Category: OSSC

We use the OSSC to run over 400 million univariate regressions using Dutch administrative data at the secure servers of Statistics Netherlands (CBS). We document how, when, and where the opportunity gap in the Netherlands opens up. We show how circumstances in childhood are associated with outcomes from birth through adulthood. We look at outcomes…

The research group POPNET has been analyzing the 2018 social network of the Netherlands to explore its network properties and their relationship with socio-economic indicators. Recently, data on the family, school, work, neighbour, and household connections became available annually from 2009 to 2022 via CBS’s microdata environment. The new primary goal of the group is…

Previous studies suggest that Autism Spectrum Disorder (ASD) is associated with cardiometabolic diseases, but the full risk profile in autistic individuals remainsunclear. Most existing evidence comes from small sample sizes. To address this, we conducted a large-scale cohort study using data from the entire population of theNetherlands, comprising, after exclusion criteria, over 8 million individuals.…

Agent-Based Models (ABMs) are increasingly being used to model interactions and dynamics within social systems. Consequently, they are also considered to be a very suitable tool for studying the dynamics of school choice and school segregation. However, empirically calibrated ABMs have received limited attention in this field. Mostly due to computational challenges (it is often…

The project focused on a question of how well can we determine ex ante individuals’ life outcomes? Traditional low dimensional models employed in the social sciences prove of limited effectiveness when given such objective. We seek to improve the performance of such models by applying modern machine learning methods on CBS microdata. Neural network models…

A data challenge is a competition where many researchers work on the same dataset to make the best predictions of a well-defined outcome. Data challenges led to major progress in data science by fostering the development of new algorithms with increasing predictive ability. Data challenges can also accelerate scientific progress because the ability to compare…

Calibration is a crucial step in developing agent-based models. Agent-based models are notorious for being difficult to calibrate as they can express various degrees of freedom when model parameters are unknown. Models that appear correctly calibrated to match macro-level observed data perform poorly when micro-level insights need to be inferred. As a result, policymakers cannot…