Forecasting the 2020 Electoral College Winner: The State Presidential Approval/State Economy Model

Peter Enns and I predicted 49/50 states + D.C. correctly. Our only miss was Georgia.

The model was published in PS. We discuss our results and the potential implication of presidential approval being disconnected from reality in a Washington Post Monkey Cage article. We also published an article in the University of Miami Law Review in which we discuss how legal and social science researchers can use early forecasting to estimate the effects of campaigns, media, and laws.

I talked about our model and the American Presidential Election 2020 to the German media outlet “” (in German) and Argentinian newspaper “Infobae” (in Spanish). Our work was also cited in “Le Figaro” (in French).

Our model focuses on fundamentals and presidential approval. Instead of predicting the national vote share we predict the outcome of the Electoral College. To receive State-level presidential approval rates we used MRP models using 86 polls and almost 90,000 respondents between 1980-2020 to estimate the State-level presidential approval rate in each state. Additionally, we use economic indicators on state-level as well as some political variables like home-state of the presidential candidates. 104 days before the election, our model predicted about a 4 in 10 chance that Donald Trump would be re-elected and about a 6 in 10 chance that Joe Biden is the next president. The figure below shows the distribution of the 70,000 simulated Electoral College outcomes. The modal value was a victory of Trump by a margin of one vote. However, in ~60% of the simulations, we predicted that Joe Biden would win more than 50% of the Electoral College votes.

The graph below shows the distribution of our simulations on state-level. Higher histograms indicate greater importance for the Electoral College. The only state we missed was Georgia.

Immigrant-citizens in the German 2021 Elections

#VielfaltGewinntWahlen #DiversityWinsElections

The share of citizens with a migration background (immigrant-citizens) has never been larger in Germany. They can decide the Bundestag election in 2021. Together with the NGO Citizens for Europe, Arndt Leininger and I collected data on the share of imigrant-citizens in each federal election district for the 2017 and the 2021 German federal elections. For the first time, researchers and policy makes can access data on the share of citizens with a migration background on electoral district! We collected the data by accessing the German Micro Census from 2017 and 2018 in a data vault in Berlin. The estimates are based on ~1.5 Million respondents and is publicly available here on Harvard Dataverse.

Together with Daniel Gyamerah and Deniz Yıldırım-Caliman in cooperation with CorellAid we published a policy paper (in German) to inform the German public ahead of the election of the 2021 federal elections summarizing our findings:

“Eligible voters with a migration background could significantly influence the distribution of seats in the distribution of seats in the German Bundestag. […] In 167 out of 299 constituencies (56 percent), the number of eligible voters with an immigrant background exceeds the gap between the first- and second-place direct candidates in the last Bundestag election.”

Find the policy paper here. We presented our results in an online press-conference together with the Türkische Gemeinde Deutschland e.V. (Turkish Society of Germany). You can find a recording of the press conference here (in German). CorrelAid also used the data in an English blogpost visualizing the voting potential of immigrant-citizens in Germany here.

In relation to this publication, I was interviewed and quoted by the Associate Press. The story was widely spread internationally and syndicated by The Washington Post, ABC News, New India Express (India), or the Mainichi (Japan) among many others.

Together with Thorsten Faas and Marc Debus I also reflected on the outcomes of the German elections in the Washington Post Monkey Cage.

Map of all election districts in which the number of citizens with a migration background is larger than the number of votes between the winner of the district and the candidate with the second-most votes in the last federal election in 2017.