The Community Demographic Model International Migration (CDM-IM) Dataset : / by Raphael J. Nawrotzki and Leiwen Jiang Generating Age and Gender Profiles of International Migration Flows

By: Contributor(s): Series: | NCAR Technical NotesBoulder, CO : National Center for Atmospheric Research (NCAR), 2014Content type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISSN:
  • 2153-2397
  • 2153-2400
Subject(s): Online resources: Abstract: The demographic features of a growing number of international migrants increasingly impacts socioeconomic development in various countries of the world. However, information on international migrant’s demographic characteristics is sparse. To develop the multiregional population/urbanization projections module of the NCAR Community Demographic Model (CDM) requires information on the age and gender composition of international migration streams. This paper reports on the methods used to generate the CDM International Migration dataset, which contains information on the age and gender profiles of international migrants with approximate global coverage. We use the raw data from the United Nations Global Migration Database (UNGMD) to derive the highest quality migrant stock for two time points closest to the year 2000. We reallocate the migrants into standardized age and gender categories by using information directly from the selected file, by borrowing information from files of other years, and by applying aggregated region-level information. After accounting for the impacts of mortality and fertility, we derive the age and gender profiles of net migrant flows between the two time points for each migration stream. The newly generated dataset contains age and gender profiles of international migrants for 3,713 country-level migration streams. Validation analyses against existing data sources and against the geographical, historical, and political context demonstrate reasonably high data quality. This data set not only meets our requirement for population projections, but can also be used for the study of international migration behavior among subgroups of various socioeconomic and environmental backgrounds.
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Item type Current library Call number Copy number Status Date due Barcode Item holds
REPORT REPORT NCAR Library Mesa Lab 03709 1 Available 50583020001776
Total holds: 0

2014 April

Technical Report

The demographic features of a growing number of international migrants increasingly impacts socioeconomic development in various countries of the world. However, information on international migrant’s demographic characteristics is sparse. To develop the multiregional population/urbanization projections module of the NCAR Community Demographic Model (CDM) requires information on the age and gender composition of international migration streams. This paper reports on the methods used to generate the CDM International Migration dataset, which contains information on the age and gender profiles of international migrants with approximate global coverage. We use the raw data from the United Nations Global Migration Database (UNGMD) to derive the highest quality migrant stock for two time points closest to the year 2000. We reallocate the migrants into standardized age and gender categories by using information directly from the selected file, by borrowing information from files of other years, and by applying aggregated region-level information. After accounting for the impacts of mortality and fertility, we derive the age and gender profiles of net migrant flows between the two time points for each migration stream. The newly generated dataset contains age and gender profiles of international migrants for 3,713 country-level migration streams. Validation analyses against existing data sources and against the geographical, historical, and political context demonstrate reasonably high data quality. This data set not only meets our requirement for population projections, but can also be used for the study of international migration behavior among subgroups of various socioeconomic and environmental backgrounds.

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