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Estimating complete migration probabilities from grouped data: A methods protocol for developing a global Human

Sigurd Dyrting1, Andrew Taylor1

  • 1Northern Institute, Charles Darwin University, Darwin, Northern Territory, Australia.

Plos One
|December 10, 2024
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Summary
This summary is machine-generated.

Researchers developed a new method to estimate internal migration probabilities, creating a vital database for understanding population changes globally. This advances demographic analysis and future population projections.

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Area of Science:

  • Demography
  • Population Studies
  • Quantitative Social Science

Background:

  • Internal migration is a primary driver of population change globally.
  • Existing demographic frameworks lack comprehensive, standardized historical migration data.
  • Estimating migration probabilities is challenging due to data limitations like age aggregation and small sample sizes.

Purpose of the Study:

  • To develop a robust method for inferring age-origin-destination-specific migration probabilities.
  • To create a harmonized, multi-nation Human Internal Migration Database.
  • To address the need for accessible, high-quality internal migration data for research and policy.

Main Methods:

  • Extension of P-TOPALS and P-spline methods for smoothing migration probabilities.
  • Application to grouped age data to handle uncertainty and aggregation.
  • Utilizing microdata samples from IPUMS International for multi-country analysis.

Main Results:

  • A novel protocol for estimating complete age-origin-destination migration probabilities was developed.
  • The proposed method demonstrated superior accuracy and plausibility compared to a hybrid spline-parametric approach.
  • Complete migration probabilities were estimated for over 50 countries, forming a foundational database.

Conclusions:

  • The developed method effectively infers migration probabilities from challenging datasets.
  • The Human Internal Migration Database provides a crucial resource for demographic research.
  • This work significantly enhances the capacity for analyzing and projecting population dynamics influenced by internal migration.