Anyone who has lived in, driven through or walked by a “bad” neighborhood has a sense of the attributes that render such places unique: graffiti, litter, public intoxication and much more. According to the well-known theory of “broken windows,” these readily observable corporeal characteristics signal neighborhood disorder and lead to increased criminal behavior. This article investigates the implications of disorder for political behavior, taking particular care to distinguish between the objective tangible conditions of disorder and residents’ subjective interpretations of those conditions. Utilizing exceptionally rich data, this analysis reveals that while certain aspects of objective “reality” are consequential, perceptions of such reality are a more powerful mechanism through which neighborhood disorder impacts local political engagement. For some political outcomes, a heightened sense of the problems associated with disorder is linearly associated with an increase in participation. For others, the pattern is parabolic: those who perceive so little disorder that they remain content or so much disorder that they become disaffected are substantially less likely to take action to make their communities better. Ultimately, holding objective contextual features constant, the lenses through which residents interpret things like “broken windows” are critical determinants of grassroots politics. This information, combined with broader understandings of what shapes perceptions of disorder, lays the foundation for structuring policy in ways that facilitate grassroots activism—a vital component of the American democratic process.
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There is much contention among sociologists and criminologists about how best to define the concept of disorder (Harcourt 2001; Kubrin 2008). I will not attempt to resolve this issue here. I agree that there are problems with the idea (many of which are succinctly described by Kubrin 2008) but nonetheless contend that it captures (however imperfectly) a phenomena that is important for conceptualizing neighborhood context and is therefore worthy of study by political scientists. With this in mind, I take a practical approach and follow Skogan (2012: 174) in characterizing disorder as including, “unsettling or potentially threatening and perhaps unlawful public behaviors” (social) and “overt signs of negligence or unchecked decay as well as the visible consequences of malevolent misconduct” (physical). This definition is broadly reflective of the approach that has been taken in the literature to date.
In fact, Sampson and Raudenbush (2004) use the data leveraged in this paper to establish the connection between subjective, objective, individual and demographic factors.
Earls et al. (1997).
See the following for more details on the sampling methods associated with PHDCN: http://www.icpsr.umich.edu/PHDCN/sampling.html.
I considered creating two scales: one measuring perceptions of physical disorder and another gauging perceptions of social disorder. My primary reason for not doing so was theoretical. I had no strong theoretical impetus for assuming that individuals’ interpretations of their neighborhoods are filtered via such a dichotomy. Furthermore, a principal components analysis revealed that the combined items reflected one latent variable. More specifically, the first component had an Eigen value of 3.6 and captured over 60 percent of the variation between scale items. Finally, to be sure that this measurement decision did not impact the subsequent analyses, I re-ran the main models using two separate perceptions scales and found little difference in the results.
Earls et al. (2002).
These tracts were chosen based on a stratified probability sample designed to maximize race and class variation.
For more information about how each of these phenomenon were coded see Sampson and Raudenbush (1999): “Systematic Social Observation of Public Spaces: A New Look at Disorder in Urban Neighborhoods.”
In addition to their low correlation, both scales had relatively high Eigen values (above 1). However, it is important to note for these measures in particular that the construction of the scales was driven more by theory and prior research than by the principal components analysis.
I took this approach because ICPSR would not release the census data that was collected due to pending confidentiality issues. They also could not specify when the census data would be available nor would they provide census track numbers that would allow me to gather the data independently. Hence, to avoid entirely omitting neighborhood level demographic controls from the models, I decided to aggregate the level 1 data at the Neighborhood Cluster level. While this approach is not ideal, it is acceptable for several reasons. First, given the large sample size, NC’s contained an average of 33 (and a maximum of 62) persons each. Second, the Neighborhood Clusters were chosen based on internal homogeneity so having information about a sizeable handful of people within a cluster should be a rough proxy for information about the NC more broadly.
Initially, I considered allowing for the possibility that slope of perceptions might vary based on the race or class composition of the neighborhood. This would mean that the effect of perceptions would be different for different kinds of neighborhoods. I tried several models to this end -but there was little evidence of significant variation in the slopes across neighborhood clusters. Since varying slopes were not critical to the hypotheses I proposed, I ultimately opted for the simpler and more parsimonious varying intercept model.
I did not include a quadratic term in the level two model. There is little precedent in the literature for doing so. However, I nonetheless tested for the possibility that there is a non-linear relationship between objective disorder and participation and found that this was not the case.
In Tables 2 and 3, the results did not change substantively when I switched the ordering of models 1 and 2. In other words, models with just the basic individual level predictors and the objective measures of disorder (without demographic neighborhood predictors OR perceptions) showed similar patterns.
Note that the sample size decreases dramatically in the move from Model 1 to Model 2 and then again (although less so) in the move from Model 2 to Model 3. The initial decrease is a result of missing objective disorder data. Fortunately, the missingness of this data is random, as PHDCN Systematic Social Observation data was collected and coded only for a random subset of block faces in Chicago and thus only for a random subset of the people in the survey sample. It is instructive to note that subsetting the initial models so that they only include observations with full SSO data (e.g. dropping all observations without full data on SSO variables) produces substantively similar results as those shown in Table 2. The decrease from Model 2 to Model 3 is more problematic because it is mostly a result of missingness in response to the perceptions variable, which may not be random. To address this I took two steps. First, I re-ran the models based on subsetted data that only includes observations with no missingness on key variables, none of the substantive effects change. Hence, differences in the composition of the sample across models do not appear to drive the results. Second, I imputed the missing observations in the perceptions scale (based on research referenced above indicating how best to predict perceptions) and ran the models with this imputed data. Again, none of the substantive effects change. Overall, the sample size changes and missing data do not appear to bias any of the findings offered in the paper.
As a more direct test than this, I initially estimated Two Staged Probit Least Squares (2SPLS) models. 2SPLS is a procedure for estimating non-recursive models when one of the endogenous variables of interest is dichotomous (Keshk 2003; Alvarez and Glasgow 2000). The results were partially in favor of the core findings of the article: perceptions of disorder remained significant when the outcome was attending a meeting, even in a recursive setting. However, this did not hold for the other dependent variable (speaking to a politician). More generally, the 2SPLS results were questionable (in particular, they were highly sensitive to model specification). This is likely exacerbated by the multilevel and non-linear nature of the analysis, which the 2SPLS models I ran were not designed to account for. Given the uncertainty of these findings, I refrain from using them as evidence for or against reverse causality.
In addition, the results dis not change when I ran models that simply added a basic interaction between income and perceptions (as opposed to splitting the interactions up based on income category and running separate models).
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This research was made possible by funding from the Ford Foundation and the University of Chicago. Special thanks go to Cathy J. Cohen, Michael C. Dawson, Christopher A. Bail, Jon C. Rogowski and the helpful participants of the American Politics Workshop at the University of Chicago. I also owe a tremendous debt to the editors and anonymous reviewers at Political Behavior for their valuable insights and comments. All remaining errors are my own.
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Michener, J. Neighborhood Disorder and Local Participation: Examining the Political Relevance of “Broken Windows”. Polit Behav 35, 777–806 (2013). https://doi.org/10.1007/s11109-012-9217-x
- Political participation
- Broken windows
- Objective context
- Subjective context
- Community engagement
- Local politics