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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2402.00705 |
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| _version_ | 1866914724433625088 |
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| author | Sivak, Elizaveta Pankowska, Paulina Mendrik, Adrienne Emery, Tom Garcia-Bernardo, Javier Hocuk, Seyit Karpinska, Kasia Maineri, Angelica Mulder, Joris Nissim, Malvina Stulp, Gert |
| author_facet | Sivak, Elizaveta Pankowska, Paulina Mendrik, Adrienne Emery, Tom Garcia-Bernardo, Javier Hocuk, Seyit Karpinska, Kasia Maineri, Angelica Mulder, Joris Nissim, Malvina Stulp, Gert |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00705 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Combining the Strengths of Dutch Survey and Register Data in a Data Challenge to Predict Fertility (PreFer) Sivak, Elizaveta Pankowska, Paulina Mendrik, Adrienne Emery, Tom Garcia-Bernardo, Javier Hocuk, Seyit Karpinska, Kasia Maineri, Angelica Mulder, Joris Nissim, Malvina Stulp, Gert Machine Learning Databases 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. |
| title | Combining the Strengths of Dutch Survey and Register Data in a Data Challenge to Predict Fertility (PreFer) |
| topic | Machine Learning Databases |
| url | https://arxiv.org/abs/2402.00705 |