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Does Your Smartphone “Know” Your Social Life? A Methodological Comparison of Day Reconstruction, Experience Sampling, and Mobile Sensing

Mobile sensing is a promising method that allows researchers to directly observe human social behavior in daily life using people's mobile phones. To date, limited knowledge exists on how well mobile sensing can assess the quantity and quality of social interactions. We therefore examined the agreement among experience sampling, day reconstruction, and mobile sensing in the assessment of multiple aspects ...

Recent Methodological Advances in Panel Data Collection, Analysis, and Application: Editorial

Panel studies have become an indispensable part of today's research world especially when addressing causal questions and tracking changes over time. Three conditions are essential for effective panel data analysis: 1) having a sufficiently long time series with a substantial number of observations, 2) ensuring measurement consistency over time, and 3) using a meaningful model for selecting elements ...

Combining GPS Tracking and Surveys for a Mode Choice Model: Processing Data from a Quasi-Natural Experiment in Germany

This paper deals with the data generation process implemented for an analysis of the impact of the 9-Euro ticket on mode choice. We discuss the assumptions made and procedures used to process a raw dataset that is based on GPS traces of individuals’ movements and on survey data into the choice-set for a discrete choice model. Several steps of cleaning and merging are described in order to a) obtain ...

Do Words Hurt More than Actions? The Impact of Trade Tensions on Financial Markets

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 ...

Multiple Imputation of Ordinal Missing Not at Random Data

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 ...

Renewable Energy Targets and Unintended Storage Cycling: Implications for Energy Modeling

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”, ...

The Early Bird Catches the Worm! Setting a Deadline for Online Panel Recruitment Incentives

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 ...

Measurement Equivalence in Probability and Nonprobability Online Panels

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, ...

Assessing the Value of Data for Prediction Policies: The Case of Antibiotic Prescribing

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 ...

Setting Priorities, Pooling Resources and Accelerating Transformation: New Approaches in Industrial and Technology Policy