<abstract><title style='display:none'>Abstract</title> This article reviews unit nonresponse in cross-sectional household surveys, the consequences of the nonresponse on the bias of the estimates, and methods of adjusting for it. We describe the development of models for nonresponse bias and their utility, with particular emphasis on the role of response propensity modeling and its assumptions. The article explores the close connection between data collection protocols, estimation strategies, and the resulting nonresponse bias in the estimates. We conclude with some comments on the current state of the art and the need for future developments that expand our understanding of the response phenomenon.</abstract>

Unit Nonresponse and Weighting Adjustments: A Critical Review

<abstract>
<title style='display:none'>Abstract</title>
Artificial neural networks (ANNs) have been the catalyst to numerous advances in a variety of fields and disciplines in recent years. Their impact on economics, however, has been comparatively muted. One type of ANN, the long short-term memory network (LSTM), is particularly well-suited to deal with economic time-series. Here, the architecture’s performance and characteristics are evaluated in comparison with the dynamic factor model (DFM), currently a popular choice in the field of economic nowcasting. LSTMs are found to produce superior results to DFMs in the nowcasting of three separate variables; global merchandise export values and volumes, and global services exports. Further advantages include their ability to handle large numbers of input features in a variety of time frequencies. A disadvantage is the stochastic nature of outputs, common to all ANNs. In order to facilitate continued applied research of the methodology by avoiding the need for any knowledge of deep-learning libraries, an accompanying Python (Hopp 2021a) library was developed using PyTorch. The library is also available in R, MATLAB, and Julia.
</abstract>

Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)

<abstract><title style='display:none'>Abstract</title> Recent research has attempted to examine the proportion of interviewer variance that is due to interviewers systematically varying in their success in obtaining cooperation from respondents with varying characteristics (i.e., nonresponse error variance), rather than variance among interviewers in systematic measurement difficulties (i.e., measurement error variance) - that is, whether correlated responses within interviewers arise due to variance among interviewers in the pools of respondents recruited, or variance in interviewer-specific mean response biases. Unfortunately, work to date has only considered data from a CATI survey, and thus suffers from two limitations: Interviewer effects are commonly much smaller in CATI surveys, and, more importantly, sample units are often contacted by several CATI interviewers before a final outcome (response or final refusal) is achieved. The latter introduces difficulties in assigning nonrespondents to interviewers, and thus interviewer variance components are only estimable under strong assumptions. This study aims to replicate this initial work, analyzing data from a national CAPI survey in Germany where CAPI interviewers were responsible for working a fixed subset of cases.</abstract>

Survey Methodology Program (SMP), Survey Research Center (SRC), Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, MI 48106, U.S.A.

Joint Program in Survey Methodology (JPSM), Ludwig Maximilian University of Munich, Institute for Employment Research (IAB), Nuremberg, Germany

Institute for Employment Research (IAB), Nuremberg, Germany

“Interviewer” Effects in Face-to-Face Surveys: A Function of Sampling, Measurement Error, or Nonresponse?

<abstract><title style='display:none'>Abstract</title> We propose a convenient method of estimating the within-group, between-group, and interaction components of the overall traditional Gini index from the estimated parameters of underlying “trick regression models” involving known forms of heteroscedasticity related to income. Two illustrative examples involving both real and artificial data are provided. The issue of appropriate standard error of the subgroup decomposition is also discussed.</abstract>

A Convenient Method of Decomposing the Gini Index by Population Subgroups

<abstract><title style='display:none'>Abstract</title> Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test statistics for the coefficients of multiple regression forms of GVF models. The second set uses smoothed estimators of the mean squared error (MSE) of GVF-based variance estimators. The smooth version of the MSE estimator can provide a useful measure of the performance of a GVF estimator, relative to the variance of a standard design-based variance estimator. Some of the proposed methods are applied to sample data from the Current Employment Statistics survey.</abstract>

Evaluation of Generalized Variance Functions in the Analysis of Complex Survey Data

<abstract><title style='display:none'>Abstract</title>Every five years, the U.S. Census Bureau conducts the Economic Census, the official count of US businesses and the most extensive collection of data related to business activity. Businesses, policymakers, governments and communities use Economic Census data for economic development, business decisions, and strategic planning. The Economic Census provides key inputs for economic measures such as the Gross Domestic Product and the Producer Price Index. The Economic Census requires businesses to fill out a lengthy questionnaire, including an extended section about the goods and services provided by the business.To address the challenges of high respondent burden and low survey response rates, we devised a strategy to automatically classify goods and services based on product information provided by the business. We asked several businesses to provide a spreadsheet containing Universal Product Codes and associated text descriptions for the products they sell. We then used natural language processing to classify the products according to the North American Product Classification System. This novel strategy classified text with very high accuracy rates - our best algorithms surpassed over 90%.</abstract>

Applying Machine Learning for Automatic Product Categorization

<abstract><title style='display:none'>Abstract</title> There is a risk of introducing survey error at every stage of any study involving a survey: design, data collection, processing, and analysis. Effectively managing the survey sample from the time of sample selection through the survey lifecycle is essential to producing highquality data on schedule and within budget. Managing the survey lifecycle using software systems that are not fully integrated can result in error and cost inefficiencies. The development of an integrated data collection and management system that supports monitoring of survey error has the potential to reduce errors and improve operational efficiencies. This system, referred to as Nirvana, uses a standardized database, protocol, and terminology. It integrates case status and history information across modes of data collection and tracing as well as sample and contact information. Nirvana also centralizes questionnaire development, quality monitoring, and support for real-time survey management decisions.</abstract>

The General Survey System Initiative at RTI International: An Integrated System for the Collection and Management of Survey Data

<abstract><title style='display:none'>Abstract</title> In response to a changing environment, Statistics Netherlands has embarked on a large-scale redesign of the way statistics are produced. The aim is to increase the capability to respond to changing information demand, to lower the response burden for surveys, especially for businesses, and to improve efficiency, while preserving the overall quality level. The redesign is carried out within the framework of a so-called enterprise architecture, which gives overall guidance when structuring the processes of the organisation, including statistical methods and IT tools used. The article describes the redesign approach and explains the main features of the architecture. The emphasis is on experiences that may be relevant to other national statistical institutes operating in a similar environment.</abstract>

Redesign of Statistics Production within an Architectural Framework: The Dutch Experience

<abstract><title style='display:none'>Abstract</title> This article focuses on methods for enhancing access to survey data produced by government agencies. In particular, the National Center for Health Statistics (NCHS) is developing methods that could be used in an interactive, integrated, real-time online analytic system (OAS) to facilitate analysis by the public of both restricted and public use survey data. Data from NCHS’ National Health Interview Survey (NHIS) are being used to investigate, develop, and evaluate such methods. We assume the existence of public use microdata files, as is the case for the NHIS, so disclosure avoidance methods for such an OAS must account for that critical constraint. Of special interest is the analysis of state-level data because health care is largely administered at the state level in the U.S., and state identifiers are not on the NHIS public use files. This article describes our investigations of various possible choices of methods for statistical disclosure control and the challenges of providing such protection in a real-time OAS that uses restricted data. Full details about the specific disclosure control methods used by a working OAS could never be publicly released for confidentiality reasons. NCHS is still evaluating whether to implement an OAS that uses NHIS restricted data, and this article provides a snapshot of a research and developmental project in progress.</abstract>

Westat, 1600 Research Boulevard, Rockville, MD 20850, U.S.A

National Center for Health Statistics, 3311 Toledo Road, Hyattsville, MD 20782, U.S.A.

Addressing Disclosure Concerns and Analysis Demands in a Real-Time Online Analytic System

<abstract><title style='display:none'>Abstract</title> This article outlines a framework for formal description, justification and evaluation in development of architectures for large-scale statistical production systems. Following an introduction of the main components of the framework, we consider four related issues: (1) Use of some simple schematic models for survey quality, cost, risk, and stakeholder utility to outline several groups of questions that may inform decisions on system design and architecture. (2) Integration of system architecture with models for total survey quality (TSQ) and adaptive total design (ATD). (3) Possible use of concepts from the Generic Statistical Business Process Model (GSBPM) and the Generic Statistical Information Model (GSIM). (4) The role of governance processes in the practical implementation of these ideas.</abstract>

Bureau of Labor Statistics, Postal Square Building, 2 Massachusetts Avenue, NE Washington DC, U.S.A.

RTI, Research Triangle Park, NC 27709-2194, U.S.A.

Statistics Sweden, Box 24300, SE-701 89 Örebro, Sweden

A Potential Framework for Integration of Architecture and Methodology to Improve Statistical Production Systems

<abstract><title style='display:none'>Abstract</title> In 2011, Statistics NZ embarked on an ambitious ten-year programme of change called Statistics 2020 Te Kāpehu Whetū: Achieving the statistics system of the future. This article outlines a key component of this approach to transforming how Statistics NZ delivers its statistics. This involves modernising all aspects of statistical production - from identifying need, right through to dissemination - to ensure the organisation has the functionality and capacity to deliver now and into the future. Standardisation of processes, methods, tools, and systems to increase flexibility and efficiency is a key component of this transformation.</abstract>

Innovative Production Systems at Statistics New Zealand: Overcoming the Design and Build Bottleneck

Prelude to the Special Issue on Systems and Architectures for High-Quality Statistics Production

Index to Volume 39, 2023

<abstract>
<title style='display:none'>Abstract</title>
In this study, we advocate a two-stage framework to deal with the issues encountered in surveys with long questionnaires. In Stage I, we propose a split questionnaire design (SQD) developed by minimizing a quadratic cost function while achieving reliability constraints on estimates of means, which effectively reduces the survey cost, alleviates the burden on the respondents, and potentially improves data quality. In Stage II, we develop a block weighted least squares (BWLS) estimator of linear regression coefficients that can be used with data obtained from the SQD obtained in Stage I. Numerical studies comparing existing methods strongly favor the proposed estimator in terms of prediction and estimation accuracy. Using the European Social Survey (ESS) data, we demonstrate that the proposed SQD can substantially reduce the survey cost and the number of questions answered by each respondent, and the proposed estimator is much more interpretable and efficient than present alternatives for the SQD data.
</abstract>

Block Weighted Least Squares Estimation for Nonlinear Cost-based Split Questionnaire Design

<abstract>
<title style='display:none'>Abstract</title>
The modernization of the production of official statistics faces challenges related to technological developments, budget cuts, and growing privacy concerns. At the same time, there is a need for shareable and scalable platforms to support comparable data, leading to several online data collection strategies being rolled out. Time Use Surveys (TUS) are particularly affected by these challenges and needs as they (while producing rich data) are complex, time-intensive studies (because they include multiple tasks and are administered at the household level). This article introduces the Modular Online Time Use Survey (MOTUS) data collection platform and explains how it accommodates the challenges of and changes in the production of a TUS that is carried out in line with the Harmonized European Time Use Survey guidelines. It argues that MOTUS supports a shift in the methodological paradigm of conducting TUS by being timelier and more cost efficient, by lowering respondent burden, and by improving the reliability of the data collected. Importantly, the modular structure allows MOTUS to be easily deployed for various TUS configurations. Moreover, this versatile structure allows comparable, complex diary surveys (such as the household budget survey) to be performed on the same platform and with the same applications.
</abstract>

Answering Current Challenges of and Changes in Producing Official Time Use Statistics Using the Data Collection Platform MOTUS

<abstract>
<title style='display:none'>Abstract</title>
In this article, we propose a framework for small area estimation with multiply imputed survey data. Many statistical surveys suffer from (a) high nonresponse rates due to sensitive questions and response burden and (b) too small sample sizes to allow for reliable estimates on (unplanned) disaggregated levels due to budget constraints. One way to deal with missing values is to replace them by several plausible/imputed values based on a model. Small area estimation, such as the model by Fay and Herriot, is applied to estimate regionally disaggregated indicators when direct estimates are imprecise. The framework presented tackles simultaneously multiply imputed values and imprecise direct estimates. In particular, we extend the general class of transformed Fay-Herriot models to account for the additional uncertainty from multiple imputation. We derive three special cases of the Fay-Herriot model with particular transformations and provide point and mean squared error estimators. Depending on the case, the mean squared error is estimated by analytic solutions or resampling methods. Comprehensive simulations in a controlled environment show that the proposed methodology leads to reliable and precise results in terms of bias and mean squared error. The methodology is illustrated by a real data example using European wealth data.
</abstract>

Small Area with Multiply Imputed Survey Data

<abstract>
<title style='display:none'>Abstract</title>
Sampling variance smoothing is an important topic in small area estimation. In this article, we propose sampling variance smoothing methods for small area proportion estimation. In particular, we consider the generalized variance function and design effect methods for sampling variance smoothing. We evaluate and compare the smoothed sampling variances and small area estimates based on the smoothed variance estimates through analysis of survey data from Statistics Canada. The results from real data analysis and simulation study indicate that the proposed sampling variance smoothing methods perform very well for small area estimation.
</abstract>

Application of Sampling Variance Smoothing Methods for Small Area Proportion Estimation

Editorial Collaborators

<abstract><title style='display:none'>Abstract</title>More and more data are being produced by an increasing number of electronic devices physically surrounding us and on the internet. The large amount of data and the high frequency at which they are produced have resulted in the introduction of the term ‘Big Data’. Because these data reflect many different aspects of our daily lives and because of their abundance and availability, Big Data sources are very interesting from an official statistics point of view. This article discusses the exploration of both opportunities and challenges for official statistics associated with the application of Big Data. Experiences gained with analyses of large amounts of Dutch traffic loop detection records and Dutch social media messages are described to illustrate the topics characteristic of the statistical analysis and use of Big Data.</abstract>

Statistics Netherlands, Division of Process development, IT and methodology P.O. Box 4481, 6401 CZ, Heerlen, The Netherlands.

Statistics Netherlands, Division of Process development, IT and methodology P.O. Box 4481, 6401 CZ, Heerlen, The Netherlands

Big Data as a Source for Official Statistics

<abstract><title style='display:none'>Abstract</title>The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.</abstract>

Department of Economics and Management – University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy

Department of Economics and Management - University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy.

Department of Economics and Management - University of Pisa, Via Ridolfi 10, 56124 Pisa, Italy

KDD Lab – ISTI – National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy.

Department of Computer Science – University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy

KDD Lab - ISTI - National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy

Department of Information Engineering – University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy.

Small Area Model-Based Estimators Using Big Data Sources

<abstract><title style='display:none'>Abstract</title> This article describes a project conducted in conjunction with the Central Statistics Office of Ireland in response to a planned national rollout of smart electricity metering in Ireland. We investigate how this new data source might be used for the purpose of official statistics production. This study specifically looks at the question of determining household composition from electricity smart meter data using both Neural Networks (a supervised machine learning approach) and Elastic Net Logistic regression. An overview of both classification techniques is given. Results for both approaches are presented with analysis. We find that the smart meter data alone is limited in its capability to distinguish between household categories but that it does provide some useful insights.</abstract>

Household Classification Using Smart Meter Data

<abstract><title style='display:none'>Abstract</title>Survey researchers have been investigating alternative approaches to reduce data collection costs while mitigating the risk of nonresponse bias or to produce more accurate estimates within the same budget. Responsive or adaptive design has been suggested as one means for doing this. Falling survey response rates and the need to find effective ways of implementing responsive design has focused attention on the relationship between response rates and nonresponse bias. In our article, we re-examine the data compiled by <xref ref-type="bibr" rid="j_jos-2017-0034_ref_013_w2aab2b8c10b1b7b1ab1ac13Aa">Groves and Peytcheva (2008)</xref> in their influential article and show there is an important between-study component of variance in addition to the within-study variance highlighted in the original analysis. We also show that theory implies that raising response rates can help reduce the nonresponse bias on average across the estimates within a study. We then propose a typology of response propensity models that help explain the empirical findings, including the relative weak relationship between nonresponse rates and nonresponse bias. Using these results, we explore when responsive design tools such as switching modes, giving monetary incentives, and increasing the level of effort are likely to be effective. We conclude with some comments on the use of responsive design and weighting to control nonresponse bias.</abstract>

Responsive Survey Designs for Reducing Nonresponse Bias

<abstract><title style='display:none'>Abstract</title>Mobile phone data are an interesting new data source for official statistics. However, multiple problems and uncertainties need to be solved before these data can inform, support or even become an integral part of statistical production processes. In this article, we focus on arguably the most important problem hindering the application of mobile phone data in official statistics: detecting home locations. We argue that current efforts to detect home locations suffer from a blind deployment of criteria to define a place of residence and from limited validation possibilities. We support our argument by analysing the performance of five home detection algorithms (HDAs) that have been applied to a large, French, Call Detailed Record (CDR) data set (~18 million users, five months). Our results show that criteria choice in HDAs influences the detection of home locations for up to about 40% of users, that HDAs perform poorly when compared with a validation data set (resulting in 358-gap), and that their performance is sensitive to the time period and the duration of observation. Based on our findings and experiences, we offer several recommendations for official statistics. If adopted, our recommendations would help ensure more reliable use of mobile phone data vis-à-vis official statistics.</abstract>

Assessing the Quality of Home Detection from Mobile Phone Data for Official Statistics

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 The December 2023 Issue is now online.     The Journal of Official Statistics (JOS) is a quarterly...