Cluster member Tom Zimmermann calls for more data transparency

15.03.2021

In the current edition of the Cologne University Magazine, cluster member Dr. Tom Zimmermann, assistant professor at the University of Cologne, calls for more transparent access to data – even after the pandemic.

Since the beginning of the Corona pandemic in spring 2020, economist Tom Zimmermann has been collecting and visualizing data on Covid-19 in a dashboard to compare the local development of the pandemic across Germany. However, he is also interested in more: showing that many areas of society can benefit when more data is publicly available.

The dashboard shows various statistics on Covid-19 at the state and county level. In addition to widely discussed figures, such as cases per 100.000 inhabitants or the 7-day incidence, Zimmermann visualizes the data by gender or age, for example.

“Representative data sets are crucial”

The economist usually conducts research on stock markets as well as monetary and fiscal policy. However, the projects overlap at one key point: data. “You need a reasonable basis on which to discuss. For that, large, representative data sets are crucial,” says Zimmermann. Many results of scientific studies cannot be replicated, he says, because it is not clear how the underlying data came about. While Covid-19 dashboards can be updated daily, there are still many areas where it is difficult for researchers to get access to data from public institutions, especially in Germany. Zimmermann therefore wishes for the future: “Public institutions in Germany should make data more easily and quickly available.”

Click here to read the full article.

Current research project: Database for the analysis of investment strategies

Together with Andrew Chen, an economist at the Federal Reserve in the U.S., Zimmermann evaluated studies on more than 300 investment strategies in a recent research project. Each of these studies examined hundreds of stock market investment predictions using huge data sets and computationally intensive algorithms. However, the results are often difficult to replicate because the algorithms used are not publicly available. Zimmermann and Chen rebuilt 98 percent of the algorithms that could be replicated and collected them in a publicly available database that can now be accessed by researchers, firms or private investors.

Click here to access the database.

Press and communication

Carolin Jackermeier

M jackermeier@wiso.uni-koeln.de