Covid-19 as amplifier of social inequality- SMall ARea Analyses with German neighborhood Data (SMARAGD)

在这个项目中，我们试图了解居民平均社会经济地位较低的弱势社区的作用。人们通常认为低收入社区的个人避免接触Sars-Cov2感染者的可能性有限。这可能是由于恶劣的住房状况（例如，更多的人住在更小的房间里），工作没有在家工作的选择，等等。德国仍然缺乏关于社会不平等和感染率的严格经验证据，主要是因为没有全面获得低于地区（Kreise）水平的小区域信息。然而，有证据表明，城市内的感染率存在很大差异，因此有必要在小得多的区域层面收集和分析数据。在这个项目中，我们的目标是通过提供适当的经验证据来填补这一空白，从而增加我们对流行病如何运作的理解。此外，我们在这个项目上的工作可以为有针对性的政策处方提供信息，以克服流行病。例如，如果我们了解到弱势社区在病毒传播中发挥了很大作用，那么疫苗接种运动就应该把重点放在这些地区，比目前做的要多得多。然而，只有当这些社区的疫苗接种意愿处于可接受的水平时，这才有希望，我们也试图通过收集的数据进行分析。我们分三步达到我们的目标：收集有关内城和内区感染率的数据，并将这些数据与同级住房和社会经济状况的丰富数据结合起来。我们还收集了在家工作的可能性和疫苗犹豫的数据。用最先进的方法分析这些数据，了解弱势社区的作用，并提出基于证据的政策建议。在项目完成后，为科学研究提供小规模数据。这将由RWI的FDZ Ruhr和LMU-ifo EBDC完成。因此，我们增加了这一主题的研究基础设施。鲁尔FDZ是一个小型区域数据研究数据中心。LMU-ifo EBDC是一个研究数据中心，专注于为大数据研究提供基础设施。这两个数据中心都获得了德国数据论坛RatSWD的认证。

In this project we seek to understand the role of disadvantaged neighborhoods with low average socio-economic status of their inhabitants. Individuals in low-income neighborhoods are often assumed to have limited possibilities to avoid contacting people infected with Sars-Cov2. This can be due to bad housing situations (e.g., more individuals live in smaller rooms), holding jobs without the option to work from home, etc. Rigorous empirical evidence on social inequality and infection rates is still lacking for Germany, mainly because small area information below the level of the districts (Kreise) is not comprehensively available. However, suggestive evidence indicates strong differences in infection rates within cities, necessitating the collection and analysis of data at much smaller regional levels.In this project, we aim to fill this gap by providing the appropriate empirical evidence, thereby adding to our understanding of how pandemics work. Additionally, our work on this project can inform targeted policy prescriptions to overcome pandemics. For instance, if we learn that disadvantaged neighborhoods play a large role in the spread of viruses, vaccination campaigns should focus on these areas, much more than currently done. This, however, is only promising as long as vaccination willingness is on an acceptable level in these neighborhoods which we also seek to analyze by the collected data.We reach our goal in three steps:1. Collect data on inner-city and inner-district infection rates and combine these with rich data on housing and socio-economic status at the same level. We also collect data on the possibility to work from home and on vaccine hesitancy.2. Analyze these data with state-of-the-art methods to learn about the role of disadvantaged neighborhoods and to draw evidence-based policy recommendations.3. Make the data on small-scale level available for scientific research on completion of the project. This will be done by the FDZ Ruhr at RWI and the LMU-ifo EBDC. Thus, we add to the research infrastructure on this topic. The FDZ Ruhr is a research data center for small scale regional data. The LMU-ifo EBDC is a research data center with a focus on providing infrastructure for big data research. Both data centers are accredited by the German DataForum RatSWD.