Elections and Public Policy in Times of Crisis

• Applicant: Professorin Dr. Zohal Hessami
• Subject Area: Economic Policy, Applied Economics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 506165485

The aim of this project is (i) to explore how major crises affect electoral outcomes and the selection of politicians into office and (ii) to understand whether and how these crises can be an opportunity to monitor incumbents more closely, to improve political selection, and to achieve economic prosperity and fiscal stability by choosing and implementing appropriate policy responses. To study these questions, we use data on the universe of municipalities in two German states - Bavaria and Hesse. This is a useful setting to explore the questions outlined above especially since the available number of observations allows for credible identification of causal effects and an extensive analysis on transmission mechanisms.In the first part of the project, we will study the effect of a looming pandemic (local council elections in 2056 municipalities in Bavaria in March 2020) and of a pandemic already experienced by voters (local council elections in 426 municipalities in Hesse in March 2021) on candidates' performance in elections. This will allow us to draw conclusions as to whether voters prefer specific types of leaders when facing a crisis, whether incumbency advantages are affected in general and whether prospective and retrospective voting have played a role in these elections. We will rely on difference-in-differences approaches (variation in local school and daycare closures in February/March 2020 in Bavaria to define the treatment- and control-group) as well as approaches that rely on geographic spillovers of Covid-19 infections across state borders (e.g. Hessian municipalities sharing a border with Bavaria).In the second part of the project, we will study the effect of refugee intake in Hessian municipalities, in particular as of 2015. First, we will study whether local exposure to refugees affects voters' biases towards local council candidates with a foreign background. This background will be proxied by machine learning (ML) classification algorithms and existing databases on the provenance of names. We will then investigate whether the individual electoral performance of candidates with a foreign background was positively or negatively affected (contact vs. backlash hypothesis) in local council elections in Hesse in March 2016 and March 2021. Second, we will study how local refugee intake has impacted local public finances (especially revenues) and local economic development (employment, economic growth, demographics, local companies). Our identification strategy in these two subprojects relies on an IV approach that exploits initial heterogeneity in the availability of large common housing facilities.

• DFG Programme: Research Grants