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Reverse pivot counties: How McCain-Romney-Clinton counties voted in the 2018 U.S. House elections

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2016 Pivot Counties

Analysis
Pivot counties overview
List of counties
Historical voting patterns
Historical voting analysis, 1992-2004
Demographics
Reverse-Pivot Counties
Voter turnout
Pivot Counties by state
Pivot Counties in congressional districts
Pivot Counties in state legislative districts
Pivot Counties and ballot measures

2018 elections analysis
Pivot counties in U.S. House elections
Pivot counties in state legislative elections
Reverse pivot counties in U.S. House elections

Reverse pivot counties are counties that voted for John McCain in 2008, Mitt Romney in 2012, and Hillary Clinton in 2016. There are six reverse pivot counties in the country from four states: Orange County, California; Cobb County, Georgia; Gwinnett County, Georgia; Henry County, Georgia; Anne Arundel County, Maryland; and Fort Bend County, Texas. Their opposite, pivot counties, voted for Barack Obama twice and Donald Trump in 2016.

Democratic candidates for the U.S. House won all six reverse pivot counties in 2018. The average margin of victory in these counties was 11.05 percent. Cobb County, Georgia, had the lowest margin of victory with 7.18 percent and Henry County, Georgia, had the highest with 14.59 percent.

In five of the six counties, the Democratic U.S. House candidate in 2018 had a larger margin of victory than Clinton did in 2016. Orange County, California was the only exception, where the Democratic U.S. House candidate had a 0.26 percent smaller margin of victory than Clinton. Clinton’s average margin of victory in 2016 in these counties was 4.96 percent while for Democratic U.S. House candidates in 2018 it was 11.05 percent. Clinton’s median margin of victory was 5.08 percent while for Democratic U.S. House candidates it was 11.77 percent.

For further analysis of pivot counties in the 2018 elections, see:

Reverse pivot counties in 2018 elections

The following table lists each reverse pivot county, margins of victory in each of the last three presidential elections, and the Democratic U.S. House candidates' margin of victory in 2018.

Democratic candidates in these counties performed better than Clinton. Their average margin of victory was 11.05 percent, while Clinton's was 4.96 percent. Clinton won three of these counties by more than five percent, but none of them by 10 percent or more. U.S. House candidates in 2018 won all six counties by more than five percent, and by more than 10 percent in four of those. Collectively, the candidates won these six counties by more than 220,000 votes.

2018 election results in reverse pivot counties
County State McCain MOV (2008) Romney MOV (2012) Clinton MOV (2016) 2018 U.S. House Winner Party 2018 MOV (Votes) 2018 MOV (Percent) 2016 to 2018 MOV Change
Orange County California 2.56% 6.23% 8.59% Electiondot.png Democratic 89,395 8.33% -0.26%
Cobb County Georgia 9.43% 12.45% 2.16% Electiondot.png Democratic 22,127 7.18% 5.02%
Gwinnett County Georgia 10.24% 9.22% 5.79% Electiondot.png Democratic 39,080 12.57% 6.78%
Henry County Georgia 7.46% 3.30% 4.36% Electiondot.png Democratic 14,170 14.59% 10.23%
Anne Arundel County Maryland 1.80% 0.08% 2.23% Electiondot.png Democratic 24,843 10.97% 8.74%
Fort Bend County Texas 2.39% 6.83% 6.64% Electiondot.png Democratic 31,437 12.64% 6.00%
Average 5.65% 6.35% 4.96% n/a 36,842 11.05% 6.09%
Median 5.01% 6.53% 5.08% n/a 28,140 11.77% 6.39%

The following map displays 2018 U.S. House results in reverse pivot counties:

Results by Congressional district

In the 2018 U.S. House elections, there were 22 districts that intersected with a reverse pivot county. Heading into the election, Republicans controlled 11 (50 percent) of these districts, while Democrats also controlled 11 (50 percent). As a result of the elections, Republicans lost five seats to Democratic candidates, while Democrats lost no seats to Republican candidates. At the start of the 116th Congress, Democrats held 16 (72.8 percent) seats while Republicans held six (27.3 percent) seats.

The table below shows U.S. House districts that intersected with reverse pivot counties as of February 2019. The middle three columns show the margin of victory (MoV) for Clinton in 2016, Romney in 2012, and McCain in 2008. The third column from the right shows the incumbent's party at the time of the 2018 election. The column second from the right shows the party of the winning candidate in 2018. The column furthers to the right indicates whether the district changed party hands in 2018.

U.S. House districts intersecting with reverse pivot counties as of February 2019
County State Clinton MoV 2016 Romney MoV 2012 McCain MoV 2008 District 2018 incumbent 2018 winner Flipped in 2018?
Orange California 8.59% 6.23% 2.56% District 38 D D No
District 39 R D Yes
District 45 R D Yes
District 46 D D No
District 47 D D No
District 48 R D Yes
District 49 R D Yes
Cobb Georgia 2.16% 12.45% 9.43% District 6 R D Yes
District 11 R R No
District 13 D D No
Gwinnett Georgia 5.79% 9.22% 10.24% District 4 D D No
District 7 R R No
District 10 R R No
Henry Georgia 4.36% 3.30% 7.46% District 3 R R No
District 10 R R No
District 13 D D No
Anne Arundel Maryland 2.23% 0.08% 1.80% District 2 D D No
District 3 D D No
District 4 D D No
District 5 D D No
Fort Bend Texas 6.64% 6.83% 2.39% District 9 D D No
District 22 R R No

Demographics

The table below shows the demographics of the entire U.S. population compared with the demographics of the six Reverse-Pivot Counties and 206 Pivot Counties. Averages for the United States used in population estimates were calculated using 3,156 counties. The numbers in the "Average" column were found by averaging the demographic percentages of each of the six Reverse-Pivot Counties and 206 Pivot Counties—not by calculating demographics across the populations of those counties. This table was compiled by Ballotpedia staff based on data made available via the U.S. Census Bureau website.[1]

The data reveals that Reverse-Pivot Counties were far larger than Pivot Counties, with a median population of 636,727 to 32,956. In terms of racial and ethnic makeup, the six counties were far less white (57.9 percent to 92.5 percent), more African-American (22.6 percent to 2.0 percent), more Asian (7.6 percent to 0.7 percent), and more Hispanic (16.2 percent to 3.3 percent). Reverse-Pivot Counties had a more college-educated population, with 38.9 percent receiving college degrees compared to 18.6 percent in Pivot Counties. Economic statistics were also different between the two sets of counties: Reverse-Pivots had almost double the annual household income ($80,173 to $47,427) and half as many individuals living below the poverty level (7.5 percent to 14.2 percent).

Demographics of 2016 Reverse-Pivot Counties
United States Reverse-Pivot Counties Pivot Counties
Demographic U.S. Average Average Median Average Median
County population 97,828[2] 971,164 636,727 78,018 32,956
Female persons, percent 50.8% 51.0% 50.8% 49.9% 50.2%
White alone, percent 77.1% 59.6% 57.9% 85.5% 92.5%
Black or African American alone, percent 13.3% 20.7% 22.6% 7.9% 2.0%
American Indian and Alaska Native alone, percent 1.2% 0.4% 0.4% 3.6% 0.5%
Asian alone, percent 5.6% 9.4% 7.55% 1.1% 0.7%
Native Hawaiian and Other Pacific Islander alone, percent 0.2% 0.1% 0.1% 0.1% 0.1%
Hispanic or Latino, percent 17.6% 17.0% 16.2% 6.2% 3.3%
High school graduate or higher, percent of persons age 25 years+, 2011-2015 86.7% 89.0% 89.3% 87.9% 89.0%
Bachelor's degree or higher, percent of persons age 25 years+, 2011-2015 29.8% 38.3% 38.9% 19.6% 18.6%
Median household income (in 2015 dollars), 2011-2015 $53,889 $77,974 $80,173 $47,228 $47,427
Persons in poverty, percent[3] 13.5% 6.6% 7.5% 15.6% 14.2%
Source: United States Census Bureau

About the data

The raw presidential data for this study was provided by Dave Leip of Atlas of U.S. Presidential Elections.

See also

Footnotes

  1. United States Census Bureau, "State Legislative Districts Relationship Files," accessed April 18, 2018
  2. 2010 U.S. population estimate: 308,745,538
  3. The U.S. Census Bureau states that "geographic level of poverty and health estimates are not comparable to other geographic levels of these estimates."