Collective efficacy and arson: the case of Malmö

Abstract

The burning of cars and containers during social unrest has become a topic of both scholarly and public interest in Sweden over the past decade. Studies have suggested that social disorganization theory, or its modern expression collective efficacy, may be important for explaining why some neighbourhoods experience elevated levels of arson and unrest while others do not. To date, no study has explicitly studied the link between arson and collective efficacy at the neighbourhood level. The present study attempts to fill this gap by analysing the association between arson and collective efficacy in the city of Malmö, Sweden. Collective efficacy data from 96 neighbourhoods were collected in 2012 in the Malmö Community Survey (N = 4051). Arson data were collected from the rescue services, which employ GPS-receivers to accurately record the location of incidents. Regression models were fitted for arson, controlling for concentrated disadvantage, ethnic heterogeneity and residential stability. Results show no significant link between collective efficacy and arson following appropriate controls. This suggests that arson may depend on mechanisms other than collective efficacy, at least at the neighbourhood level. Future research may need to focus on alternative theoretical explanations such as strain, or on other levels of analysis than neighbourhoods.

Keywords:

• Arson
• collective efficacy
• social disorganization
• riots
• fire-setting

Acknowledgements

The author wishes to acknowledge the major contribution made by Karl Kronkvist of Malmö University in the collection, management and coding of the independent variable data.

Notes

1. In addition to the above referenced geographical-structural analyses of arson or rioting, there are several other Swedish studies based on different perspectives, such as co-offending and arson, typologies for school arson and micro-sociological explanations for the spread of riots (cf. Björk, 2013; Uhnoo, 2016; Uhnoo, Persson, Ekbrand, & Lindgren, 2015).

2. When the proportion of youth is included in the factor analysis, the loadings drop for the two variables that were included in the residential instability index. Number of years as resident drops from .933 to .757 and proportion of residents who moved away from the neighbourhood drops from .824 to .789.

3. Indexes were then re-weighted with new factor scores, but as Table 1 shows, the differences are very minor. The bivariate correlation for concentrated disadvantage and concentrated disadvantage with ethnic heterogeneity is .997, and the bivariate correlation for residential stability based on the two different factor scores is 1.

4. For a more detailed discussion of the arson data see Gerell (2016).

5. The number of neighbourhoods registering 0 on the outcome variable was 29 for 2013, 17 for 2012–2014, and 49 for vehicle-related arson in 2013.

6. One neighbourhood has average levels of collective efficacy but high levels of arson (the easternmost high arson neighbourhood in Figure 2). Excluding this neighbourhood has no substantive impact on the results, however.