Texas Blues – Interstate Migration and the Electoral College

How has interstate migration affected the vote during recent Presidential elections in Texas?

Lucas Bernui

Rob Fehnel


The ever-shifting population movements from state to state are continually changing the makeup of party affiliation within each state, and therefore the electoral college vote.  For example, blue California has been the number one state that people are migrating from.  And where are they mostly migrating too?  Red Texas (86k+ in 2018) (Martichoux 2019).  This migration is helping to “blue” the state that Republicans have counted on for its large number of electoral votes (with 38, second only to California).   

Our research is focusing on the state of Texas and attempts to quantify how interstate migration to the state is affecting the electorate in terms of Republican or Democratic leanings.  We utilized a purposive sampling method and have selected the 15 biggest counties by population in Texas in 2019. Using Census Bureau data, we then logged the top 20 counties (outside of Texas) that these in-migrants moved from and their voting pattern based on the 2016 Presidential election within each of those counties. Our model found that a net 8,153 more Democrats than Republicans moved in during that time. Assuming that a voter’s ideological and party preferences are not affected by their environment, then this would suggest that Texas is going to see more Democratic votes. Will this have an effect on the 2020 elections and beyond? We will see.

What We Have Read

In Article II, Section 1 of the U.S.  Constitution, the founding fathers established the Electoral College as the means of selecting the Chief Executive. The American President is elected by the states, not by a national popular vote. Therefore, interstate population movement will continue to have an influence over states and how red or blue they vote in national elections.  Recent trends indicate that states that have been solid red are now shifting purple (and even blue) and at a pace that could have dramatic effects on upcoming elections. This shift is most notable in states with large in-migration numbers like Texas and other electorally important Sun Belt states. States not long ago considered solid red are now in play for the Democrats in the 2020 presidential election and beyond. We propose that in-migration to these states from traditional Democratic states is affecting the political leanings (and the vote) in these traditional Republican strongholds (Texas in particular).  The following literature review will examine this issue.

Forest (2017) discussed the increasing popularity of electoral geography and how the rapid improvements of digital technology (GIS, etc) and availability of near real-time electronic data has made it easier to create meaningful maps to study the relationships between geography, voting, and political power.  The author focuses on 3 major approaches: The geography of voting (mapping and visualizing votes), geographic influences on voting (the effect of place on political preferences and behavior), and the geography of representation (the analysis of electoral systems). He concludes that the ongoing improvements in electoral geography will better define the spatial patterns and deviations and the related casual relationships revealed by advances in survey and polling data. In other words, political geography will have an increasingly important role in predicting future elections. 

Using some of these tools, Kearns and Locklear (2019) examined the most current state migration patterns in their recent U.S. Census Bureau article.  The article summarizes migration patterns by states, regions, and counties. The authors help us to understand not only which states are gaining/losing residents but also where people are coming from or going to (down to county levels). They concluded that in 2018, the southern states had a net gain of about 512,000 people. States with the most in-migration (in order) were Florida (556.5k), Texas (524.5k), California (523.1k) and states with the most out-migration (in order): California (661k), Texas (467.3k), New York (452.6k). Most of Florida’s in-migration was from heavily Democratic New York (63.7k) and most of the migration into Texas was from heavily Democratic California (63.2k).

Backing up this migration trend, Frey (2005) in his Brooking Institution article used Census data to detail past, current and projected future migration from Snow Belt states to Sun Belt states and the electoral ramifications of that ongoing migration. His work shows that the Sun Belt in the 1970s only had 4 more net electoral votes than the Snow Belt, but that this difference has been steadily increasing (to a net 88 electoral vote advantage in 2000). He projects that by 2030, this will increase to 146. Frey’s data reinforces that the shift of electoral college influence from the blue-leaning populous states in the north to the red-leaning states in the south is real and will continue to have political consequences.

But will this population movement benefit Republicans or Democrats more?  While the favored current opinions (and election results) may be that this move is benefitting Democrats, some political scientists are not so sure.  Gimpel and Schuknecht (2001) conclude that population mobility and its effect on partisan realignment has been difficult to study due to lack of survey data and other factors such as if political views of migrants remain the same or change due to new environmental influences after they move.  Bermila (2007) using statistical projections of state populations hypothesized the net increase/loss in electoral votes for Republican and Democratic candidates and predicted that while migration would benefit some solidly Democratic states, on balance, Republican presidential candidates would most likely benefit the most from migration in future presidential elections through 2028. (Though the opposite appears to be happening).

So, can it be determined that migration to red states from traditional Democratic states is affecting the political leanings (and therefore the electoral vote) in these traditional Republican strongholds like Texas?  While there continues to be improvements in data collection and geographical tools, this is an area that warrants further consideration and study.

Our Methodology

Our methodology attempts to predict how many Texas in-migrants are going to vote Republican, Democrat or other. First, we looked at the number of in-migrants to each of the 15 most populous counties in Texas and multiplied it by the voter turnout percent from each of the top 20 counties (in number of in-migrants) they arrived from, using the 2016 Presidential election as a baseline. This is our predictor of turnout of the new in-migrants. Voter turnout percent is calculated by total votes in the 2016 election in that county divided by population of that county in 2016. The reason we took the total population and not registered voters is because in-migrants could be voters but they could also be non-voters or ineligible to vote (under 18, non-citizen etc.) Next found the percentage of the vote that Clinton and Trump won in 2016 in that county. To calculate the “other” percentage we used 100-(Clinton%+Trump%). Next we multiplied the predicted turnout by the percentage of Republican votes in that county in 2016, Democrat votes in that county in 2016 and other votes in that county in 2016. The output is predicted voters for in-migrants who will vote Republican, Democrat or other. Note that we only took only out of state in-migrants into account and we look at top 20 counties that people are in-migrating into each of the 15 Texas counties chosen. This method was used to predict how incoming voters will vote based on their previous county’s voter turnout and party preference.

See our data here.

Our Data and FiguresAnd What it all Means

Analysis: Our model predicts that Democrats have gained a net 8,153 total voters from in-migrants across the top 15 counties from 2014 to 2018.

Analysis: Margin of Victory over the last three presidential election cycles and the difference in voting percentage. We use this as a metric to identify voting trends by party.

Analysis: Does the presidential margin of victory line up with our predicted gains for Democrats or Republicans? For 13 of 15 counties it does.

Analysis: A quick look at the 15 least populated counties and their in-migration statistics from 2014 – 2018.

Texas County Maps – Recent Presidential Elections

What We Have Learned

Our findings showed a net increase of 8,153 Democratic voters in just four years (from 2014 – 2018) in the 15 most populated Texas counties.  In our analysis we looked at presidential elections from 2012 to 2020. What we found was that our prediction of how in-migrants would vote showed 13 out of 15 times the trend of that county for presidential elections. While this is not evidence that in-migration is a predictor of election outcomes it deserves a closer look. Considering there are 254 counties in Texas, this number is likely to be much higher.  However, our focus was on big counties with large urban cores which generally lean Democratic, even in Texas. When taking a look at the 25 lowest populated counties and their in-migrants, although it was a small sample size, it did have more people moving in from red counties. If all counties are taken into account , there may be an increase in immigrating Republican voters in more rural counties, as one of patterns our research recognized was that as the county became smaller the margin between in-migrating Democrats and Republicans narrowed. Further research on these numbers and the effect on the 2020 election is warranted.


Feel free to contact us:

Lucas Bernui at lbernui@muhlenberg.edu or Rob Fehnel at rfehnel@muhlenberg.edu


“2016 Election Results: President Live Map by State, Real-Time Voting Updates.” Election Hub. Accessed November 7, 2020. https://www.politico.com/2016-election/results/map/president/. 

Alix Martichoux, SFGATE. “691,000 People Moved out of California Last Year. Here’s Where They Went.” SFGate. San Francisco Chronicle, November 5, 2019. https://www.sfgate.com/expensive-san-francisco/article/move-california-where-to-go-cheap-states-best-14811246.php.

Bureau, U.S. Census. “Moves to and From the South and West Dominate Recent Migration Flows.” The United States Census Bureau, May 23, 2019. https://www.census.gov/library/stories/2019/04/moves-from-south-west-dominate-recent-migration-flows.html. 

Burmila, Edward M. “The Electoral College after Census 2010 and 2020: The Political Impact of Population Growth and Redistribution.” Perspectives on Politics 7, no. 4 (2009): 837-47. http://www.jstor.org/stable/40407082.

Clifford Pugh, “The Great Divide: President Obama Carries Top 4 Texas Cities and Still Gets Trounced in Lone Star State,” CultureMap Houston, November 7, 2012, https://houston.culturemap.com/news/city-life/11-07-12-the-great-divide-obama-carries-top-four-texas-cities-and-loses-by-15-point/.

Forest, Benjamin. “Electoral Geography: From Mapping Votes to Representing Power.” Geography Compass 12 (1): 1. doi:10.1111/gec3.12352.

Frey, William H. “The Electoral College Moves to the Sun Belt.” The Brookings Institution, n.d.

Gimpel, James G., and Jason E. Schuknecht. 2001. “Interstate Migration and Electoral Politics.” Journal of Politics 63 (1): 207. doi:10.1111/0022-3816.00065.

Longman, Martin. “If Texas Goes Blue, It Will Change American Politics Permanently.” Washington Monthly, June 18, 2019. https://washingtonmonthly.com/2019/06/17/if-texas-goes-blue-it-will-change-american-politics-permanently/.

Ross Ramsey, “Analysis: The Blue Dots in Texas’ Red Political Sea,” The Texas Tribune (The Texas Tribune, November 11, 2016), https://www.texastribune.org/2016/11/11/analysis-blue-dots-texas-red-political-sea/.

Steven ShepardSenior campaigns and elections editor11:12 p.m. et al., “Live Election Results: 2020 Texas Results,” POLITICO, accessed December 9, 2020, https://www.politico.com/2020-election/results/texas/.

U.S. County Migration Patterns. Accessed November 7, 2020. https://flowsmapper.geo.census.gov/map.html.