Sen. Ted Cruz, Donald Trump and Sen. Marco Rubio stand on stage at the start of the Republican presidential candidate debate on Feb. 13. (Daniel Acker/Bloomberg News)

Prediction markets are pessimistic about Republican presidential prospects. PredictWise gives the Republicans a 35 percent chance of winning the White House.

That seems surprising. The underlying fundamentals actually aren’t that favorable to the Democrats, and so we might expect the race to be more competitive.

This raises the question: Are Republicans paying a penalty for their contentious (to put it mildly) nomination process?

After the 2012 election, Vanderbilt political scientist Larry Bartels presented one of the simplest and most robust economic voting models. In this model, the incumbent party’s popular-vote margin depends on just two factors: per capita income growth and fatigue with the incumbent party. He measures those factors using change in real disposable income (RDI) per capita in the second and third quarters of the election year and the number of consecutive terms served by the incumbent party.

We replicated Bartels’s model with revised data from the Bureau of Economic Analysis. This simple model explains quite a lot — 83 percent — of the variation in presidential election outcomes since 1948.

We can use this model to make an election prediction based strictly on economic forecasts. What might be a reasonable forecast? Unfortunately, forecasting organizations do not report predictions for RDI. But they do forecast growth in the gross domestic product (GDP), a good proxy for RDI growth.

The Economist Intelligence Unit predicts that U.S. real GDP will grow by 2.4 percent in 2016, a value that is slightly below the average real GDP growth rate of 2.6 percent in the post-1979 period. So, mean RDI growth in the second and third quarters for the post-1979 period seems like a reasonable forecast, if a bit optimistic. That value is 0.77.

When we plug these values into the Bartels model, the “tenure-adjusted” value for income growth is –0.52. (As Bartels describes, this is just RDI growth, 0.77, minus 1.29 for every term the president has held office after the first term.) For that value, the model’s prediction is indicated below by the big red dot. Basically, the model predicts a dead heat: The Democratic Party’s popular-vote margin is forecast to be only 0.1 percentage points.

A prediction of a 0.1 percentage-point margin for the incumbent party is associated with a 51 percent probability that the Democratic Party wins the popular vote (when we take account of the small amount of noise in this figure). Compare this with the probability in the prediction markets — 65 percent — and you can see that the Republican Party is underperforming this model’s prediction by 14 percentage points.

Prospects for Republican success were not always so dire. In fall 2015, as the campaign was getting underway, betting exchanges were giving the Republicans a 45 percent probability of winning the presidency. Since last fall, economic forecasts for 2016 have become more pessimistic, which should hurt the Democrats and help the Republicans. Instead, we’ve seen the Republican prospects diminish to the current 35 percent.

Why are Republicans underperforming? Is this all because of Donald Trump? Not entirely. Consider what happened in the days after the Iowa caucus. Trump’s prospects took a tumble — from a 50 percent probability of winning the nomination to 20 percent — and Rubio rose from a 35 percent probability of winning the nomination to 60 percent. Yet the general election market remained fairly steady. So, Republican underperformance appears to be more than just Trump’s weakness as a candidate.

Political scientists have long thought that parties pay a modest penalty, if any, for a contentious nomination process. The current prediction markets appear to believe that the penalty is far more substantial.

Matthew Atkinson is a visiting assistant professor of political science at Miami University. Darin DeWitt is an assistant professor of political science at California State University at Long Beach.