Phil Keisling, former Oregon secretary of state and current director of the Center for Public Service at Portland State University, wrote the cover article in the current edition of the Washington Monthly titled: Vote from Home, Save Your Country. In it, he makes the case for universal vote by mail (UVBM) as it is now practiced in Oregon, Washington and Colorado.

Academic researchers have studied UVBM. But in an article by Keisling titled: What Critics Get Wrong about Universal Vote By Mail, he notes this:

The core question these academic studies address seems straightforward enough. Controlling for all other relevant factors, how much difference does Election Reform X seem to make? Every election is different—by year, between states, and within states. Was Reform X, first used in 2010, really responsible for that 8 percent higher voter turnout compared to the 2006 midterms? Or were other factors responsible? In fact, might turnout have otherwise been 12 percent higher, except for X?

Every electorate is different, too. Election researchers have long known that four demographic factors significantly correlate with the propensity of a given state’s population to register and to vote: age, personal income, educational attainment, and race/ethnicity. Latino and Asian citizens, for example, have voting rates—especially in non-presidential elections like midterms and primaries—that are roughly half the rates of both black and white citizens, who now vote at roughly equal rates in such elections.

Researchers use various assumptions and statistical models to try to account for these and other differences between elections and electorates. But there’s no one broadly accepted framework for making these adjustments; assumptions and models vary widely among academics. And the resulting studies—rife with various correlations, weighted least squares logit models, and regression analyses—are often opaque to nonspecialists.

Keisling has provided us with a thorough review of the research on UVBY, outlining where these different assumptions and models have been used and demonstrating why that produces varying results.

Nancy LeTourneau

Follow Nancy on Twitter @Smartypants60.