Statistical Studies of Income, Poverty and Inequality in Europe: Computing and Graphics in R using EU-SILC, 1st Edition (Hardback) book cover

Statistical Studies of Income, Poverty and Inequality in Europe

Computing and Graphics in R using EU-SILC, 1st Edition

By Nicholas T. Longford

Chapman and Hall/CRC

375 pages | 81 B/W Illus.

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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.

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

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

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.

About the Series

Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General