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Record W3116580741 · doi:10.1002/psp.2413

Housing costs, self‐employment, and fertility

2020· article· en· W3116580741 on OpenAlexaff
Richard Florida, Charlotta Mellander, Karen King

Bibliographic record

VenuePopulation Space and Place · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChildbirthFertilityDemographic economicsEconomicsHuman capitalWork (physics)Panel dataLabour economicsTotal fertility rateEconomic growthPopulationDemographySociologyPregnancyFamily planningResearch methodology

Abstract

fetched live from OpenAlex

Abstract The decline in fertility across advanced nations is a well‐known fact. Becker famously argued that declining rates of childbirth were the by‐product of higher levels of economic development and human capital. Recently, it has been suggested that two additional factors might lead to declining rates of childbirth—the higher housing costs of expensive cities and the change in the nature of work and employment from more regular and secure full‐time work to less secure arrangements, like self‐employment. Our research examines the effect of these two classes of factors—housing costs and self‐employment—on fertility, in regard to both the rate of childbirth and the delay in the age at which people have children. We use detailed panel data covering all Swedish individuals in their prime childbearing years (20–45) for the 10‐year period 2007–2016. Our findings indicate that the likelihood of having a child is affected negatively by increased housing costs and positively by self‐employment. Both result in a delay in the parental age at which children are born. Of the two, self‐employment has a relatively large effect.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.304
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2020
Admission routes1
Has abstractyes

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