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P O L I T I C S SY M P O S I U M
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The New Blue: Northern In-Migration
in Southern Presidential Elections
Seth C. McKee, Texas Tech University
Jeremy M. Teigen, Ramapo College
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J
ames Carville once quipped, “Pennsylvania is Philadelphia and Pittsburgh with Alabama in between.”1
This southern Democratic strategist’s assessment of
political geography informs Obama’s controversial
comments regarding a segment of the American electorate in 2008. Shortly before the Keystone State’s primary,
Obama said that in difficult times, many small-town residents
“cling to their guns and religion.”2 His campaign rival, Hillary
Clinton, jumped on those remarks; indeed, Obama stumbled
in Pennsylvania. However, he carried Pennsylvania and the
rest of the Northeast in the general election, decisively defeating Republican John McCain in 2008. To be sure, the voters
that Obama disparaged were not aligned with him anyway
because those with the greatest penchant for clinging to God
and guns—like actual Alabamians—were disproportionately
located well below the Mason–Dixon Line. Indeed, as has been
true since the late 1960s (Black and Black 1987), the South
in 2008 was a Republican stronghold. Nevertheless, Obama
notched notable victories in Florida and Virginia in both of his
elections and in North Carolina in 2008—an impressive feat
given that his immediate Republican predecessor, George W.
Bush, swept the South in both of his presidential runs.
Regional differences in America appear to be durable and
strong. Harking back to the Founding and eventually manifesting in the Civil War, the political divide between the
Northeast and the South seems to be permanent. However,
a complete partisan reversal of these sectional strongholds
transpired in the late twentieth century. At one time, Republicans dominated presidential politics in the Northeast (Rae
1989; Reiter and Stonecash 2011), whereas Democrats monopolized the electoral votes in the South (Black and Black 1992).
Today, the South is reliably red and the Northeast is solidly
blue. This article examines one important reason of how
Obama broke that pattern to carry some southern states:
population migration.
Despite the remarkable strength of the Democratic Party
in the Northeast and the equally formidable prowess of the
GOP in the South, it is the evidence of increasing competitiveness in certain parts of the South that inspired this study.
Only a few decades ago, the migration of residents from the
Northeast and the Midwest infused the South with a healthy
dose of Republicanism (Scher 1997). Today, native Southerners are decidedly more Republican than recent Northern
in-migrants, who are more Democratic in affiliation and
presidential voting preferences (Hillygus, McKee, and Young
2015). This article examines the impact of migration to the
228
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South on voting behavior in the 2008 and 2012 presidential
elections. Our analysis of county- and individual-level data
demonstrates the extent to which Northern newcomers are
tilting Dixie in a Democratic direction.
SOUTHERN MIGRATION PATTERNS
We classify Southern newcomers according to the five stateregional categories defined by Black and Black (2007): the
Northeast, the South, the Midwest, the Mountains/Plains,
and the Pacific Coast.3 Because we are interested in the influence of in-migration to the South on presidential voting patterns, South-to-South migration is not discussed. Figure 1
shows the percentage of movers to the South in each southern state and South-wide, according to the region from which
newcomers migrated (i.e., the Northeast, the Midwest,
the Mountains/Plains, and the Pacific Coast). The states
are rank-ordered from the least to the greatest percentage of
movers from the Northeast. The data total 100% based on the
percentage breakdown of migrants from the Northeast, the
Midwest, the Mountains/Plains, and the Pacific Coast in each
state. These are one-year American Community Survey (ACS)
data estimated by the US Census Bureau in 2012.
On the lower end, only 8% of migrants to Arkansas hail
from the Northeast, whereas on the higher end, a majority
(52%) of Northern newcomers to Virginia came from the
Northeast. After Arkansas, Texas and Tennessee exhibit
the lowest rates of Northeastern in-migration; these Rim
South states are then followed by three Deep South states—
Mississippi, Louisiana, and Alabama—which have proportionally higher Northeastern residents.4 All of the previously
mentioned states and Georgia—with the next highest percentage of Northeastern in-migrants at 30%—have a higher
percentage of newcomers from the Midwest than from the
Northeast. Texas is the only state with a plurality of in-migrants
from the GOP-dominant Mountains/Plains. After Georgia,
a notable Eastern Atlantic Seaboard pattern manifests with
Northeasterners constituting a clear plurality of in-migrants to
Florida, North Carolina, South Carolina, and Virginia. Overall, the most recent arrivals to the South are slightly more than
33% from the Northeast, 31% from the Midwest, 20% from The
Mountains/Plains, and 15% from The Pacific Coast.
The map in figure 2 is a more fine-grained visual inspection of recent southern migration patterns, indicating
the plurality regional category (the Northeast, the Midwest, the
Mountains/Plains, and the Pacific Coast) of newcomers to the
South at the county level. Unlike the state-level data in figure 1,
© American Political Science Association, 2016
doi:10.1017/S1049096516000081
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which were based on one-year ACS data in 2012, the countylevel data in figure 2 are based on five-year ACS data from
2008 to 2012. By highlighting the modal region from which
movers come, figure 2 is an insightful illustration of recent
migration patterns to the South.
Starting in the West with Texas, the predominant pattern of
Mountains/Plains in-migrants is evident. We also see a fairly
broad scattering of counties with a plurality of newcomers hailing from the Pacific Coast, whereas Midwest in-migrants are
Carolina, and Florida partly explains their presidential battleground status and Obama’s success there, despite narrowly
losing North Carolina in 2012 (Hood and McKee 2010). Of
course, the presentation in figure 2 can only go so far—perhaps
providing more “grist” for speculative arguments than actual
confirmation. Hence, we conducted multivariate assessments
at the county and individual levels to estimate the extent that
recent Northern in-migration contributes to voting behavior
in the South.
Given that states with high Yankee in-migration are also those that flipped blue in 2008
and 2012, the previous summary data provides an initial impression that migration
patterns co-vary with Southern presidential voting.
prevalent in several counties in Central, East, and South
Texas (with a few in the panhandle). Moving eastward into
Arkansas and Louisiana, the pattern of in-migration found
in Texas begins to give way to a new pattern in which
in-migrants from the Midwest dominate the middle of Dixie:
Eastern Arkansas, Mississippi, most of Alabama, Tennessee,
Western Virginia, Western North Carolina, and the South
Carolina upcountry.
The in-migration pattern found in Georgia is interesting
because of a fairly broad and representative distribution of
newcomers from the four Northern regions. Indeed, it is evident that Georgia is an outlier versus the other four Atlantic
Seaboard states that exhibit an obvious in-migration pattern of Northeastern dominance. With the exception of the
Deep South state of South Carolina, the massive influx of
Northeasterners to the Rim South states of Virginia, North
DATA AND METHODS
Given that states with high Yankee in-migration are also those
that flipped blue in 2008 and 2012, the previous summary data
provides an initial impression that migration patterns co-vary
with Southern presidential voting. However, multivariate
analysis is necessary to demonstrate that the role of migration
in ballot preferences is not due to confounding factors. We analyzed both county- and individual-level survey data to provide
multilayered evidence for understanding how migration influences presidential voting in the southern United States. The
individual-level analysis used the 2008 American National Election Survey (ANES) data, whereas the county-level models relied
on data collected from multiple sources including presidential
election returns for 2008 and 2012 from Dave Leip’s Atlas of US
Presidential Elections and control variables from the ACS fiveyear county-level estimates, as well as the Association of Religion
Data Archives (ARDA).5 As was
the case for descriptive data preFigure 1
viously presented, we used Black
Percentage of Regional In-Migration by Southern State, 2012
and Black’s (2007) regional classifications to assess variations in
the relationship between Southern in-migration and presidential
voting behavior in the 2008 and
2012 elections.
To estimate the relationship
between migration and voting
behavior, we began with countylevel models. The South has 1,143
counties and county equivalents.
Regarding analysis, counties
have a few shortcomings (e.g.,
population heterogeneity) but
the advantages exceed the
deficiencies. Unlike cities and
legislative districts, county
boundaries are mostly stable
over time. They also are exhaustive, with counties and their
equivalents encompassing the
Source: American Community Survey, 2012.
entire nation. Finally, counties
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Po l i t i c s Sy m p o s i u m : N o r t h e r n I n - M i g r a t i o n i n S o u t h e r n P r e s i d e n t i a l E l e c t i o n s
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migration as a proportion
of a southern county’s
Modal Regional Origin of Movers into The South, 2008–2012
population, we totaled all
of the migrants from each
northern region and
divided the result by each
county’s population. This
proportion is generally
very low, of course, with
means of less than 1% and
maximum percentages in
the single digits. The
dependent variable was the
presidential vote, which
was measured by Obama’s
average two-party, countylevel vote share between
2008 and 2012.
Our second analysis used cross-sectional
individual-level ANES
data from the 2008 presidential election to gauge
the relationship between
population migration and
presidential voting behavior. To the extent possible,
we attempted to mirror
the county-level analysis;
therefore, the models
are the smallest common unit for which we could aggreinclude several control variables akin to the county models.
gate our control variables and our two variables of interest:
There are some differences worth describing. First, there are
migration and election data. Similar to other studies using
necessary differences in the way that variables were measured.
geographic units to explain individual-level behavior, countyWith aggregate data, we used percentages for most measures;
level estimates suffer from potential ecological-inference
with survey data, the variables were all coded as dummies.
problems; therefore, we reinforce our findings with an
Second, the typical measurement risks inherent in survey
individual-level model. However, with smaller units, there is
work applied. Over-reporting political participation and other
less risk of this inferential problem. Although there are outliers
socially desirable behaviors influences survey-based inferwithin large urban-dominated counties of more than a million
ences. Third, we were able to control for party identification,
residents (e.g., Harris County, Texas, and Miami–Dade,
unlike in the spatial models. Fourth, there is a difference in
Florida), the average county population in the South is far
the way migration was measured. For the county-level ACS
less—approximately 85,000.
data, the survey asked about relocating in the past year. For
In general, our analytic strategy was to regress two-party
the ANES data, the migration question was: “Where is it that
presidential votes onto in-migration percentages from nonyou grew up?,” which captured a somewhat different sense of
South regions with a spatial maximum-likelihood model. The
migration—one that spanned a longer time frame.6
independent variable of interest was migration into the South
RESULTS
from the four Northern regions (the Northeast, the Midwest,
the Mountains/Plains, and the Pacific Coast); a within-South
To convey the relationship between migration patterns and
migration measure also is included. To measure this relationpresidential voting preferences, table 1 displays both countyship at the county level required the use of the ACS five-year
and individual-level regression results. The county model
migration-flow estimates. Respondents were asked whether
shows that rising levels of in-migration from the North“they had lived in their current residence one year ago” and,
east and the Midwest shape higher Democratic presidential
if so, where that was. In essence, this created a large matrix of
voting tendencies. In contrast, increasing the number of
migration among all of the counties in the United States in a
migrants from the Republican stronghold of the Mountains/
given five-year period, using 2008—2012 data. For each of the
Plains into southern counties moves the tendencies in the
1,143 counties in the South, we counted the number of people
other direction.7 Figure 3 shows the salient parameter estiwho migrated from non-South counties and coded them for
mates, highlighting the importance of population flows from
their region of origin (e.g., the Northeast). To make sense of
the Northeast, the Midwest, and the Mountains/Plains in
Figure 2
230 PS • April 2016
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affecting Southern presidential voting in the 2008 and 2012
elections. For instance, a 10% increase in newcomers from
the Northeast to a southern county increases the Democratic
presidential vote in 2008–2012 by approximately five percentage points.
Table 1 also displays results for a logistic regression of
vote choice in the case of non-black Southern voters (McCain
and Obama voters only) in the 2008 presidential election.8
With “Growing up in The South” as the omitted comparison category, we found that respondents who grew up in the
respondents grew up and their voting preferences in
2008. These first differences in the probability of voting
for Obama in 2008 show the point estimate as well as the
95% confidence interval for the independent variables
from the second model in table 1. Most of the control variables behave as conventional wisdom would predict, with
Southern Latinos more likely to vote for Obama, older
Southerners and Born Again Christians preferring McCain,
and party identification indicators in the expected direction
as well.
Finally, among those who did not identify with a major party, the swing was by far the
largest: native Southern Independents (including “leaners”) were approximately 39%
likely to cast a vote for Obama, whereas otherwise identical Southern residents raised in
the Northeast had an approximate 70% chance of supporting the Democrat in 2008.
Northeast and the Midwest were much more likely to vote
Democratic. We ran postestimation marginal effects on the
logit coefficients from table 1 and plotted them in figure 4.
Figure 4 reveals the substantive relationship between where
For the southern in-migration variables—even after controlling for known correlates of vote choice—growing up in
the Northeast or the Midwest significantly increased the likelihood of preferring the Democratic presidential candidate.
Ta b l e 1
County- and Individual-Level Analysis of the Southern Presidential Vote in 2008
and 2012
County Analysis (2008–2012)
Coefficient (S.E.)
South Movers (%)
−0.103 (0.09)
Individual Analysis (2008)
Coefficient (S.E.)
Northeast Movers (%)
4.917 (0.70)**
Grew up in Northeast
1.288 (0.56)*
Midwest Movers (%)
1.347 (0.51)**
Grew up in Midwest
1.426 (0.48)**
Mountains/Plains Movers (%)
Pacific Coast Movers (%)
Bachelors (%)
Median Income ($10K)
−1.913 (0.48)**
−0.640 (0.79)
0.404 (0.07)**
−3.027 (0.32)**
Grew up in Mountains/Plains
Grew up in Pacific Coast
Bachelors or more
Income more than $60K
0.156 (0.35)
−0.520 (0.39)
Hispanic (%)
0.276 (0.02)**
Black (%)
0.469 (0.02)**
Hispanic
1.489 (0.48)**
Age 65+ (%)
0.243 (0.08)**
Age 65+
−0.970 (0.36)**
Age 20–24 (%)
0.030 (0.10)
Age 35 or younger
Evangelical Christian (%)
Population Density (log)
Married (%)
−0.137 (0.02)**
1.978 (0.02)**
−0.095 (0.02)**
Income less than $20K
−1.475 (1.83)
−0.230 (0.64)
0.173 (0.45)
0.587 (0.39)
Born Again Christian
−0.825 (0.31)**
Rural resident
−0.461 (0.41)
Married
−0.507 (0.37)
Spatial Lag
0.339 (0.02)**
Female
0.300 (0.33)
Constant
12.92 (3.70)**
Democrat
1.936 (0.36)**
Observations
1,143
Republican
−2.130 (0.41)**
R-Squared
0.797
Constant
−0.119 (0.51)
Observations
Pseudo R-Squared
369
0.452
Notes: The county analysis is a spatial maximum-likelihood model (**p ≤ 0.01, *p ≤ 0.05) of the average vote won by Obama between 2008 and 2012. Sources include
ACS five-year estimates, Dave Leip’s Atlas of US Presidential Elections, and the Association of Religion Data Archives. The individual analysis is a logistic regression
of vote choice (**p ≤ 0.01, *p ≤ 0.05, 1 = Obama vote, 0 = McCain vote) based on data from the 2008 ANES. Black voters were omitted due to a lack of voting variability
(100% for Obama). “Grew up in The South” is the omitted variable of comparison.
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Po l i t i c s Sy m p o s i u m : N o r t h e r n I n - M i g r a t i o n i n S o u t h e r n P r e s i d e n t i a l E l e c t i o n s
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Figure 3
Regional Origin of Movers to Southern Counties and the
Presidential Vote in 2008–2012
Notes: Model (n = 1,143) includes controls for education (Bachelors percentage); median income; percentages of Hispanics,
blacks, those older than age 65, those ages 20–24, Evangelical Christians, and married; population density (logged); and
a spatial lag (see table 1). Sources include ACS five-year estimates, Dave Leip’s Atlas of US Presidential Elections, and the
Association of Religion Data Archives. Positive estimates indicate factors predicting an Obama vote.
Figure 4
Predicted Probabilities of Southern Presidential Vote in 2008
Consider the presidential
preferences of native Southerners vis-à-vis voters who grew
up in the Northeast. Assuming
a typical Republican (i.e., setting the other controls at their
modal values), a native Southerner had an approximate 7%
probability of voting for Obama
in 2008, whereas a Republican
who grew up in the Northeast
had an approximate 22% probability of backing the Democrat.
Comparing a typical native
Southern Democrat with an
otherwise identical Democrat
who grew up in the Northeast
shifts the likelihood of voting
for Obama from 82% to 94%.
Finally, among those who did
not identify with a major party,
the swing was by far the largest:
native Southern Independents (including “leaners”) were
approximately 39% likely to cast
a vote for Obama, whereas
otherwise identical Southern
residents raised in the Northeast
had an approximate 70% chance
of supporting the Democrat in
2008.
FINAL THOUGHTS
Before 2008, the last time that
Virginia had given its electoral
votes to a Democrat was in 1964
for Lyndon Johnson. Indeed,
even in 1976, when the South’s
own Jimmy Carter won 10 of
the 11 ex-Confederate states,
it was the Old Dominion that
narrowly backed Gerald Ford.
In 1992 and 1996, Bill Clinton
managed to pluck four Southern states; however, in neither
election did the Arkansan wrest
Virginia from the Republicanwin column. This 44-year Democratic presidential drought is
remarkable, and an important
reason for its eventual end was
Notes: Postestimation first differences were calculated from table 1 results (with 95% confidence intervals). Positive
observed within select commuestimates indicate factors predicting an Obama vote; negative values favor McCain. Estimates with confidence intervals
that cross the zero point are not statistically significant (e.g., rural resident).
nities in Virginia.
Consider the case of ManasWhere a respondent grew up did more to explain voting
sas Park, a small city of fewer than 20,000 people who reside
tendencies in this model than other factors often discussed
within the orbit of the Washington, DC suburbs. With its popin predicting presidential preferences among Southerners,
ulation rapidly increasing via Northern in-migration, Manassas
including gender, marital status, and education level.
Park exemplifies the more dynamic, growing communities
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in the South. It has a racially diverse demographic profile
resembling the national average (i.e., 10% black, 30% Latino,
and slightly more than 50% white); however, its above-average
population growth is attributable to in-migration from the
Northeast and the Midwest. Another noteworthy attribute
of Manassas Park is that it voted reliably Republican in the
1980s and 1990s but has tilted dramatically in the other direction since 2008. In 1988, Michael Dukakis earned only 30%
of its votes; in 1992 and 1996, Bill Clinton picked up only 42%
and 40%, respectively; and Al Gore managed about the same
percentages in 2000. In 2004, John Kerry did slightly better with
45%, but Barack Obama took 60% in 2008 and 62% in 2012.
How much does in-migration influence southern presidential voting patterns? Manassas Park was an archetype
for our thesis; however, we found that, in general, Southern
communities that witness moving vans coming in from the
Northeast and the Midwest tilt toward Democratic voting
habits. Manassas Park and similar locales are microcosms
of the latest changes to Southern politics. It may be true that
within various regions of the United States, partisan sorting
has contributed to greater political homogeneity and, on a
macro-scale, has accounted for the large swaths of red and
blue regions coloring the presidential map (Bishop and Cushing
2008; Black and Black 2007; Levendusky 2009; McKee and
Teigen 2009). However, this article demonstrates that migratory patterns into the South are disrupting this partisan
uniformity. In fact, it is because most of the regions outside
of the South are considerably more Democratic that Dixie
in-migration leads to these important changes in presidential
voting behavior. When enough Manassas Parks are aggregated, a deep-red presidential stronghold such as Virginia
turns purple and has an important role in shaping the outcome
of presidential elections. Q
NOTES
1. This is the popularized version of Carville’s quote, which dates back to
1986; see “Rewriting Pennsylvania Political Geography,” by Carrie Budoff
Brown. Available at www.cbsnews.com/news/rewriting-pennsylvaniapolitical-geography (accessed on August 28, 2015).
2. See The Guardian article, “Obama Angers Midwest Voters with Guns and
Religion Remark.” Available at www.theguardian.com/world/2008/apr/14/
barackobama.uselections2008 (accessed on August 2, 2015).
3. In addition to the Northeast (i.e., New England states: Connecticut,
Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont;
and Mid-Atlantic states: Delaware, Maryland, New Jersey, New York,
Pennsylvania, and Washington, DC) and the South (i.e., Alabama,
Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina, South
Carolina, Tennessee, Texas, and Virginia), there are the Mountains/Plains
(i.e., Arizona, Colorado, Idaho, Kansas, Oklahoma, Montana, Nebraska,
Nevada, New Mexico, North Dakota, South Dakota, Utah, and Wyoming),
the Midwest (i.e., Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota,
Missouri, Ohio, West Virginia, and Wisconsin), and the Pacific Coast
(i.e., Alaska, California, Hawaii, Oregon, and Washington). We used this
regional classification scheme in a previous article to examine the role
of geography in presidential voting patterns (McKee and Teigen 2009).
Generally speaking (some states are exceptions), The Northeast and The
Pacific Coast were Democratic strongholds in recent presidential elections,
whereas The South and The Mountains/Plains were Republican redoubts.
The Midwest is the most competitive region and therefore consists of the
greatest number of swing/battleground states.
4. Scholars of Southern politics routinely divide The South into two subregions:
The Deep South (i.e., Alabama, Georgia, Louisiana, Mississippi, and South
Carolina) and The Rim South (i.e., Arkansas, Florida, North Carolina,
Tennessee, Texas, and Virginia). This distinction is drawn mainly by
the reality that African Americans comprise a greater proportion of the
5.
6.
7.
8.
populations of The Deep South (Black and Black 1987; Key 1949), which
has notable political repercussions (Hood, Kidd, and Morris 2012; McKee
2010; McKee and Springer 2015; White 2014).
The authors purchased the county-level data from Dave Leip and his
Atlas of US Presidential Elections website can be found here: http://
uselectionatlas.org/. The religious affiliation county-level data (Evangelical
Christian percentage) come from the “2010 Religious Congregations and
Membership Study,” which we retrieved from the Association of Religion
Data Archives (ARDA): http://www.thearda.com/Archive/browse.asp.
We would have included an analysis of the 2012 presidential election with
the ANES data; however, at this time, the variable of interest (“Where is it
that you grew up?”) is not publicly available.
Relative to a given county, some counties are very near to one another and
may influence neighboring counties, whereas distant counties might well
be considered independent. Geographers refer to this spatial dependency
as Tobler’s Law. To guard against this problem, we included a spatial-lag
variable that places nearby counties’ values of the dependent variable on
the right-hand side of the regression for a given county. Anselin and Rey
(2014) discussed the need for and use of spatial regression.
African Americans were omitted from the models in table 1 because the
oversample of (Southern) blacks voted unanimously in favor of Obama.
Thus, African American voting behavior in the 2008 ANES functions as a
constant rather than a variable.
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