Statistical Studies of Income, Poverty and Inequality in Europe: Computing and Graphics in R using EU-SILC (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Longford, Nicholas T.

ISBN 10: 1466568321 / ISBN 13: 9781466568327

Published by Chapman and Hall/CRC, 2014

About this Item

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Bibliographic Details

• Title: Statistical Studies of Income, Poverty and Inequality in Europe: Computing and Graphics in R using EU-SILC (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2014
• Language: English
• ISBN 10: 1466568321
• ISBN 13: 9781466568327
• Binding: Hardcover
• Condition: like_new

About this title

Synopsis

There is no shortage of incentives to study and reduce poverty in our societies. Poverty is studied in economics and political sciences, and population surveys are an important source of information about it. The design and analysis of such surveys is principally a statistical subject matter and the computer is essential for their data compilation and processing.

Focusing on The European Union Statistics on Income and Living Conditions (EU-SILC), a program of annual national surveys which collect data related to poverty and social exclusion, Statistical Studies of Income, Poverty and Inequality in Europe: Computing and Graphics in R presents a set of statistical analyses pertinent to the general goals of EU-SILC.

The contents of the volume are biased toward computing and statistics, with reduced attention to economics, political and other social sciences. The emphasis is on methods and procedures as opposed to results, because the data from annual surveys made available since publication and in the near future will degrade the novelty of the data used and the results derived in this volume.

The aim of this volume is not to propose specific methods of analysis, but to open up the analytical agenda and address the aspects of the key definitions in the subject of poverty assessment that entail nontrivial elements of arbitrariness. The presented methods do not exhaust the range of analyses suitable for EU-SILC, but will stimulate the search for new methods and adaptation of established methods that cater to the identified purposes.

About the Author

Nicholas T. Longford is Director of SNTL Statistics Research and Consulting and Academic Visitor at Universitat Pompeu Fabra, Barcelona, Spain. His previous appointments include Educational Testing Service, Princeton, NJ, U.S.A., and De Montfort University, Leicester, U.K.