About the Project

Project Partners

This project is a collaboration between Data Science for Social Good Foundation Fellowship UK and German Federal Ministry of Economic Affairs and Energy (BMWi).

The DSSG Foundation helps NGOs and government bodies achieve more with their data by enhancing their services, interventions and outreach, helping fulfil their mission of improving the world and people’s lives. Its fellowship programme trains students to create industry-standard data science products in collaboration with these agencies to deliver positive social impact.

German Federal Ministry of Economics and Energy (BMWi)

The main responsibilities of BMWi include supporting SMEs and start-ups, promoting digitization within industrial companies, and designing the path for energy transition. Ultimately, the goal pf BMWi is to ensure Germany’s economic competitiveness and a high level of employment.

Project Goal

The goal of the project is to robustify BMWi’s economic forecasts by developing a bottom-up model for predicting economic development in Germany based on regional data. The project drew on fine-grained data on the demographic, economic and sectoral structure of county-level data with the aim of improving economic forecasts during times of shocks.

More specifically, our team built a open-source tool where BMWi as well as the public would be able to predict unemployment rate in Germany on a county-level for the next three months.

Project Data

All the data used in this project is available to the public.

Regional Statistics Office

The unemployment rate data we use for this project is mainly drawn from the labor market statistics section provided by the Regional Statistics Office.

Corona Data Platform

The structural data we use for this project is mainly drawn from the Corona Data Platform. We use the numerical and categorical data from the platform

The Corona Data Platform is part of a project under BMWi in September 2020. The platform contains a combination of epidemiological and socioeconomic variables for analytical evaluations. The socioeconomic variables on the platform are predominantly collected from the Regional Statistics Office.