Analysis of poverty data by small area estimation

www.ebook3000.com Analysis of Poverty Data by Small Area Estimation www.ebook3000.com WILEY SERIES IN SURVEY METHODOLOGY Established in part by Walter A Shewhart and Samuel S Wilks Editors: Mick P Couper, Graham Kalton, Lars Lyberg, J N K Rao, Norbert Schwarz, Christopher Skinner Editor Emeritus: Robert M Groves A complete list of the titles in this series appears at the end of this volume Analysis of Poverty Data by Small Area Estimation Edited by Monica Pratesi University of Pisa, Italy www.ebook3000.com This edition first published 2016 © 2016 John Wiley and Sons Ltd Registered offic John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and Patents Act 1988 All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents Act 1988, without the prior permission of the publisher Wiley also publishes its books in a variety of electronic formats Some content that appears in print may not be available in electronic books Designations used by companies to distinguish their products are often claimed as trademarks All brand names and product names used in this book are trade names, service marks, trademarks or registered trademarks of their respective owners The publisher is not associated with any product or vendor mentioned in this book Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose It is sold on the understanding that the publisher is not engaged in rendering professional services and neither the publisher nor the author shall be liable for damages arising herefrom If professional advice or other expert assistance is required, the services of a competent professional should be sought Library of Congress Cataloging-in-Publication Data applied for A catalogue record for this book is available from the British Library ISBN: 9781118815014 Set in 10/12pt, TimesLTStd by SPi Global, Chennai, India 2016 Contents Foreword xv Preface xvii Acknowledgements xxiii About the Editor xxv List of Contributors 1.1 1.2 1.3 1.4 xxvii Introduction on Measuring Poverty at Local Level Using Small Area Estimation Methods Monica Pratesi and Nicola Salvati Introduction Target Parameters 1.2.1 Definitio of the Main Poverty Indicators 1.2.2 Direct and Indirect Estimate of Poverty Indicators at Small Area Level Data-related and Estimation-related Problems for the Estimation of Poverty Indicators Model-assisted and Model-based Methods Used for the Estimation of Poverty Indicators: a Short Review 1.4.1 Model-assisted Methods 1.4.2 Model-based Methods References 1 2 7 12 15 Part I DEFINITION OF INDICATORS AND DATA COLLECTION AND INTEGRATION METHODS Regional and Local Poverty Measures Achille Lemmi and Tomasz Panek 21 2.1 2.2 Introduction Poverty – Dilemmas of Definition 21 22 www.ebook3000.com vi 2.3 2.4 2.5 2.6 2.7 Contents Appropriate Indicators of Poverty and Social Exclusion at Regional and Local Levels 2.3.1 Adaptation to the Regional Level Multidimensional Measures of Poverty 2.4.1 Multidimensional Fuzzy Approach to Poverty Measurement 2.4.2 Fuzzy Monetary Depth Indicators Co-incidence of Risks of Monetary Poverty and Material Deprivation Comparative Analysis of Poverty in EU Regions in 2010 2.6.1 Data Source 2.6.2 Object of Interest 2.6.3 Scope and Assumptions of the Empirical Analysis 2.6.4 Risk of Monetary Poverty 2.6.5 Risk of Material Deprivation 2.6.6 Risk of Manifest Poverty Conclusions References 23 23 25 25 26 30 31 31 31 32 32 33 37 38 39 Administrative and Survey Data Collection and Integration Alessandra Coli, Paolo Consolini and Marcello D’Orazio 41 3.1 3.2 Introduction Methods to Integrate Data from Different Data Sources: Objectives and Main Issues 3.2.1 Record Linkage 3.2.2 Statistical Matching Administrative and Survey Data Integration: Some Examples of Application in Well-being and Poverty Studies 3.3.1 Data Integration for Measuring Disparities in Economic Well-being at the Macro Level 3.3.2 Collection and Integration of Data at the Local Level Concluding Remarks References 41 3.3 3.4 43 43 46 50 51 53 56 57 Small Area Methods and Administrative Data Integration Li-Chun Zhang and Caterina Giusti 61 4.1 4.2 Introduction Register-based Small Area Estimation 4.2.1 Sampling Error: A Study of Local Area Life Expectancy 4.2.2 Measurement Error due to Progressive Administrative Data Administrative and Survey Data Integration 4.3.1 Coverage Error and Finite-population Bias 4.3.2 Relevance Error and Benchmarked Synthetic Small Area Estimation 4.3.3 Probability Linkage Error Concluding Remarks References 61 63 63 65 68 68 70 75 80 81 4.3 4.4 vii Contents Part II IMPACT OF SAMPLING DESIGN, WEIGHTING AND VARIANCE ESTIMATION 5.1 5.2 5.3 Impact of Sampling Designs in Small Area Estimation with Applications to Poverty Measurement Jan Pablo Burgard, Ralf Münnich and Thomas Zimmermann 85 Introduction Sampling Designs in our Study Estimation of Poverty Indicators 5.3.1 Design-based Approaches 5.3.2 Model-based Estimators Monte Carlo Comparison of Estimation Methods and Designs Summary and Outlook Acknowledgements References 85 87 90 90 92 96 105 106 106 Model-assisted Methods for Small Area Estimation of Poverty Indicators Risto Lehtonen and Ari Veijanen 109 6.1 Introduction 6.1.1 General 6.1.2 Concepts and Notation Design-based Estimation of Gini Index for Domains 6.2.1 Estimators 6.2.2 Simulation Experiments 6.2.3 Empirical Application Model-assisted Estimation of At-risk-of Poverty Rate 6.3.1 Assisting Models in GREG and Model Calibration 6.3.2 Generalized Regression Estimation 6.3.3 Model Calibration Estimation 6.3.4 Simulation Experiments 6.3.5 Empirical Example Discussion 6.4.1 Empirical Results 6.4.2 Inferential Framework 6.4.3 Data Infrastructure References 109 109 110 111 111 114 116 117 117 119 120 122 123 124 124 125 125 126 Variance Estimation for Cumulative and Longitudinal Poverty Indicators from Panel Data at Regional Level Gianni Betti, Francesca Gagliardi and Vijay Verma 129 Introduction Cumulative vs Longitudinal Measures of Poverty 7.2.1 Cumulative Measures 7.2.2 Longitudinal Measures Principle Methods for Cross-sectional Variance Estimation 129 130 130 131 131 5.4 5.5 6.2 6.3 6.4 7.1 7.2 7.3 www.ebook3000.com viii 7.4 7.5 7.6 7.7 7.8 7.9 Contents Extension to Cumulation and Longitudinal Measures Practical Aspects: Specification of Sample Structure Variables Practical Aspects: Design Effects and Correlation 7.6.1 Components of the Design Effect 7.6.2 Estimating the Components of Design Effect 7.6.3 Estimating other Components using Random Grouping of Elements Cumulative Measures and Measures of Net Change 7.7.1 Estimation of the Measures 7.7.2 Variance Estimation An Application to Three Years’ Averages 7.8.1 Computation Given Limited Information on Sample Structure in EU-SILC 7.8.2 Direct Computation 7.8.3 Empirical Results Concluding Remarks References 133 134 136 136 138 139 140 140 141 141 141 144 145 146 147 Part III SMALL AREA ESTIMATION MODELING AND ROBUSTNESS 8.1 8.2 8.3 8.4 8.5 Models in Small Area Estimation when Covariates are Measured with Error Serena Arima, Gauri S Datta and Brunero Liseo Introduction Functional Measurement Error Approach for Area-level Models 8.2.1 Frequentist Method for Functional Measurement Error Models 8.2.2 Bayesian Method for Functional Measurement Error Models Small Area Prediction with a Unit-level Model when an Auxiliary Variable is Measured with Error 8.3.1 Functional Measurement Error Approach for Unit-level Models 8.3.2 Structural Measurement Error Approach for Unit-level Models Data Analysis 8.4.1 Example 1: Median Income Data 8.4.2 Example 2: SAIPE Data Discussion and Possible Extensions Acknowledgements Disclaimer References 151 151 153 154 156 156 157 160 162 162 165 169 169 170 170 Robust Domain Estimation of Income-based Inequality Indicators Nikos Tzavidis and Stefano Marchetti 171 9.1 9.2 9.3 Introduction Definition of Income-based Inequality Measures Robust Small Area Estimation of Inequality Measures with M-quantile Regression Mean Squared Error Estimation 171 172 9.4 173 176 Appendix on Software and Codes Used in the Book 21.4.13.3 425 Required Data and their Structure Not applicable 21.4.13.4 How to Use and Run the Script Available on the Website Not applicable Users can use the guidelines provided by the R package listed in Section 21.4.13.2 21.4.13.5 Output Provided by the Script Not applicable 21.4.14 A Quick Guide to Chapter 18 - (Small Area Estimation Using Both Survey and Census Unit Record Data: Links, Alternatives, and the Central Roles of Regression and Contextual Variables) 21.4.14.1 Analysis Provided by the Script Not applicable The script associated with this chapter is not available on the website because the World Bank software PovMap has been used This software can be downloaded from the following link: http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/ EXTPROGRAMS/EXTPOVRES/0,,contentMDK:22717057∼pagePK:64168182∼piPK: 64168060∼theSitePK:477894,00.html System requirements for using the package are: Microsoft Windows® NT or later; and a minimum memory requirement of 128M 21.4.14.2 Required Software and Packages See Section 21.4.14.1 21.4.14.3 Required Data and their Structure Not applicable 21.4.14.4 How to Use and Run the Script Available on the Website Not applicable Users can use the guidelines provided by PovMap 21.4.14.5 Output Provided by the Script Not applicable www.ebook3000.com 426 Analysis of Poverty Data by Small Area Estimation References Arima, S., Datta, G.S., and Liseo, B 2015 Bayesian estimators for small area models when auxiliary information is measured with error Scandinavian Journal of Statistics 42, 518–529 Battese, G E., Harter, R M & Fuller, W A (1988), An error-components model for prediction of county crop areas using survey and satellite data, Journal of the American Statistical Association, 83, 28–36 Breidenbach J (2015) Packae ‘JoSAE’ (https://cran.r-project.org/web/packages/JoSAE/JoSAE.pdf) Breslow N E., Clayton D G (1993) Approximate Inference in Generalized Linear Mixed Models, Journal of the American Statistical Association, Vol 88, No 421, pp 9–25 Molina I and Marhuenda Y 2015 sae: An R package for small area estimation The R Journal 7(1), 81–98 Fay R.E., Diallo M (2015) Package ‘sae2’ (https://cran.r-project.org/web/packages/sae2/sae2.pdf) Lopez-Vizcaino E., Lombardia M.J and Morales D (2013a) Package ‘mme’ (https://cran.r-project.org/web/ packages/mme/mme.pdf) Lopez-Vizcaino, ME, Lombardia, MJ and Morales, D (2013b) Multinomial-based small area estimation of labour force indicators Statistical Modelling, 13, 153–178 Pratesi M, Ranalli MG, and Salvati N 2009 Nonparametric M-quantile regression using penalised splines Journal of Nonparametric Statistics 21(3), 287–304 Salvati N, Chandra H, Ranalli MG, and Chambers R 2010 Small area estimation using a nonparametric model-based direct estimator Computational Statistics & Data Analysis 54(9), 2159–2171 Sarndal, C E (1984) Design-consistent versus model-dependent estimation for small domains, Journal of the American Statistical Association, 79, 624–631 Schoch T 2015 rsae: Robust small area estimation R package version 0.1-5 Author Index Abel-Smith, B., 22, 25 Aitchinson, J., 306 Akaike, H., 359 Alfons, A., 96, 301, 302 Alkire, S., 25, 400 Anselin, L., 210 Arima, S., 151–170, 415, 416 Arnold, S., 207 Asher, J.L., 365 Atkinson, A.B., 2, 21, 25, 85, 86 Ballin, M., 49 Banerjee, S., 210, 228 Barr, R.S., 47 Basel, W.W., 349–376, 394 Battese, G., 157, 187, 208, 228, 282, 407 Bayarri, M., 169 Beaumont, J.F., 11 Belin, T.R., 46 Bell, W.R., 349–376, 102, 166–169, 281 Berg, E., 279–296 Berger, Y.G., 113, 120, 227, 357 Betti, G., 129–147, 25, 26, 30, 195, 252 Biggeri, L., xxii, xxix Bocci, C., 245–259 Bourguignon, F., 25 Bradshaw, J., 25 Bramley, G., 328 Branscum, A.J., 300 Breckling, J., 13, 174 Breidt, F.J., 187–202 Brundson, C., 15 Burgard, J.P., 85–106 Carroll, R.J., 151, 152 Carter, G., 386 Casas-Cordero, C., 379–401 Cerioli, A., 26 Chambers, R., 263–276, 279–296, 9, 12–15, 45, 74, 80, 114, 183–184, 192, 194, 198, 200, 201, 228, 246, 248–251, 327, 328, 334, 418 Chandra, H., 263–276, 279–296, 13, 14, 194, 198, 227, 246, 248–251 Chatterjee, S., 400 Cheli, B., 3, 26 Cheng, C.L., 155 Chiang, C.L., 63 Choudry, G.H., 210 Christen, P., 45 Christiaensen, L., 344 Claeskens, G., 190 Clemen, R.L., 309 Cockran, W.G., 3, 265 Coelho, P.S., 228 Coli, A., 41–57 Consolini, P., 41–57 Contiero, P., 76 Copeland, P., 21 Costa, A., 88 Analysis of Poverty Data by Small Area Estimation, First Edition Edited by Monica Pratesi © 2016 John Wiley & Sons, Ltd Published 2016 by John Wiley & Sons, Ltd Companion Website: www.wiley.com/go/pratesi/poverty www.ebook3000.com 428 Cressie, N., 11, 210, 228, 246, 247, 329, 338 Cruse, C., 369 D’Agostino, A., 405 Da Silva, C.Q., 300 Datta, G.S., 151–170, 189, 211, 214, 357, 366 Davidson, R., 301 Deltas, G., 301, 302 Demidenko, E., 118, 208 Demombynes, G., 332, 339 Deutsch, J., 25 Deville, J.C., 91, 266 Diallo, M., 324, 407 Di Consiglio, L., 227 Dick, P., 208 D’Orazio, M., 41–57 Dreassi, E., 191, 281, 292, Drewnowski, J., 22 Duan, N., 14, 175 Efron, B., 335, 385, 386, 395 Elbers, C., 12, 92, 172, 227, 316, 324, 328, 331, 332, 335, 339, 344, 345, 393 Elfadaly, F.G., 310 Elfeky, M., 45 Encina, J., 379–401 Ericksen, E.P., 208 Esteban, M.D., 207–224 Fabrizi, E., 299–313, 9–11, 268, 270, 281 Falorsi, P.D., 110, Fay, R., 68, 93, 153, 208, 228, 281, 317, 356, 394 Faraway, J.J., 96 Fellegi, I.P., 44 Fellner, W.H., 194 Fernandez, G., 45 Ferrante, M.R., 299–313 Ferrari, S.L.P., 299, 300, 303 Ferraz, V.R.S., 279, 281 Ferretti, C., 317, 322 Fesseau, M., 52 Findley, D.F., 355 Fisher, G.M., 365 Fosen, J., 66, 68, 69 Author Index Foster, J., 2, 25, 92, 299, 316, 336, 400 Franco, C., 374 Frey, J., 11 Fujii, T., 328, 332 Fuller, W.A., 153, 279, 281, 282, 288 Fusco, A., 300 Gabler, S., 88 Gagliardi, F., 129–147, 405 Gelfand, A.E., 339 Gelman, A., 303, 312, 359 Ghosh, M., 157, 159–162, 208, 211, 280, 281, 374 Giusti, C., 61–81 Goebel, J., 288 Goldstein, H., 208 Gosh, M., Graf, E., 91 Graf, M., 87 Graybill, F.A., 207 Griswold, M Grosh, M., 246, 251 Hagenaars, A.J.M., 22, 25, 269 Hansen, M.H., Harms, T., 264, 265 Haslet, S.J., 327–346, 50, 279, 280, 286, 295 Hastie, T.J., 246, 247 Hawala, S., 303, 374 Hedlin, D., 65 Henderson, C., 12 Hentschel, J., 343 Hocking, R.R., 207 Hogan, H., 68 Horvitz, D.G., 89, 90, 110, 152, 251, 265 Huang, E., 166, 167, 281, 306, 361, 374 Isidro, M., 279, 280, 334, 341, 343, 344 Jaro, M.A., 44 Jasso, J., 301, 302 Jiang, J., 9, 11, 92–95, 152, 155, 190, 192, 208, 228, 230, 280 429 Author Index Kammann, E., 191, 246, 247 Kaufman, L., 253 Kieschnick, R., 299 Kim, J.K., 50, 80 Kish, L., 3, 70, 73, 136, 138, 139 Kloek, W., 57 Kolb, J.P., 85 Lahiri, P., 379–401, 9, 45, 92, 93, 95, 152, 189, 192, 208, 214, 280, 357, 366, 374 Langel, M., 112, 124, 301, 302 Lehtonen, R., 109–126, 8, 10, 90–92, 227, 334, 413 Lemmi, A., 21–39, Lepik, N., 105 Linkletter, C.D., 65 Liseo, B., 151–170 Little, R.J.A., 47 Liu, B., 300, 302 Lohr, S.L., 140, 153, 154, 155, 162–168 Longford, N.T., 208, 229 Luna-Hernandez, A., 74 Lunn, D., 312 Maples, J.J., 349–376 Marchetti, S., 171–185, 114 Marhuenda, Y., 211, 228, 229, 407 Marker, D.A., 95 Marshall, A., 22 Martin, N., 199, 202 McCullogh, C.E., 208 Militino, A.F., 191 Minot, N., 343 Molina, I., 315–324, 12, 13, 90, 92, 93, 96, 105, 172, 192, 199, 202, 210, 227, 229, 263, 295, 328, 329, 332–334, 336, 339–342, 393, 394, 407 Morales, D., 207–224 Moriarity, C., 48 Moura, F.A.S., 210, 279, 281 Munnich, R., 85–106 Myrskyla, M., 92 Nelsen, R.B., 300 Neri, L., 246, 252, 258, 405 Neter, J., 45 Nirel, R., 68 Noble, A., 70, Olsen, M.K., 14 O’Muircheataigh, C., 140 Opsomer, J.D., 187–202, 248, 249 Osier, G., 90, 91, 133 Ospina, R., 303 Otto, M.C., 374 Pagliarella, M.C., 227–242 Panek, T., 21–39 Pebesma, E., 237 Pérez, A., 207–224 Petrucci, A., 245–259, 14, 15, 210, 228 Pfeffermann, D., 10, 14, 70, 92, 119, 125, 152, 187, 208, 211, 227–229, 234, 281, 327, 328, 334, 336, 340, 343 Potter, F.J., 383 Prasad, N.G.N., 8, 12, 155–157, 168, 188, 214, 227, 229, 283, 257, 366, 424 Pratesi, M., xxiii–xxvii, 1–15, 46, 47, 49, 235, 265, 266 Pudney, S., 329 Puntanen, S., 339 Purcell, N.J., 70, 73 Ranalli, M.G., 187–202 Rao, J.N.K., 315–324, 3, 7–13, 50, 64, 69, 90–96, 102, 105, 140, 152, 155–157, 168, 170, 172, 188, 189, 192, 194, 198, 201, 202, 208, 2010, 211, 214, 227–233, 263, 265, 282–286, 295, 299, 304, 327–336, 339–345, 357, 366, 374, 393, 394, 407, 424 Rassler, S., 55 Raymer, J., 70 Reis, F., 57 Rencher, A.C., 207 Renssen, R.H., 49, 50 Rivest, L.P., 279 Royall, R.M., 202, 251 Rubin, D.B., 46–49 Rueda, C., 192, 265 Ruppert, D., 189, 190, 248 www.ebook3000.com 430 Author Index Saei, A., 263, 265 Salvati, N., 1–15, 263–276, 193, 194, 198–202, 210, 228, 229, 285, 418 Salvatore, R., 227–242 Samart, K., 80 Sariyar, M., 45 Särndal, C.E., 3, 4, 49, 91, 92, 110, 111, 266, 407 Scheuren, F., 45 Searle, S.R., 207, 208 Seber, G.A.F., 207 Selukar, R., 237 Shorrocks, A.F., 26 Silver, H., 22 Simpson, L., 70 Singh, A.C., 48, 110, 210, 211, 228, 234, 242 Sinha, S.K., 157, 159, 227 Skinner, C.J., 227, 334 Slud, E.V., 279, 281, 365, 374 Smith, P., 10, 22, 25, 334 Sperlich, S., 191, 192 Stillwell, J., 329, Sturtz, S., 312 Tsui, K.Y., 25 Tzavidis, N., 171–185, 9, 12–14, 114, 171–174, 192, 194, 195, 199, 201, 202, 258, 263, 265, 281, 282, 285 Taciak, J., 374 Tancredi, A., 45 Tarozzi, A., 227, 334, 339, Thibaudeau, Y., 44 Thomas, A., 312 Torabi, M., 9, 160, 161, 281 Torelli, N., 46, 281 Townsend, P., 22, 25 Trevisani, M., 281 Trivisano, C., 299–313 Ybarra, L., 153–155, 162–168 Yancey, W.E., 45 You, Y., 8, 10, 210, 211, 304 Ugarte, M.D., 191 Valliant, R., Van der Valk, J., 57 Van der Weide, R., 324 Veijanen, A., 109–126, 8, 10, 90–92, 227 Verma, V., 129–147, 26, 30 Wall, M., 228 Wallgren, A., 62 Wand, M.P., 189, 191, 246, 247, 306 Wang, J., 10, 71, 281 Wells, J.E., 140 Whelan, C.T., 25, 28 Wiekzorek, J., 303, 375 Winkler, W.E., 44, 45 Wolf, P., 22 Wolter, K., 69 Wu, C., 120, 250 Zadeh, L.A., 26 Zhang, L., 61–81, 296 Zhao, Q., 328 Zhou, Q., 210, 328 Zhu, V.J., 45 Zimmermann, T., 85–106 Subject Index absolute bias, 178, 179 absolute poverty, 381 absolute relative bias (ARB), 115, 122, 177 adaptive rejection sampling, 162 administrative data sources, 125, 354, 356, 419 administrative register data, 5, 61, 125, 413 Advanced Methodology for European Laeken Indicators (AMELI), xxvi, 15, 86 Akaike Information Criterion (AIC), 359 Albania, 188, 195, 246, 251–259, 418 American Community Survey (ACS), 162–170, 352–375 area level models, xxvi, 229, 232, 299, 305, 312, 316, 317, 408, 411 at-risk-of-poverty, 91, 96, 299 at-risk-of-poverty rate, 24, 86, 269, 300–302, 308, 310, 312, 414 auxiliary information, 3, 5, 9, 10, 14, 43, 48, 50, 53, 56, 86, 91, 93, 110, 114, 117, 119, 120, 122, 124, 147, 153, 155, 156, 169, 190, 191, 280, 299, 302–306, 317, 319, 411 auxiliary variable, 120, 156, 162, 169, 176, 177, 189, 192, 216, 275, 289, 323, 341, 389, 390, 392 back-transformation, 93, 94, 396, 397 balanced repeated replication (BRR), 132 Bayesian beta regression models, 299–313 Bayesian methods, 156, 159, 163, 164, 292, 416 benchmarking, 6, 10, 14, 70, 71, 72, 74, 281, 343, 400 best linear unbiased estimator (BLUP), 12, 230 bootstrap, 12, 93, 114, 117, 124, 132, 176, 178, 179, 190, 191, 192, 202, 211, 214, 219, 220, 229, 250, 286, 296, 302, 303, 305, 309, 320, 322, 323, 331, 334, 335, 340, 343, 396, 399, 400, 407, 417, 424 borrowing strength, 7, 15 BUGS, 312, 423, 424 burn-in period, 292, 312 Cambodia, 337, 341, 345 Campania, 237, 264, 268, 269, 270, 271, 275, 276, 422 census data, 48, 65, 162, 173, 256, 271, 272, 273, 274, 328, 329, 331, 332, 333, 334, 338, 339, 341, 342, 343, 361, 369, 370, 373, 417, 418, 422 census unit record data, 327–347, 425 Chile, 379–403 coefficient of variation, 5, 116, 144, 197, 200, 201, 237, 238, 239, 257, 292, 315, 413, 422 co-incidence of risks, 30 Analysis of Poverty Data by Small Area Estimation, First Edition Edited by Monica Pratesi © 2016 John Wiley & Sons, Ltd Published 2016 by John Wiley & Sons, Ltd Companion Website: www.wiley.com/go/pratesi/poverty www.ebook3000.com 432 Comisión Económica para América Latina y el Caribe (CEPAL), 381 comparative analysis, 31, 32 components of design effect, 138 composite estimator, 154 Comuna, 379–403 conditional autoregressive (CAR) models, 228, 339 confidence intervals for the poverty rates, 396, 399 consumer expenditure, 202, 315 contextual effects, 331–344 correlated sampling errors, 232 county crop, 208 county poverty models, 354, 356, 357, 367, 373 coverage error, 63–68, 80 cross-sectional variance estimation, 130, 131 cross-validation, 267 cumulation, 6, 130, 133, 141 cumulative distribution function (CDF), 13, 28, 29, 91, 173, 286, 309, 422 cumulative measures, 130, 140, 141 Current Population Survey (CPS), 162, 415 custom-made scripts, 405 data cloning, 281 data integration, 2, 42–46, 50, 51, 57, 61–81 data-related problems, design-based approaches, 90 design bias, 90, 92, 95, 96, 109, 110, 114, 120, 125, 208, 289, 412 design consistent estimators, design effect, 94, 135–140, 143, 303, 308, 387, 388 design-unbiased estimators, 90, 91 diagnostics, 173, 240, 323, 342, 389 direct computation, 144 direct estimate, 71, 265, 306, 365, 367, 384, 389 Subject Index disease mapping, 64 disparity measurement, 51, 52 education, 1, 25, 41, 51, 53, 62, 63, 171, 180, 195, 229, 236, 237, 245, 246, 252, 254, 270, 323, 329, 341, 349, 401, 409, 417 effect of the sample design, ELL method, 12, 13, 328, 331, 332, 335, 345 EM algorithm, 44 Emilia Romagna, 300, 311 empirical Bayes method, 161, 321 empirical best (EB), 7, 11, 12, 13, 14, 71, 93, 118, 155, 156, 159, 162, 172, 188, 202, 211, 228, 230, 249, 264, 265, 280, 295, 332, 340, 407, 424 empirical best linear unbiased prediction (EBLUP), 7, 12, 14, 71, 118, 162, 188, 211, 228, 230, 249, 265, 280 estimation-related problems, 5, EURAREA, 11, 408 EU regions, 22, 31, 32 EU-SILC data, 32, 75, 130, 135, 145, 171, 180, 182, 236, 301, 406, 409, 410, 412, 414, 419 EU-SILC survey, 28, 75, 180, 236, 270, 300, 409, 416, 419, 422, 423 excess of zero values, 6, 14 externally benchmarked, 10, 14 fence method, 190 finite-population bias, 68 fitting-of-constants method, Foster-Greer-Thorbecke (FGT) poverty measures, 2, 34, 36, 42, 46, 238, 356–357, 359, 360–362, 377 frequentist methods, 154, 158, 164 Functional Measurement Error (FME) approach, 153–159, 416 Fuzzy Monetary Depth (FMD), 26 Fuzzy Supplementary Depth (FSD), 26 433 Subject Index gamma distribution, 160, 280, 281, 287, 292, 294, 295 generalized linear mixed models, 68, 263, 374 generalized regression (GREG) estimator, 8, 91 generalized responses, 192 general linear mixed models, 189 Geoadditive models, 191, 246, 247 Geographically Weighted Regression (GWR), 15, 329 Gini index, 24, 109–125, 172, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 301, 308–314, 317, 383, 412–414, 423 Hajek type estimator, 251 head count ratio (HCR), 2, 264, 275, 414 Henderson’s estimator, 12, 213 hierarchical Bayes method, 161, 322 Horvitz-Thompson (HT) estimator, 89, 90, 110, 152, 251, 265, 412 household equivalized income, 180, 269, 417 household per-capita consumption expenditure, 195–201, 246–258, 420 indirect estimators, 92, 111, 115, 116, 265 inequality measures, 130, 172, 173 inferential framework, 109, 125 informative sampling, 119, 320, 323, 336 InGRID, 106, 408 integration methods, 42–50 internally benchmarked, 10 Jackknife, 94, 95, 155, 159, 161 Jackknife Repeated Replication (JRR), 130, 414 Kalman filter, 229, 234, 235, 238 Kernel methods, 191 Laeken indicators, 2, 23, 24, 41, 51, 56, 86, 129, 171–174, 183 life expectancy, 63, 64, 80 limited information, 141 limited translation empirical Bayes estimator, 395–399 linear mixed models (LMM), 13, 14, 68, 176, 182, 194, 228, 230, 231, 240, 246, 247, 249, 263, 282, 289, 318, 374, 408 linear unit-level mixed models, 280, 282 linking model, 93, 95, 208, 209, 228, 317, 394 living standard, 246, 251, 259 living standards measurement study (LSMS) in Albania, 188, 195, 246, 251–253, 418, 421 local labour system, 268 logistic regression, 90–92, 118, 374 lognormal distribution, 279–281, 286, 294, 295, 366 lognormal models, 279–296 Lombardy, 264, 268–276, 422 longitudinal poverty, xxv, 129, 130, 414 manifest poverty, 30, 31, 37, 38 Markov chain Monte Carlo (MCMC), 162, 300, 304, 312, 322 material deprivation, 23–34, 299–306, 312, 410, 423 maximum likelihood (ML), 8, 74, 118, 153, 188–190, 211, 214, 230, 240, 249, 283, 318, 321, 341, 357, 395, 397, 407 mean squared error (MSE) estimation, 176, 241 measurement error, 12, 63, 65–68, 80, 151–161, 168, 416 measurement error in the covariates, 12 measures of net change, 130, 140, 141 median income data, 162 merging, 43, 53, 54, 105, 247, 330 minimum norm quadratic estimation method, 230 model-assisted methods, 7, 109–127, 412 model-based direct estimator (MBDE), 194, 250, 264, 268, 280, 285, 422 www.ebook3000.com 434 model-based methods, 2, 3, 7, 12, 86, 105, 118, 125, 214, 324 model calibration estimation, 120 modifiable area unit problem (MAUP), 7, 258 monetary poverty, 1, 2, 21–33, 409 Monte Carlo simulation, 175, 357 moving average, 211 M-quantile, 7, 8, 9, 10, 11, 13, 14, 15, 114, 172, 173, 174, 175, 176, 180, 183, 192, 193, 194, 195, 200, 201, 202, 268, 281 M-quantile coefficients, 9, 13, 15, 174, 192, 193 M-quantile weighted estimators, multidimensional Fuzzy approach, 25 multidimensional measures of poverty, 25 multidimensional poverty, 70, 80, 401 National Socioeconomic Characterization Survey (Casen), 379–402 Nested error regression models, 8, 172, 173, 188, 194, 231, 236, 237 non-monetary poverty, 2, 31, 32 nonparametric methods, 9, 187, 188, 417 NUTS (Nomenclature of Statistical Territorial Units), 23, 24, 32, 33, 36–38, 41, 52, 53, 75, 114–116, 122–124, 145, 146, 171, 236, 237, 413, 414 optimal design, 102 outlier detection, 6, 355 outlier robust estimation, 11 out of sample areas, 5–7, 12 out of sample predictors, 14 oversampling, 4, 5, 54, 55, 171 panel data, 6, 129–147, 414 penalized quasi-likelihood (PQL), 407 penalized splines, 189–194, 247 Population and Housing Census (PHC), 251, 252, 421 posterior density, 94, 156, 160, 169 poverty count, 393 Subject Index poverty gap, 2, 26–28, 32, 114, 172, 230, 268, 316, 417 poverty incidence, 26, 31–38, 215, 230, 286, 315, 319, 323, 332, 336, 337 poverty indicators, 2–15, 21–23, 32, 42, 56, 68, 70, 86, 90, 105, 109–127, 128–147, 172, 182, 202, 210, 211, 229, 230, 236, 264, 269, 270, 319, 322, 324, 412, 414, 416 poverty mapping, 3, 8, 12, 329, 335, 338, 379 poverty severity, 26, 316 poverty threshold, 24, 68, 90, 91, 94, 96, 105, 117, 123, 215, 301, 316, 354, 381 PovMap, 328, 343, 405, 425 prior distribution, 161, 316, 322 probability linkage error, 63, 75 Programa de las Naciones Unidas para el Desarrollo (PNUD), 380 progressive administrative data, 63, 65, 66 pseudo-EBLUP estimators, 8–12, 231 Purchasing Power Parities (PPP) indicators, 32 Purchasing Power Standard (PPS), 32 Quintile share ratio, 172–184 raking, 73, 291, 383, 395, 397, 398, 400 random effects models, 14, 180, 266 random grouping of elements, 139 R and SAS software, 405 Rao–Kovar–Mantel estimator, ratio adjustment, 373 regional poverty, 86, 105, 125 register-based SAE, 63, 66 regression analysis, 216, 389, 393 relative bias, 96–98, 102, 103, 163, 165, 420 relative design bias (RDB), 289 relative design root mean squared error (RDRMSE), 289 relative root mean squared error (RRMSE), 96, 100, 115 relevance error, 62, 63, 70, 74 435 Subject Index replication, 93, 130, 132, 133, 135, 139, 141, 144, 145, 151, 175, 414, 415 restricted maximum likelihood (REML), 8, 211–217, 230–237, 249, 253, 286, 294, 318, 321, 341, 395, 407, 420 robust approaches, 192, 201, 202 robustification, 11, 13 robust small area estimation, 173 R packages, 405 sample size, 4, 41, 42, 49, 63, 69, 75, 88–90, 94, 102, 105, 111, 115, 118–124, 130, 135, 136, 143, 147, 160, 163, 172, 173, 176, 207, 208, 283, 288, 292, 302–308, 315–319, 332, 342, 353, 365, 367, 374, 383, 386, 387, 389, 407, 413 sampling error, 63, 69, 80, 129, 134, 138, 167, 209, 212, 352, 357, 358, 360, 364, 365 sampling model, 93–95, 209, 317, 394 SAS macros, 410, 414 school district poverty, 354–357, 368–376 selection bias, 54, 55, 320 self benchmarked, 10 semi-parametric models, 246 shrinkage effect, 5, 7, 10, 310, 312 simulation experiments, 110, 114, 122, 124, 229, 412, 413 simultaneous autoregressive (SAR) models, 228 skewed data, 246, 248, 279, 280 skewed variables, 284 small area estimation (SAE) methods, 2–15, 61–81, 187–193, 200, 202, 246–275, 327–347, 380, 381, 386, 391–401 small area income and poverty estimates (SAIPE), 165, 349 small area methods for poverty and living condition estimates (SAMPLE), 51, 75, 86 social exclusion, 2, 21–25, 22, 23, 25, 31, 41, 42, 51, 53, 85, 86, 109, 130, 171, 299, 300, 409, 423 social inclusion, 2, 21, 51 spatial correlation, 14, 210, 211, 228, 247, 344 spatial EBLUP (SEBLUP), 14 spatial information, 7, 14, 245 spatial microsimulation, 328, 329, 338 spatial relationships, 5, 245 spatial structure, 228, 234, 236, 240 spatio-temporal models, 211, 220, 227–243, 419 specification of the model, 5, 47, 159 spline models, 194, 266 state poverty models, 166, 357 state space models, 234, 236, 238, 240, 241 statistical matching, 43, 46 stratified sampling, 87, 119 structural measurement error approach, 160, 161 structure preserving estimation (SPREE), 70–75 Supplemental Nutrition Assistance Program (SNAP) benefits, 351, 355 survey data sources, 351 Survey on Income and Living Conditions (SILC), 236, 319, 323 survey regression estimator, 10 synthetic estimation, 70, 80, 336 target parameters, 2, 4–6, 178, 299 time-varying effects models, 232, 238 totally fuzzy and relative (TFR), 26 Tuscany, 53, 172, 180–184, 245, 264, 268–276, 300, 311, 422 two-level models, 8, 93 undercount, 208 under/over shrinkage effect, unemployment, 63, 211, 352 unit level models, 189, 191, 231, 234, 235, 237, 317, 323, 420 unplanned domains, 54, 56, 87, 110–113, 119, 172, 180, 183, 416 U.S Current Population Survey (CPS), 162–170, 352–376, 415 U.S Federal Income Tax Data, 354 U.S Social Security Administration, 356 www.ebook3000.com 436 variable selection, 169 variance components, 8, 12, 65, 188–194, 213, 249, 267, 282, 292, 312, 324, 328, 330, 335, 342, 345, 407 welfare, 12, 13, 22, 51, 53, 130, 173, 195, 316–320, 344, 345, 361, 381, 393, 424 Subject Index well-being, 1, 42, 43, 50–56, 61–63, 80, 130, 251, 268 World Bank (WB), 12, 25, 109, 172, 195, 246, 251, 258, 279, 316, 319, 342, 406, 425 zero excess, The Wiley Series in Survey Methodology covers topics of current research and practical interests in survey methodology and sampling While the emphasis is on application, theoretical discussion is encouraged when it supports a broader understanding of the subject matter The authors are leading academics and researchers in survey methodology and sampling The readership includes professionals in, and students of, the fields of applied statistics, biostatistics, public policy, and government and corporate enterprises ALWIN ⋅ Margins of Error: A Study of Reliability in Survey Measurement BETHLEHEM ⋅ Applied Survey Methods: A Statistical Perspective *BIEMER, GROVES, LYBERG, MATHIOWETZ, and SUDMAN ⋅ Measurement Errors in Surveys BIEMER and LYBERG ⋅ Introduction to Survey Quality BIEMER ⋅ Latent Class Analysis of Survey Error BRADBURN, SUDMAN, and WANSINK ⋅ Asking Questions: The Definitive Guide to Questionnaire Design—For Market Research, Political Polls, and Social Health Questionnaires, Revised Edition BRAVERMAN and SLATER ⋅ Advances in Survey Research: New Directions for Evaluation, No 70 CALLEGARO, BAKER, BETHLEHEM, GORITZ, KROSNIK and LAVRAKAS (Editors) ⋅ Online Panel Research: A Data Quality Perspective CHAMBERS and SKINNER (editors) ⋅ Analysis of Survey Data COCHRAN ⋅ Sampling Techniques, Third Edition CONRAD and SCHOBER ⋅ Envisioning the Survey Interview of the Future COUPER, BAKER, BETHLEHEM, CLARK, MARTIN, 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and Inference in Finite Population Sampling HUNDEPOOL, DOMINGO-FERRER, FRANCONI, GIESSING, NORDHOLT, SPICER and DE WOLF ⋅ Statistical Disclosure Control www.ebook3000.com KALTON and HEERINGA ⋅ Leslie Kish Selected Papers KISH ⋅ Statistical Design for Research *KISH ⋅ Survey Sampling KORN and GRAUBARD ⋅ Analysis of Health Surveys KREUTER (Editor) ⋅ Improving Surveys with Paradata: Analytic Uses of Process Information LEPKOWSKI, TUCKER, BRICK, DE LEEUW, JAPEC, LAVRAKAS, LINK, and SANGSTER (editors) ⋅ Advances in Telephone Survey Methodology LESSLER and KALSBEEK ⋅ Nonsampling Error in Surveys LEVY and LEMESHOW ⋅ Sampling of Populations: Methods and Applications, Fourth Edition LUMLEY ⋅ Complex Surveys: A Guide to Analysis Using R LYBERG, BIEMER, COLLINS, de LEEUW, DIPPO, SCHWARZ, TREWIN (editors) Survey Measurement and Process Quality LYNNE (editor) ⋅ Methodology of Longitudinal Surveys MADANS, MILLER, MAITLAND and WILLIS ⋅ Question Evaluation Methods: Contributing to the Science of Data 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Direct Estimation Small Area Estimation of the At-risk -of- poverty Rate 16.3.1 The Model 16.3.2 Data Analysis Small Area Estimation of the Material Deprivation Rates 16.4.1 The Model 16.4.2 Data Analysis. .. Groves A complete list of the titles in this series appears at the end of this volume Analysis of Poverty Data by Small Area Estimation Edited by Monica Pratesi University of Pisa, Italy www.ebook3000.com... evidence V Small area estimation of the distribution function of income and inequalities (Chapter 14 Model-based direct estimation of a small area distribution function; Chapter 15 Small area estimation