The Road to Division: How Interstate Highways Caused
Geographic Polarization
Clayton Nall 1
March 24, 2013
1 Assistant
Professor, Department of Political Science, Stanford University, 616 Serra Street, Stanford CA
94305. Phone: 617–850–2062. Email: [email protected]. Web:http://www.nallresearch.com. Thanks to Nate
Baum-Snow for kindly sharing replication data. Gary King, Dan Carpenter, Claudine Gay, Justin Grimmer,
Karen Jusko, Jonathan Rodden, Eitan Hersh, Ryan Enos, Ed Glaeser, and participants at Harvard workshops
provided helpful comments, as did attendees at talks at multiple campus workshop talks. Thanks to the Center for American Political Studies and Taubman Center for State and Local Government at Harvard University and the Stanford Institute for Research in the Social Sciences for fellowship support. For replication data,
http://dvn.iq.harvard.edu/dvn/dv/claynall.
Abstract
In the postwar era, Republicans have become increasingly more likely than Democrats to live in nonurban counties, and the two parties serve increasingly distinct geographic constituencies. Introducing a
theory of geographically-induced policy feedback, this paper shows that policies that shape geographic
space have contributed to these changes. It examines the effect of the Interstate Highway System, the
largest public works project in American history. Interstates are hypothesized to facilitate partisan geographic polarization by catalyzing residential migration and land use changes. These hypotheses are
tested by exploiting Interstates’ conditionally exogenous placement in suburban counties and metropolitan areas. Two studies show that suburban counties with Interstates became about 2 to 3 points less
Democratic than they would have been otherwise (about 5 points in the South), and increasing metrolevel highway density from the 25th to the 75th percentile increases the urban-suburban partisan gap by
up to 4 points.
American partisans are increasingly sorted by population density. While both parties have been
suburbanizing for decades, Republicans have become much more likely to live in low-density suburban
and exurban areas, while the Democrats have been more likely to live in central cities and inner suburbs
(Schneider, 1992; Gainsborough, 2001). By various measures, metropolitan areas have become more
polarized along an urban-to-rural continuum, a form of geographic polarization has grown along with
Congressional polarization, growing income inequality, and increasing residential income segregation
(McCarty, Poole and Rosenthal, 2006; Reardon and Bischoff, 2011).
The sorting of Democrats and
Republicans by residential density has helped dictate which issues reach the parties’ national agendas,
how public goods are distributed and metropolitan area disputes resolved (Einstein, 2011; Gerber and
Gibson, 2009), and how votes are translated into seats in state legislatures and Congress (Chen and
Rodden, 2011). Politicians in the two parties speak to partisan audiences that have become increasingly
sorted not just on policy issues (Levendusky, 2009), but also by space: Democrat-held Congressional
districts are 11 times as densely populated, on average, as Republican-held districts (Cooper, 2011). As
a result, policies tied to residential density have been tied to the parties’ ideologically coherent agendas.
In 1968, for example, many white swing voters lived in urban counties, and both parties’ platforms
dedicated over 500 words to urban policy (Democratic Party, 1968; Republican Party, 1968a). In 2012,
the Democratic platform devoted 350 words to a mix of policies such as rail and transit that primarily
appeal to constituents in urbanized areas, while the 2012 Republican platform’s sole discussion of urban
issues was to criticize President Obama for “pursuing an exclusively urban vision of dense housing and
government transit” (Baker, 2012; Democratic Party, 2012; Republican Party, 1968b). The two parties’
electoral strategies pit urban Democrats against suburban and rural Republicans. Voter identification
laws pushed by Republicans and Democrats’ push for reduced voter wait times would likely have the
greatest impact in Democrats’ urban (often African American and Latino) precincts (Stewart, 2012, 22).
1
In states from Michigan to Florida, debates on issues from urban financial control to mass transit and
intercity rail set Republican leaders based in rural and suburban constituencies against urban Democrats.
Like any phenomenon, partisan geographic sorting and the resulting urban-suburban polarization
have many causes. Most explanations of residential sorting on other dimensions—including correlates
of partisanship such as race—focused on either individual preferences for places (e.g., Schelling 1971;
Farley 1995) or on local attempts to attract preferred residents (Tiebout, 1956; Lewis and Neiman, 2009).
Others focus on exclusionary policies such as zoning, redlining, and racially exclusive covenants that restrict residential opportunities (Levine, 2006; Massey and Denton, 1993). This paper shows that federal
spatial policies—those that shape geographic space—have similarly influenced residential choice by influencing mobility and the range of residential options available to citizens. A theory of geographicallyinduced policy feedback holds that such policies are central to American political geography, influencing
the geography of election outcomes and influencing public policies focused on these constituencies. This
article tests this theory by examining one of the largest “spatial policies”: the Federal Aid Highway Act
of 1956, more broadly known as the Interstate Highway Act. The largest public works project in American history, the planned 41,000-mile system connected urban, suburban, and rural areas. By reducing
the cost of living in suburban and rural areas during a period of rapid suburbanization and white flight,
and by catalyzing preexisting partisan residential preference differences, Interstates created conditions
for increased partisan residential sorting and associated geographic polarization. Previous scholars have
shown that highways stimulated suburban growth (Baum-Snow, 2007) and rural development (Chandra and Thompson, 2000). This article examines highways’ concomitant effects on partisan residential
patterns, testing two empirical hypotheses on data combining county-level presidential election returns
with the historical Interstate construction record. Because highways can be thought of as a catalyst for
underlying preferences and political trends, the first hypothesis is that Interstates drew Republican voters
2
and voters likely to become Republican (who tended to prefer non-urban housing) to live in low-density
counties along highways. These effects were most likely in regions that had not already developed
around previous highway projects and mass transit, and where overall regional partisan changes were
the largest. The South is therefore expected to account for much of the effect. The second, related hypothesis is that Interstates increased the magnitude of the urban-suburban gap. These hypotheses are
tested by applying matching, difference-in-difference, and linear regression to a new database that combines 1952-2008 county-level presidential returns, county-level Census and geographic characteristics,
and historical highway construction records. These two empirical tests demonstrate that Interstates facilitated partisan residential sorting and increased urban-suburban polarization. They show that public
policies can change politics not only through individual-level behavioral change, but also by influencing
the spatial distribution of voters.
1
The History of Partisan Geographic Polarization and the Role of
Transportation Infrastructure
At first glance, urban-suburban polarization appears to be nothing new. Democrats have long been more
likely to live in cities, where ethnic immigrants and racial minorities were mobilized into (usually Democratic) machine voting blocs (Erie, 1990), while Republicans have long been more likely to live in peripheral areas, prompting speculation that suburbanization stimulates political conservatism (Wolfinger
and Greenstein, 1969). Political scientists in the 1950s noticed an apparent relationship between “size of
place” and party (Epstein, 1950). Nor is urban-suburban polarization uniquely American. In competitive
democracies, parties of the left tend to be urban, a “legacy” of the Industrial Revolution that has persisted
even as cities deindustrialized (Rodden, 2010; Chen and Rodden, 2013). Urban and non-urban legisla-
3
tive blocs have often been at cross purposes (Burns et al., 2009), and resolving urban-suburban divisions
over public goods provision has been a major concern of urban politics scholars (Orfield, 1994).
Even though urban-suburban polarization has been a long-running feature of metropolitan areas, its
growth over the last half century has been exceptional. The urban-suburban gap presidential voting gap
has grown monotonically since 1970, with the Republican Party getting a smaller fraction of its votes
from counties containing major cities, and Democratic presidential voters suburbanizing at a slower rate.
Figure 1 presents geographic polarization measures based on the county-level two-party presidential vote
in Census 2000 metropolitan statistical areas (MSAs) that currently contain a city of at least 200,000
persons (Leip, 2012). These measures reveal a steady increase in urban-suburban partisan segregation in
metropolitan areas, as measured by: the mean urban-suburban difference in the Democratic proportion of
the two-party vote (left), the mean relative centralization index (center), and the mean dissimilarity index,
a non-spatial measure of the proportion of Democrats who would need to change counties to produce
an even distribution (Massey and Denton, 1988, 284).1 All three measures have more than doubled
since World War II and grown monotonically since the 1970s.2 Metropolitan geographic polarization
has grown monotonically at about the same time as other forms of polarization and segregation: income
segregation (Reardon and Bischoff, 2011), national income inequality, and Congressional polarization
(McCarty, Poole and Rosenthal, 2006).
Geographic polarization also occurred after the construction of the massive Interstate Highway SysP
P
1
The relative centralization index, RCE = ( ni=1 (Xi−1 Yi ) − ( ni=1 Xi Yi−1 ), where the n units
are counties defined by geographic centroids, “ordered by increasing distance from the central business
district,” defined by the place marker in the 2008 ESRI cities file. See (Massey and Denton, 1988, 292).
2
These indices increased faster in the South, but also increased in non-Southern metropolitan areas
after 1970.
4
Relative Centralization of Dems
Mean Partisan Dissimilarity
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Dissim Index
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RCE Index
Difference in Urban, Suburban Dem Vote
Urban−Suburban Dem Gap
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1932 1952 1972 1992 2012
1932 1952 1972 1992 2012
1932 1952 1972 1992 2012
Year
Year
Year
Figure 1: Mean values of three partisan segregation measures for the two-party vote in Census 2000
metropolitan statistical areas. Left panel: Mean urban-suburban difference in Democratic share of twoparty vote. Center panel: Relative centralization of Democratic (versus Republican) votes. Right panel:
Mean dissimilarity index. 95% confidence intervals accompany each estimate.
tem, though the consequences of this project have not previously captured the attention of political
scientists. Political scientists have shown that transportation infrastructure’s effects on political geography are sometimes both unintentional. The first major American infrastructure project, the Erie Canal,
promoted westward expansion and solidified New York’s dominance as a financial capital, but also facilitated the Yankee diaspora and abolitionism’s expansion across the Great Lakes region (Cross, 1950).
The transcontinental railroads closed the frontier, but also stimulated prairie populism and radical labor
politics (White, 2011; Slez, 2011). Streetcar and rapid transit lines around the turn of the century facilitated racial and ethnic segregation, which covaries with partisan segregation (Oliver 2010, 100, Einstein
2011). Other uses of infrastructure have been more clearly about political and social control. For example, local governments commonly favored routing of highways so they would act as a physical barrier
between white and black neighborhoods (Connerly 2002, Chicago Historical Society 2004, Kruse 2005,
5
86).
It is likely that none of these examples of infrastructure programs were as significant a federal intervention into American social geography, or had as great an effect on American life as the Interstate
Highway System. Previous federal infrastructure programs consisted of piecemeal projects typically
micro-managed through Congressional appropriations (Weingast and Wallis, 2005). Interstates were,
by contrast, a large, exogenously imposed program that changed American transportation patterns and
daily life. The dedicated Highway Trust Fund under the Federal Aid Highway Act of 1956 covered 90%
of the cost of a single network, the planning and administration of which was managed by state and
federal highway engineers (Seely, 1987). Critically, the Interstate system is unrivaled in its enormity.
For example, a single section of I-95 running through New York City carried 289,000 vehicles per day
in 2010 (Federal Highway Administration, 2010). The entire Long Island Railroad, the nation’s busiest
commuter railroad, carried only 283,000 daily passengers in 2011, and all of Amtrak carried only 82,000
passengers per day (Metropolitan Transit Authority, 2013; Corporation, 2013).3 The automobility of the
average American depends on Interstates. The average American spent 56 minutes per day in a personal
vehicle in 2011 (Santos et al., 2011, 31) and about 25% of vehicle-miles are on Interstates (Cox and
Love, 1996).
The increased mobility brought about by Interstates also stimulated partisan change. While previous scholarship has suggested that transportation routes maintain neighborhood segregation (Ananat
and Washington, 2009), the metropolitan-level changes brought about by increased mobility were as
important. Directly, highways increase mobility and influence individual-level residential preferences,
enabling members of the two parties, who have different residential preferences, to act them out more
3
Residents of other metro areas are substantially more dependent on automobile for urban transporta-
tion, typically using transit at rates below 10% (Pucher and Renne, 2003; Fitch and Ruggles, 2003).
6
easily. Indirectly, highways influence land use decisions in metropolitan areas, which are in response to
and influence the choices available to different groups. Preferences for these different types of land use
are correlated with partisanship. The final mechanism is that highways may be economically exclusionary, providing mobility to middle- and high-income residents who live in suburbs, while the carless poor
choose to remain in cities and near urban transit service (Pucher and Renne, 2003; Glaeser, Kahn and
Rappaport, 2007).
A potential catalyst of residential sorting, highways “alter the time-space ratio” and reduce the effective (i.e., cost-based) distance between places (Lichter and Fuguitt, 1980, 494). This function seems, at
first glance, to be politically neutral, equally permitting the exurban Republican and the urban Democrat
to live where they like. But because residential preferences are typically correlated with partisanship,
even as a “neutral” phenomenon highways influence partisan sorting. Citizens need not consciously select on partisanship for partisan sorting to occur, because they move for seemingly apolitical reasons that
may strongly correlate with political party, including housing affordability, safety, school quality, and
cultural amenities (Gans, 1991; Los Angeles Times, 1999; Pew Research Center, N.d.; Gimpel, Cho and
Hui, N.d.). One of the most important of such correlates is race: white Americans have tended to avoid
racial diversity, while racial minorities prefer heterogeneity (Farley et al., 1978, 1994; Oliver, 2010).
Since non-white race positively correlates with Democratic voting, sorting on this variable is likely a primary factor in partisan geographic polarization (Einstein, 2011). Whatever the underlying motivations,
however, Republicans and Democrats have had consistently different residential preferences. Gallup
and Roper surveys in 1970, 1976, and 1983 asked respondents where they would live if they could live
anywhere they wanted to (Gallup Organization, 1983a; Roper Organization, 1976; Gallup Organization,
1983b). Republicans were 6 to 8 percentage points more likely than Democrats to say they preferred
suburbs or small towns. This gap is larger in more recent surveys. In 2011, the National Association of
7
Realtors found that Republicans were 17 points more likely than Democrats to prefer a rural residence
and 23 points more likely to favor communities with “sprawl” versus “smart growth” traits (Belden, Russonello & Stewart, 2011).4 In addition to enabling the expression of partisan differences in residential
preferences, highways facilitate land use changes that may attract different partisan groups.5 The classic
monocentric model of metropolitan land use assumes a single central business district surrounded by
a “zone of commuters” and uniform transportation costs within a metropolitan area (Baldassare, 1991;
Burgess, 1925; Mills, 1967; Mieszkowski and Mills, 1993). While this model breaks down under only
modest weakening of this assumption (Ogawa and Fujita, 1980), highways upturn these assumptions,
as demonstrated, inter alia, by the growth of “edge cities” around highway beltways and interchanges
(Garreau 1991, Mieszkowski and Mills 1993, 144). Interstates drove the creation of housing and employment markets on the periphery that otherwise would not exist. These included new industrial areas
and racially and economically exclusionary communities that were appealing to white residents who
abandoned central cities (Sugrue 2005, 127; Self 2003, 135-136, 149-151). Highways enticed developers to build communities tailored to particular types of residents and business districts (Burns 1994,
35-37, Lewis and Neiman 2009). In the mid- to late-1960s, for example, developers placed newspaper advertisements explaining that subdivisions built near Interstates would provide access to low taxes,
privacy, and good schools while maintaining access to downtown jobs (e.g., What’s Happening in the
New World Should be Happening to You 1968). Throughout most of the twentieth century, this highwayinduced development accompanied racial and economic real estate discrimination that disproportionately
4
Before around 1970, few such questions appeared on currently available national surveys. One
early question asked respondents where they would like to “live and work” (a double-barreled question)
(Gallup Organization, 1937a,b).
5
I thank a referee for suggesting this.
8
excluded Democratic-leaning income and racial groups from the suburbs (Oliver, 2010; Massey and Denton, 1993). While overt discrimination is now less common, indirect discrimination through measures
such as exclusionary zoning limits opportunities for high-density housing more favored, on average, by
Democrats (Levine, 2006).6 Where highways are built, they may stimulate housing built under these
restrictions and thereby catalyze such policies’ effects on residential sorting.
Highways may also increase partisan geographic sorting through socioeconomic bias. The poor are
more likely to be Democratic, and also less likely to drive on Interstates. Among those making less
than $20,000 per year, 27% have no motor vehicle, versus only 5% of other households. The poor make
17 percent of their trips by non-motorized means, and walk for nearly twice as many of their trips as
higher-income Americans (Pucher and Renne, 2003, 56, 59). Interstates may thus disproportionately
aid those who want to and can afford to drive and live in the suburbs. This contributes to the “spatial
mismatch” through which the disproportionately Democratic urban poor are isolated from suburban jobs
and opportunity (Kain, 1968; Wilson, 1987). Glaeser, Kahn and Rappaport (2007), for example, argue
that the availability of public transit makes urban neighborhoods attractive to the car-less poor.
1.1
Causal Strategy
What do we want to know about Interstates’ political effects, and what can we learn? Ideally, we would
like to infer a historical counterfactual in which Interstates had not been built, or an alternative national
policy had been adopted. This is the foundational logic of some of the grandest claims about highways’
effects, which are often based on qualitative historical research. The most widely cited suburban histories
note that the Interstate system was crucial to suburban growth (Jackson 1985, 249-250; Fishman 1989,
6
Twenty-eight percent of Democrats and 14 percent of Republicans rent their primary residence
(Belden, Russonello & Stewart, 2011).
9
190-191). Interstates’ causal effect on suburbanization and associated political effects would thus appear
to be widely known. While such claims seem valid on their face, few claims based in the urban history
literature use even the most assumption-laden forms of causal inference, such as cross-country comparative studies, or model-based approaches to historical counterfactual inference favored in the economic
history literature (Fogel, 1964).
A different approach, adopted in urban and regional economics and in this article, exploits spatial and
temporal variation in highway placement to ascertain highways’ effects. Such an approach permits more
accurate estimates, with the tradeoff of obtaining a different estimand: highways’ effects where they
were built (or, where more were built) compared to the counterfactual inferred from comparable units
that had no (or fewer) highways. Under this approach, highways are assumed to be“as-if” randomly assigned to counties, conditional on observables (Dunning, 2012). Previous work on infrastructure’s effects
has adopted two main approaches to satisfy this assumption.7 One approach is to search for and defend
an exogenous instrument that predicts highways’ placement (Baum-Snow, 2007; Duranton and Turner,
2008), or to make a plausible argument that where Interstates were located was effectively random (e.g.,
Chandra and Thompson 2000, 482). The second approach, and the one used here, is to account for the assignment mechanism under which highways were built in some places, not others (Rubin, 1991), using a
combination of nonparametric and parametric methods including difference-in-difference, nonparametric matching, and linear regression.8 Compared to other policies that contributed to suburbanization,
the Interstate program fits well with this research design: a well-documented planning project began
before construction. State highway builders rarely deviated from the plans except to permit modest local
7
Early work on Interstates’ effects used basic interrupted time series approaches. See, Garrison et al.
(1959).
8
For a recent example, see Rephann and Isserman (1994).
10
discretion over mile-by-mile placement. Key criteria used in planning appear in the 1944 Interregional
Highways report, the first coherent proposal for a national expressway system that looks like the modern Interstate System (National Interregional Highway Committee, 1944). These criteria can be used to
approximate the assignment mechanism, and is operationalized in counties and metropolitan areas using
county-level Census data and geographically referenced data from the 1944 report.
This causal strategy is adopted in two studies. The first tests whether suburban counties with Interstates became less Democratic than they would have been otherwise. Because such changes are expected
where highways had a potentially strong influence and where they would be catalyzing the already large
secular trends in geographic sorting (through phenomena such as white flight), such effects are expected
to be larger in the South. The second study uses metropolitan areas as the unit of analysis and examines
the gap between the urban and suburban counties that constitute each metropolitan area. All else equal,
metro areas with more Interstates are expected to become more geographically polarized (that is, have a
larger urban-suburban split in the Democratic presidential vote).
2
Highways and Suburban Political Development
The first empirical tests estimate highways’ effects on suburban development, using data from a Federal
Highway Administration Interstate highway construction database that runs through 1996, combined
with county-level Census data through 1950 and presidential-election data. Combining identification
approaches with a set of similar assumptions (nonparametric matching, difference-in-difference, and
linear regression), highways’ total effect on Democratic suburban support is estimated.
11
2.1
Data and Methods
Suburban counties, the population of interest, are defined as those with geographic centroids 20 to 100
kilometers from the center of the 100 most populous cities in 1950.9 Counties have numerous advantages
in a study focused on multi-decade political change. Their constant boundaries for most of the past
century, permit over-time comparisons that are impossible using higher-resolution data that cover more
recent periods (e.g., King and Palmquist 1998). Counties often organize school districts, public services,
taxation, and other factors in residential sorting (Tiebout, 1956). The full suburban county sample of
n = 988 counties appears in Figure 2.10 Summary statistics for the suburban counties appear in Table 1.
The outcome variable is defined as the the county-level change in the county-level Democratic presidential vote share between 1952 and each election year between 1960 and 2008 (Clubb, Flanigan and
Zingale, 2006; Inter-university Consortium for Political and Social Research, 1995; CQ Press, 2010;
Leip, 2010).11
The Interstate highway “treatment” is indicated by the variable Z, assigned value zit for unit i each
election year t if an Interstate opened in the county by year t − 4. This variable is derived from the
Federal Highway Administration PR-511 construction database, which covers Interstate construction
through 1996 (Baum-Snow, 2007; Michaels, 2008; Chandra and Thompson, 2000).12 This definition
9
10
The city center is defined by the location in the StreetMap USA Cities layer (ESRI, 2008a).
To exclude urbanized counties falling inside the catchment area of large cities, counties with a 1950
Census population greater than 300,000 are excluded.
11
Election returns through 1988 are drawn from ICPSR sources, while data after 1992 came from the
CQ Voting and Elections database and the Dave Leip archive. Post-1992 data were also used to correct
ICPSR errors.
12
The open-highway date is defined by taking the earliest “open” date from the PR-511 data for one-
12
Variable
Mean
SD
Min.
Max.
Earliest Interstate Opening, (Treated Counties)
1965
6.717
1942
1992
Interstate Highway Built At Any Time
0.554
0.497
0
1
Republican Presidential Vote, 1948
0.413
0.192
0.006
0.841
Republican Presidential Vote, 1952
0.548
0.152
0.081
0.930
Republican Presidential Vote, 1956
0.556
0.154
0.095
0.926
Median Family Income, $, 1949 (9-category)
5.16
1.524
2
9
Proportion Non-White, 1950
0.110
0.164
0
0.818
Log(Persons Per Acre, 1950)
-2.54
0.872
-7.26
0.2825
Proportion in Urban Residence, 1950 (10-category)
2.60
2.231
0
9
Crop Value Per Capita, 1950 ($)
112
133
0
1619
Mfg. Establishments, 1939
36
51
0
457
On Strategic Route, 1941
0.657
0.475
0
1
Proportion Non-Resident One Year Earlier, 1950
0.013
0.13
0.000
0.192
Region=South
0.42
0.49
0
1
Treatment Variables
Covariates
Table 1: Summary statistics for suburban county sample (n = 988).
13
Figure 2: Map of the suburban-county sample. Counties that contained an Interstate highway at any
point through 1996 are grey, while counties without Interstate highways through 1996 are black.
implies that any county with an Interstate highway for at least 4 years received the same cumulative
highway “dosage” up to year t, a reasonable assumption since most counties saw a new highway in the
narrow window between 1956 and 1970.
Covariates are included in matching and regression to account for the assignment mechanism and
improve balance between Interstate and non-Interstate counties. These come from county-level Census
variables measured through 1950 (Fitch and Ruggles, 2003) or from the same election results as before.
Census variables are selected to match factors listed as criteria for highway placement in Interregional
Highways. Several covariates pick up preexisting suburban and rural development, including Log population density in 1950, Crop value per capita, 1950, and a variable for the Percentage urban. The
mile Interstate segments in the county.
14
Number of manufacturing establishments in 1939 captures local industrialization, also mentioned in the
1944 report. Military requirements, whose role in highway planning is often overstated, are represented
by a dummy variable for whether a county was on or near a 1941 strategic route map that appeared in
the 1944 report (National Interregional Highway Committee, 1944, 33). Median family income in 1950
is a correlate of automobile ownership. Other covariates account for other potential confounders. The
Percentage of households that lived outside the county in 1949 measures pre-existing suburbanization
trends. The Republican presidential vote share in 1948 and 1956 accounts for partisan interventions
in highway planning and to predict unobserved partisan realignment trends. The Percentage nonwhite
in 1950 is a strong correlate of partisanship and partisan change, and is a key correlate of geographic
polarization. Finally, a dummy variable for the South is included to account for Southern influence over
highway policy and to detect regionally heterogeneous effects.
A three-step process of non-parametric matching, linear regression (with difference-in-difference),
and temporal smoothing is adopted to estimate Interstates’ total effect on the partisan composition of
suburban counties between 1960 and 2008.
First, the covariates listed above are used to construct a separate matched sample at each election year
using coarsened exact matching (CEM), an alternative to propensity-score matching for “non-parametric
preprocessing to reduce model dependence” (Iacus, King and Porro, 2011a; Ho et al., 2007). CEM
places observations in multidimensional bins defined using coarsened versions of the covariates above,
then assembles a sample only of the treated counties (those with an Interstate at year t − 4) and untreated
counties (those without) that fall in the same bin.13 The default method is to trim unmatched treated and
untreated units.14 CEM removes almost all preexisting imbalance on each included covariate during the
13
Each covariate is placed in at most four bins.
14
Estimates on this sample yield a local effect among treated units that were well matched.
15
years in which effects are detected. Imbalance is reduced less in early years, mostly because the treated
group (counties that had early Interstates) is small and unusual, and the resulting matched sample is
therefore small. The standardized difference in means for the covariates in Interstate (treated) and nonInterstate (untreated) counties appears in Figure 3.
15
Next, Interstates’ effect on suburban county-level
Democratic presidential vote share is estimated independently on each year’s matched sample using least
squares regression, for each year from 1960 to 2008:
Yt − Y1952 = β0t + βzt zt + β1t x1 + . . . + βkt xk + (1)
where zt represents that an Interstate was open in year t − 4, βzt the effect of interest, and x1 . . . xk the
covariates. Because the outcome is a gain score, for βzt to capture the causal effect of the treatment of
interest, the key assumption is that, in the absence of interstate highways, interstate highway counties
would have undergone the same change in Democratic vote share in the absence of highways as nonhighway counties in the matched sample, conditional on the additive linear effect of included covariates.
Matching makes this assumption more plausible, since observations with unusual pre-trends are excluded
from the data.
Finally, confidence intervals are constructed using bootstrapping and lowess smoothing. One thousand samples are drawn from the CEM-matched data sets for each election year. Two regressions are
estimated on each sample: one as described above, a second that interacts the Interstate variable with
the South variable.16 The resulting 1,000 bootstrapped point estimates for the national and regional
effects were obtained by adding relevant coefficients (Braumoeller, 2004) and stored as a matrix of
15
Before matching, the sample is truncated to exclude “early-adopter” states that built Interstate-
quality highways before 1956. This is similar to excluding “always-takers” from an instrumentalvariables design (Angrist, Imbens and Rubin, 1996).
16
Matching weights are used at the regression stage (Iacus, King and Porro, 2011b).
16
Standardized Imbalance
Log Population Density, 1950
1.0
0.5
0.0
F
F F F F F F F F F F F
M M
Standardized Imbalance
F
0.5
0.0
F
M M
F F
F F F F F F F
M M M M M M
M M M M
0.5
0.0
−0.5
−1.0
−1.0
−1.0
1980
1996
1964
Year
1952 GOP Pres. Vote
1.0
1980
1996
F
F F F
F F F F F F F
M M M M M M M M M M M
1980
1996
Year
Percentage Non−White, 1950
1.0
0.5
F
M M F
F F F F F F F F F
M M
M M M M M M M M
M
1964
Year
1948 GOP Pres. Vote
1.0
F
0.5
0.0
F
1956 GOP Pres. Vote
1.0
−0.5
0.5
M
0.0
F
F
F F
M M M
F M
M M
F M
F M
F M
F M
F M
F
0.0
M M
M M M
F M
F M
F M
F M
F
M M M
F F F
F
F F
−0.5
−0.5
−0.5
−1.0
−1.0
−1.0
1964
Standardized Imbalance
F
−0.5
1964
1980
1996
1964
Year
Pct. Urban, 1950
1.0
1980
1996
1964
Year
Crop Value Per Capita, 1950
1.0
1980
1996
Year
Pct. Incoming Residents, 1950
1.0
F
0.5
F F F F F F F F F F F
0.5
0.0
M M M M M M M
M M M M M
0.0
0.5
M M M M M M M M M M M
F F F F F
M
F F F F F
F F
0.0
−0.5
−0.5
−0.5
−1.0
−1.0
−1.0
1964
Standardized Imbalance
M M M M M M M
M M M
F
Median Family Income
1.0
1980
1996
1964
Year
South
1.0
1996
M M M M M M M
M M M M M
F F F F F F F
F
F F
1964
Year
Mfg. Estab., 1939
1.0
0.5
0.0
1980
F F F F F F F F
M M M F M
M M M M M M M
F F M
F
F
F
1.0
0.5
F
0.0
M M M M M M M M M M M M
F F F F F F F F F F F
1980
F
1996
Year
Mil Need
F
F F F F F F F F
0.5
F
0.0
M M M M M M M M M M M M
F
−0.5
−1.0
F
1964
1980
1996
−0.5
−0.5
−1.0
−1.0
1964
1980
1996
1964
1980
1996
Figure 3: Standardized imbalance of included covariates (difference in means between the treated and
control group, divided by the standard deviation in the unmatched treated group) under CEM. Standardized imbalance in the [M]atched and [F]ull (original) sample are presented for the matched sample for
each election year, 1960-2008.
17
vectors containing one column for each year. To construct accurate confidence intervals and smooth
over election-specific vagaries, the vector associated with the bootstrapped estimates in each year was
smoothed by calculating a locally weighted regression (lowess) curve of the estimates against election
year. This approach shrinks point and interval estimates by averaging effects from proximate elections.17
The quantiles of these smoothed values are used to construct 95% and 80% confidence intervals.18
2.2
Results
Interstate highways made suburban counties in which they were built less Democratic than they would
have been otherwise, with most of the effects concentrated in Southern counties (Figure 4). Estimates on
the matched national sample and on the South are in the expected direction. The bulk of the effect appears
in fast-growing postwar Southern suburbs. These Southern suburban rings would, over the course of the
study, become part of the Republican Southern ascent (Black and Black, 2002, 6-7). On average, the
presence of an Interstate highway in a county helped reduce the Democratic vote share by 2 to 3 points
in each year between 1976 and 2004, with slightly smaller effects in later years (point estimates remain
negative and differences between the significant and non-significant results are negligible). As expected,
highways’ effect on partisanship in Southern counties has been larger in magnitude and stable over time.
Southern counties with Interstates became about 5 points less Democratic than they would have been
otherwise, an effect that is significant at the 5% level (two-tailed test) in all years after 1972. Effects in
non-Southern counties were, as expected, smaller and less persistent, though highways still made such
counties 1 to 2 points less Democratic than they would have been otherwise between 1980 and 1996.
(Point estimates were in the expected direction over a longer time period.)
17
The lowess function uses a span of one-third of the data points and uses three “robustifying itera-
tions” (Becker, Chambers and Wilks, 1988).
18
Other methods, including bias-corrected confidence intervals, may be used (Keele, 2008, 181).
18
Highway Effect on Change in County Dem Vote Share (Points), 1952−[Year]
All Suburban Counties
8
7
6
5
4
3
2
1
0
−1
−2
−3
−4
−5
−6
−7
−8
−9
● ●
● ●
1964
●
1976
●
● ● ●
1988
●
●
2000
South
●
8
7
6
5
4
3
2
1
0
−1
−2
−3
−4
−5
−6
−7
−8
−9
●
●
●
Non−South
●
●
●
●
1964
1976
Year
● ●
● ● ●
1988
2000
8
7
6
5
4
3
2
1
0
−1
−2
−3
−4
−5
−6
−7
−8
−9
● ●
● ●
1964
● ●
●
● ●
1976
Year
1988
●
●
●
2000
Year
Figure 4: Interstate highways facilitated drop in the suburban Democratic vote share. Smoothed OLS estimates of effect of Interstate in a county, using CEM-matched sample and excluding early-adopter states.
80% (thick line) and 95% (thin line) confidence intervals accompany each estimate. Large intervals in
the early 1960s are due to the small number of counties with Interstates at year t − 4.
19
The large effects in the South are expected and consistent with highways’ role as a catalyst. NonSouthern suburbs were both more developed and had more developed transportation infrastructure (McShane, 1994), while postwar Southern road building provided the more basic (and consequential) benefit
of helping a region catch up in rural and suburban development. Schulman (1994) notes that “the interstate highway and federal airport programs brought isolated counties without access to markets or
distribution centers into the national economy. The impact was greatest where existing systems were
the poorest and resources to improve them the scantest” (158). The result is also consistent with Shafer
and Johnston (2006), who note that Southern economic development contributed to the movement of
middle- and upper-income whites into the Republican Party (Ch. 2). (Black and Black, 2002, 6-7) note
that fast-developing suburbs such as Cobb County, Georgia grew around Interstates and became the geographic base of the Southern Republican Party. By contrast, non-Southern areas dominated by older
housing—mostly cities in the Northeast and Midwest—were already developed, and may have been less
susceptible to infrastructure-induced change. This result is similarly consistent with previous findings in
regional economics (Mieszkowski and Mills, 1993).19
These results are built on a core assumption that the placement of Interstates in suburban counties
was “as-if random.” The Online Appendix presents several tests of related modeling assumptions . The
first assumption is that Interstate and non-Interstate counties have comparable potential trends over time.
This assumption is tested further by administering “placebo tests” on outcomes measured before the
Interstate Highway Act, including on presidential results from 1948, 1952, and 1956 (Figure A-6), and
1930 and 1940 Census variables that were not included in matching. Figure A-7 presents the t-statistics
for the placebo effects for these pre-treatment variables, almost all of which fail to reach significance at
19
CEM, which produces a local effect estimate, may explain some of this difference. However, even
without matching, effects are larger in the South than elsewhere.
20
the 95% level (Dunning, 2012).20
3
Highways and Urban-Suburban Sorting
Because highways contributed to a growing Republican vote in suburban counties where they were built,
it is expected that they also increased urban-suburban polarization. This hypothesis is tested on a sample
of major metropolitan areas, using Interstate exits per square mile as the explanatory variable of interest.
3.1
Data and Methods
Major metropolitan areas are conceived as “couplets” formed from each metropolitan area’s urban and
suburban portions. The urban portion of each couplet contains counties that contain the 100 most populous cities in 1950. The suburban portion contains other counties with centroids within 80 kilometers
of the central city (cities). All urban counties in each metropolitan area are combined to form the urban portion, while other counties are combined to form the suburban portion. This process produces 70
20
These show that good balance has been achieved not just with respect to baseline, but also with
respect to pre-trends. Another assumption is that highway construction does not, itself, induce additional
highway construction. To confirm that reverse causation is not a problem, the 1944 Interregional Highways map is used to instrument highway construction in each year included in the main analysis (Online
Appendix Section C). Another assumption is that no interference exists between units (Rubin, 1986).
This is typically a strong assumption in any geographic study. If highways induced political change,
such change would have diffused across neighboring units. We would expect this to bias estimates of the
effect magnitude downward (Gelman and Tuerlincx, 2000). Since such diffusion is more likely to occur
in later years, this could explain the diminishing effect magnitude in the non-Southern sample in later
years. The Online Appendix (p. 84) also includes a test of findings’ sensitivity to an omitted confounder.
21
urban-suburban couplets.
The outcome variable is defined as the difference between election year t and 1952 in the urbansuburban Democratic vote share gap in each couplet. D̄ist (D̄iut ) represents the urban (suburban) Democratic vote share in metro area i in year t. The outcome of interest is the following:
∆i,t = (D̄iut − D̄iu,1952 ) − (D̄ist − D̄is,1952 )
(2)
‘ This use of the gain score as the outcome variable permits unbiased estimation of highways’ effects, as
long as the potential trends between 1952 and year t are identical for metropolitan areas with more and
fewer highways, after linear adjustment with included covariates.
The explanatory variable, Interstate highway exits per square mile of land area at year t − 4, captures
Interstates’ spatial influence in each metropolitan area. Interchanges connect communities in metropolitan areas and collapse cost-based relational space. A GIS shapefile containing year 2008 exits was spatially merged with the nearest segment in the FHWA PR-511 highway construction database, yielding a
database exits’ imputed year of construction. (ESRI, 2008b).21 The exit count in each year from 1956 to
1996 is divided by metropolitan land area to capture Interstate highways’ spatial “dosage.” To account for
the highway assignment mechanism in metropolitan areas, metro-level covariates are constructed from
county-level data. The log transformation of Metropolitan area population density in 1950 addresses
a predictor of highway placement mentioned in Interregional Highways: highways were built to serve
population centers. The Proportion of counties on a military strategic route in 1941 accounts for the
perceived military importance of each metro area. The Mean number of manufacturing establishments
in 1939 accounts for preexisting industrialization. To account for future changes in the urban-suburban
21
Coordinates for this database come from Baum-Snow (2007). Spatial matching includes only Inter-
states built through 1996.
22
Variable
Mean
SD
Min.
Max.
Exits Open Per Square Mile, 1956
0.005
0.008
0
0.050
Exits Open Per Square Mile, 1964
0.023
0.027
0
0.155
Exits Open Per Square Mile, 1968
0.032
0.033
0
0.162
Exits Open Per Square Mile, 1972
0.038
0.038
0
0.197
Exits Open Per Square Mile, 1976
0.041
0.040
0.004
0.214
Exits Open Per Square Mile, 1980
0.044
0.042
0.004
0.214
Exits Open Per Square Mile, 1984
0.045
0.042
0.004
0.214
Exits Open Per Square Mile, 1988
0.046
0.042
0.004
0.217
Exits Open Per Square Mile, 1992
0.047
0.043
0.006
0.220
Exits Open Per Square Mile, 1996
0.049
0.043
0.006
0.220
Urb-Sub Diff. in Dem. Pres. Vote, 1948
0.034
0.086
-0.201
0.210
Urb-Sub Diff. in Dem. Pres. Vote, 1952
0.033
0.093
-0.169
0.197
Urb-Sub Diff. in Dem. Pres. Vote, 1956
0.024
0.092
-0.202
0.226
Urb-Sub Diff. in Proportion Nonwhite, 1950
0.026
0.056
-0.132
0.185
Urb-Sub Mean, Proportion Nonwhite, 1950
0.098
0.112
0.003
0.440
Urb-Sub Mean, Mfg. Estabs., 1939
801
1804
0
14320
Urb-Sub Mean, Co. on Strategic Route
0.83
0.19
0.1
1
Log Persons/Sq. Mi., 1950
5.10
0.93
3.74
8.013
South
0.275
0.449
0
1
Table 2: Summary statistics for explanatory variables and covariates for urban-suburban couplets (n=70).
23
split, the models include the Lagged urban-suburban difference in the Democratic presidential vote share
in 1948, 1952, and 1956. The importance of race as a strong correlate of the urban-suburban partisan
divide, and its role as a factor in white flight, is represented by the Urban percentage non-white minus
the suburban percentage non-white and the Mean of urban and suburban percentage non-white. A South
dummy variable accounts for preexisting regional differences in infrastructure and accounts for political pre-trends that anticipated the post-Civil Rights realignment. Summary statistics for these variables
appear in Table 2.22
The effect of the exit variable and its logarithmic transformation (which implies diminishing marginal
impact of increasing highway density) are estimated by least-squares regression:
∆t = β0 + βzt zt + β2 x1 + . . . + βk xk + (3)
where βzt represents the effect of a one exit-per-square-mile (or logged exit-per-square-mile) difference
in exit density at year t − 4 and x1 , . . . , xk are included covariates. Due to sample size limitations,
regional interaction effects are not estimated. No nonparametric matching is conducted, but the analysis
otherwise follows the same bootstrapping and smoothing procedure discussed in Section 2.1. βzt is
estimated on the full sample for each election year.23
22
Within-couplet urban-suburban differences are a population or vote-weighted difference in means.
The within-couplet mean is the unweighted mean of the weighted urban and suburban averages in each
couplet.
23
A small positive value, 10−4 , was added to each value before log transformation to permit calculation
of log density. This is mostly relevant in the earliest years of the program, when some major metropolitan
areas had no Interstates.
24
3.2
Results
Figure 5 plots predicted differences in the urban-suburban Democratic voting gap associated with a shift
from the 25th percentile to 75th percentile of Interstate density in 1996, an outcome chosen to provide a
consistent comparison across years while providing easy substantive interpretation of the results. The left
panel displays the effect for the untransformed exits variable, the right panel its log-transformed version.
These results show that increasing the number of Interstates in a metropolitan area increases urbansuburban polarization in the presidential vote. While point estimates are uniformly positive under both
versions of the exit-density variable beginning in 1972, the 80% confidence intervals permit rejection of
the null in the log-transformed case by 1992, and the 95% confidence intervals permit rejection of the
null by the 2000s. Under the untransformed version of the treatment variable, a change in exit density
across the interquartile range (IQR) led to a 1.5 to 2 point increase in polarization starting in the 1990s.
Under the log-transformed version, the difference associated with an exit-density increase across the IQR
reached 3 points by the early 1990s, increasing to four points by 2008.
24
These estimates are substantively large. The magnitude of the effect associated with moving across
the interquartile range is about a quarter as large as the average urban-suburban polarization gap. It is
also large compared to other effects commonly estimated in political science. For example, this predicted difference across the interquartile range is about as large as experimental estimates of direct voter
contact’s effect on turnout (Gerber and Green, 2000), and about the same size as the incumbency advantage in state legislatures across the same study period, also about four percentage points (Cox and
Morgenstern 1993, 501; King 1991, 125). The effect sizes are also on par with other observational stud24
The IQR of the number of exits per square mile was [0.003, 0.1] in 1960, rising to [0.19, 0.56] in
1996.
25
Exits Per Sq. Mi.
Log(Exits Per Sq. Mi.)
8
5
7
4
6
3
●
●
1
●
●
●
●
●
●
5
●
First Difference, Points
First Difference, Points
2
●
0
●
−1
●
●
4
●
●
●
3
●
●
●
2
−2
●
●
●
−3
1
●
●
−4
0
●
●
−5
−1
−6
1960
1968
1976
1984
1992
2000
2008
1960
Year
1968
1976
1984
1992
2000
2008
Year
Figure 5: Interstates’ predicted effect on the urban-suburban Democratic voting gap. Difference across
the interquartile range of exits per square mile in 1996. Left: exit density. Right: log-transformed exit
density. Bootstrapped 80% and 95% confidence intervals accompany the estimates.
ies related to the political effects of distributive politics. Each presidential federal disaster declaration
adopted in a state, for example, increases the incumbent president’s vote share by 1 point (Reeves, 2011,
1150). (Levitt and Snyder, 1995) find that an increase in federal non-transfer spending of $100 per capita,
“approximately $50 million” per house district, boosts incumbent House member vote share by about 2
percentage points.
One concern that could be raised about this analysis is that the metropolitan areas are defined by
the researcher, but any definition of a metropolitan area is subject to researcher discretion (Rosenbaum,
1999). Allowing metropolitan boundaries to vary with changing population density over time, including
via adoption of Census-based statistical definitions of urban and rural areas, would select on the dependent variable, because fast-growing metropolitan areas have also become more polarized. The radius
26
was chosen according to a principle, to capture a roughly one-hour commuting radius from the central
cities at the 1950 baseline under typical Interstate highway speeds. To justify these coding rules further,
the findings’ sensitivity to codings based on density and different radius choices appear in the Online
Appendix (p. 47).
Another potential concern is that exit and highway placement was sensitive to reverse causality arising from local interventions into highway planning. State and local highway officials deviated from
official highway plans by adding or dropping metropolitan highways. However, exits are rarely added
to the system after initial highway construction. A sampling of Rand McNally atlas maps of Houston,
Atlanta, and Phoenix from 1965 through 1985 shows that only 7% of the exits were added after the
local Interstates themselves were built (Online Appendix Table A-1). While some measurement error
exists, then, it is unlikely to bias the results. To account for the potential of reverse causality or positive
feedback in highway construction, an instrumental variables model similar to Baum-Snow (2007) is also
presented in the Online Appendix (Figure A-18).
As in the suburban analysis, additional analyses and robustness checks appear in the Online Appendix. One of these robustness checks is a matching-based analysis, conducted with placebo tests
and balance checks, in which the exit-density variable is dichotomized at the median (Online Appendix
p. 52). This yields comparable estimates for effects in recent years. A parametric sensitivity analysis
assesses findings’ robustness to an unobserved omitted variable U (Online Appendix Section ??).
4
Migration, Sorting, and Demographic Change
It could be argued that the effects observed here are second-order effects of demographic and socioeconomic change. This is not a concern, because partisan segregation is the outcome of interest and is a
known concomitant of other geographic changes. Because key individual-level traits (race, income, gen27
der, age) are correlated with partisanship, we expect geographic changes in these variables to covary with
partisan geographic change, as a result of the same mechanisms that lead partisans to sort. (Indeed, if
they did not, the results would be unusual.) A matching and smoothed bootstrapped regression procedure
similar to the main analyses was used to estimate highways’ effects on Census variables for the years
1970 through 2000. While the effects appear at varying levels of certainty, Interstates facilitated higher
property values, more new home construction, higher incomes, and higher rates of out-of-county commuting in counties where they were built, but had no discernible effect on non-white racial composition.
(Online Appendix Figure A-9).
Another concern is that large effects observed in the South are due to conversion, not sorting.
A priori, it is unclear why highways would lead partisan conversion to occur more rapidly in places
with highways than in those without. Likely, a combination of both mechanisms were likely at work,
and neither is inconsistent with the infrastructure-induced policy feedback discussed here. While Southern whites were converting to the Republican party, the region was also more mobile than the rest of
the country. Over most of the study period, residents of Southern suburban counties have been more
likely than non-Southerners to be new to their counties. Southerners in counties with Interstates were
about 5 points more likely to be newcomers than residents in non-Interstate counties. This strongly
suggests that highway-induced suburban in-migration, and not just localized highway-induced changes,
were responsible for highways’ observed suburban effects.
Whether partisan geographic segregation has a contextual effect on individual political attitudes remains an open question. Previous work suggests that an adverse consequence of sorting and geographic
polarization is that people isolate themselves from competing political viewpoints and adopt more extreme positions (Mutz, 2006; Sunstein, 2009; Bishop and Cushing, 2008). While there is reason to be
skeptical of such claims since voters rarely talk to their neighbors about politics (Abrams and Fiorina,
28
2012), if partisans are sorting the types of political discussion, and the issues that appear on the local
agenda and in political conversation may change.
5
Conclusion
Where Americans live within a metropolitan area is increasingly linked to their partisanship. This fact
has extensive consequences for elections and public policies. Suburbanization has coincided with the
rapid dispersal of Republicans into more suburban areas. Not coincidentally, over the same period, faster
automobile transportation has expanded Americans’ residential options between and within metropolitan
areas. While they are not the sole factor, Interstate highways have been a major input of this changed
partisan geography. The size of their effect is comparable to other estimates of the effect of distributive
spending on politics.
Highways have operated via local political geography, and the magnitude of their effects will be
contingent on both the baseline partisan geography where they were built and preexisting sorting trends.
Built at a point of rapid social and economic change during the postwar era, Interstates facilitated ongoing suburbanization and white flight, contributing to a larger urban-suburban split where they were built.
Unsurprisingly, then, highways appear to have had their strongest effect where their potential influence
was strongest in the South, counties with Interstates became 5 points less Democratic than they would
have been otherwise. Highways contributed to greater urban-suburban polarization. Metro areas with
Interstate density at the 75th percentile saw, over time, a four-point larger gap than if their highway density had been at the 25th percentile. This finding holds after accounting for baseline population density,
racial polarization, political polarization, and factors that determined where highways were built. The
magnitude of the partisan divide, then, has been a function not only of individual sorting decisions or
technological change, but a result of “place-making policies” (Glaeser and Gottlieb, 2008). In addition to
29
linking public policy attitudes, residential preferences, and partisan geography, this study suggests many
opportunities to evaluate public policies effect on spatial outcomes. While research in other political science subfields has focused on infrastructure’s instrumental social uses (e.g., Scott 1998, Ch. 2), American
politics research has been limited almost entirely to the distributive politics of transportation infrastructure spending. Political scientists know how infrastructure “greases the wheels” of the Congressional
lawmaking (Evans, 2004; Lee, 2003) or helps members curry favor in the bureaucracy and their districts
(Ferejohn, 1974; Stein and Bickers, 1997). We have had a less clear sense of the long-term political
effects that arise when infrastructure reworks the social, economic, and political fabric of communities.
The findings here suggest that scholarship on policies’ political effects should account for geographic
mechanisms. Most scholarship on public policy’s political effects have focused on individual-level behavioral influences of policies such as Social Security (Campbell, 2003) or the GI Bill (Mettler, 2002),
mostly be influencing socioeconomic status and engagement (Verba, Schlozman and Brady, 1995). Geographic policy feedback mechanisms, however, can change politics not just by changing individual
attitudes and behavior, but by changing individuals’ location and assessing aggregate-level implications
for public policy. Such geographic mechanisms and outcomes cannot be identified in random-sample
survey data, since such data assume independence and typically ignore geography Sampson (2012).
The geographic distribution of voters in space dictates how individual preferences influence the political
agenda and political outcomes.
Infrastructure has other implications for political geography that are worthy of future research. The
location of Republicans in low-density suburban and rural areas and the concentration of Democratic
voters in high-density urban areas constrains redistricting and may contribute to pro-Republican bias in
redistricting plans (Chen and Rodden, 2013). Just as significant is that Interstates have helped place
Republicans and Democrats in different socioeconomic and population-density contexts with different
30
political economies (Costa and Kahn, 2010). Republicans’ preference for sprawl, for example, is consistent with geographic polarization and divergent consumption preferences and issue attitudes (Belden,
Russonello & Stewart, 2011). A potential implication is that partisan issue sorting (Levendusky, 2009)
combined with physical sorting of Democrats and Republicans, leads to a stronger relationship between
spatial location, partisanship, and issue attitudes. Residents of homogeneous communities may have
their interests better represented, but policy compromise on important, geographically focused issues
such as urban housing, transportation, and education may be more difficult.
While various public policies have influenced the growth of suburbia and the political economies underlying the two parties, Interstate highways have been at the center of changes. The suburbanization of
industry, urban renewal, racial segregation, the relative quality of suburban and rural school districts, and
a plethora of other factors co-occurred with growth of the Republican vote in suburban areas. Highways
were at the center of these changes, enabling residents to express residential preferences linked to their
partisanship. A policy intended to connect Americans instead allowed them to live apart. An unintended
consequence for American politics was the creation of suburban Republican enclaves where highways
were built, and a larger partisan gap between cities and their periphery.
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44
Appendices
A County-Level Analysis
46
A.1 Defining the Suburban County Sample . . . . . . . . . . . . . . . . . . . . . . . . . . .
46
A.2 Simple Difference in Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
48
A.3 Balance Checks and Placebo Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
48
A.4 The Influence of Desegregation Orders . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
A.5 Highway-Induced Changes in Suburban Correlates of Partisanship . . . . . . . . . . . .
49
A.6 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
B Urban-Suburban Analysis
51
B.1 Illustration of an Urban-Suburban Couplet . . . . . . . . . . . . . . . . . . . . . . . . .
51
B.2 Sensitivity of Urban-Suburban Findings to Choice of Outer Radius . . . . . . . . . . . .
51
B.3 Highway Effects on Urban-Suburban Gap in Correlates of Partisanship . . . . . . . . . .
51
B.4 Supplemental Matching Analyses for Urban-Suburban Analysis . . . . . . . . . . . . .
52
B.5 Exogeneity of Exits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
C Instrumental Variables Estimation
54
D Addressing Regional Heterogeneity
74
D.1 Differences and Similarities Between the South and Non-South . . . . . . . . . . . . . .
74
E Results Without Smoothing of Point Estimates
80
F Differences in Public Goods in Interstate/Non-Interstate Suburbs
82
45
G Sensitivity Analysis
84
H A Metro-Level Case Study on the Municipal-Level Impacts of Highways on Geographic
Partisan Sorting
97
H.1 Municipal-Level Effects in the Full Metro Sample . . . . . . . . . . . . . . . . . . . . . 100
I
SUTVA
A
102
County-Level Analysis
A.1
Defining the Suburban County Sample
This section addresses one of the basic concerns that arises in the study of American metropolitan areas:
the potential sensitivity of findings to rule-based sample definitions. The results presented in the body of
the paper define suburban counties by including only counties more than 20 kilometers from the central
city and less than 100 kilometers from the central city. Findings are robust to both selection of the inner
and outer radius and to a sampling frame based on population density.
First, findings are robust to selection of inner and outer radii. Results for the full suburban county
sample, excluding pre-1956 freeway adopters and applying coarsened exact matching, appear in Figures
A-1 and A-2. To begin, the full sample, excluding early-adopter states, is subsampled using a grid-search
procedure. This grid search covers three different values of the inner radius (10, 20, and 30 kilometers)
and six values of outer radius (40, 50, 60, 70, 80, 90, and 100 kilometers). This yields a total of 21
different subsamples. On each of these samples, coarsened exact matching and least squares regression
are used to estimate the average treatment effect of highways on the suburban county vote, following
the procedure described in the text. For all samples with an outer radius greater than 50 kilometers, the
results corroborate those presented in the main text.
46
Figure A-3 displays a similar grid search over sample definition rules. Instead of defining the suburban sample for each metropolitan county in terms of radius, it defines the sample in terms of county
population density. Each sample is defined by selecting different minimum and maximum county population densities, using sample quantiles. This grid search applies the same matched sampling and regression methods to samples defined by shrinking the density window progressively. Samples that include
low-population-density counties yield results closer to those obtained.
47
A.2
Simple Difference in Means
One may be concerned that the estimates presented here are a result of specification hunting. One way
to allay these concerns is to present a simple difference-in-means estimate. Figure A-4 presents the
difference in the mean Democratic vote (in percentage points) for these three sets of estimates. A simple
scatterplot of urban-suburban polarization against exits per square mile is presented in Figure A-5.
A.3
Balance Checks and Placebo Tests
A.3.1
Placebo Test
I present a placebo test of the effect of Interstate highway construction on the pre-treatment Democratic
presidential vote and on a range of pre-treatment variables in the suburban-county sample, including
several variables from earlier Censuses that were not included in the matching procedure. These tests
use the same methods used to generate the main set of estimates, with the exception that the difference-indifference analysis is not applicable in this setting. The first of these placebo tests, in Figure A-6, presents
an estimate of the presence of an Interstate highway in each county in each election year on the average
Democratic vote share in the 1944 thru 1956 presidential elections. Figure A-7 presents t-statistics from
placebo tests for a range of pre-1956 measures under the 1930, 1940, and 1950 Censuses, as well as the
presidential vote results included in the above placebo test. Note that the placebo tests in Figure A-6
and in Figure A-7 are done without exploiting difference-in-difference estimation. This makes them a
difficult test for balance, because in practice with difference-in-difference estimation, some imbalance in
the potential outcomes in election year t is acceptable as long as the potential trends between 1952 and
year t in the average presidential vote are balanced between treatment and control groups, conditioning
on the covariates. This makes this particular placebo test a difficult one.
48
A.4
The Influence of Desegregation Orders
Another robustness check examines the effect of school desegregation orders and agreements on the
growth of Republican suburbs. Metropolitan areas with such orders may, according to hypotheses presented in the late 1970s, lead to more white flight. Of course, such orders may be at least partially
enabled by the construction of the highways themselves, and controlling for a post-treatment variable
may bias the results (Rosenbaum, 1984). Busing is a more practical solution in a metropolitan area with
a highly developed metropolitan highway system. Nevertheless, we can observe how robust findings are
to the coding of these desegregation orders. Using the dataset assembled in Welch and Light (1987), a
variable is created for each election year to indicate whether a desegregation program had been adopted
in the central city (or cities) of a metropolitan area at least four years before each election year.25 Results
of the suburban regressions, with the added desegregation variable included, appear in Figure A-8.
A.5
Highway-Induced Changes in Suburban Correlates of Partisanship
Partisan change in suburban counties and across metropolitan areas did not occur in a controlled environment. Highways had multiple effects, including changing the demographic and economic composition of
communities, and, while we cannot completely disentangle sorting and compositional effects, we would
expect highway-induced influences on both the political and demographic makeup of communities. In
the case of the suburban-county vote, highways may have aided the sorting of ex ante Democratic and
Republican identifiers, or placed voters in a context in which they are more likely to vote for one of
the major parties. We could attempt causal mediation analysis to engage these demographic changes’
relative influence over the total effect of highways on partisanship, but even in an experimental setting
such an analysis would require strong assumptions (Imai et al., 2011). As an alternative, we can estimate
25
No desegregation orders had been adopted as of the 1960 election.
49
highways’ effect on a range of correlates of partisanship to demonstrate the aggregate-level outcomes
that were most affected by the presence of highways. Available evidence from both the suburban counties and urban-suburban couplets during time period in question (using county-level Census data from
1970 to 2000) suggests that in the suburban comparative analysis, highways’ most pronounced effect
was to aid construction of wealthier commuter neighborhoods in suburban counties in which they were
built. Within urban-suburban couplets, it appears that racial change was a more important consequence
of highway construction.
The analyses presented here present compositional changes brought about by highway construction.
Estimated effects on five correlates of partisan voting behavior are presented, using the same models
used to estimate highways’ effect on cross-suburban and metropolitan area differences. Four of these are
socioeconomic correlates of partisanship, measured using data from the National Historical GIS (Fitch
and Ruggles, 2003): Average home value (2010 dollars), Per capita income (2010 dollars), Percentage
of workers over 16 working outside the county (a measure of a county’s commuter status), and the
Percentage of homes built in the previous decade. To test for highways’ effect on racial compositional
change, I estimate the effect on the Nonwhite percentage of the population.
The first of these findings applies the methods for the suburban-county analysis as presented in Section 2. Results of this analysis (Figure A-9) suggest that an explanation of the partisan differences between suburban counties may stem from highway-induced changes in counties’ economic composition.
Specifically, counties with Interstate highways have had higher income, higher home values, and a higher
out-of-county commuter rate than comparable counties without them. These results are consistent with
the model of residential sorting laid out earlier: highways enable creation of new, upper-middle-class
and wealthy commuter suburbs in previously rural areas. They offer little evidence that highways have
brought about differences in racial composition between suburban counties, though this is unsurprising
50
given the overwhelmingly white population in the suburban sample.
Even in the South, changes in non-white racial composition were not drastic. Simple statistics of the
non-white percentage of the population in control counties (those that never had an Interstate) and treated
counties (those that had an Interstate built at some point during the study period) show little difference
between the groups and little divergence over time as a function of Interstate status. These results appear
in Figure A-10.
A.6
B
B.1
Sensitivity Analysis
Urban-Suburban Analysis
Illustration of an Urban-Suburban Couplet
An example of a typical urban-suburban couplet, the Atlanta metro area, appears in Figure A-11. An 80kilometer buffer centered on the city of Atlanta delimits the area used to calculate Interstate exit density,
while exits appear as points.
B.2
Sensitivity of Urban-Suburban Findings to Choice of Outer Radius
The sensitivity of the effect of the log-transformed variable is assessed by varying the outer radius used
to define each metropolitan area. The outer radius of each metropolitan area is varied and the effects
estimated by the same methods used on p. 24. These results, presented in Figure A-12, show that the
direction of point estimates is insensitive to choice of outer radius.
B.3
Highway Effects on Urban-Suburban Gap in Correlates of Partisanship
One may speculate about the degree to which the urban-suburban divide is similarly tied to correlates
of partisanship influenced by highway-induced residential sorting. Once again, we would not expect the
51
mechanisms at work in a comparison of suburban counties to carry over to an urban-suburban analysis,
and, as with the analysis of the urban-suburban Democratic gap, estimates are likely to be imprecise.
Here, we can estimate the effect of highway density on the urban-suburban gap in per capita income,
age of housing stock, home values, percent non-white, and the percentage of residents working outside
the county. Lowess-smoothed estimates of highway density’s effects on urban-suburban differences in
selected Census variables are presented in Figure A-13. These are estimated using the same linear regression and smoothing process (without matching) that appears in the main text, p. 24. These estimates
control for the same baseline covariates included in the text.
Across all categories, the estimates of the differences are in the expected direction. A larger positive
value indicates that the urban-minus-suburban gap in the outcome is larger. The negative estimates
obtained for the effect on per capita income, for example, indicate that the income gap between suburbs
and cities is larger in metropolitan areas with more highways. The large positive estimate for non-white
racial composition indicates that metro areas with more highways tend, on average, to have a greater
urban-suburban racial segregation.
B.4
Supplemental Matching Analyses for Urban-Suburban Analysis
A second analysis, presented here primarily to check the robustness of findings to modeling assumptions,
dichotomizes the exit-density treatment variable around the sample median for each year, then generates
matched samples of treated and untreated units to estimate the effect of highway construction.26 Using genetic matching (Diamond and Sekhon, 2012), I create a separate matched sample of treated and
similar untreated units for each year, using all of the covariates subsequently used in a linear regression
26
Dichotomizing the treatment variable introduces the assumption that the “dose” of the treatment of
interest is identical for all units on each side of the median.
52
analysis, conducted using the same bootstrapping and lowess-smoothing method used in the previous
two analyses. This method often yields better improvements in observed balance in small samples than
other, propensity-score based matching methods, while tolerating some additional increase in imbalance
on observed and unobserved covariates.27
Matching observations with highway exit density above the median to units with density below the
median, then applying linear regression to the matched samples in each year yields substantively similar, though less precise estimates. Across all covariates across all years, matching reduced the average
standardized imbalance between treatment and control groups to 0.25 standard deviations of the controlgroup values in the pre-matched population. Applying a linear regression model to reduce the consequences of the remaining imbalance (and accepting remaining model dependence) produces estimates
of the average treatment effect on the treated (ATT). The estimated treatment effect reaches more than
4 points by the 2008 election, with overall effect size is statistically significant at approximately the
α = 0.2 level (two sided test) from 1996 to the present (Figure A-14). A graph of the absolute average
standardized balance obtained on covariates after matching appears in Appendix Figure A-15.
B.5
Exogeneity of Exits
One concern raised about the exits analysis is that exit construction may be endogenous to local demands
for highway construction. That is, as highways are built, they induce demand for more highways, and
may also induce demand for exits. As discussed in the text, the construction of and placement of exits
on Interstate highways in order to avoid excessive traffic jams was a key concern of highway planners.
The merging delays and congestion now known to occur in urbanized areas with many access and exit
27
Coarsened matching is not adopted here, as doing so using a reasonable number of bins discards too
many observations.
53
ramps was a concern that prompted highway planners to place restrictions on exit construction. While
there were key exceptions to the rule, most exits along the Interstate system are original to the Interstates,
and few additional exits appear after construction. To demonstrate the stability of the number of exits
over time, and how exits follow new highway construction and are not simply a response to new traffic
demands, I present historical exit counts from three Sun Belt cities: Phoenix, Houston, and Atlanta.
These three cities were selected because they were among the fastest growing cities over the study period,
and because highways were essential to their outward growth. Thus, they constitute a “hard case” for my
claim that exits were placed exogenously. Four quantities are presented from metropolitan-area maps
from 1965, 1975, and 1985 Rand McNally Road Atlases: total exits on Interstates, total exits added
in the previous decade, the proportion of exits that were inserted (i.e., that were added after a highway
segment had been completed, rather than with new construction) and the percentage of exits that were
these “inserted” exits. Of the three cities, Houston appears to be the worst-case scenario, with 12.2% of
exits qualifying as “inserted” exits in the year 1985. The number of freeways added post-construction is
minimal in the other two cities.
C
Instrumental Variables Estimation
Among the alternatives to the matching and regression framework applied in the paper is an instrumental
variables framework, in which an exogenous instrument is used to predict an endogenous regressor. One
reason to seek out and use such an instrument is that it helps to address the widely held concern that
once highways were built, they created traffic and, in turn, induced additional highway construction. We
would want to identify an exogenous instrument that effectively predicts highway construction. Previous
scholarship has used highways plans as instruments for eventual highway construction. Using twostage least squares or other instrumental variables methods, we can then estimate the effect of highways
54
City
Year
Interstate Exits
New Exits
Inserts
% Inserts
Atlanta
1965
39
-
-
0%
Atlanta
1975
66
27
0
0
Atlanta
1985
83
14
3
3.6
Houston
1965
37
-
-
0%
Houston
1975
77
40
3
3.9
Houston
1985
115
38
14
12.2
Phoenix
1965
16
-
-
0%
Phoenix
1975
26
10
0
0
Phoenix
1985
40
4
0
0
Table A-1: The exogeneity of exits in three Sun Belt cities in 1965, 1975, and 1985.
55
only in counties whose highway construction can reasonably be construed to be induced by the original
plan. If the instrument is uncorrelated with the outcome variables, we can interpret such an estimate as
an unbiased local effect among those units whose construction occurred because of the highway plan.
Highways that were built in places that did not appear on the plan and highways that were built without
appearing on the plan will effectively be excluded from the analysis.
Instrumental variables analysis requires strong assumptions. The first is that the instrument must be
a strong predictor of where highways were built. In this respect, the highway plan is a strong instrument
for Interstate construction, as noted in the paper. Across most of the period, very few Interstates were
built that did not appear on original plans. A second requirement is that any instrument must satisfy
the exclusion restriction: after conditioning on shared predictors of the instrument and the outcome, the
instrument must be “uncorrelated with any other determinants of the dependent variables” (Angrist and
Pischke, 2009, 116-117). With respect to this project, the highway-plan instrument must not just act
upon the outcome, but must be correlated with the outcome only by way of the endogenous regressor
(whether a highway was built).
Do highway plans that pre-date highway construction satisfy these requirements? It is clear that this
instrument was not randomized. As noted in the text, the factors considered in drawing the plan are also
factors that anticipated future changes in metropolitan areas. Thus, to use highways as an instrument, we
must depend very heavily on the other covariates included in the first-stage and second-stage regressions.
Thus, the primary advantage of the instrumental variables approach is to eliminate highways that were
built “off the plan” from the analysis.
The results of two instrumental variables (two-stage least squares) regressions presented in Figures
A-16 and A-18 offer a robustness check on the findings presented in the body of the paper. The top
figure is a check on the suburban-county analysis. In lieu of matching, an instrumental variables analysis
56
was applied to all suburban counties in non-early-adopter states. We are interested in the second-stage
coefficient estimate on the interstate highway variable (an indicator that an Interstate was open in a
county at least four years earlier). The instrumental variable used in the analysis is an indicator variable indicating whether a county was included in the 1944 highway plan, represented by georeferencing
the 1944 Interregional Highways report, tracing the lines from the report, then adding a five-mile-wide
buffer to account for imprecision in the original map and in coding. The 2SLS regression includes the
following county-level covariates in both the first and second stage: median family income in 1950, log
population density in 1950, percent non-white in 1950, percent urban in 1950, crop value per capita in
1950, percentage of residents who did not live in the county in 1949, and the number of manufacturing establishments that appeared in the 1939 manufacturing census. The outcome variable used in the
second-stage estimate is the gain score measure. The bootstrap/lowess smoothing procedure described
in the text is used. These instrumental variables regressions are run separately on the full set of suburban
counties, Southern counties, and non-Southern counties.
The second analysis is a similar type of design, but applies to the urban-suburban analysis, and more
closely follows the analyses that appear in the literature. The exogenous instrument is the metropolitanlevel number of “rays,” a count of the number of radial highways, on a 1947 highway plan, while the
variable being instrumented is the number of radial highways built as of the beginning of each decade
preceding highway construction, from 1950 through 1990 (Baum-Snow, 2007). The first and second
stage regressions each include the following covariates: the urban-suburban difference in the 1948, 1952,
and 1956 presidential votes, the urban-suburban difference in the nonwhite percentage of the population
in 1950, the unweighted urban-suburban mean of the percentage nonwhite in 1950, a dummy variable
for the South, and 1950 log population density. This analysis is run only on the full sample and the
bootstrap/lowess smoothing procedure described in the text is used.
57
The results from both of these analyses do nothing to contradict the findings in the main text, and even
suggest that the regional effect heterogeneity observed in the South and non-South may be overstated.
Across all suburban counties, counties whose highways were induced by the 1944 highway plan were,
across most of the study period starting around 1976, about 4 points less likely to vote for the Democratic
presidential candidate than comparable counties without highways. While regional effects are estimated
with less precision, across much of this period the effect sizes in the South were about twice as large:
around 6 to 8 points, though most of these estimates only border on statistical significance at customary
levels. The same analysis was conducted with the same gain-score outcome but without including pretreatment covariates in the system of equations. These results (Figure A-17) are substantially similar.
The rays analysis also yields effects that are qualitatively similar to those presented using exits. The
plotted estimates represent the effect of a shift from the 25th percentile to the 75th percentile in the
number of rays on the planned map. The dots on each plot represent the unsmoothed point estimates,
while the blue line and dashed and full line represent the lowess-smoothed point estimates and 80% and
95% confidence intervals.
58
−0.2
−0.3
0.00
2.0
1.5
1.0
0.5
0.0
−0.5
−1.0
−1.5
1988
●
● ● ● ● ● ● ● ● ● ●
1964
Outer radius: 40 km
Inner radius: 30 km
Obs: 64
0.1
0.0
−0.1
● ● ● ●
●
● ● ● ●
● ●
−0.2
−0.3
1988
1964
1988
Year
Year
Year
Outer radius: 50 km
Inner radius: 10 km
Obs: 168
Outer radius: 50 km
Inner radius: 20 km
Obs: 147
Outer radius: 50 km
Inner radius: 30 km
Obs: 119
●
●
●
●
●
−0.05
●
●
●
●
● ●
−0.10
1964
0.00
●
●
●
●
−0.05
●
●
●
●
●
−0.10
●
●
−0.15
1988
1964
Effect of Hwy in Cnty
Effect of Hwy in Cnty
1964
Outer radius: 40 km
Inner radius: 20 km
Obs: 92
Effect of Hwy in Cnty
● ●
● ● ●
● ● ● ●
● ●
−0.1
Effect of Hwy in Cnty
0.0
Effect of Hwy in Cnty
Effect of Hwy in Cnty
Outer radius: 40 km
Inner radius: 10 km
Obs: 113
● ●
0.4
●
0.2
●
0.0
● ● ●
−0.2
●
●
−0.4
1988
1964
1988
●
Year
Year
Year
Outer radius: 60 km
Inner radius: 10 km
Obs: 255
Outer radius: 60 km
Inner radius: 20 km
Obs: 234
Outer radius: 60 km
Inner radius: 30 km
Obs: 206
●
−0.02
●
●
●
−0.06
●
●
●
−0.08
1964
0.01
0.00
−0.01
−0.02
−0.03
−0.04
−0.05
1988
●
●
●
●
●
●
●
●
●
1964
● ●
0.04
0.02
0.00
−0.02
−0.04
−0.06
−0.08
1988
●
● ●
●
● ●
● ●
● ● ●
1964
1988
Year
Year
Year
Outer radius: 70 km
Inner radius: 10 km
Obs: 347
Outer radius: 70 km
Inner radius: 20 km
Obs: 326
Outer radius: 70 km
Inner radius: 30 km
Obs: 298
●
●
● ●
● ●
●
●
●
●
●
1964
1988
Year
0.02
●
0.00
−0.02
●
● ●
●
−0.04
●
●
●
●
● ●
−0.06
1964
1988
Year
Effect of Hwy in Cnty
Effect of Hwy in Cnty
●
●
●
−0.04
0.02
0.00
−0.02
−0.04
−0.06
−0.08
−0.10
Effect of Hwy in Cnty
●
Effect of Hwy in Cnty
0.00
Effect of Hwy in Cnty
Effect of Hwy in Cnty
●
0.02
0.00
−0.02
●
●
−0.04
●
●
●
●
● ●
● ●
●
−0.06
1964
1988
Year
Figure A-1: Robustness of findings to definition of a suburban county sample based on the inner and outer
radius defining rings drawn around the metropolitan central city. Outer radii of 40 to 70 kilometers.
59
●
●
●
−0.05
−0.06
●
●
●
●
●
●
●
●
Outer radius: 80 km
Inner radius: 30 km
Obs: 393
0.01
0.00
−0.01
−0.02
−0.03
−0.04
−0.05
−0.06
1988
●
●
●
●
● ●
●
1964
●
●
●
●
1988
Year
Year
Outer radius: 90 km
Inner radius: 10 km
Obs: 528
Outer radius: 90 km
Inner radius: 20 km
Obs: 507
Outer radius: 90 km
Inner radius: 30 km
Obs: 479
● ●
●
●
●
●
●
●
●
●
●
−0.04
1964
−0.01
−0.02
●
●
● ●
●
−0.03
●
●
−0.04
●
●
●
●
−0.05
−0.06
1988
1964
Effect of Hwy in Cnty
Year
−0.02
0.00
−0.01
−0.02
−0.03
−0.04
−0.05
−0.06
1988
●
● ● ●
●
●
●
● ●
1964
●
●
1988
Year
Year
Outer radius: 100 km
Inner radius: 10 km
Obs: 615
Outer radius: 100 km
Inner radius: 20 km
Obs: 594
Outer radius: 100 km
Inner radius: 30 km
Obs: 566
●
−0.01
● ●
−0.02
●
●
●
● ●
●
−0.04
1964
1988
Year
●
●
0.00
●
−0.01
−0.02
● ●
●
●
●
●
●
●
●
●
−0.03
−0.04
1964
1988
Year
Effect of Hwy in Cnty
Year
0.00
−0.03
●
● ●
1964
−0.01
−0.03
−0.01
−0.02
−0.03
−0.04
−0.05
−0.06
−0.07
1988
Effect of Hwy in Cnty
Effect of Hwy in Cnty
● ●
●
1964
Effect of Hwy in Cnty
●
●
−0.03
−0.04
● ●
Outer radius: 80 km
Inner radius: 20 km
Obs: 421
Effect of Hwy in Cnty
●
−0.02
Effect of Hwy in Cnty
−0.01
Effect of Hwy in Cnty
Effect of Hwy in Cnty
Outer radius: 80 km
Inner radius: 10 km
Obs: 442
0.01
0.00
−0.01
−0.02
−0.03
−0.04
−0.05
●
●
●
1964
●
● ●
●
●
● ●
●
1988
Year
Figure A-2: Robustness of findings to definition of a suburban county sample based on the inner and outer
radius defining rings drawn around the metropolitan central city. Outer radii of 80 to 100 kilometers.
60
0.6
0.4
0.2
●
●
●
●
●
●
●
●
●
1976
1988
0.04
0.02
●
0.00
●
●
●
●
●
●
●
●
●
●
−0.02
−0.04
2000
1964
1976
1988
2000
Year
Log Density Quantile Interval: [0.3,0.7]
Obs:303
Log Density Quantile Interval: [0.25,0.75]
Obs:356
Effect of Hwy in Cnty
Year
0.04
0.02
0.00
●
●
●
●
●
●
●
●
●
●
−0.02
●
−0.04
1976
1988
0.02
●
0.00
●
●
●
●
●
●
−0.02
●
●
●
●
−0.04
2000
1964
1976
1988
2000
Year
Year
Log Density Quantile Interval: [0.2,0.8]
Obs:413
Log Density Quantile Interval: [0.15,0.85]
Obs:471
Effect of Hwy in Cnty
Effect of Hwy in Cnty
●
−0.4
1964
Effect of Hwy in Cnty
●
−0.2
1964
0.02
●
0.00
●
−0.02
●
●
●
●
●
●
●
●
●
−0.04
1964
Effect of Hwy in Cnty
Effect of Hwy in Cnty
0.8
0.0
Log Density Quantile Interval: [0.35,0.65]
Obs:226
1976
1988
0.02
0.00
●
●
−0.02
●
●
●
●
●
●
●
●
−0.04
●
−0.06
2000
1964
1976
1988
2000
Year
Year
Log Density Quantile Interval: [0.1,0.9]
Obs:520
Log Density Quantile Interval: [0.05,0.95]
Obs:566
0.02
0.01
0.00
−0.01
−0.02
−0.03
−0.04
●
●
1964
●
●
●
1976
●
●
●
●
1988
●
●
Effect of Hwy in Cnty
Effect of Hwy in Cnty
Log Density Quantile Interval: [0.4,0.6]
Obs:149
2000
0.01
0.00
−0.01
−0.02
−0.03
−0.04
●
●
●
1964
Year
●
●
1976
●
●
●
●
1988
●
●
2000
Year
Figure A-3: Robustness of findings to definition of suburban county sample by population density.
61
Simple Difference in Means
Suburban Counties
Difference in Democratic Vote, (Treated−Untreated)
10
●
●
0
●
●
●
●
●
●
●
●
●
●
●
−10
−20
1960
1968
1976
1984
1992
2000
2008
Year
Figure A-4: Difference in mean Democratic vote share between counties with and without an Interstate
at year t − 4. Counties in early-adopter states excluded.
62
0.30
●
0.00
0.15
0.30
0.2 0.4
1968
−0.2
0.2 0.4
−0.2
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●●
Urban−Suburban D Vote Gap
0.15
Urban−Suburban D Vote Gap
0.2 0.4
−0.2
Urban−Suburban D Vote Gap
0.00
● ●
● ●●
●●
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0.30
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0.30
0.00
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0.15
0.30
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0.30
−0.2
●
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0.2 0.4
1984
Urban−Suburban D Vote Gap
1980
0.2 0.4
1976
Urban−Suburban D Vote Gap
1972
0.2 0.4
Exits Per Sq. Mi.
Urban−Suburban D Vote Gap
Exits Per Sq. Mi.
●
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0.15
0.30
1992
1996
2000
0.00
0.15
0.30
●
●
0.00
0.15
0.30
Exits Per Sq. Mi.
Exits Per Sq. Mi.
2004
2008
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Exits Per Sq. Mi.
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1988
Urban−Suburban D Vote Gap
Exits Per Sq. Mi.
0.2 0.4
Exits Per Sq. Mi.
Urban−Suburban D Vote Gap
Exits Per Sq. Mi.
0.2 0.4
Exits Per Sq. Mi.
−0.2
0.2 0.4
−0.2
0.30
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1964
Exits Per Sq. Mi.
0.00
0.2 0.4
0.15
1960
Exits Per Sq. Mi.
Urban−Suburban D Vote Gap
−0.2
0.2 0.4
0.00
Urban−Suburban D Vote Gap
0.2 0.4
−0.2
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Urban−Suburban D Vote Gap
Urban−Suburban D Vote Gap
Urban−Suburban D Vote Gap
Urban−Suburban D Vote Gap
1956
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Figure A-5: Simple bivariate plots of the urban-suburban Democratic voting gap, by year, against metroarea exits per square mile.
63
All Suburban Counties
2
1
0
●
●
●
●
●
●
●
●
●
●
●
●
−1
Avg Effect on Dem %
(ATT, Matched Sample)
−2
−3
−4
−5
1964
1972
1980
1988
1996
2004
Year
Figure A-6: Placebo test: effect of Interstate highways in each year on the average Democratic presidential vote at baseline, 1944-1956.
64
4
Placebo Test t Statistic
2
% Urban, 1940
Value
PerCapita,
Capita,1940
1940
Crop Added
Value Per
% Non−White 1930
% Urban,
1930 1940
Mfg Wages
Per Capita,
% Non−White, 1940
%
Negro,
1940
%Family
Negro,
1930 1950
Median
1950
Crop%Value
PerIncome,
Capita,
Non−White,
1950
%
Urban,
1950
0
Mfg Ests Per Capita, 1940
Mfg Ests Per Capita, 1930
Value Added Per Capita, 1930
Mfg Wages Per Capita, 1930
−2
% Homes Owned, 1930
−4
1964
1972
1980
1988
1996
2004
Year
Figure A-7: T-statistics of placebo tests on a range of pre-treatment Census and political covariates.
Abbreviated labels for each variable appear at right. Absolute values of these t-statistics are mostly less
than two, suggesting that conditional ignorability has been satisfied.
65
●
●
●
●
1964
1976
● ●
1988
●
●
2000
●
South
3
2
1
0
−1
−2
−3
−4
−5
−6
−7
−8
●
● ●
●
●
●
●
● ●
●
1964
1976
Year
1988
Year
●
●
2000
Diff. in Change in Dem. Vote Share, 1952−(Year)
● ●
●
Diff. in Change in Dem. Vote Share, 1952−(Year)
Diff. in Change in Dem. Vote Share, 1952−(Year)
All Suburban Counties
3
2
1
0
−1
−2
−3
−4
−5
−6
−7
−8
Non−South
3
2
1
0
−1
−2
−3
−4
−5
−6
−7
−8
● ●
● ●
●
●
● ● ●
1964
1976
1988
●
●
●
2000
Year
Figure A-8: Results, with addition of variable indicating whether a desegregation order had been put in
place in a metropolitan area at least four years earlier.
66
Per Capita Income (2010 $)
% Homes Built Last Decade
Avg Home Value (2010 $)
15000
2000
●
1500
●
4
●
2
●
●
5000
1000
0
500
●
1990
2000
●
●
1970
1980
1980
1990
2000
1970
1980
1990
2000
% Work Outside County
0.5
●
●
−10000
1970
% Non−White
1.0
●
●
●
−2
0.0
●
0
●
1980
●
−5000
0
1970
10000
−0.5
7
6
5
4
3
2
1
●
●
●
●
−1.0
1990
2000
1970
1980
1990
2000
Figure A-9: Interstate highways’ impact on economic and racial correlates of partisanship in suburban
counties (average treatment effect on the treated in the population represented by the matched sample).
67
Interstate
No Interstate
Proportion Non−White
0.24
0.22
0.20
0.18
0.16
1970
1975
1980
1985
1990
1995
2000
Year
Figure A-10: Average proportion non-white residents in Southern counties with and without Interstates
as of 1996.
68
¯
!
(
Legend
!
(
Central City
80-Kilometer Radius
Interstate Highway Exits
Fulton County (Urban)
0
Suburban Counties
10
20
40 Kilometers
Other Counties
Figure A-11: Map of the Atlanta metro area, illustrating the construction of an urban-suburban couplet
and exits used to construct the exit-density measure.
69
Log(Exits Per Sq. Mi.)
Log(Exits Per Sq. Mi.)
10
8
9
8
7
7
6
5
4
●
●
●
3
●
2
●
●
●
●
●
First Difference, Points
First Difference, Points
6
5
●
●
●
4
●
●
●
●
3
●
●
1
2
●
0
●
●
●
●
1
●
−1
●
−2
0
●
−3
1960
1968
1976
1984
1992
2000
2008
1960
1968
1976
1984
Year
Log(Exits Per Sq. Mi.)
2000
2008
Log(Exits Per Sq. Mi.)
8
7
7
6
6
5
5
●
4
●
●
●
3
●
●
First Difference, Points
First Difference, Points
1992
Year
4
●
●
●
●
●
3
●
●
2
●
●
2
●
●
●
1
●
●
1
●
●
●
0
0
●
●
●
−1
−1
1960
1968
1976
1984
1992
2000
2008
1960
Year
1968
1976
1984
1992
2000
2008
Year
Figure A-12: Highways’ effect on urban-suburban polarization across interquartile range of logged exits,
as a function of outer metropolitan radius. Top left: 50 kilometers; top right: 70 kilometers; bottom left:
80 kilometers, bottom right: 100 kilometers.
70
Per Capita Income (2010 $)
●
% Homes Built Last Decade
0.02
●
−1000
●
−2000
●
−3000
First Difference
First Difference
0
0.00
●
●
●
−0.02
−0.04
−0.06
●
−0.08
−4000
1970
1990
2000
1970
1980
1990
Year
Year
Avg Home Value (2010 $)
% Non−White
●
2000
●
−5000
●
−10000
−15000
●
−20000
First Difference
First Difference
0
1980
0.06
●
0.04
●
●
0.02
−25000
●
0.00
1970
1980
1990
2000
Year
1970
1980
1990
2000
Year
% Work Outside County
First Difference
0.00
●
●
−0.05
●
●
−0.10
1970
1980
1990
2000
Year
Figure A-13: Highways and urban-suburban polarization on correlates of partisanship.
Lowess-
smoothed estimate of the first difference in urban-suburban polarization in five key Census variables
for the 25th to 75th percentile of the logged exit-density variable, without matching. Bootstrapped 80%
and 95% confidence intervals accompany the point estimates.
71
OLS Estimates of ATT
Exits Per Sq. Mi. > Median
11
10
9
8
Polarization Effect, Points
7
6
5
4
3
2
1
0
−1
−2
−3
−4
−5
−6
1960
1968
1976
1984
1992
2000
2008
Year
Figure A-14: Effect of highways on the urban-suburban Democratic presidential vote gap, estimated
by least squares regression on the matched sample. The estimate captures the difference in the urbansuburban Democratic presidential vote difference, shifting the number of built exits from below the
median in year t to above the median. Bootstrapped 95% and 80% confidence intervals are generated
from the lowess-smoothed curves to incorporate information from proximate observations.
72
0.8
0.6
Standardized Imbalance
(Mean for All Covariates)
Full Sample
0.4
0.2
Matched Sample
0.0
1960
1968
1976
1984
1992
2000
Year
Figure A-15: Mean absolute standardized imbalance (mean for all covariates)
73
The rays analysis also yields effects that are qualitatively similar to those presented using exits. The
plotted estimates represent the effect of a shift from the 25th percentile to the 75th percentile in the
number of rays on the planned map. The dots on each plot represent the unsmoothed point estimates,
while the blue line and dashed and full line represent the lowess-smoothed point estimates and 80% and
95% confidence intervals.
D
Addressing Regional Heterogeneity
D.1
Differences and Similarities Between the South and Non-South
Infrastructure was able to have a larger influence in the South because metropolitan areas in the region
had lower density suburban regions than the non-South before the construction of the Interstate Highway System. At the same time, the differences between the South and other states were small enough
to be addressed by including controls that represent the different starting points for suburbanization in
the two regions. The scale of these differences is presented in the Figure A-19, which captures these
suburban pre-trends using two variables whose effect was accounted for in matching and linear regression. The horizontal axis of each graph represents the distance of each county from the nearest central
city in each metropolitan area, while the vertical axis represents the county-level mean. These graphs
demonstrate that counties on the outskirts of Southern cities were slightly lower-density than suburban
counties elsewhere, and such counties also had a slightly higher proportion of new residents. Both of
these are consistent with the hypothesized mechanisms and the larger effect sizes observed in the South.
While these analyses are presented as examples of regional differences, the differences observed
here are addressed in the analysis in several respects. Both of the variables presented here are included
as linear covariates, so their linear additive influence over the results is accounted for in the models. If
74
Instrumented Effect of Highways on Suburban Democratic Vote (Points)
All Suburban Counties
10
8
6
4
2
0
−2
−4
−6
−8
−10
−12
−14
−16
−18
−20
●
● ●
● ● ● ● ●
●
●
●
1964
1976
1988
2000
South
●
10
8
6
4
2
0
−2
−4
−6
−8
−10
−12
−14
−16
−18
−20
● ● ●
●
Non−South
● ● ● ● ● ●
●
●
1964
1976
Year
1988
Year
2000
10
8
6
4
2
0
−2
−4
−6
−8
−10
−12
−14
−16
−18
−20
● ● ● ● ●
● ● ● ● ●
●
●
1964
1976
1988
2000
Year
Figure A-16: Instrumental variables estimates using early highway plans as an instrument for eventual
adoption. Effect of highways on suburban Democratic vote. Full set of pretreatment covariates included
in estimation.
75
Instrumented Effect of Highways on Suburban Democratic Vote (Points)
All Suburban Counties
10
8
6
4
2
0
−2
−4
−6
−8
−10
−12
−14
−16
−18
−20
●
●
●
1964
●
● ● ● ● ●
1976
1988
●
● ●
2000
South
10
8
6
4
2
0
−2
−4
−6
−8
−10
−12
−14
−16
−18
−20
Non−South
●
● ● ● ●
1964
1976
Year
● ● ●
1988
Year
● ●
● ●
2000
10
8
6
4
2
0
−2
−4
−6
−8
−10
−12
−14
−16
−18
−20
●
●
1964
●
● ● ●
● ● ● ●
1976
1988
● ●
2000
Year
Figure A-17: Instrumental variables estimates using early highway plans as an instrument for eventual
adoption. Effect of highways on suburban Democratic vote. No covariates included in estimation.
76
that proves insufficient to account for the differences presented here, a region fixed effect is included,
which encompasses any non-time-varying differences between the South and non-South not accounted
for in the other covariates.
77
Effect of Rays, 2SLS Estimates
First Difference Across IQR of Planned Rays
0.09
First Diff. in Change in Dem Vote Proportion, 1952−(Year)
0.08
0.07
0.06
0.05
●
0.04
●
●
0.03
●
●
●
0.02
●
●
1988
1992
●
●
●
0.01
●
0
−0.01
●
−0.02
1960
1964
1968
1972
1976
1980
1984
1996
2000
2004
2008
Year
Figure A-18: Instrumental variables estimates using early highway plans as an instrument for eventual
adoption. Effect of radial highways on the urban-suburban Democratic gap.
78
Regional Differences in Pre−Existing Density
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Persons Per Sq Mi, 1950
1000
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Proportion in Different County, 1949
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Regional Differences in Migration Rates
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20
40
60
80
100
20
Distance to Nearest City (km)
40
60
80
100
Distance to Nearest City (km)
Figure A-19: Southern suburban counties were only slightly lower density than non-Southern suburban
counties in 1950, and residents in Southern counties were slightly more likely to have lived in a different
county a year earlier.
Another concern about the South is the extent to which conversion in place, rather than geographic
mobility. While this distinction is not important to the estimating highways’ effect on geographic change
(since highways could act by indirect means to help induce partisan change at higher rates in counties
with highways), descriptive statistics from historical Censuses (Fitch and Ruggles, 2003) indicate a larger
role for geographic migration in suburbs in the South, and a larger difference associated with highways,
than outside the South. Figure A-19 presents the proportion of migrants who lived outside the county five
years earlier. The regional averages for the South and non-South are presented, along with averages for
counties that had an Interstate highway five years earlier and those that did not. While this figure does not
provide evidence of a causal effect, about 25% of residents of counties with Interstates were newcomers,
79
while just around 20 percent of residents in counties without highways were newcomers. The effect is
also in the expected direction (though, unsurprisingly, smaller in magnitude) across this period. In all
Censuses, in both regions, the average proportion of new arrivals in counties with highways exceeds the
proportion in counties without highways. Also consistent with a model of sorting in which highways
have larger effects when they are built in new areas, the highway-related gap grows smaller over time
as more areas have Interstates for longer periods. Some of this convergence of places with and without
Interstates may be due to the diffusion of residential growth from counties with highways into those
without them.
E
Results Without Smoothing of Point Estimates
For reasons outlined in Section 2.1, it is desirable to exploit information across adjacent election years
by using a smoothing technique. Confidence intervals should use information across adjacent elections,
and shrink the estimates towards a common mean to provide a better estimate of underlying partisanship, rather than candidate-specific effects. However, as a check on the sensitivity of results to these
assumptions, Figures A-20 and A-21 present the results of the two main analyses in the text without
added lowess smoothing. In both cases, the point estimates are approximately the same (in some cases,
larger in magnitude than the smoothed estimates) while the confidence intervals are slightly tighter, as
expected. For both the suburban and urban-suburban analysis, the findings are robust to the smoothing
decision.
80
Highway Effect on Change in County Dem Vote Share (Points), 1952−[Year]
Unsmoothed Estimates
All Suburban Counties
8
7
6
5
4
3
2
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1976
1988
2000
South
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Year
1988
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2000
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Year
Figure A-20: Estimates of highways’ effect on the suburban Democratic vote, without lowess smoothing
of point estimates.
81
Exits Per Sq. Mi.
Log(Exits Per Sq. Mi.)
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1976
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1992
2000
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Year
1960
1968
1976
1984
1992
2000
Year
Figure A-21: Estimates of highways’ effect on the urban-suburban gap in the Democratic vote, without
lowess smoothing of point estimates.
F
Differences in Public Goods in Interstate/Non-Interstate Suburbs
Do suburban counties with Interstates deliver different types of public goods? If so, in what spending
categories do counties with suburban Interstates differ from those without? To assess this, I examine
basic differences in means in per capita spending in different budget areas within all counties in the full
suburban-county sample. These data were assembled by adding together all expense areas for all governments that fell within counties in the 1972 Census of Governments (U.S. Department of Commerce,
Bureau of the Census, 2008), then dividing by 1970 population (Fitch and Ruggles, 2003). Figure A-22
presents the estimated coefficient on a binary variable indicating whether a county had an open Interstate
82
2008
All Suburban Counties
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6
5
4
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2
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$ Thousands Per Capita
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expgas72pc
exptransit72pc
expwater72pc
expelectric72pc
expliquor72pc
expsanit72pc
expwatertrans72pc
expwelf72pc
expsewer72pc
expcashassist72pc
expparks72pc
exppolice72pc
expparking72pc
explibr72pc
expnatres72pc
exphwy72pc
exphouse72pc
exphosp72pc
exphealth72pc
expgen72pc
expbldg72pc
expfin72pc
expfire72pc
expempsec72pc
exphied72pc
expeduc72pc
expschools72pc
expcorrect72pc
exptot72pc
expairport72pc
−10
Outcome
Figure A-22: Difference in means in various public-goods categories for counties with and without
highways, 1972.
by 1968. Point estimates, which capture the difference between Interstate and non-Interstate counties
in thousands of dollars per capita, are accompanied by 95% confidence intervals. Counties with and
without Interstates adopt about the same spending practices, except in budget areas clearly explained by
population growth. Counties with Interstates spent more on fire, parks, police, sewer, sanitation, and water supply than non-Interstate counties in 1972. This is unsurprising when one considers how highways
influence local development. Population growth stimulates public works construction and investment in
public safety services. Curiously, the budget area in which Interstate highway counties spent much less
83
money per capita was on highways (by almost $5,000 per capita in 1972). State and federal highway
spending in such counties may offset local spending, and counties without Interstates, which are, on the
whole, more rural, may receive more aid for county road programs.
G
Sensitivity Analysis
One can adopt various approaches to test sensitivity of findings to the inclusion or exclusion of an unobserved omitted variable. All of these approaches adopt some method to introduce this omitted variable,
and test the effects of its inclusion on point estimates and standard errors. Many widely preferred methods designed for nonparametric estimation of differences in means on matched samples (e.g., Rosenbaum
2002; Keele 2012. While such methods have many advantages, especially when only a difference-inmeans test is applied after matching, they are more difficult to apply in a parametric modeling framework.
Several alternatives to nonparametric sensitivity analyses have been proposed and developed (Imbens,
2003; Harada, 2012). These methods adopt various approaches to generating a simulated unobserved
covariate correlated with both the treated variable and the outcome. They then include this covariate in
the modeling procedure, and identify the set of correlation values that break the result. One such way to
define a “broken” result is to examine the effect the p-value of point estimates and exclude observations
above a standard cutoff p-value. These related approaches have the advantage of not requiring a binary
treatment, and by working within a generalized linear modeling framework.
This sensitivity analysis adopts aspects of the Imbens (2003) model-based sensitivity analysis for
both the suburban-county analysis (which entails least squares regression on the matched sample) and
the urban-suburban polarization analysis, which entails least squares regression on the full sample. Approximately the same procedure is followed in both instances.
For each bootstrapped sample (for each year t) we draw an instance of the unobserved confounder,
84
U , which is drawn from a conditional normal so as to be correlated with the treatment and outcome
variables. Each element of this conditional normal vectors is drawn from a normal distribution with an
identical variance and a different mean: Ui ∼ N (µi , Σ̄), where
Σ̄ = 1 − ρU Y σY
ρU Z σZ
Σ−1
YZ
T
ρU Y σY
ρU Z σZ
(A-1)
, ρU Y and ρU Z are specified by the researcher and represent, respectively, the correlation between U and
the residualized outcome variable Y and residualized treatment variable Z, and ΣY Z is the variancecovariance matrix for Y and Z, and σZ and σY are the standard deviations of Y and Z. µi is the ith
element of the vector:
µ̄ =
ρU Y σY
−1
ρU Z σZ ΣY Z (a − µY Z )
(A-2)
where a is n × 2 matrix of bootstrapped Y and Z values and µY Z is the n × 2 matrix of column means.
The sensitivity analysis consists of a grid search with ρU Z and ρU Y each set at evenly spaced, discrete
values on the closed interval [-0.8, 0.8]. U is drawn from the conditional normal distribution and added
as a covariate. The point estimates and 95% confidence intervals are then calculated as usual, but without
lowess smoothing. For the suburban-county analysis, this is done on the matched samples for each year,
while for the urban-suburban analysis unmatched sample with U calculated separately for each year. The
same quantities of interest presented in the paper are then calculated for each value on the grid.
To aid presentation, two quantities of interest are presented for each combination of ρU Y and ρU Z ,
and presented in a heat map. The first is predicted difference in the suburban vote or urban-suburban
polarization. The second is the upper (lower) limit of the 95% confidence interval for the suburban
(urban-suburban) analysis. When this value crosses zero it indicates that the results are sensitive.
The point estimates for the national sample and for the interaction effects appear in Figures A-23,
A-24, A-25. The upper bounds of the 95% confidence intervals (which we desire to keep below zero)
85
appear in Figures A-26, A-27, and A-28. The point estimates for the urban-suburban analysis appear in
Figures A-30 and A-29. The confidence-interval estimates (which we desire to keep above zero) appear
in Figures A-31 and A-32.
As expected, very large correlations between U and the residualized treatment and outcome variables,
typically with a magnitude of about 0.25 or larger for both values, are necessary before the results break.
This frontier is visible in the plots for each election year. Results for the South are especially large, with
larger correlations necessary to break the results in the years for which statistically significant effects
are reported. (Because the hypothesized effect is negative, correlations with opposite signs weaken the
results.) As expected, smaller ρ values are necessary to move the confidence interval margins in the
urban-suburban polarization case, but correlations of around 0.2 are necessary before the substantive
interpretation of the results is undermined. Note, in both cases, that the omitted variable increases the
magnitude and reduces the associated p-value.
86
Point Estimate, All Suburbs
−0.5
2000
0.0
0.5
2004
2008
0.5
0.7
0.0
−0.5
1988
1992
0.6
1996
0.5
0.5
0.0
cor(resid(Z),U)
0.4
−0.5
1976
1980
1984
0.3
0.5
0.0
0.2
−0.5
1964
1968
1972
0.1
0.5
0.0
0.0
−0.5
−0.1
−0.5
0.0
0.5
−0.5
0.0
0.5
cor(resid(Y),U)
Figure A-23: Sensitivity of point estimates, effect of Interstates on Democratic suburban vote. Full
suburban sample excluding early adopters.
87
Point Estimate, South
−0.5
2000
0.0
0.5
2004
2008
0.5
0.7
0.0
−0.5
1988
1992
0.6
1996
0.5
0.5
0.0
cor(resid(Z),U)
0.4
−0.5
1976
1980
1984
0.3
0.5
0.0
0.2
−0.5
0.1
1964
1968
1972
0.5
0.0
0.0
−0.1
−0.5
−0.5
0.0
0.5
−0.5
0.0
0.5
cor(resid(Y),U)
Figure A-24: Sensitivity of point estimates, effect of Interstates on Democratic suburban vote in Southern
suburban counties.
88
Point Estimate, Non−South
−0.5
2000
0.0
0.5
2004
2008
0.5
0.7
0.0
−0.5
1988
1992
0.6
1996
0.5
0.5
cor(resid(Z),U)
0.0
0.4
−0.5
1976
1980
1984
0.3
0.5
0.0
0.2
−0.5
1964
1968
1972
0.1
0.5
0.0
0.0
−0.5
−0.5
0.0
0.5
−0.5
0.0
0.5
cor(resid(Y),U)
Figure A-25: Sensitivity of point estimates, effect of Interstates on Democratic suburban vote in nonSouthern suburban counties.
89
95% CI Upper Bound, All Suburbs
−0.5
2000
0.0
0.5
2004
2008
0.8
0.5
0.0
0.7
−0.5
0.6
1988
1992
1996
0.5
0.5
cor(resid(Z),U)
0.0
−0.5
0.4
1976
1980
1984
0.5
0.3
0.0
0.2
−0.5
1964
1968
1972
0.1
0.5
0.0
0.0
−0.5
−0.5
0.0
0.5
−0.5
0.0
0.5
cor(resid(Y),U)
Figure A-26: Sensitivity of 95% CI upper bound, effect of Interstates on Democratic suburban vote. Full
suburban sample excluding early adopters.
90
95% CI Upper Bound, South
−0.5
2000
0.0
0.5
2004
2008
0.8
0.5
0.0
0.7
−0.5
0.6
1988
1992
1996
0.5
0.5
cor(resid(Z),U)
0.0
−0.5
0.4
1976
1980
1984
0.5
0.3
0.0
0.2
−0.5
1964
1968
1972
0.1
0.5
0.0
0.0
−0.5
−0.1
−0.5
0.0
0.5
−0.5
0.0
0.5
cor(resid(Y),U)
Figure A-27: Sensitivity of 95% CI upper bound, effect of Interstates on Democratic suburban vote in
Southern suburban counties.
91
95% CI Upper Bound, Non−South
−0.5
2000
0.0
0.5
2004
2008
0.8
0.5
0.0
0.7
−0.5
0.6
1988
1992
1996
0.5
0.5
cor(resid(Z),U)
0.0
−0.5
0.4
1976
1980
1984
0.5
0.3
0.0
−0.5
1964
1968
0.2
1972
0.1
0.5
0.0
0.0
−0.5
−0.5
0.0
0.5
−0.5
0.0
0.5
cor(resid(Y),U)
Figure A-28: Sensitivity of 95% CI upper bound, effect of Interstates on Democratic suburban vote in
non-Southern suburban counties.
92
Predicted Difference Across IQR of Exit Density
0.10
2008
0.5
0.0
0.08
−0.5
1992
1996
2000
2004
0.5
0.06
cor(resid(Z),U)
0.0
−0.5
1976
1980
1984
1988
0.04
0.5
0.0
−0.5
0.02
1960
1964
1968
1972
0.5
0.0
0.00
−0.5
−0.5 0.0
0.5
−0.5 0.0
0.5
cor(resid(Y),U)
Figure A-29: Sensitivity of point estimates in urban-suburban polarization findings. Log-transformed
version of explanatory variable.
93
Predicted Difference Across IQR of Exit Density
2008
0.5
0.0
0.15
−0.5
1992
1996
2000
2004
0.5
0.10
cor(resid(Z),resid(U))
0.0
−0.5
1976
1980
1984
1988
0.5
0.05
0.0
−0.5
1960
1964
1968
1972
0.00
0.5
0.0
−0.05
−0.5
−0.5
0.0
0.5
−0.5
0.0
0.5
cor(resid(Y),resid(U))
Figure A-30: Sensitivity of point estimates in urban-suburban polarization findings. Untransformed
version of explanatory variable.
94
95% CI Lower Bound, Predicted Difference Across IQR of Exit Density
2008
0.5
0.1
0.0
−0.5
0.0
1992
1996
2000
2004
0.5
−0.1
0.0
cor(Z,U)
−0.5
−0.2
1976
1980
1984
1988
0.5
−0.3
0.0
−0.5
1960
1964
1968
−0.4
1972
−0.5
0.5
0.0
−0.6
−0.5
−0.5 0.0
0.5
−0.5 0.0
0.5
cor(resid(Y), U)
Figure A-31: Sensitivity of lower bound of 95% confidence interval in urban-suburban polarization
findings. Untransformed version of explanatory variable.
95
95% CI Lower Bound, Predicted Difference Across IQR of Log Exit Density
2008
0.5
0.08
0.0
−0.5
1992
1996
2000
2004
0.06
0.5
0.0
0.04
cor(Z,U)
−0.5
1976
1980
1984
1988
0.02
0.5
0.0
0.00
−0.5
1960
1964
1968
1972
−0.02
0.5
0.0
−0.04
−0.5
−0.5 0.0
0.5
−0.5 0.0
0.5
cor(resid(Y), U)
Figure A-32: Sensitivity of lower bound in 95% confidence interval urban-suburban polarization findings. Log-transformed version of explanatory variable.
96
H
A Metro-Level Case Study on the Municipal-Level Impacts of
Highways on Geographic Partisan Sorting
While municipal-level data are not available for most states prior to recent elections, states with a tradition of municipal-level recordkeeping offer substantial data to examine patterns below the county level.
Wisconsin, a state in which election results are reported and handled at the municipality level, is a particularly useful setting for these studies. I examine the relationship between highways and local political
development in the Milwaukee metropolitan area over the years 1944-2008. The Milwaukee area is a
suitable proxy for other major industrial Rust Belt cities. It entered the postwar period with a strong
industrial base and dense urban population, and, like many of its regional counterparts, lost population
to surrounding areas over the next six decades. Construction of freeways between the 1950s and 1970s
appears to have sped the decline by subsidizing the growth of suburban residential communities in radial
spokes extending from the urban core. Many of these areas became critical to subsequent Republican
dominance of the suburban Milwaukee metro area.
As in other metropolitan areas, Milwaukee’s central city underwent a major decline in population
while suburban development favored the ascendancy of new Republican base areas in the suburbs. Comparing the two largest counties in southeast Wisconsin reveals the wrenching political changes brought
about by suburbanization during the second half of the twentieth century. While Milwaukee County’s
population remained approximately stable across the second half of the twentieth century–failing to
keep pace with the state’s overall population growth–neighboring Waukesha County, long a Republican
stronghold, grew relentlessly. So-called “greenfield” development in these rural areas caused the Waukesha County population to grow by 386% between 1940 and 2000, the fastest rate in Wisconsin, while
97
maintaining its Republican partisanship.28
The growth of increasingly affluent and Republican suburbs in Waukesha and other suburban Milwaukee counties can be linked to the counties’ freeway connections. To examine the role of freeway
construction in the Milwaukee area, a bubble-plot map (Figure A-33) captures the growth of Republican
suburbs around Milwaukee freeways between 1952 and 2000. Each point represents a municipality, its
diameter the total number of votes cast. The familiar red-blue coding scheme captures areas won in which
Republicans (red) or Democrats (blue) won a majority of the two-party vote. The major freeways that
existed in each year, extracted from historical maps and the Federal Highway Administration’s database
of highway construction (Baum-Snow, 2007), appear as dotted lines. In 1952, lightly populated rural
areas were primarily Republican, but many of the Republican votes were in the inner suburbs near the
City of Milwaukee (represented by a large black dot on the maps). By 1976, the region’s portion of the
Interstate Highway System had been completed for nearly a decade, and I-94, the major east-west route
28
In 1952, Eisenhower captured 65.6% of Waukesha County’s 46,111 votes, accounting for only 10%
of his Milwaukee-area vote total. Eisenhower’s main reservoir of votes was in Milwaukee County, where
he won 51% of the county’s 426,000 votes, nearly seven times as many votes as he obtained in Waukesha
County (and more than twice as many votes as he obtained in all four outlying counties combined). By
the 2000 presidential election, the urban-suburban divide was well established and the Republican center
of gravity had shifted to Waukesha and other suburbs. Governor George W. Bush won 65.3% of the
Waukesha County vote, nearly identical to Eisenhower’s vote share. But these votes accounted for 32.2%
of his overall vote total in the five-county metro area. Bush’s 37.7% share of the Milwaukee County vote
accounted for only 39.9% of his Milwaukee-area vote total. These changes dramatically changed the
targeting behavior of the two political parties.
98
between Madison and Milwaukee, had made possible substantial and sustained growth in an east-west
highway corridor extending across a thirty-mile stretch of Waukesha County. Other suburban counties,
including Ozaukee County and Washington County, underwent substantial Republican growth as well.
In the meantime, the previously suburban communities in Milwaukee County had become inner suburbs
and transformed from Republican bastions into swing areas, or from swing districts into Democratic
strongholds.
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Figure A-33: Municipal-level presidential election returns in the Milwaukee area, 1952, 1980, and 2008.
The size of each bubble is proportional to the total presidential vote in the municipality. Areas in red
were won by Republicans and areas in blue were won by Democrats. Highway segments built as of each
election year appear as dotted lines. (Source: Wisconsin Blue Book and Elections Division, Wisconsin
Government Accountability Board.)
99
H.1
Municipal-Level Effects in the Full Metro Sample
How much did the findings for these two archetypal freeway communities replicate throughout the Milwaukee metropolitan area? Applying the same logic to the population of municipalities in the Milwaukee
metropolitan area as a whole that was applied to the study of the two towns above, we can observe impacts of highway construction on all communities at least 15 kilometers from the Milwaukee central
business district. For all suburban communities in the sample, the election results for 1944 to 2000 are
centered on the year of construction of the nearest major freeway. Did the relative Republican strength
of communities vary, on average, after freeway construction? Figure A-34 presents the results of this
analysis. The vertical axis plots the difference in the Democratic share of the two-party presidential vote
between each locality and the City of Milwaukee, measured in each available election year. A locally
weighted regression curve is fitted, and the vertical line is the year of nearest freeway construction in
each municipality. This curve shows that monotonic growth for the Democrats reverses in the period
after freeway construction that converted to a steady decline in the Democratic vote in suburban communities, relative to the City of Milwaukee, after freeway construction. Though a strong assumption
is required to establish the change in partisan polarization as a causal effect, these results provide additional descriptive evidence that even if suburbanization was already underway in the period before
freeway construction, both the pace and partisan aspects of suburbanization shifted dramatically after
freeway construction. By 30 years after freeway construction, included communities had become, on
average, thirty points less Democratic than the City of Milwaukee.
Another way to measure highways’ impact on sorting using the case study data is by estimating communities’ propensity to deliver a “landslide” in a presidential election–that is, casting a two-party vote
more than 10 points ahead of or behind the average in the complete metropolitan area. A locally weighted
100
Urban−Suburban Gap in Democratic Vote, (Suburb−Milwaukee)
−0.15
−0.20
−0.25
−0.30
−0.35
−0.40
−0.45
−20
−10
0
10
20
30
40
50
Years Pre/Post Freeway Construction
Figure A-34: The difference between the suburban municipal Democratic presidential vote and the City
of Milwaukee Democratic presidential vote, centered on the year of construction of the nearest freeway.
Values appear as a scatterplot, and a lowess curve is fitted to the points. Source: Wisconsin Blue Book,
1944-2000.)
regression curve is again used, in this case to estimate the proportion of communities that qualified as
“landslide” communities before and after local freeway construction. As expected, highways not only
appear to make the municipalities near which they are built more Republican, but also make them more
politically homogeneous, as measured by their landslide status. By stimulating the construction of new
places, highways also appear to have generated local political homogeneity, with a sudden turnaround
in the proportion of landslide communities immediately after major freeways were built locally (Figure
A-35).
101
1.0
Probability of Landslide
0.8
0.6
0.4
0.2
0.0
−20
−10
0
10
20
30
40
50
Years Pre/Post Freeway Construction
Figure A-35: Locally weighted probability that a suburban community is a “landslide” community,
delivering a Democratic presidential vote more than ten points ahead of or behind the regional average
presidential vote in the presidential election. All municipal-level time series are centered on the year of
construction of the nearest freeway. Source: Wisconsin Blue Book, 1944-2000, Wisconsin Government
Accountability Board Elections Division.
I
SUTVA
As with most analyses founded on geographic units, SUTVA is a strong assumption. The current draft
takes up this issue by noting that if highway construction produces certain types of economic development in rural and suburban areas, these effects will diffuse across boundaries. If we assume that
highways’ effects diffuse, this would bias the estimates towards zero. It is unlikely that SUTVA would
then be explaining the magnitude and direction of the results, and may be making effect sizes (particularly in later years) lower. The other aspect of this assumption is that intercity migration accounts for
some changes; for example, the Great Migration into Northern cities persisted after World War II. Inter102
nal migrants were driven to move to different cities for various reasons. The assumption in the current
draft is that after accounting for baseline characteristics of places and regional location, and performing
difference-in-difference analysis, predictors of migration pick up any diffusion effects.
Appendix References
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URL: http://sekhon.berkeley.edu/papers/GenMatch.pdf
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