Advances in Business Statistics, Methods and Data Collection
- Length: 896 pages
- Edition: 1
- Language: English
- Publisher: Wiley
- Publication Date: 2023-02-07
- ISBN-10: 1119672309
- ISBN-13: 9781119672302
- Sales Rank: #1279176 (See Top 100 Books)
This edited volume is based on significant papers presented at The Sixth International Conference on Establishment Statistics (ICES-VI). In addition to providing broad introductions in specialized topic areas, the volume discusses a wide spectrum of new issues related to business statistics, its methods and collecting business data, including: new developments in business surveys (like non-probability sampling, developments in web surveys, adaptive business designs), statistical process control, use of alternative/secondary data sources like registers and big data, register methodology, Internet of Things possibilities (smart farming data, smart industries data), new computer technologies, consequences of globalization, and new indicators for the economy (like Sustainable Development Goals). Over the past few decades, methodologists around the world have worked actively on optimizing approaches for the development, conduct and evaluation of modern business programs. Furthermore, there are emerging data sources and new technologies, like big data, machine learning and advanced visualization, that are used in modern applications and are not discussed in other books, simply because they did not previously exist. Like the conference itself, the edited volume provides a broad overview of modern developments in collecting business data and producing business statistics. Both the conference and the resulting book summarizes the status quo of business statistics methods and serves as a proactive forum to prepare researchers to meet future challenges.
Cover Title Page Copyright Contents List of Contributors Part 1 Introduction to New Measures/Indicators for the Economy Chapter 1 Advances in Business Statistics, Methods and Data Collection: Introduction 1.1 The ICES‐VI Edited Volume: A New Book on Establishment Statistics Methodology 1.2 The Importance of Establishment Statistics 1.3 ICES Trends 1.4 Organization of This Book 1.4.1 Section 1: Introduction to New Measures/Indicators for the Economy 1.4.2 Section 2: Topics in the Production of Official Establishment Statistics and Organizational Frameworks 1.4.3 Section 3: Topics in the Use of Administrative Data 1.4.4 Section 4: Topics in Business Survey Data Collection 1.4.5 Section 5: Topics in the Use of New Data Sources and New Technologies 1.4.6 Section 6: Topics in Sampling and Estimation 1.4.7 Section 7: Topics in Data Integration, Linking and Matching 1.5 To Conclude … Disclaimer References Chapter 2 GDP and the SNA: Past and Present 2.1 Introduction 2.2 The Origins of National Income Statistics – A Brief History 2.2.1 Early Developments 2.2.2 Invention of Gross National Product (GNP) 2.2.3 The Debate on Including Government 2.2.4 Toward a System of National Accounts 2.2.5 Global Proliferation of GDP 2.3 SNA and GDP Today 2.3.1 The System at Large 2.3.2 Supply and Use Tables 2.3.3 Institutional Sector Accounts 2.3.4 The Link Between Supply and Use Tables and the Institutional Sector Accounts 2.3.5 Consistency and Coherence 2.3.6 The Relationship Between National Accounts and Business Statistics 2.3.6.1 Definitional Adjustments 2.3.6.2 Adjustments for Exhaustiveness 2.3.6.3 Adjustments for Time Consistency 2.3.6.4 Balancing Adjustments 2.4 Most Recent and Important Revisions to SNA (Implications for Business Statistics) 2.4.1 International Standards Not Set in Stone 2.4.2 From SNA 1968 to SNA 1993 2.4.3 From SNA 1993 to SNA 2008 2.4.4 The SNA and Source Statistics for Enterprises 2.5 Conclusions and Implications for Business Statistics References Chapter 3 GDP and the SNA: Future Challenges 3.1 Introduction 3.2 An Agenda for the Future 3.3 The Tangled Web of Globalization 3.4 The Digital Revolution 3.5 Moving Beyond GDP: GDP Impeached 3.6 Including a Measure of Well‐being 3.7 Putting a Value on the Environment 3.8 Challenges Replacing GDP 3.9 Conclusions and Implications for Business Statistics References Chapter 4 Bridging the Gap Between Business and Macroeconomic Statistics: Methodological Considerations and Practical Solutions 4.1 Introduction 4.2 Global Production and Statistics 4.2.1 Concepts of Nationality and Economic Ownership 4.2.2 Case Finland: Global Production in Economic Statistics 4.2.2.1 Identification of Enterprises Involved in Global Production 4.2.2.2 Case on Automotive Industry 4.2.2.3 Foreign Trade of Goods Based on Economic Ownership 4.2.2.4 Challenges Related to Global Production Recordings 4.3 Co‐operation Between National Statistical Offices and National Central Bank Statistics Functions Tackling Globalization Problems 4.3.1 Foreign Direct Investment Network as an Example of Co‐operation 4.3.2 Early‐Warning System (EWS) 4.3.3 A Roadmap for Solving the Globalization‐Related Issues in Monetary, Financial, and Balance of Payments – Statistics 4.4 Bridging the Gap Between Business and Economic Statistics Through Global Data Sharing 4.4.1 Product Innovation – One‐Off or Regular Data Sharing for Better Quality 4.4.2 Service Innovation – Improving Respondent Service for MNEs 4.4.3 Process Innovation to Statistical Production by Data Sharing 4.4.4 Innovating User Experience – Better Relevance and Consistency for Users 4.4.5 Organizational Innovation – Changing the Business Model of Official Statistics 4.4.6 Cultural Innovation – Key to Making it Happen 4.4.7 Innovation in Other Industries to Learn From References Chapter 5 Measuring Investment in Intangible Assets 5.1 Introduction 5.2 Data Sources on Intangibles 5.2.1 Past Surveys on Intangibles 5.2.2 Comparison of Past Surveys on Intangibles 5.3 Measurement Challenges in Surveys 5.3.1 Intangibles Are Intangible and Mobile 5.3.2 Own‐Account Investment Prevails 5.3.3 Pricing of Intangibles Is Difficult 5.3.4 In Search of the Most Suitable Respondent for Intangibles 5.3.5 Investments in Intangibles Take Time 5.3.6 Data Existence Questioned 5.3.7 Evidence of Inconsistent Respondent Behavior 5.3.8 Summarizing the Challenges in Intangible Surveys: The 4 “F” Words 5.4 Intangibles and the Productivity Puzzle 5.4.1 Analytical Considerations 5.4.2 Role of Global Value Chains 5.5 Collecting Data on Intangibles: The Way Ahead 5.5.1 Methodological Improvements 5.5.2 Data Needs Today and Tomorrow 5.5.2.1 Current and Potential Users 5.5.2.2 A (Single) IA Survey or a Bundle of Data Sources? 5.5.2.3 A Parallel Development Path: Assessing Intangible Asset Stocks 5.6 Conclusion Acknowledgment References Chapter 6 Measuring the US Digital Economy 6.1 Introduction 6.2 Experimental Digital Economy Measures 6.2.1 Methodology 6.2.1.1 Defining the Digital Economy 6.2.1.2 Calculating Results 6.2.2 Domestic Trends 6.2.2.1 Value Added 6.2.2.2 Gross Output 6.2.2.3 Prices 6.2.3 International Collaboration and Alignment 6.2.3.1 The Organization for Economic Co‐operation and Development Working Party on National Accounts 6.2.3.2 International Comparisons 6.2.4 Other Areas of Research 6.2.4.1 “Free” Digital Media 6.2.4.2 Measurement and Treatment of Data 6.2.4.3 Prices 6.3 Measuring Digital Services Trade 6.3.1 Defining Digital Services for International Trade 6.3.2 Trends in ICT and ICT‐enabled Services 6.3.3 Areas of Research 6.4 Conclusion and Way Forward References Chapter 7 Establishment Based Informal Sector Statistics: An Endeavor of Measurement from Economic Census 2018 of Nepal 7.1 Introduction 7.2 Issues of Informal Sector in Legislation and Policies in Nepal 7.2.1 Constitution 2015 7.2.2 Labor Act 2017 7.2.3 Contribution Based Social Security Act 2017 7.2.4 Fifteenth Periodic Plan (2019/20–2023/24) 7.2.5 National Employment Policy 2014 7.3 Concept and Definition of Informal Sector 7.3.1 Definition of Informal Sector from Statistical Perspective 7.4 Endeavors of Measuring Informal Economic Activities in Nepal 7.4.1 Nepal Labor Force Survey 7.4.2 Nepal Living Standard Surveys (NLSS) 7.4.3 Population Censuses 7.5 Economic Census 2018 7.5.1 Contents of Economic Census 2018 7.6 Status of the Informal Sector Statistics 7.6.1 Informal Sector Statistics from Nepal Labor Force Survey 1998 and 2008 7.6.2 Informal Sector Statistics from Nepal Labor Force Survey 2017/18 7.6.3 Informal Sector Statistics from National Population Census 2011 7.6.4 Informal Sector Statistics from National Economic Census 2018 7.6.5 Status of Keeping Accounting Record 7.6.6 Informality in Micro Small and Medium Establishments (MSME) 7.6.7 Street Business Situation 7.7 Annual Revenues/Sales, Operating Expenses in Not‐Registered Establishments 7.8 Need of Regular Measurement Informal Sector 7.9 Conclusion References Part 2 Topics in the Production of Official Establishment Statistics and Organizational Frameworks Chapter 8 Statistical Producers Challenges and Help 8.1 Introduction 8.2 A Brief Overview of the Evolution of Economic Statistics, and the Establishment of National Statistical Institutes 8.3 Our Statistical Ecosystem 8.4 Help Available to Us 8.4.1 International Governance 8.4.2 Statistical Principles to Produce and Disseminate Official Statistics 8.4.3 Statistical Production Models and Frameworks 8.4.3.1 Quality Assurance Frameworks 8.4.4 Statistical Manuals and Handbooks 8.4.5 Classifications 8.4.5.1 Classifying Businesses 8.4.5.2 Classifying Employment and Workers 8.4.5.3 Classifications Overview 8.4.6 Statistical Tools 8.4.7 International Collaboration and Support 8.5 Summary Before the Case Study 8.6 Standardization Leads to Efficiency: Canada's Integrated Business Statistics Program 8.7 IBSP Objectives 8.8 Cornerstones of an Integrated Infrastructure System 8.9 Metadata‐Driven Model 8.10 Integrated Infrastructure 8.11 Information Management 8.12 Standardization and Cooperation Within IBSP 8.13 The Business Register 8.13.1 The BR as the Common Frame 8.13.2 Allocation Factors on the BR 8.13.3 Commodities and Activities on the BR 8.13.4 Robust Methodologies and Generalized Systems 8.14 Standard Tools for Developing EQ 8.15 Developing a Harmonized Content Model 8.16 The IBSP Data Mart and Analytical Tools 8.17 Managing Response Burden 8.18 Electronic Questionnaires 8.19 Large and Complex Enterprises 8.19.1 EPM/LAOS Programs 8.19.2 Customized Collection 8.20 Tax Replacement Strategy 8.21 Active Collection Management 8.22 Rolling Estimate Model 8.23 IBSP Growth and Adaptation 8.24 Efficiencies Gained and Learned 8.25 Conclusion References Chapter 9 The Development and Maintenance of Statistical Business Registers as Statistical Infrastructure in Statistics Indonesia and the Australian Bureau of Statistics 9.1 Introduction 9.2 The Indonesian and Australian Context 9.3 The Definition of a Statistical Business Register 9.4 The Evolution of SBRs in Statistics Indonesia and the Australian Bureau of Statistics 9.4.1 Development of the Statistical Business Register in Statistics Indonesia 9.4.1.1 Phase 1 (1970–2012): Business Directory 9.4.1.2 Phase 2 (2013–2015): Integrated Business Register 9.4.1.3 Phase 3 (2015–2021): The Statistical Business Register 9.4.2 Development of the Statistical Business Register in the Australia Bureau of Statistics 9.5 Statistical Business Register Designs 9.5.1 The Design of the BPS SBR 9.5.1.1 Statistical Unit Model 9.5.1.2 Unit Coverage 9.5.1.3 Data Sources 9.5.1.4 Main Processes 9.5.1.5 SBR Integration 9.5.2 The Design of the ABS Statistical Business Register 9.5.2.1 Centralized Maintenance 9.5.2.2 Dissemination 9.5.2.3 Uses 9.6 Statistical Business Register Benefits 9.7 Statistical Business Register Challenges 9.7.1 SBR Governance and Policy 9.7.2 Business Process Integration 9.7.3 System Development 9.8 Opportunities in SBR Implementation 9.8.1 Transformation Program 9.8.2 National Policy and Initiative 9.9 The Future Spine Concept 9.10 Conclusion Acknowledgment References Chapter 10 Managing Response Burden for Official Statistics Business Surveys – Experiences and Recent Developments at Statistics Netherlands, Statistics Portugal, and Statistics Sweden 10.1 Introduction 10.2 Understanding and Measuring Response Burden 10.2.1 The Concept of Response Burden 10.2.2 Measuring and Monitoring Response Burden 10.3 Organization of Response Burden Management 10.3.1 Legal Context and Cooperation with Other Government Bodies 10.3.2 Organization of Burden Management Within the NSI 10.4 Burden Reduction Measures 10.4.1 Using Alternative Sources 10.4.2 Improving Primary Data Collection 10.4.2.1 Redesigning Content to Fit Data Provision Capacities 10.4.2.2 Sample Coordination 10.4.2.3 Business Survey Communication 10.4.2.4 Feedback 10.4.2.5 File Transfer and Other Techniques (Hybrid Data Collection) 10.4.3 Survey‐assisted Modeling with Mixed Sources 10.4.4 Reducing Burden Through Cooperation 10.4.4.1 Coordination of Metadata 10.4.4.2 Technical Cooperation and Standards 10.4.4.3 Standard Business Reporting 10.5 Discussion Disclaimer and Acknowledgments References Statistics Netherlands – Time Measurement in Dutch and English Statistics Portugal – Burden Questionnaire Statistics Portugal Burden Questionnaire Translation Statistics Sweden Chapter 11 Producing Official Statistics During the COVID‐19 Pandemic 11.1 Introduction 11.2 Managing the Australian Statistical Business Register During COVID‐19 Contributed by Luisa Ryan and AJ Lanyon. Thanks to Richard Mumford, Nick Skondreas and Julie Cole for their management of the ABS BR administrative data during this period, and for Justin Farrow, Jack Steel, Melanie Black and Anthony Russo for methods support. 11.2.1 ABS Business Register and COVID‐19 11.2.2 Changes to Business Reporting 11.2.3 Potential Impacts to the ABS BR 11.2.4 Increasing Number of Employers 11.2.5 Industry Recoding 11.2.6 Business Cancellations 11.2.7 The New Normal? 11.3 Mitigating COVID‐19 Response Rate Risks in the Collection of the ABS Producer and International Trade Price Indexes Contributed by Marie Apostolou and Tanya Price. Thanks to Andrew Tomadini, Robert Villani, and Darryl Malam for their open, adaptive leadership of statistical production through this challenging time. Many analysts in the Producer Prices and International Trade Price Indexes made implementation of the strategy possible. We acknowledge our colleagues in Policy & Legislation, Information Security, and the National Data Acquisition Centre for their willingness to adapt existing policy paradigms and support innovative solutions. A special thanks to Kylie Patman for her excellent project management and execution of the strategy, creative wrangling of management information for the statistics presented in this chapter and her positive resilience throughout. 11.3.1 Overview of ABS Producer and International Trade Price Indexes 11.3.2 ABS Producer and International Trade Prices Data Collection 11.3.3 Developing the COVID‐19 Response 11.3.4 The Optimizing Response Strategy 11.3.5 Prefield Preparation 11.3.6 Field Development and Operations 11.3.7 Outcomes of the Optimizing Response Strategy 11.3.8 Lessons Learned 11.4 The Impact of Changing Data Collection Modes in the IAB Establishment Panel in Response to the COVID‐19 Pandemic Contributed by Corinna König, Marieke Volkert, and Joseph W. Sakshaug. 11.5 Classification and Statistical Implementation of Australian COVID‐19 Government Policies Contributed by Dane Mead and Helen Baird. 11.5.1 Building a New Work Program 11.5.2 Changes to Business Surveys 11.5.3 Policy Case Study – JobKeeper 11.5.3.1 Classification of the Policy 11.5.3.2 Implementation of Policy Classification 11.5.4 Lessons for the Organization 11.6 Seasonal Adjustment and Trend During and Post–COVID‐19 Contributed by Duncan Elliott, Jacqui Jones and Craig H. McLaren. We would like to thank the following country participants that provided information on their NSIs approach to seasonal adjustment and trend estimates during COVID‐19: Jennie Davies and Julian Whiting (Australian Bureau of Statistics); Jan van den Brakel (Statistics Netherlands/Maastricht University); Steve Matthews (Statistics Canada); Andrew Richens, Steve White, Ben Brodie, Richard Penny (Statistics New Zealand). 11.6.1 Pre‐COVID Publication and Presentation of Data 11.6.2 Seasonal Adjustment and Trend Estimation in Practice 11.6.3 Publication and Presentation During COVID‐19 11.6.4 Options for Time Series Publications During COVID‐19 11.6.5 Modeling Outliers 11.6.6 Forward Factors 11.6.7 Option to Suspend Series 11.6.8 Use of High‐Frequency Estimates 11.6.9 Other Time Series Challenges 11.6.10 Conclusion References Part 3 Topics in the Use of Administrative Data Chapter 12 Methodology for the Use of Administrative Data in Business Statistics 12.1 Introduction 12.2 Receive the Data 12.3 Inspect the Data 12.4 Link to Population Frame 12.4.1 Basic Linkage Methods 12.4.2 Linkage of Data Sets in the Presence of Different Unit Types 12.5 From Actual to Target Population 12.5.1 Estimation Methods to Adjust for Undercoverage 12.5.2 Temporary Coverage Issues 12.6 From Observed to Targeted Variables 12.6.1 Harmonization Methods 12.6.2 Editing Methods for Measurement Errors 12.6.3 Correcting for Bias Due to Decentralized and Autonomous Organizations 12.7 From Observed to Targeted Periods 12.7.1 Estimation Methods When Data Are Not Available on Time 12.7.1.1 Benchmarking 12.7.1.2 Forecasting from Previous Complete Data 12.7.1.3 Estimation Techniques 12.7.2 Estimation Methods to Adjust for Periodicity 12.8 Assess Data Quality 12.8.1 Throughput Quality 12.8.2 Output Quality 12.8.3 Analysis of Differences Between Survey and Administrative Data Estimates 12.9 Unsolved Issues 12.10 Conclusion References Chapter 13 Developing Statistical Frameworks for Administrative Data and Integrating It into Business Statistics. Experiences from the UK and New Zealand 13.1 Introduction 13.1.1 Background 13.1.2 Administrative Data Methods Research Program 13.2 Quality Frameworks for Administrative Data 13.2.1 Statistics Netherlands and UNECE Framework 13.2.2 Stats New Zealand and Zhang Framework 13.2.3 The ESSnet Komuso Project 13.2.4 The Southampton University Error Project 13.3 Case Study One – The Use of Value Added Tax Data in the United Kingdom 13.3.1 Organizational Context Within the Office for National Statistics 13.3.1.1 Administrative Data in Practice 13.3.1.2 Dealing with the Data 13.3.1.3 Complex Units in UK Tax Data 13.3.2 Developing Statistical Pipelines for Processing 13.3.3 The Use of Administrative Data in UK Monthly Short‐term Indicators 13.3.4 The Use of Administrative Data for Regional Estimation 13.3.5 Example: Comparison of VAT Data to Survey Data 13.4 Case Study 2 – A Greater Use of Administrative Data in New Zealand's Labor Market Statistics 13.4.1 Organizational Context 13.4.2 Stats NZ's New Monthly Employment Indicator 13.4.3 Redesigning the Quarterly Employment Survey 13.4.3.1 Phase One 13.4.3.2 Phase Two 13.4.4 Introducing Stats NZ's New Quarterly Business Employment Data 13.5 Concluding Remarks References Chapter 14 The Evolution of Integrating Administrative Data in Business Statistics in Ireland 14.1 Introduction 14.2 Administrative Data 14.2.1 Benefits 14.2.1.1 Resources 14.2.1.2 Coverage 14.2.1.3 Timeliness 14.2.1.4 Response Burden 14.2.2 Challenges 14.2.2.1 Access 14.2.2.2 Quality 14.2.2.3 Statistical Units 14.3 Administrative Data in CSO Business Statistics 14.3.1 Legal Mandate 14.3.2 Business Register 14.3.3 Structural Business Statistics (SBS) 14.4 Data Linkage Using Administrative Data 14.4.1 Exporting Enterprises in Ireland 14.4.1.1 Trade Data Sources 14.4.1.2 Linking 14.4.1.3 Linking Trade to Business Register 14.4.1.4 Results 14.4.2 Business Signs of Life 14.4.2.1 Linking 14.4.2.2 Results 14.4.3 Lessons Learned 14.4.3.1 Quality 14.4.3.2 Profiling 14.4.3.3 Coverage 14.4.3.4 Transparency 14.5 The Use of VAT Data in Business Statistics 14.5.1 The Current Situation 14.5.2 VAT Data Available in Ireland 14.5.3 VAT Data for Short‐Term Business Statistics 14.5.4 VAT Data as a Timely Indicator of Business Signs of Life 14.5.5 Business Signs of Life Series 14.6 Summary References Part 4 Topics in Business Survey Data Collection Chapter 15 What Computerized Business Questionnaires and Questionnaire Management Tools Can Offer 15.1 Introduction 15.2 Business Survey Challenges 15.2.1 Concepts 15.2.2 Units 15.2.3 Time References 15.3 The Path to Competent and Motivated Respondents 15.4 What Computerization Can Offer 15.4.1 Source‐Oriented Instruments 15.4.2 Combined Communication Means 15.4.3 Designed Dialogues 15.5 What We Know That We Don't Know Acknowledgments References Chapter 16 Tailoring the Design of a New Combined Business Survey: Process, Methods, and Lessons Learned 16.1 Introduction 16.2 Toward the New “CBS‐DNB Finances of Enterprises and Balance of Payments” Survey 16.3 Achieving Coherent Statistics 16.4 Questionnaire Communication: Tailoring the Design 16.4.1 Steps in the Questionnaire Development Process 16.4.2 The Feasibility Study 16.4.3 What Data and Where Are the Data? 16.4.4 The Business Response Process 16.4.5 How: Questionnaire Design Requirements 16.4.6 Questionnaire Development 16.5 Introducing the Survey in the Field: The Survey Communication Strategy 16.5.1 The Three Phases in the Survey Communication Strategy 16.5.1.1 Pre‐field Phase 16.5.1.2 Field Phase of the First Wave of the Survey 16.5.1.3 Post‐field Phase of the First Wave of the Survey 16.5.2 Evaluation of the Communication Strategy: Was the Strategy Effective? 16.5.2.1 Effectiveness of the Pre‐field Strategy 16.5.2.2 The Response Rate Development in the Next Quarters of 2019 16.6 Lessons Learned Acknowledgment Disclaimer References Chapter 17 Advances in Question(naire) Development, Pretesting, and Evaluation* 17.1 Introduction 17.2 Adaptation and Innovation in Pretesting Methods 17.2.1 Case Study 1: Emerging Topic of Robotics – it takes a village 17.2.1.1 Background 17.2.1.2 Pretesting Methodology 17.2.1.3 Summary 17.2.2 Case Study 2: Multiple Modes and Methods – different strokes for different folks 17.2.2.1 Background 17.2.2.2 Pretesting Methodology 17.2.2.3 Summary 17.2.3 Case Study 3: Record‐Keeping Study – “I don't keep my records that way” 17.2.3.1 Background 17.2.3.2 Pretesting Methodology for Phase I 17.2.3.3 Pretesting Methodology for Phase II 17.2.4 Case Study 4: Usability Testing – when human meets computer 17.2.4.1 Background 17.2.4.2 Pretesting Methodology 17.2.4.3 Summary 17.2.5 Case Study 5: Remote Testing, Logistics, and COVID – reality is virtual 17.2.5.1 Background 17.2.5.2 Remote Testing at NASS 17.2.5.3 Remote Testing at the Census Bureau 17.2.5.4 Summary 17.2.6 Case Study 6: Pretesting Plus Paradata – a look underneath the hood 17.2.6.1 Background 17.2.6.2 Pretesting Methodology 17.3 Pretesting Methodologies: Current Features and Future Needs 17.3.1 Current Features and Consequences 17.3.1.1 Finding “the Missing Link”: Collaborative Partnerships 17.3.1.2 The Odyssey: Exploratory Methods 17.3.1.3 Leave No Stone Unturned: Multiple Methods 17.3.1.4 Methuselah: The Many Roles of Technology 17.3.1.5 Trust the Process: The Response Process 17.3.2 Future Needs and Implications Acknowledgments References Chapter 18 Using Paradata in Electronic Business Survey Questionnaires* 18.1 Introduction 18.2 Paradata 18.3 Questionnaire Completion Paradata 18.4 Looking Inside the Questionnaire Completion Process 18.4.1 Completing the CBS‐DNB Quarterly Survey on Finances of Enterprises and Balance of Payments 18.4.1.1 Questionnaire Completion Profiles 18.4.1.2 Usage of Download and Import Functions 18.4.1.3 Usage of Dutch and English Versions of the Questionnaire 18.4.1.4 Time Needed to Complete the Questionnaire 18.4.1.5 Effect of the Communication Strategy 18.4.2 Business Survey Use of Paradata at Statistics Canada 18.5 Conclusions Acknowledgment Disclaimer References Chapter 19 Recent Findings from Experiments in Establishment Surveys 19.1 Introduction 19.2 Experiments with Mailed Survey Packets to Improve Recruitment Strategies in a National Establishment Survey (BLS) 19.2.1 Motivation 19.2.2 Advance Letter Study 19.2.2.1 Experiment Design 19.2.2.2 Results 19.2.3 Folder Design Study 19.2.3.1 Experiment Design 19.2.3.2 Results 19.2.4 Discussion 19.3 Experiments Testing Changes to Data Collection Timing and Content of Contacts in the US Census of Agriculture (NASS) 19.3.1 Motivation 19.3.1 Survey Design 19.3.1 Results 19.3.1 Experiment 1 Discussion 19.3.1 Survey Design 19.3.1 Results 19.3.1 Experiment 2 Discussion 19.3.1 Survey Design 19.3.1 Results 19.3.1 Experiment 3 Discussion 19.3.1 Overall Summary 19.4 Comparing FedEx to Traditional Postage in a Survey of Substance Abuse and Mental Health Facilities (by Mathematica for SAMHSA) 19.4.1 Motivation 19.4.2 Experiment Design 19.4.3 Results 19.4.4 Discussion 19.5 Addressing Item Nonresponse with Clarifying Information – Evidence from the IAB Job Vacancy Survey (IAB) 19.5.1 Motivation 19.5.2 Experiment Design 19.5.3 Results 19.5.3.1 Item Duration 19.5.3.2 Item Nonresponse 19.5.3.3 Spillover Effects 19.5.4 Discussion 19.6 Summary Acknowledgment References Chapter 20 Web Portals for Business Data Collection 20.1 Introduction 20.2 The NSI Web‐Portal Study 20.2.1 More About the Survey 20.2.2 Survey Results and Other Findings 20.2.2.1 Type and Size of Portals 20.2.2.2 Common Features and Status 20.2.2.3 Registration, Authentication, and Authorization 20.2.2.4 Data Import and Transfer 20.2.2.5 Identifying the Right Business Unit 20.2.2.6 Returning Data to the Respondents 20.2.2.7 Contact Options and Communication 20.2.2.8 Strengths and Weaknesses 20.3 Investigating How to Build a Customized Portal at Statistics Netherlands 20.4 Recommendations and Future Developments 20.4.1 Recommendations 20.4.2 Future Web Portal Developments Disclaimer and Acknowledgements Web Survey Portals .2 Section 1 .2 Section 2 .2 Section 3 .2 Section 4 .2 Section 5 References Chapter 21 A Creative Approach to Promoting Survey Response1 21.1 Introduction 21.2 Background 21.3 Approach and Methods 21.3.1 Public Sector – Private Sector Partnership 21.3.2 Strategic Objectives 21.3.3 Target Segmentation and Focus Groups 21.3.4 Communications Plan 21.4 Results from Focus Groups 21.4.1 Focus Groups – Round One 21.4.2 Focus Groups – Round Two 21.4.3 Intermediary Meeting at Census 21.4.4 Focus Groups Round Three 21.4.5 Additional Meetings 21.5 Development of Campaign Materials 21.5.1 Brochures 21.5.2 Videos 21.5.3 Campaign Website 21.5.4 Partner Briefing Presentation Content 21.5.5 Island Areas 21.6 Campaign Implementation 21.6.1 “9‐8‐7” Campaign 21.6.2 Webinar 21.6.3 Email Awareness Campaign 21.6.4 Internal Communications 21.6.5 Economic Census Day for Census Bureau Staff 21.6.6 Media Relations 21.6.7 Congressional and Intergovernmental Affairs 21.6.8 Social Media 21.6.9 Meetings and Events 21.7 Moving Forward 21.7.1 Respondent Portal Changes 21.7.2 Adaptation for Current Surveys 21.8 Conclusion References Part 5 Topics in the Use of New Data Sources and New Technologies Chapter 22 Statistical Data Production in a Digitized Age: The Need to Establish Successful Workflows for Micro Data Access 22.1 Introduction 22.2 Building Blocks for Successful Workflows Enabling Access to Micro Data 22.2.1 Building Block 1: Laying the Technical and Procedural Foundations 22.2.2 Building Block 2: Generating Safe Results 22.2.3 Building Block 3: Generating Value for All Stakeholders 22.3 An Alternative Approach to Measuring Value: FAIR Data 22.4 Applying the BUBMIC Model to Research Data Centers 22.4.1 Building Block 1: Laying the Technical and Procedural Foundations 22.4.2 Building Block 2: Generating Safe Results 22.4.3 Building Block 3: Generating Value for All Stakeholders 22.4.4 Examples of Generating Value for All Stakeholders 22.4.4.1 Rules for Visiting Researchers at the RDSC For more information, see Research Data and Service Centre (). 22.4.4.2 SDC Packages in Stata The Stata ado files (“nobsdes5”, “nobsreg5” and “maxrdsc”) are available on the BBk‐RDSC's website: https://www.bundesbank.de/en/bundesbank/research/rdsc/data-access. and R The R package “sdcLog” is available on GitHub https://github.com/matthiasgomolka/sdcLog/issues. 22.4.4.3 Dobby, the BBk‐RDSC's High‐Performance and Streamlined Data Production Pipeline See Gomolka et al. () for more information on dobby. 22.4.4.4 RDSC Contract Generator See Blaschke et al. () for more information on the RDSC Contract Generator. 22.4.4.5 Annodata Schema See Bender et al. () for more information on the Annodata schema, and INEXDA Working Group on Data Access () for a specific Annodata use case. 22.5 Conclusion Acknowledgments Disclaimer References Chapter 23 Machine Learning in German Official Statistics1 23.1 Introduction 23.2 Terminology and a Short Introduction to Machine Learning 23.3 Machine Learning in Official Statistics – International Overview 23.4 History and Current Status of Machine Learning in German Official Statistics 23.4.1 Federal Statistical Office of Germany 23.4.2 History and Current Status in the German Official Statistics Network 23.5 Some Current Projects at the Federal Statistical Office 23.5.1 Overview 23.5.2 Machine Learning to Increase Analysis Capabilities in the Area of Minimum Wage Using Official Statistics 23.5.3 Machine Learning for Editing and Imputation 23.5.3.1 Relevance 23.5.3.2 Editing and Imputation in the New Digital Earnings Survey The author thanks Natalie Peternell who is carrying out the project for providing texts on which this section is based. 23.5.3.3 Studies on the Preservation of the Distribution under Imputation 23.6 Summary and Outlook References Chapter 24 Six Years of Machine Learning in the Bureau of Labor Statistics 24.1 Introduction 24.2 Why Official Statistics? 24.3 How Should We Do It? 24.4 Is It Good Enough? 24.5 How Should We Use It? 24.6 How Do We Integrate It? 24.7 How Do We Maintain It? 24.8 Conclusion References Chapter 25 Using Machine Learning to Classify Products for the Commodity Flow Survey 25.1 Background 25.1.1 Commodity Flow Survey (CFS) Background 25.1.2 CFS Data Collection Challenges 25.1.2.1 Nonrespose 25.1.2.2 Data Quality 25.1.2.3 Respondent Burden 25.1.2.4 Related Work 25.2 Data 25.3 Methods 25.3.1 Filtering and Text Cleansing 25.3.1.1 Filtering CFS Response Data 25.3.1.2 Text Preprocessing 25.3.1.3 De‐duplication and Disambiguation 25.3.2 Deriving Variables (Features) from Text Data 25.3.3 Other Features Incorporated into Model – NAICS Code 25.3.4 Resolving Previously Unseen Variables during Prediction 25.3.5 Model 25.3.6 Training and Evaluation 25.3.7 Imputing and Correcting Data – Edit Rule Agreement 25.4 Results 25.4.1 Model Results 25.4.2 Applications to 2017 CFS and Impact of ML 25.4.2.1 Applications 25.4.2.2 Impact of ML 25.5 Conclusion and Future Work Disclaimer References Chapter 26 Alternative Data Sources in the Census Bureau's Monthly State Retail Sales Data Product 26.1 Introduction/Overview 26.2 History of State‐Level Retail Sales at Census 26.3 Overview of the MSRS 26.4 Methodology 26.4.1 Directly Collected Data Inputs 26.4.2 Frame Creation 26.4.3 Estimation and Imputation 26.4.3.1 Composite Estimator 26.4.3.2 Synthetic Estimator 26.4.3.3 Hybrid Estimator 26.4.4 Quality Metrics 26.5 Use of Alternative Data Sources in MSRS 26.5.1 Input to MSRS Model 26.5.2 Validation 26.6 Conclusion Disclaimer References Part 6 Topics in Sampling and Estimation Chapter 27 Introduction to Sampling and Estimation for Business Surveys 27.1 Introduction 27.2 Statistical Business Registers 27.3 Sampling 27.3.1 Stratified Sampling 27.3.2 Cut‐off Sampling 27.3.3 Probability Proportional to Size Sampling 27.3.4 Indirect Sampling 27.3.5 Balanced Sampling 27.4 Estimation 27.4.1 Model‐assisted and Calibration Estimation 27.4.2 Outliers 27.5 Model‐based Estimation 27.5.1 Small Area Estimation 27.5.2 Nowcasting 27.5.3 Model‐based Estimators 27.6 Conclusion References Chapter 28 Sample Coordination Methods and Systems for Establishment Surveys 28.1 Introduction 28.2 Sample Coordination 28.2.1 Notation and Definitions 28.2.2 Methods for Sample Coordination 28.2.3 Methods Based on PRNs 28.2.4 Non‐PRN Methods 28.3 Comparing Sample Coordination Methods 28.3.1 Measures Used in Sample Coordination 28.3.2 Criteria for Sample Coordination 28.4 Sample Coordination Systems 28.4.1 Optimization Measures in Sample Coordination Systems 28.5 Overview of Sample Coordination Systems 28.5.1 Coordinated Poisson Sampling/Conditional Selection 28.5.2 SAMU 28.5.3 Synchronized Sampling 28.5.4 Burden‐Based Coordination 28.5.5 Coordination Functions 28.6 Discussion 28.6.1 Distinguishing Sample Coordination Methods and Sample Coordination Systems 28.6.2 Further Challenges 28.7 Conclusion Acknowledgments References Chapter 29 Variance Estimation for Probability and Nonprobability Establishment Surveys: An Overview 29.1 Estimation for Probability Business Survey Data 29.1.1 Probability Sampling in Practice 29.1.2 Theories of Population Inference 29.1.3 Basic Weighting Steps 29.1.4 Variance Estimation for Probability Surveys 29.1.4.1 Exact Formulas 29.1.4.2 Linearization Methods 29.1.4.3 Replication Methods 29.1.5 Variance Estimation with Imputed Values 29.1.6 Variance Estimation Applications Among Probability Establishment Surveys 29.2 Estimation with Nonprobability Establishment Survey Data 29.2.1 Nonprobability Sampling in Practice 29.2.2 Analytic Objectives 29.2.2.1 Methods for Nonprobability Estimation 29.2.2.2 Methods for Hybrid Estimation 29.2.3 Variance Estimators 29.2.3.1 Quasi‐randomization Methods 29.2.3.2 Superpopulation and Model‐based Methods 29.3 Concluding Remarks References Chapter 30 Bayesian Methods Applied to Small Area Estimation for Establishment Statistics 30.1 Introduction 30.2 Bayesian Hierarchical Modeling for Dependent Data 30.3 Area‐Level Models 30.4 Unit‐Level Models 30.4.1 Basic Unit‐Level Model 30.4.2 Accounting for Survey Design 30.4.3 Models for Non‐Gaussian Data 30.5 Empirical Simulation Study 30.6 Data Analysis 30.7 Discussion Acknowledgments References Chapter 31 Variance Estimation Under Nearest Neighbor Ratio Hot Deck Imputation for Multinomial Data: Two Approaches Applied to the Service Annual Survey (SAS) 31.1 Introduction 31.2 Basic Setup 31.3 Single Imputation Variance Estimation 31.4 Multiple Imputation Variance Estimation 31.5 Simulation Study 31.5.1 Data Generation 31.5.2 Imputation Methods Implemented 31.5.3 Evaluation of Performance 31.5.4 Results 31.6 Empirical Application 31.6.1 Background 31.6.2 Results 31.7 General Conclusion Acknowledgments Disclaimer References Chapter 32 Minimizing Revisions for a Monthly Economic Indicator 32.1 Introduction 32.2 MARTS and MRTS Background and Motivation 32.2.1 Sample Design 32.2.2 Unit Definitions 32.2.3 Response Rates 32.2.4 Estimation Methodology 32.2.5 Imputation Methodology and Procedures 32.3 Estimation Evaluation 32.3.1 Estimation Methods Considered 32.3.1.1 Link Relative Estimation 32.3.1.2 Modified Link Relative Estimator 32.3.1.3 Weighting Class Estimator 32.3.1.4 Ratio Estimator 32.3.2 Estimation Evaluation Criteria 32.3.3 Estimation Empirical Results 32.3.4 Estimation Discussion 32.4 Automating the Detection of High‐Priority Units for Imputation 32.4.1 Methods for Identifying Units for Analyst Imputation 32.4.1.1 Influence Measure Method (Month‐to‐Month Change) 32.4.1.2 Size Identification Method 32.4.1.3 Prioritization 32.4.2 Identifying High‐Priority Units for Analyst Imputation Evaluation 32.4.3 Identifying High‐Priority Units for Analyst Imputation Discussion 32.5 Automating Imputation Procedures 32.5.1 Imputation Model 32.5.1.1 RegARIMA Time Series Model 32.5.1.2 Hierarchical Bayesian Regression (HBR) Model 32.5.2 Simulation Study 32.5.2.1 Simulation Study Design 32.5.2.2 Evaluation Statistics 32.5.2.3 Simulation Results 32.5.3 Imputation Discussion 32.6 Conclusion Acknowledgments Disclaimer References Part 7 Topics in Data Integration, Linking and Matching Chapter 33 Record Linkage for Establishments: Background, Challenges, and an Example 33.1 Introduction 33.2 Variables for Linking Records 33.3 Exact, Deterministic, and Probabilistic Matching 33.3.1 Exact Matching and Deterministic Matching with Multiple Passes 33.3.2 Probabilistic Matching 33.3.3 Combining Deterministic and Probabilistic Linkage 33.4 Additional Considerations in Record Linkage 33.4.1 Structural Considerations in Record Linkage 33.4.2 One‐to‐One Matching 33.4.3 Using Linked Files in Analysis 33.4.4 Confidentiality and Computing 33.5 A Practical Example of Business Data Linking: The Business Longitudinal Analysis Data Environment 33.5.1 Overview 33.6 BLADE Linking Methodology 33.6.1 BLADE Integrating Frame 33.6.2 Linking Data to BLADE 33.6.2.1 ABS Survey Data 33.6.2.2 BLADE Core 33.6.2.3 Linkage of Other Administrative Data 33.6.3 Maintaining BLADE 33.6.3.1 Static and Dynamic BLADE 33.7 BLADE Access Model 33.7.1 Accessing BLADE 33.7.2 Customized BLADE Products 33.7.3 How BLADE Is Being Used 33.8 Conclusion References Chapter 34 Methods for Estimating the Quality of Multisource Statistics 34.1 Introduction 34.2 Representation Error 34.2.1 Estimating and Correcting Selectivity 34.2.1.1 Estimating Output Accuracy with Respect to Representation Errors 34.2.1.2 Correcting for Representation Errors 34.2.2 Case Study on Estimating Bias Due to Selectivity 34.2.3 Case Study on Correcting Output for Selectivity 34.3 Linkage Error 34.3.1 Estimating the Effect of Linkage Errors on Outputs 34.3.2 Adjusting Outputs to Correct for Linkage Errors 34.3.3 Simulation Study on Correcting Contingency Tables for Linkage Errors 34.4 Measurement Error 34.4.1 Quantifying Measurement Error 34.4.2 Estimating the Effect of Measurement Error on Outputs 34.4.3 Case Study on Turnover Growth Rates by Economic Activity 34.4.4 Case Study on Internet Purchases 34.4.5 Obtaining a Bias‐corrected Estimator for Measurement Errors 34.4.6 Simulation Study Comparing the Bias‐corrected Estimators 34.5 Conclusion References Chapter 35 Adopting Previously Reported Data into the 2022 Census of Agriculture: Lessons Learned from the 2020 September Agricultural Survey 35.1 Introduction 35.2 Agricultural Survey and Previously Reported Data (PRD) 35.3 Study Design 35.3.1 Survey Questions and PRD Included in the Study 35.3.2 Design of the Study 35.3.3 Data Collection 35.3.4 Analysis of the Study 35.4 Study Results 35.4.1 Comparison of Response Rates 35.4.2 Comparison of Completion Times 35.4.3 Analysis of Update Rates 35.4.4 Analysis of Edit Rates 35.5 Discussion Acknowledgments References Chapter 36 Integrating Alternative and Administrative Data into the Monthly Business Statistics: Some Applications from Statistics Canada 36.1 Context for Integrating Alternative and Administrative Data 36.2 Replacement of Survey Data by Tax Data in the Monthly Survey of Food Services and Drinking Places 36.2.1 Previous Methodology Under a Survey Design 36.2.2 The GST Administrative File 36.2.3 The New Methodology Proposed to Replace Survey Data with Administrative Data 36.2.4 Assessment and Requirements Before the Implementation 36.2.5 Implementation 36.2.6 Conclusion and Future Work 36.3 Replacement of Survey Data by Scanner Data in the Retail Commodity Survey 36.3.1 The Retail Commodity Survey 36.3.2 Scanner Data File and Coding by Commodity 36.3.3 Methodology to Replace Survey Data with Scanner Data 36.3.4 Quality Assurance Process 36.3.5 Future Work and Conclusion 36.4 Integration of Survey Data with Administrative Data in the Monthly Survey of Manufacturing Using Small Area Estimation Techniques 36.4.1 Methodology of the Monthly Survey of Manufacturing (MSM) 36.4.2 Small Area Estimation (SAE) Method and Implementation 36.4.3 Confidentiality Strategy for the SAE Estimates 36.4.4 Conclusion and Future Work 36.5 Impact of the COVID‐19 Pandemic in the Monthly Business Programs and the Administrative and Alternative Data Sources Used 36.5.1 Survey Responses 36.5.2 Imputation Methods 36.5.3 Scanner Data and Administrative GST Data 36.5.4 Advance Estimates and Supplementary Questions 36.6 Future Initiatives for the Monthly Business Programs 36.7 Conclusion Acknowledgments References Index EULA
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