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Bilateral international migration flow estimates for 200 countries (1990-1995 to 2010-2015)

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posted on 04.08.2020 by Guy Abel, Joel E. Cohen
Version Details

Update of estimates of international migration flows from Abel & Cohen (2019) based on newly published International Migrant Stock (IMS2019) data inputs by the United Nations and the most recent WPP (WPP2019).

Also includes a correction for the treatment of Serbia, Montenegro, Sudan and South Sudan as separate countries prior to 2005. During 1990-1995, 1995-2000 and 2000-2005 periods there are now 198 countries, where the old three letter alpha numeric codes for Serbia and Montenegro (SCG) and Sudan (SUD) are used. The combination of these countries follows their representation in United Nations migrant stock data. In periods after 2000-2005 there are 200 countries, where Serbia, Montenegro, Sudan and South Sudan are separate (as in the paper and previous versions). A description of the changes in the estimates can be found here.

See Version 1 (link above) for estimates presented in the paper, based on WPP2017 and IMS2017.

Data Details

Row for each migration corridor - period combination (198 origins x 198 destinations x 3 periods + 200 origins x 200 destinations x 2 periods = 197,612).

year0 - first year of five year period
orig - origin ISO three letter country code
dest - destination ISO three letter country

Columns for estimates based on the following migration flow estimation methods:

Stock Differencing Approaches:


sd_drop_neg - see for example Beine, M., Docquier, F., & Özden, Ç. (2011). Diasporas. Journal of Development Economics, 95(1), 30–41. https://doi.org/10.1016/j.jdeveco.2009.11.004

sd_rev_neg - see for example Beine, M., & Parsons, C. R. (2015). Climatic Factors as Determinants of International Migration. The Scandinavian Journal of Economics, 117(2), 723–767. https://doi.org/10.1111/sjoe.12098

Migration Rate Approach:

mig_rate - see Dennett, A. (2016). Estimating an Annual Time Series of Global Migration Flows - An Alternative Methodology for Using Migrant Stock Data. In Global Dynamics (pp. 125–142). Chichester, UK: John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118937464.ch7

Demographic Accounting Approaches:

da_min_open - see Abel, G. J. (2013). Estimating global migration flow tables using place of birth data. Demographic Research, 28(March), 505–546. https://doi.org/10.4054/DemRes.2013.28.18

da_min_closed - see Abel, G. J. (2018). Estimates of Global Bilateral Migration Flows by Gender between 1960 and 2015. International Migration Review, (Fall), imre.12327. https://doi.org/10.1111/imre.12327

da_pb_closed - see Azose, J. J., & Raftery, A. E. (2018). Estimation of emigration, return migration, and transit migration between all pairs of countries. Proceedings of the National Academy of Sciences, 201722334. https://doi.org/10.1073/PNAS.1722334116

Funding

National Science Foundation of China funding research fund for International Young Scientists (Grant Agreement Number: 41650110483).

U.S. National Science Foundation grant DMS-1225529

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