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conjunction with Geographic Information Systems (GIS) and Hierarchical Linear
Modeling (HLM) to evaluate the effects of local context on the decision to vote. In
particular, we consider interaction effects of neighborhood and partisanship, in-migration,
education, income, gender, and age on turnout in some of the most hotly contested
venues of the last election. We will show how predictable correlates of voting are
influenced by local and neighborhood contexts. As a practical matter, we demonstrate
how voter lists, GIS, and HLM can be used to make useful predictions about political
behavior, thereby enhancing the ability of the campaign to target and mobilize voters.
The paper proceeds in a straightforward manner. The next section discusses how
aggregate-level characteristics might affect individual-level voting decisions. This is
followed by a description of the study’s data, design, and estimation techniques, and a
presentation of results. Finally, we discuss the broader implications of the approach and
analysis.
Local and Neighborhood Effects on Turnout
A number of studies have tested the relationship between neighborhood context
and individual political participation (Cohen and Dawson 1993; Eulau and Rothenberg
1986; Giles and Dantico 1982; Huckfeldt 1979; Kenny 1992; Krassa 1988; Straits 1990).
Collectively, their findings are consistent with the idea that neighborhoods have an
influence beyond the individual attributes of voters; although often neighborhoods matter
less when explanatory variables are introduced that describe voters’ particular networks
or influential neighbors (Eulau and Rothenberg 1986; Beck, Dalton, Greene and
Huckfeldt 2002). Neighborhoods and localized context also appear to matter less for
Convention