Abstract
Most previous empirical comparative studies of homicide examine homicide rates across nations or subnational units within a single country. This study is one in which a European cross-national city comparison is made. We aim to provide insight into the extent that the homicide rates are related to the social and economic forces characterizing a sample of European cities. Derived from theories rooted in classic works, including social disorganization, strain, and urbanism, are six hypothesized effects of structural forces on homicide rates. Data are obtained from Eurostat’s Urban Audit file for the 2001 time frame. Analyses show that cities’ deprivation and population structure indexes are strong predictors of homicide rates. The predicted effects of unemployment rates, population heterogeneity, and age structure on homicide rates, however, were not consistently corroborated by these results. Comparing Eastern and Western European countries, there is also support for the influence of the country’s level of development on city-level homicide rates.
This paper is based on two earlier papers: (a) McCall, P. L. & P. Nieuwbeerta’s Structural Covariates of Homicide Rates. A European City Cross-National Comparative Analysis. http://hsx.sagepub.com/content/11/3/167.full.pdf+html. The final, definitive version of this paper has been published in Homicide Studies 11(3) Aug. 2007, 167–188 by Sage Publications, All rights reserved.©; and (b) McCall, P.L., P. Nieuwbeerta, R.L. Engen & K.M. Thames. (2009). Explaining Variation in Homicide Rates across Eastern and Western European Cities: The Effects of Social and Economic Forces. Paper presented at the Annual Meeting of the American Society of Criminology.
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Notes
- 1.
For a thorough review on the history of European and North American research on urban crime see Bruinsma (2007).
- 2.
For the sake of brevity, the term “Eastern Europe” will be used to refer to Eastern and Central Europe.
- 3.
National-level data used in cross-national analyses primarily have been compiled over time by the World Health Organization (WHO), Interpol, and the United Nations’ Demographic Yearbook (a source for covariates of crime) and provide researchers with measures of homicide and some of the classic covariates of homicide.
- 4.
The culture of violence is another perspective found in the U.S. homicide literature but is excluded from the current analysis because this unique characteristic associated with the southern region of the United States does not have an identified corollary in Europe. Furthermore, because the body of homicide studies is so vast, to make the review more manageable we omit cross-national level homicide studies and those U.S. and cross-national studies that employ disaggregated (race-, sex-, relationship-specific) homicide rates, as well as intranational and times-series, cross-national homicide studies.
- 5.
Some of the same ideas from classic versions of social disorganization have generated the systemic theory that also has been used to explain urban crime.
- 6.
For more details about these data, we refer the reader to the Urban Audit Methodological Handbook on the Eurostat Web site at http://www.europa.eu.int/comm/eurostat/ and the Urban Audit data on “New Cronos” at http://www.esds.ac.uk/international/support/user_guides/eurostat/cronos.asp#description.
- 7.
The “sampling” procedure was designed by Eurostat, each European country’s national statistical organizations, and the cities within the countries (Eurostat, 2004, p. 9).
- 8.
On closer inspection of the homicide data, homicides for Belgium cities seemed suspiciously high is – for example, more than 3 times higher than data reported for Brussels by the British Home Office. Therefore our analyses were conducted excluding Belgium cities except Brussels, for which we estimated their homicides (30 for 2001) based on data from the Home Office report (Barclay & Tavares, 2003, p. 11). To determine the degree to which our subsample of 117 cases may create a selection bias, we compared descriptive statistics for the full set of data 285 with our reduced sample of 117 cases. The only substantial difference between the cities’ mean statistics was the percentage of non-EU nationals, which was larger in our subsample than the total sample (6% vs. 3.5%). This is largely because much of the missing homicide data was in some of the Eastern European countries.
- 9.
We caution the reader that any one of these city’s data may not represent typical homicide rates because we have access only to one year of data from the Urban Audit. Nevertheless, we are somewhat confident with these data to the extent that they are comparable with the 15 cases from the British Home Office.
- 10.
We have no indication of the extent to which the data included in this analysis represent cause of death statistics or police statistics (see LaFree, 1999, for a discussion of data sources for cross-national homicide).
- 11.
Five cities reported zero homicides. Therefore when calculating the natural logarithm we added 0.5 to each homicide rate.
- 12.
Unfortunately, the Urban Audit data set does not provide the detailed income information to allow us to compute a Gini index. Therefore, we have no measure of income inequality in our model.
- 13.
The factor scores generated from the principal components analysis (using pairwise deletion of missing data) are percentage lone-parent households (0.693), percentage households with less than one-half the national average income (0.622), percentage households reliant on Social Security (0.769), and median disposable household income (−0.686). We multiplied median household disposable income by −1 to make it a theoretically consistent indicator with the other “deprivation” indicators.
- 14.
Therefore, some cities’ measure of deprivation is based on fewer than four variables. Sixty-seven (55%) cases included data for all 4 variables, 12 (10%) for 3 of the variables, 7 (5%) for 2 variables, and 36 (30%) for 1 variable (typically the percentage lone-parent households – mostly made up of the UK cities – but also median disposable income – mostly Spain) in the index. Our decision was based on our attempts to maintain more than 100 cases to enable us to invoke the central limit theorem.
- 15.
Many EU countries do not systematically collect information regarding the racial composition of their population (Tonry, 1997). Some will record information regarding country of birth and also information for second-generation immigrants. Therefore, the percentage of the population who are non-EU nationals was the best indicator of population heterogeneity available from this data set.
- 16.
Currency conversion rates according to the European Central Bank – http://www.ecb.int/stats/exchange/eurofxref/html/—which shows the 2001 euro valued approximately 0.85 of the dollar (1 € = 85 cents). This EU “disposable” income is what they have after their medical insurance and other similar types of living expenses are covered by the government. See also Eurostat (2005) for detailed discussion of data collection.
- 17.
Without more substantive studies of the impact of nationality, race, and ethnicity on homicide offending in European countries, and without systematically collected measures available to best capture the differential social and economic backgrounds they entail, homicide studies of European cities will not be able to assess the relationship between population heterogeneity and homicide rates.
- 18.
Because our measure of welfare support was a component of the deprivation index, we were not able to distinguish its unique effects on homicide.
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Appendices
Appendix 1
The 16 Countries Included in the Analysis (N of Cities = 117)
Austria (1): Linz; Belgium (1): Bruxelles; Czech Republic a (3): Ostrava, Praha, Usti nad La; Denmark (4): Aalborg, Aarhus, Köbenhavn, Odense; Estonia a (1): Tallinn; Finland (1): Helsinki; France (23): Amiens, Besançon, Bordeaux, Caen, Clermont-Fe, Dijon, Grenoble, Le Havre, Lille, Limoges, Lyon, Marseille, Metz, Montpellier, Nancy, Nantes, Orléans, Reims, Rennes, Rouen, Saint-Etien, Strasbourg, Toulouse; Germany (33): Augsburg, Berlin, Bielefeld, Bochum, Bonn, Bremen, Darmstadt, Dresden, Düsseldorf, Erfurt, Essen, Frankfurt, Freiburg, Göttingen, Halle an der Saale, Hamburg, Hannover, Karlsruhe, Köln, Leipzig, Magdeburg, Mainz, Mönchenglad, Mülheim, Moers, München, Nürnberg, Regensburg, Schwerin, Trier, Weimar, Wiesbaden, Wuppertal; Hungary a (4): Budapest, Miskolc, Nyiregyhaza, Pecs; Lithuania a (2): Kaunas, Vilnius; Luxembourg (1): Luxembourg; Latvia a (1): Riga; The Netherlands (5): Arnhem, Eindhoven, Heerlen, Rotterdam, Gravenhage; Slovakia a (3): Banska, Byst, Nitra; Spain (17): Badajoz, Barcelona, Las Palmas, Logroño, Madrid, Málaga, Murcia, Oviedo, Palma di Majorca, Pamplona/Ir, Santiago de Compostela, Sevilla, Toledo, Valencia, Valladolid, Vitoria/Gas, Zaragoza; United Kingdom (18): Aberdeen, Belfast, Birmingham, Bradford, Cardiff, Derry, Edinburgh, Exeter, Gravesham, Leeds, Leicester, Lincoln, Liverpool, Manchester, Newcastle, Sheffield, Stevenage, Worcester.
aEastern European countries.
Appendix 2
Bivariate Correlations and Descriptive Statistics for Variables in the Indexes, N = 117 Unless Otherwise Indicated
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | Mean | SD | |
|---|---|---|---|---|---|---|---|---|---|---|
Homicide rate (logged) | – | 0.34* | 0.16 | 0.32* | −0.35* | 0.31* | 0.26* | 0.36* | 0.36 | 1.04 |
% Lone-parent households a | – | 0.12 | 0.39* | −0.45* | 0.10 | 0.06 | 0.50* | 6.90 | 2.90 | |
% Households w/ half national mean income b | – | 0.72* | 0.08 | −0.02 | 0.27 | −0.08 | 20.58 | 17.02 | ||
% of Households reliant on Social Security b | – | −0.61* | −0.12 | −0.07 | 0.55* | 15.39 | 12.18 | |||
Median disposable household income c | – | −0.12 | 0.22 | −0.68* | 14,676 | 5,823 | ||||
Population size (logged) | – | 0.47 | −0.02 | 12.63 | 0.82 | |||||
Population per square kilometer (logged) | – | −0.08 | 0.48 | 0.74 | ||||||
Eastern Europe indicator | – | 0.11 | 0.32 | |||||||
Population size (thousandths) | 442.9 | 492.0 | ||||||||
Population per square kilometer | 2.09 | 1.83 |
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McCall, P.L., Nieuwbeerta, P., Engen, R.L., Thames, K.M. (2012). Explaining Variation in Homicide Rates Across Eastern and Western European Cities: The Effects of Social, Political, and Economic Forces. In: Liem, M., Pridemore, W. (eds) Handbook of European Homicide Research. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0466-8_8
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