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Do People Really Know Their Fertility
Intentions?
Correspondence between Self-Reported
Fertility Intentions and Narratives
Xu Xiao, Anne Gauthier, Gert Stulp, Antal van den Bosch
Edinburgh, Scotland
Jun 13, 2024
Background: fertility intention
● Prevalent uncertainty and instability in fertility intentions suggest they are constructed
through time and contextual (Bhrolcháin and Beaujouan 2019, Müller et al. 2022)
● Data collection method on fertility intentions has rarely been evaluated
● Natural Language Processing (NLP) methods enable us to automatically analyze
open-ended data
● Do people’s self-reported fertility intentions correspond with the conditions and
narratives reflected in open-ended answers?
Data: open questions
● 433 Dutch women between 21-44 in 2021 from LISS panel
● The standard survey question on fertility intention (GGS: FERXX):
○ Do you intend to have a child in the next three years?
○ If you don't have children for the next three years, would you still plan to
have children after that?
● After the module, we added a follow-up open question*
○ Can you tell us more about what makes you (un)certain about whether or
not to have children?
* Questionnaire and responses originally in Dutch.
Data: annotation
● Each response annotated by three individuals on our
interpretation of the most likely long-term fertility outcome
● Annotations follow the same scheme as the close-ended
questions
● Results compared to respondents’ self-reported intentions
Method: topic modeling and
classification model
● Binomial logistic regression
● Target: whether a respondent’s self-reported long-term fertility
intention is the same as our annotated label
● Features: age, education level, income, partnership, number of
children, topics in open answers (extracted by an NLP model)
Intentions match with or deviate from
our estimations
Discussion
● Fertility intention often not in line with life conditions
● Uncertainty in existing measurements could be underestimated
● People with strong positive fertility intentions are more likely to
have harder-to-achieve intentions
○ health-related issues or being students
● People with strong negative fertility intentions are more likely to
have more realistic expectations
Thanks for your attention!
Twitter (X): @Xiao_Xu_nidi
Email: xu@nidi.nl
LinkedIn: willskywalker
xuxiao.nl
Do People Really Know Their Fertility Intentions?  Correspondence between Self-Reported Fertility Intentions and Narratives

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Do People Really Know Their Fertility Intentions? Correspondence between Self-Reported Fertility Intentions and Narratives

  • 1.
  • 2. Do People Really Know Their Fertility Intentions? Correspondence between Self-Reported Fertility Intentions and Narratives Xu Xiao, Anne Gauthier, Gert Stulp, Antal van den Bosch Edinburgh, Scotland Jun 13, 2024
  • 3. Background: fertility intention ● Prevalent uncertainty and instability in fertility intentions suggest they are constructed through time and contextual (Bhrolcháin and Beaujouan 2019, Müller et al. 2022) ● Data collection method on fertility intentions has rarely been evaluated ● Natural Language Processing (NLP) methods enable us to automatically analyze open-ended data ● Do people’s self-reported fertility intentions correspond with the conditions and narratives reflected in open-ended answers?
  • 4. Data: open questions ● 433 Dutch women between 21-44 in 2021 from LISS panel ● The standard survey question on fertility intention (GGS: FERXX): ○ Do you intend to have a child in the next three years? ○ If you don't have children for the next three years, would you still plan to have children after that? ● After the module, we added a follow-up open question* ○ Can you tell us more about what makes you (un)certain about whether or not to have children? * Questionnaire and responses originally in Dutch.
  • 5. Data: annotation ● Each response annotated by three individuals on our interpretation of the most likely long-term fertility outcome ● Annotations follow the same scheme as the close-ended questions ● Results compared to respondents’ self-reported intentions
  • 6.
  • 7.
  • 8. Method: topic modeling and classification model ● Binomial logistic regression ● Target: whether a respondent’s self-reported long-term fertility intention is the same as our annotated label ● Features: age, education level, income, partnership, number of children, topics in open answers (extracted by an NLP model)
  • 9. Intentions match with or deviate from our estimations
  • 10. Discussion ● Fertility intention often not in line with life conditions ● Uncertainty in existing measurements could be underestimated ● People with strong positive fertility intentions are more likely to have harder-to-achieve intentions ○ health-related issues or being students ● People with strong negative fertility intentions are more likely to have more realistic expectations
  • 11. Thanks for your attention! Twitter (X): @Xiao_Xu_nidi Email: xu@nidi.nl LinkedIn: willskywalker xuxiao.nl
  翻译: