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Survey Methodology Catalogue no. 12-001-X ISSN 1492-0921 Survey Methodology Survey Methodology 46-1 Release date: June 30, 2020 How to obtain more information For information about this product or the wide range of services and data available from Statistics Canada, visit our website, www.statcan.gc.ca. You can also contact us by Email at [email protected] Telephone, from Monday to Friday, 8:30 a.m. to 4:30 p.m., at the following numbers: • Statistical Information Service 1-800-263-1136 • National telecommunications device for the hearing impaired 1-800-363-7629 • Fax line 1-514-283-9350 Depository Services Program • Inquiries line 1-800-635-7943 • Fax line 1-800-565-7757 Standards of service to the public Note of appreciation Statistics Canada is committed to serving its clients in a prompt, Canada owes the success of its statistical system to a reliable and courteous manner. To this end, Statistics Canada long-standing partnership between Statistics Canada, the has developed standards of service that its employees observe. citizens of Canada, its businesses, governments and other To obtain a copy of these service standards, please contact institutions. Accurate and timely statistical information Statistics Canada toll-free at 1-800-263-1136. The service could not be produced without their continued co-operation standards are also published on www.statcan.gc.ca under and goodwill. “Contact us” > “Standards of service to the public.” Published by authority of the Minister responsible for Statistics Canada © Her Majesty the Queen in Right of Canada as represented by the Minister of Industry, 2020 All rights reserved. Use of this publication is governed by the Statistics Canada Open Licence Agreement. An HTML version is also available. Cette publication est aussi disponible en français. SURVEY METHODOLOGY A Journal Published by Statistics Canada Survey Methodology is indexed in The ISI Web of knowledge (Web of science), The Survey Statistician, Statistical Theory and Methods Abstracts and SRM Database of Social Research Methodology, Erasmus University and is referenced in the Current Index to Statistics, and Journal Contents in Qualitative Methods. It is also covered by SCOPUS in the Elsevier Bibliographic Databases. MANAGEMENT BOARD Chairman E. Rancourt Past Chairmen C. Julien (2013-2018) Members G. Beaudoin J. Kovar (2009-2013) S. Fortier (Production Manager) D. Royce (2006-2009) W. Yung G.J. Brackstone (1986-2005) R. Platek (1975-1986) EDITORIAL BOARD Editor W. Yung, Statistics Canada Past Editor M.A. Hidiroglou (2010-2015) J. Kovar (2006-2009) M.P. Singh (1975-2005) Associate Editors J.-F. Beaumont, Statistics Canada E. Lesage, L’Institut national de la statistique et des études M. Brick, Westat Inc. économiques S. Cai, Carleton University K. McConville, Reed College P.J. Cantwell, U.S. Census Bureau I. Molina, Universidad Carlos III de Madrid G. Chauvet, École nationale de la statistique et de l’analyse de J. Opsomer, Westat Inc l’information D. Pfeffermann, University of Southampton J. Chipperfield, Australian Bureau of Statistics J.N.K. Rao, Carleton University J. Dever, RTI International L.-P. Rivest, Université Laval J.L. Eltinge, U.S. Bureau of Labor Statistics F. Scheuren, National Opinion Research Center W.A. Fuller, Iowa State University P.L.N.D. Silva, Escola Nacional de Ciências Estatísticas J. Gambino, Statistics Canada P. Smith, University of Southampton D. Haziza, Université de Montréal D. Steel, University of Wollongong M.A. Hidiroglou, Statistics Canada M. Torabi, University of Manitoba B. Hulliger, University of Applied and Arts Sciences Northwestern D. Toth, U.S. Bureau of Labor Statistics Switzerland J. van den Brakel, Statistics Netherlands D. Judkins, ABT Associates Inc Bethesda C. Wu, University of Waterloo J. Kim, Iowa State University A. Zaslavsky, Harvard University P. Kott, RTI International L.-C. Zhang, University of Southampton P. Lahiri, JPSM, University of Maryland Assistant Editors C. Bocci, K. Bosa, C. Boulet, S. Matthews, C.O. Nambeu, Z. Patak and Y. You, Statistics Canada EDITORIAL POLICY Survey Methodology publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves. All papers will be refereed. However, the authors retain full responsibility for the contents of their papers and opinions expressed are not necessarily those of the Editorial Board or of Statistics Canada. Submission of Manuscripts Survey Methodology is published twice a year in electronic format. Authors are invited to submit their articles through the Survey Methodology hub on the ScholarOne Manuscripts website (https://mc04.manuscriptcentral.com/surveymeth). For formatting instructions, please see the guidelines provided in the journal and on the web site (www.statcan.gc.ca/surveymethodology). To communicate with the Editor, please use the following email: ([email protected]). Survey Methodology A Journal Published by Statistics Canada Volume 46, Number 1, June 2020 Contents Regular Papers Jean-François Beaumont Are probability surveys bound to disappear for the production of official statistics? ................................. 1 Marius Stefan and Michael A. Hidiroglou Local polynomial estimation for a small area mean under informative sampling .................................. 29 María Guadarrama, Isabel Molina and Yves Tillé Small area estimation methods under cut-off sampling .............................................................................. 51 Robert Graham Clark Model-assisted sample design is minimax for model-based prediction ..................................................... 77 Stefan Zins and Jan Pablo Burgard Considering interviewer and design effects when planning sample sizes .................................................. 93 Yousung Park and Tae Yeon Kwon A new double hot-deck imputation method for missing values under boundary conditions .................. 121 In Other Journals ................................................................................................................................................. 141 Statistics Canada, Catalogue No. 12-001-X Survey Methodology, June 2020 1 Vol. 46, No. 1, pp. 1-28 Statistics Canada, Catalogue No. 12-001-X Are probability surveys bound to disappear for the production of official statistics? Jean-François Beaumont1 Abstract For several decades, national statistical agencies around the world have been using probability surveys as their preferred tool to meet information needs about a population of interest. In the last few years, there has been a wind of change and other data sources are being increasingly explored. Five key factors are behind this trend: the decline in response rates in probability surveys, the high cost of data collection, the increased burden on respondents, the desire for access to “real-time” statistics, and the proliferation of non-probability data sources. Some people have even come to believe that probability surveys could gradually disappear. In this article, we review some approaches that can reduce, or even eliminate, the use of probability surveys, all the while preserving a valid statistical inference framework. All the approaches we consider use data from a non- probability source; data from a probability survey are also used in most cases. Some of these approaches rely on the validity of model assumptions, which contrasts with approaches based on the probability sampling design. These design-based approaches are generally not as efficient; yet, they are not subject to the risk of bias due to model misspecification. Key Words: Statistical matching; Calibration; Non-probabilistic data; Data integration; Fay-Herriot model; Propensity score. 1 Introduction In 1934, Jerzy Neyman laid the foundation for probability survey theory and his design-based approach to inference with an article published in the Journal of the Royal Statistical Society. His article (Neyman, 1934) piqued the interest of a number of statisticians at the time, and the theory was developed further in the following years. Still today, many articles on this topic are published in statistics journals. Rao (2005) provides an excellent review of various developments in probability survey theory during the 20th century (see also Bethlehem, 2009; Rao and Fuller, 2017; Kalton, 2019). Nowadays, national statistical agencies, such as Statistics Canada and the Institut National de la Statistique et des Études Économiques (INSEE) in France, use probability surveys most often to get the information they seek on a population of interest. The popularity of probability surveys for producing official statistics stems largely from the non- parametric nature of the inference approach developed by Neyman (1934). In other words, probability surveys allow for valid inferences about a population without having to rely on model assumptions. This is appealing ‒ even fundamental, according to Deville (1991) ‒ to national statistical agencies that produce official statistics. In fact, these agencies have historically
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