. Angrist J, Imbens G (1995) Two-stage least squares estimates of average causal effects in models with variable treatment intensity. J Am Stat Assoc 90(430):431–442CrossRefGoogle Scholar
  2. Annest JL, Mercy JA, Gibson DR, Ryan GW (1995) National estimates of nonfatal firearm-related injuries. J Am Med Assoc 273:1749–1754CrossRefGoogle Scholar
  3. Audit Bureau of Circulations (1993) Supplementary data report, covering county paid circulation for gun and related sports magazines. Audit Bureau of Circulations, Schaumburg, ILGoogle Scholar
  4. Ayres I, Donohue JJ III (2003) Shooting down the ‘More Guns, Less Crime’ hypothesis. Stanf Law Rev 55:1193–1312Google Scholar
  5. Azrael D, Cook PJ, Miller M (2004) State and local prevalence firearms ownership: measurement structure, and trends. J Quant Criminol 20:43–62Google Scholar
  6. Baum CF, Schaffer ME, Stillman S (2003) Instrumental variables and GMM: estimation and testing. Stata J 3:1–31Google Scholar
  7. Baum CF, Schaffer ME, Stillman S (2007) Enhanced routines for instrumental variables/generalized method of moments estimation and testing. Stata J 7:465–506Google Scholar
  8. Baum C, Schaffer ME, Stillman S (2008) IVREG2: Stata module for extended instrumental variables/2SLS and GMM estimation. http://ideas.repec.org/c/boc/bocode/s425401.html
  9. Bordua DJ, Lizotte AJ (1979) Patterns of legal firearms ownership: a cultural and situational analysis of Illinois counties. Law Policy Quart 1:147–175CrossRefGoogle Scholar
  10. Clarke RV, Mayhew P (1988) The British gas suicide story and its criminological implications. In: Tonry M, Morris N (eds) Crime and justice, vol 10. University of Chicago Press, Chicago, pp 79–116Google Scholar
  11. Cook PJ, Ludwig J (1997) Guns in America. Police Foundation, Washington, DCGoogle Scholar
  12. Cook PJ, Ludwig J (2003a) Guns and burglary. In: Ludwig J, Cook PJ (eds) Evaluating gun policy. Brookings Institution Press, Washington, DCGoogle Scholar
  13. Cook PJ, Ludwig J (2003b) Pragmatic gun policy. In: Cook and Ludwig (2003a), op. cit Google Scholar
  14. Cook PJ, Ludwig J (2004) The social costs of gun ownership. NBER working paper 10736. http://www.nber.org/papers/w10736
  15. Cook PJ, Ludwig J (2006) The social costs of gun ownership. J Public Econ 90:379–391CrossRefGoogle Scholar
  16. Deaton A (1997) The analysis of household surveys: a microeconometric approach to development policy. World Bank/Johns Hopkins University Press, Washington, DCCrossRefGoogle Scholar
  17. Decker SH, Curry GD, Catalano S, Watkins A (2005) Strategic approaches to Community Safety Initiative (SACSI) in St. Louis. Final Report. Research report submitted to the US Department of Justice, Document Number 210361, Award Number 2000-IJ-CX-K008, June. http://www.ncjrs.gov/pdffiles1/nij/grants/210361.pdf
  18. Duggan M (2001) More guns, more crime. J Polit Econ 109:1086–1114CrossRefGoogle Scholar
  19. Duggan M (2003) Guns and suicide. In: Cook and Ludwig (2003a), op. cit Google Scholar
  20. Frishman F (1971) On the arithmetic means and variances of products and ratios of random variables. Army Research Office, Durham, North Carolina, publication AD-785 623. http://handle.dtic.mil/100.2/AD0785623
  21. Greene, WH (2008) Econometric analysis, 6th edn. Prentice Hall, New JerseyGoogle Scholar
  22. Hayashi F (2000) Econometrics. Princeton University Press, PrincetonGoogle Scholar
  23. Heckman J (1997) Instrumental variables: a study of implicit behavioral assumptions used in making program evaluations. J Human Resourc 32(3):441–462CrossRefGoogle Scholar
  24. ICPSR (1995) General election data for the United States, 1950–1990 [Computer file]. ICPSR ed. Inter-university Consortium for Political and Social Research [producer and distributor], Ann Arbor, MIGoogle Scholar
  25. Imbens G, Angrist J (1994) Identification and estimation of local average treatment effects. Econometrica 62(2):467–475CrossRefGoogle Scholar
  26. Kates DB, Mauser G (2007) Would banning firearms reduce murder and suicide? A review of international and some domestic evidence. Harv J Law nd Public Policy 30(2):649–694Google Scholar
  27. Killias M (1993) Gun ownership, suicide, and homicide: an international perspective. In: del Frate A, Zvekic U, van Dijk JJM (eds) Understanding crime: experiences of crime and crime control. UNICRI, Rome, pp 289–303Google Scholar
  28. Killias M, van Kesteren J, Rindlisbacher M (2001) Guns, violent crime, and suicide in 21 countries. Can J Criminol 43:429–448Google Scholar
  29. Kleck G (1979) Capital punishment, gun ownership, and homicide. Am J Sociol 84:882–910Google Scholar
  30. Kleck G (1984) The relationship between gun ownership levels and rates of violence in the United States. In: Kates DB Jr. (ed) Firearms and violence: issues of public policy. Ballinger, Cambridge, MAGoogle Scholar
  31. Kleck G (1988) Crime control through the private use of armed force. Soc Probl 35:1–21CrossRefGoogle Scholar
  32. Kleck G (1997) Targeting guns: firearms and their control. Aldine, NYGoogle Scholar
  33. Kleck G (2004) Measures of gun ownership levels for macro-level crime and violence research. J Res Crime Delinquency 41(1):3–36CrossRefGoogle Scholar
  34. Kleck G, DeLone M (1993) Victim resistance and offender weapon effects in robbery. J Quant Criminol 9:55–82CrossRefGoogle Scholar
  35. Kleck G, Hogan M (1999) A national case-control study of homicide offending and gun ownership. Soc Probl 46(2):275–293CrossRefGoogle Scholar
  36. Kleck G, Kates DB (2001) Armed: new perspectives on gun control. Prometheus, Amherst, NYGoogle Scholar
  37. Kleck G, McElrath K (1991) The effects of weaponry on human violence. Soc Forces 69:669–692Google Scholar
  38. Kleck G, Patterson EB (1993) The impact of gun control and gun ownership levels on violence rates. J Quant Criminol 9:249–288CrossRefGoogle Scholar
  39. Kovandzic T, Vieraitis LM, Yeisley MR (1998) The structural covariates of urban homicide. Criminology 36:569–600CrossRefGoogle Scholar
  40. Kovandzic T, Schaffer ME, Kleck G (2012) Gun prevalence, homicide rates and causality: a GMM approach to endogeneity bias. In: Gadd D, Karstedt S, Messner SF (eds) The Sage handbook of criminological research methods. Sage, LondonGoogle Scholar
  41. Land K, McCall PL, Cohen LE (1990) Structural covariates of homicide rates. Am J Sociol 95:922–963CrossRefGoogle Scholar
  42. Lott JR Jr (2000) More guns, less crime, 2nd edn. University of Chicago Press, ChicagoGoogle Scholar
  43. Marvell TB, Moody CE Jr (1991) Age structure and crime rates: the conflicting evidence. J Quant Criminol 7(3):237–273CrossRefGoogle Scholar
  44. McDowall D, Loftin C (1983) Collective security and the demand for handguns. Am J Sociol 88:1146–1161CrossRefGoogle Scholar
  45. Moody CE, Marvell TB (2005) Guns and crime. South Econ J 71:720–736CrossRefGoogle Scholar
  46. Newey WK (1985) Generalized method of moments specification testing. J Econom 29:229–256CrossRefGoogle Scholar
  47. Okoro CA, Nelson DE, Mercy JA, Balluz LS, Crosby AE, Mokdad AH (2005) Prevalence of household firearms and firearm-storage practices in the 50 states and the District of Columbia. Pediatrics 116:e370–e376CrossRefGoogle Scholar
  48. Rice DC, Hemley DD (2002) The market for new handguns. J Law Econ 45:251–265CrossRefGoogle Scholar
  49. Sampson RJ (1986) Crime in cities. In: Reiss AJ Jr, Tonry M (eds) Communities and crime. University of Chicago Press, Chicago, pp 271–311Google Scholar
  50. Schaffer ME (2007) XTIVREG2: Stata module to perform extended IV/2SLS, GMM and AC/HAC, LIML and k-class regression for panel data models. http://ideas.repec.org/c/boc/bocode/s456501.html
  51. Sloan JH, Kellermann AL, Reay DT, Ferris JA, Koepsell T, Rivara FP, Rice C, Gray L, LoGerfo J (1990) Handgun regulations, crime, assaults and homicide. N Engl J Med 319:1256–1262CrossRefGoogle Scholar
  52. Southwick L Jr (2000) Self-defense with guns: the consequences. J Criminal Just 28:351–370CrossRefGoogle Scholar
  53. Staiger D, Stock JH (1997) Instrumental variables regression with weak instruments. Econometrica 65:557–586CrossRefGoogle Scholar
  54. Stock JH, Watson MW (2007) Introduction to econometrics, 2nd edn. Addison-Wesley, PearsonGoogle Scholar
  55. Stock JH, Yogo M (2005) Testing for weak instruments in linear IV regression. In: Andrews DWK, Stock JH (eds) Identification and inference for econometric models: essays in honor of Thomas Rothenberg. Cambridge University Press, Cambridge, pp 80–108. Working paper version: NBER Technical Working Paper 284, 2002. http://www.nber.org/papers/T0284
  56. Tark J, Kleck G (2004) Resisting crime: the effects of victim action on the outcomes of crimes. Criminology 42(4):861–909CrossRefGoogle Scholar
  57. US Bureau of the Census (1990) Census 1990 summary file 3 (SF3)—sample data, table P006 urban and rural. Retrieved 7 February 2005 from US Census http://factfinder.census.gov
  58. US Bureau of the Census (1994) County and City Data Book, 1994. US Government Printing Office, Washington, DCGoogle Scholar
  59. US Federal Bureau of Investigation (FBI) 1990–2000, 2006. Crime in the United States 1989 [–1999] Uniform crime reports. US Government Printing Office, Washington, DCGoogle Scholar
  60. US National Center for Health Statistics (1997) Limited access versions of mortality detail files, 1987–1993, with location detail, supplied to third author. US Department of Health and Human Services, Hyattsville, MDGoogle Scholar
  61. Vieraitis LM (2000) Income inequality, poverty, and violent crime: a review of the empirical evidence. Soc Pathol 6(1):24–45Google Scholar
  62. White H (1994) Estimation, inference and specification analysis. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  63. Wright JD, Rossi PH (1986) Armed and considered dangerous. Aldine, New YorkGoogle Scholar
  64. Zimring FE, Hawkins G (1997) Crime is not the problem. NY, OxfordGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Tomislav Kovandzic
    • 1
  • Mark E. Schaffer
    • 2
    • 3
    • 4
  • Gary Kleck
    • 5
  1. 1.Program in CriminologyUniversity of Texas at DallasRichardsonUSA
  2. 2.Heriot-Watt UniversityEdinburghUK
  3. 3.CEPRLondonUK
  4. 4.IZABonnGermany
  5. 5.College of Criminology and Criminal JusticeFlorida State UniversityTallahasseeUSA

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