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
Replications. Fixed and Random
Estimation. Sample Quantities
Sampling Variation. Bootstrap
Fragility of Unbiasedness and Efficiency
Relative and log-Poverty Gaps
Lorenz Curve and Gini Coefficient
Income Inequality. Kernels, Scores and Scaling
Mixtures of Distributions
Components as Clusters
Analysis of Regions
Using Auxiliary Information
Regions of Spain
Regions of France
Absolute and Relative Rates of Transition
Partial Scoring of Transitions
Transitions over Several Years
Multivariate Normal Distributions
Mixture Models for the Countries in EU-SILC
Stability of Income
Confusion and Separation
The Capacity of Social Transfers
Impact of Social Transfers
Potential and Effectiveness
The Perils of Indices
Causes and Effects. Education and Income
Background and Motivation
Definitions and Notation
The Missing-Data Perspective
Propensity and Matched Pairs
Index of User-Defined R Functions
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.
"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