Abhishta Abhishta (Twente University – Faculty of Behavioural, Management and Social Sciences), Exploring the role of Internet measurements for economic decision making
Internet presents unique opportunities and challenges for economic decision-making. This talk explores the role of active and passive internet measurements in facilitating this process. Active measurements involve introducing specific packets into the Internet network, providing insights into network performance and user interactions. Passive measurements involve monitoring existing traffic without altering it, offering detailed data on network usage and user behaviour. Through detailed case studies, this talk demonstrates the practical applications of these internet measurements in understanding the economic impacts of significant events, such as the COVID-19 pandemic, elections, and cyber attacks. For instance, it examines the changes in internet traffic patterns during the pandemic, revealing how different regions and demographics adjusted their online activities in response to lockdowns and remote work mandates. The analysis of behavioural changes following major cyber incidents, such as the 2016 DDoS attack on a DNS service provider, illustrates how consumer trust can be influenced by cybersecurity events.
These examples show the potential of internet-based data to inform economic decision-making, marketing strategies, and security measures. Policymakers can use traffic data to assess the effectiveness of public health interventions, while businesses can adjust their marketing strategies based on changes in consumer behaviour observed through internet measurements. Security measures can also be refined by understanding the patterns and impacts of cyber attacks on different business sectors on the internet.
This talk also discusses the mechanics of search engines and the utilisation of Google Alerts as data collection tools, showing their relevance for security decision-making. Google Alerts can help researchers monitor changes and patterns in real-time. These example prove the importance of integrating internet and service-based measurements into empirical research in economic decision-making. By leveraging these tools, economists and business leaders can gain deeper insights into market phenomena, leading to more informed and effective decision-making processes.
Mert Akay (TU Delft – Faculty of Industrial Design Engineering), Abhigyan Singh (TU Delft – Faculty of Industrial Design Engineering), Mapping the Public Participation in Climate Resilience Studies through Structured Topic Modeling
Climate resilience is crucial for equipping cities to handle climate-related challenges. It emphasises the critical need for public participation, i.e., engaging people and communities in processes addressing climate-related issues. Public participation presents a significant challenge to climate resilience, yet limited research offers a comprehensive overview of the relevant discussions on this topic. Existing studies employ conventional literature review methodologies, focusing on narrower perspectives. Therefore, gaining deeper insights requires exploring diverse thematic perspectives across time, disciplines, and context. Addressing this gap necessitates the use of novel and emerging methodological approaches. Hence, this presentation examines contemporary discussions on climate resilience through the lens of public participation by employing Structured Topic Modelling (STM) as a methodological approach. It addresses two interconnected questions: (1) What are the predominant themes, gaps, and challenges in current scholarly debates on climate resilience and public participation in urban settings? (2) How can STM be operationalised to identify these themes and gaps? As a novel perspective for literature review in climate resilience studies, the STM enables identifying and examining the fundamental themes, research gaps, trends, and discussions for public participation in climate resilience by analysing the bibliographic dataset of scientific articles from the Web of Science. In this context, the objectives of this presentation are twofold: (1) illustrate the result of STM analysis and reflect on the strengths and limitations of utilising STM as an emerging methodology for literature review (2) combine interdisciplinary perspectives on urban planning, climate resilience, and computational social science through STM, addressing the relevant conference theme of using computational methods for social science research. We believe that the presentation reveals the potential of STM in better understanding and communication of research findings by providing a better visual representation and ensuring a consistent and objective methodology for the literature review.
Anastasiya Alferova (University of Utrecht), Ethical / Legal Implications of Data Control and Privacy in the Digital Marketplace
Big tech companies’ (BTC) unprecedented access to vast amounts of user data has given them a significant competitive advantage, allowing them to consolidate market dominance, target advertising, and create barriers to entry for new competitors. This article examines the ethical and legal implications of data control and privacy in the digital marketplace, focusing on the intersection of competition and data protection laws.The dominance of BTC in the digital market raises a number of concerns, including privacy violations, unfair competitive practices, and potential violations of fundamental rights. This study examines how these companies use user data to strengthen their market position and the regulatory challenges this creates. The integration of advanced AI technologies such as GPT chat into Apple devices further exacerbates these challenges by introducing new aspects of data usage and competitive dynamics.Digitalization determines the dynamics of legislative norms lagging behind modern market conditions. Governments have considered various tools for adapting to such changes, for example, behavioral economics. The article takes an interdisciplinary approach, combining legal analysis, policy assessment, and ethical review. It includes a comprehensive literature review, case studies of major technology companies, and a comparative analysis of global regulatory strategies. The conclusions will include topics for further discussion on the subject, as well as recommendations aimed at improving the EU regulatory framework to more effectively address the ethical and legal issues associated with data control by large technology companies. The research aims to provide actionable ideas and innovative solutions that offer a balanced approach to protecting the public interest, promoting fair competition, and ensuring data privacy. The purpose of this work is to promote a greater understanding of the ethical and legal complexities of the digital age and stimulate debate about effective regulatory practices.
Lianne Bakkum (VU Amsterdam – Faculty of Behavioural and Movement Sciences), Carlo Schuengel (VU Amsterdam – Faculty of Behavioural and Movement Sciences), Age of entry into the Dutch child protection system of children of parents with intellectual disability: A case-control study
Background
Parents with intellectual disabilities (ID) face challenges in accessing care, meaning that risks to their children can go long undetected. However, heightened scrutiny and lower use of preventive supports may accelerate their entry into the child protection system. To sort out these conflicting expectations, we assessed children’s age at the first child protection measure, comparing parents labelled with and without ID. In addition, we compared the duration of the first measure, and the likelihood of having a sibling in child protection.
Methods
We used a case-control design with microdata from Statistics Netherlands. The population consisted of children in child protection (total N = 91,174; reporting years: 2015-2021). Using proximity score matching, children with at least one parent with ID (N = 4,526) were labelled based on indications for benefits, long term care, and/or sheltered employment (all based on ID), and were matched 1:1 with children with parents without ID, by socioeconomic status and having only one registered parent. Linear and logistic regression models were used for the analyses.
Results
Children with at least one parent labelled with ID were younger at the first child protection measure (Mdifference = 177 weeks, B = -176.76, SE = 5.57, p <.001), had longer child protection measures (Mdifference = 35 weeks, B = 34.68, SE = 4.46, p <.001), and more often had a sibling in child protection (OR = 1.28, SE = 0.04, p <.001), compared to control children.
Conclusions
The findings point to the direction of heightened scrutiny of parents labelled with ID and lower use and/or efectiveness of preventive interventions. The longer duration of child protection measures further supports heightened scrutiny and lower effectiveness of current interventions. This study is a starting point for further exploration of the representation of children with parents labelled with ID in child protection.
Mohammad Behbahani (Utrecht University – Faculty of Social Sciences), Mahdi Shafiee Kamalabad (Utrecht University – Faculty of Social Sciences), Emmeke Aarts (Utrecht University – Faculty of Social Sciences), Hidden state detection in Relational event history data: an extension of Hidden Markov Model for the Relational Event Model
Relational Event History (REH) data are interactions between actors in some way, over time. The Relational Event Model (REM) is a gold standard for analyzing REH data, allowing for the study of how social interactions evolve over time and which factors shape these social interactions. This model accounts for the dynamic patterns and dependencies between actors, parameterizing interaction rates based on both exogenous and endogenous statistics.
Social dynamics are inherently variable, and it is likely that these dynamics shift in response to changing often unobserved circumstances, leading to variations in the model’s parameters. However, traditional REM assumes a constant effect for each statistic throughout the observation period, which may not hold in all contexts. For example, in high-stress environments like surgery rooms, communication dynamics among surgeons may alter dramatically in response to emergencies. Addressing this, research has identified time zones where changepoints occur, enhancing the understanding of how communication behavior shifts instantaneously, such as Apollo 13 mission data.
Nonetheless, changepoint models have limitations; once a data segment is exited, it cannot be revisited. This is in contrast with scenarios like surgical teams, where communication may return to normal after an emergency. To overcome this, we propose integrating REM with Hidden Markov Models (HMMs). This extension, termed HMM-REM, allows for modeling the complexities of temporal relationships and dependencies in REH data more effectively. Thus, this new model helps researchers detect hidden states that influence interactions.
In this work, we apply the HMM-REM model to both synthetic and real-world data to demonstrate its functionality. We use synthetic data to illustrate the model’s capability to detect hidden states and explore social dynamics. This exploration aids in understanding how various entities adjust their interaction patterns dynamically in different hidden states, enhancing our comprehension of complex social interactions in varied situations.
Thales Bertaglia (Utrecht University – Faculty of Law, Economics and Governance), Catalina Goanta (Utrecht University – Faculty of Law, Economics and Governance), Adriana Iamnitchi (Maastricht University – Faculty of Science and Engineering), The Monetisation of Toxicity: Analysing YouTube Content Creators and Controversy-Driven Engagement
YouTube, one of the most popular social media platforms, remains understudied within computational social sciences. Content creators are central to YouTube’s ecosystem and significantly influence their followers, especially vulnerable individuals such as children. These creators often engage in controversial behaviour to generate engagement, despite negative consequences. This study investigates the relationship between monetisation, controversy, and toxicity on YouTube.
We conducted a quantitative analysis of controversial content, focusing on monetisation strategies, engagement patterns, and the prevalence of toxic comments. Using a curated dataset of controversial YouTubers sourced from Reddit, we classified channels into two categories: Consistent Controversy, frequently involved in scandals, and Spike Controversy, which experience temporary or recent surges in controversy. Our dataset included 20 channels, covering 16,349 videos and over 100 million comments.
We analysed video descriptions for monetisation cues to identify monetisation strategies, categorising 15,952 unique URLs linked to various revenue sources. We identified six primary monetisation models, with merchandise sales being the most prevalent. Notably, Spike Controversy Channels exhibited a higher intensity of monetisation efforts than Consistent Controversy Channels, suggesting a more aggressive approach to monetising their content.
To measure toxicity, we employed a Ridge regression model trained on a dataset of YouTube comments labelled for abusive language detection. Our analysis shows that while toxic content tends to generate higher engagement through increased comments, it negatively impacts the number of likes and monetisation cues. This result indicates that controversy-driven engagement does not necessarily translate to financial benefits for content creators.
Our study also uncovers self-moderation practices among YouTubers, where creators alter their content or marketing strategies to mitigate backlash. For example, a YouTuber might change URLs for controversial merchandise to redirect attention away from problematic products. These insights lay the groundwork for future research, particularly in understanding monetisation’s qualitative aspects and toxicity’s delayed effects on audience interaction.”
Marissa Bultman (Netherlands Court of Audit), Eline Smit (Netherlands Court of Audit), Evaluating the Effectiveness of the Wi2021 Integration Law in Accelerating Labor Market Participation of Asylum Status Holders in the Netherlands (Work in Progress)
Despite significant labor shortages in the Netherlands, the labor market participation of refugees with an asylum residence permit (i.e. status holders) lags far behind that of the general working population. Moreover, most status holders who do work, are employed with part-time and temporary contracts. As a consequence, they highly depend on social benefits and often live in poverty.
Status holders’ integration in Dutch society is regulated by the Integration Law, which was recently amended in 2021. Earlier research found the previous integration law (Wi2013) to hinder the labor market integration of asylum migrants due to the lack of a link between integration obligations and societal participation. The new integration law (Wi2021) aims to contribute to ‘quick and full participation in Dutch society, preferably through paid work’. The law seeks to achieve this by focusing on ‘duality’: the combination of societal participation and learning the Dutch language in a classroom setting. While the Wi2013 centralized integration, Wi2021 decentralizes integration by placing municipalities primarily in charge of organizing an integration program for status holders. Nonetheless, the minister of Social Affairs and Employment remains responsible for the policy and partially for its implementation.
This research aims to assess whether the implementation of the new law is likely to achieve its societal goal of contributing to ‘quick and full participation, preferably through paid work’. As part of this study, we seek to evaluate to what extent the Wi2021 is more effective in accelerating labor market participation of status holders compared to the Wi2013 law. To achieve this, we will analyze data from the Dutch Central Bureau of Statistics (CBS) Employing methods that estimate causal effects (such as interrupted time series or regression discontinuity), we will assess the likely effect of the Wi2021 implementation on employment rates among status holders in the Netherlands.
Elena Candellone (Utrecht University – Faculty of Social Sciences), Erik-Jan van Kesteren (Utrecht University – Faculty of Social Sciences), Sofia Chelmi (University of Bologna), Community detection in bipartite signed networks is highly dependent on parameter choice
Decision-making processes often involve voting. Human interactions with exogenous entities such as legislations or products can be effectively modeled as two-mode (bipartite) signed networks-where people can either vote positively, negatively, or abstain from voting on the entities. Detecting communities in such networks could help us understand underlying properties: for example ideological camps or consumer preferences. While community detection is an established practice separately for bipartite and signed networks, it remains largely unexplored in the case of bipartite signed networks. In this paper, we systematically evaluate the efficacy of community detection methods on bipartite signed networks using a synthetic benchmark and real-world datasets. Our findings reveal that when no communities are present in the data, these methods often recover spurious communities. When communities are present, the algorithms exhibit promising performance, although their performance is highly susceptible to parameter choice. This indicates that researchers using community detection methods in the context of bipartite signed networks should not take the communities found at face value: it is essential to assess the robustness of parameter choices or perform domain-specific external validation.
María Ángeles Caraballo (University of Seville), Oksana Liashenko (University of Seville), Social attitudes do matter. A worldwide perspective
There is a vast literature showing a negative relationship between various categories of social diversity and quality of institutions and economic growth. The explanation underlying these results is that politicians and bureaucrats may have incentives to favor and/or receive favors from certain groups which induces an inefficient allocation of resources and, in turn, hinders economic growth. In addition, the conflict of interests between groups over, for instance, the consumption of a shared public good and the receipt of transfers, also negatively affects to quality of institutions and economic performance.
Our research is related to this strand of the literature. We focus on ideological diversity and, more precisely, on three of the hottest topics in the current political arena: gender, immigration and environment. The ideological position of the citizenship towards these issues is proxied through questions selected from the World Values Survey. The questions have been classified attending to three categories: attitudes, engagement and confidence. This has allowed us to distinguish several types of individuals according to their responses. By means of computational methods for social science research, we analyze the relationships between the different groups of individuals and quality of institutions and economic growth. To measure quality of institutions and economic growth, we have use data from the World Bank and BTI. We also consider that these relationships can be weakened or strengthened by some characteristic of the society such as political participation, trust and tolerance that can be inferred from selected questions from the World Value Survey.
Our analysis permits to identify the groups that have a most relevant impact on the quality of institutions and economic growth, and the shaping elements of the society that also influence this impact.
Giovanni Cassani (Tilburg University, Tilburg School of Humanities and Digital Sciences), Stijn Rotman (Tilburg University, Tilburg School of Humanities and Digital Sciences), Drew Hendrickson (Tilburg University, Tilburg School of Humanities and Digital Sciences), Boosting fertility predictions: a bottom-up, data-driven, cross-sectional Light Gradient Boosting Machine model for fertility prediction from survey data
In the context of the Predicting Fertility (PreFer) challenge, we implemented a bottom-up, data-driven, cross-sectional solution which scored third in the leaderboard on F1 (with the best precision score). Over multiple runs, we noticed that no variable before 2017 ever appeared among the most useful predictors in a Light Gradient Boosting Machine (LGBM) model trained on all available variables. The model’s F1 was consistently around 0.7 on the validation sets, which were generated based on a random 50% split of the unique households in the available data. We thus pruned features to exclude variables from earlier waves in the LISS panel than 2017 and refitted the model. Feature importance highlighted expected predictors, such as those pertaining to household income, fertility intentions, age, and marital status. We then considered the issue of missing data. Using only dichotomized predictors that indicate whether a response was missing, a LGBM model produced an F1 above 0.6, suggesting that missingness carries information for the task. We suspect this depends on the propensity of people to answer certain questions relating to children if they do not have (or plan on having) any. In addition to adding these binary features that indicate missingness per wave, we created new variables that collapse across year and keep the last known value ( if 2020 was missing but 2019 was not, the feature reflected the value of 2019). Finally, we added features pertaining to changes in income, housing, and marital status. The final model’s F1 was between 0.75 and 0.8 on the validation splits, , while the F1 on the competition hold-out set was 0.71. Altogether, our attempt suggests that bottom-up approaches can reach state-of-the-art performance in predicting fertility; variables related to fertility in the literature influenced predictions the most; imputing variables may mask informative non-response patterns in survey data.
Qian Chen (Tilburg University, Tilburg School of Social and Behavioural Sciences), Jonas Everaert (Tilburg University, Tilburg School of Social and Behavioural Sciences), Bennett Kleinberg (Tilburg University, Tilburg School of Social and Behavioural Sciences), Measuring inflexible and biased interpretations using linguistic analyses to reveal pathways to depression and anxiety
Everyday life is full of ambiguous social situations. People need to interpret such situations to resolve this ambiguity and understand what is happening to them. Imagine receiving a stranger’s gaze while you are giving a talk at a conference. You may interpret this gaze as either positive admiration or negative dissatisfaction. How you interpret the situation may influence how you feel about your public performance. When interpretations are negatively biased and inflexible, they may even set the stage for severe mental health conditions such as depression and anxiety. Theorists and previous studies have implicated distorted interpretation processes (interpretation bias and inflexibility) in the onset, maintenance, and relapse of depression and anxiety. However, current work is limited because it employs questionnaires and cognitive-experimental laboratory tasks with limited ecological validity. This study, combining language analysis, opens new opportunities for understanding and treating depression and anxiety. A total of 210 college students will be invited to complete an online survey, which includes the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, Lack of Emotional Awareness and Lack of Emotional Clarity scales, the emotional variant of the Bias Against Disconfirmatory Evidence (BADE) task, and a revised open-ended Interpretation Bias Questionnaire (IBQ-R). Then, based on LIWC and other language markers of interest (e.g., first-person and emotional words), we will examine language features associated with interpretations, build a language-based interpretation model, and examine its predictive power for depression and anxiety. The knowledge generated by this project will not only deepen the theoretical understanding of depression and anxiety and their risk factors, but also facilitate the identification of treatment targets and, ultimately, better treatment response.
Juliette de Wit (University of Groningen, Faculty of Economics and Business), Maite Laméris (University of Groningen, Faculty of Economics and Business), Sjoerd Beugelsdijk (Darla Moore School of Business, University of South Carolina), National identification and voting behaviour
We study if and how identification with the nation state relate to individual’s voting behaviour. We theorize that a limited number of ideal types exist that best capture the sources of identification with the nation state, and as a result, people identify with the nation state in distinctive ways. Using a unique survey dataset of Dutch respondents, we identify three ideal types: the ethnic type, who identifies strongly with traditions, symbols, and history; the civic type, who identifies via civic liberties and religious freedom; and the indifferent type, who does not identify strongly with the nation state via any characteristic. We then suggest that the way in which individuals identify with the nation state translates into different political issues being salient to them, namely those issues that are salient to their identity. This, in turn, affects how individuals vote. Considering the three types of national identification (i.e., ethnic, civic, indifferent) and voting behaviour (i.e., turnout and party preferences), our results show that the types of national identification are related to turnout and party preferences in significantly distinctive ways. This is corroborated by findings on individuals’ positions on relevant political issues. We draw two main conclusions. First, it is not only the strength, but also the type of national identification that matters for voting. Second, there is a strong and persistent tension between the ethnic and civic types of national identification that suggests a clash of two normative world views.
Qixiang Fang (Utrecht University – Faculty of Social Sciences), Solichatus Zahroh (Utrecht University – Faculty of Social Sciences), Daniel Oberski (Utrecht University – Faculty of Social Sciences), Enhancing Human Values Prediction from Digital Trace Data through Measurement Knowledge Integration
Computational social scientists are increasingly interested in using digital trace data (e.g., social media posts, user logs) to measure/predict social science constructs like human values. This task is challenging due to inherent issues: “ground-truth” labels, which are typically based on crowd-workers or survey responses, often contain measurement errors, and the data is frequently high-dimensional relative to the number of observations. To address these challenges, we propose two solutions. First, we incorporate measurement models into the loss function of prediction models to mitigate measurement error. Second, we utilise an automatic selection method based on semantic similarity to handle high-dimensional data. Our contributions are validated using a dataset of human values measurements and individuals’ social media likes history. We also compare our methods against state-of-the-art approaches aimed at preventing models from learning spurious correlations including data augmentation and invariant learning.
Jack Fitzgerald (VU Amsterdam – School of Business and Economics), The Need for Equivalence Testing in Economics
Equivalence testing methods can provide statistically significant evidence that relationships are practically equal to zero. I demonstrate their necessity in a systematic reproduction of estimates defending 135 null claims made in 81 articles from top economics journals. 37-63% of these estimates cannot be significantly bounded beneath benchmark effect sizes. Though prediction platform data reveals that researchers find these equivalence testing `failure rates’ to be unacceptable, researchers actually expect unacceptably high failure rates, accurately predicting that failure rates exceed acceptable thresholds by around 23 percentage points. To obtain failure rates that researchers deem acceptable, one must contend that over 75% of published effect sizes in economics are practically equivalent to zero, implying that Type II error rates are likely quite high throughout economics. This paper provides economists with empirical justification, guidelines, and commands in Stata and R for conducting credible equivalence testing in future research.
Jérôme Francisco Conceicao (TU Delft – Faculty of Architecture and the Built Environment), Ana Petrović (TU Delft – Faculty of Architecture and the Built Environment), Maarten Van Ham (TU Delft – Faculty of Architecture and the Built Environment), The operationalisation of contextual poverty from individual and household perspectives: Does it matter how we measure income when estimating neighbourhood effects?
The ever-growing neighbourhood effect literature, analysing the effects of contextual poverty on individual outcomes, predominantly uses the personal incomes of neighbourhood residents to measure contextual poverty. However, using individual income is inconsistent with the literature on poverty measures, which focusses on the household income. The choice of using individual or household income could have an effect on the measure of income distribution, economic segregation and, consequently, the neighbourhood effect estimation. Constructing a poverty indicator based solely on the individuals’ income overlooks, for example, the financial composition of the household, a critical unit when evaluating the individuals’ purchasing power. Additionally, there is quite some variation between studies in the use of gross or net incomes, as well as methodological choices such as selecting the income of male residents. These variation makes it difficult to understand to what extent differences between studies are real differences in estimated neighbourhood effects, or differences caused by choices made by researchers and data availability. This paper investigates the sensitivity of the neighbourhood effect estimates to the operationalisation of contextual poverty. To what extent does selecting an income variable when creating a poverty indicator determine the modelling outcomes? We use longitudinal micro-data from Dutch population registers spanning from 2011 to 2020, which provides detailed income data at the individual and household levels. We consider four potentially influential factors when measuring poverty: income unit (individual vs household), income redistribution (gross vs disposable), household standardisation (equivalence factor), and sample selection (gender groups). We conduct a systematic analysis modelling the effect of each poverty indicator while keeping all other factors constant. The results of this study will shed light on how using individual or household income would lead to different estimations of neighbourhood effects.
Santiago Gómez-Echeverry (Statistics Netherlands (CBS)), Arnout van Delden (Statistics Netherlands (CBS)), Ton de Waal (Tilburg University, Tilburg School of Social and Behavioural Sciences), Modeling Total Error using Linked Survey and Administrative Data: A Simulation and an Application to the Italian Labor Market
The expansion of administrative and Big Data and the increase in the survey’s non-responses have highlighted the relevance of assessing the quality of non-probability samples. To tackle this issue, people usually resort to the Total Error (TE) framework, which divides the error into a measurement and a representation component. Extensive literature focuses on measurement error, often using a combination of data from different sources to evaluate whether the observed variables adequately capture the concept intended to be measured. Another branch of the literature has centered on the representation error, assessing how respondents are selected in the sample, leading to systematic differences between the population and the observed units. However, research modeling both of these components simultaneously is still scant. In the present study, we address this gap by jointly modeling the measurement and the representation errors, combining recent advances in both areas. We conducted a simulation study to evaluate our TE model under different specifications of measurement and representative errors. Additionally, we performed a case study analysis using a combination of Italian administrative registers and the Labor Force Survey (LFS) to evaluate the total error in the income variable. Our preliminary results show that our model adequately captures the different error sources and provides a good strategy for assessing the TE when using a combination of probability and non-probability data.
Anirudh Govind (KU Leuven, Belgium), Ate Poorthuis (KU Leuven, Belgium), Ben Derudder (KU Leuven & Ghent University, Belgium), Cocooning? A multi-scalar analysis of the determinants of persistent activity space segregation
Recently, ethnic segregation studies have looked beyond residential neighbourhoods to include the set of locations people visit during their daily activities (i.e., activity spaces). Such work has suggested that segregation may be experienced and deliberately (re)produced across locations — an idea researchers have likened to people seeking the safety of cocoons. Currently, it remains unclear if this (re)production of segregation is an outcome of urban structure, i.e., the relative distribution of people and activities, or deliberate, i.e., people seeking cocoons. That is, are people making trips only to locations physically accessible from their residential neighbourhoods? Or, are people being more discerning and visiting subsets of accessible locations corresponding to their desired experiences of segregation?
We investigate using the case of Rotterdam with data from the Dutch Central Bureau of Statistics (CBS). For each residential neighbourhood, we determine three ethnic activity space (AS) segregation values, at multiple scales, up to ten kilometers. These segregation values are based on
– the total population accessible based on travel along the extant street network (the maximal theoretical AS),
– the population people come into contact with based on their actual AS (the experienced), and,
– the hypothetical population determined by randomizing people’s AS (the counterfactual).
Minimal differences between the first two values would indicate that segregation is an outcome of urban structure.
However, large differences would suggest the influence of people’s choices and necessitate a comparison of the latter two values to determine cocooning (i.e., counterfactual > experienced = cocooning).
Initial findings suggest that segregation is an outcome of people’s choices rather than urban structure. However, such segregation cannot always be categorized as cocooning and is dependent on the neighbourhood under consideration and the scale of analysis.”
Andrea Gradassi (University of Amsterdam – Faculty of Social and Behavioural Sciences), Scarlett Slagter (University of Amsterdam – Faculty of Social and Behavioural Sciences), Lucas Molleman (University of Amsterdam – Faculty of Social and Behavioural Sciences), Social influence of high-status peers in adolescents social networks
Dispositions for prosociality undergo major changes during adolescence, a period of increased sensitivity to peer influence and incipient internalization of societal norms. However, the proximate mechanisms for the development of prosocial preferences are poorly understood. Here, we show that high-status peers affect adolescents’ prosocial decision making. Participants repeatedly chose to either donate money to a charity or keep it for themselves and could revise their decision upon observing the (opposite) decisions of either a high-status or low-status peer from their classroom. Participants tended to conform to peer behavior (both generous and selfish), often reversing their initial preference. This pattern was especially strong when observing a high-status peer. Our findings suggest that high-status peers act as important signalers of prosocial norms and can be instrumental for the diffusion of prosocial behaviour. By using an incentivized task in a naturalistic setting and extending the experimental work with computer simulations, we bring evidence for the role of real world (high-status) peers in the development of prosocial preferences, and provide a potential path for interventions aimed at spreading cooperative norms
Rolf Granholm (University of Groningen – Faculty of Behavioural and Social Sciences), Anne Gauthier (Netherlands Interdisciplinary Demographic Institute (NIDI)), Gert Stulp (University of Groningen – Faculty of Behavioural and Social Sciences), Measuring the relative importance of fertility determinants for recent birth cohorts across 7-19 countries with GGS II data using microsimulation
While demographic research has uncovered a wide range of determinants of fertility outcomes, it has been difficult to estimate the relative importance of these determinants. This is because the fertility process is complex, and many of the determinants of fertility outcomes are interdependent. It is therefore difficult to estimate independent effects of these determinants with statistical techniques commonly used in fertility research, like event history analysis and life table approaches. Another shortcoming in fertility studies is that the physiological constraints on human fertility have rarely been explicitly modelled, despite the fact that they are the most proximate determinants of fertility outcomes, and now more relevant than ever with increasing mean ages at first birth. We address these issues with a microsimulation model we developed that reproduces the entire reproductive life courses of individual women. The model includes information on reproductive physiology, reproductive behaviour, educational attainment, and union events. The reproductive physiology part of the model is based on modelling work by Henri Leridon and clinical data. For the behavioural part of the model, we use data mainly from the Generations and Gender Survey II. We apply our simulation model to a wide range of countries. By doing this we can not only uncover the most important determinants of fertility outcomes within each country, but we can also compare determinants between countries. What makes our approach different from statistical and life table approaches is that we explicitly model the mechanisms of the fertility process based on empirical data and fertility theory, and treat fertility as a process over the entire reproductive life course of a woman.
Christian Olesen (University of Amsterdam – Faculty of Humanities), Isadora Paiva (University of Amsterdam – Faculty of Humanities), The CLARIAH Media Suite: An introduction to qualitative media analysis using automatic data enrichments and annotation
The Media Suite is one of the research environments developed within the Dutch CLARIAH research infrastructure. As an innovative digital research environment, the Media Suite is a networked, university-level access point to a large variety of digital collections – comprising key broadcast, film, paper and oral history collections from NISV, Eye Filmmuseum, the KB and DANS. Moreover the environment offers new ways of browsing, searching and analyzing the collections made available with digital tools developed specifically for the environment. The environment’s tools facilitate among other approaches exploratory research and browsing, close reading and qualitative analysis based on video annotation tools as well as data-driven modes of distant reading and visualization of collection data. Beyond digitized collection items – amounting to a couple of millions of audiovisual items – the environment is also the unique access point to automatic data enrichments, such as automatic speech recognition (ASR) data of broadcast collections and optical character recognition (OCR) of historical paper collections. In addition to introducing The Media Suite, this presentation will discuss the steps of creating, annotating and analyzing a corpus of materials from different collections available in the environment by discussing 1) the scope and aims of the Media Suite environment as a tool for distant and close reading of multimedia items, and 2) how to create a corpus of items from several collections in the environment and explore and annotate them.
Jiri Kaan (Wageningen University & Research – Wageningen Social Science Group), Yara Khaluf (Wageningen University & Research – Wageningen Social Science Group), Kristina Thompson (Wageningen University & Research – Wageningen Social Science Group), An Agent-Based Model Comparing Two Reward Learning Algorithms In Dynamic Food Environments
Food environments are riddled with ultra-processed foods and cues that encourage overeating. Repeated consumption of these foods, coupled with exposure to their cues, can condition people to eat beyond their metabolic needs at the sight or smell of them. Although conditioning is not the only pathway that influences eating behavior, understanding how conditioning affects eating behavior is critical when simulating policies intended to tackle the obesogenic environment. Although well-validated reward learning algorithms exist to formalize conditioning, direct comparisons of these algorithms in food environment models are lacking. This gap is significant because comparing these algorithms under similar conditions is essential to understanding the role of the food environment in eating behavior and designing effective public health policies. In this study, we replicate a previous agent-based model that used Temporal Difference Learning (TD) as reward learning algorithm. We then extend the agent-based model by incorporating the Rescorla-Wagner (RW) model, which is beneficial for scenarios involving immediate associations, such as classical conditioning, where understanding the strength of an association in response to immediate outcomes is crucial. Our replication and extension result in a similar “lock-in” effect, in which early exposure to a food environment dominated by ultra-processed foods leads to a non-trivial preference for these foods. To further enhance our understanding of learning dynamics in varying food environments, we introduce two additional model extensions: a generalized RW model and a two-phased RW model. These extensions significantly alter learning trajectories, providing a more comprehensive understanding of how different learning algorithms respond to changes in food environments. Overall, our results underscore the importance of a health-promoting food environment, as preferences for ultra-processed foods appear robust across all models.
Jiri Kaan (Wageningen University & Research – Wageningen Social Science Group), Kristina Thompson (Wageningen University & Research – Wageningen Social Science Group), Yara Khaluf (Wageningen University & Research – Wageningen Social Science Group), Agent-Based Models Of Social Network Interventions Promoting Health And Well-being: A Systematic Review
Social networks are complex adaptive systems characterized by dynamic processes that feedback into one another, influencing health and well-being in a nonlinear manner. However, most health interventions focus solely on individuals and do not leverage these social network processes. Social network interventions, which seek to use or modify the characteristics of social networks to improve the effectiveness of health interventions, have yielded promising results. Yet, they often lack designs that fully estimate the impact of these networks. The complexity and dynamic nature of social networks pose challenges that traditional methodologies may not fully address. Computational approaches, such as agent-based modeling, offer powerful tools to estimate the impact of social networks in interventions. These tools can potentially be used by policymakers and health practitioners to forecast various scenarios for social network interventions. Consequently, agent-based models are increasingly employed to test the effectiveness of social network interventions under different circumstances. Despite their growing use, there is currently no comprehensive overview of social network interventions tested with the aid of agent-based models and their performance in various contexts. Therefore, we will conduct a systematic literature review to address this knowledge gap. The review will adhere to PRISMA-S guidelines. We searched the Scopus, Web of Science, and PubMed databases for papers on agent-based models simulating social network interventions to enhance the effectiveness of health interventions. Our search identified 1,282 papers with 16 papers remaining after exclusion. Data will be extracted to determine which type of social network intervention was simulated, how it performed, and in what health and well-being context. Furthermore, special attention will be paid to the theories and processes that underpin the agent-based models. Overall, we will provide understanding of how agent-based models have been utilized in social network interventions, thereby guiding future research and health interventions.
Pim Kastelein (Netherlands Bureau for Economic Policy Analysis), Brinn Hekkelman (Netherlands Bureau for Economic Policy Analysis), Suzanne Vissers (CPB), Predicting Persistence of Labor and Health Shocks
Life is inherently marked by challenges, and the ability to recover from adverse life events varies among individuals. This study investigates the predictability of recovering from setbacks in the domains of labor and health using rich administrative data on the entire Dutch population. Employing machine learning techniques, we estimate the likelihood of individuals overcoming adverse shocks within a foreseeable time frame. The results demonstrate that recovery from a labor and health shock is to a large extent predictable, especially in the labor domain. It is not only possible to accurately forecast the recovery probability, but also the specific recovery path (such as the route via various welfare benefits before work resumption or the extent to which health care costs decline after an initial spike). Furthermore, the estimated recovery probability distributions highlight that there is a lot of inequality across the population in chances of recovery. A distinct group of individuals has a near-zero probability of recovering from a setback within a year, while another group of individuals is virtually guaranteed to recover. The fact that this heterogeneity is forecastable implies that policy can be tailored towards this, for example by means of targeted prevention policies. We supplement these findings with ex-ante shock probabilities from the study of Cammeraat et al. (2024), who investigate the predictability and concurrence of risks in the domains of labor and health for the same sample. It turns out that individuals with the highest ex-ante likelihood of facing setbacks also encounter greater challenges in the recovery process. This insight underscores the critical interplay between pre-existing risk factors and the difficulties individuals face in bouncing back from setbacks. There is a distinct vulnerable group that faces predictably high ex-ante susceptibility and low ex-ante resilience, again suggesting that targeted prevention policies could be useful supporting policies.
Rishabh Kaushal (Maastricht University – Faculty of Science and Engineering), Adriana Iamnitchi (Maastricht University – Faculty of Science and Engineering), Nicole Kilk (Maastricht University – Faculty of Science and Engineering), The Needle in the Haystack: What Platforms Report to the DSA Transparency Database When They Don’t Have To
To promote transparency, the European Union has adopted the Digital Services Act (DSA), which requires that very large online platforms (VLOPs) share their content moderation decisions with meaningful related information, referred to as Statement of Reasons (SoRs), to the DSA Transparency Database maintained by the EU. This database contains daily data dumps with all SoRs from all platforms. A dashboard with basic data analysis visualizations is also provided and includes an advanced search option. However, this search tool returns only a limited number of SoRs, and it can only be performed based on mandatory fields of SoRs. Therefore, the free-form text fields in the SoRs may carry important information that is missed by the advanced search tools.
In this work we analyze what platforms report in optional fields. We make three contributions. First, we propose an automated approach to retrieve SoRs on a large scale based on user-supplied keywords that are searched in free text optional fields. Second, we perform text analysis to study frequent words and phrases used by platforms in the explanations of their content moderation decisions. Third, we verify that platforms use specific terminology that uniquely identifies them.
Our results contribute to a better understanding of the moderation decisions that are not easily visible in the Transparency Database dashboard.
Paul Keuren (UU-FSW and CBS), Marc Ponsen (Statistics Netherlands (CBS)), Ayoub Bagheri (Utrecht University – Faculty of Social Sciences), Expert Embedding Alignment
In this research, we look into the possibility of measuring the alignment between two expert-created Knowledge Systems and multiple different embeddings. For both the contained Thesaurus and Taxonomy, various metrics are defined and applied in conjunction with other classical and state-of-the-art methods. On top of this, we fine-tune a state-of-the-art model with information from the knowledge system to find whether this will improve the final performance.
To determine the validity of the results, we use both a dimensional reduction, as well as a plot where the retrieval chance is offset by the number of retrieved items. We found that state-of-the-art methods might not outperform classical methods depending on the number of items retrieved. That fine-tuning an embedding on a knowledge structure, does not yield a better-performing network. And that the best-performing embeddings, do not show agreement with the expert. Finally, we conclude that the applied metrics do not indicate an alignment between the expert and the embedding.
Saurabh Khanna (University of Amsterdam – Faculty of Social and Behavioural Sciences), Knowing Unknowns in an Age of Incomplete Information
The technological revolution of the Internet has digitized the social, economic, political, and cultural activities of billions of humans. While researchers have been paying due attention to concerns of misinformation and bias, these obscure a much less researched and equally insidious problem — that of uncritically consuming incomplete information. The problem of incomplete information consumption stems from the very nature of explicitly ranked information on digital platforms, where our limited mental capacities leave us with little choice but to consume the tip of a pre-ranked information iceberg. This study makes two chief contributions. First, I leverage the context of Internet search to propose a novel metric quantifying ‘information completeness’, i.e. how much of the information spectrum do we see, when browsing the Internet. I then validate this metric using 6.5 trillion search results extracted from daily search trends across 48 nations for one year. Second, I find causal evidence that awareness of information completeness while browsing the Internet reduces resistance to factual information, hence paving the way towards an open-minded and tolerant mindset.
Jonas Klingwort (Statistics Netherlands (CBS)), Yvonne Gootzen (Statistics Netherlands (CBS)), Daniëlle Remmerswaal (Utrecht University – Faculty of Social Sciences), Validating a smart survey travel app: how do GPS measurements compare to reported behavior?
Smart surveys combine passive data collection by the device sensors (e.g., accelerometer, GPS) with (inter)active data provided by the respondent (e.g., response to prompts based on the passively collected data). The interest in such smart surveys in official statistics is increasing because traditional diary surveys, such as travel surveys, are burdensome for respondents and suffer from measurement errors (e.g., underreporting and recall errors). In recent years, a substantial amount of research has been conducted into the feasibility of smart travel surveys. However, more attention must be paid to validating respondents’ measurements and provided information in smart surveys. Such empirical validation studies and their results are crucial for a deeper understanding of the data and the quality of the data collected by smartphones. We present such a validation study based on a large-scale experiment using data from the general population and conducted by Statistics Netherlands. The data are collected in 2022-2023 and include respondents for whom both app data and responses from a web questionnaire are available for the identical reporting period. The data from the web questionnaire are used to validate the app data in combination with the applied algorithms. Two target variables are considered: first, a binary variable of whether the respondent is stationary (stop) or moving (track). Second, a categorical variable regarding the mode(s) of transport used during a track. The results of this comprehensive analysis yield important conclusions about how similar app measurements and web responses are. In addition, we will report on the effects of respondent interaction and the extent to which these interactions influence the app data quality. Furthermore, we will shed light on lessons learned from this pioneering smart survey validation study, what to consider in such validation studies, and what recommendations we derive for future validation studies of smart (travel) surveys.
Pim Koopmans (Leiden University – Faculty of Law), Max van Lent (Leiden University – Faculty of Law), Marike Knoef (Tilburg University, Tilburg School of Economics and Management), The Impact of Retirement on Household Finances: Causal Evidence from Transaction Data
This paper contributes to the literature that studies the impact of retirement on household finances and financial behavior, often using survey or yearly administrative data. We use high-quality Dutch transaction data to estimate the causal effect of retirement on households’ financial outcomes. We use the discontinuity imposed by Statutory Retirement Age (SRA) and variation in the SRA in order to measure causal effects. The monthly data allow us to estimate the direct short-run impact using RD and DiD designs. Our findings show a positive spike in net flow balance at retirement, which financially constrained households use to pay off debts. Debts decline especially for low income, low wealth, blue collar workers, and social insurance recipients. In addition, we see a gradual increase in the end of month balance over time, that is not directly caused by retirement itself.
Sander Kraaij (University of Cologne), Jan Kabátek (University of Melbourne), Sacha Kapoor (Erasmus University Rotterdam, School of Economics), Systemic Discrimination in Firing
Do firms discriminate against women and ethnic minorities in firing decisions? Why? We investigate these questions using administrative data from the Netherlands and discontinuous increases in minimum wages at the birthdays of youth workers. Age-wage increases are orthogonal, therefore, to other worker characteristics that may be relevant for firing decisions, including their race, gender, or productivity. We leverage the orthogonality of these wage increases to measure the willingness of firms to pay (WTP) to retain workers of different ethnicities and genders. We identify WTP distributions across large firms to identify firms with extremely low or high WTP for disadvantaged workers. We find that the market is willing to pay less to retain workers from certain ethnic groups compared to natives, but willing to pay more to retain those from certain other groups. We find no gender differences in WTP. We also find significant dispersion across firms in the WTP for migrant relative to native workers. We consider various mechanisms that can explain our results, including heterogeneous hiring standards across ethnic groups, learning, discriminatory preferences of managers and coworkers, and costly coordination among decision-makers. Our approach lets regulators identify extreme discriminators in the market and enables firms themselves to identify extreme discriminators within the firm using observational data.
Pradeep Kumar (Centerdata), Joris Mulder (Centerdata), Investigating the Impact of Survey Methodologies on Predictive Accuracy in Time Use Modeling
Gender differences in time use are a critical factor distinguishing the lives of men and women, particularly in developing countries. Research consistently shows that men tend to allocate more time to market activities or productive labor, while women spend a disproportionate amount of time on unpaid care and domestic work, often referred to as reproductive labor (Ilahi, 2000; Rubiano-Matulevich and Viollaz, 2019). This imbalance creates challenges for women who choose to participate in the labor market, forcing them to balance personal and family responsibilities.
The primary source of time use data comes from time use surveys or multi-purpose surveys that include a time use module. These surveys generally ask respondents to recall their activities over the previous 24 hours (Fisher, 2015). However, one major issue with this method is recall bias, where respondents may not accurately remember their activities.
An innovative approach to measuring time use involves imputing activity-specific time allocations from physical activity data collected via accelerometers worn by respondents. Our earlier research in Malawi used machine learning techniques and a unique socioeconomic survey conducted in 2017.
In a follow-up experiment in Malawi, 720 households and 1,440 respondents were divided between two survey methods: (1) a 24-hour recall time use diary and (2) a real-time smartphone Time Tracker App (Daum et al., 2019). Respondents were balanced by gender and urban/rural residence. Each participant wore a research-grade physical activity tracker (ActiGraph wGT3X-BT) for nine days, and their height and weight were recorded. Participants were visited three times during data collection, with the first group completing the 24-hour recall module three times and the second group using the Time Tracker App throughout.
This presentation examines whether the effectiveness of machine learning models depends on whether they are trained on 24-hour recall data versus real-time app diary data. It also explores the results and treatment effects from both survey methods in reporting time use activities.
Angel Lazaro (Wageningen University & Research – Wageningen Social Science Group), Roger Cremades (Wageningen University & Research – Wageningen Social Science Group), Eveline van Leeuwen (Wageningen University & Research – Wageningen Social Science Group), Steering sustainable food systems: the complex co-evolution of consumer preferences, sustainable restaurants, and policymaking
The transition towards more sustainable food systems is essential for achieving climate targets and the Sustainable Development Goals (SDGs). Achieving goal 12, responsible consumption and production, in particular, will require changes in consumer behavior, the adoption of sustainable practices by suppliers, and, most importantly, the implementation of policies that foster such sustainable behaviors. Whether it is the availability of supply or the increasing demand that drives sustainability transient consumption is often discussed in economic circles. From the complex systems perspective, it is their interaction that matters. This article explores how consumer preferences and restaurant menus co-evolve to contribute to a more sustainable food system and how this co-evolution is affected by different policy instruments. We use a spatially explicit agent-based model of the catering industry in Amsterdam as a case study. The model is built using spatial microsimulation to expand on survey data from a discrete choice experiment about restaurants in Amsterdam. We observe that in the absence of policy interventions, it takes quite a lot of time for changes in diet patterns to occur. Multiple cities are working on their sustainability. Based on the generated insights, we expect our research to contribute to the current debate on the policy interventions to achieve sustainable urban food systems and the SDGs, and to be of consequence across multiple cities worldwide
Maël Lecoursonnais (Linköping University – Institute for Analytical Sociology), Selcan Mutgan (Linköping University – Institute for Analytical Sociology), Life-Course Trajectories of Experienced Segregation
Motivation
Dynamics and consequences of segregation are commonly studied within isolated domains (e.g., neighborhoods or schools) and use point-in-time exposure measures. Recent advancements emphasize that these domains (1) are likely to influence each other and (2) are expected to have both independent and multiplicative effects on socioeconomic outcomes. Given that individuals with distinct socioeconomic backgrounds experience everyday life differently, it is difficult to infer the implications of diverse experiences in separate domains from isolated contexts.
Objective, data and methods
In this study, we investigate how various facets of segregation collectively shape life-course trajectories of individuals. Drawing on Swedish administrative data, we track cohorts of primary school students into adulthood over almost 30 years. We use individual-level, annual information on classmates during education years, colleagues at the workplace and nearby neighbors. We focus our empirical analyses on three domains: the neighborhood, the school, and the workplace as they make up the majority of individuals’ time use. For each individual-year, we measure exposure to the top and bottom 20% of the income distribution. The combination of these six measures constitutes what we term experienced segregation.
Results
Preliminary results suggest several stylized facts on experienced segregation over the life-course. First, segregation is higher among affluent groups than disadvantaged ones, with greater concentration of the affluent groups. Second, we observe strong positive correlations in exposure to affluence and poverty between neighborhood and school, but lower associations at workplace and university. Third, exposure levels follow similar patterns over the life-course across socioeconomic groups. Finally, levels of exposure follow a socioeconomic gradient, with students from the highest income quintile consistently more exposed to affluence and less to poverty across all activity domains throughout their lives.
Yue Li (Twente University – Faculty of Behavioural, Management and Social Sciences), Marcello A. Gómez-Maureira (Twente University – Faculty of Electrical Engineering, Mathematics and Computer Science), Stéphanie van den Berg (Twente University – Faculty of Behavioural, Management and Social Sciences), Generalizing Behavior Prediction Models from VR Experiences
Virtual Reality (VR) has become a prevalent tool for presenting stimuli in social psychology research. Although the human behaviors in VR environment (VRE) is widely explored, there remains a gap in capturing and modelling the behavioral outcomes in VRE and explore how these indications affect behavior in real-life. To address this, we propose an approach aimed at predicting behavior in Virtual Reality experiments, which will allow us to assess the impact of tasks on prosocial behaviors. In this study, we will make use of experiments that systematically manipulate interaction levels and sensory settings within VR environments. Using complementary measures, including physiological responses, interaction, position, and stimulus properties, this multi-faceted analysis approach is designed to enable us to construct an advanced statistical model that predicts participants’ behavior based on their experience in VRE’s. The resulting behavior prediction models will provide valuable insights into the mechanisms driving prosocial behavior and demonstrate the utilization of VR as a robust tool for behavioral research. This research is aimed to advance our understanding of how immersive VR experiences can influence human behavior, offering empirical evidence to support the development of reliable, generalizable behavior prediction models. Furthermore, it has potential to enable innovations in social psychology, contributing to the potential of VR to generate deep insights into human behavior.
Angelica Maria Maineri (Erasmus University Rotterdam, School of Social and Behavioural Sciences), Laura Boeschoten (Utrecht University – Faculty of Social Sciences), Niek de Schipper (University of Amsterdam – Faculty of Social and Behavioural Sciences), Claartje ter Hoeven (Erasmus University Rotterdam, School of Social and Behavioural Sciences), The impact of constant connectivity on employees’ well-being: a data donation pilot study
In the knowledge economy, the use of digital platforms, e.g., Slack, to connect employers among each other is extensive, to the point that thousands of teams worldwide employ them. Next to the advantages of such platforms, which enable to connect people who work remotely and also to carve out space for entertainment and release, the inherent danger of these tools is that they blur the boundaries between work and personal life. This constant connectivity, defined as a constant availability to the work organisation beyond working hours, can be detrimental for worker’s well-being because it lowers psychological detachment, e.g., taking (mental) breaks from work, which is important for employees to restore energy (Büchler et al., 2020 [1]). The detrimental effect could be attenuated by a preference for integration (i.e., when individuals like to have blurry boundaries between work and private life) instead of segmentation (i.e., when individuals prefer to keep work and private life separate).
In this study, we replicate part of Buchler et al. 2020’s findings using novel data. We investigate whether constant connectivity has a detrimental effect on employees’ well-being mediated by psychological detachment, and we also investigate whether this effect is stronger for segmenters (vs. integrators). To answer our question, we use a combination of survey data and actual Slack access logs, acquired via data donation using the D3I infrastructure, which allows us to quantify how much individual employees connect to work via Slack outside of their working hours. In this way, we further improve on the original study by being able to compare self-reported to actual constant connectivity. The use of digital trace data to measure constant connectivity in this context may allow us to better quantify and understand an important phenomenon of nowadays’ work environments, whereby self-reported measures may be inaccurate due to recall bias.
Gabriele Mari (Erasmus University Rotterdam – School of Social and Behavioural Sciences), Emanuele Fedeli (University of Milan “La Statale”), Child Penalties and Public Childcare Provisions Under Fiscal Austerity
Child penalties affecting women’s earnings are relatively large in Italy and contribute to gender pay gaps worldwide. Evidence on the effectiveness of public policies in mitigating child penalties is mixed. We revisit the question of whether, how, and for whom child penalties might be influenced by public childcare provisions. Whilst previous studies have studied expansions, we focus on the effects of constraints limiting these provisions in times of fiscal austerity.
Specifically, we examine public spending restrictions enforced since 2001 by the Domestic Stability Pact (DSP) for Italian municipalities above 5,000 inhabitants. In a fuzzy regression discontinuity (FRD) design, we leverage the DSP-induced discontinuity as an instrument for the supply of public childcare. The design allows us to tease out the causal effects of a more limited supply of public childcare.
We systematised more than a decade of data from municipal budgets with rich information on public childcare provisions. We find a sizeable decrease in public childcare supply under the fiscal austerity regime dictated by the DSP. The decrease is driven by total childcare spots and accepted applications rather than number of centres, educators, and total applications. However, when combining municipal data with social security records on payslips and employment histories for over 400,000 women over the period 2001-2015, our FRD results suggest little influence of austerity-related limits in public childcare provisions on women’s average earnings after childbirth. In the next month, we will complete our paper with – among others – an analysis of disparities depending on women’s socioeconomic status.
Jordy Meekes (Leiden University – Faculty of Law), Maddalena Ronchi (Northwestern University), Mind the Cap: The Effects of Regulating Bankers’ Pay
In this paper we investigate how restrictions to the possibility of paying large bonuses affect employees’ pay schemes and firms’ ability to attract and retain workers. Using administrative data from Statistics Netherlands, we study the Dutch bonus cap that sets a 20% limit to the ratio of variable to fixed pay for all workers employed in the banking industry. Comparing banks to financial institutions not covered by the regulation, results based on a dynamic differences-in-differences specification show that treated employees experience a sharp drop in variable pay which is not fully compensated by an increase in fixed pay, especially for talented workers. We also study whether the policy affects banks’ ability to attract and retain talented workers.
Adrienne Mendrik (Eyra), Emiel van der Veen (Eyra), Jeroen Vloothuis (Eyra), The Next platform: What do data donation, benchmark challenges and participant recruitment have in common?
Data donation, benchmark challenges, and participant recruitment are all software services available on the Next web platform. They are developed by Eyra, co-created with social sciences and humanities researchers from various universities and co-funded by ODISSEI (https://www.eyra.co/software-services). They are integrated into the Next web platform and share reusable modules within the open source Next mono codebase (https://github.com/eyra/mono). The Next web platform functions as an online operating system. In other words, researchers sign in and can readily use the available software services. Like Apps on an operating system, software services can be made available to some or all Next platform users. They are integrated into the project structure on the Next platform as templates. Researchers simply create a new project, choose a project item, such as data donation or benchmark challenge and they can immediately start filling out the required information to publish their study or challenge online. By using the Next web platform you contribute to the maintenance of the sustainable software ecosystem. Each software service benefits from and contributes to the collaborative ecosystem. This collaborative approach not only provides resource efficient maintenance but also enhances efficiency in software development for academic research. On top of this, the Next platform also functions as an integration hub for third party software services, such as services from SURF, Centerdata, and Qualtrics. These services are connected to the Next platform enabling interoperability. For instance, performing data donation studies within the LISS panel (through integration with Centerdata), using university credentials to sign in on the Next platform (through SURFconext), transferring donated data to SURF research cloud for analyses and having participants fill out a Qualtrics questionnaire. In this presentation we will demo the Next platform, talk about future plans and welcome new ideas for valuable software services on Next.
Mohsen Monji (Concordia University, Montreal, Canada), Machine Learning Approaches for Exploring the Social Determinants of Mental Health in Canada: Findings from the Canadian Community Health Survey (2017-2018)
In recent years, there has been growing concern about a rise in mental health problems in Canada, with reports showing an increase in the prevalence of anxiety and depression in Canadian society. However, these mental health problems are not proportionately distributed across the population and significant disparities in mental health outcomes exist among sub-populations. Among the key contributors to these disparities are social determinants, which are the conditions in which individuals are born, grow, live, work, and age. These include factors such as age, gender, race, socio-economic status, housing conditions, and food security, which influence individuals’ access to resources and opportunities, leading to inequalities in mental health. To reduce population mental health disparities, it is crucial to have a comprehensive understanding of the social factors shaping inequalities in mental health outcomes.
Existing research on the social determinants of mental health in Canada mainly relies on traditional statistical analyses, with limited application of machine learning approaches, especially in sociological research on mental health.This study aims to bridge this gap by developing and comparing several machines learning models, including logistic regression, SVM, and decision trees (random forests) to analyze the social determinants of mental health in Canada. Using data from the nationally representative Canadian Community Health Survey (2017-2018, N=130,000), this study examines the application of machine learning models to predict self-rated mental health and psychological distress and to identify social drivers of mental health outcomes in Canada. By using machine learning approaches to study the social determinants of mental health, this study contributes to both computational sociological research and broader research on population mental health. The findings of this study inform data-driven and evidence-based policies, promoting more inclusive and targeted strategies for enhancing mental well-being in Canada at the population level.
Tamara Mtsentlintze (Utrecht University – Faculty of Science), Esmee Dekker (Utrecht University – Faculty of Science), Damion Verboom (Utrecht University – Faculty of Science), European Value Maps: can data visualizations contribute to increased tolerance in society?
One of the most complicated challenges of our time is deeply rooted polarization in our society. To tackle the different crises (e.g., climate, migration) that we are facing today, it is important for people to work together. However, the deep social, political, and economic divides lead us to distrust each other, which only increases conflict. Social media filter bubbles, selected exposure and “echo chambers” further contribute to this polarization and especially the perception of polarization. Namely, one important distinction is the difference between the actual and the perceived polarization in society. To build bridges in our society, we need people to become more open minded to the opinions of others. We therefore propose an intervention to reduce the perception of polarization and thereby increasing the tolerance to opposite opinions on central societal and political topics.
This intervention is based on European Value Maps (EVM), a visual representation of data collected within European Value Study. The maps visualize opinions of people on crucial societal topics such as gender equality, environment protection, trust to societal and governmental organisations and moral beliefs. EVMs allow to: -observe the sheer diversity of opinions held by the participants of the survey, -interact with the maps to explore the underlying data, -locate themselves on the maps. EVM are built on the hypothesis that showing the actual distribution of opinions to people, leads observers to think that the world is not black and white, but instead exists out of a variety of different gradients of opinions. This may consequently lower the threshold to start the conversation with someone with a (slightly) different opinion and eventually become more open minded. In this talk, we will present the project EVM and the results of the initial user studies to assess the effectiveness of the maps.
Vittorio Nespeca (TU Delft – Faculty of Technology Policy and Management ), Tina Comes (Tu Delft – Faculty of Technlogy, Policy and Management), Frances Brazier (TU Delft – Faculty of Technlogy, Policy and Management), Learning to select information exchange hubs: Capturing the emergence of boundary spanning in volatile conditions
To be resilient, different formal and informal groups, such as governmental agencies, NGOs, and communities, need to coordinate effectively when responding to crises. Crucial to this coordination is the exchange of information across these groups, particularly in the volatile settings typical of crisis response. Informational Boundary Spanners (IBSs) serve as promising information exchange ‘hubs’ to facilitate this inter-group communication. However, the current understanding of the mechanisms leading to the emergence of IBSs remains limited. First, a metric is necessary to quantitatively analyze the emergence of IBSs, yet such a metric is currently unavailable. Second, while a potential mechanism for IBS emergence is the ability to learn who provides high-quality information, this mechanism has not been systematically tested. This study advances crisis resilience by providing key components for measuring and understanding the emergence of IBSs. It introduces a novel metric to identify emergent IBSs and uses this metric to investigate the role of learning as a fundamental mechanism for IBS emergence in volatile environments. The metric and the learning mechanism are formalized using an Agent-Based Model. A case study on information sharing in a disaster scenario demonstrates the metric’s validity and confirms that learning is indeed a mechanism for effective IBS emergence in high-volatility settings. Such emergence is, however, contingent upon sufficient inter-group connections and stable information sources. This study aims to establish a foundation for exploring the mechanisms underlying IBS emergence, thereby enhancing inter-group information exchange and supporting crisis resilience.
Samin Nikkhah Bahrami (Utrecht University – Faculty of Law, Economics and Governance), Karlijn Morsink (Utrecht University – Faculty of Law, Economics and Governance), Chris Barret (Cornell University- Department of Economics ), On targeting; Predictors of Expected Consumer Welfare from Catastrophic Drought Insurance
Financial products are increasingly complex, and consumers often struggle to make financial decisions that enhance their welfare. The scope to improve these decisions through additional information provision and changing choice architectures of financial products is limited. Recent efforts try to improve the choice quality of decision-makers — also outside of the financial decision-making space – by providing advice about optimal choices based on the characteristics of decision-makers. One approach is to use individual-specific information, such as past health expenditures for health insurance advice. Another approach is to identify characteristics that predict good or bad quality choices and provide advice by targeting product information and advice to individuals with similar traits. Using this approach for financial decision-making is, however, challenging because optimal choice quality is often heterogeneous and depends on the preferences and beliefs of consumers, which are difficult to easily observe.
This study explores predictors of expected consumer welfare from catastrophic drought insurance for pastoral households in Ethiopia. The insurance uses a satellite-based normalized difference vegetation index to monitor pasture quality, triggering indemnity payments when pasture quality falls below a certain threshold. This insurance can enhance welfare for low-income pastoral households. Still, its expected consumer welfare depends on household characteristics like risk attitudes, loss expectations, and the correlation between pasture quality and livestock losses. We use Group Lasso, a machine-learning feature selection method, to identify positive expected consumer welfare predictors from insurance. The objective is to determine household characteristics that predict whether they will benefit from this insurance.
This research reveals that different livestock types and decisions to purchase insurance on extensive and intensive margins result in distinct predictors. Key factors that positively influence welfare from insurance choices include education, religion, familiarity with the insurance and its promoter, and previous purchase of the insurance, among others.
Ceciel Pauls (VU Amsterdam – Faculty of Science), Michel Klein (VU Amsterdam – Faculty of Science), Stef Bouwhuis (VU Amsterdam – Faculty of Social Sciences), Objective or subjective employment precariousness? Comparing definitions to a topic model based on user-generated data.
There exists great heterogeneity in the literature regarding both the definition and the operationalization of employment precariousness (EP). Because of the lack of consensus on what EP entails, there is an unfulfilled need for a universally recognized definition and operationalization of EP. As a consequence, authors often operationalize EP based on the variables available in the data. The literature on EP can be roughly divided into two approaches: one of which relates to the objective contractual arrangement (objective EP) and the other to an individual’s experience (subjective EP). However, it is unclear how theoretically and data-driven definitions of EP, such as such and subjective EP, compare to data ‘in the wild’. In this study, we use an objective definition of employment precariousness based on the Employment Precariousness Scale (EPRES) and subjective EP as formulated by Mai et al. to determine how prevalent user-generated topics in relation to EP compare to both the objective and subjective definitions of EP. We aim to gain understanding in the ways in which individuals generate discourse about the various dimensions of EP and how this discourse related to objective and subjective EP. We apply Bi-term Topic modeling on user-generated data from Dutch employment forums to identify a number of k latent employment-related topics. Our preliminary results demonstrate that the dimensions in the EPRES scale are not in alignment with the topics regarding EP discussed in online discourse.
Dimitris Pavlopoulos (VU Amsterdam – Faculty of Social Sciences), Roberta Varriale (Sapienza University), Mauricio Garnier-Villarreal (VU Amsterdam – Faculty of Social Sciences), Cross-country differences in employment mobility in the presence of measurement error. A multiple-group hidden Markov model using linked administrative and survey data
In this paper, we investigate whether measurement error can bias cross-country differences in employment mobility. We particularly focus on the comparison between two countries with very different labour markets: the Netherlands and Italy and we study mobility between permanent employment, temporary employment, self-employment and non-employment. For this purpose, we define a multigroup mixed hidden Markov model (MgHMM) with two independent observed indicators for employment status. These two indicators come from linked administrative data from the National Statistical Institutes of Italy (Istat) and the Netherlands (CBS) and survey data from the Labour Force Survey for the years 2017-19. The dimensionality of the data is therefore quite different for the two sources: administrative data cover the whole population, while survey data cover only a sample. The MgHMM is flexible to model measurement error in both data sources in both countries. The results of our analysis indicate that, when correcting for random measurement error, cross-country differences in employment mobility over time are smaller than originally thought. Error-corrected estimates of over time mobility from temporary to permanent employment, self-employment and non-employment to temporary or permanent employment are much smaller than the relevant observed mobility rates. For example, the 3-month transition rate from temporary to permanent employment was never larger than 9.7% in Italy or 7.7% in the Netherlands, while the 3-month transition rate from non-employment to temporary employment never exceeded 4.1% in Italy or 13.0% in the Netherlands. Random error seems more present in the Labour Force Survey than administrative data in both countries. In administrative data, random error seems to bias only estimates on self-employment in the Netherlands and on temporary employment in Italy.
Sanne Peereboom (Tilburg University, Tilburg School of Social and Behavioural Sciences), Inga Schwabe (Tilburg University, Tilburg School of Social and Behavioural Sciences), Bennett Kleinberg (Tilburg University, Tilburg School of Social and Behavioural Sciences), Cognitive phantoms in large language models through the lens of latent variables
Large language models (LLMs) increasingly reach real-world applications but are poorly understood. The size and complexity of LLMs complicate the study of potential higher-order constructs such as attitudes or behavioural tendencies. Inspired by ethology and psychology, an alternative approach to studying LLMs is to treat them as participants in psychology experiments. Recent studies administering psychometric questionnaires to LLMs report human-like traits in LLMs. However, using psychometric instruments developed for humans presupposes equivalence in the internal representation of a construct in LLMs and humans. In psychometrics, suchlike constructs are known as latent variables: unobservable, abstract constructs that are measured through observable variables. Yet, typical analytical procedures rarely investigate the internal representations of latent phenomena in LLMs, and resort to comparisons of aggregate dimension scores instead. This study corrects this misalignment and applies formal psychometric methods to investigate if and how the latent representations of psychological traits differ between humans and LLMs. A latent variable modelling approach was used to compare a representative human sample with LLM responses on two validated personality inventories (HEXACO-60 and Dark Side of Humanity Scale). Our findings indicate that administering psychometric inventories can create the illusion of human-like traits in LLMs, which does not withstand formal psychometric analyses and introduces the risk of misattributing “human-like” latent phenomena to LLMs. We highlight the need for psychometric analyses of LLM responses to avoid chasing cognitive phantoms.
Keenan Ramsey (Twente University – Faculty of Behavioural, Management and Social Sciences), Anne van Dongen (Twente University – Faculty of Behavioural, Management and Social Sciences), Robbert Sanderman (Twente University – Faculty of Behavioural, Management and Social Sciences), Assessing the scope of mental health (non)-recovery in the aftermath of the COVID-19 pandemic
Background: The COVID-19 pandemic had profound impacts on mental health globally. While research highlights the resilience of mental health in the general population, some may be left behind unanimous recovery is assumed post-pandemic. Specifically, attention is needed for those who do not experience recovery alongside broader population trends. Utilizing existing datasets presents an opportunity to harmonize disparate data sources into a robust sample to comprehensively address this issue.
Objectives: This project aims to leverage data from multiple cohort studies to create a harmonized dataset for investigating mental health (non)-recovery in the Dutch population after the COVID-19 pandemic. The initial objective is to develop a versatile pipeline capable of integrating heterogeneous datasets. The resulting harmonized dataset is the foundation for subsequent aims identifying, characterizing, and predicting mental health (non)-recovery. Ultimately, we seek to clearly communicate findings through visual decision aids, providing stakeholders with actionable insights to identify and prioritize support for individuals at risk.
Methods: Longitudinal data from Dutch cohort studies, LISS Panel and Lifelines, with data pre-, during, and post-pandemic were selected. Harmonization is facilitated by developing a pipeline for generic data-cleaning and pre-processing. In the harmonized dataset, (non)-recovery is operationalized for a three-step strategy to identify, characterize, and predict (non)-recovery. Descriptive analyses identify persistent mental health impairments, while further characterization explores factors associated. Machine learning models are used to identify and create visual mappings of potential risks.
Results: Preliminary results are expected to demonstrate the feasibility and effectiveness of the pipeline created to facilitate harmonization.
Conclusions/Implications: Using computational techniques to understand (non)-recovery post-pandemic advances both research practices and understanding concerning mental health in crisis. These insights are pivotal for informing monitoring, interventions and policies aimed at supporting populations at risk. Moreover, the methodological framework developed offers a scalable solution for robust evaluation of mental health impacts in future crises.
Raoul Schram (Utrecht University – Faculty of Social Sciences), Samuel Spithorst (Utrecht University – Faculty of Social Sciences), Erik-Jan van Kesteren (Utrecht University – Faculty of Social Sciences), Metasyn: Generate synthetic tabular data in a transparent, understandable, and privacy-friendly way.
Social scientists often handle highly privacy-sensitive information, such as mental health questionnaires, personal income data, or social media information. This makes it ethically and legally problematic for the researcher to share the real data publicly as part of the research process, even if the data is pseudonymized. Among other issues, this can create reproducibility problems: while the analysis code for the research might be published, other researchers cannot reproduce the results without the original data. One solution that can improve the situation is to crea
Mónika Simon (University of Amsterdam – Faculty of Social and Behavioural Sciences), The anatomy of a conspiracy theory: A multi-modal investigation of the
evolution of distrust narratives surrounding “”Kastepiracies””
Much has been uncovered about the psycho-social predictors and correlates of conspiracy theories, highlighting their profound impact on interpersonal relationships, inter-group relationships, and democratic institutions at large. These simplified explanations of complex realities have a powerful appeal and persuasive impact. They create an environment ripe for misinformation by undermining trust in traditional gatekeepers, encouraging extremist belief systems and actions, and leading to disengagement from conventional political activities. However, there is a significant gap in understanding how narrative features like simplified language, logical fallacies and biases, sensationalism, and negativity present in various (social) media content contribute to the emergence and spread of conspiracy theories, fostering societal distrust. To address this gap, we employ state-of-the-art automated content analytic techniques (textual and visual) to study ‘Katespiracies’ — a series of conspiracy theories that arose around the prolonged public absence of the Princess of Wales following major abdominal surgery in early 2024. A failed attempt by Kensington Palace to debunk these theories with a “”doctored”” family photo, which was given a kill notice by the Associated Press, exacerbated the spread of conspiracy theories and raised more suspicion. This persisted until a video was shared by Kate detailing her cancer diagnosis on March 21st, 2024 that effectively put an end to almost all conspiracy theorizing around her absence. This clear timeline of the emergence and rapid demise of Katespiracies allowed us to examine the evolution of (dis)trust narratives in social media and the British press. Drawing on theories of narrative persuasion, agenda setting, and framing, we study how specific narrative elements and textual and visual features shape the evolution of ‘Katespiracies’ in both social and legacy media. Our insights derived from a unique multi-modal dataset offer actionable insights for combating conspiracy theories by identifying and addressing problematic narrative features that fuel their spread on (social) media.
Abhigyan Singh (TU Delft – Faculty of Industrial Design Engineering), Natalia Romero Herrera (TU Delft – Faculty of Industrial Design Engineering), Razieh Torkiharchegani (TU Delft – Faculty of Industrial Design Engineering), Converging design methods and computational analysis to support community lifelong learning practices
Energy learning communities are institutions involving various stakeholders that engage in lifelong learning practices integrating knowledge from research, government, industry, and society to accelerate energy transition. A challenge that learning communities often face is the context-dependent nature of problems and strategies, making it difficult to understand what changes to make, when, how, and with whom. We present the work of Greengage, an interactive digital platform that supports energy learning communities in developing knowledge about their local socio-technical environment.
In this presentation, we showcase our ongoing work and discuss the intended direction of three data-oriented stages of Greengage to support energy learning communities in the context of secondary school. In the data collection stage, buildings’ performance from indoor climate comfort sensors and energy meters is integrated with social data on experiences, opinions, and community preferences to prompt individuals’ contribution to community learning. This stage aims to support engaging and playful reporting activities as well as inclusive and representative lifelong monitoring practices. In the data processing stage, both technical and social computational analyses are explored, including machine learning and text analysis, to develop integrated benchmarks of building performance and community learning performance. In the feedback loop stage, a technical-social radar aims to provide real-time and historical indicators of the alignments and misalignments of the community’s values, preferences, and experiences and the connection to factual knowledge from sensors and meters.
The development and implementation of these stages follow a participatory and iterative approach to exploring schools’ needs and abilities to develop and apply knowledge about their local environment to experiment and transform current practices. Overall, our presentation contributes to the discussion on using design methods combined with computational techniques and highlights the benefits and challenges of integrating technical and social contextual data to support lifelong learning practices.
Weronika Sojka (Wageningen University & Research – Wageningen Social Science Group), Erkinai Derkenbaeva Derkenbaeva (Wageningen University & Research – Wageningen Social Science Group), Eveline van Leeuwen (Wageningen University & Research – Wageningen Social Science Group), Agent-based modeling and Urban Digital Twin simulations: state of the art for complex circular systems
Digital Twins simulations do not have a consistent definition in the literature. Nowadays, we can already identify multiple definitions and sets of sub-tools that they include, depending on their purpose or specificities of the industry that they are being designed for. Nonetheless, in contemporary urban practice, cities are increasingly turning to various kinds of Urban Digital Twins and other simulation models, e.g. Agent-Based Models (ABM), to simulate and analyse complex issues. This study aims to address the knowledge gap lying at the intersection of these two practices and to indicate opportunities for their integration.
In addition, the transition to sustainable policies requires significant resources and timely implementation based on evidence. Therefore, encompassing technological, social, environmental, and financial aspects affecting complex systems and their ongoing interplay is crucial. Lack of coherence in this aspect, hinders efforts to optimize current city management systems and align policies with the principles of the circular economy. Integrating ABM into the Digital Twins simulations is an evident solution to bridge those aspects.
In this study, we conduct a literature review and analyse real-life examples and pilot projects to investigate how they approach the system holistically and to broaden the understanding of those tools including their similarities and differences. We aim to explore whether ABMs can be considered as a component or a sub-system of a Digital Twin and construct a conceptual framework for our next studies.
We expect that the results of this exploration will determine the benefits of existing practices together with challenges that the cities might have encountered both during their creation and after their occurrence. A practice review will serve for determining the variables causing changes, risks, and opportunities for our system’s design in the future.
Koen Steenks (VU Amsterdam – Faculty of Social Sciences), Stef Bouwhuis (VU Amsterdam – Faculty of Social Sciences), Dimitris Pavlopoulos (VU Amsterdam – Faculty of Science), Classifying employer orientation: how do firms combine wage policy and the use of non-standard employment
There is a growing scholarly interest in the pivotal role that employer policies play in shaping social inequality. Particularly, employer orientation (EO), i.e. how employers combine wage policy and the use of non-standard employment (NSE), may influence social disparities as they influence inequalities regarding job security and income. Previous empirical research on employer policies has mostly focused on either wage policy or the use of NSE. Research on how employers combine this is mostly theoretical. These studies usually distinguish between two types of orientations: external and internal. Externally oriented entities base their policies on the fluctuating dynamics of supply and demand within the labour market, and often use NSE and performance-based wages. Internally oriented firms more often use permanent contracts and offer their employees a continuously increasing wage based on administrative rules such as wage scales.
To our knowledge, this study is the first to empirically investigate how employers combine wage policy and the use of NSE. To do so, we use register data on employment from Statistics Netherlands linked to the structure of earnings survey. Since employer orientation is a multi-dimensional latent variable, we employ Latent Class Analysis (LCA) to cluster employers with similar orientations. In addition, we explore how firm size, sector, whether a firm is a multinational, and labour union activity influence a firm’s EO.
The preliminary results show that most employers are either flexible with respect to wages or contracts, indicating that there exists a trade-off between wage policies and the use of NSE to generate flexibility. This contrasts with the theoretical distinction between internal and external orientation where high (or low) wage flexibility goes together with high (or low) contract flexibility.
Eduard Suari-Andreu (Leiden University – Faculty of Law), Max van Lent (Leiden University – Faculty of Law), Time to Give: Health Shocks as a Trigger for Inter-Vivos Transfers
In this study we investigate the effects of health status as a predictor of giving via inter-vivos wealth transfers. To that end, we use high-quality administrative data for the whole Dutch population. We construct a measure of negative health shocks and use it to carry out an event study that exploits the random timing of the shock. Our results show a significant and positive increase in the probability of giving wealth during the years following a health shock. Several extensions of our baseline analysis (using the diagnoses related to the shock as well as data on the children of individuals who experience the health shocks) indicate that the transfers we observe are mostly intergenerational transfers. In addition they show that the wealth transfers respond to increased mortality and that they fit in a model of intergenerational altruism. Our findings are relevant for social policy and for tax policy.
Kristina Thompson (Wageningen University & Research – Wageningen Social Science Group), Johan van Ophem (Wageningen University & Research – Wageningen Social Science Group), Investigating socio-economic status’s role in the intergenerational transmission of mortality
What makes someone long-lived? Although lifespans worldwide are generally increasing, some people enjoy a longevity advantage, while others do not. In the Netherlands, there is similarly evidence of disparities in life expectancy. Understanding the sources of this variation is crucial to identifying ways of narrowing the gap between the short-and long-lived.
Already, research suggests that an individual’s survival chances are heavily tied to the lifespans of their family members. That is, longevity can considered something that is inherited from one generation to the next. Likewise, research suggests that socio-economic status is a key determinant of health and mortality, and is also influenced by the socio-economic status of previous generations.
To fully unravel how mortality and socio-economic status are related, intergenerational data on both are necessary. Such datasets are extremely rare. The newly-linked Historical Sample of the Netherlands (HSN) and the System of Social-statistical Datasets (SSD) offers researchers a unique ability to study how socio-economic status and mortality are related across generations. This dataset stands out for the ability to link up to three generations of kin, and for containing the cause of death of the final generation.
With this dataset, we will explore the moderating role of socio-economic status on the intergenerational transmission of mortality. Exploiting cause-of-death information will further illuminate any inherited and/or socio-economic patterns in mortality.
Agata Troost (University of Groningen – Faculty of Spatial Sciences ), Jaap Nieuwenhuis (University of Groningen – Faculty of Behavioural and Social Sciences), Jonathan Mijs (Boston University), Exploitation-based class scheme, social inequality and contemporary conflicts: a novel empirical approach
Much of current quantitative social research on inequality uses proxies (income, education) to assess a person’s social position and experiences. We argue that we need better data on social class dynamics, as well as on personal assets and wealth. Data capturing people perception of social classes, including their own class identity, can help with explaining the mechanisms of social inequality and related societal conflicts. In December 2023 we won the ODISSEI LISS panel data grant financing a survey collecting such data, which can be matched with Dutch administrative register datasets and other LISS surveys on topics such as one’s housing situation, political views and activities. The grant gave us a unique opportunity to design survey questions on individual perceptions of inequality, work autonomy, and social class.
Our novel data, in combination with the existing LISS and Microdata datasets, allow us to answer a key question in contemporary research on social classes, based on theories inspired by thinkers such as E.O. Wright and Bourdieu. Is class identity predominantly determined by the economic or cultural capital? Can the relative lack of class-based political mobilisation be explained by low levels of class consciousness? Or are attitudes about ethnic divisions, or maybe feelings of powerlessness and precarity more important? This research allows for exploring issues surrounding social inequality among the growing concerns about divisions and polarisation of the Dutch society.
Stéphanie van den Berg (Twente University – Faculty of Behavioural, Management and Social Sciences), Ulrich Halekoh (SDU), Jacob Hjelmborg (SDU), Using computer vision to estimate a Linfoot correlation
As long as data are multivariate normally distributed, Pearson correlation coefficients are perfect estimators of bivariate relationships. But many traits in social and medical sciences are not normally distributed. Existing methods to estimate mutual information (the basis for correlation) show bias and large variance. Deep convolutional neural networks have shown great performance on image data. Here we make use of this strength by transforming data sets into images, and let deep learning do what it is good at. We compare performance with numerical methods to estimate mutual information.
Sterre van der Kaaij (National Institute for Public Health and the Environment (RIVM)), Lenneke Vaandrager (Wageningen University & Research – Wageningen Social Science Group), Hanneke Kruize (National Institute for Public Health and the Environment (RIVM)), Using Agent-Based Modeling and Group Model Building to Understand How Spatial Interventions Affect Health Behaviours: A Research Protocol
Background: Western society is structured in a way that is conducive to physical inactivity and unhealthy food consumption. Spatial interventions in the built environment, such as restructuring greenspaces, might be important in promoting healthy behaviours. However, insights into the system that influences these behaviours are lacking, and the effectiveness of spatial interventions remains unclear. This research protocol outlines the methodology that will be used to understand how spatial interventions in the built environment affect physical activity (PA) and healthy food consumption, particularly focusing on greenspace interventions, using a systems approach. Methodology: We will design Agent-Based Models (ABM) to study adult health behaviour at the neighborhood level in the Netherlands. ABMs are particularly suited for this research because they can simulate heterogeneous individual behaviours and their adaptations to interventions in the built environment. The ABM will be based on existing theories (such as COM-B) and data collected from 2-3 living labs in the Netherlands. A living lab involves municipalities working together with citizens to co-create and implement spatial interventions aimed at promoting PA and/or healthy food consumption. The data collection for this study will involve two phases. First, Group Model Building (GMB) will engage citizens, neighborhood professionals, and scientific experts to identify factors and relationships in the living environment that contribute to healthy behaviour. In the second phase, these factors will be measured among citizens before and after the intervention to determine its impact. Measurements will include walkability, levels of physical activity, and citizens’ experience with the spatial intervention.
Oskar Veerhoek (Radboud University Nijmegen – Faculty of Management Sciences), The Springboarding Organization
As wage setters, organisations play a pivotal role in upward intergenerational mobility. Which types of organisations contribute to upward intergenerational mobility remains unclear. As a theoretical contribution, this paper proposes that springboarding organisations shape upward mobility. Springboarding organisations are organisations that substantially increase lifetime earnings of employees by boosting their future earnings. They achieve this by facilitating rapid accumulation of their employees’ cultural and social capital. Cultural and social capital are built through extensive on-the-job training, networking, and employer reputation. The boost in future earnings occurs when employees convert cultural and social capital into economic capital. This paper makes two empirical contributions: (1) identification of springboarding organization characteristics and (2) estimation of impact on upward intergenerational mobility. For identification, the characteristics are size, sector, types of employees, and age. For estimation of impact, the method is variance decomposition. This research is made possible by the 2022 ODISSEI Microdata Access Grant. This grant covers access to the CBS microdata, a comprehensive administrative data set of the Dutch government. With the CBS microdata, 4.5 million people in the Dutch labor market are followed from 2006 to 2022. In the data, an organization is classified as a springboarding organization based on the ratio between employees’ future wages and their wage during first year of employment. The CBS microdata are based on tax records, family links, and organization registers. This study is based on theory from social stratification (Bourdieu) and labor economics (Becker). It aims to contribute to the relatively new field of intergenerational mobility within labor economics.
Thom Volker (Utrecht University – Faculty of Social Sciences), Carlos Gonzalez Poses (Utrecht University – Faculty of Social Sciences), Erik-Jan van Kesteren (Utrecht University – Faculty of Social Sciences), densityratio: An R-package for density ratio estimation
The density ratio (i.e., the ratio of the distribution of two datasets) is a workhorse in many computational social science tasks, such as sample selection bias adjustment, non-parametric two-sample testing, change-point detection and synthetic data utility evaluation. The key advantage of the density ratio in these applications lies in its ability to identify where and how two distributions differ. Over the past years, advanced methods have been developed to accurately estimate the density ratio from two samples. Despite these innovations, tools for density ratio estimation are rather inaccessible, because existing software only implements a narrow range of estimation techniques, is relatively slow, and/or lacks user-friendliness. To make the tools from the density ratio estimation literature available to computational social science researchers and beyond, we present the R-package densityratio. The densityratio-package is designed to support novice and advanced users in a wide range of practical situations. It contains a comprehensive suite of methods for density ratio estimation, including novel extensions to deal with high-dimensional data. All methods efficiently estimate the density ratio between two input datasets using non-parametric kernel-based estimation techniques implemented in C++. Automatic hyperparameter tuning through fast, multi-core cross-validation minimizes the need for model specification on behalf of the end-user, allowing researchers to focus on their substantive questions. Densityratio makes it easy for users to not only estimate density ratios, but also to inspect, validate, and extend their functionality; two-sample testing, prediction, and plotting are built-in, allowing researchers to use the estimated density ratio in subsequent tasks and visualize the output of the model. In the presentation, we demonstrate the densityratio-package in two empirical examples in the domain of sample selection bias and two-sample testing. As such, we illustrate how densityratio makes density ratio estimation a useful and accessible tool in the toolbox of computational social scientists.
Anastasiia Voloshyna (University of Groningen, Faculty of Economics and Business), Agnieszka Postepska (University of Groningen, Faculty of Economics and Business), Can hybrid work help close the labor market gender gaps?
The work-from-home experiment initiated by the Covid pandemic has transformed the global work environment. Employers now offer the option to continue working remotely, and policymakers recognize the potential of hybrid work to address labour market shortages by enabling greater participation and longer working hours for those with informal care responsibilities. This study empirically tests Goldin’s (2014) hypothesis using Dutch administrative data and sub-sample population surveys. We investigate whether remote work has led to a more equal labour market regarding promotion rates, job hopping, participation, weekly hours, and wages among men and women in the Netherlands, particularly those with informal caregiving duties. We analyse post-pandemic data, abstracting from the detrimental effects of lockdowns, and examine both the impact of individual hybrid work and the hybrid work of partners, aiming to uncover new pathways toward a gender-equal labour market. The study begins by merging work-from-home data from the Labour Force Survey with administrative employment and family condition records to explore the labour outcomes of interest. Using this sub-sample, we train a Random Forest Model to predict the probability of working from home for the larger population and identify changes in work-from-home patterns before and after the Covid pandemic. Additionally, we introduce heterogeneity through between/within-industry variation in statutory provisions on flexible work arrangements in collective agreements. We conduct a difference-in-difference analysis employing matching techniques to correct for individual characteristic imbalances. Preliminary results indicate increased labour force participation among women with minor children and an increase in average hours worked, primarily among those sometimes/always working from home. Furthermore, women with children employed by companies that allow remote work are less likely to switch jobs. Finally, we find that a male partner’s status of sometimes/always working from home is significantly associated with higher employment probabilities and increased hours worked after childbirth of mothers, as well as with a somewhat shorter time to the next birth within the household.
Thorid Wagenblast (TU Delft), Social influence in the context of climate change adaptation: analyzing cross-national survey data
To adapt against the impacts of climate change, adaptation across all scales is needed. This includes adaptation on individual or household, community or local, and national and international levels. Social interaction and influence connect individuals in their communities. Furthermore, they are, next to risk and coping perceptions, key drivers of private climate change adaptation decisions and community-level adaptation. Nonetheless, so far, there is limited knowledge of how people influence each other in the context of climate change adaptation. Using cross-national survey data (Netherlands, US, Indonesia, England), we identify patterns of social interaction in this context. We describe different groups based on interaction frequency and content. There appear to be stark differences in communication based on context and country. Indonesian respondents interact more on the topic of flooding and adaptation compared to their Western counterparts, and so do people who perceive the risk as higher. Discussion often evolve around the perceived risk and coping strategies to mitigate the risk, both on individual and community level. Furthermore, we link the likelihood of being connected within a social network to household characteristics like income or risk perception. These archetypes of interactions and interactors are then used to update social influence in an agent-based model of household flood adaptation uptake, testing different communication policies and their effectiveness under the influence of the refined social interaction.
Shuai Wang (VU Amsterdam – Faculty of Science), Maria Adamidou (VU Amsterdam – Faculty of Science), Examining LGBTQ+-related Concepts and Their Links in the Semantic Web
The past years witnessed a significant adoption of LGBTQ+ ontologies and structured vocabularies in libraries. Some of them are published as linked data in the semantic web. Homosaurus is among the most popular ones with links from/to the QLIT, GSSO, Wikidata, and LCSH, etc. Over the past years, three versions of Homosaurus have been released with updates every half a year. Despite its rapid development, little has been reported about the properties of these links. In this study, we first retrieve all the mappings and links between them as well as links about concept replacement to form an integrated knowledge graph, together with some obtained links about redirection. Using them, we perform qualitative and quantitative analysis. We discuss the discovery of missing links using weakly connected components. We analyze concept drift and change by providing examples of the convergence and divergence of concepts. Finally, we discuss some potential issues with publishing related multilingual information in the semantic web and the consequences of our findings in practice in libraries, heritages, and online literature databases.
Jari Zegers (Tilburg University, Tilburg School of Social and Behavioural Sciences), Bennett Kleinberg (Tilburg University, Tilburg School of Social and Behavioural Sciences), Understanding psychological responses to the COVID-19 pandemic with latent class growth analysis
There is increasing evidence that the psychological responses to the COVID-19 pandemic are heterogenous. In this paper, we model subgroups in a dataset of emotional responses to the pandemic using latent class growth analysis on a panel dataset of UK-based participants. Participants (n=868) rated eight emotions on a nine-point Likert scale in April of 2020, 2021, 2022, and 2023, and provided demographic variables, data on perceived social support and the impact of life events. Using a latent class trajectory analysis, we found evidence for six latent classes with some classes showing patterns of well-coping (decreasing negative emotions and increasing positive emotions) while others showed patterns of poor adjustment. Social support and negative life events were predictive of class membership in a multinomial logistic regression: having lower social support and experiencing negative life events increased the probability of participants belonging to poorly adjusted classes. Our study suggests that most individuals – over the period of four years – were able to adjust well to the pandemic, while a smaller subgroup of individuals struggled considerably. Social support may be of interest as a protective factor and could aid policy makers during future crises.
Bente Zuijdam (Maastricht University – Faculty of Science and Engineering), Adriana Iamnitchi (Maastricht University – Faculty of Science and Engineering), Demographic and Political Differences in Twitter Abuse: The Case of Dutch Politicians
In this study we look at abusive messages on Twitter (now named X) addressed to Dutch politicians. We investigate the impact of the political stance and the politician’s demographic characteristics, such as gender or religion, on the levels of abuse targeted at them. To do this, we employ computational methods to analyse a dataset of more than a million tweets that mention Dutch politicians during the year 2022. We use gpt-3.5 (ChatGPT) to identify who is the target of the abusive message that mention the politician: is it the politician or a different group? We then focus only on the messages that attack the politician and determine which characteristics influence received abuse: are they demographic characteristics (such as gender, race, religion) or aspects of the the political/ideological platform?
We discover evidence that demographic characteristics and political stance matter in received abuse. We report the differences in the effect and significance for the demographic and engagement characteristics (gender, ethnicity, religion, number of tweets and number of followers) and political stance. Moreover, we show that these factors alone cannot fully account for received levels of abuse. We also found that extreme right and conservative orientated politicians are mentioned on the largest number of messages that contain abusive language targeted at other groups. Our study extends prior research and helps inform and guide policies for a diverse and safe digital political discourse.
