Abstract
Critics have long denounced the design of suburban communities for fostering political apathy. We disaggregate the concept of suburban design into four distinct attributes of neighborhoods. We then use tract-level Census data, the Social Capital Community Benchmark Survey, and multilevel models to measure the relationship between these design features and political participation. Certain design aspects common in suburban neighborhoods are powerful predictors of reduced political activity, illustrating a potential link between neighborhood design and politics. Yet low-density environments appear to facilitate some types of participation. Suburban designs vary, and so do their likely impacts on political participation.
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Notes
The data and questionnaire are available at http://www.cfsv.org/communitysurvey.
Ewing et al. compiled an index of sprawl for metropolitan areas based on four components: residential density, the integration of homes, jobs, and services; the strength of centers, such as business districts; and accessibility via the street network. Subsequently Ewing et al. compiled a sprawl index for 951 metropolitan counties based on two of these factors: density and street accessibility. Our thanks to Reid Ewing for providing us with the index of county-level sprawl scores.
Specifically, we selected 290 census tracts from our survey respondents, over-sampling to represent the surveyed communities as well as the tails of the distribution. Two independent coders then used satellite images of the census tract from Google Earth to identify whether the tract had a traditional street grid, meaning that “the majority of streets visible in the image follow a traditional grid, with frequent intersections and few cul-de-sacs or dead-ends.” Coders could respond “yes,” “no,” or “mixed/ambiguous.”
To determine if there was much to be gained from a tract-level analysis, for each of our key independent variables, we calculated the proportion of the variance that was attributable to differences across metropolitan areas. The proportions ranged from 0.15 (for density) to 0.35 (for commuting time), indicating that in all cases, the majority of the variation is within rather than across metropolitan areas.
In part, the reason for this is that contextual measures of design across different levels of aggregation show surprising correlations. For example, the logged density of SCCBS national respondents’ census tracts correlates with the logged density of their counties at 0.74.
9,215 of these respondents did not have available census tract information, and they were assigned to a census tract based on the geographic center of their ZIP code.
For other measures, the figures are 0.004 (attendance at public meetings), 0.04 (voting), 0.004 (registering to vote), 0.003 (joining local reform group), 0.010 (participating in a march or demonstration), and 0.03 (signing a petition).
Voting measures whether the respondent reported voting in the 1996 presidential election, while the other activities are measured according to whether the respondent had engaged in the activity within the previous year.
The question wording for key variables is available in the Appendix. For other variables, please see http://www.cfsv.org/communitysurvey/docs/survey_instrument.pdf.
Although originally designed to measure industrial concentration, the Herfindahl index can measure the diversity of any population sorted into a finite number of mutually exclusive and exhaustive groups. Mathematically, it indicates the probability that two chosen units will be from the same group. Within studies of racial and ethnic politics, it is commonly employed to measure ethnic and racial diversity (e.g. Alesina et al. 1999; Branton and Jones 2005). To calculate a Herfindahl index, one sums the squared proportion of each group within a population. We do so using four census-defined groups: non-Hispanic whites, non-Hispanic blacks, non-Hispanic Asians, and Hispanics. The Herfindahl index can be interpreted as the probability that two members of a community are of the same racial or ethnic group.
A similar pattern of results also appears when we remove the 4,055 respondents who live outside metropolitan areas, affirming that these results are not driven by rural respondents. Still, our purpose is to capture the influence of spatial features, and to remove rural residents from our standard models would limit both the available variation and the generality of our findings.
Given that we measure design in part through the age of the median home, we also explored whether there were non-linear effects that might be evidence that certain time periods had especially influential designs. To do so, we broke up the age-based measure into five categories, and explored the impact of the resulting indicator variables. Our results showed a continual decline as homes grow younger, and did not give strong evidence of non-linear effects. However, with the rise of “New Urbanism” and related design principles, scholars could productively retest this possibility with data from more recent years.
Here, the negative correlation is surprising until one considers that mass transit use correlates positively with average commuting times.
The pattern of results also holds when using standard logistic regression with clustered standard errors, indicating that it is robust to modeling decisions.
To address the possibility that the results are driven by people’s underlying propensity toward social and public life, we estimated the same model using an index of social trust as our dependent variable. Living in a neighborhood with more solo commuters correlates with higher social trust, leading us to believe that the core results do not reflect differences in respondents’ psychological orientations toward social life.
The 95% confidence interval runs from 0.6 percentage points to 2.4 percentage points.
Here, the 95% confidence interval runs from −0.2 to 1.7 percentage points.
The 95% confidence interval for the impact of density on public meetings runs from 5.6 to 8.7 percentage points.
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Appendix
Appendix
Question Wording
This section provides the question wording for the dependent variables—all of which are indicator variables—and select independent variables.
Respondents to the SCCBS were asked: “Which of the following have you done in the past twelve months?”
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“signed a petition?”
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“attended a political meeting or rally?”
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“participated in any demonstrations, protests, boycotts, or marches?”
Other questions used as measures of participation are:
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“Are you currently registered to vote?”
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“As you may know, around half the public does not vote in presidential elections. How about you–did you vote in the Presidential election in 1996 when Bill Clinton ran against Bob Dole and Ross Perot, or did you skip that one?”
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“Just answer ‘yes’ if you have been involved in the past 12 months with this kind of group… Other public interest groups, political action groups, political clubs, or party committees”
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“How many times in the past twelve months have you attended any public meeting in which there was a discussion of town or school affairs?” (Coded as 1 for people who attended any meetings, 0 otherwise)
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“Did any of the groups that you are involved with take any local action for political or social reform in the last 12 months?”
Respondents were also asked questions about their tenure in the community, political interest, and ideology, such as:
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“How many years have you lived in your community? Less than 1 year, 1–5 years, 6–10 years, 11–20 years, more than 20 years, or all your life?” (Coded 1–6)
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“How interested are you in politics and national affairs? Are you very interested, somewhat interested, only slightly interested, or not at all interested?” (Coded 1–4)
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“Thinking politically and socially, how would you describe your own general outlook–as being very conservative, moderately conservative, middle-of-the-road, moderately liberal or very liberal?” (Coded 1–5)
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Hopkins, D.J., Williamson, T. Inactive by Design? Neighborhood Design and Political Participation. Polit Behav 34, 79–101 (2012). https://doi.org/10.1007/s11109-010-9149-2
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DOI: https://doi.org/10.1007/s11109-010-9149-2