Statistical Studies of Income, Poverty and Inequality in Europe : Computing and Graphics in R using EU-SILC book cover
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Statistical Studies of Income, Poverty and Inequality in Europe
Computing and Graphics in R using EU-SILC




ISBN 9781466568327
Published July 7, 2014 by Chapman and Hall/CRC
375 Pages - 81 B/W Illustrations

 
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Book Description

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.

Table of Contents

Poverty Rate
Background
Income distribution
Comparisons
Sampling Weights
Programming Notes

Statistical Background
Replications. Fixed and Random
Estimation. Sample Quantities
Sampling Variation. Bootstrap
Horvitz-Thompson Estimator
Fragility of Unbiasedness and Efficiency

Poverty Indices
Poverty Index
Relative and log-Poverty Gaps
Lorenz Curve and Gini Coefficient
Scaled Quantiles
Income Inequality. Kernels, Scores and Scaling

Mixtures of Distributions
Introduction
Fitting Mixtures
Examples
Improper Component
Components as Clusters
Programming Notes

Regions
Introduction
Analysis of Regions
Small-Area Estimation
Using Auxiliary Information
Regions of Spain
Regions of France
Simulations
Programming Notes

Transitions
Panel Data
Absolute and Relative Rates of Transition
Substantial Transitions
Partial Scoring of Transitions
Transitions over Several Years
Imputed Patterns
Programming Notes

Multivariate Mixtures
Multivariate Normal Distributions
EM Algorithm
Example
Improper Component
Mixture Models for the Countries in EU-SILC
Stability of Income
Confusion and Separation
Programming Notes

Social Transfers
The Capacity of Social Transfers
Impact of Social Transfers
Potential and Effectiveness
Nonparametric Regression
The Perils of Indices
Programming Notes

Causes and Effects. Education and Income
Background and Motivation
Definitions and Notation
The Missing-Data Perspective
Propensity and Matched Pairs
Application
Programming Notes

Epilogue

Bibliography

Subject Index

Index of User-Defined R Functions

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Author(s)

Biography

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.

Reviews

"In this book, the analyses of surveys conducted by EU-SILC are carried out using the statistical language R. … One noteworthy section … is devoted to Horvitz–Thompson estimation and is methodologically solid. … The presented methods … are illustrative in the use of software codes, figures, tables, and graphics."
International Statistical Review, 2015