Volume 28 - Article 18 | Pages 505–546

Estimating global migration flow tables using place of birth data

By Guy J. Abel

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Date received:02 Aug 2012
Date published:15 Mar 2013
Word count:8343
Keywords:conditional maximisation, international migration, international migration flows, international migration stocks, log-linear model, migration estimation
DOI:10.4054/DemRes.2013.28.18
Updated Items:On June 3, 2013 several typing mistakes were corrected on pages 521, 522, and on page 524. On April 23, 2014 one mistake was corrected in equation (13) on page 516.
Additional files:flows (Excel file, 646 kB)
 

Abstract

Background: International migration flow data often lack adequate measurements of volume, direction and completeness. These pitfalls limit empirical comparative studies of migration and cross national population projections to use net migration measures or inadequate data.

Objective: This paper aims to address these issues at a global level, presenting estimates of bilateral flow tables between 191 countries.

Methods: A methodology to estimate flow tables of migration transitions for the globe is illustrated in two parts. First, a methodology to derive flows from sequential stock tables is developed. Second, the methodology is applied to recently released World Bank migration stock tables between 1960 and 2000 (Özden et al. 2011) to estimate a set of four decadal global migration flow tables.

Results: The results of the applied methodology are discussed with reference to comparable estimates of global net migration flows of the United Nations and models for international migration flows.

Comments: The proposed methodology adds to the limited existing literature on linking migration flows to stocks. The estimated flow tables represent a first-of-a-kind set of comparable global origin destination flow data.

Author's Affiliation

Guy J. Abel - Asian Demographic Research Institute, China [Email]

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» Non-zero trajectories for long-run net migration assumptions in global population projection models
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» Integrating uncertainty in time series population forecasts: An illustration using a simple projection model
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