Indikatoren sollen der Steuerung von (sozialen) Prozessen dienen. Sie beschreiben jedoch die Realität in der Regel nur deskriptiv und unvermeidlich mit mehr oder weniger großen und systematischen Messfehlern behaftet. Insofern ist es im Allgemeinen alles andere als einfach mit Hilfe von Indikatoren zu steuern; insbesondere dann, wenn für Problembereiche (fehlerbehaftete) Zielwerte vorgegeben werden, ...
We introduce a selection model-based imputation approach to be used within the Fully Conditional Specification (FCS) framework for the Multiple Imputation (MI) of incomplete ordinal variables that are supposed to be Missing Not at Random (MNAR). Thereby, we generalise previous work on this topic which involved binary single-level and multilevel data to ordinal variables. We apply an ordered probit ...
Nonprobability online panels are commonly used in the social sciences as a fast and inexpensive way of collecting data in contrast to more expensive probability-based panels. Given their ubiquitous use in social science research, a great deal of research is being undertaken to assess the properties of nonprobability panels relative to probability ones. Much of this research focuses on selection bias, ...
Public debates and current research on “digitalization” suggest that digital technologies could profoundly transform the world of work. While broad claims are common in these debates, empirical evidence remains scarce. This calls for reliable data for empirical research and evidence-based policymaking. We implemented a data module in the Socio-Economic Panel to gather information on digitalization ...
We use machine learning techniques to quantify trade tensions between the United States and China. Our measure matches well-known events in the US-China trade dispute and is exogenous to the developments on global financial markets. Local projections show that rising trade tensions leave US markets largely unaffected, except for firms that are more exposed to China, while negatively impacting stock ...
The literature on the effects of incentives in survey research is vast and covers a diversity of survey modes. The mode of probability-based online panels, however, is still young and so is research into how to best recruit sample units into the panel. This paper sheds light on the effectiveness of a specific type of incentive in this context: a monetary incentive that is paid conditionally upon panel ...
To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as “unintended storage cycling”, ...
We quantify the value of data for the prediction policy problem of reducing antibiotic prescribing to curb antibiotic resistance. Using varying combinations of administrative data, we evaluate machine learning predictions for diagnosing bacterial urinary tract infections and the outcomes of prescription rules based on these predictions. Simple patient demographics improve prediction quality substantially ...