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

(Universitat Pompeu Fabra, Barcelona, Spain)
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"Preface A majority of the population in the established members of the European Union (EU) has over the last few decades enjoyed prosperity, comfort and freedom from existential threats, such as food shortage, various forms of destruction of our lifes, homes and other possessions, judicial excesses or barred access to vital services, such as health care, education, insurance and transportation. New technologies, epitomised by the internet and the mobile phone, but also micro-surgery and cheap long-distance travel, have transformed the ways we access information, communicate with one another, obtain health care, education, training and entertainment, and how public services and administration operate. Our economies and societies have a great capacity to invent, apply inventions and package them in forms amenable for personal use by the masses. These great achievements have not been matched in one important area, namely, tackling poverty. Poverty is about as widespread in our societies as it was a few decades ago when, admittedly, our standards for what amounts to prosperity were somewhat more modest (Atkinson, 1998). Yet, there is no shortage of incentives to reduce poverty in our societies. The purely economic ones are that the poor are poor consumers, and much of our prosperity is derived from the consumption by others; the poor are poor contributors to the public funds (by taxes on income, property and consumption), which pay for some of the vital services and developments. More profound concerns are that the poor are a threat to the social cohesion, are more likely to be attracted to criminal and other illegal activities, and represent a threat to all those who are not poor, because we would not like ourselves and those dear to us to live in such circumstances"--

"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"--

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.

Arvustused

"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 HorvitzThompson estimation and is methodologically solid. The presented methods are illustrative in the use of software codes, figures, tables, and graphics." International Statistical Review, 2015

Preface xiii
List of Figures xvii
List of Tables xxi
1 Poverty Rate 1(36)
1.1 Background
1(6)
1.1.1 Median and Percentiles
3(1)
1.1.2 Populations and Samples
4(3)
1.2 Income Distribution
7(5)
1.2.1 Poverty-Rate Curves
8(4)
1.3 Comparisons
12(2)
1.4 Sampling Weights
14(6)
1.4.1 Trimming
19(1)
1.A Appendix. Programming Notes
20(17)
1.A.1 Data Input
20(3)
1.A.2 Estimating a Quantile
23(2)
1.A.3 Summarising the Weights
25(2)
1.A.4 Some Auxiliary Functions
27(2)
1.A.5 Plotting a Set of Curves
29(2)
1.A.6 Final Touch in a Diagram
31(4)
1.A.7 Countries in EU-SILC
35(2)
2 Statistical Background 37(16)
2.1 Replications. Fixed and Random
37(1)
2.2 Estimation. Sample Quantities
38(2)
2.2.1 Weighted Sample Median
39(1)
2.3 Sampling Variation. Bootstrap
40(5)
2.4 Horvitz-Thompson Estimator
45(2)
2.5 Fragility of Unbiasedness and Efficiency
47(2)
2.5.1 Lognormal Distribution
48(1)
2.A Appendix
49(4)
2.A.1 Bootstrap
49(3)
2.A.2 Moments of the Lognormal Distribution
52(1)
3 Poverty Indices 53(44)
3.1 Poverty Index
53(7)
3.1.1 Which Kernel?
56(4)
3.2 Relative and Log-Poverty Gaps
60(3)
3.3 Lorenz Curve and Gini Coefficient
63(10)
3.4 Scaled Quantiles
73(2)
3.4.1 Permutation Test
75(1)
3.5 Income Inequality. Kernels, Scores and Scaling
75(2)
3.A Appendix
77(20)
3.A.1 Negative Values of eHI
77(3)
3.A.2 Newton Method in R
80(2)
3.A.3 More on Poverty Indices
82(5)
3.A.4 Lorenz Curve and Gini Coefficient
87(4)
3.A.5 Scaled Quantiles
91(2)
3.A.6 Permutation Test
93(4)
4 Mixtures of Distributions 97(38)
4.1 Introduction
97(3)
4.2 Fitting Mixtures
100(2)
4.3 Examples
102(11)
4.3.1 Exploration of the Fitted Probabilities
104(6)
4.3.2 Results for Several Countries
110(3)
4.4 Improper Component
113(3)
4.5 Components as Clusters
116(5)
4.5.1 Confusion Matrix
120(1)
4.A Appendix. Programming Notes
121(14)
4.A.1 EM Algorithm for Mixtures of Normal Distributions
121(10)
4.A.2 Improper Component
131(1)
4.A.3 Confusion Index
132(3)
5 Regions 135(40)
5.1 Introduction
135(2)
5.2 Analysis of Regions
137(5)
5.3 Small-Area Estimation
142(4)
5.4 Using Auxiliary Information
146(2)
5.5 Regions of Spain
148(8)
5.5.1 Composite Estimation of the Poverty Rates
150(6)
5.6 Regions of France
156(2)
5.7 Simulations
158(2)
5.A Appendix
160(1)
5.A.1 Estimation of Region-Level (Co-)Variances
160(1)
5.A.2 Report Card for Austria and Its Regions
161(1)
5.B Programming Notes
161(14)
5.B.1 Composite Estimation
168(2)
5.B.2 Multivariate Composition
170(3)
5.B.3 Graphics
173(2)
6 Transitions 175(40)
6.1 Panel Data
175(7)
6.2 Absolute and Relative Rates of Transition
182(4)
6.3 Substantial Transitions
186(5)
6.4 Partial Scoring of Transitions
191(3)
6.5 Transitions over Several Years
194(3)
6.6 Imputed Patterns
197(6)
6.A Appendix. Programming Notes
203(12)
6.A.1 National Panel Databases
203(4)
6.A.2 Rates of Transition
207(8)
7 Multivariate Mixtures 215(36)
7.1 Multivariate Normal Distributions
215(2)
7.1.1 Finite Mixtures of Normal Distributions
216(1)
7.2 EM Algorithm
217(1)
7.3 Example
218(9)
7.4 Improper Component
227(3)
7.5 Mixture Models for the Countries in EU-SILC
230(2)
7.6 Stability of Income
232(2)
7.7 Confusion and Separation
234(2)
7.A Appendix
236(1)
7.A.1 What Can Go Wrong in Iterations of EM
236(1)
7.B Programming Notes
237(14)
7.B.1 Improper Component
245(2)
7.B.2 Stability of Income
247(2)
7.B.3 Confusion Index
249(2)
8 Social Transfers 251(34)
8.1 Capacity of Social Transfers
251(3)
8.2 Impact of Social Transfers
254(7)
8.3 Potential and Effectiveness
261(3)
8.4 Nonparametric Regression
264(8)
8.4.1 Smoothing Sequences
270(2)
8.5 Perils of Indices
272(2)
8.A Appendix. Programming Notes
274(11)
8.A.1 Nonparametric Regression
277(4)
8.A.2 Graphics for Nonparametric Regression
281(4)
9 Causes and Effects. Education and Income 285(42)
9.1 Background and Motivation
285(2)
9.2 Definitions and Notation
287(3)
9.2.1 Treatment-Assignment Mechanism
288(2)
9.3 Missing-Data Perspective
290(3)
9.4 Propensity and Matched Pairs
293(3)
9.4.1 Regression as an Alternative
294(2)
9.5 Application
296(12)
9.5.1 Results for Other Countries
304(1)
9.5.2 Regression of Outcome on Treatment and Background
305(2)
9.5.3 Potential Versions of Variables
307(1)
9.A Appendix. Programming Notes
308(19)
9.A.1 Second-Level Functions
313(9)
9.A.2 Graphics for the Balance Diagnostics
322(5)
Epilogue 327(4)
Bibliography 331(10)
Subject Index 341(12)
Index of User-Defined R Functions 353
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.