Ebook: Developing New National Data on Social Mobility: A Workshop Summary
Author: National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Committee on Population, Amy Smith
- Year: 2014
- Publisher: National Academies Press
- City: Washington, D.C., United States
- Edition: 1
- Language: English
- epub
Developing New National Data on Social Mobility summarizes a workshop convened in June 2013 to consider options for a design for a new national survey on social mobility. The workshop was sponsored by the National Science Foundation and convened by the Committee on Population and the Committee on National Statistics Division of Behavioral and Social Sciences and Education of the National Research Council. Scientific experts from a variety of social and behavioral disciplines met to plan a new national survey on social mobility that will provide the first definitive evidence on recent and long-term trends in social mobility, with the objectives of coming to an understanding of the substantial advances in the methods and statistics for modeling mobility, in survey methodology and population-based survey experiments, in opportunities to merge administrative and survey data, and in the techniques of measuring race, class, education, and income. The workshop also focused on documenting the state of understanding of the mechanisms through which inequality is generated in the past four decades. In the absence of a survey designed and dedicated to the collection of information to assess the status of social mobility, a wide variety of data sources designed for other purposes have been pressed into service in order to illuminate the state of social mobility and its trends. Developing New National Data on Social Mobility discusses the key decision points associated with launching a new national level survey of social mobility. This report considers various aspects of a major new national survey, including identifying relevant new theoretical perspectives and technical issues that have implications for modeling, measurement, and data collection.
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