Abstract The transition to online data collection in general population surveys has accelerated in recent years. Although high-quality web surveys now achieve response rates comparable to those of telephone surveys, they frequently display greater educational bias. This study analyzes complete employment biographies of both respondents and non-respondents to address three key questions: (1) How do different stages of the online panel recruitment process contribute to educational bias? (2) Are specific subgroups within the lower-educated population underrepresented in online surveys? (3) Are there interaction effects between education and other predictors of nonresponse, such as age, nationality, or employment status? In 2023, the Institute for Employment Research (IAB) in Germany launched the IAB-OPAL online panel survey using a push-to-web approach. The sample was drawn from a comprehensive administrative database containing social security, unemployment insurance, and basic income records, allowing for detailed analyses of employment histories. Leveraging detailed employment histories enables a granular assessment of how educational bias emerges across recruitment stages and whether response tendencies within educational groups vary due to typically unobserved factors such as benefit receipt, occupational history, or wages. The results indicate that educational bias increases at each stage of the recruitment process. Nonresponse is particularly common among individuals with lower educational attainment, especially those aged 50 and above. Although response probabilities for foreigners and Germans without an educational degree are similar, the disparity between these groups increases by at least a factor of four among individuals with a university degree. Additionally, men are less likely to participate unless they possess advanced degrees or lack formal qualifications.

Data protection laws in many jurisdictions grant users access to information that online platforms collect about them. Researchers capitalize on this legal requirement through data donation studies, where they ask users to request their personal data from an online platform and then share them as part of a survey. We experimentally tested two strategies to boost participation in a pre-registered study with over 2000 YouTube, Instagram or LinkedIn users from Germany: we varied (1) how we invited people, either framing the study as a web survey or directly describing it as a data donation study, and (2) the motivational appeals highlighting either prosocial advantages or personal gains from donating data. We found that neither study framing nor motivational appeals significantly affected people's willingness to donate data or their actual donation rates. However, framing the study from the start as data donation research led to significantly more people starting the study. Our results demonstrate that researchers need to consider how they design invitations to data donation studies, while revealing that study framing or motivational appeals fail to increase respondents' willingness to donate or their actual donation behavior.

Panel surveys provide particularly rich data for implementing adaptive or responsive survey designs. Paradata and survey data as well as interviewer observations from all previous waves can be utilized to predict fieldwork outcomes in an ongoing wave. This manuscript contributes to the literature on how to best make use of these data in an adaptive design framework applying machine learning algorithms. In a first step, different models were trained based on past panel waves. In a second step, we assess which model best predicts fieldwork outcomes of the following wave. Finally, we apply the superior model to predict response propensities and base case prioritizations of households at risk of attrition on these predictions. An experimental design allows us to evaluate the effect of these prioritizations on response rates and on nonresponse bias. Increasing prepaid respondent incentives from 10 to 20 euros substantially decreases attrition of low propensity cases in personal as well as telephone interviews and thereby helps reduce nonresponse bias in important target variables of the panel survey.

Short-time work (STW) is a policy measure whose prominence increases during economic crises and is intended to stabilize the labor market. Employers can temporarily reduce employees' working hours, which are in turn paid by the social security system in the meantime. Although short-time work-by design-saves employers a fraction of their wage costs, little is known about free riding behavior when using this option. Accordingly, we analyze the employee-reported free riding experience with respect to longer actual working hours than accounted for in employees' short-time work allowances, the unchanged workloads experienced by these employees, and announced lay-off decisions. Since these questions are certainly sensitive, we employ the crosswise model, a privacy-preserving technique, in a random half of the sample. Our results show significant employee-reported prevalences across all dimensions and a significant association between free riding and workers' job dissatisfaction. These findings thus highlight the importance of the crosswise model in uncovering these findings and demonstrate a specific drawback in the application of short-time work.

Abstract Who accomplishes a work-related exit from welfare benefit receipt for job-seekers and what prevents others from achieving it is a key question for practitioners and researchers. We use data from the Panel Study Labour Market and Social Security (PASS) to examine the relevance of individual labour market barriers in this context. In particular, an age of 51 years or older, long-term benefit receipt, severe health limitations, lack of vocational qualifications, and poor German language skills worsen the chances of leaving welfare benefit receipt. The presence of children in the household reduce the chances of taking up employment for women only.

Linking digital trace data to existing panel survey data may increase the overall analysis potential of the data. However, producing linked products often requires additional engagement from survey participants through consent or participation in additional tasks. Panel operators may worry that such additional requests may backfire and lead to lower panel retention, reducing the analysis potential of the data. To examine these concerns, we conducted an experiment in the German PASS panel survey after wave 11. Three quarters of panelists (n = 4,293) were invited to install a research app and to provide sensor data over a period of 6 months, while one quarter (n = 1,428) did not receive an invitation. We find that the request to install a smartphone app and share data significantly decreases panel retention in the wave immediately following the invitation by 3.3 percentage points. However, this effect wears off and is no longer significant in the second and third waves after the invitation. We conclude that researchers who run panel surveys have to take moderate negative effects on retention into account but that the potential gain likely outweighs these moderate losses.

Numerous panel surveys around the world use multiple modes of data collection to recruit and interview respondents. Previous studies have shown that mixed-mode data collection can improve response rates, reduce nonresponse bias, and reduce survey costs. However, these advantages come at the expense of potential measurement differences between modes. A major challenge in survey research is disentangling measurement error biases from nonresponse biases in order to study how mixing modes affects the development of both error sources over time. In this article, we use linked administrative data to disentangle both nonresponse and measurement error biases in the long-running mixed-mode economic panel study “Labour Market and Social Security” (PASS). Through this study design we answer the question of whether mixing modes reduces nonresponse and measurement error biases compared to a single-mode design. In short, we find that mixing modes reduces nonresponse bias for most variables, particularly in later waves, with only small effects on measurement error bias. The total bias and mean-squared error are both reduced under the mixed-mode design compared to the counterfactual single-mode design, which is a reassuring finding for mixed-mode economic panel surveys.

Researchers are combining self-reports from mobile surveys with passive data collection using sensors and apps on smartphones increasingly more often. While smartphones are commonly used in some groups of individuals, smartphone penetration is significantly lower in other groups. In addition, different operating systems (OSs) limit how mobile data can be collected passively. These limitations cause concern about coverage error in studies targeting the general population. Based on data from the Panel Study Labour Market and Social Security (PASS), an annual probability-based mixed-mode survey on the labor market and poverty in Germany, we find that smartphone ownership and ownership of smartphones with specific OSs are correlated with a number of sociodemographic and substantive variables. The use of weighting techniques based on sociodemographic information available for both owners and nonowners reduces these differences but does not eliminate them.

Article Editorial: Implications of the COVID-19 pandemic for the welfare state, its actors and benefit recipients was published on May 1, 2022 in the journal Zeitschrift für Sozialreform (volume 68, issue 1).

Employment relationships are embedded in a network of social norms that provide an implicit framework for desired behaviour, especially if contractual solutions are weak. The COVID-19 pandemic has brought about major changes that have led to situations, such as the scope of short-time work or home-based work in a firm. Against this backdrop, our study addresses three questions: first, are there social norms dealing with these changes; second, are there differences in attitudes between employees and supervisors (misalignment); and third, are there differences between respondents' average attitudes and the attitudes expected to exist in the population (pluralistic ignorance). We find that for the assignment of short-time work and of work at home, there are shared normative attitudes with only small differences between supervisors and nonsupervisors. Moreover, there is evidence for pluralistic ignorance; asked for the perceived opinion of others, respondents over- or underestimated the consensus in the (survey) population. Such pluralistic ignorance can contribute to the upholding of a norm even if individuals do not support the norm, with potentially far-reaching consequences for the quality of the employment relationship and the functioning of the organization. Our results show that, especially in times of change, social norms should be considered for the analysis of labour markets.

Research apps allow to administer survey questions and passively collect smartphone data, thus providing rich information on individual and social behaviours. Agreeing to this novel form of data collection requires multiple consent steps, and little is known about the effect of non-participation. We invited 4,293 Android smartphone owners from the German Panel Study Labour Market and Social Security (PASS) to download the IAB-SMART app. The app collected data over six months through (a) short in-app surveys and (b) five passive mobile data collection functions. The rich information on PASS members from previous survey waves allows us to compare participants and non-participants in the IAB-SMART study at the individual stages of the participation process and across the different types of data collected. We find that 14.5 percent of the invited smartphone users installed the app, between 12.2 and 13.4 percent provided the different types of passively collected data, and 10.8 percent provided all types of data at least once. Likelihood to participate was smaller among women, decreased with age and increased with educational attainment, German citizenship, and PASS tenure. We find non-participation bias in substantive variables, including overestimation of social media usage and social network size and underestimation of non-working status.

Abstract We investigate the general effect of the COVID-19 pandemic on subjective well-being and determine whether this effect differs between recipients of basic income support (BIS) and the rest of the working-age population in Germany. BIS recipients constitute one of the most disadvantaged groups in Germany and might lack resources for coping with the crisis. Thus, our analysis contributes to investigations of whether the pandemic exacerbates or equalises preexisting social inequality. Our analysis employs data from the panel survey “Labour Market and Social Security” (PASS). These data have the key advantage that the collection in 2020 started prior to implementation of the first COVID-19-related policies. This situation enables us to apply a difference-in-differences approach to investigate the causal change in subjective well-being. Our results suggest that well-being declined during the first phase of the COVID-19 pandemic. However, we find no difference in this decline between BIS recipients and other German residents. Thus, our results suggest that the first phase of the COVID-19 pandemic neither exacerbated nor equalised pre-existing inequalities.

As smartphones become increasingly prevalent, social scientists are recognizing the ubiquitous data generated by the sensors built into these devices as an innovative data source. Passively collected data from sensors that measure geolocation or movement provide an unobtrusive way to observe participants in everyday situations and are free from reactivity biases. Information on day-to-day geolocation could provide valuable insights into human behavior that cannot be collected via surveys. However, little is known about the quality of the resulting data. Using data from a 2018 German population-based probability app study, this article focuses on the measurement quality of geolocation sensor data, with a strong focus on missing measurements. Geolocation sensor data are an example of an available data type that is of interest to social science research. Our findings can be applied to the wider subject of sensor data. In our article, we demonstrate (1) that sensor data are far from error-free. Instead, device-related error sources, such as the manufacturer and operating system settings, design decisions of the research app, third-party apps, and the participant, can interfere with the measurement. To disentangle the different influences, we (2) apply a multistage error model to analyze and control the error sources in the specific missingness process of geolocation data. We (3) raise awareness of error sources in geolocation measurement, such as the use of GPS falsifier apps, or device sharing among participants. By identifying the different error sources and analyzing their determinants, we recommend (4) identification strategies for future research.

Smartphones sind für viele Menschen zu einem selbstverständlichen Bestandteil des Alltags geworden. Sie werden neben der Nutzung zur Kommunikation, Unterhaltung und Information auch bei der Jobsuche und im Arbeitsalltag genutzt (Perrin 2017). Dies bietet Möglichkeiten Smartphones als Datenerhebungsinstrument für die wissenschaftliche Forschung einzusetzen.

We investigate the effect of several characteristics of long-term unemployed’s social networks on subsequent job take-up. Our main findings are that active memberships in different types of organizations (e.g. clubs, churches, unions) and the social support in everyday life increase the unemployed`s job chances. (Author's abstract, IAB-Doku) ((en))

Many studies document the positive association between accessed social capital and wages. It is widely accepted that the underlying relationship is causal. However, most studies use cross-sectional data, and only a few test causal mechanisms. In our analysis, we first test a broad range of social capital indicators by applying fixed-effects panel data regression to a sample of currently employed and a sample of newly employed individuals. Second, we test reservation wages, network search, being offered a job without prior job search, and the number of job interviews as some of the theoretical mechanisms put forward to explain positive social capital effects. Overall, we find no empirical evidence for wage effects of the social capital measure and no evidence that any of the proposed mechanisms are empirically relevant.

This chapter analyzes the effects of different incentive schemes on participation rates in a study combining self-reports and passive data collection using smartphones, as well as breaks out these effects by economic subgroups. Providing some form of incentive, whether monetary or some other kind of token of appreciation, is common for studies recruiting respondents to answer survey questions. The chapter provides a brief review of the literature on the effectiveness of incentives and the postulated mechanisms explaining these effects. It explains the study design with an emphasis on the experimental conditions. The chapter also presents an analysis of the results of the study, including overall effects of the different incentive treatments on app installation, number of initially activated data-sharing functions, deactivation of data-sharing functions, retention of app, and overall costs of data collection.

This article presents results from an experimental study in Germany designed to test the effectiveness of a novel protocol for matching participants in a national panel survey with interviewers employing computer-assisted telephone interviewing (CATI) on selected sociodemographic features, including sex, age, and education. We specifically focus on the ability of the protocol to engender close matches between respondents and interviewers in terms of these features, using both theory and empirical evidence to suggest that this type of matching will improve cooperation rates in surveys employing CATI. We also focus on indicators of “success” at first contact (defined as a successful interview or establishment of an appointment for an interview) as a function of whether the matching protocol was in use on a given day and whether specific types of matches generated higher rates of success overall. We find strong evidence of the protocol effectively establishing close matches, and we also observe that matches based on education proved especially effective for rates of “success” in a panel survey that focused primarily on labor market topics. We conclude with thoughts on practical implementation of this approach in other settings and suggested directions for future work in this area.

The new European General Data Protection Regulation (GDPR) imposes enhanced requirements on digital data collection. This article reports from a 2018 German nationwide population-based probability app study in which participants were asked through a GDPR compliant consent process to share a series of digital trace data, including geolocation, accelerometer data, phone and text messaging logs, app usage, and access to their address books. With about 4,300 invitees and about 650 participants, we demonstrate (1) people were just as willing to share such extensive digital trace data as they were in studies with far more limited requests; (2) despite being provided more decision-related information, participants hardly differentiated between the different data requests made; and (3) once participants gave consent, they did not tend to revoke it. We also show (4) evidence for a widely-held belief that explanations regarding data collection and data usage are often not read carefully, at least not within the app itself, indicating the need for research and user experience improvement to adequately inform and protect participants. We close with suggestions to the field for creating a seal of approval from professional organizations to help the research community promote the safe use of data.

Data Resource Profile: Panel Study Labour Market and Social Security (PASS) Mark Trappmann,* Sebastian Bähr, Jonas Beste, Andreas Eberl, Corinna Frodermann, Stefanie Gundert, Stefan Schwarz, Nils Teichler, Stefanie Unger and Claudia Wenzig Panel Study Labour Market and Social Security, Institute for Employment Research, Nuremberg, Germany, Faculty for Social Sciences, Economics, and Business Administration, University of Bamberg, Bamberg, Germany and University of Erlangen-Nuremberg, Institute of Labor Market and Socioeconomics, Nuremberg, Germany