Why We Need to Rethink the Definition of Tipping Point State

It’s no secret that winning the popular vote isn’t enough to get the keys to the White House. As the winner of presidential elections is decided through the Electoral College, the paramount goal of presidential hopefuls is to cobble together 270 Electoral Votes, regardless of the number of votes obtained nationwide. 

This quirky system devised by the Founders makes it useful in political science to define an indicator that measures the asymmetry between the Electoral College and the popular vote results: the “Electoral College Bias”, which provides a theoretical nationwide threshold for candidates to meet in order to come on top when Electoral Votes are counted on January 6^th. For example, if we say that the Electoral College is biased by 2% in favor of Republicans, then the democratic nominee would need to win the national popular vote by more than this margin in order to obtain a majority in the EC, and anything short of that would result in a GOP victory. 

What is the “tipping point state”?

Finding the “tipping-point state” is what helps us assess the bias of the Electoral College. Begin by sorting the states carried by the winning candidate by decreasing margin of victory, then take away as many states – and electoral votes – as you can from their tally until you identify the one that gets the winner over the 270 EVs threshold: this is our “tipping-point state”. In 2024, for instance, when Trump clinched 312 EVs, his margin of victory was the smallest in Wisconsin (0.6%), followed by Michigan (1.4%) and Pennsylvania (1.7%). As per the standard procedure, start taking away the Badger State’s 10 electoral votes, and then Michigan’s 15, which leaves Trump at 287. This means that Pennsylvania is the state that got him over the line, and that would deny him the victory if removed from his column, therefore securing the much-coveted title of Tipping-Point State for the 2024 presidential election.

At this point, calculating the Electoral College Bias is straightforward: you just have to subtract the popular vote margin to the tipping-point state results. Trump won the Keystone State by 1.7% and the popular vote by 1.5%, resulting in an Electoral College bias of 0.2% in Trump’s favor. In a hypothetical world where shifts across states are uniform, this implies that Kamala Harris would have needed to actually popular vote by 0.2% in order to carry Wisconsin, Michigan and – critically – Pennsylvania, and with them the presidency.

The only problem? Assuming that a given nationwide shift would translate into the same swing in every state is a big approximation that doesn’t factor in states’ elasticity.

What is elasticity?

Elasticity refers to states’ responsiveness to national shifts from the previous cycles. For instance, if the U.S. were to shift 3% to the right in a given election compared to the previous one, and one state registered a 6% swing, we’ll say that state is very elastic, while a state that only trended 1.5% to the right would be less responsive to changes in the national environment, therefore less elastic. 

My elaboration considering presidential elections from 2008 to 2020 found the states with the highest elasticity scores to be located in the Midwest region, with Western states less susceptible to environment shifts. Notably, the most elastic states in 2024 were exactly the least elastic ones in previous cycles. That’s because Trump gains last fall were concentrated among minorities, especially Latinos, resulting in major shifts in states like California, Texas, New York and New Jersey, which had registered only minor swings in previous elections. On the other hand, white-majority states in the Midwest and the Plains swung by smaller amounts compared to the national 5.9% rightward trend.

Here comes the issue with the current definition of the tipping-point state. Suppose a hypothetical scenario in which in a given election the US as a whole registers a 6.0% rightward shift, let’s say from D+6 to D+0, while the tipping-point state goes from D+4 to D+2, trending right only by 2.0% – i.e. one third of the national swing. It’s a bit of a stretch to think that had the nation shifted by 9% instead of 6%, the tipping-point state would have trended right by 5% instead of 2%. It makes more sense to imagine that, within certain limits, the ratio of Nation/State shifts would remain constant: hence, a 9% national swing would translate only into a 3% state shift.

It follows that, according to this reasoning, the GOP candidate would need to shift the nation by 12% to bring about the 4% local swing needed to win the state. This is a much bigger national shift that the 8% needed according to the standard tipping-point definition- after all, the state already registered a smaller shift compared to the nation – that would change the Electoral College Bias from D+2% to D+6%. With these numbers, the republican candidate would need to win by 6% nationwide in order to carry the decisive state, assuming a more elastic one doesn’t replace it as the tipping-point – more on this later on.

Electoral college bias

I tried to devise a new definition of tipping-point state that also considers state elasticity when measuring the Electoral College Bias. I came up with an alternative formula to determine the tipping-point state and Electoral College Bias that accounts for states’ elasticity as well as “raw shifts” – this is because we cannot even assume the ratio of Nation/State shifts to be constant in every scenario, particularly when considering huge differences from actual results. In fact, elasticity weighs more when looking at small differences from election day results – and took a look at the latest presidential cycles.

The advantage of including elasticity scores is that they allow us to look at specific dynamics as well as campaign roles in shaping the election. There is a lot of evidence showing how campaigns’ focus on certain groups or states can significantly affect outcomes. It’s not a case that while turnout dropped by around 2 points nationwide in 2024, swing states actually registered higher levels of participation compared to 2020 – the likely result of the massive amount of money spent in ads and grassroot campaign efforts.

Which states shifted least?

The Midwest registered some of the smallest pro-Trump shifts. Wisconsin shifted right only by 1%, Pennsylvania by 3% and Michigan by 4.2%, all well below the national 5.9% threshold: a result many ascribe to the turnout effort put in place by the Harris apparatus, which helped reduce the bleeding of historically democratic white voters in the region. The standard tipping-point state definition implies that had Harris done 1.7% better nationally, she would have carried the trio, even winning Wisconsin by 1.1%. But it looks like Harris already maximized her support in a region that seemed to move quite independently of the nation. Without much more leeway in the Midwest, she would have probably needed to do significantly better nationwide in order to win the three critical states. 

On the other hand, Trump gains came from states home to a big Latino population. It stands to reason to argue that a hypothetical drop in nationwide support for Trump would have disproportionately affected those same states, among which rank Texas, Arizona, Nevada and New Jersey. This explains why according to the new methodology, it would take a smaller nationwide swing to flip Nevada in Harris’ favor than Pennsylvania and Wisconsin. Due to the Silver State’s scant number of Electoral Votes, the tipping-point state would remain Pennsylvania, but the shift needed to flip the Keystone State would increase from 1.7% to 3.6%. As a consequence, the Electoral College Bias would go from R+0.2% to R+ 2.1%, much closer to the 2020 bias, which hovered around 4% calculated in both methods – Biden small margins of victory in Arizona, Georgia and Wisconsin only change the math by 0.5%, bringing the bias from R+4.0 to R+3.5, with Wisconsin still the tipping-point state.

Looking back at the 2016 election, when Hillary Clinton won the national popular vote by 2.1%, Midwestern states registered massive swings from 2012, while nation only shifted right by 1.8%, making them extremely elastic. This means that according to the new definition, Clinton would have needed to do only 0.21% better nationwide in order to win the WIMIPA trio and the White House, compared to 0.77% according as per the standard definition. The Electoral College bias would therefore decrease from R+2.86 to R+2.30, while the tipping-point state would change from Wisconsin to Pennsylvania. The Badger state was more elastic: hence, Clinton would have needed only a major national shift to flip it, despite it voting 0.05% to the right of Pennsylvania.

Including states’ elasticity into the definition of tipping-point state may provide useful insights and more realistic information on hypothetical scenarios and Electoral College Bias, taking into account the particular contingencies of every election and campaign. An alternative definition may work particularly well in close contests and polarized environments, when candidates already tend to maximize their chances in particular geographical areas which weakens the assumption that shifts propagate uniformly nationwide.