Book section : Chapter
Leveraging digital and computational demography for policy insights
- Abstract:
- Situated at the intersection of the computational and demographic sciences, digital and computational demography explores how new digital data streams and computational methods advance the understanding of population dynamics, along with the impacts of digital technologies on population outcomes, e.g. linked to health, fertility and migration. Encompassing the data, methodological and social impacts of digital technologies, we outline key opportunities provided by digital and computational demography for generating policy insights. Within methodological opportunities, individual-level simulation approaches, such as microsimulation and agent-based modelling, infused with different data, provide tools to create empirically informed synthetic populations that can serve as virtual laboratories to test the impact of different social policies (e.g. fertility policies, support for the elderly or bereaved people). Individual-level simulation approaches allow also to assess policy-relevant questions about the impacts of demographic changes linked to ageing, climate change and migration. Within data opportunities, digital trace data provide a system for early warning with detailed spatial and temporal granularity, which are useful to monitor demographic quantities in real time or for understanding societal responses to demographic change. The demographic perspective highlights the importance of understanding population heterogeneity in the use and impacts of different types of digital technologies, which is crucial towards building more inclusive digital spaces.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
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(Preview, Version of record, pdf, 365.6KB, Terms of use)
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- Publisher copy:
- 10.1007/978-3-031-16624-2_17
Authors
Contributors
+ Bertoni, E
- Role:
- Editor
+ Fontana, M
- Role:
- Editor
+ Gabrielli, L
- Role:
- Editor
+ Signorelli, S
- Role:
- Editor
+ Vespe, M
- Role:
- Editor
- Publisher:
- Springer
- Host title:
- Handbook of Computational Social Science for Policy
- Pages:
- 327-344
- Chapter number:
- 17
- Publication date:
- 2023-01-24
- Edition:
- 1st
- DOI:
- ISBN-10:
- 303116623X
- ISBN-13:
- 9783031166235
- Language:
-
English
- Keywords:
- Subtype:
-
Chapter
- Pubs id:
-
1325056
- Local pid:
-
pubs:1325056
- Deposit date:
-
2023-01-24
Terms of use
- Copyright holder:
- Kashyap and Zagheni
- Copyright date:
- 2023
- Rights statement:
- © 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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