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An Exploratory Study of Honor Crimes in the United States

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Abstract

There is a lack of research on honor crimes within the United States. We used an open source search methodology to identify the victim-offender relationship and motivations for this crime within the United States. Using data collected based on the protocol for the United States Extremist Crime Database (ECDB), we identified a total of 16 honor crimes with 40 victims that occurred between January 1st 1990 and December 31st 2014 in the United States. Based on our findings, the overarching motivations for honor crimes in the United States were the perpetrator’s former partner beginning the process of separation and the westernized behavior of the victim, typically the offender’s daughter or step-daughter. Honor crimes were not limited to current/former intimate partners or daughters, as they also included the death of extended family members (e.g. in-laws, nieces, and cousins). Policy implications and directions for future research on honor crimes are discussed.

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Notes

  1. Freilich et al. (2014) provide an appendix that lists these sources. For a detailed discussion of the incident identification process, see Chermak et al. (2012), Freilich et al. (2014); Gruenewald (2011), and Gruenewald & Pridemore (2012).

  2. We recently looked at 10 sources (such as the FBI, the Anti-Defamation League, etc.) that the ECDB had used to identify homicides committed by far-right extremists in the United States between 1990 and 2010 (Chermak et al. 2012). After examining these sources similarities and differences, we normalized their inclusion criteria to accurately assess variations in the events they included. We used a “catchment-re-catchment” analysis and found that the inclusion of additional sources resulted in an increasing number of events that were identified in previous sources. Further, the ECDB’s use of key word searches identified over 10 cases that should have been included but yet were missed by all the other sources. Thus, the ECDB’s strategy of using multiple sources- and ideally all relevant sources- to identify the cases interested in minimized the danger of selectivity bias and resulted in a more complete universe.

  3. These web-engines include: 1. Lexis-Nexis; 2. Proquest; 3. Yahoo; 4. Google; 5. Copernic; 6. News Library;7. Westlaw; 8. Google Scholar (both articles & legal opinions); 9. Amazon; 10. Google U.S. Government;11. Federation of American Scientists; 12. Google Video; 13. Center for the Study of Intelligence; 14. Surf Wax;15. Dogpile; 16. Mamma; 17. Librarians’ Internet Index; 18. Scirus; 19. All the Web; 20. Google News; 21. Google Blog; 22. Homeland Security Digital Library; 23. Vinelink; 24. The inmate locator; 25. Bureau of Prisons; 26. Individual State Department of Corrections (DOCs); 27. Blackbookonline.info; 28. Quantloos; 29. Anti-Defamation League; 30. Southern Poverty Law Center; and 31. Center on Law and Security

  4. Freilich et al. (2014) explained how the larger ECDB study that relies upon multiple coders addressed inter-rater reliability. They write that “we addressed this important issue in a number of ways. First, coders were trained. New coders initially coded previously coded cases and both sets of values were compared. We created a listserv of ECDB personnel and instructed coders to share difficult issues. In this way, inconsistencies were addressed early in the coding process. Second, coding abnormalities were continually checked across coders. Third, filling in values for certain ECDB variables required little interpretation as the variables captured basic facts such as a suspect’s race, age, or gender….. we [also] conducted an initial measurement of inter-rater reliability for selected individual and situational characteristics of far-right homicides and found coder agreement between 89 % and 98 % of the time. When coders disagreed it was usually not because of differences in the values coded, but because one coder found a document that contained information that could be coded, while the second coder did not find it (p. 374–375).” Importantly, as noted, we conducted multiple open source searches of each of our 16 honor killings to insure that we did not miss important information. In addition, all the authors participated in the training of coders, and all the points noted in this footnote.

  5. Databases that were searched included state, local, and federal inmate locators; online local court dockets; the social security death index; and online national record aggregators such as Ancestry.com, Archives.com, BeenVerified.com and other news aggregates. If the case was missing the victim’s date of birth for instance, in Google the coder would search “Victim’s Name” and “date of birth” to find information on the missing values.

  6. All web-engines were searched using key terms such as “dowry murders” “Indian honor crime” and “Indian honor killing” in conjunction with “United States”.

  7. For a more detailed description of the coding of variables, see Freilich et al. (2014). All coding is conducted in an Access database.

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Authors and Affiliations

  1. Department of Criminal Justice and Criminology, Sam Houston State University, Huntsville, TX, USA

    Brittany E. Hayes

  2. Doctoral Program in Criminal Justice & Department of Criminal Justice, John Jay College, CUNY, New York, NY, USA

    Joshua D. Freilich

  3. School of Criminal Justice, Michigan State University, East Lansing, MI, USA

    Steven M. Chermak

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  1. Brittany E. Hayes
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  2. Joshua D. Freilich
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  3. Steven M. Chermak
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Correspondence to Brittany E. Hayes.

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Hayes, B.E., Freilich, J.D. & Chermak, S.M. An Exploratory Study of Honor Crimes in the United States. J Fam Viol 31, 303–314 (2016). https://doi.org/10.1007/s10896-016-9801-7

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