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Can Social Capital Account for Differences in Political Participation Across American Cities?
Unformatted Document Text:  absence of such a strong link suggests that, insofar as social capital has en effect on political participation, it is through mobilization. This, however, begs the question: if social capital is telling us that people get mobilized into political participation through involvement with different kinds of civic groups, what is this theory adding that we do not already know from the vast literature dealing explicitly with mobilization (e.g. Gerber & Green 2000, Leighley 1996, Rosenstone & Hansen 1994)? 5.1. Empirical Strategy The data I use here are nested, or clustered, in nature. I have data on individuals from the Benchmark survey and these individuals are clustered in cities, on which I also have data; as such observations have not been sampled independently of each other. As Snijders & Bosker (1999) note, dependence can be seen as both a nuisance and as an interesting phenomenon in itself (1999, 6–9). The nuisance is that dependence of observations needs to be corrected for in some way in order to avoid drawing incorrect inferences; for example, standard errors will tend to appear smaller than they actually are if dependence is ignored. However, I am also interested in analyzing the effects of different city characteristics on individual behavior. That is, I want to draw inferences on cities as well as individuals, making the clustering of observations of interest. In this paper, the question is whether living in a more ethnically diverse city affects an individual’s propensity to take political action. There are a number of empirical strategies for handling data structures of this kind. Steenbergen & Jones (2002, 220) note that political scientists have tended to opt for either “dummy variable models” or “interactive models.” Dummy vari- able models, by assigning fixed effects for each higher-level unit, are able to over- come the statistical problems associated with dependence of observations in clus- tered data (Steenbergen & Jones 2002, Rahn & Rudolph 2001). However, one is often interested in how various aspects of different higher-level units impact on lower-level units; say how different city characteristics influence individuals’ chances 19

Authors: Rubenson, Daniel.
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absence of such a strong link suggests that, insofar as social capital has en effect on
political participation, it is through mobilization. This, however, begs the question: if
social capital is telling us that people get mobilized into political participation through
involvement with different kinds of civic groups, what is this theory adding that we
do not already know from the vast literature dealing explicitly with mobilization (e.g.
Gerber & Green 2000, Leighley 1996, Rosenstone & Hansen 1994)?
5.1.
Empirical Strategy
The data I use here are nested, or clustered, in nature. I have data on individuals from
the Benchmark survey and these individuals are clustered in cities, on which I also
have data; as such observations have not been sampled independently of each other.
As Snijders & Bosker (1999) note, dependence can be seen as both a nuisance and
as an interesting phenomenon in itself (1999, 6–9). The nuisance is that dependence
of observations needs to be corrected for in some way in order to avoid drawing
incorrect inferences; for example, standard errors will tend to appear smaller than
they actually are if dependence is ignored. However, I am also interested in analyzing
the effects of different city characteristics on individual behavior. That is, I want to
draw inferences on cities as well as individuals, making the clustering of observations
of interest. In this paper, the question is whether living in a more ethnically diverse
city affects an individual’s propensity to take political action.
There are a number of empirical strategies for handling data structures of this
kind. Steenbergen & Jones (2002, 220) note that political scientists have tended
to opt for either “dummy variable models” or “interactive models.” Dummy vari-
able models, by assigning fixed effects for each higher-level unit, are able to over-
come the statistical problems associated with dependence of observations in clus-
tered data (Steenbergen & Jones 2002, Rahn & Rudolph 2001).
However, one
is often interested in how various aspects of different higher-level units impact on
lower-level units; say how different city characteristics influence individuals’ chances
19


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