This paper examines the effect of asylum seeker intake on the number of new businesses being registered. We combine two datasets: exclusive data on new district-level business registrations and data on asylum seeker intake in about 400 German districts (Kreise) between 2007 and 2021. To address endogeneity in asylum seeker allocation, we use official within-state allocation quotas as an instrument. A one SD higher intake of asylum seekers (≈ 10 asylum seekers) per 1,000 inhabitants leads to 0.7 new businesses per 1,000 inhabitants being registered (≈ 8.8% increase). These new businesses, mainly registered by Germans (not asylum seekers themselves), directly create 2.6 new full-time jobs per 1,000 inhabitants as well as about 8 new full-time jobs per 1,000 inhabitants in the district outside the new businesses. On mechanisms, we also find that the new business registrations are both supply- (new workforce) and demand-driven (new demand patterns).

We present a new methodological approach to measure the redistributive preferences of political parties based on their election proposals. This approach builds on the marginal value of public funds (MVPF) framework. We recover the welfare weight associated with a small reform as the inverse of its MVPF. The aggregated welfare weights of multiple small reform proposals for each party and election year provide measures of the parties’ redistributive preferences along the income distribution. Leveraging this approach, we use a rich structural microsimulation model to estimate the MVPFs and their associated welfare weights for more than 300 proposed reforms of the tax-transfer system by Germany’s five largest parties from 1990 until 2021. Our results allow to study the differences in the redistributive preferences between German parties

and over time.

Does the sudden arrival of a new wave of immigrants distort the electoral performance of local council candidates with an immigrant background? We combine hand-collected candidate-level data on council elections (2001-2021) with detailed administrative data on asylum seekers across Hessian municipalities. We infer candidates’ immigrant background from their names via existing machine learning classification tools. Using a difference-indifferences strategy with a continuous treatment (change in the share of asylum seekers per municipality), we find that candidates with an immigrant background benefit from the arrival of relatively large numbers of asylum seekers such as in late 2015. Further results show that this effect exists exclusively for candidates with a Southern or Eastern European background which are culturally and ethnically relatively similar to native Germans.

What is the causal effect of conflict on refugees’ return and integration? To answer this question, we launched a panel survey of Ukrainian refugees across Europe in June 2022 and combined it with geocoded conflict data. Most refugees plan to return, and initial return intentions strongly predict actual return. Those who initially plan to settle outside Ukraine integrate faster. Increased conflict intensity in the home municipality discourages return there, but not to Ukraine as a whole. It also has no effect on the likelihood of working. Liberation of the home district increases return, while increased pessimism about the outcome of the war reduces return intentions.