Combining the strengths of Dutch survey and register data in a data challenge to predict fertility (PreFer)

Elizaveta Sivak; Paulina Pankowska; Adriënne Mendrik; Tom Emery; Javier Garcia-Bernardo; Seyit Höcük; Kasia Karpinska; Angelica Maineri; Joris Mulder; Malvina Nissim; Gert Stulp

doi:10.1007/s42001-024-00275-6

Combining the strengths of Dutch survey and register data in a data challenge to predict fertility (PreFer)

Elizaveta Sivak^*, Paulina Pankowska, Adriënne Mendrik, Tom Emery, Javier Garcia-Bernardo, Seyit Höcük, Kasia Karpinska, Angelica Maineri, Joris Mulder, Malvina Nissim, Gert Stulp

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

The social sciences have produced an impressive body of research on determinants of fertility outcomes, or whether and when people have children. However, the strength of these determinants and underlying theories are rarely evaluated on their predictive ability on new data. This prevents us from systematically comparing studies, hindering the evaluation and accumulation of knowledge. In this paper, we present two datasets which can be used to study the predictability of fertility outcomes in the Netherlands. One dataset is based on the LISS panel, a longitudinal survey which includes thousands of variables on a wide range of topics, including individual preferences and values. The other is based on the Dutch register data which lacks attitudinal data but includes detailed information about the life courses of millions of Dutch residents. We provide information about the datasets and the samples, and describe the fertility outcome of interest. We also introduce the fertility prediction data challenge PreFer which is based on these datasets and will start in Spring 2024. We outline the ways in which measuring the predictability of fertility outcomes using these datasets and combining their strengths in the data challenge can advance our understanding of fertility behaviour and computational social science. We further provide details for participants on how to take part in the data challenge.

Original language	English
Pages (from-to)	1403-1431
Number of pages	29
Journal	Journal of Computational Social Science
Volume	7
Issue number	2
Early online date	13 Apr 2024
DOIs	https://doi.org/10.1007/s42001-024-00275-6
Publication status	Published - 2024

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Funding

This work is supported by a VIDI grant (VI.Vidi.201.119) from the Netherlands Organization for Scientific Research (NWO) to GS. The LISS panel data was collected by the non-profit research institute Centerdata (Tilburg University, the Netherlands). Funding for the panel\u2019s ongoing operations comes from the Domain Plan SSH and ODISSEI since 2019. The initial set-up of the LISS panel in 2007 was funded through the MESS project by the Netherlands Organization for Scientific Research (NWO). The ODISSEI Benchmark Platform, the ODISSEI-SICSS Summer School, and the development of the LISS harmonized dataset are financed by the ODISSEI Roadmap Project financed by NWO.

Funders	Funder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
ODISSEI-SICSS

Keywords

Benchmark
Data challenge
Fertility
Out-of-sample prediction
Register data
Survey data

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Cite this

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AU - Karpinska, Kasia

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Combining the strengths of Dutch survey and register data in a data challenge to predict fertility (PreFer)

Abstract

Bibliographical note

Funding

Keywords

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