Background Mandatory COVID-19 certification (showing vaccination, recent negative test, or proof of recovery) has been introduced in some countries. We aimed to investigate the effect of certification on vaccine uptake. Methods We designed a synthetic control model comparing six countries (Denmark, Israel, Italy, France, Germany, and Switzerland) that introduced certification (April–August, 2021), with 19 control countries. Using daily data on cases, deaths, vaccinations, and country-specific information, we produced a counterfactual trend estimating what might have happened in similar circumstances if certificates were not introduced. The main outcome was daily COVID-19 vaccine doses. Findings COVID-19 certification led to increased vaccinations 20 days before implementation in anticipation, with a lasting effect up to 40 days after. Countries with pre-intervention uptake that was below average had a more pronounced increase in daily vaccinations compared with those where uptake was already average or higher. In France, doses exceeded 55 672 (95% CI 49 668–73 707) vaccines per million population or, in absolute terms, 3 761 440 (3 355 761–4 979 952) doses before mandatory certification and 72 151 (37 940–114 140) per million population after certification (4 874 857 [2 563 396–7 711 769] doses). We found no effect in countries that already had average uptake (Germany), or an unclear effect when certificates were introduced during a period of limited vaccine supply (Denmark). Increase in uptake was highest for people younger than 30 years after the introduction of certification. Access restrictions linked to certain settings (nightclubs and events with >1000 people) were associated with increased uptake in those younger than 20 years. When certification was extended to broader settings, uptake remained high in the youngest group, but increases were also observed in those aged 30–49 years. Interpretation Mandatory COVID-19 certification could increase vaccine uptake, but interpretation and transferability of findings need to be considered in the context of pre-existing levels of vaccine uptake and hesitancy, eligibility changes, and the pandemic trajectory. Funding Leverhulme Trust and European Research Council.

This study investigates the presence of environmental inequality in Germany and analyses its spatial pattern on a very fine grained level. Using the 2011 German census and pollution measures of the E-PRTR, the study relies on nearly 100,000 one squared km census cells over Germany. SLX and community-fixed SLX models incorporate spatial spillover-effects into the analysis to account for the spatial distribution of socio-demographic characteristics. Results reveal that the share of minorities within a census cell indeed positively correlates with the exposure to industrial pollution. Furthermore, spatial spillover effects are highly relevant: the characteristics of the neighbouring spatial units matter in predicting the amount of pollution. Especially within urban areas, clusters of high minority neighbourhoods are affected by high levels of environmental pollution. This highlights the importance of spatial clustering processes in environmental inequality research.

Fixed effects (FE) panel models have been used extensively in the past, as those models control for all stable heterogeneity between units. Still, the conventional FE estimator relies on the assumption of parallel trends between treated and untreated groups. It returns biased results in the presence of heterogeneous slopes or growth curves that are related to the parameter of interest (e.g., selection into treatment is based on individual growth of the outcome). In this study, we derive the bias in conventional FE models and show that fixed effects individual slope (FEIS) models can overcome this problem. This is a more general version of the conventional FE model, which accounts for heterogeneous slopes or trends, thereby providing a powerful tool for panel data and other multilevel data in general. We propose two versions of the Hausman test that can be used to identify misspecification in FE models. The performance of the FEIS estimator and the specification tests is evaluated in a series of Monte Carlo experiments. Using the examples of the marital wage premium and returns to preschool education (Head Start), we demonstrate how taking heterogeneous effects into account can seriously change the conclusions drawn from conventional FE models. Thus, we propose to test for bias in FE models in practical applications and to apply FEIS if indicated by the specification tests.

Socio-economically disadvantaged and ethnic minorities are affected by a disproportionately high exposure to environmental pollution. Yet, it is unclear if selective migration causes this disproportionate exposure experienced by low-income and minority households. The study uses longitudinal data from the German Socio-Economic Panel to investigate the process of selective migration and its connection to the perceived exposure to air pollution in Germany. Consistent with the selective migration argument, movers experience a decrease in exposure according to their income, whiles stationary households do not experience a reductive effect due to income. Furthermore, the moving returns differ by minority status. While native German households experience less exposure to pollution when moving to a new place of residence, minority households do not. Additional analyses show that this minority effect cannot be explained by socio-economic differences, but completely vanishes in the second immigrant generation.

ImportanceGovernments have introduced non-pharmaceutical interventions (NPIs) in response to the pandemic outbreak of Coronavirus disease (COVID-19). While NPIs aim at preventing fatalities related to COVID-19, the previous literature on their efficacy has focused on infections and on data of the first half of 2020. Still, findings of early NPI studies may be subject to underreporting and missing timeliness of reporting of cases. Moreover, the low variation in treatment timing during the first wave makes identification of robust treatment effects difficult.ObjectiveWe enhance the literature on the effectiveness of NPIs with respect to the period, the number of countries, and the analytical approach.Design, Setting, and ParticipantsTo circumvent problems of reporting and treatment variation, we analyse data on daily confirmed COVID-19-related deaths per capita from Our World in Data, and on 10 different NPIs from the Oxford COVID-19 Government Response Tracker (OxCGRT) for 169 countries from 1st July 2020 to 1st September 2021. To identify the causal effects of introducing NPIs on COVID-19-related fatalities, we apply the generalized synthetic control (GSC) method to each NPI, while controlling for the remaining NPIs, weather conditions, vaccinations, and NPI-residualized COVID-19 cases. This mitigates the influence of selection into treatment and allows to model flexible post-treatment trajectories.ResultsWe do not find substantial and consistent COVID-19-related fatality-reducing effects of any NPI under investigation. We see a tentative change in the trend of COVID-19-related deaths around 30 days after strict stay-at-home rules and to a slighter extent after workplace closings have been implemented. As a proof of concept, our model is able to identify a fatality-reducing effect of COVID-19 vaccinations. Furthermore, our results are robust with respect to various crucial sensitivity checks.ConclusionOur results demonstrate that many implemented NPIs may not have exerted a significant COVID-19-related fatality-reducing effect. However, NPIs might have contributed to mitigate COVID-19-related fatalities by preventing exponential growth in deaths. Moreover, vaccinations were effective in reducing COVID-19-related deaths.