Pandemic economics 2

Einleitung / Introduction

The COVID-19 pandemic altered everyone’s life-trajectory in unprecedented ways. No domain of individuals’ lives was speared by what has been coined the largest challenge since the second World War. Consequently, it’s of utmost importance for policy makers to compensate inequalities which are caused by the COVID-19 pandemic and hence, out of the individuals’ control. At the same time, policy makers face budget constraints. Therefore, understanding which groups are of risk of infections, earnings losses or consequences in the private domain is crucial to allocate scarce resources efficiently. Thus, this also opens possibilities for ex-post evaluations: Have the right persons received the right aid?

Methode / Method

This is where we contribute to: We aim at describing which groups are at risk of adverse consequences in the wake of and during the COVID-19 pandemic. For this, we make use of state-of-the-art statistical learning methods and data, which is uniquely suited to identify the population at risk in the wake of the COVID-19 pandemic. Our target outcomes are: earnings losses, individuals' well-being and changes in household production, i.e., gender division of childcare. Our data consists of the Socio-Economic Panel (SOEP), SOEP-CoV and the SOEP-RKI study. The SOEP-CoV is a special two-wave telephone survey in which respondents provided rich information about their experiences during COVID-19. The SOEP-CoV’s sample is a random draw from the SOEP-population in 2020. The first SOEP-CoV wave took place in spring and summer 2020 and the second wave in winter 2021. The SOEP is a longitudinal and representative survey of households and their members in Germany. Thus, we can enrich the SOEP-CoV with an unparalleled set of predetermined characteristics which help us to predict and classify groups at risk. Lastly, in the SOEP-RKI study in autumn/winter 2020, we tested the SOEP-respondents with antigen- and antibody-tests, equipping us with representative information about (retrospective) infections in the complete population. This allows us to consider how actual and undetected COVID-19 infections contributed to the outcomes. With this data at hand, we apply random forests to predict which groups are at particular risk of infection, earnings losses, or severe stress in the household. Random forests are part of the class of supervised machine learning algorithms, which provide accurate (out-of-sample) predictions in contrast to model-based inferences. One major advantage of random forests is that they do not rely on functional form assumptions. As such it is a non-parametric approach allowing us to identify highly non-monotonous and complex patterns in data.

AutorInnen

Daniel Graeber, DIW Berlin

Lorenz Meister, DIW Berlin

Carsten Schroeder, DIW Berlin

Sabine Zinn, DIW Berlin

Einleitung / Introduction

This study examines the immediate and intermediate effects of the COVID-19 pandemic on the well-being of two high school graduation cohorts (2020 and 2021). We also investigate how changes in well-being at the transition to post-secondary education affect educational plans and outcomes.

Methode / Method

Our unique panel data contain prospective survey information on three dimensions of well-being: mental health problems, self-rated health, and life satisfaction for 3,697 students. Data is collected several months before (fall 2019), shortly before and soon after (spring 2020), and several months after (fall/winter 2020/21) the outbreak of the COVID-19 pandemic. We apply difference-in-differences designs, random effect growth curve models, and linear regression models.

Ergebnisse / Results

We find that school closures had a positive immediate effect on students’ well-being. Over the course of the pandemic, however, well-being strongly declined, mainly concentrated among the 2021 graduation cohort. Finally, we show that a strong decline in mental health is associated with changes in educational and career plans and transition outcomes.

Zusammenfassung / Conclusion

As adverse life experiences in adolescence are likely to accumulate over the life course, this study is the first to exhibit potential long-lasting negative effects of the COVID-19 pandemic on education and careers of young individuals.

AutorInnen

Malte Sandner, Institut für Arbeitsmarkt und Berufsforschung (IAB)

Alexander Patzina, Institut für Arbeitsmarkt und Berufsforschung (IAB)

Silke Anger, Institut für Arbeitsmarkt und Berufsforschung (IAB)

Sarah Bernhard, nstitut für Arbeitsmarkt und Berufsforschung (IAB)

Hans Dietrich, Institut für Arbeitsmarkt und Berufsforschung (IAB)

Einleitung / Introduction

The suggested paper reviews the responses of five countries to the first wave of the Covid-19 pandemic: Denmark, Germany, Israel, Spain, and Sweden. It focusses the hospital care structures before the pandemic, as well as governance and capacity expansion during the crisis, in particular in intensive care. In line with the criteria suggested by the OECD, we consider the following key characteristics of resilient health care systems: 1. flexibility and adaptability in the use of existing resources, as well as planning for responding to surge in demand; 2. the ability to create surge capacity in the three fronts: staff, supplies, and space; and 3. the ability to avoid excess idle capacity The paper aims to understand to what extent the crisis response of five different health care systems during the COVID-19 pandemic was resilient. The aim is to learn from these responses to improve health systems’ resilience in the currently ongoing pandemic.

Methode / Method

The method is a comparative case study analysis. We chose these countries because they differ in terms of hospital ownership structures, health system design, and associated governance structures. Data were collected by country experts, the co-authors of this paper, following a structured questionnaire built specially for this study. The comparative analysis allows examining the structural similarities and differences, while being sensitive to context-specific phenomena such as resources, policy and decision-making.

Ergebnisse / Results

A high level of acute care and intensive care beds (Germany) is only of limited use in reaction to the pandemic as the number of non-occupied beds is decisive. Rather, the decisive factor in the crisis situation is the availability of qualified personnel, especially in (intensive) care, and the ability to mobilize personnel reserves available in the system. Staffing of hospital beds, and in particular the availability of (highly) qualified nurses, is the crucial “bottleneck” in all countries. A functional equivalent to high bed capacities is rapid adjustment of capacity as needed, as observed in Sweden, or a good preparedness plan, as perceived in Israel. A main deficiency in all countries, that data on nursing staff in intensive care are not available. Specialization and centralization in the inpatient sector together with the close integration of the outpatient sector (Denmark, Sweden) are structural advantages that have been conducive to targeted and rapid navigation of COVID-19 cases through the healthcare system from the outset. A centralized governance structure is crucial for flexibility and adaptability in the use of existing resources and a timely expansion of capacities. Limitations of this study can be seen in particular in the numerous contextual factors that could not be included, or only to a limited extent.

AutorInnen

Mirella Cacace, Katholische Hochschule Freiburg

Jan Böcken, Bertelsmann Stiftung

Kristin Edquist, Eastern Washington University, USA

Tanja Klenk, Helmut Schmidt University, Hamburg

Mario Martinez-Jimenez, Lancaster University, UK

Uwe Preusker, Health System Analysis / Health Policy Research, Vantaa, Finland

Karsten Vrangbaek, University of Copenhagen, Denmark

Ruth Waitzberg, Ben-Gurion University of the Negev / Myers-JDC-Brookdale Institute, Israel / TU Berli

Einleitung / Introduction

Governments around the world have implemented various non-pharmaceutical interventions (NPI) to slow down the spread of Covid-19. Most of these interventions severely restrict the lives of the affected populations. While nighttime curfews are less severe restrictions compared to full lockdowns, there is surprisingly little evidence on their effectiveness -- and the existing literature provides rather mixed evidence. This paper examines the effectiveness of nighttime curfews, based on the curfew in the German city of Hamburg that came into effect on April 2, 2021. The curfew prohibited people from leaving their home between 9 p.m and 5 a.m, except for physical training, walking the dog, and commuting to work.

Methode / Method

We compare the development of Covid-19 cases in Hamburg around the introduction of the nighttime curfew with the development of Covid-19 cases in other German regions in the same period, applying both difference-in-differences regressions and synthetic control group methods.

Ergebnisse / Results

Our empirical analyses show that the nighttime curfew was effective in reducing the number of Covid-19 cases: While the development of Covid-19 cases in Hamburg and a synthetic Hamburg was similar before the implementation of the curfew, a few days after the curfew cases in Hamburg begin to fall relative to the control group.

Zusammenfassung / Conclusion

Although many regions (e.g., in Canada, France, Germany, Spain) have implemented nighttime curfews at some point, there is surprisingly little evidence on the effectiveness of these curfews to contain the Covid-19 pandemic. Our findings are import for the general public as it can strengthen the adherence to an intervention if people are convinced of its effectiveness.

AutorInnen

Johannes Apel, Universität Hamburg

Niklas Rohde, Universität Hamburg

Jan Marcus, Universität Hamburg/HCHE