Youth voters (ages 18-44) consistently have lower turnout rates compared to older voters, often by as much as 50% relative to those 65 and older. This presents a significant challenge for Democrats, as the youth voter population is twice as large as the 65+ population, yet their actual turnout lags considerably behind. The 2024 presidential election is just around the corner, and the polls indicate a tight race. Given the uncertainty in polling and the likely underrepresentation of Democratic support, sensitivity analysis is one of the best tools for evaluating potential election outcomes. By calculating the minimum youth voter turnout needed to secure battleground states, this approach provides a clearer picture of what it would take to win. Ultimately, it is youth voter turnout—more than the electoral college alone—that determines whether these states can become reliably blue.
Example: North Carolina
If we do a sensitivity analysis of turnout across older voters using the combined poll data (Atlas and Emerson, some of these are subject to red-wave bias), we find that the high voter participation across 65+ carries the election for Trump, almost irrespective of turnout across 45-64-year-olds (makes sense as that age group is almost split 50/50 with just under 1 point favoring Trump if we combine polls). However, if we were to increase youth voter turnout by 15%, Harris would carry a rather dramatic advantage (assuming voter sentiment is reflected in the combined poll data).
Sensitivity analyses of voter turnout in mid-aged voters using Washington Post poll data, assuming increased voter turnout across youth voters ages 18-34 (62%, up 10% from 2020).
Nini Munoz is a Senior Fellow at Portulans Institute. She hails from Barranquilla, Colombia. Her area of expertise spans sensor design to neural interfaces, with an emphasis on nanofabrication, data science & statistics, process control, and risk analysis. She completed her Ph.D. at Cornell University in the Department of Electrical and Computer Engineering with a minor in food microbiology and substantial hands-on experience in electrochemistry and neurophysiology. She simultaneously focuses on sensor design and data analytics, while also using her spare time to teach scientific literacy online and create infographics that simplify complex topics for easier understanding.
