A Study of Quality Management of Official Statistics in China (Research Series on the Chinese Dream and China’s Development Path) [1st ed. 2022] 981336601X, 9789813366015

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Research Series on the Chinese Dream and China’s Development Path

Wuyi Zeng

A Study of Quality Management of Official Statistics in China

Research Series on the Chinese Dream and China’s Development Path Series Editors Yang Li, Chinese Academy of Social Sciences, Beijing, China Peilin Li, Chinese Academy of Social Sciences, Beijing, China

Drawing on a large body of empirical studies done over the last two decades, this Series provides its readers with in-depth analyses of the past and present and forecasts for the future course of China’s development. It contains the latest research results made by members of the Chinese Academy of Social Sciences. This series is an invaluable companion to every researcher who is trying to gain a deeper understanding of the development model, path and experience unique to China. Thanks to the adoption of Socialism with Chinese characteristics, and the implementation of comprehensive reform and opening-up, China has made tremendous achievements in areas such as political reform, economic development, and social construction, and is making great strides towards the realization of the Chinese dream of national rejuvenation. In addition to presenting a detailed account of many of these achievements, the authors also discuss what lessons other countries can learn from China’s experience. Project Director Shouguang Xie, President, Social Sciences Academic Press Academic Advisors Fang Cai, Peiyong Gao, Lin Li, Qiang Li, Huaide Ma, Jiahua Pan, Changhong Pei, Ye Qi, Lei Wang, Ming Wang, Yuyan Zhang, Yongnian Zheng, Hong Zhou

More information about this series at https://link.springer.com/bookseries/13571

Wuyi Zeng

A Study of Quality Management of Official Statistics in China

Wuyi Zeng Jilin University of Finance and Economics Changchun, China Translated by Li Wenda

Sponsored by the Chinese Fund For the Humanities and Social Sciences ISSN 2363-6866 ISSN 2363-6874 (electronic) Research Series on the Chinese Dream and China’s Development Path ISBN 978-981-33-6601-5 ISBN 978-981-33-6602-2 (eBook) https://doi.org/10.1007/978-981-33-6602-2 Jointly published with Social Sciences Academic Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Social Sciences Academic Press. ISBN of the Co-Publisher’s edition: 978-7-5097-8790-8 © Social Sciences Academic Press 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Series Preface

Since China’s reform and opening began in 1978, the country has come a long way on the path of Socialism with Chinese characteristics, under the leadership of the Communist Party of China. Over 30 years of reform, efforts and sustained spectacular economic growth have turned China into the world’s second largest economy, and wrought many profound changes in the Chinese society. These historically significant developments have been garnering increasing attention from scholars, governments, and the general public alike around the world since the 1990s, when the newest wave of China studies began to gather steam. Some of the hottest topics have included the so-called “China miracle”, “Chinese phenomenon”, “Chinese experience”, “Chinese path”, and the “Chinese model”. Homegrown researchers have soon followed suit. Already hugely productive, this vibrant field is putting out a large number of books each year, with Social Sciences Academic Press alone having published hundreds of titles on a wide range of subjects. Because most of these books have been written and published in Chinese, however, readership has been limited outside China—even among many who study in China— for whom English is still the lingua franca. This language barrier has been an impediment to efforts by academia, business communities, and policy-makers in other countries to form a thorough understanding of contemporary China, of what is distinct about China’s past and present may mean not only for her future but also for the future of the world. The need to remove such an impediment is both real and urgent, and the Research Series on the Chinese Dream and China’s Development Path is my answer to the call. This series features some of the most notable achievements from the last 20 years by scholars in China in a variety of research topics related to reform and opening. They include both theoretical explorations and empirical studies, and cover economy, society, politics, law, culture, and ecology, the six areas in which reform and opening policies have had the deepest impact and farthest reaching consequences for the country. Authors for the series have also tried to articulate their visions of the “Chinese Dream” and how the country can realize it in these fields and beyond. All of the editors and authors for the Research Series on the Chinese Dream and China’s Development Path are both longtime students of reform and opening and v

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recognized authorities in their respective academic fields. Their credentials and expertise lend credibility to these books, each of which having been subject to a rigorous peer-review process for inclusion in the series. As part of the Reform and Development Program under the State Administration of Press, Publication, Radio, Film, and Television of the People’s Republic of China, the series is published by Springer, a Germany-based academic publisher of international repute, and distributed overseas. I am confident that it will help fill a lacuna in studies of China in the era of reform and opening. Shouguang Xie

Foreword

Professor Wuyi Zeng has completed the Key Project of the National Social Science Fund—“Research on Quality Management of National Statistical Data” he hosted and the book will be published. I am very honored to be invited by Prof. Zeng to write the preface for his book. After reading the manuscript, I’d like to share my ideas from the perspective of a government statistician. Government statistics are important information reflecting China’s economic and social development. It is an important tool for the governments at all levels as well as the enterprises and the public to understand China’s economic operations and social development. It is the important basis for the governments at all levels to conduct management and make decisions on economic and social issues. It is also an important reference for enterprises and the public to make decisions on production and operation, investment and consumption. The quality of government statistics is extremely important. It is not only a key concern of the society, but also a core issue of government statistics. The National Bureau of Statistics has always attached great importance to the quality of statistical data. In particular, in recent years, a series of more targeted measures have been taken to improve the quality of statistical data. The first is to strengthen the construction of the basic units database and improve its authenticity, unity, and integrity. In particular, taking the opportunity of the third economic census, a variety of alternative means of information have been adopted to identify the basic units and check against the administrative records provided by the departments of industry and commerce, taxation, and civil affairs, hence systematical update of the database. The second is to establish a statistical system with a set of forms for enterprises to improve the design quality of statistical survey forms. It has changed the previous situation of different statistical survey forms designed for different industries, inconsistency of statistical survey indicators, and different meanings of the same indicators, thereby realizing the integration of the design of statistical survey forms for various enterprises. The third is to establish the online direct reporting system. The National Bureau of Statistics has established the online direct reporting system. In the economic census and most of the regular statistical surveys, the respondents or investigators can directly submit the original data to the vii

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National Bureau of Statistics through the Internet, which has changed the previous mode of reporting by the statistical agencies of local governments from level to level and has effectively curbed the interference of some local governments with statistical data through intermediate links. The fourth is to strengthen inspections of statistical law enforcement and maintain the seriousness of statistical investigation work. We have been seriously investigating and punishing various violations of the Statistics Law and related regulations, such as false reports, concealment, commanded substituted reporting, prompted fabricating data, etc. and adopting means of notification and online exposure to maintain the seriousness of statistical investigations and ensure the normal ecological environment for statistical investigations. The fifth is to make more efforts in audit, inspection, and verification work to improve the quality of basic statistical data. The National Bureau of Statistics conducts a rigorous review of the primary level statistical questionnaires, and selects professional statisticians to conduct regular inspections of the statistical investigations of the primary level units and conduct on-site inspections of the problems found at the primary level units, and urge them to improve the quality of filling statistical questionnaires and minimize the error of filling in the basic data. The sixth is to strengthen the data quality assessment of important statistical indicators and improve their coordination with other relevant indicators and administrative record data. The National Bureau of Statistics conducts data quality assessments of the important statistical indicators, such as the GDP, CPI, industrial added value, the fixed assets investment of the whole society, and total retail sales of social consumer goods on a monthly or quarterly basis to improve coordination of these important indicators with the other related indicators and administrative record data. The seventh is to release various statistical data in time to improve the availability of statistical data to users. However, due to various reasons, there are still some problems in the quality of government statistics in China, and the requirements of users cannot be well met yet. How to further improve the quality of governmental statistical data is an important issue that needs to be studied for current statistical theory research and practical departments. The project hosted by Prof. Zeng Wuyi has conducted a comprehensive and systematic study on the quality of government statistics in China, revealing the existing problems, exploring the causes, and proposing corresponding countermeasures, thus playing a positive role in improving the quality of government statistics and making government statistics better serve the governments at all levels as well as the enterprises and the public. At the same time, the theory and methods of statistical data quality management studied by this research will also promote the development of statistics effectively. Therefore, the research conducted by Prof. Zeng is not only of great practical significance, but also has very important theoretical value. After reading the manuscript, I was deeply impressed and greatly inspired by many parts of the study. First, based on the systematical review of related basic concepts, the author classifies the concept of statistical data quality into three levels. The first level is the basic attribute of statistical data quality, which refers to the accuracy of statistical data, that is, the extent to which the statistical data can truly reflect the quantitative characteristics and quantitative laws of objective things. This is the core and most basic requirement for statistical data quality. The second level is

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the extension of connotation of the statistical data quality. This is the attribute of the statistical data quality inspected from the perspective of users, including timeliness, comparability, applicability, and availability. The third level is to inspect the connotation of statistical data quality from the perspective of overall quality management, that is, to define the connotation of statistical data quality from the various links of statistical data production, including the quality of statistical ecological environment; the quality of statistical design, investigation, and processing; and the quality of the release, revision, and evaluation of statistics. The three-level concept of statistical data quality put forward in this study is well structured, which not only highlights the focus of statistical data quality work, but also defines the connotation of statistical data quality from different angles, and offers the direction of efforts in improving the quality of statistical data, so it is of great value to the government statistics. Second, with the concept of statistical data quality defined and the production process of government statistics systematically reviewed, the author advances a basic framework for implementing overall quality management of government statistics. The basic framework proposes to start with a further reform of the statistical management system to improve the statistical system and method as well as the statistical practice, and focus on the quality control in the whole process of pre-stage, interstage, and post-stage statistical data production. Every link of statistical data production will have an impact on data quality. Therefore, the basic framework for overall quality management of government statistics emphasizes the quality control of the whole process of data production, which is of great significance for improving the quality of government statistics. Third, he has conducted an in-depth study on the data quality of important statistical indicators such as GDP, CPI, real estate price index, and revealed the data quality problems of these indicators from different aspects or different links. Then based on the discussion of the causes of these data quality problems, he puts forward some solutions. For example, he studies the gap between national and regional GDP data and discusses the causes of the problems that lie in institutional mechanisms and statistical system and methods. And then based on that, he puts forward a series of suggestions, such as to evaluate cadres with scientific views of achievements, to evaluate the local economic development comprehensively with scientific development concepts, to further advance the reform of the statistical management system, and to further improve the statistical investigation system and the methods of GDP accounting. All these are very constructive suggestions for solving the problem of gap between the national GDP data and regional ones. Fourth, he has conducted a deep study on the construction of statistical ecological environment. On the basis of summarizing the existing concepts of statistical ecological environment, he extracts the core connotation of statistical ecological environment and puts forward an explicit definition. Besides, he analyzes the way and extent of impact of the statistical ecological environment, including the socioeconomic environment, political legal environment, and institutional environment, on the quality of statistical data, and suggests constructing a statistical ecological environment conducive to improving the quality of government statistics. These studies

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have a positive effect on promoting the construction of good statistical ecological environment and improving the quality of government statistics. I hope that, based on the existing achievements, the research team led by Prof. Zeng will make further research to achieve more fruitful results, thus making more valuable contributions to the improvement of the quality of government statistics. Beijing January 2015

Xu Xianchun Deputy Director of the National Bureau of Statistics

Contents

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Research Background and Its Significance . . . . . . . . . . . . . . . . . . . 1.1.1 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Significance of Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Contents of Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Research on Basic Theories on Management of Statistics Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Research on the Diagnosis and Evaluation Methods of Statistics Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Diagnosis and Analysis of the Quality of Major Macroeconomic Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.4 Study on the Construction of Ecological Environment of Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic Theories of Statistics Quality Management . . . . . . . . . . . . . . . . . 2.1 Basic Concepts of Statistics Quality . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Evolution of Concept of Statistics Quality . . . . . . . . . . . . . 2.1.2 Summarization and Re-Discussion of Concepts of Statistical Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Study on Evaluation Standards of Statistical Data Quality . . . . . . 2.2.1 The Evaluation Criteria for Accuracy of Statistical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 The Quality Standards of Data Dissemination . . . . . . . . . . 2.2.3 A Comparative Analysis of Assessment Frameworks of Statistical Quality Commonly Used in the World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 The Basic Idea of Formulating China’s Statistical Data Quality Evaluation System . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Research on the Framework of Overall Quality Management System of Statistical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 The Producing Process of Government Statistics in China . . . . . . 3.1.1 Pre-stage: Statistical Design . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Intermediate Stage: The Collection and Processing of Statistical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 The Post-stage: Assessment, Release and Revision of the Report Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Construction of a Framework for Overall Quality Management of Statistical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 The Basic Framework of the Statistical Data Quality Management System . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 The Quality Control at the Pre-stage . . . . . . . . . . . . . . . . . . 3.2.3 Quality Control at the Intermediate Stage . . . . . . . . . . . . . . 3.2.4 Quality Control at the Post-stage . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic Methods for Inspection and Assessment of Statistical Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Traditional Analysis Method and Survey Error Assessment Method for Data Quality Inspection . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Traditional Analysis Method . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Survey Error Assessment Method . . . . . . . . . . . . . . . . . . . . 4.2 Statistical Distribution Method and Econometric Modeling Method for Data Quality Inspection . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Statistical Distribution Method . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Econometric Model Method . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Comprehensive Evaluation Method of Data Quality . . . . . . . . . . . 4.3.1 Basic Ideas of the Comprehensive Evaluation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 To Build a Comprehensive Evaluation Index System for Statistical Data Quality . . . . . . . . . . . . . . . . . . . 4.3.3 To Provide the Evaluation Value of the Basic Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 To Determine the Weight of Indicators of Each Level . . . 4.3.5 Comprehensive Evaluation of the Quality of Statistical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.6 Evaluation of Comprehensive Evaluation Method and the Thinking on Further Improvement . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research on the Revision Method of Statistical Data . . . . . . . . . . . . . . 5.1 Basic Principles of Statistical Data Revision . . . . . . . . . . . . . . . . . . 5.1.1 Basic Concept and Types of Statistical Data Revision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Standard of Statistical Data Revision . . . . . . . . . . . . . . . . .

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5.1.3 How to Better Revise Statistics . . . . . . . . . . . . . . . . . . . . . . Survey Data Method for Statistical Data Revision . . . . . . . . . . . . . 5.2.1 The Basic Idea of the Survey Data Method . . . . . . . . . . . . 5.2.2 Several Commonly Used Revision Methods for Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model Method for Statistical Data Revision . . . . . . . . . . . . . . . . . . 5.3.1 The Basic Idea of the Model Method . . . . . . . . . . . . . . . . . 5.3.2 Comparative Analysis and Thinking on the Revised Results of Quantity Input Method and Grey System Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Research on the Quality of China’s GDP Data . . . . . . . . . . . . . . . . . . . 6.1 Research on the Connectivity of National and Regional GDP Data of China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Question Raised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 The Evolution of the Gap Between the Regional Aggregate Data and National Data of GDP . . . . . . . . . . . . 6.1.3 Quality Inspection of National and Regional Data of China’s GDP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Conclusions and Suggestions . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Study on the Connectivity of China’s GDP Data in the Census Year and Regular Year . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Question Raised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 The Difference Between the GDP Accountings in the Census Years and the Regular Years . . . . . . . . . . . . . 6.2.3 Conclusions and Suggestions . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Research on the Connectivity Between China’s GDP Accountings by Production Approach and Expenditure Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Question Raised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 The Evolution of the GDP Data Gap Between Production Approach and Expenditure Approach in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Analysis of the Reasons for the Gap Between GDP Data Calculated by Expenditure Approach and Production Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4 Analysis of the Reasons Why the GDP of Production Approach Is Underestimated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Problems in the GDP Accounting by the Expenditure Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Conclusions and Suggestions . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Evaluation and Analysis of CPI Data Quality in China . . . . . . . . . . . . 7.1 CPI and Its Compilation in China . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Principles of Design and Compilation of CPI in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Statistical Investigation of CPI in China . . . . . . . . . . . . . . . 7.1.3 Compilation, Release and Revision of CPI . . . . . . . . . . . . . 7.2 Analysis of CPI Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Question Raised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.2 Analysis of CPI Bias Based on Time Series . . . . . . . . . . . . 7.2.3 Analysis of CPI Bias Based on Engel Coefficient . . . . . . . 7.3 Aggregate and Connectivity Analysis of CPI . . . . . . . . . . . . . . . . . 7.3.1 Assessment of the Aggregate Scheme of CPI Total Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Connectivity Test of MOM Index and YOY Index . . . . . . 7.4 Multidimensional Quality Assessment of CPI Data in China . . . . 7.4.1 International Comparing Basis for CPI Quality Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Evaluation of CPI Data Quality in China Based on International Comparison . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research on CPI Data Quality in China . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Quality Problems in China’s CPI Data . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 The Problems in the CPI Design . . . . . . . . . . . . . . . . . . . . . 8.1.2 The Problems in the CPI Source Data Survey . . . . . . . . . . 8.1.3 The Problems in the Compilation Methods, Release and Revision of CPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Suggestions on Further Improving the Quality of CPI Data . . . . . 8.2.1 Suggestions on Optimization of Statistical Design . . . . . . 8.2.2 The Suggestions on the Quality Optimization of Statistical Data Production Process . . . . . . . . . . . . . . . . . 8.2.3 Optimization of Control During Data Entry, Review and Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.4 Suggestions for Improvement in the Evaluation and Revision of Statistical Data . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research on the Quality of China’s Real Estate Price Index . . . . . . . 9.1 Compilation of China’s Real Estate Price Index and Its Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.1 China’s Current Real Estate Price Index . . . . . . . . . . . . . . . 9.1.2 The Current Problems of China’s Real Estate Price Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Assessment of Accuracy of Housing Price Index . . . . . . . . . . . . . . 9.2.1 Theoretical Model for Assessing the Accuracy of Housing Price Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

9.2.2 Empirical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3 Research on Housing Price Index Based on Repeat Sales Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Question Raised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.2 The Repeat Sales Model and Its Estimation Method . . . . . 9.3.3 The Processing of Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.4 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Research on Ecological Environment of Statistics . . . . . . . . . . . . . . . . 10.1 Impact of Statistical Ecological Environment on Data Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 How to Build a Statistical Ecological Environment that is Conducive to Improving Data Quality . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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182 185 186 186 187 189 192 196 196 199 199 209 213

Epilogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

About the Author

Wuyi Zeng who was born in January 1953 is a native of Quanzhou, Fujian Province. He is currently a Distinguished Professor of Jilin University of Finance and Economics and Shanghai University of International Business and Economics, as well as a member of the National Advisory Committee of Experts on Statistics and a member of the Statistics Section of the National Social Science Fund of China. His researches mainly focus on economic statistics. He has been a teacher of such courses as Statistics. He has presided over and participated in more than 30 research projects, published more than 30 books and more than 150 papers. He has won 49 provincial and ministerial awards for his teaching and research achievements. His remarkable performance endows him with the State Council Special Allowance for Experts and he is cited as an outstanding teacher of discipline of statistics nationwide. He was selected into the third group of China Outstanding Scientists of Humanities and Social Sciences and the fourth group of Outstanding People’s Teacher of Fujian Province.

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Chapter 1

Introduction

The quality of statistics is the lifeline of government statistical work. How to further improve the quality of the government statistics is now at the top of the agenda for the current statistical theory and practice. This chapter provides a brief introduction to the research background of the book, the theoretical basis and practical significance, in addition to the main contents of the study.

1.1 Research Background and Its Significance 1.1.1 Research Background Since the reform and opening up, China’s statistical agencies of government has made great efforts to improve the quality of statistics. In December, 1983, the promulgation of Statistics Law of People’s Republic of China (hereinafter referred to as Statistics Law) marked that the country’s statistical work began to step on the track of law. After two revisions in May, 1996 and June, 2009, the latest Statistics Law was put into effect on January 1st, 2010. In order to reduce improper intervention in statistical work, the government also introduced relevant rules and regulations: including Circular on Opposing and Restraining Statistical Fraud promulgated in 1998, and Provisions on Disciplinary Actions against Violations of Statistical Laws and Disciplines coming into operation on May 1st, 2009. In the specific data quality management, the National Bureau of Statistics of PRC announced the Management Methods of National and Provincial Bureau of Statistics on Assessment of Quality of Main Statistical Indicators for the first time in early 1999. The statistical quality of 12 indicators, such as regional GDP, total value of agriculture output, grain output, rural per capita net income, the industrial added value and growth rate, fixed asset investment and price index, was assessed © Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_1

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1 Introduction

through self-examination and evaluation within the statistical system. The quality assessment of statistics was included by the National Bureau of Statistics in the statistical inspection system in 2003. The National Bureau of Statistics promulgated the Reform on GDP Accounting and Data Release System (Documentation No. 70 issued by National Bureau of Statistics [2003]) in November, 2003 and the Circular on Improving and Regularizing Regional GDP Accounting (Documentation No. 4 issued by National Bureau of Statistics [2004]) in January, 2004, thus establishing the joint review system for regional GDP data and making the measures to improve and standardize GDP accounting and release. Internationally, China joined the General Data Dissemination System (GDDS) in April, 2002 in order to keep in line with the international standard. These laws and regulations provide important legal and institutional guarantee for preventing and punishing the statistical illegal acts, regulating the statistical order, and improving the quality of statistics. There is no doubt that a great leap has been made for China’s government statistics both in quantity and quality compared with the period before the reform and opening up. However, it should be noted that, with the acceleration of economic globalization and the arrival of the information age, the government and the public have an increasingly high requirement on statistics as there are more and more statistics users; meanwhile, it becomes increasingly difficult to control and manage the quality of statistics since the situation for statistical work is becoming more and more complex. Therefore, in China, there is a certain gap between the quality of government statistics and the requirements of the government and public. Some of China’s official statistics were often questioned by the domestic and overseas media and scholars in recent years. These questions are embodied in two aspects: the first is incoordination, that is, the official data are contradictory to each other, and the second is inconsistency, that is, there is a big gap between the official statistics and people’s perception. For example, Thomas G. Rawski, a professor of economics at the University of Pittsburgh, published an article entitled What Happened to China’s GDP Statistics in China Economic Review in 2001, in which the author listed the facts that China’s energy growth and traffic growth were not synchronized with the GDP growth data during the period of the 10th “Five-year Plan”, thus believing that China’s official statistics overstated the economic growth; Li Deshui, the member of CPPCC National Committee and former Commissioner of National Bureau of Statistics, disclosed a list of figures in March, 2005: there was a total gap of 2658.2 billion yuan between the annual GDP aggregate data reported by all provinces and municipalities and the total volume released by the National Bureau of Statistics in 2004; in July, 2009, the National Bureau of Statistics released the average wage of the country’s urban workers in first half of the year, i.e., 14,638 yuan, an increase of 12.9% compared with the same period of previous year, which was far higher than the 7.1% economic growth rate of the same period, and some netizens mocked that their income “was” increased; and in March, 2010, the National Bureau of Statistics released that the house price in 70 large and medium

1.1 Research Background and Its Significance

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cities nationwide rose by 1.5%, but there was a vast distance between this figure and people’s perception, which aroused serious questions among the NPC deputies and CPPCC members. It should be noted that China’s government statistics had rarely been published to the public before the reform and opening up, therefore there had been no way for the public to query the statistics. In a highly centralized economic management system, the public paid much less attention than today to the statistics. The situation has undergone great changes since the reform and opening up. As more and more statistics were released by the government statistical agencies to the society, the public have paid closer and closer attention to the official statistics due to the diversification of interests and decision-making bodies. Therefore, the emergence of the above queries is rather a manifestation of social progress. In addition, although some media and scholars had some misunderstandings or even prejudice on China’s government statistics, these queries are reasonable to some extent as some quality problems did exist in some of China’s official statistics due to various reasons. Internationally, the research on the quality of official statistics has become a hot topic concerned by the statistical agencies of various countries and some international statistical agencies since 1990s. According to the analysis of Ito Yoichi and Takeshi Mizunoya in Japan, the main reasons that the study of quality of official statistics has become a hot issue for the international statistical agencies in recent years are as follows. Firstly, the economic globalization has been accelerated significantly in recent years. While the knowledge economy with the computer and information technology as the representative sprang up rapidly, the virtual economy corresponding to the real economy also has had a very great development. But the original official statistics failed to adapt to this change and couldn’t reflect the real economic and social situation. In particular, the official statistics of various countries failed to provide adequate and effective warning information for the Asian Economic Crisis in the latter half of 1990s and the global financial crisis in 2008. Secondly, as it was difficult to guarantee the required funds and human resources of official statistics due to the constraints of government budget, the credibility of official statistics declined. To this end, the international statistical agencies and the government statistical agencies of many countries expected to arouse attention and support to statistics from governments and the public through the study of official statistics quality, and, through relevant researches and discussions, to improve the governments’ statistical work and adopt better statistical system and methods to meet better the needs of governments and the public for high-quality statistics.

1.1.2 Significance of Research Under the above background, it is of great practical and theoretical significance to carry out an in-depth research on the quality of China’s official statistics comprehensively and systematically.

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1 Introduction

First of all, the government statistics is not only the basic source of information for the government and the public to get to know the performance of macroeconomy and social development, but also the main basis of their actions and decisions. If the quality of statistics is not good, it is likely to mislead the government and the public and result in unnecessary losses. In addition, taken as a standard to evaluate the performance of different regions or agencies in the current system, the statistics, to a great extent, not only serve as the main basis to appraise the officials’ performance and decide their promotion, but also can affect directly the allocation of government resources. If the quality of statistics is not good, neither the performance of the regions and agencies can be evaluated properly, nor the allocation of government resources can be optimized. Therefore, by finding the problems of China’s government statistics and the specific reasons that affect their quality and putting forward the corresponding countermeasures, this study will greatly improve the quality of China’s government statistics to serve better the government and the public, thereby playing an important role in promoting China’s national economy and social development. Secondly, the queries from all sectors of society have been serious challenges against the authority of China’s government statistics. A comprehensive and scientific diagnosis of the quality of China’s government statistics and the strengthened management on this basis will improve the credibility and authority of China’s statistics, hence a far-reaching significance for the good image of China’s statistical agencies. Finally, it will explore and summarize a set of scientific theories, diagnosis methods and overall management methods on statistical quality, thus playing a very important role in promoting the development of statistics itself.

1.2 Contents of Study 1.2.1 Research on Basic Theories on Management of Statistics Quality In order to solve the quality problems of China’s government statistics, it is necessary to have studies on the basic theories of data quality management at first and make a systematical summary of the basic theory of quality management of statistics combined with China’s national conditions, which will provide a solid theoretical framework for the study of this book. The basic theory research mainly includes the following parts.

1.2.1.1

Research on the Basic Concepts of Statistics Quality

Different statistical agencies and scholars have different definitions of the statistics quality. For example, Statistics Canada has identified six criteria to measure the

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data quality, namely, applicability, accuracy, timeliness, availability, connectivity, and interpretability; the standards of data quality proposed by the Office for National Statistics of UK are accuracy, timeliness, effectiveness and objectivity; Dalenius (1983) proposed the “measurement vector” of statistics quality, including the data’s accuracy, economy, confidentiality, relevance, timeliness, and level of specificity, etc.; Brackstone [1] proposed six dimensions of statistics quality, including relevance, accuracy, timeliness, availability, interpretability and consistency. We believe that the quality of statistics is not limited to the common sense of accuracy. As the result of statistical work, the statistics quality is closely related to its ecological environment and the quality of work in all links. As a “product” of statistical work, the statistics are constantly evolving and changing, and meanwhile, the concept of its quality also needs to keep pace with the time. Therefore, in order to study the quality of statistics in depth, it is necessary to summarize a set of specific, multidimensional and comprehensive concepts of statistics quality on the basis of a systematic summary of relevant research results at home and abroad.

1.2.1.2

Study on Evaluation Criteria of Statistics Quality

The evaluation standard is the basic measurement for data quality, including the quality standard of data dissemination and the framework of data quality assessment. In order to develop the research on China’s evaluation standard for statistics quality, the standards and practice of the developed countries and international organizations researches should be studied first. Then, on the basis of international experience and China’s situation, China’s evaluation standard of statistics quality should be put forward. Second, the general reasons that affect China’s statistics quality and the basic links of statistics quality management should be analyzed further so as to construct the basic standards of China’s statistics quality assessment.

1.2.1.3

Theoretical Research on the Construction of a Comprehensive Quality Management System for Government Statistics

The government statistics are the production of governments’ statistical agencies. The producing process of government statistics is composed of statistical design—statistical investigation—data processing—statistical estimation and analysis—release of statistics—assessment and revision of statistics quality. Ignorance in any link of statistics production may cause quality problems. Therefore, it is necessary to take the overall quality management theory as reference to analyze the various factors affecting the statistics quality and put forward the basic idea of strengthening quality management and quality control from all links, thus building the national quality management system for statistics.

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1.2.2 Research on the Diagnosis and Evaluation Methods of Statistics Quality In order to carry out the study of statistics quality management, some scientific diagnosis and evaluation methods of the statistics quality are needed as the technical support. The existing methods of testing and assessing the statistics quality have their strengths and limitations. The study will not only make systematical summary and conclusion, but will also focus on the analysis of the applicable premises and occasions, and, on the basis of it, make some researches to develop some new methods, especially the composed methods for testing and evaluating the statistics quality by integrating various ways.

1.2.3 Diagnosis and Analysis of the Quality of Major Macroeconomic Statistics The specific diagnosis of the quality of statistics in China in the past was mainly made by some scholars when they studied China’s economic growth or other related economic issues. In regard to the study objects, the previous researches focused mainly on the data quality of such a few indicators as China’s GDP and economic growth, but less on other important indicators. Besides, there were various opinions but no consistent conclusion on the quality of China’s statistics. We believe that the specific reasons affecting the data quality of different indicators are quite different, and some are of the management system and the interest mechanism, while some of the statistical methods and systems. In order to establish China’s overall quality management system for statistics, it is necessary to make comprehensive and systematical diagnose and analysis of the major socio-economic indicators from every dimension by using comprehensively various methods, and, on this basis, to make further analysis of the specific reasons affecting the data quality of different indicators to provide supports for specific solutions. There are a wide range of indicators for the macroeconomy and this study focuses on the following aspects. (1)

(2)

(3)

Diagnosis and analysis of the data quality of the national GDP. This study will make a statistical diagnosis and evaluation of China’s GDP data and other economic indicators by using a variety of methods and relevant data at home and abroad. The research on the connectivity of national and local GDP. At present, there is a big gap between the national GDP data and local aggregate GDP data in China, and the latter is far greater than the former. An in-depth analysis will be conducted to explore the crux of problems and the solutions. The research on the connectivity of annual GDP data in the census year and non-census years. According to the results of national economic census, there

1.2 Contents of Study

(4)

(5)

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were obvious omissions in China’s national GDP statistics in regular years. The study will be made on the issue to find ways of improvement. The research on the coordination among the fixed asset investment, household consumption, government consumption, total volume of import and export, and production and expenditure of GDP. Theoretically, a balance should be maintained between production and expenditure of GDP. Due to various reasons, a big error exists between China’s GDP calculated by expenditure approach and GDP by production appoach. An analysis will be made on this problem to explore the credibility of the two methods and seek for a method to further reduce the calculation errors. The research on the quality problems of main price index, including the consumer price index (CPI) and housing price index. In recent years, the public questioned a lot the data in this area. A variety of methods will be used comprehensively to diagnose its data quality and analyze the specific causes of quality problems. And then a further study will be made on improving the quality of price index in accordance with the concept of overall quality management.

1.2.4 Study on the Construction of Ecological Environment of Statistics The ecological environment of statistics refers to all the external factors and conditions that affect the statistical work. It includes the statistical management system, statistical laws and regulations, statistical concepts, the competence of statistical personnel, the attitude of government and the public towards statistics, etc. The previous research on the construction of ecological environment of statistics mainly focused on the statistical system, statistical law enforcement, survey methods and competence of statistical personnel, etc. In the long run, the fundamental way to improve the quality of statistical data is to build a good statistical ecological environment. There are still some issues in need of further studies and solutions in the statistical ecological environment construction in China, mainly including: the basic concept of statistical ecological environment; the main factors affecting the statistical ecological environment; analysis of the influence of statistical ecological environment on the quality of statistical data; how to construct a statistical ecological environment that is conducive to improving the quality of statistical data. Therefore, the study of statistical ecological environment is also one of the important parts of this book.

Reference 1. Brackstone G (1999) Managing data quality in a statistical agency. Survey Methodol (2)

Chapter 2

Basic Theories of Statistics Quality Management

This chapter provides a basic theoretical framework for the study of the book. On the basis of a systematical study of the basic concepts of statistics quality, the chapter will make systematical study on several major evaluation systems of data quality in the world, and put forward the basic idea of developing an evaluation system for China’s data quality.

2.1 Basic Concepts of Statistics Quality 2.1.1 Evolution of Concept of Statistics Quality In order to make further study on the quality management of statistics, it is necessary to give a scientific definition of the basic concept and connotation of quality management of statistics. In the previous researches on the basic concepts and connotations of statistics quality management, only several properties of the statistics quality were listed without any detailed discussion and, particularly, without any elucidation of their relationships, which also affected the further research on the quality management and quality control of statistics. To this end, this study will trace briefly back the evolution of basic concept of statistics quality on the basis of the relevant research results, and then get a systematical conclusion and summary of the basic connotation of statistics quality. The government statistics, as an achievement of human producing activities, are the social public products provided by the government statistical agencies. Therefore, we can learn from the basic theories of quality management of products to understand the concept and connotation of statistics quality. Historically, the basic connotation of product quality management has been expanding with the social development and the © Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_2

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improvement of requirements on product quality. The evolution of the connotation of the general product quality management can be divided into three stages: The first stage is the quality management based on the producers’ standpoint (i.e. the conformity quality management). Its connotation is that the quality management is measured by how much it is conformed to the standard proposed by the producers. The second stage is quality management based on the standpoint of consumers (i.e. satisfaction quality). Its connotation is that the quality management is measured by how much it can meet consumers’ needs. The product quality is defined on the basis of its utilization, and its quality means the “applicability”, i.e. “the degree to which the product can be used to meet its customers’ needs”. The third stage is the quality management from various angles (i.e. charm quality). This connotation is very broad. It reflects the fact that the product should not only meet the requirement of certain standards, but also meet the demands of such related parties as customers, society (environment, sanitation), employees, and investors. The object of quality evaluation is extended from the product to all the aspects, including process, system, etc. Similar to general product quality management, the basic concept of statistics quality management has also gone through three similar phases. Phase 1. Statistics quality management with “accuracy” as the core. The international statistical circle defined the statistics quality basically as the accuracy of data before the 1980s. The researches on the statistics quality management mainly took the improvement of data accuracy as the basic starting point. From the prospect of sampling technique, the researchers at that time made large amount of researches on how to organize the survey scientifically, trying to reduce the statistical survey error and control the data quality. Some researchers tried to ensure the statistics accuracy through the selection of survey methods, typical samples, control of sampling error, etc. For example, the Norwegian statistician A. N. Kiar (1895) proposed a representative sampling method, the British statistician A. L. Bowley (1915) studied the error in random sampling and non-sampling, and M. H. Hansen and W. N. Hurwitz (1961) put forward a survey error model [1]. Phase 2. Statistics quality management with “users’ needs” as the core. As the connotation of the statistics quality has been expanded since the 1980s, accuracy is no longer the only criterion to measure the statistics quality, and meeting the needs of the users has become a topic concerned by the scholars and statistical agencies of various countries when they make researches on statistics quality. The Handbook of Statistical Organization published by the United Nations Statistics Division in 1980 presented eight requests for government statistics as following: the statistical work must have a clear idea of users’ needs for decision-making and research; the statistical service should be aimed to various users rather than some single type of users; the statistical data should form an organic system through the interrelation of indicators; the statistical material should keep a historical continuity to reflect the historical change in a systematic time sequence; the statistical data should be collected, processed and released in time; the interests of the resondents who provided the investigative materials should be guaranteed and their secrets

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should be kept; the statistical agencies should be fair, objective and free from any prejudice; there should be competent business and administrative leaders for the accuracy and timeliness of statistical materials. Dalenius proposed the “measurement vector” of statistics quality, including accuracy, economy, confidentiality, correlation, timeliness, level of specificity, etc. Wang and Strong determined by means of survey four aspects of data quality that met the users’ needs: the inherent quality of data, the environmental quality of data, the presentation quality of data, and the availability quality of data, which were divided into 15 dimensions as follows: credibility, accuracy, objectivity, data reputation; additional information, correlation, timeliness, integrity, data volume; interpretability, understandability, consistency, simplicity, availability, and safety. Phase 3. Overall quality management of statistics “from source to terminal”. With the development of theories of total quality management, many international organizations and countries have established their own data quality management and evaluation frameworks since 2000. Focusing on users’ needs, these frameworks require further that the government statistical agencies have the prerequisites and environment for production of high quality statistics. It is an overall quality management from source to terminal, including the comprehensive control and management of the ecological environment of statistics, process of production and release of statistics. IMF’s Data Quality Assessment Framework (DQAF) includes not only the basic conditions of statistics quality, but also the requirements in statistical legal environment and statistical work. The Quality Framework and Guidelines for OECD Statistical Activities and the European Statistical System also emphasize the importance of overall statistics quality management from multiple links.

2.1.2 Summarization and Re-Discussion of Concepts of Statistical Data Quality In the quality management standard system (ISO 9000:2000) proposed by the International Organization for Standardization, quality is defined as the extent to which a set of inherent characteristics fulfills the requirements or expectations (demands) that are stated, usually implied or must be fulfilled. The definition of quality in this system contains two aspects. On the one hand, quality is a comprehensive concept, and a set of characteristics of things; on the other hand, quality is the degree to which the demand or expectation is met, and, accordingly, the quality assessment should start from the demand. Taking this scientific definition for reference, we can also understand the quality of official statistics as the extent to which a set of characteristics of statistics can meet the needs of users. The higher the degree of satisfaction, the higher the quality of statistics. Therefore, what specific characteristics statistics need to cover depends on the users’ requirements for the data. The previous researchers have listed out various kinds of ideal characteristics of statistics according to their

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own understanding, some even as many as 10 kinds. We don’t think it necessary. It may blur the nature of the problem by listing all kinds of properties equally and simply, which is not conducive to the management and control of the statistics quality. Therefore, it is necessary to summarize the ideal attributes that the data should have. The concept of statistics quality can be summarized into the following three levels accordingly.

2.1.2.1

Basic Attributes of Connotation of Statistics Quality

Accuracy of statistical data is the most central and basic requirement of data quality. If the data cannot meet the demand of accuracy, that is, the data cannot truly reflect the objective economic phenomenon, the research results from such data will certainly lead to the systematic bias, which will not only result in the loss of its reference value, but also mislead the government, enterprises and the public. Therefore, only when the data meet the requirements of accuracy to a certain extent, is it practical to evaluate the other properties of the data. The accuracy of statistical data is one of the most concerned and questioned aspects of official statistics in China in recent years. The accuracy of statistical data refers to the extent to which statistical data can truly reflect the characteristics and laws in quantity of objective things. The realization of accuracy can be examined from two different perspectives, “precise” and “correct”, that is, “real” and “reliable”. “Fidelity” refers to the closeness between the statistical result and the realistic target that is being measured. To improve the “fidelity” of statistical data, special attention must be paid to statistical design, especially the design of statistical indicators, statistical measures and systems as well as the process control. Taking the statistics of disposable income of residents as an example, we must first define the concept of disposable income of residents scientifically on the basis of clarifying its theoretical connotation, and stipulate the specific content included, and also design scientifically the specific methods of data collection. If the definition of the basic concepts is unscientific, the specific content stipulated is not correct, or the statistical survey methods designed are not appropriate, or there are some improper interferences, it is impossible to obtain the data which can reflect the real disposable income of the residents. For example, the loans obtained from banks by residents have also been used as the disposable income of residents in some economic statistics works in the past, which, however, is not the disposable income of residents according to the economic theories. For another example, if the income tax paid by residents is directly used to estimate the disposable income in investigation and calculation, or the residents are asked about the income directly without necessary explanation, there will often be underreporting or failing to report the income. Therefore, the disposable income statistics of residents obtained in this way may also be much lower than the real income of residents. “Reliability” refers to the degree of credibility of the statistics. The level of reliability can be measured by the magnitude of probability that the statistical indicator value falls within a certain interval (also known as degree of confidence). In order to

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improve the reliability of statistical data, not only the statistical methods and systems are required to be designed scientifically, but also the statisticians are demanded to implement the statistical methods and systems strictly, to exclude various kinds of interference and eliminate irregularities such as fraud and false reports to reduce various errors in investigations so that the authenticity of the statistical process can be ensured. “Fidelity” and “reliability” are two basic measurements for the accuracy of statistical data. The former focuses on whether the measuring results can illustrate clearly the issue to be studied, while the latter stresses whether there are large errors in the measuring results of investigation, concerning nothing about whether the result itself reflects correctly the objective reality. The difference between them is that the involved errors are different. “Reliability” measures the influence of the errors between the observed value of the indicators and the true value of the selected statistical indicators; while “fidelity” needs to reflect, in addition to the above influence of errors, the systematic errors caused by the fact that the selected indicators actually contain variables irrelative to the measuring purpose. As for the accuracy of statistical data, “fidelity” is the essential requirement, while “reliability” is an indispensable auxiliary means for its improvement. The introduction of the concepts of fidelity and reliability to examine the connotation of statistical data quality will be conducive to further management and control of the statistical data quality. As introduced above, the early assessment and control of data quality focused mainly on the selection of survey methods and the evaluation and control of survey errors. This is actually an inspection of the reliability of statistical data, which is undoubtedly necessary but still not enough. To improve the quality of statistical data, it is also necessary to pay attention to whether the designed statistical indicators can truly reflect the issues to be studied, just as a shooter, who wants to achieve good results, must first find the real target, otherwise it will be difficult for his shooting technique, however superb it is, to play a role.

2.1.2.2

Extendedness of the Connotation of Statistical Data Quality

From the users’ perspective, in addition to accuracy, which is the most basic attribute, the ideal official statistics must also have the following properties: (1)

Timeliness. It refers to the extent to which statistical data is provided to meet the users’ need in time. It can usually be reflected by the interval between the time when the economic phenomenon occurs and the earliest time when the data that reflects the phenomenon are available. To research the economic phenomena and formulate the economic policies usually need to keep abreast of the latest development of the economy. If the government departments and statistical agencies cannot provide the necessary data in time, it is likely to become an umbrella offered after the rain, which cannot play its due role.

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2 Basic Theories of Statistics Quality Management

Comparability. It refers to the comparability of the same data indicator in time and space. In terms of the time, since the statistical system and statistical methods of the data may be adjusted, in order to maintain consistency, the statistical agencies are required to make retrospective adjustments to the historical data after each change so that the longitudinal connection of the data can be maintained. In terms of the space, it is required that the statistical standards referred by different countries are as consistent as possible and that the statistical scale of the same indicator should be consistent in different regions in a country. Applicability. It refers to the extent to which statistical data can meet the users’ need for analysis and application. In order to improve the applicability of statistical data as a public product, the government statistical agencies need to understand the needs of different types of users, and the statistical data provided should coincide with the users’ need in terms of the types, definition, components, and classification of indicators. In addition, in order to promote the users’ correct understanding and application of the relevant statistical data, they should provide as much as possible the auxiliary information, including the interpretation of indicators, the calculating methods, and the connection between the published statistical data and the source data. Availability. It refers to the degree of difficulty for the users in obtaining the statistical data and its related auxiliary information. It contains two meanings: one is the degree of difficulty in obtaining statistical data itself; the other is the difficulty in getting the consulting services on relevant statistical information.

It should be pointed out that it is difficult to make the statistical data conform fully to the above attributes at the same time in the real statistical work. For example, in terms of accuracy and timeliness of data, it is often the case that fish and bear’s paws are difficult to coexist. In order to obtain accurate data, it takes a lot of time to make detailed investigation and statistics. Therefore, it is necessary to make appropriate trade-offs or take other remedies. For example, when calculating the GDP, a quick report is often made by using the progress statistics to meet the requirements of timeliness on a basis of general accuracy, and then the detailed accounting of GDP will be made by using the annual report statistics and accounting statements. Finally, a further revision will be made for the GDP by using the economic census data at set intervals to meet the need of accuracy.

2.1.2.3

The Connotation of Statistical Data Quality in Context of Overall Quality Management

The overall quality management is a general management from source to terminal. The quality of statistical data is affected by all aspects of data from production to use. Therefore, the connotation of statistical data quality can also be defined by various production links of data.

2.1 Basic Concepts of Statistics Quality

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Quality of the statistical ecological environment (Prerequisites) The so-called statistical ecological environment refers to all external factors and conditions that affect the statistical work. The high-quality statistical ecological environment requires a high-efficiency, anti-interference statistical management system, a set of sound statistical regulations, a competent statistician team, adequate funding and software and hardware inputs, and scientific attitude of the government and public to statistics, etc.

(2)

The quality of statistical design (Pre-stage) The high-quality statistical design requires the government statistical agencies to give adequate consideration of the national conditions to design the scientific and reasonable systems of statistical indicators and national economic accounting based on the research objectives and relevant international standards, and propose scientific and feasible schemes for statistical survey and statement preparation as well as the standardized texts of the detailed, integrated and normative instruction manuals and guidelines for the professional technicians.

(3)

The quality of statistical investigation and processing (intermediate stage) The key to improving the quality of statistical data at this stage is how to ensure that the source data can be collected and processed according to the designed scheme, and the errors can be minimized in the data collection and processing, and the accuracy of statistical calculation can be improved as much as possible in the process of “source data → report data”.

(4)

Quality of publication, revision and evaluation of statistical data (poststage) The key to improving the quality of statistical data at this stage lies in the following aspects: firstly, the data should be released in time in accordance with the international standards and a variety of data product forms should be provided according to users’ needs; secondly, in terms of data interpretation, explanation of indicators, method descriptions and necessary statistical analysis that are provided should be as detailed as possible; and thirdly, the effective assessment and revision of report data quality should be carried out actively, thus forming a positive interaction between data producers and users.

2.1.2.4

The Relationship Between the Three Concepts of Data Quality

Figure 2.1 is a schematic diagram of the relationship between the three concepts of statistical data quality described above.

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Fig. 2.1 Correlation of three concepts of data quality

It can be seen from the diagram that the concept of data quality from the perspective of overall quality management covers the widest range, and the dimension of the concept from the perspective of users takes the second, while the concept of quality in terms of accuracy is the core part of the concept, though it is relatively simple. These three concepts are derived respectively from the advanced, intermediate, and early stages of management of data quality. It should be noted that, in the three concepts of data quality described above, accuracy is the primary concern of average researchers. In a certain period of time, the normative statistical documents implemented in a country are relatively stable, and the number of statistical indicators released, statistical classification and its timeliness will not change significantly in the short term. Therefore, in the short term, the assessment of the quality of economic statistics is actually mainly about the accuracy of the data. In many cases, the accuracy of data becomes a core issue in assessment of data quality. Therefore, in order to avoid conceptual confusion, we will distinguish the concept of data quality in a broad and narrow sense. The concept of statistical data quality in broad sense includes the latter two concepts mentioned above, which is a multi-dimensional concept. The data quality in narrow sense refers specifically to the accuracy of data. The broad-sensed data quality is a long-term and dynamic comprehensive evaluation system of statistics of a country by international organizations (or a higher-level department to a lower-level agency), reflecting the efforts of a country (or region) to improve the quality of its statistical data; while the narrow-sensed data quality is a short-term requirement of users for statistical data, and it is also a prerequisite for specific researches.

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2.2 Study on Evaluation Standards of Statistical Data Quality 2.2.1 The Evaluation Criteria for Accuracy of Statistical Data In the previous section we have provided the basic meaning of the accuracy of statistical data. In real work, an operational evaluation standard is needed to judge whether the statistical data is accurate or not. Before the 1980s, when evaluating the accuracy of statistical data, the international statistical community mainly explored the error of sampling survey from the perspective of mathematical statistics and sampling techniques, and used the size of error in sampling survey as an important criterion to measure the accuracy of statistical data. They were committed to designing a variety of scientific sampling schemes to reduce sampling errors, thus improving the accuracy of statistical data. But then with the development of statistical theory and practice, people gradually realized that it is very inadequate to use sampling error as the criterion to measure data accuracy. In Table 2.1 we can find various statistical errors that may occur in reality. It tells us that in real life, only part of the errors between the statistical indicators and the true value of the actual target to be observed are caused by the error of the sample survey. In addition, because the true value of the actual target we are observing is in fact unknown to us, it is difficult to quantitatively determine the size of statistical error directly by subtracting the true value of actual targets from the value of statistical indicators, and then to evaluate the accuracy of the statistical data. So, how should the accuracy of statistical data be evaluated in reality? We believe that the following alternative criteria can be applied to determine the accuracy of the statistics. First, to analyze, based on relevant theories, the definition, measurement scales and calculating method of statistical indicators to make a judgment on whether it can theoretically reflect the actual target to be observed. For example, in the past, indexes Table 2.1 Classification of statistical errors

Total errors of statistics I. Statistical design errors

II. Statistical implementation errors

i. Statistical Definition Errors

i. Counting Errors

ii. Sampling Design Errors

ii. Recording Errors

1. Overall Definition Error

iii. Understanding Errors

2. Sample Frame Error

iv. Deception Errors

3. Systematic Sample Error

v. Data Processing Error

4. Random Sample Error iii. Estimation Error iv. Other Design Errors

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of total industrial and agricultural output value were used in China to reflect the rate of economic growth. Since industry and agriculture are only part of the national economy and there are a large number of repeated calculations in the total output value index, even if it is statistically correct, it is not appropriate to use this indicator to reflect the development speed of the entire national economy, nor can it satisfy the basic requirement of accuracy. Second, to observe whether the quantitative characteristics or law of transformation reflected by the data are interpretable in terms of economic significance and real conditions and consistent with the overall direct perception of the vast majority of the public. The interpretability of economic significance and real conditions here refers to a reasonable explanation of theoretical or realistic events for trends and structural adjustment reflected in statistical data. For example, in March 2010, in the real life, housing prices at large rose sharply, but according to the data released by the State Statistical Bureau of China, the housing prices in 70 large and medium-sized cities nationwide in the previous year only rose by 1.5%, which was far apart from the people’s perception, thus causing questions on the accuracy of the current real estate price index. Third, to observe the connectivity of statistical data with each other. Theoretically speaking, there are certain links and achievable connections between various statistical data. For example, the aggregate of GDP of regions should theoretically be equal to the national GDP, or at least there should be no excessive gap between the two. The indicators of economic growth should grow in step with those of energy supply, or with those of transportation. If they failed to connect with each other without any special reasons, it is likely that there are some quality problems in the statistical data (of one or more parts). Fourth, to discover outliers of statistical data through statistical diagnostic methods. There are certain rules for the change of statistical data. If there is an outlier, but no time or reason to explain it in reality, it can be decided that the data may be inaccurate. Fifth, to evaluate the various statistical errors listed in Table 2.1 separately, and priority should be given to quantitative analysis, otherwise, qualitative analysis and relatively rough evaluation should be done. The above evaluation criteria for quality requirements are mainly for the final published indicator data. If to discuss further, the quality requirements for source data, intermediate data and revised data, as well as the related processing methods, should also be included. Source data are the original products of statistical indicators. Only high-quality source data can ensure the accuracy of the final data. Statistical technical methods and evaluation methods of related intermediate data are the control elements to ensure the quality of the productive process. Only the scientific and reasonable technical methods can guarantee the effective implementation of statistical production. The revision of statistical data is a measure to improve the accuracy. Whether it is a revision in the data productive process or a revision of the report data afterwards, either is indispensable to the improvement of the accuracy of government statistics.

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Table 2.2 Quality factors and quality requirements for the “accuracy and reliability” dimension under DQAF Quality factors

Quality requirements

Source data

Source data come from the acquisition projects of comprehensive data that reflect the specific situation of a country Source data is fairly close to the requirements of definition, scope, classification, valuation, and recording time The source data is timely

Assessment of source data

To assess the source data (including censuses, sample surveys and management records) regularly, such as scope of assessment, sampling error, returning error, and non-sampling errors, the results of which are monitored and can be used to guide statistical procedures

Statistical technique

To use sound statistical techniques to process source data To use sound statistical techniques in other statistical procedures (eg, data adjustment and format conversion, data analysis)

Assessment to confirm To confirm the intermediate result based on the corresponding other intermediate data and information statistical outputs To evaluate and investigate the differences in intermediate data Statistical differences and other indications and problems in the data produced by the survey Revision study

To conduct research and analysis of revisions regularly to provide information to statistical procedures internally

Note The “accuracy” of the data referred to in this chapter is basically the same as the “accuracy and reliability” dimension under the DQAF

Table 2.2 contains the basic factors that can impact accuracy and reliability and their specific quality requirements under the DQAF (Data Quality Assessment Framework) proposed by the International Monetary Fund.

2.2.2 The Quality Standards of Data Dissemination Data dissemination is an important part of statistical work, and the quality of data dissemination is also an important aspect of the quality of statistical data. Currently, the data dissemination standards accepted internationally include the Special Data Dissemination Standard (SDDS) and General Data Dissemination Standard (GDDS) formulated by the International Monetary Fund (IMF). In order to supervise the economic and financial situation of member states in a more timely and effective manner, the IMF has formulated these two documents to provide uniform norms and standards for the dissemination of economic and financial statistics of member states. Among them, SDDS is the data dissemination standard for countries that have participated in (or are participating actively in) international financial markets, while

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Fig. 2.2 Main content and quality requirements of GDDS and SDDS. Note Sorted out according to Refs. [2, 3]

GDDS is a data dissemination standard for those member countries that have not yet reached the SDDS standard. As for the difference between the two, GDDS emphasizes efforts to improve the quality of statistical data dissemination, especially in terms of frequency and timeliness of release. The SDDS puts forward higher requirements on the coverage, frequency and timeliness of the data dissemination. The main contents involved in the two types of standards can be illustrated by Fig. 2.2: besides the differences in statistical scope, frequency of release and timeliness, GDDS and SDDS share three common requirements: the quality of released data, the integrity of released data, and public access. We intend to analyze the similarities and differences between the two evaluation frameworks from the following aspects.

2.2.2.1

Common Quality Requirements

As can be seen in Fig. 2.2, the GDDS and SDDS frameworks share the same quality requirements in terms of the quality of released data, the integrity of released data, and public access. Among them, in terms of the quality of released data, the two frameworks focus on data compilation methods, source data, data verification methods, and a statistical framework to ensure rationality of data. In terms of the integrity of

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the published data, the two frameworks emphasize the conditions and regulations of the compilation and the comment of released data as well as the disclosure in advance of the revision methods. In terms of public access, both frameworks believe that the schedule for data dissemination and other related statistical activities should be disclosed in advance, and that the data should be sent to all parties at the same time.

2.2.2.2

Differences in Scope of Statistics

As can be seen in Fig. 2.2, the GDDS covers five departments of national economy, that is, the real sector, the financial sector, the banking sector, the external sector and the social population sector; while the SDDS covers four, namely, the real sector, the financial sector, the banking sector, and the external sector. The data like total population are only schedules which are encouraged to be released. For each sector, both frameworks select the most important data categories based on criteria “that can reflect actual performance, policies, or can help understand economic development and structural transition”. Among them, the data categories selected by the GDDS framework can be divided into two types: “regulated” and “encouraged”; while the SDDS framework divides the selected data categories into “regulated”, “encouraged” and “indicators depending on relevance”. Only the classification of data categories under GDDS and SDDS is given here, as shown in Table 2.3. Table 2.3 Classification of data categories of GDDS and SDDS Sector (data)

GDDS framework

Real sector

Total volume of national accounts, National accounts (nominal, actual production index, price index, and related prices), production labor market indicators index, price index, labour markets

Financial sector

Total budget of the Central Government, debt of the central government

operations of general government or public sectors(depending on its relevance), operations of central government, debt of central government

Banking sector

Total volume of broad money and credit, total volume of central bank, interest rates, stock markets

Analytical account of the banking department, analytical account of the central bank, interest rate, stock market

External sector

The overall balance of international payments, international reserves, commodity trade, exchange rate

Balance of international payments, international reserves, commodity trade, international investment positions, exchange rates

Social population data

Population, education, health care, Schedule: Population poverty

Note Sorted out according to Refs. [2, 3]

SDDS framework

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2.2.2.3

Differences in Releasing Frequency and Timeliness

The GDDS and SDDS frameworks have some differences in the frequency and timeliness of data release. The GDDS framework specifies the frequency of release of different categories of data and the initial time limit of the release interval, as shown in Table 2.4. The SDDS framework puts higher demands on the frequency and timeliness of data dissemination, emphasizing that member states (regions) publish statistics with faster frequency and higher timeliness, as shown in Table 2.5. It can be seen from the comparison between Tables 2.4 and 2.5 that, in general, the SDDS framework has higher requirement on frequency and timeliness of data release than the GDDS framework.

2.2.3 A Comparative Analysis of Assessment Frameworks of Statistical Quality Commonly Used in the World The assessment framework for statistical data quality is a tool for overall assessment of data quality. At present, there are various official guidelines and rich international experience on quality assessment of statistical data which present systematic features. Many international organizations and statistical agencies in developed countries have proposed assessment frameworks for data quality. Among the many frameworks, the DQAF framework of the International Monetary Fund, the Statistical Quality Assurance Framework of the European Statistical System (ESS), and Quality Framework for OECD Statistical Activities are very typical. In the following part, we will briefly introduce the overall framework of these three assessment systems of statistical data quality and conduct comparative analysis accordingly so as to provide useful reference for Chinese government to establish China’s assessment system of statistical data quality.

2.2.3.1 (1)

Three Representative Data Quality Assessment Frameworks [1]

IMF’s DQAF framework

The DQAF framework, developed by the Department of Statistics of International Monetary Fund in 2001, was a set of data quality assessment systems designed for the IMF staff and various national statistical offices as a complement to the GDSS and SDDS frameworks. The design of the DQAF framework refers to the basic provisions of the GDDS and SDDS frameworks, covering various aspects of data quality, such as statistical management, statistical procedures, and statistical products [4]. This framework builds “a precondition and five quality dimensions” of data quality and offers corresponding quality factors and evaluation indicators by comparing current regulations and good practices. In the structural design of the evaluation framework, the DQAF framework takes the multi-layer structure. The

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Table 2.4 Requirements for frequency and timeliness of data release under the GDDS framework Sector

Data category

Release frequency

Timeliness

Annual (encouraging quarterly)

6–9 months

Production index

Monthly (accordingly)

6–12 weeks

Price index

Monthly

1–2 months

Labor market indicators

Annual

6–9 months

Total budget of central government

Quarterly

1 quarter

Debt of central government

Annual (encouraging quarterly)

1–2 quarters

Total volume of broad money and credit

Monthly

1–3 months

Total volume of central bank

Monthly

1–2 months

Interest rate

Monthly

Stock market

Monthly

Overall balance of international payments

Annual (encouraging quarterly)

6 months

External debt and debt repayment arrangements

Differences existing among indicators

Differences existing among indicators

International reserve

Monthly

1–4 weeks

Commodity trade

Monthly

8–12 weeks

Exchange rate

Daily

Population

Annual

year

Education

Annual (a census per ten years)

3–6 months for the annual data and 9–12 months for the census data

Health care

Annual (frequency should be increased for the epidemics, etc.)

3–6 months after the reference period

Poverty

3–5 years

6–12 months after the investigation

Real sector Total national accounts

Financial sector

Banking sector

External sector

Social population data

Note In the category of “External Debt and Debt Repayment Arrangement”, the data of “public sector and its guaranteed external debt” is released on a quarterly basis (with timeliness of 1–2 quarters) and the data of “public sector and its repayment arrangements of guaranteed external debt” is released twice a year (with timeliness of 3–6 months), and the data of “Private Foreign Debt and Debt Repayment Arrangement without Guarantee from the Public Sector” is released annually (with timeliness of 6–9 months). The data is from Ref. [2]

first three layers are applicable to all data sets. The subsequent layers are specific to different types of data sets which can be evaluated in detail through focuses and essentials.

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Table 2.5 Requirements for frequency and timeliness of data dissemination under the SDDS framework Sector

Data category

Release frequency

Timeliness

Real sector

National accounts: nominal, actual and related prices

Quarterly

Quarterly

Production index

Core indicator: monthly (or accordingly)

6 weeks (encouraging to release on a monthly or related basis)

Forward-looking indicators: monthly or quarterly

Monthly or quarterly

Price index

Monthly

Monthly

Labor market indicators

Quarterly

Quarterly

Operation of the general government (public sector)

Annual

2 quarters

Financial sector

Banking sector

External sector

Operations of central Monthly government

Monthly

Debt of central government

Quarterly

Quarterly

Analytical account of Monthly the banking sector

Monthly

Analytical account of Monthly (encouraging to the Central Bank release by week)

2 weeks (encouraging to release by week)

Interest rate

Daily

No strict requirements

Stock market

Daily

No strict requirements

Balance of international payments

Quarterly

Quarterly

International investment position

Annual

2 quarters (encouraging quarterly release)

International reserve

Monthly (encouraging to release by week)

Weekly

Commodity trade

Monthly

8–12 weeks

Exchange rate

Daily

No strict requirements

Schedule: population

Annual

Note The information is from Ref. [3]

(2)

Statistical Quality Assurance Framework of ESS

The Statistical Quality Assurance Framework of the European Statistical System (ESS) consists of five basic elements and is evaluated in four forms. The basic framework is as follows:

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Based on this quality assurance framework, ESS also develops a series of evaluation guidelines and practice standards of statistical data, including practice norms, sets of quality indicator, assessment method manuals, reporting standards and management frameworks. These guidelines and norms provide a set of operational methods for statistical data, such as quality indicators, quality reports, statistical audits, quality certifications and quality improvement measures, to guide the statistical practice of EU member states. Among them, the “Statistical Quality Management Framework” is very efficient in guiding government’s statistical work. Based on the quality framework proposed by the EU Statistical Manual, the framework is divided into three parts: system quality, quality assurance and overall quality management. In March 2009, the new European Statistical Regulations came into being which defines relevant quality standards and offers definitions of quality indicators and their minimum standards, forming a better quality assurance framework than the previous one within the EU system (Fig. 2.3). (3)

Quality Framework for OECD Statistical Activities

OECD Quality Activities Framework and Guidelines for Statistical Activities mainly covers four aspects, the definition and dimensions of data quality, the assurance procedures for the quality of emerging statistical activities, the regular assessment procedures of the quality of existing statistical activities, and the basic norms and quality guidelines for statistical activities. This framework and guidelines can be illustrated by Fig. 2.4. As can be seen in Fig. 2.4, the Quality Framework for OECD Statistical Activities aims to improve data quality by constraining the relevant content of statistical activities with norms. This framework provides an efficient theoretical reference and method guidance for government statistical work of member states (regions) and has

Fig. 2.3 Diagram of statistical quality assurance framework of ESS

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Fig. 2.4 Quality framework for OECD statistical activities

played a very positive role in improving the quality of government statistics in those countries (regions).

2.2.3.2

Comparative Analysis of Three Management Frameworks of Statistical Data Quality

In order to further understand these three frameworks, we will make a comparative analysis of the three frameworks from the overall framework and the quality dimension, thus drawing conclusions that can provide reference to China. (1)

Comparison of the overall frameworks

The DQAF framework of the International Monetary Fund adopts a layered and progressive structure to regulate the management of statistical system, statistical procedures and statistical products related to data quality. From the perspective of its framework structure, its multiple layers include comprehensive quality, quality

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dimension, assessment factors and assessment indicators. The layer of quality dimension includes “prerequisite of quality” and five quality dimensions of “guaranteed integrity”, “soundness of methods”, “accuracy and reliability”, “applicability”, and “availability”. “One quality precondition and five quality dimensions” can be further divided into different quality factors which in turn can be reflected by different specific indicators of quality assessment. On the whole, the DQAF framework represents a process of decomposing from a general dimension to specific elements, and then refining the specific elements to detailed indicators. Statistical Quality Management Framework of ESS is in accordance with the quality framework of the European Statistical Manual. It is divided into three levels according to the relationship with quality management factors: quality assurance framework, system quality framework and overall quality management. The quality assurance framework of ESS is designed to provide methods and guidance for ensuring the quality of statistical producing processes and statistical products. Standardization of production processes has greatly facilitated assessment of data quality. The corresponding quality assurance measures is related to the document description of the quality requirements, the strict definition of each process (requiring common awareness), the regular supervision of the process, the continuous supervision and report of product quality and process quality, informing the users of the relevant information and ensuring the implementation and evaluation of schemes of improving measures. ESS believes that the organizational environment for official statistics is not fully contained in the overall quality management model because it can not be directly controlled by the statistical department itself. The basic principles of the ESS Data Quality Framework stipulate basic measures that can be taken in data product and process, and realize the improvement of organizational quality and credibility of statistical data by defining and assessing performance. The quality assurance framework of OECD is largely linked to the phases of its statistical activities. In this framework, the statistical activities are divided into seven phases, each with corresponding quality requirements. On the whole, the OECD’s quality framework consists of two major requirements, one is the self-assessment of the quality of existing statistical activities, and the other is the quality assurance procedure for the anticipated new statistical activities. Through the above analysis, we can summarize the similarities and differences of the three types of frameworks in Table 2.6. (2)

Comparison of quality dimensions (quality requirements)

The quality dimensions (quality requirements) of the three frameworks are shown in Table 2.7. It can be seen from it that the three frameworks have many similarities in the quality requirements of statistical data. For example, all include dimensions of accuracy (namely “accuracy and reliability” in the DQAF framework), availability, etc.; both the DQAF framework and the OECD’s statistical activity quality framework include requirements of integrity (respectively, integrity and reliability, although different in names, the same in basic meaning); both the ESS statistical quality

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Table 2.6 Comparative analysis of three data quality assessment frameworks Frame

Similarities

Differences

DQAF

Same purpose, to ensure the quality of the statistical data to meet self-defined quality requirements

Focusing on the assessment of data quality, and gradually refining the quality requirements of statistical data from multiple layers of quality dimension (preconditions), quality factors and assessment indicators

Statistical quality management framework of ESS

Focusing on measures to ensure data quality, and laying different emphasis on quality management from the different perspectives of quality assurance framework, institutional quality framework and overall quality management

OECD quality framework and guidelines for statistical activities

Focusing on the control of data quality, and proposing quality requirements of different stages of statistical activities

Table 2.7 Quality dimensions (Quality Requirements) of the three data quality assessment frameworks Frame

Quality dimensions (quality requirements)

DQAF

Prerequisites for quality, assuring integrity, soundness of methods, accuracy and reliability, applicability, availability

ESS statistical quality management framework

Appropriateness, accuracy, timeliness, availability, comparability, integrity, consistency

OECD quality framework and guidelines for statistical activities

Relevance, accuracy, credibility, promtness, availability, interpretability, consistency; in addition, the framework pays special attention to cost efficiency

management framework and the OECD statistical activity quality framework contain the promptness (time limitation) and the consistency dimension (requirements). Of course, there are some differences in the quality dimensions of the three. On the whole, the focuses of the three frameworks are not exactly the same. The DQAF framework focuses more on the quality requirements of the report data, while the ESS statistical quality management framework and the OECD statistical activity quality framework put emphasis on the control of the data production process and the specific measures to ensure data quality, which includes not only the quality requirements

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for the reported data but also the specific quality requirements for the source data. In addition, the DQAF framework not only covers the five dimensions of data quality, but also considers the preconditions of quality, such as legal and institutional environment, resources, relevance, etc., while no inspection of the preconditions for data quality are included in the statistical quality management framework of ESS or the statistical activity quality framework of OECD. (3)

Comparison of assessment methods

The basic evaluation methods of the three frameworks are listed in the Table 2.8. As can be seen in the Table, the three frameworks differ to some degree in assessment type, focus, specific methods, evaluation process and aggregation. The DQAF framework is mainly aimed at the needs of data users, so it is more inclined to evaluate data quality; the statistical quality management framework of ESS and the statistical activity quality framework of OECD not only focus on the assessment of data quality, but also tend to inspect the guarantee conditions of the data quality in all links of data production. In the perspective of the construction of statistical agencies, these two frameworks need to be revised according to the emerging issues or new situations in the process of data production.

2.2.4 The Basic Idea of Formulating China’s Statistical Data Quality Evaluation System There are many factors affecting the statistical data quality of Chinese government. In addition to the causes in statistical scheme, statistical methods and systems, the lack of a comprehensive, systematic and operational assessment system of statistical data quality is also an important reason. According to the comparison and analysis of the three international statistical data quality management frameworks mentioned above, we find that DQAF is mainly a framework for evaluating the quality of data according to the needs of users. The frameworks proposed by ESS and OECD are essentially for quality management and control of the production process of statistical data. Relatively speaking, the latter two frameworks have higher requirements for the statistical department. In accordance with the current statistical level and conditions in China, it is more appropriate to establish a quality management and control framework for government statistics in two steps. The first step is to learn from DQAF framework to meet the needs of users and establish a basic framework for quality assessment of major macroeconomic indicators. The second step is to refer to the framework proposed by ESS and OECD, and, in combination with the real situation in China and from the perspective of data production, to establish an overall quality management system of the statistical data that can meet the needs of users. The basic framework for overall quality management of government statistics is intended to be further explored in Chap. 3 of this book. Here, in reference to the DQAF framework, we propose a basic framework for the quality assessment of Chinese government statistics, as shown in Table 2.9.

Assessment of the data to assess by using production process questionnaires, quality lists, etc

OECD quality framework and guidelines for statistical activities

Formally quantitative assessment, but actually focuses on qualitative assessment

Assessment based on the needs of data users and data production processes

ESS statistical quality Focus on quantitative management framework assessment

to obtain relevant quantitative indicators based on surveys and to assess them based on quality assessment checklists

to conduct assessments using tools such as the “Related Semantic Scale” and the “Expert Advice Method”

Assessment method

Assessment based on the needs of data users

Focus on qualitative assessments as well as quantitative assessments with relevant data

DQAF

Focus

Assessment type

Frame

Table 2.8 Comparison of quality assessment methods of the three frameworks

The statistical process is divided into different steps and projects, and based on the quality criteria to determine whether the specific steps and projects meet the standard requirements, based on which to make assessment

The assessment methods and tools are divided into three levels: the first level is the document and measurement mainly used to obtain relevant assessment information; the second level is assessment, which is producer’s self-inspection and external inspection of quality based on the information of the first level; the third level is qualification, to further refine the information of quality assessment and present it to the data users

First, it is necessary to classify the assessment conclusions of the evaluation indicators. Second, the assessment is carried out by comparing the target data and the quality standards. Third, the pooled analysis of the assessment conclusions is carried out from the perspective of the quality dimension to obtain overall quality assessment results

Assessment process and aggregation

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Table 2.9 Comprehensive assessment framework for data quality Dimensions corresponding to DQAF

Quality dimensions

Quality indicator

Quality indicator description

Quality Precondition Guarantee integrity

Objectivity

A1: Independence of statistical Are statistical agencies and agencies and personnel statisticians independent from other departments, organizations and respondents in form and in substance? Can relevant laws or other regulations support and ensure their professional independence? A2: Openness and transparency Do the statistical policy and of statistical survey statistical survey process maintain the rightful transparency? A3: The extent to which statisticians follow the professional ethical standards

Applicability

Applicability

Is there a clear set of professional ethical standards and guidelines for personnel behavior that are familiar to the personnel?

B1: The suitability of indicator Are the primary users of the preparation for users’ needs statistical product identified? Is the users’ demand for this statistical product identified? B2: Satisfaction of indicator preparation to users’ needs

How satisfied are users with this statistical product? Is appropriate response given to users’ comments and suggestions?

Method soundness

Method soundness

C: Conformity between statistical survey and indicator preparation methods and international standards

Does the methodological basis of statistics follow the internationally accepted standards, guidelines or good practices, including the applied concepts, scope, classifications, etc.?

Accuracy and reliability

Accuracy

D: The degree to which statistical data accurately reflects the real situation

Is the design of the indicator reasonable? Are the data true and can they reflect the reality accurately?

Reliability

E1: The credibility of data processing and quality assessment results

Do statisticians consider the choice of data sources and statistical techniques from a statistical perspective? Are the sound statistical techniques used to compile and analyze data? (continued)

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Table 2.9 (continued) Dimensions corresponding to DQAF

Applicability

Applicability

Quality dimensions

Comparability

Timeliness

Quality indicator

Quality indicator description

E2: Rationality and scientific nature of data revision rules and procedures

Is there research and analysis on the difference in data? Is the adjustment of the difference tracked? Are the relevant investigation means and methods revised?

F1: Comparability in time

Are the concepts and measuring methods of statistical data comparable before and after a period of time?

F2: Comparability in space

Are the concepts and measuring methods of statistics comparable between regions, nationally or internationally?

G1: Timeliness of data release

How long is the interval from the end of the survey to the data release? How long is the interval between the end of the survey and the launch of publication of relevant statistical data?

G2: Frequency of data release

Are the users informed in advance of the time and interval of the release of the statistics? Is the frequency of release of important indicators in line with people’s needs? Is the actual date of the statistical release close to the time agreed in advance?

Availability

Availability

Integrity

Availability

H1: Integrity of the data results Is information about how to collect, process and publish statistics disclosed to users? H2: Disclosure of statistical environmental changes

Are users informed in advance of the major changes that are going to happen in raw data, statistical methods, and statistical techniques?

I1: The ease of obtaining data

Are the type and number of media used to release data sufficient? Can the unpublished (but not confidential) related documents be provided in accordance with users’ requests? (continued)

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Table 2.9 (continued) Dimensions corresponding to DQAF

Quality dimensions

Quality indicator

Quality indicator description

I2: Help to users

Are the users informed how to get help when they have difficulty using data? Are catalogues of publications, documents and other services, including all fee information, widely available?

The concept of data quality in Table 2.9 adopts the multidimensional concept and expands the idea of “one precondition and five quality dimensions” proposed by the DQAF framework. The data quality assessment framework takes a two-tier structure. The first level lists the corresponding data quality dimensions. Among them, objectivity, applicability and soundness of methods are mainly the preconditions for the quality of statistical data and the quality requirements for the design stage of statistical activities; accuracy, reliability and comparability are mainly the quality requirements for the production stage of statistical data; timeliness, integrity and availability are primarily quality requirements for the statistical release phase. Under each quality dimension, a number of specific indicators are set up, through which the inherent requirements of the corresponding data quality dimension can be reflected more accurately.

References 1. Cheng K (2010) Research on diagnosis and management of statistical data quality. Zhejiang Gongshang University Press 2. Department of Comprehensive Statistics of National Bureau of Statistics (2006) GDDS in China. China Statistics Press 3. GDDS, SDDS–the International Standards of Data Release, the web site of National Bureau of Statistics. http://www.stats.gov.cn/ztjc/TJZDGGZGJRGDDS/200203/t20020331_ 29929.HTML 4. Chang N (2004) Data quality assessment framework and implications of IMF. Stat Res (1)

Chapter 3

Research on the Framework of Overall Quality Management System of Statistical Data

In this chapter, first the producing process of statistics of Chinese government will be analyzed, and then, on this basis, the basic framework for the overall quality management of Chinese government statistics will be constructed, with a clear introduction of the key points of quality control that should be noticed in all aspects of data production.

3.1 The Producing Process of Government Statistics in China Government statistics is a general term for a series of activities under the guidance of the government, that is, with the assistance of the government statistical organization system and according to certain statistical theories and methods, to collect the data that reflect the basic situation of national economic and social development, and on this basis, to process and analyze the data and provide corresponding statistical information and consulting services. Government statistical data are the main result of official statistical activities. According to the connection with the direct production links of statistical data, the overall producing process of government statistics in China can be divided into the following three stages: “statistical design (pre-stage)–collection and processing of statistical data (intermediate stage)–evaluation, revision and release of statistical data (post-stage).

© Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_3

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3.1.1 Pre-stage: Statistical Design The pre-stage refers to the stage at which specific statistics have not yet been collected and processed. This stage is also known as the statistical design stage. Statistical design, as the beginning of a statistical activity, is the arrangement for the production of statistical data in advance. The main work task is shown in Fig. 3.1. The work in the pre-stage mainly focuses on the following four questions: What kind of statistical indicators are needed? What data are needed to compile these indicators? How to obtain the data? How to process and analyze the data?

3.1.1.1

Selection and Design of Statistical Indicators

Statistical indicators are concepts and values that reflect the characteristics of an objective overall quantity. The selection and design of statistical indicators is the most important work in the phase of statistical design. According to the nature of the problem to be studied, a unified definition of measurement scales and methods and corresponding classification criteria of statistical indicators should be given under the guidance of relevant substantive discipline theories. Scientific selection and design of statistical indicators and their classification system is the foundation of the quality of statistical data.

3.1.1.2

Source of Data and Design of Coverage

The raw data is the first-hand information for the compilation of statistical indicators. It is of great importance for the follow-up work to select the raw data adapting to the

Fig. 3.1 Work content of the pre-stage

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indicators to be prepared and to determine reasonably their sources and coverage. For example, if it is determined that GDP indicators are to be calculated from the perspective of production, distribution, and application, it is necessary to identify the channels from which to obtain the raw materials needed and the specific scope they cover. Otherwise, it is impossible to work out the GDP data that meet the requirements. When considering the source and coverage of the data, in addition to meeting the needs of the statistical department to compile statistical indicators, we must also take into consideration such factors as the cost of collecting the original data and the basic conditions (personnel, equipment, etc.) required to conduct the investigation, etc.

3.1.1.3

Design of Statistical Survey

Statistical surveys are the basic method of collecting raw data. At present, the statistical survey methods commonly used in China’s statistical practice mainly include: regular statistical statements system, census, sample survey and key survey [1]. Different survey methods have different characteristics. The so-called survey design is, in accordance with the realities and the characteristics of the data to be collected, to put forward specific survey methods in advance, and to formulate basic plans for data collection.

3.1.1.4

Statistical Processing and Analysis Design

The raw data obtained in surveys must undergo necessary processing and analysis to generate statistics that can be released. In the phase of statistical design, it is also necessary to make plans and arrangement in advance on the basic methods of data processing and analysis as well as the progress of work.

3.1.2 Intermediate Stage: The Collection and Processing of Statistical Data The intermediate stage refers to the direct producing phase of the statistics from the start of collecting the raw data at the beginning to the completion of the calculation of the reported data. The main work of the stage is shown in Fig. 3.2.

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Fig. 3.2 The work of the intermediate stage

3.1.2.1

Implementation of Statistical Survey

A statistical survey is conducted based on a statistically designed survey program to obtain raw data. The raw data, which can also be called “source data”, are the first-hand statistics obtained from the respondents and the foundation for calculating various statistical indicators. To a large extent, the quality of the final data of statistical indicators released by the government depends on the quality of the raw data obtained.

3.1.2.2

Entry and Review of Raw Data

Data entry is to import and store various raw data and codes into a computer. The overall process of data entry includes four steps: “coding—pre-auditing—entry— reviewing”. The specific work is shown in Fig. 3.3. Among them, the coding is mainly used for the text information or “enclosure information” in the survey materials, and the correctness of the coding will directly affect the subsequent processing of such data information. Pre-auditing is the quality control before the entry of raw data. Before inputting the data, it is necessary to check the logics, legality and equilibrium relationship to make sure that the raw data are complete, normal, clear, and correct. Data entry includes manual entry and photoelectric input.1 The review of the data is mainly applied for the raw data that has been entered to analyze the basic statistical characteristics of the obtained raw data, and to have a view of the possible missing data, outliers as well as the measurement errors, thus preparing for filling the missing data, identifying and processing outliers, etc.

1

Photoelectric input mainly refers to the application of photoelectric recognition technology developed in recent years in statistical surveys, such as handwritten character recognition technology and logo recognition technology.

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Fig. 3.3 Quality control and measures for raw data entry

3.1.2.3 (1)

Processing of Raw Data

Problems in code matching and the processing highlight

The first step of processing the raw data is to encode and match the corresponding data. The raw data usually consist of a series of qualitative and quantitative attributes of the survey object (that is to be filled in the report). For the data of a single point of time, a given survey unit will involve multiple data of its relevant features. In addition to the encoding of some characteristic data, the matching process should be conducive to obtaining data sequence that reflect the particular use. (2)

Data missing and its treatment

The so-called data missing refers to the inability to obtain some of the required data. The main reasons for data missing are: no respond, invalid respond or omission in the survey. The specific types include the overall missing of a survey unit (the missing of an individual case) and the missing of part of the survey items (the missing of items). Appropriate remedial measures should be taken for the missing data to avoid adverse effect on the quality of the reported data [2].

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(3)

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Outliers and its treatment

Outliers, or abnormal data, refer to the individual statistical values observed that deviate significantly from the observed value of the rest of the statistics group to which they belong. The raw data obtained through a statistical survey (including statements) is usually the data of time-point (or period). It must be pointed out that not all outliers are data all with quality problems. Only those that are statistically abnormal and cannot be explained by economic realities are the data with real quality problems. Therefore, after identifying outliers with statistical methods, it is also necessary to make a comprehensive judgment by combining the corresponding economic theories and the realities.

3.1.2.4

Accounting (Estimation) of the Report Data

That is, according to the accounting (estimation) scheme given in the design stage, the raw data that have been analyzed and processed are used to calculate and compile statistical indicators (also called report data) that can be released.

3.1.3 The Post-stage: Assessment, Release and Revision of the Report Data The post-stage refers to the stage after the relevant statistical indicators have been worked out. The main task of this stage includes the following three aspects:

3.1.3.1

Assessment of Data

After being worked out, the statistical indicators need to be assessed as necessary. The assessment of data can be divided into two levels in general, one is the assessment on accuracy of statistical data; the other is the multi-dimensional comprehensive assessment of statistical data quality. The assessment of data requires not only a comprehensive use of various methods, but also a clear understanding of relevant socio-economic conditions and statistical work.

3.1.3.2

Release of Data

Only after the data have been assessed and considered to meet the basic quality requirements can they be officially released to the public in accordance with the releasing standards. There will usually be multiple releases for some important national economic indicators to better balance the requirements of accuracy and timeliness. For example, as for China’s GDP, the data of the previous quarter will be

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roughly estimated based on the available information at the beginning of each quarter and released to the public in time; and the GDP of the year will be calculated based on the detailed annual data when they are available. Every few years, the GDP will be calculated using more comprehensive and detailed economic census data, and the GDP of previous years will be revised accordingly and published in the statistical yearbook.

3.1.3.3

Revision of Report Data

After the release of statistical data, if serious quality problems are found, or the measurement scales of statistical indicators and the applied basic data have changed greatly, it is necessary to revise the original data. Compared with the raw data obtained from the survey, most of the report data finally published have characteristics of small samples, and most of the revisions are made for data of time series. In reality, the revision of the report data by the official statistical agencies is made mostly to meet the users’ needs, and to provide necessary data support for the government’s macro-control, business’s decision-making and to the public. The revision can not only improve the accuracy of the statistics, but also provide a more comparable long-term time series. The revision of the report data can be classified into two types: The first is to revise the abnormal data and missing data with quality problems. This revision only deals with several data in question, or fills in missing data by certain methods, but makes no change for other data. The second is to reassess all data. Such revisions are generally applicable to statistical indicators that have large questions or have large changes in the measurement scales and that have conditions for reassessment.

3.2 Construction of a Framework for Overall Quality Management of Statistical Data 3.2.1 The Basic Framework of the Statistical Data Quality Management System Based on the understanding of the concept of statistical data quality and the overall generation process of China’s official statistics, we have initially designed a framework for the overall quality management of government statistical data (see Fig. 3.4). Now, we will make more brief explanations of the overall framework. The fundamental goal of overall quality management of statistical data is to ensure the quality of government statistics. All management and control are carried out for this goal. The quality of statistical data can be comprehensively assessed from a number of

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Fig. 3.4 The framework for the overall quality management of government statistical data

different dimensions, such as pre-conditions for ensuring quality, integrity, soundness of methods, accuracy and reliability of data, applicability and availability of data, etc. In order to enable statistical data to meet the above quality standards, it is necessary to further reform and improve the statistical management system, advance the statistical methods and systems, and modify the statistical practice. From the perspective of process management, it is necessary to comprehensively control and manage the pre-stage, the intermediate-stage and the post-stage of data production, and achieve the goal of improving the quality of government statistical data through quality control of the whole generating process of statistical data.

3.2.2 The Quality Control at the Pre-stage The pre-stage is when the statistical design is made. Quality control in the pre-stage has a crucial impact on the quality of government statistics. If the production of statistical data is based on a low-quality statistical design, no matter how high the level of statistical practice is, it is not likely to produce high-quality statistics. The actual situation in China is that the statistical methods and systems fail to advance

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with the times, which is one of the dominating factors affecting the quality of government statistics. Therefore, great importance must be attached to the optimization of statistical design. The optimization of government statistical design mainly focuses on the soundness and standardization of the statistical method system. Since the 20th century, especially since the 1990s, the international organizations such as the United Nations and the IMF have formulated and revised a large number of international statistical norms to guide and standardize the design of statistical systems and methods in various countries. We believe that in optimizing China’s statistical design, we should keep in line with the international community and improve China’s relevant statistical method system in accordance with these guidelines. There are several reasons. First of all, the operation of all modern large-scale production and modern national economy shares considerable things in common. To grasp quantitatively the basic measuring methods for main statistical indicators of social and economic phenomena are applicable to all countries in the world. International measurement scales and calculation methods can be adopted to calculate the relevant statistical indicators. Of course, every country has its own unique features which, however, are not mainly reflected in the different measurements and calculation methods of the statistical indicators, but reflected in the different quantitative performance of the same indicators. For example, the development level of different countries and regions can be reflected by GDP. Nevertheless, the unique feature of each country is not manifested in using different output indicators to reflect the scale and level of the national economic activities, but in different levels of GDP per capita and different industrial structure proportion, etc. Second, all the international standards and normative texts formulated by international organizations, such as the United Nations, are products of the wisdom of experts from all countries, so they are the common treasures of mankind. We can do more with less if we improve and perfect China’s statistical method system according to these guidelines. In the future, China’s statistical experts and government statistical agencies should also take the initiative to participate in the formulation of international statistical standards and normative documents, so that China can have a greater say in the international government statistics community. Third, to standardize China’s statistical method system in accordance with the international norms is conducive to strengthening the comparability of China’s statistics and international statistics and will be beneficial to China’s adaptation to the economic globalization, thus strengthening China’s international competitiveness. It should be pointed out that we advocate a full use of international statistical standards and relevant normative documents when optimizing our statistical design. It does not mean to copy practices of other countries in all details, because there are many differences in the specific national conditions, statistical systems and statistical resources between different countries. Another important goal of optimizing statistical design is to maximize the efficiency of limited statistical resources to invest scarce statistical resources more to the practice that can meet the needs of the public in the country for statistical data. Therefore, under the premise that the basic measurement scales and calculation methods are as consistent as possible with

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the international standards, the optimization of statistical design should also be in line with the actual situation of the country to make appropriate arrangement for the source of data, the scope of coverage, the method of obtaining raw data and the processing method of specific data. Next, we will discuss more about what we need to notice in different aspects of the statistical design stage.

3.2.2.1

Quality Control in the Selection and Design of Statistical Indicators

China’s existing statistical indicator system is still greatly affected by the traditional system. For various reasons, a large number of indicators mainly serving for the management of planned economy have been retained, while the new indicators that the society urgently needs to know have not been supplemented and increased in time. As a result, the large amount of redundancy and serious shortage of statistical information coexist at the same time, so it is difficult to meet the needs of statistical information of all levels of government and the public. In order to solve this problem, in the selection and design of statistical indicators, the following principles should be paid special attention to. First, there must be a clear goal, that is, to figure out what is intended to show through statistical indicators. When designing statistical indicators and statistical indicator system, we need not only “additions” but also “subtractions”. It is necessary to strengthen communication with various users of statistical information, and to add some indicators that are really needed by the society. At the same time, based on the principle of saving resources, it is necessary to clean up the existing indicators and resolutely delete some outdated or unnecessary indicators. And it is also necessary to avoid repeated setting indicators. Second, the established indicators and indicator system must conform to the scientific connotations defined by the relevant socio-economic theories and can be quantitatively measured. Third, we must meet the needs of international comparison as much as possible.

3.2.2.2

Quality Control of Data Sources and Coverage Design

In the existing statistical practice in China, it is very common that, in the statistical methods and systems published officially, the definition and calculation method of a certain statistical indicator seem to be relatively clear, while the specific source and coverage of the raw data used are not explained clearly. Only those who operate personally know how the specific data are obtained, but there is no way for other people to verify and check. This situation has greatly affected the credibility of government statistics, and it is adverse to the further improvement of the statistical method system and the quality of government statistics. To solve this problem, quality control of data source and coverage design should be strengthened.

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The source and the coverage of the data should be determined to meet the needs of the index preparation as much as possible, and meanwhile, the availability of the data, as well as the savings of statistical resources and the quality of the raw data, should also be considered. Besides, it must be released to the public in time to improve its transparency. Specifically, the following points should be noted. First, we should make full use of the existing raw data sources, and new surveys will be organized only for those data that are really needed but cannot be obtained from existing materials. Different statistical indicators may have the same requirements for raw data. Therefore, it is necessary to strengthen the sharing of relevant statistical data between different statistical indicators. By doing so, not only statistical resources will be saved, but also the quality of statistical data be improved. Second, in the case where some direct data required for the preparation of indicators is not available, raw data of similar nature may also be considered as substitution. It should be noted, however, that the substitute data should be highly correlated with the replaced ones and can be obtained in time over a relatively long period of time. Third, in recent years, with the advancement of science and technology, a large number of various types of data that can be quantified, stored and continuously expanded through modern information technology and tools have begun to appear in daily business operations and transactions as well as various social activities. This type of data is called “big data”, such as e-commerce data, financial transaction data, electronic monitoring data, information obtained by online access through various digital sensors, etc. How to make better use of “big data” to compile relevant indicators of government statistics is an important subject that needs attention in the future. Fourth, we should adjust the coverage of data in time. In today’s world, the social and economic conditions are changing rapidly. In order to have statistical indicators reflecting these changes, it is often necessary to make some adjustments on the coverage of the raw data at regular intervals. For example, when compiling CPI, it is often necessary to adjust the selected typical specification products. The goods that have already withdrawn from the market or are not mainstream in the market are no longer typical specification products, while others that are newly emerged with growing market share should be included in the scope.

3.2.2.3

Optimization and Quality Control of Survey Design2

The main problems in the existing statistical surveys in China are as follows: First, although as early as the beginning of 1990s, the National Bureau of Statistics of China had proposed to establish a comprehensive method system of statistical survey based on periodic statistical survey, taking regular sample survey as the 2

Optimization of survey design is one of the key points to improve data quality. However, the optimization of specific survey program involves a wider range of content. Due to space limitations, there is not much discussion here. It is only necessary to point out the key points that need to be noticed in current survey design optimization. Readers interested in the optimization of specific survey options can refer to the references in this chapter.

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principal part supplemented by necessary statistical statements, key surveys, and scientific estimates. But for various reasons, this target system has not yet been fully realized. The sample survey is far from being the principal body of the entire survey system. Second, the application of various statistical survey methods lacks necessary connections and support. There is a disconnect between regular statistical statements and sample surveys, and there is no organic integration between census and sample surveys and daily statistical statements. All of these make the combination of various methods fail to achieve the expected effect, but instead, become a main reason for “data obtained from various sources”, causing unnecessary confusion and waste of resources. In order to further improve the efficiency of statistical surveys, we must attach great importance to the optimization of survey design and further strengthen the quality control of it. Specifically, we need pay special attention to the following points: Firstly, the specific survey method should be determined according to the specific conditions in China and the characteristics of the raw data to be collected. For example, for industrial enterprises above designated size, periodic statements can be used to collect relevant raw data. For industrial enterprises below the scale, sample surveys are appropriate. For the questions that need to estimate the total quantity characteristics through samples, a sample survey should be adopted. For the questions that need to learn the process of development and the specific reasons for the occurrence of the phenomenon, it is suitable to adopt the typical survey method. Secondly, we must further promote the sample survey. Compared with the comprehensive survey, the sample survey is characterized not only by its low cost and high timeliness, but by its less external anti-interference and fewer registration errors. Therefore, sample surveys should be used as much as possible to collect raw data. Of course, we must first solve many problems that currently affect its further promotion. For example, how to further improve the multi-objective and hierarchical sampling methods to adapt to the current needs of the system that governments at all levels are managing the social economy? How to better construct the sampling frame and perform sample rotation? How to determine an appropriate specific sampling plan in accordance with the actual situation of different departments. Thirdly, we must vigorously promote and apply new survey techniques. The popularity of telephones and the Internet and the advancement of computer and electronic sensing technologies in recent years have provided new efficient tools for government statistics to obtain raw data. These new techniques can not only greatly improve the efficiency of statistical surveys, but also bring about major changes in the methods of statistical surveys. Therefore, we should value the promotion and application of new survey techniques, and study carefully when to apply these new technologies and how to better integrate them with various traditional statistical survey methods. Fourthly, we must actively promote the scientific and standardized statistical estimation. The statistical estimation refers to calculation and estimation under certain assumptions about the overall quantitative characteristics and relationship of the

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socio-economic phenomena. It’s carried out in accordance with the objective connection and changing laws of things, on the basis of the information already mastered which have to be done [3]. As a method of obtaining statistical information indirectly, it has many advantages. Scientific statistical estimation can not only greatly reduce the workload of the survey, but also make the data obtained in the survey play a greater role. That’s why it has been widely used in many developed countries. Accordingly, we should coordinate efforts to give clear provisions on the occasions where various statistical estimation methods can be applied, the principles of application, and the linkage of indicators between various materials applied (accounting materials, business materials and statistical data) and the specific details of implementation. The scientific and standardized statistical estimation is an important guarantee against inappropriate use of estimation methods. Fifthly, we should take practical measures to promote integration and convergence between various survey methods. Comprehensive consideration and design should be carried out on the basis of in-depth and meticulous research on various existing methods and systems. In the process, the focus should be laid on the research of various inconsistencies and incoherencies in the existing methods and systems to eliminate their contradictions and conflicts. Besides, it is also essential to carry out necessary integration according to the characteristics of various methods and to arrange scientifically the division of labor, cooperation and convergence among various survey methods. Sixth, we should make a good design of the relevant statistical questionnaire. In order to enable respondents to better cope with relevant surveys and reduce errors or no-response, the questionnaire should be as clear and easy to understand as possible, and necessary explanations should be made for the survey items to help the respondents understand and answer the relevant ones correctly. Randomized question-and-answer techniques can be adopted for some sensitive items.3

3.2.2.4

Optimization and Quality Control of Statistical Processing and Analysis Design

Statistical indicators can be compiled in different ways. For example, the price indices can be compiled in different forms, and indices of different forms have different characteristics, so the calculating result may be different even if the same raw data are used. Therefore, in order to ensure that the resulted statistical indicators are comparable, it must be clearly defined in advance during the statistical design stage. The specific method of preparation should be selected on the basis of a full consideration of the characteristics and limitations of various methods and possible sources of information. In many occasions, it is necessary to use a variety of methods to 3

Randomized question-and-answer technology is a method for investigating sensitive problems. The basic idea is to use randomization technology, so that the investigator can not directly know the specific respondents’ answers to sensitive questions, but can use all respondents. The results of the answer are used to estimate the overall situation. For the specific content of the randomized question and answer technology, please refer to the third chapter of the main Ref. [4] in this chapter.

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calculate separately from different angles. For example, GDP can be calculated by using “production approach”, “income approach” and “expenditure approach” from three different perspectives of production, distribution and application. In this way, not only mutual verification can be achieved and statistical errors can be reduced, but also more useful information can be obtained. In addition, reasonable arrangements for the processing method of raw data, and the specific work schedule, etc. should be made on the basis of full consideration of the opinions of all parties concerned, and followed with necessary summaries, thus facilitating future improvements.

3.2.3 Quality Control at the Intermediate Stage In-process control mainly refers to the control of the collection and processing of statistical data, involving the collection and pre-processing of raw data, accounting of statistical indicators, etc.

3.2.3.1

Quality Control in Implementation of Statistical Survey

Investigators and respondents are the two subjects that influence the quality of statistical survey. The impact on the quality of survey data is mainly reflected by the investigator’s effect, the errors in respondent’s answers and no answer. The investigators’ effect refers to the impact of the competence of investigators on the quality of the survey data. Investigators’ understanding of the survey project and their expressiveness directly affect the information transfer between the investigator and the respondent. Their work attitudes, values, etc. also exert impact on the statistical investigation process. The investigator effect can be classified into two types: firstly, the error caused by the incorrect information transmitted by the investigator to the respondent (including: 1. the investigator fails to correctly understand the survey item and makes a mis-transmission to the respondent. 2. although the investigator understands the survey item correctly, mistakes occur in their expressions which leads to respondent’s misunderstanding.) Secondly, the deviation caused by the insufficiency of number of investigators. In such a case, the quality of the survey data is likely to be lowered if an investigator undertakes too many tasks. The error in the respondent’s answers refers to the error caused by the respondent’s incorrect understanding of the questionnaire or non-cooperation. In real life, due to various reasons, such as the low degree of education of the respondents, or the unwillingness of the respondents to reveal the real situation, the phenomenon of wrong filling, skipping some information, intentional concealment or exaggeration often occurs, hence great deviation of the survey data. The so-called “no answer” mainly refers to the missing of data in the process of statistical survey due to the respondent’s refusal to answer or invalid answer. Specifically, there are two cases for “no answer”: “no answer for a unit” and “no

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answer for the item”. The reason for the former case is mostly due to the omission in the survey, or the non-cooperation of the respondent. While the latter case is that the respondent feels reluctant to answer because of the sensitivity of the survey item. If “no answer” occurs in a large amount, it will have a rather great impact on the integrity and representativeness of the survey data, thus leading to great deviation and error in the statistical inference made through the survey data. In the past statistical practice, the sample replacement method (ie, to find other respondents who are willing to answer) was adopted more frequently by the government statistical agencies in China to deal with the problem of no-answer, while other specialized technical methods were used less frequently. In addition, even when the sample replacement technique was used, there was no strict operation control procedures, and consequently, poor effect. In response to the above problems, corresponding control measures can be designed in three aspects, as shown in Table 3.1. For the quality control of the implementation of surveys, most importantly, a standard implementation system should be built to implement process management for the entire survey. All aspects of the process should be standardized and the investigators should be urged to implement strictly the plan to minimize the actual statistical survey error. For some important large-scale surveys, such as economic censuses and population censuses, subsequent sampling surveys can also be organized, that is, to set more stringent requirements (creating more ideal survey conditions, implementing more rigorous operating procedures, and arranging more experienced investigators, etc.) for a random check for the second time on respondents, and, by comparing the results of the two surveys, to examine the possible errors in the census, based on which to make necessary revisions of the the census results of [1]. Table 3.1 Quality control measures for statistical surveys Error type

Control measures

Investigator effect

1.To improve the statistical professionalism of investigators 2.To allocate the number of units for the investigators reasonably 3.To build a standardized guidance for the survey

Errors in the respondents’ answers 4.To optimize the survey tools, such as statements and questionnaires 5.To improve survey efficiency and reduce the burden on respondents 6.To optimize sample replacement technique 7.To carry out statistical publicity and education campaigns 8.To reduce the impact of external factors (problems that may be of concern to respondents) No answer

9.To inform in advance and make reviews 10.To Innovate in survey tool design 11.To apply effectively of randomized answering techniques

Note Items 4, 10 and 11 of the above control measures should be included, to a large extent, in the “statistical design”

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The quality control measures associated with the survey reality include: to further strengthen the professional education and training of statistical personnel, to improve the effectiveness of the transmission of survey information by direct network reporting, and to reduce the undue interference of external factors on the respondents’ responses, etc.

3.2.3.2

Quality Control of Entry and Review of Raw Data

In order to minimize the frequency of entry errors, the raw data should be tested with necessary methods before the entry.4 Data review is the final step of the process, mainly to check the original error of data coding and the regenerative errors that may occur during the entry phase. In this step, a clear and standardized operation manual should be formulated and a corresponding reward and punishment system be established to keep a close watch on the data entry, thus improving the overall quality of the data entered.

3.2.3.3 (1)

Quality Control of Raw Data Processing

Key point on addressing coding and matching problem

There is vertical matching for newly entered data with past data, that is, in a database containing multiple time points, there should be unique data corresponding to a given attribute of a given time point and a specific unit. In the perspective of workflow, from the acquisition of raw data to the establishment of corresponding database after entry, it is just the process of coding and matching of samples, and in this process the input sample data must be matched with the specific attributes of a specific unit. (2)

Addressing the problem of data missing

There are many relatively mature methods to deal with the missing of raw data, which provide effective processing tools for filling up the missing data. The key to the problem is to figure out the type of missing data and the generation mechanism before filling them up, and choose an appropriate processing method. In addition, it is also necessary to optimize the process by combining the economic relationships between specific data. (3)

Treatment of outliers

Generally speaking, the sample of raw data is large in size, and the method for identifying such outliers of data is relatively mature. For the identification of outliers, the most important point is to find out whether they really have quality problems. There are mainly (but not limited to) the following methods to address the outliers that are confirmed to have quality problems. 4

The specific method can be found in Chap. 4 of this book.

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First, To re-check the data. For data whose quality problem was detected through comprehensive analysis, it can be processed to obtain more “accurate” data by referring to the original record, verifying them with the respondent, etc. This method is the most reliable one for the processing of outliers with quality problems, but because of the large and complex workload, it is mainly applicable for the grassroots statistical departments and personnel to process specific types of data. Second, To smooth data. For those problem data that cannot be effectively processed by checking the original record or implementing re-survey to the respondents, a certain technical method can be used for smoothing. Third, To eliminate the outliers and fill the missing data accordingly. Fourth, to delete directly. If the sample size is large enough and the amount of data to be deleted is relatively small, the problem data can be deleted directly as long as it does not affect the generation and aggregation of statistical data or the stability of the statistical analysis results and the requirements on sample size and representativeness can be ensured.

3.2.4 Quality Control at the Post-stage Quality control at the post-stage involves quality assessment, revision and release of data. It should be noted that in the actual statistical activities, the above three links are often carried on alternately. Before the report data is released, a quality assessment is required. If data quality problems are found, the necessary revisions are required before the data can be officially released. After the release of the report data, there will often be problems with quality assessment and revision of data.

3.2.4.1

Quality Control of Data Quality Assessment

In China’s statistical practice, it’s an urgent task to establish and improve the methods and system of quality assessment of statistical data. The specific methods for data quality assessment will be analyzed and discussed in detail in Chaps. 4, 5 and 6. Here, only some basic points will be listed out that should be paid special attention when carrying out data quality assessment. Firstly, different methods for data quality inspection have their strengths and limitations. Therefore, we must pay attention to the applicable occasions and conditions of various methods, and select the appropriate method for data quality inspection according to the characteristics of the data to be tested. If necessary, a variety of methods should be used integratedly for testing and comparison. Secondly, it is necessary to establish a standardized data quality assessment procedure for various statistical indicators with reference to relevant international standards. Meanwhile, it should be further clarified that only the data that has been subjected to standardized data quality assessment and confirmed to meet basic requirements can be released.

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Finally, for the comprehensive assessment of statistical data quality, based on the relevant international standards, an appropriate assessment framework should be established, and scientific and feasible evaluation methods should be adopted, and the assessment results should be analyzed and explained reasonably.

3.2.4.2

Quality Control of Data Revision

The purpose of data revision is to obtain more comprehensive, accurate and reliable report data. The specific methods of data revision will be discussed in detail in Chap. 7. The focus here is on the basic points that should be noted when implementing data revision. First, the revised object should be the indicator data that have been found quality problems in test or assessment. Second, the revision of the data must have a clear theoretical and practical basis, and must avoid “back-door operation”. Only in this way can the results of the revision be credible. Finally, the revision should be able to better respond to the public’s questions about the quality of the data, and the revised results can better match the overall perception of the public. The revised data can meet the requirements for the connotation and denotation of relevant statistical indicators. Whether time series data or cross-section data, they must be connected and comparable, and at the same time, the changes reflected by the revised data can be interpreted in accordance with theories or economic facts.

3.2.4.3

Quality Control of Data Release

Data release is an important part of generation of government statistics. In the past, there were the following three main problems in the release of government statistics in China: first, some data were not released in time, and some data, such as the statistical data of enterprises below the scale, investigated, though, weren’t released for various reasons. Second, the phenomenon that “data come from various sources and conflict with each other” occurred from time to time. Third, the description of the source of the statistical data, the calculation method and measurement scales was not sufficient, which affected the users’ correct understanding and application of the statistical data. The key to quality control of data release is to further improve and standardize of the data release system by referring to the international standards. It should be made clear when and how often to release the data. Sources and specific processing methods for statistical data should be published to the public in as much details as possible. The division of functions of the central comprehensive statistical system, the local comprehensive statistical system and the statistical department system should be

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clarified in the form of statistical regulations, so as to avoid repetition and omission and achieve data sharing, thus realizing that the macroscopic statistical data can be officially released by only one institution.

References 1. Du X, Hu G (2008) Suggestions on the survey work after China’s 2010 population census. Northwest Popul J (1) 2. Jin Y (2009) Processing of missing statistical data. China Statistics Press 3. Wuyi Z (1999) Theory method and application of statistical estimation. China Financial Publishing House 4. Wuyi Z (2009) Research on statistical survey system and survey methods. China Statistics Press

Chapter 4

Basic Methods for Inspection and Assessment of Statistical Data Quality

In order to effectively solve the quality problems of statistical data, there must be a set of scientific and easily-operated inspection and assessment methods for data quality. By doing so, people can have a clearer understanding of their data quality, thereby enabling statistical data producers to further refine statistical work and improve data quality, and guiding statistical data users to select appropriate statistics for more effective statistical analysis. In this chapter, the author intends to make a systematic summary of the existing inspection methods of data quality, in addition to a brief introduction of the basic principle, and an analysis of the preconditions and characteristics of its application, thus providing necessary foundations for further research.

4.1 Traditional Analysis Method and Survey Error Assessment Method for Data Quality Inspection 4.1.1 Traditional Analysis Method The so-called traditional analysis method refers to some conventional logic test methods widely used in current statistical practice. It mainly includes the following. 1.

Assessment Based on Balance Relationship

Theoretically, there is a certain balance between many economic indicators. For example, GDP can be calculated from the perspective of production, distribution and utilization. According to the “three-way equivalence” principle in national economic accounting, the GDP calculated by the three methods should be roughly equal theoretically. The assessment based on the balance is to collect and observe all kinds of relevant statistical data to inspect whether the data have proper balance between © Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_4

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each other. If it is found that a certain set of statistical indicator data clearly violate the balance between them, it indicates that there is a quality problem in this group, which needs further analysis and verification. 2.

Assessment Based on Correlation

In a certain period of time, many social and economic phenomena are relatively interdependent stably. For example, the value added rate, the proportion of fiscal revenue in GDP, and the ratio of the value added of the three industries are relatively stable in a relatively short period of time, and output and input should generally maintain a roughly synchronous change. The assessment method based on correlation is to observe a group of indicators that are highly correlated theoretically and check whether the correlation has abnormal changes. In statistical practice, this method is mainly used to make judges based on the proportional relationship between indicators, the structural relationship between part of indicators and the overall indicators, as well as the change of the elasticity coefficient of the related indicators. In this regard, it is usually possible to give a range that allows for changes. When the observed change exceeds this range, it is considered that the observed statistical data may have quality problems, which require further analysis and verification. 3.

Assessment of Traditional Analysis Methods

The greatest advantage of traditional analysis is that it is easy to understand and simple to apply. Besides the assessment of source and intermediate data, it can also be used for for the evaluation of integrated statistics. Therefore, it has been widely used in practice. Taking it as the most basic analytical tool, many researchers and even the media often question the accuracy of Chinese government statistics. For example, during the “Two Sessions of NPC and CPPCC” in 2010, the NPC deputies and CPPCC members questioned the fact that the sum of GDP of each region was significantly larger than the national GDP. Rawski clearly raised the question of China’s GDP growth since he cited that the actual GDP growth rate between 1997 and 2000 was 24.7%, while the energy consumption in the same period fell by 12.8%. However, the use of traditional analytical methods must meet certain preconditions, that is, there is indeed a balance or a high correlation between the observed indicators. If this premise does not hold, these methods may lead to wrong conclusions. In addition, the results of assessment using such methods are relatively rough and have multiple directivity. On the one hand, as long as there is no great logical contradiction between the statistical indicator data being assessed, the data of the group may be accepted, but the logical balance is only a necessary condition rather than a sufficient condition for the accuracy of the statistical indicator data. On the other hand, when the assessed statistical data violates a specific logical balance or correlation, although it indicates that there may be quality problems in the data though, it is impossible to determine which indicator has a problem. Taking Rawski’s question as an example. Ren Ruo’en points out in his comments on Rawski’s research as well as the researches by Meng Lian and Wang Xiaolu, that the assumption that economic growth rate and energy growth rate should be roughly equal does not necessarily hold in reality, and it is also difficult to stabilize the correlation between industrial

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added value and cargo transportation, energy consumption and other factors [1]. In addition, between 1997 and 2000, the closure and amalgamation of small coal mines had certain impact on the correct statistics of coal industry. Therefore, we believe that China’s energy consumption data in this period of time was likely to be underestimated. We cannot determine that China’s GDP growth in this period of time was exaggerated based solely on the decline in energy consumption data.

4.1.2 Survey Error Assessment Method 1.

Types of Survey Errors

The assessment of statistical data accuracy is, in the end, an assessment of errors contained in the data. From the perspective of the production of data, the error mainly manifests as statistical survey error. There are two major sources of error in statistical surveys. One is sampling error and the other is non-sampling error. Sampling error, which only exists in the sample survey, is inevitable in estimating the overall through the sample. At present, the academic community has made very advanced researches on sampling errors. As long as the specific sampling method is designed and the sample estimator is given, the corresponding error formula for the estimator will be obtained. All other errors except the sampling error are nonsampling errors. Non-sampling errors can be divided into sampling frame errors, noanswer errors, and measurement errors. Relatively speaking, non-sampling errors are more difficult to calculate and control, but they are common in various forms of surveys, which may occur from design, implementation, data processing to data release. It can cause considerable bias in the results of the survey, which affects the quality of the statistics. The survey error in the following discussion mainly refers to the non-sampling error. 2.

Measure of Non-sampling Errors

In statistical practice, non-sampling errors are usually assessed and measured based on repeated investigations in the post-stage.1 The method is, after the first survey, a sample survey is organized under more ideal and standardized conditions (such as selecting more experienced investigators, strengthening the monitoring of the sample survey process, etc.), and by comparing the random subsample data obtained in the two surveys, the extent of deviation contained in the entire initial survey data is estimated accordingly. When estimating the overall deviation size, two estimation methods are usually adopted: one is to use dual-system estimation, that is, both the initial survey data and the quality sampling data in the post-stage are used for estimation; the other is to make estimation based on the post-stage quality inspection 1

In China, repeated investigations afterwards are often called spot checks afterwards. Post-hoc quality checks not only have the effect of assessing the accuracy of the data, but also play the role of supervising and investigating the implementation staff and verifying the effectiveness of the methods used in this survey.

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data. The population censuses of some countries, such as the United States and Switzerland, are based on post-stage sampling surveys, using a stratified dual-system estimation method to assess the extent of error in national census data. In the second economic census of China, based on the data of the post-stage sample, the survey error in the census data was assessed by calculating the filling rate of the unit in the census and the error rate of the main indicators. The survey error assessment method is mainly applicable to the control and inspection of the quality of the original survey data. The precondition for applying this method is that the repeated surveys in the post-stage must be in high quality. Only in this way can the quality of the first survey be assessed based on the results of the repeated surveys. At the same time, this method requires more resources. In addition, further research is needed on how to inspect and measure various types of non-sampling errors, especially the measurement errors caused by various reasons.

4.2 Statistical Distribution Method and Econometric Modeling Method for Data Quality Inspection 4.2.1 Statistical Distribution Method 1.

Basic Ideas

The statistical distribution method assumes that the statistical indicators to be tested are generally subject to a certain distribution, and the statistical distribution consistency test is performed on the observed statistical data. If the consistency test is passed, the statistical data is considered to be generally credible, otherwise it indicates that there may be quality problems in the statistical data, so further observation and analysis are needed, and relevant criteria are used to find out the exceptional points of the indicators to be evaluated on each individual unit. On this basis, further investigation of the exceptional points is required. 2.

Types of Statistical Distribution Method

According to the different distribution used for the inspection, the statistical distribution method can be divided into the conventional statistical distribution test method and other statistical distribution test methods. The application of the conventional statistical distribution test method is mainly found in a series of papers by Cheng Bangwen et al. Cheng Bangwen et al. argue that in the socio-economic system, the statistical data (such as output, output value, etc.) reflecting the size of the research object approximate a logarithmic normal distribution. They propose that the logarithmic normal distribution can be tested by K-S test and Chi-square test method. If the statistical data does not conform to the normal distribution, the exceptional point is further identified according to the 3σ law; finally, the identified exceptional point will be compared with the ones in the

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early stage. If the data does not change drastically, the data is considered normal; otherwise, there is a quality problem. Fu Deyin proposed to explore the exceptional points in individual data by using exploratory data analysis methods, such as Stem-and-Leaf display, letter value, boxplots, coding table, suspended histogram, negative linear or nonlinear smoothing method and median polish method, etc., to control the quality of the aggregated data [2]. This method makes up for the inadequacy of the conventional statistical distribution test method which is difficult to apply when the theoretical distribution cannot be obtained, so it can also be regarded as an unconventional statistical distribution test method. In addition, the Benford method, which has been widely used abroad in recent years, is also an unconventional statistical distribution test [3]. 3.

Assessment of the Statistical Distribution method

The statistical distribution test method makes good use of statistical theory and methods, and its operation is relatively simple, so it is easy to be accepted by grassroots statisticians. The precondition for the assessment of data accuracy through the statistical distribution test is that the indicators to be assessed obey a specific distribution. But in the vast majority of cases, people do not know whether the indicators to be assessed actually obey this prior distribution. It is the biggest shortcoming of this method. Besides, a relatively large size of sample is required to test the quality of statistical data by statistical distribution methods. Most of the official comprehensive statistics, especially the annual data, often have the characteristics of small samples and are, therefore, less suitable for application of statistical distribution method.

4.2.2 Econometric Model Method 1.

Basic Ideas

The econometric model is an equation based on certain economic theories and actual statistical data that reflects the quantitative relationship between a certain economic variable and its main influencing factors. The econometric model in data quality testing assumes that the relationship between economic variables (statistical indicators) in the real economy can be well described by a certain form of econometric model. Under this premise, first, based on certain economic theories, statistical data and measurement methods are used to construct the econometric model, and then the agreement between the actual statistical data and the models are analyzed. If the model fits well, with the estimated parameters in line with the analysis of economic theory, and meanwhile, there are relatively few exceptional points found by residual analysis, then the statistical data applied can be considered to have a high quality. Otherwise, the statistical data applied may have quality problems, and further analysis is needed.

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The Basic Form of the Panel Data Model

The current measurement model for statistical data quality diagnosis is mainly a single equation structure model which has many forms, of which the panel data model is the most general one. Now, we’ll make a brief introduction of it. The panel data is two-dimensional data composed of time series of different items. Viewed from a vertical perspective, it is time-series data containing N individual members; from a horizontal perspective, it is cross-sectional data formed by individuals at T points in time. The most general form of the panel data linear model can be expressed by the Eq. (4.1). yit = αit + xit βit + u it i = 1, 2, . . . , N ; t = 1, 2, . . . , T

(4.1)

Among them, yit represents the explained variable; x it (x 1 it, x 2 it, …, x k it ), the explanavariable of 1 × k dimension; αit , the constant term of the model; β it = tory β it1 , β it2 , . . . β itk the parameter vector of k × 1 dimension; uit , the disturbance; N, the number of individual section members; T, the total number of observation periods of each section member; and k, the number of explanatory variables. Equation (4.1) can be further evolved into the following three forms according to the different constraints of the components of the intercept term vector α and the parameter vector β. (1)

Constant Coefficient Model yit = α + xit β + u it i = 1, 2, . . . , N ; t = 1, 2, . . . , T

(4.2)

This model is also called a hybrid regression model. In the model, the intercept and slope are the same on different items. The original hypothesis of the model is H01 : α i = α j , β i = β j , (i, j = 1, 2, …, N). (2)

Variable Intercept Model yit = αi + xit β + u it i = 1, 2, . . . , N ; t = 1, 2, . . . , T

(4.3)

The slope of the model is constant, the intercept term varies from individual to individual, and the individual influence can be accounted for by the difference in the intercept term αi (i = 1, 2, . . . , N ). The corresponding model hypothesis is H02 : α = α, βi = β j (i = j; i, j = 1, 2, . . . , N ). (3)

Variable Coefficient Model yit = αit + xit β + u it i = 1, 2, . . . , N ; t = 1, 2, . . . , T

(4.4)

In this model, the intercept term and slope vary with the individual item, and it is an unconstrained model.

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When applying the panel model to data quality inspection, the statistical indicators to be tested are generally used as explained variables, and other related indicators that are simultaneously considered to be relatively accurate and reliable are used as explanatory variables. 3.

Model Setting Test

To use panel data for analysis, we must first set the form of the model. If the model form is not set correctly, the estimation result will deviate far from the simulated economic reality. The test of the form of the model is mainly about whether the parameters α i and β i are constants to the point and time of all individual samples, that is, to check the above H01 and H02 . In the case of the hypothesis H01 : F1 =

(S1 − S3 )/[(N − 1)(k + 1)] ∼ F[(N − 1)(k + 1), N (T − k − 1)] S3 /[N (T − k − 1)]

(4.5)

If the hypothesis H02 holds: F2 =

(S2 − S3 )/[(N − 1)k] ∼ F[(N − 1)k, N (T − k − 1)] S3 /[N (T − k − 1)]

(4.6)

In it, S 1 , S 2 , S 3 represent respectively the residual sum of square of the constant coefficient model, the variable intercept model and the varying coefficient model, which have no individual influence. Given the significance level α, the critical values F1a and F2a for the test are obtained from the F-distribution table. First, let’s test the hypothesis H01 . If F1 < F1α , the original hypothesis H01 is accepted, and the model is a constant coefficient model. If F1 > F1a , the hypothesis H02 should also be tested. If F2 < F2a , H02 is accepted and the model is a variable intercept model; but if F2 > F2a , the model is a varying coefficient model. 4.

Estimation of the Panel Model

Mathematically, the ideal method to testify the estimated panel model is the generalized least squares (GLS) method. 5.

Panel Unit Root Test

In order to prevent spurious regression caused by the non-stationary nature of the variables, the stability of each variable should be tested first before estimating the parameters. The unit root test method for variables in the constant coefficient model is the same as the test method for the general regression model. The unit root of heterogeneous panel data can be tested by Pesaran test (2007) [1]. The principle of the method is as follows. Let the data be generated by the following process, yit = ai + bi yi,t−1 + ci y t−1 + di y t + eit

(4.7)

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The original hypothesis H 0 : bi = 0 is for all i, and the alternative hypotheses H 1 bi < 0, i = 1, 2, …, N 1 , bi = 0, i = N 1 + 1, N 1 + 2, …, N. Based on (4.1), the statistic t of the coefficient bi can be obtained, which is recorded as t i (N, T ). ti (N , T ) =

y i Mw yi,−1 σˆ i (y i,−1 Mw yi,−1 )1/2

(4.8)

In it,    = yi0 , yi1 , . . . , yi,T −1 , yi = (yi1 , yi2 , . . . , yi T ) yi,−1   −1    M w = IT − W W W , W , W = τ, y, y −1 , τ = (1, 1, . . . 1) ,     y = y 1 , y 1 , . . . , y r , y −1 = y 0 , y 1 , . . . , y r −1  −1   yi − Mi,w yi σˆ i2 = , Mi,w = IT − G i G i G i G  , G i = W , yi,−1 . T −4 The statistic can be constructed from the above formula: C I P S(N , T ) =

N 1  ti (N , T ) → N (0, 1) N i=1

(4.9)

This statistic can be used to test the stability of the variables. 6.

Parameter Reliability Analysis

The parameter reliability analysis is to observe and analyze the parameter estimates obtained by using statistical data on the premise that the constructed model is effective enough, that is, the equation can fit very well. According to the different analysis angles, it can be divided into parametric analysis of economic significance and parametric analysis of stability. Parametric analysis of economic significance is to analyze the economic significance of the estimated value of the explanatory variable coefficient, and judge whether it is consistent with the economic theory or the expectation when constructing the model. If the two are inconsistent, the sample data may have quality problems. This method is common in the diagnosis of GDP data quality. Klein and Ozmucur have argued that there is no obvious overestimation of China’s GDP growth rate [4] based on the result of their analysis that the relationship between the rate of change of 15 basic economic variables and China’s GDP growth rate is in full compliance with the economic law. The parameter stability analysis compares the explanatory variables coefficients of different periods and different items, and observes the amount of variation to see if it is within an acceptable range. If it exceeds the acceptable range but without any other reason, then we can tell that the sample data may have some quality problems. Meng Lian and Wang Xiaolu built a model based on Klein and Ozmucur’s method

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and introduced instrumental variables of time and region to analyze the accuracy of GDP data. It is the very example of application of this method [5]. 7.

Analysis of Exceptional Points

Based on the result that the fitting model can pass the relevant tests, we can further analyze the exceptional points. The exceptional point analysis is, based on the calculation of the model residuals, to find the sample points that may have abnomly by using a certain method. The specific steps of the exceptional point analysis are as follows. The first step is to calculate the residual of the model fitting: eit = Yit − Yˆit

(4.10)

In the second step, the residual is tested by statistical methods to check whether it is abnormal. There are three main types of statistical test methods commonly used in practice. (1)

Nair Verification method. This method is suitable for situations where the population standard deviation σ is known. If the statistic of the Nair Verification is Rn , there will be: Rn =

|xd − x| σ

(4.11)

In it, x d is observed residual, x is sample mean of residual, and σ is the population standard deviation. According to the detection level α, the rejection level α* and the sample capacity n, we check the “critical value table for outliers by Nair Verification Method” to obtain the detection critical value R1−α (n) and the rejection critical value R1−α (n). If Rn > R1− (n), x d is the rejected outliers. If Rn > R1−α∗ (n), x d is the outlier which should be rejected. When there may be multiple outliers, the observation data can be arranged in non-descending order. Then, firstly, we check the value at the farthest end (the extreme value). If it is an outlier, we reject it and continue to check the n−1 observation value, and so on, until no outlier can be detected. (2)

Grubbs’ test. The Grubbs’ test can be used when a population follows a normal distribution and the population standard deviation is unknown. The specific method is as follows: If the Grubbs’ test statistic is Gn, then: Gn =

|xd − x| √ V

(4.12)

In√the formula, the meanings of x d and x are the same as in the Nair Verification, and V is the sample standard deviation. The procedure of the Grubbs’ test is the same as the Nair Verification. That is, the test statistic Gn is calculated first, and the

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Table 4.1 Dixon statistics calculation formula Sample size n

Verify high-end outliers

3–7

D = r10 =

x (n) −x (n−1) x (n) −x (1)

Verify low-end outliers  = D  = r10

x (2) −x (1) x (n) −x (1)

8–10

D = r11 =

x (n) −x (n−1) x (n) −x (2)

 = D  = r11

x (2) −x (1) x (n−1) −x (1)

11–13

D = r21 =

x (n) −x (n−2) x (n) −x (2)

D = r21 =

14–30

D = r22 =

x (n) −x (n−2) x (n) −x (3)

 = D  = r22

x (3) −x (1) x (n−2) −x (1)

30–100

D = r22 =

x (n) −x (n−2) x (n) −x (3)

 = D  = r22

x (3) −x (1) x (n−2) −x (1)

x (n) −x (n−2) x (n) −x (2)

detection critical value G1−α (n) and the rejected critical value G1−α * (n) are obtained by checking the table; if Gn > G1-α (n), the endpoint value is the detected outlier. If Gn > G1-α * (n), the endpoint value is the outlier which should be rejected. The test procedure for multiple outliers is the same as the Nair Verification. (3)

Dixon’s test. The Dixon’s test is also applicable if the population standard deviation is unknown. The statistic in the Dixon’s test is recorded as D. Firstly, the observation sequence is arranged in non-descending order to obtain the order statistic x (1) , x(2) , …, x (n) , and after calculating the statistics of Dixon’s test according to the formula given in Table 4.1, we check the “Critical Value Table of Dixon Test Method for Two Tailed Outliers”, when the detection level is α, the detection critical D˜ 1−α (n) value is obtained, and when the deletion level is α*, the deletion D˜ 1−α∗ (n) critical value is obtained. If D > D˜ (1−α) (n) and D˜ 1−α (n), then x (n) is judged as the detected outlier, and if D > D andD  > D˜ 1−α∗ (n), then x (1) is judged as the detected outlier, otherwise there is no outlier. When D > D and D  > D˜ 1−α (n), x (n) is judged as the high-degree outlier; and when D > D and D  > D˜ 1−α (n), x (1) is judged as the high-degree outlier, otherwise there is no high-degree outlier which should be rejected.

In the third step, it is judged whether the statistical data of the sample points which are considered to be high-degree outliers have a quality problem. There are many reasons for the occurrence of outliers in reality, either because of the data quality of the explained variables or because of the selection of inappropriate model or the quality problem of the chosen explanatory variables. This kind of abnormality even happens to be the reflection of the real situation. Therefore, after finding out the outliers, we must go further to learn about the specific situation. After basically excluding other reasons, we can conclude that the statistical data to be tested on the sample point does have quality problems. 8.

Evaluation of the Econometric Model Method

The econometric model method is essentially an improvement on the traditional correlation-based test method. By introducing modern econometric analysis methods in data quality detection and assessment, empirical analysis can be built on the basis of economic theory and statistical theory, and it can help to absorb more fully all

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kinds of useful information and make the traditional analysis less superficial and accidental, thus making the inspection and assessment methods of data quality more scientific to a certain extent. However, it must also be pointed out that the method still has many limitations. First of all, the basic premise for correct application of this method is that the constructed model can well reflect the objective quantitative relationship and variation between the statistical indicators (explained variables) to be tested and other indicators (explanatory variables). At the same time, the data of other indicators as explanatory variables should be true and reliable. In reality, this basic premise is hard to be satisfied. Secondly, even if the above premise is true, due to other reasons beyond the quality problems of various non-statistical data (such as poor identification of economic data, possible multi-collinearity of models, etc.), it is possible that the parameters of the model are inconsistent with economic theory or lacking in stability. So are outliers, which is also difficult to fully attribute to data quality. Finally, the diagnosis method for the outliers described earlier implies still a logical defect. That is, it acquiesced in the existence of outliers in the sample. However, it is well known that when estimating the parameters of a model, if the sample used has outliers, the parameter estimation may be deviated. That is to say, the model residual obtained in this case is not credible, and the outliers obtained based on the model residual analysis are less reliable, either. All in all, at least to date, we cannot be confused by the “scientific outerwear” of the econometric model, nor overestimate its role in inspection and assessment of statistical data quality.

4.3 Comprehensive Evaluation Method of Data Quality 4.3.1 Basic Ideas of the Comprehensive Evaluation Method The methods described above are mainly methods for evaluating and verifying the accuracy of statistical data. As the concept of statistical data quality develops from one dimension to multiple dimensions, the evaluation of statistical data quality also extends from the data itself to all aspects of the entire data production process, thus generating a comprehensive evaluation method for the quality of statistical data. The ROSC-DM method (method of presenting data quality reports according to certain standards and norms) proposed by the International Monetary Fund (IMF) falls into this category. Based on its connotation, the quality of statistical data is divided into a number of specific requirements which are further reflected by the corresponding basic indicators. And then through self-assessment, mutual evaluation or questionnaire, judgment and analysis are made on the basic indicators to provide the corresponding evaluation value as well as the weight of each indicator and various quality requirements.

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Finally, the evaluation values of each index are collected by using some method and a comprehensive evaluation of the overall quality of the statistical data is made. That’s the feature of this method. Combined with an empirical study we have conducted, we will discuss in the following part the specific application of comprehensive evaluation method in the quality evaluation of government statistics in China.

4.3.2 To Build a Comprehensive Evaluation Index System for Statistical Data Quality 1.

Principles for Constructing a Data Quality Evaluation Index System

To carry out comprehensive evaluation of data quality, the first step is to construct a scientific evaluation system for indicators of statistical data quality. It can be fulfilled only on the basis of a correct understanding of the scientific connotation of concept of the statistical data quality and a comprehensive and profound view of the statistical workflow. The construction of a comprehensive evaluation index system should follow the following principles. First, the principle of comprehensiveness. Comprehensiveness means that the system should cover the evaluation of the whole process of the entire statistical work, and, at the same time, be able to meet the needs of various users for statistical data. Second, the principle of being scientific. Being scientific means that the design of various indicators must have a scientific theoretical basis, clear in meaning and easy to measure. Third, the principle of operability. The selection of evaluation indicators should be compatible with the development level of the national statistics, and the designed indicators should be practical and operable. Fourth, the principle of mutual independence. The selected indicators should avoid being highly correlated with each other as much as possible, which can reduce the redundancy of the indicator system and avoid deviation in the results of comprehensive evaluation caused by overlapping information. 2.

Comprehensive Evaluation Index System for Statistical Data Quality

Table 4.2 is the indicator system we have built to comprehensively assess the quality of government statistics. The data quality assessment framework takes the form of a four-tier structure. The first level is the overall quality of government statistics; the second level divides the whole process of government statistical activities into different stages; the third level lists the corresponding data quality dimensions. Among them, objectivity, applicability and soundness of method are mainly the prerequisite of statistical data quality and the quality requirements of statistical design stage; accuracy, reliability and

4.3 Comprehensive Evaluation Method of Data Quality

67

Table 4.2 Four-level evaluation index system for government statistical data quality First-level indicator

Second-level indicators

Third-level indicator

A government B1 Prerequisite and C1 Objectivity statistics data quality statistical design stage

C2 Applicability

Fourth-level indicator D1 Independence of statistical agencies and personnel D2 Openness and transparency of statistical survey D3 Professional ethics of the statisticians D4 Suitability of indicator preparation for users’ needs D5 Satisfaction of the indicator preparation to the user’s needs

C3 Method soundness D6 Conformity of statistical surveys and indicators preparation methods to international standards B2 Statistical data production stage

B3 Statistical data release stage

C4 Accuracy

D7 Accuracy that statistical data reflects the actual situation

C5 Reliability

D8 Credibility of data processing and quality assessment results D9 Rationality and scientificity of data revision rules and procedures

C6 Comparability

D10 Comparability in time D11 Comparability in space

C7 Timeliness

D12 Timeliness of data release D13 Frequency of data release

C8 Integrity

D14 Integrity of the data results D15 Disclosure of changes in statistical environment

C9 Availability

D16 Easiness of data acquisition D17 Help to users

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4 Basic Methods for Inspection and Assessment …

comparability are mainly the quality requirements of statistical production stage; timeliness, integrity and availability are primarily quality requirements for the statistical release phase. In each quality dimension, a number of basic indicators are set, through which the inherent requirements of the corresponding dimension of data quality can be reflected more accurately.

4.3.3 To Provide the Evaluation Value of the Basic Indicators 1.

To Set Standards for Evaluation Values of Basic Indicators

In the general statistical comprehensive evaluation, since the types, units of measurement and quantity levels of the basic indicators that constitute the indicator system are often different, in order to facilitate the integration, it is necessary to pre-process the actual values of the basic indicators to transform them into comparable evaluation values of indicators. However, in this empirical study, since the basic indicators given in the Table 4.2 are all qualitative indicators, this step is omitted, and the personnel involved in the data quality assessment are directly required to score the basic indicators according to the five levels of “Highly Unsatisfactory”, “Unsatisfactory”, “Neutral”, “Satisfactory” and “Highly Satisfactory” with the corresponding scores as 1–2, 3–4, 5–6, 7–8, and 9–10 points. 2.

Ways to Obtain the Evaluation Value of the Index

Specific evaluation values reflecting the basic indicators of data quality can be obtained through self-evaluation, mutual evaluation or questionnaire. Self-evaluation and mutual evaluation are often used in the evaluation of data quality by international organizations and some developed countries. The self-evaluation is the evaluation made by the government statistical agencies themselves, while the mutual evaluation is the one made by the government statistical agencies of different countries or regions mutually. The questionnaire is a form of evaluation of data quality by inviting relevant experts and public figures who are familiar with and interested in statistical work to participate in it. In this empirical study, we issued questionnaires to experts, scholars, university teachers and graduate students who attended the 3rd China Statistical Annual Meeting. The respondents are all users of statistical data, so it can also be called a user satisfaction survey on government statistics. 3.

Preliminary Analysis of the Results of the Questionnaire

A total of 160 questionnaires were distributed in this survey with 155 reclaimed, of which 10 were eliminated as invalid questionnaires, and 150 obtained as valid ones. The valid response rate was 93.8%. The respondents included 24 professors (16%), 25 associate professors (17%), 23 lecturers (15%), and 30 doctoral students (including postdoctoral researchers) (20%), 38 graduate students (25%), 10 other types (7%). For the research field of respondents, there are 50 of mathematical statistics (33.3%),

4.3 Comprehensive Evaluation Method of Data Quality Table 4.3 Results of Bartlett spherical test of validity

69

KMO measure of sampling adequacy

0.917

Bartlett test of sphericity

Approx. Chi-square

1785.035

Df

136

P

0.000

62 of economic statistics (41.3%), 72 of social statistics (48%), and 7 of bio and medical statistics (4.7%). All respondents considered statistical data indispensable in their own researches. Among them, 140 respondents regarded statistical data as basic materials, and 10 as auxiliary materials, accounting for 93.3% and 6.7%, respectively. The assessment scale in the questionnaire must have sufficient reliability and validity to ensure the reliability of the research conclusions.2 Table 4.3 shows the results of the validity of the questionnaire measured by the KMO Test and the Bartlett’s Test of Sphericity. As shown in the table, the KMO value is 0.917, and the significance probability of value x 2 of Bartlett’s Test of Sphericity is ε0 for the specified ε0 > 0, the model is called the qualified correlation degree model. Let X (0) be the original sequence, Xˆ (0) be the corresponding simulation sequence, 2  (0) ε the residual sequence, then x = n1 nk=1 x (0) (k), S12 = n1 nk=1 x (0) (k) − x is the mean and variance of X (0) respectively, and ε = n1 nk=1 ε(k), S22 = n 2 1 k=1 [ε(k) − ε] is the mean and variance of the residual. n (1)

C = SS21 is called the mean variance ratio. For the given C 0 > 0, when C < C 0 , the model is called the qualified mean variance ratio model.

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Table 5.6 Accuracy inspection level reference table Accuracy level

Index critical value Relative error (%)

Correlation ε0

Mean variance ratio C0

Small error probability P0

First

1

0.90

0.35

0.95

Second

5

0.80

0.50

0.80

Third

10

0.70

0.65

0.70

Forth

20

0.60

0.80

0.60

(2)

p = P(|ε(k) − ε| < 0.6745S1 ) is called the small error probability. For a given P0 > 0, when p < p0 , the model is called qualified small error probability model. See Table 5.6 for commonly used accuracy levels.

5.3.2 Comparative Analysis and Thinking on the Revised Results of Quantity Input Method and Grey System Method In terms of the direction of revision, except for several years, the revisions of the official GDP data by the two methods are in the same direction. That is, China’s official GDP data may have been underestimated before the mid-1990s, but from the mid-1990s to around 2004, China’s GDP data may have been overestimated, so the data of this period have been revised downwards to some extent. In terms of the revision range, except for several years, the range by grey system method is smaller than by the volume input method. It may be because the metabolic gray system model adopted can better reflect the constant changes of the economic system, while the quantity input law assumes a stable relationship between GDP and quantity input but ignores the constant changes in the economic structure. In summary, we believe that the two revision methods have their own advantages and disadvantages. In an era of rapid economic development with constant changes in the economic system, there is no theoretical or realistic foundation for the assumption that GDP has a stable linear relationship with one or some quantity inputs in the quantity input method. While, taking the economic system as a gray system, the metabolic GM(1,1) constantly updates the economic system to approach the reality, which can overcome the shortcomings of the assumption that the economic system is stable in the quantity input method. However, the method only revises GDP data, hence insufficient utilization of information of the economic system. It should also be pointed out that both the two methods estimate through models the theoretical value which is used as the revised value. The main limitations of this type of methods are as follows: Firstly, the accuracy of the revised results depends entirely

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95

on the rationality of the model hypothesis and the reliability of other related data. Secondly, the original statistical data are needed to estimate the relevant parameters of the model. If there’s systematic bias for the original statistical data, the estimated parameters will also be biased, thus resulting in systematic deviations in the revised results. Thirdly, the parameters estimated through actual data are actually a mean estimation of the quantitative relationship between the variables of sampling period. The theoretical value calculated by models will be higher than the original actual value in some periods, but inevitably lower than the original actual value in other periods. In view of the above possible problems, when applying the methods in statistical practice, we must pay great attention to the preconditions for applying. At the same time, it is not appropriate to take the theoretical values estimated by a model as the revised values. Rather, after diagnosing the data by the model, only the data on the sample points that deviate greatly from the theoretical values should be revised by using the theoretical values and referring to other information available.

Chapter 6

Research on the Quality of China’s GDP Data

Gross domestic product (GDP) is the most important macroeconomic indicator that reflects a country’s economic size and structure. For a period of time, great gaps have emerged in China’s GDP accounting between the national data and regional data, the data of the economic census year and of the non-census year, the data calculated by the production approach and by the expenditure approach, which has aroused doubts among experts and scholars at home and abroad as well as in the public. This chapter intends to make a systematic and in-depth research on these issues to reveal the causes and explore effective countermeasures to improve their connectivity.

6.1 Research on the Connectivity of National and Regional GDP Data of China 6.1.1 Question Raised At present, China’s GDP adopts a system of hierarchy accounting, that is, the GDP must be accounted statistically by the nation and each region. Theoretically, the national GDP and the aggregate data of each region (hereinafter referred to as the national value and regional aggregate value) should be basically consistent. Even if there is an error, it should be within the controllable range. However, the statistics of China’s GDP show that the gap between the national value and the regional aggregate value has expanded significantly since 2002 (see Fig. 8.1). The huge gap between the two will not only lead to the public’s question about the quality of statistical data, but also affect the credibility of statistical agencies seriously. Therefore, it is extremely urgent to make the research on the connectivity between the two. Chinese scholars have made some researches on the connectivity between the national and regional GDP data of the country. In terms of the existing research © Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_6

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Fig. 6.1 Evolution of gap between the regional aggregate value and the national value of China’s GDP. Note Percentage of balance = (regional aggregate value of GDP—national value of GDP)/national value of GDP (%)

achievements, there are still some problems in needs of further studies and solutions as follows. First, most of the studies only made general analysis of the possible reasons for the gap between the two, but no specific reason has been found for the systematic error caused by the differences in the basic data and the calculation methods. Second, no convincing conclusion has been put forward as to which is more accurate, the national data or the regional aggregate data. Third, although the “accounting at a lower level” solution can solve the problem of connectivity between the two superficially, the existing practice is not very standardized and transparent, so it is not truly “convinced” to the subordinated institutes. Therefore, starting with the basic data, we will firstly review the evolution of the gap between the national value and regional aggregate value of GDP over the years to give a specific quantitative analysis of the causes of the gap between the two. Then we will inspect and analyze the quality of national and regional GDP data by using the economic census data and relevant statistical methods. On this basis, the suggestions for further improvement will be proposed.

6.1.2 The Evolution of the Gap Between the Regional Aggregate Data and National Data of GDP Figure 6.1 is a diagram showing the percentage of balance between the regional aggregate value and the national value of GDP over the years.

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99

As can be seen in Fig. 6.1, in the initial period of reform and opening up, the gap was not very large between the regional aggregate value and the national value of China’s GDP; and before 2002, the regional aggregate value of GDP in most years was smaller than the national value. It was not until 2002 that the regional aggregate value became greater than the national value in the GDP accounting of China. Moreover, this positive deviation has been increasing year by year. The positive deviation between the regional aggregate value and the national value of China’s GDP in 2010 was as high as 3,553 billion yuan, accounting for 8.85% of the national GDP of the year. It indicates that over the past 10 years, big problem has emerged in the connectivity between the two, and there have been obvious systematic errors in GDP accounting at the national and regional levels. In fact, this problem has become one of the main arguments when some scholars and media questioned the quality of China’s GDP data in recent years. Different methods can be adopted for the GDP calculation. At present, the GDP data released officially in China are mainly calculated by two types of methods: “production approach” and “expenditure approach”. The GDP by “production approach” is equal to the sum of the added value of each industry, and the GDP by “expenditure approach” is equal to the sum of final consumption, capital formation and net exports. In order to analyze further the main causes of deviation quantitatively, we will analyze the deviations between the national value and the regional aggregate value of GDP calculated by the two methods and the proportion of balance of GDP components in the total deviation. Table 6.1 shows the deviation of the regional aggregate value from the national value of China’s GDP calculated by the “production approach” from 2002 to 2010, as well as the proportion of the deviation of the added value of the three major industries in the total deviation. It can be seen from the table that, since 2002, the positive deviation between the regional aggregate value and the national value of GDP obtained by the “production approach” has expanded year by year. The year to year expansion of positive deviation of the secondary industry is its main cause. A certain positive deviation also exists in the tertiary industry, but its impact is less significant than that of the secondary industry. The deviation of the primary industry is both positive and negative, and its impact on the total deviation is almost negligible. The secondary industry includes industry and construction industry. Table 6.2 lists the deviations between regional aggregate values and national values of China’s industrial added value and that of construction industry since 2002. It can be seen in the Table 6.2 that, since 2003, the positive deviation between the regional aggregate value and the national value of the industrial added value has gradually expanded, reaching 3259.844 billion yuan by 2010. At the same time, the deviation of the added value in the construction industry has shrunk year by year. It indicates that the deviation of the added value in the secondary industry is mainly affected by the deviation of industrial added value. Table 6.3 lists the deviations between the regional aggregate value and the national value of the GDP calculated by the expenditure approach since 2002, as well as the proportion of deviation of final consumption, capital formation and net export in

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Table 6.1 Deviation between the regional aggregate value and the national value of China’s GDP by production approach Year

GDP balance by The proportion of the Production approach added value balance (billion yuan) in the primary industry (%)

The proportion of the added value balance in the secondary industry (%)

The proportion of the added value balance in the tertiary industry (%)

2002

48.94

−70.56

37.32

132.70

2003

371.75

−6.38

68.53

37.77

2004

804.39

−8.23

78.19

29.98

2005 1428.69

2.00

68.64

29.39

2006 1652.66

0.63

73.54

25.80

2007 1392.62

−0.14

93.84

6.33

2008 1926.53

−1.13

94.70

6.46

2009 2440.27

0.01

91.20

8.76

2010 3553.05

0.00

91.99

8.01

➀ Calculated according to the data of China Economic Database http://ceicdata.securities.com/cdm Web/ ➁ GDP balance = regional aggregate value of GDP – national value of GDP ➂ The regional aggregate values are calculated based on the values of 31 provinces, autonomous regions and municipalities of China. The proportion of each industry’s balance = (the regional aggregate value of the added value of each industry—the national value of added value of each industry)/(regional aggregate value of GDP—national value of GDP) Table 6.2 Deviation between the regional aggregate value and the national value of the added value of industry and construction industry in China Year

Industrial added value (billion yuan)

Added value of construction industry (billion yuan)

National value Regional Balance National value Regional Balance aggregate value aggregate value 2002

4743.10

4670.14

−72.96

646.55

729.60

83.05

2003

5494.60

5620.18

125.58

749.08

868.66

119.58

2004

6521.00

6986.01

465.01

869.43

1025.32

155.89

2005

7723.10

8570.85

847.75 1036.70

1169.56

132.86

2006

9131.10

10256.70

1125.60 1240.90

1330.76

89.86

2007 11053.00

12349.57

1296.57 1529.60

1540.27

10.67

2008 13026.00

14848.69

1822.69 1874.30

1875.94

1.64

2009 13524.00

15749.76

2225.76 2239.90

2239.87

−0.03

2010 16072.00

19331.84

3259.84 2666.10

2674.69

8.59

➀ Based on the data from China’s Economic Database http://ceicdata.securities.com/cdmWeb/

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Table 6.3 Deviation between the regional aggregate value and the national value of China’s GDP by “expenditure approach” Year

Balance in total Value of GDP by expenditure approach (billion yuan)

Proportion of Final Proportion of consumption balance Capital formation (%) balance (%)

Proportion of Net export balance (%)

2002

25.69

−2288.93

2129.18

2003

271.48

−138.05

222.68

15.38

2004

651.54

−33.52

143.48

−9.97

2005

1143.31

−4.78

150.04

−44.41

2006

1094.85

1.00

183.36

−84.36

2007

1316.63

5.17

203.71

−108.88

2008

1938.65

13.06

158.72

−71.68

2009

1777.58

17.63

196.75

−114.34

2010

3509.04

22.81

142.16

−64.64

259.77

➀ Based on the data from the China Economic Database http://ceicdata.securities.com/cdmWeb/

the total deviation. It can be seen in the table that, since 2002, the positive deviation of regional aggregate value from the national value of GDP calculated by the “expenditure approach” has also been increasing year by year. From the perspective of composition of expenditure, a positive deviation has existed between the regional aggregate value and the national value of the capital formation over the years, and has a great impact on the GDP balance calculated by the “expenditure approach”. The deviations of final consumption and net export fluctuated greatly. Before 2005, there was a negative deviation between the regional aggregate value and the national value of the final consumption. Since 2006, a positive deviation emerged and has been enlarging year by year. Before 2003, there was a positive deviation between the regional aggregate value and the national value of the net export, but negative deviation began to appear after 2004.

6.1.3 Quality Inspection of National and Regional Data of China’s GDP As mentioned above, in recent years, there has been a considerable positive deviation between the regional aggregate value and the national value of China’s GDP. This deviation may be caused by the overstatement from each region or by the undercounts at the national level. Therefore, before further analyzing the specific reasons, it is essential to make necessary quality inspections for the national and regional data of GDP.

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Table 6.4 China’s GDP in 2004 calculated based on different data GDP

Added value of primary industry

Added value of secondary industry

Added value of tertiary industry

Calculated based on census data

159878

20956

73904

65018

Calculated based on regular data

136876

20768

72387

43721

Balance before and after revision

23002

188

1517

21297

Note Based on the data from the website of National Bureau of Statistics: http://www.stats.gov.cn/ zgjjpc/cgfb/. Data unit: 100 million yuan

1.

Analysis of the quality of national and regional data of GDP based on economic census data

The economic census is a comprehensive survey organized specially by the state. As it takes a lot of manpower, material and financial resources, it can obtain more comprehensive, systematic and detailed statistical data than any other investigations. It is generally believed that GDP calculated based on economic census data should be more accurate and can serve as an important frame of reference to measure the quality of national and regional GDP data. Table 6.4 shows the national values of GDP and the added value of the tertiary industry in 2004 calculated based on the first economic census data and the usual data respectively. As can be seen in Table 6.4, the national data of GDP and of the added value of the three industries calculated based on the census data are higher than the data calculated based on the ordinary data. However, the variations in the primary and secondary industries were less significant, but significant in the tertiary industry. The added value of the tertiary industry was increased as high as 48.71%. Combined with the previous analysis, since 2002, the regional aggregate value of added value of China’s tertiary industry has been much higher than the national value. From this point of view, there may have been more serious omissions in accounting of the added value of tertiary industry for national GDP data. The impact of the economic census on regional GDP data is relatively complicated. According to the results of the first economic census, the total GDP data of 19 regions in the country in 2004 were adjusted upwards, while the total GDP data of 12 regions were adjusted downward [1]. In general, the regional aggregate value of the added value of the primary and secondary industries calculated according to the ordinary data was overestimated, while the added value of the tertiary industry was underestimated, and the underestimation of the latter was greater than the overestimation of the former two. As a result of the combined effect of the above two aspects, the regional aggregate value of the GDP was raised by 2.67%. In terms of the regional results, the eastern and western regions made relatively large adjustment, showing the existence of underestimation; while the northeast and central regions made overestimation of GDP as a whole.

6.1 Research on the Connectivity of National and Regional GDP Data of China

2.

103

Diagnosis of GDP data quality based on logic matching test

As a comprehensive indicator reflecting the final productive performance of the national economy, GDP is highly correlated with other economic indicators. Therefore, it is possible to verify the accuracy of GDP data by examining the logical relationship between GDP and other relevant indicators. The indicators covered in this chapter include: GDP, power consumption, freight shipment volume, grain output, export volume, US CPI, fixed asset investment in the whole society, fixed asset investment price index of regions, total retail sales of social consumer goods, CPI, total wages of employees, the profit of industrial enterprises above designated size, PPI, and the fiscal revenue, etc. The time spans the period from 1991 to 2010, involving 31 provinces, autonomous regions, and municipalities directly under the central government. The data are based on China Statistical Yearbook and China Energy Statistical Yearbook. Due to the lack of data, the following quantitative analysis will not include data from Tibet. In addition, in order to ensure the comparability of historical data, we classify the data of Hainan Province and Chongqing City into Guangdong Province and Sichuan Province respectively (the quantity and value indicators are directly added, and the index takes the weighted arithmetic mean). In order to eliminate the impact of inflation, we will convert all the indicators calculated at current prices into indicators calculated based on the 1991 constant price. Specifically, the GDP index of each region is used to calculate its real GDP; the US CPI is used to adjust the export volume; the regional CPI is used to adjust the total wages of employees, fiscal revenue, and total retail sales of social consumer goods in each region; the fixed asset investment price index of each region is used to adjust its fixed asset investment of the whole society; and the PPI is used to adjust the profits of the industrial enterprises above designated size in various regions. In the test of the matching of GDP data, in order to be more convincing, we will analyze it separately by three methods of GDP accounting from three different perspectives of production, expenditure and income distribution. In addition, in order to avoid multi-collinearity, we will carry out the principal component analysis of the corresponding explanatory variables first. Using the software Stata10, we can obtain the principal component variables and their conversion coefficient matrices for each explanatory variable. Then, the regression model of GDP for each principal component variable is established. If the model is highly fitted and the coefficient of principal component variable is significant, the coefficients of the original explanatory variables are obtained by using the obtained principal component variable coefficients and the conversion coefficient matrices. By analyzing the fitting of the model and the positive and negative conditions of the explanatory variable coefficients, the matching of national and regional GDP data can be tested. In this chapter, the explanatory variables in the regression equation to examine the matching from the production perspective include: power consumption po, freight turnover (goods) go, and grain output (food) fo. The regression equation is: Table 6.5 shows the specific results of regression analysis from the production perspective.

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Table 6.5 Regression Results by “Production Approach” Goodness of fit

Production approach regression coefficient

Determinable coefficient of modified degrees of freedom

Power consumption

Freight shipment volume

Grain output

National

0.9804

0.7730

0.7788

0.6759

Regional aggregate

0.9958

2.3979

0.6640

0.6530

➀ Due to space limitations, the results of regression analysis of provinces and cities are not listed here. Only the results of regression analysis of the national value and regional aggregate values are listed. So is the following, which will not be noted one by one

In this chapter, the explanatory variables in the regression equation to examine the matching from the perspective of expenditure include: export volume ex, total fixed asset investment in the society in and total retail sales of social consumer goods co, and the regression equation is: G D Pit = ai + αi exit + βi in it + χi coit + εi , i = 1, . . . 28, t = 1, . . . 20

(6.1)

Table 6.6 shows the results of regression analysis of GDP from the perspective of expenditure. In this chapter, the explanatory variables in the regression equation to examine the matching from the perspective of income distribution include: the total wages of employees wa, the profit of industrial enterprises above designated size pr and the fiscal revenue fi, and the regression equation is: G D Pit = ai + αi wait + βi prit + χi f i it + εi , i = 1, . . . 28, t = 1, . . . 20 (6.2) Table 6.7 shows the results of regression analysis from the perspective of income distribution. From Tables 6.5, 6.6 and 6.7 we can learn that the model based on national data and regional aggregate data is fitted very successfully, whether from the perspective Table 6.6 Regression results by “Expenditure Approach” Region

Goodness of fit

Regression coefficient by expenditure approach

Determinable Export volume coefficient of modified degrees of freedom

Total fixed asset investment

Total retail sales of social consumer goods

National

0.9726

0.7270

0.7329

0.7346

Regional aggregate

0.9806

1.0069

1.0148

1.0192

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105

Table 6.7 Regression results by “Income Approach” Region

Goodness of fit

Regression coefficient by income approach

Determinable Total wages of coefficient of employees modified degrees of freedom

Profit of industrial enterprises above designated size

Fiscal revenue

National

0.9641

1.7382

1.7325

1.7382

Regional aggregate

0.9633

3.3033

3.3004

3.3073

of production, expenditure, or income distribution. The sign of the coefficient is also consistent with the interpretation of economic theory. However, as for the regression coefficient of the explanatory variables, that of the regional aggregate values is larger. It is because, in terms of the explained variables (i.e. the actual GDP data), the national data are smaller than the regional aggregate data, while the national data of each explanatory variable used in this chapter are bigger than the regional aggregate data. In particular, due to the different measurement scales of national and local fiscal revenues in China, the actual data of regional aggregate fiscal revenues are much smaller than the actual national fiscal revenue data, hence a big difference between the coefficients of the explanatory variables of the the national model and regional aggregate model in the results of quantitative analysis from the perspective of income. 3.

Analysis of the causes of deviation between national data and regional aggregate data of GDP

In the past period of time, the causes of the large deviation between the national data and the regional aggregate data of China’s GDP lied in both economic management system and statistical methods system. From the perspective of economic management system, on the one hand, GDP has been used as the most direct standard to measure the governmental work for a long period of time. The level of GDP is closely related to the promotion of officials at all levels. Therefore, the officials of local governments have been competing with each other for GDP data psychologically, which leads to the preference and tendency of over reporting or even false reporting. On the other hand, China’s current statistical management model is “double leadership, level-to-level management, integration of departments and local governments with local government oriented”. Local governments are the main administrative leaders of the local statistical agencies. Besides, the statistical activities are mainly funded by the local governments which, as a result, have affected local statistical agencies and related personnel, so it is difficult to achieve statistical independence and objectivity. It can be argued that the impulse of local governmental officials to over report GDP and the lack of necessary independence and resistance against outside interference of local statistical agencies are the deep-rooted reasons that affect the connectivity of regional and national data of China’s GDP accounting.

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From the perspective of the statistical method system, China’s current GDP accounting methods and systems are still in need of improvement. We are lacking in a specific set of scientific and normative methods and system which are detailed and transparent, especially the specific information sources of national economic accounting indicators and a detailed interpretation of the specific correspondence between the relevant indicators of national economic accounting and the source data of professional statistics, thus making it difficult for statistical departments at all levels to calculate GDP-related indicators in accordance with unified norms and operative approaches. This is also the main reason for the large difference between the two types of GDP data. We will focus on it in the following analysis. As far as the “production approach” of GDP accounting is concerned, there are two main problems: one is that the added value of the secondary industry, especially the regional aggregate value of the industrial added value, has a large positive deviation; the second is the national data of the added value of the tertiary industry may be underestimated. The large deviation between the regional aggregate value and the national value of the added value of the secondary industry are caused by the following facts: (1)

(2)

The data of cross-regional enterprises may be counted repeatedly. According to the regulations of current system, the accounting of industrial added value should be based on the principle of legal person territory. In recent years, due to the development of the market economy, some large companies and conglomerates across regions have developed rapidly, with larger and larger scale of personnel, materials and capital flows across regions. As a result, the marketing activities of the head office and the branch companies are counted repeatedly, that is, counted at the locations of the branch offices and then counted again at the location of the head office. Hence the overestimation of the regional aggregate value of the secondary industry. The source of the basic statistical data on which they are based is different. Taking industrial enterprises with annual sales below the prescribed standards as an example, each region generally relies on self-organized sample surveys in the region; while from the national perspective, the sampled data of the state-owned enterprise investigation team are used for accounting. The two are based on different data, so the results are also definitely different.

The reason for the large deviation between the regional aggregate value and the national value of the added value of the tertiary industry is mainly because the current statistical system cannot fully cover the entire tertiary industry, especially some new service industries, including securities trading, legal consultation, advertising agency, etc. There are defects in the source of information for this kind of industries, so it is difficult to conduct statistics in traditional methods. Therefor it is possible for the National Bureau of Statistics to underestimate the added value of the service industry in the process of statistics. As far as the “expenditure approach” for GDP accounting is concerned, the main reasons for the deviation between the regional aggregate value and the national value are:

6.1 Research on the Connectivity of National and Regional GDP Data of China

(1)

(2)

1

107

The understanding of the relevant indicators may be different. For example, according to the relevant regulations of the National Bureau of Statistics, “government consumption” should include: the regular business expenditures in and off the fiscal budget of operating expenses, depreciation of fixed assets of administrative units and non-profits public institutions, the balance of the regular business expenditures of urban neighborhood committees and rural village committees after deducting their revenue. If the national government and regional governments calculate strictly by this method, there should not be too large deviation between the local aggregate value and the national value. The current large deviation between the two is likely to be caused by different understandings of regular business expenditures, as well as inadequate information about extra-budgetary expenditures and revenue and expenditure of urban neighborhood committees and rural village committees. As another example, the data based on which the “resident consumption” is calculated are mainly from the “total retail sales of social consumer goods” of the Department of Trade and External Economic Relations Statistics. There is a significant difference between the retail sales of social consumer goods and “resident consumption”. It is bound to produce errors if the former is used to replace the latter simply. For one more example, in recent years, the regional aggregate value of the gross fixed capital formation has always been greater than the national value. In addition to the possible repeated calculation of fixed asset investment of large enterprises across regions, another possible reason is that the “fixed asset investment” in fixed asset investment statistics is used directly by some regions as the gross fixed capital formation.1 They are based on different basic data or adopt different processing methods. For example, the calculation of household consumption can be based on the household survey data provided by the rural and urban investigation team in addition to the information provided by the Department of Trade and External Economic Relations Statistics. The sample data at the national and local levels are inherently different, so calculations based on data of different levels can also lead to certain errors. Take the calculation of “net outflow of goods and services” as another example. At the national level, since the objects of import and export are foreign countries, the balance of international payments can be used as reference to describe scientifically and accurately the interrelationships between different economies. However, when accounting the regional total output value, the flow of products and services is more complicated, in which not only domestic and overseas imports and exports but also the inflows and outflows between regions are included, so the accounting is more difficult. In fact, many provinces and autonomous regions currently use the “reverse method” to estimate the “net outflow of goods and services”, that is, to estimate total output value by “production approach” first, and then deduct

See Section III of this chapter.

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the corresponding “capital formation” and “consumption expenditure” to estimate the indicator of this part. By doing so, the data obtained contains the error in calculating GDP by the “production approach” and the “expenditure approach”. It must also be pointed out that another reason why inconsistency of national and regional data of China’s GDP have existed for a long time and even become increasingly serious is that the coordination mechanism has not been well established. The coordination between the National Bureau of Statistics and the local statistical bureaus is not sufficient, nor is the coordination between the investigation team, the professional divisions and the accounting divisions inside the National Bureau of Statistics. Due to the difference in basic data and processing methods, it is not uncommon for inconsistency existing frequently between national data and regional data or the data of the professional departments for GDP accounting. However, when there is a big difference between the data of the parties, it is necessary to invite all parties concerned to discuss and consult together to seek a reasonable solution and make a unified revision of their data in time respectively. Otherwise, with time going by, the deviation will become larger and larger, and it will be more and more difficult to correct.

6.1.4 Conclusions and Suggestions Based on the above analysis, the following conclusions can be drawn: In the past 10 years, the gap between the national value and the regional aggregate value of China’s GDP was expanding significantly, and there has already been an obvious systematic error between the two. From the perspective of production, the added value of the secondary industry, especially its regional aggregate industrial added value is significantly larger than the national value. From the perspective of expenditure, the regional aggregate value of the fixed capital formation is significantly larger than the national value. By comparative analysis of economic census data, it is found that serious undercounts may exist in the accounting of added value of the tertiary industry for the GDP data at the national level, while the added value of the secondary industry in most regions tends to be overestimated. The results of inspection on the quality of GDP data by Benford’s law and econometric model show that no obvious evidence of deliberate fraud is found at the national or regional level, and only the GDP data of some regions have poor matching with other relevant indicators. However, the reasons of large deviation between the national data and the regional aggregate data of China’s GDP lie in both economic management system and statistical system methods. In response to the above problems, we will provide the following suggestions: Firstly, we should insist on evaluating and appointing officials with a scientific outlook on political achievements and evaluating the development of local economy

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comprehensively with a scientific outlook on development. It is necessary to gradually downplay the assessment of GDP. It should be mainly used as a comprehensive indicator to observe trends of macroeconomy and provide foundation for macroeconomic regulation. It will fundamentally suppress the impulse of some local leaders to over report or falsely report GDP. Secondly, we must further promote the reform of the statistical management system, improve the construction of statistical regulations, and provide sound legal support for statistical work, thereby further strengthening the independence of the statistical system and ensuring the authenticity of the statistical data from the system. Finally, we must further improve the system and method of GDP accounting. Specifically, there are the following points: (1)

(2)

(3)

(4)

(5)

For the indicators included in the GDP accounting, not only should we have an explicit definition, but also provide a clear and unified source of specific data and calculation methods. Besides, we must release it to the whole society, and get ready for the supervision of the society and the questioning of experts. In this way not only the existing problems can be identified to further improve the method and system of GDP accounting, but at the same time the credibility of government statistics can be improved. It is essential to further improve the statistical investigation system, especially to carry out as soon as possible the investigations that are needed for GDP accounting while not yet well established up to now. For example, to further improve the statistical survey system for service industry, to establish a statistical sampling survey system for construction enterprises below the qualification level and individual businesses, to improve the investigation system for fixed assets investment of less than 500,000 yuan in urban areas, and to further improve the producer price index and import and export price index of the service industry, etc. It is essential to further clarify the territorial principle of GDP calculation and study the specific methods and operational rules to avoid repeated calculation of added value of the cross-regional enterprises. The connection method with “top-down” and “bottom-up” combined should be adopted. For the industries whose basic data are easy to connect with each other (such as the added value of industrial enterprises above designated size), on the basis of verification, the “bottom-up” method is used to account the data and the regional aggregate data are taken as the national data. While the data that are inconvenient for convergence of the two types (such as the added value of industrial enterprises below designated size), or the data that are easier to calculate at the national level (such as the GDP indicators calculated by the expenditure approach) are calculated through “top-down” approach, that is, the national data are decomposed to each region in accordance to its proportion of relevant indicators. It is necessary to further establish and improve the system of calculating GDP by the subordinate agencies. It should be noted that calculating by lower level agencies does not mean that the superior departments can preset data freely

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to the subordinate departments or regions, but means that when determining the GDP data, the higher-level departments should convene the lower-level departments to conduct unified inspection according to the data they have. This kind of practice will undoubtedly increase the difficulty of fraud, and make it more likely to bring the dishonest behaviors to light, which will be helpful to stop corruption of falsifying for the pursuit of so-called political achievements. At the same time, it can also ensure the consistency between the regional aggregate data and the national data. A mechanism for regular data revision and timely zeroing of deviations should be established gradually. At regular intervals, necessary adjustments and revisions can be conducted for national and local GDP data in conjunction with the results of the economic census.

6.2 Study on the Connectivity of China’s GDP Data in the Census Year and Regular Year 6.2.1 Question Raised The economic census is a major reformation in China’s census system. So far, China has conducted two economic censuses (in 2004 and 2008). Since the data obtained from the economic census are more detailed than the regular data, after each census, based on the census data, the National Bureau of Statistics revised the GDP statistics of the year calculated by the conventional method. See Table 6.8 for details. After the 2004 economic census, the total GDP was revised to 15.99 trillion yuan, an increase of 2.3 trillion yuan, with a growth rate of 16.8%. In the second economic census in 2008, the total GDP was revised to 31.405 trillion yuan, an increase of 1.34 trillion yuan, grown by 4.5%. It can be seen that GDP data calculated by conventional methods in the two years enjoyed fairly high revision ratios by the two economic censuses. Such a huge revision will certainly raise questions about the accuracy of the annual GDP statistics in the regular years, and then about the accuracy of China’s economic data accounting. Therefore, how to ensure the connectivity of the Table 6.8 Revision of GDP data by economic census

Revision of annual GDP data in the first economic census year

Revision of annual GDP data in the second economic census year

Total revision ratio

Growth rate revision

Total revision ratio

Growth rate revision

16.80

0.60

4.50

0.60

Note Based on the economic census data, http://www.china. com.cn/chinese/zhuanti/yw/1092177.htm http://www.china.com. cn/news/txt/2009-12/25/content_19131034.htm Unit: %

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annual GDP data of the economic census year and non-census year and avoid ups and downs of the data is one of the key issues that need to be dealt with in the annual GDP accountings for the census years and non-census years. In the following part, we will analyze the difference between the GDP accountings in the census year and the non-census year so as to find out the main reason for the gap between GDP calculated by census data and those by data of non-census years. On this basis, feasible suggestions will be made on how to further improve the GDP accounting and how to adopt appropriate revision methods to strengthen the connectivity between annual GDP accountings in the the economic census years and the non-census years.

6.2.2 The Difference Between the GDP Accountings in the Census Years and the Regular Years In order to make better use of the economic census data, after the first economic census, the National Bureau of Statistics of China formulated the Annual GDP Accounting Methodology for Economic Census Years in 2005. Compared with non-census years (regular years), there are many improvements for GDP accounting in economic census years, including accounting scope, industrial classification, data sources, calculation methods and processing of some special issues.2 1.

Changes in the annual GDP accounting scope in the economic census years

According to the National Economic Accounting System of China (2002), when accounting China’s GDP, the main fields of production include: (1) services or goods provided or prepared for other companies; (2) self-sufficient production for self consumption or formation of fixed capital; (3) self-sufficient production of housing services provided by the owner. However, in the practice of accounting, due to the limitation of data, China’s annual GDP accounting in regular years has not fully covered these fields. Therefore, Annual GDP Accounting Methodology for Economic Census Years stipulates that the GDP accounting should include production activities that have been neglected due to the lack of data in the past. The expansion of the scope of GDP accounting in the census year is expanded to the following aspects: (1) Household surveys are included, and the rental service of self-owned houses, tutoring and housekeeping services are brought into accounting; (2) The economic census provides a questionnaire on the operation of individual businesses. In the second 2

After every economic census, the National Bureau of Statistics will formulate new annual GDP accounting scheme in census years according to the new changes in the national economic census. The differences between annual GDP accounting in the census year and the regular years introduced in this section refers to the differences between the GDP accounting scheme in the census year set by the National Bureau of Statistics after the first economic census and the annual accounting in the regular years.

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economic census, a unified census plan for individual business households was further developed. Through the survey of individual businesses (in 14 industries altogether, including industry, construction, transportation, wholesale and retail, etc.), the production activities of individual businesses that have not been registered by the industrial and commercial departments were included in the GDP accounting; (3) Most companies have other industrial activities besides their main businesses. There was no accounting for the data of this part, or they were obtained by rough estimations in annual statistics of regular years. While in the economic census year, this part can be included in the GDP accounting through the basic information form and its schedule of the units with industrial activities; (4) In the annual statistics of regular years, the accounting of service of the administrative institutions is incomplete, while in the economic census year, it can be included in the GDP accounting by using the statement of financial position of the administrative institutions; (5) In the annual accounting of regular years, due to the lack of financial information of the non-accredited construction enterprises, the accommodation and catering industry below designated size, the telecommunications and transportation industry, the wholesale and retail trade below designated size, the real estate property management industry, the intermediary services and other real estate industries, leasing and business services and other service-oriented enterprises, etc., their production and operation activities were not fully included in the GDP accounting. While in the economic census years, the statements of financial condition of these industries provide detailed information for further improvement of GDP accounting. 2.

Changes of the industrial classification of the annual GDP accounting in the economic census years

In the GDP accounting in the regular years, three-level classification is adopted, with 26 industrial sectors covered. However, due to the constraints of some factors such as data sources, the three-level classification is relatively rough, and only the data of 16 industrial sectors are released. While the economic census uses a four-level classification which is more specified. 3.

Changes of the data sources and calculation methods of the annual GDP accounting in the economic census years

The sources of information differ greatly for the annual accounting in the economic census years and the regular years, which also cause changes in the specific calculation methods. In terms of industries, it is mainly reflected in the following aspects: (1) Industry. In the regular statistics, the accounting data of industrial enterprises below designated size come from sample surveys, while for the economic census, production and operation schedule is used as supplement for these enterprises. In regular years, apart from the main business, there are no detailed accounting of other industrial activities, and the statistics of individual business are relatively inadequate. In the economic census years, the business operation questionnaire for units and individuals is used to obtain information in this regard. (2) Construction industry. The economic census provides more abundant data than in the regular statistical year

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through production and operation schedule of non-accredited construction enterprises, the basic information form of units with industrial activities and the questionnaire of operation of the individual business households. (3) Warehousing, transportation, and postal industry. In the regular statistics, most of the data of these industries are provided by the state ministries and commissions, the State Administration of Industry and Commerce and the State Administration of Taxation. In the annual accounting of the economic census year, in addition to these materials, the data of industry enterprises also come from the statement of financial position of telecommunications and transportation enterprises, the income statement and the balance sheet of the railway transportation industry. Besides, for the service business and industrial activities beyond main businesses and the individual business of the industry, there are also the statement of financial position of the service industry, the basic information form of units with industrial activities and the questionnaire of the operation of the individual business households in economic census. (4) Computer, information communication, and software industry. In the regular years, most of the industrial data are from the statistics of the Ministry of Information Industry, the National Radio and Television Administration, and the State Administration of Industry and Commerce. While in the economic census, through the statements of financial position of enterprises of the service industry and of the administrative institutions, the basic information form of units with industrial activities, as well as the questionnaire of the operation of the individual business households, the shortcomings of regular statistics have been made up. (5) Wholesale and retail, accommodation and catering. The economic census makes use of the statement of financial position of wholesale and retail enterprises below designated size and the statements of operation of accommodation and catering enterprises below designated size to make up for the inadequacy of statistical data of such enterprises. At the same time, the use of the questionnaire of the operation of the individual businesses and the basic information form of units with industrial activities can well collect the statistical data of retail, wholesale, accommodation and catering industries. (6) Real estate industry. In the economic census, the questionnaire of the operation of the individual business households and the basic information form of units with industrial activities can supplement the sources of missing data of real estate industry in the regular statistics to better measure the development of the real estate industry. (7) Rental and business services. In the regular years, only part of the sample survey data of service industry are used for the accounting of this industry, while the economic census greatly improves the accounting of the industry through the questionnaire of the operation of the individual businesses and the statement of financial position of the service enterprises and the administrative institutions. (8) Scientific research, technical services and geological surveys industry, water conservancy, environment and public facilities management industry. In the regular statistics, most of the accounting data of these two industries are provided by the Ministry of Finance and the Ministry of Construction, while the economic census make the supplements by setting the statement of financial position of the service enterprises and the administrative institutions, as well as the basic information form of units with industrial activities. (9) Culture and sports, public health and social security, as well as resident services and social welfare industry. In

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the economic census year, the accounting data of the regular years are supplemented by the questionnaire of the operation of the individual businesses, the statement of financial position of the service enterprises and the administrative institutions. (10) Public management and social organization. The economic census supplements the regular accounting through the statement of financial position of the administrative institutions. As the sources of information in the economic census year have changed, there are also many changes in the calculation methods for the corresponding accounting of production and application (expenditure) of GDP. In general, compared with the annual accounting in the regular years, the annual GDP accounting of the census year is mainly direct calculation based on the data obtained from the census, and the indirect estimation commonly used in the annual accounting in the regular years is reduced as much as possible. 4.

Adjustment of the processing method for certain special problems in the accounting of the first economic census

Compared with SNA, there were still some inappropriate processing methods in China’s regular GDP accounting in the past. But the first economic census provided data that could be used to correct these methods. It is mainly reflected in the following aspects: (1) Processing of computer software. In the past regular accounting, due to the limitation of data sources, computer software was not included in the fixed capital formation. But the computer software can be included in the future since the relevant data were provided by the first economic census„. (2) Processing methods for depreciation of private houses. In the past regular accountings, the virtual depreciation rate of self-owned houses for urban residents was set at 4%, and 2% for rural ones, with a difference of 2%. In fact, the depreciation rate of houses for rural residents is not faster than that of urban houses. Therefore, in the annual accounting of the economic census year, the two were adjusted to 3% and 2% respectively. In addition, the valuation standard of residential self-owned houses was adjusted from the historical cost price to the current construction cost price in the economic census. (3) Processing methods of import duties. In SNA, if the added value is set according to the basic price, the import duty (excluding the subsidy) is regarded as part of the product tax (excluding the subsidy) and is included in GDP by the production approach and the income approach; if the added value is set according to the production price, the import taxes (excluding subsidies) should be included as an independent part in GDP by the production approach and income approach. In the past regular accountings in China, the import tax was included in the added value of the wholesale and retail industry. Since the first economic census, China has no longer adopted this method, but been referring to the SNA regulations. (4) Processing methods of financial media services. In the past regular accounting, the net amount of interest expense was usually attributed to intermediate inputs, which led to the decrease of the added value of the whole society. In order to compensate for the added value, in GDP accounting, the interest of the residential savings was generally included in the added value. In essence, the above processing mode was quite different from the relevant methods in SNA of 1993. Therefore, in the annual

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accounting of the first economic census, the proportion of the sum of interest expense and interest income of each industrial sector and the end-user sector to the sum of total interest income and interest expense of financial institutions was used to subdivide the service output and ultimately achieved the correspondence of each part with different industrial sectors. The interest income of residential savings was no longer treated as added value of the financial sector. It is precisely the differences in accounting methods and scope, data sources, industrial classification, and processing methods of some special problems between economic census accounting and regular annual accounting that result in a large gap between the GDP accounting data of the two.

6.2.3 Conclusions and Suggestions Based on the above analysis, we can draw the following two conclusions: First, China’s annual GDP accounting in the economic census year and regular years have many differences in accounting scope, data sources and specific calculation methods, hence the large differences in the GDP data calculated according to the two schemes. Second, relatively speaking, the annual accounting in the economic census year was based on more detailed data and in more scientific methods. Therefore, the GDP data calculated according to the annual accounting scheme of the census year are more accurate. To further improve the accounting quality of China’s GDP data, it is necessary not only to further improve the annual accounting of China’s GDP in census year and regular years, but also to make necessary revisions of historical data by scientific revision methods, thereby improving its connectivity and comparability. To this end, the following suggestions are made: (1)

To further improve the national accounting scheme of the economic census year

The economic census provides a lot of valuable data for the national economic accounting which can be helpful to make up for the shortcomings of the original accounting and make China’s GDP accounting more well-established and scientific. Therefore, after each economic census, relevant experts should be organized to further revise the GDP accounting scheme of the original economic census by referring to the experience of the advanced countries in the world. First, when designing the economic census scheme, it is necessary to give a full consideration of meeting the needs of the national economic accounting; second, the accounting scope, measurement scales and calculation methods should be kept in line with the latest version of international SNA; third, for the indicators in GDP accounting, not only very explicit definitions should be provided, but also a unified and clear source of specific data and calculation methods should be stipulated, making it a normative text for national economic accounting.

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(2)

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To revise the GDP accounting scheme of the regular years

In order to ensure the comparability between the GDP data after the economic census and the GDP data of the economic census year, it is necessary to make corresponding revisions of the GDP accounting scheme in the regular years by referring to the scheme of the economic census year, including: determining the scope of GDP accounting, determining the classification of industrial sectors and expenditure items, and standardizing the sources of data and calculation methods of GDP accounting, etc. The scope and measurement scales of the GDP accounting in the regular years should be as consistent as possible with the census year. The only difference between the two lies in the differences in processing methods and data sources due to the different complexity of the data. For example, for the added value of enterprises below designated size and the individual businesses, in the census year, the data obtained from comprehensive surveys can be used for accounting. Although it is impossible to obtain as detailed and comprehensive data in the regular year as in the census year, it is also necessary to establish and improve the sample survey system for enterprises below designated size and the individual businesses, and use the data obtained to make an estimate. The accounting of added value of such enterprises and individual businesses can not be abandoned only because there is no comprehensive data. (3)

To improve the connectivity between the data of the census year and the regular years by scientific revision methods

The census year should be used as the base year for statistics, and the census accounting data as the benchmark data to make necessary revisions to the historical data, thus ensuring the stability and comparability of the trend of the GDP time series.3

6.3 Research on the Connectivity Between China’s GDP Accountings by Production Approach and Expenditure Approach 6.3.1 Question Raised The GDP accounting methods include production approach, income approach and expenditure approach. The production approach and the income approach can be used for the accounting of added value of various units and sectors. The expenditure approach must be used for accounting from the perspective of the entire national economy. In China’s accounting practice, the added value of some industrial sectors such as agriculture and industry are subject to the calculation results of the production 3

The specific revision methods for the connectivity of GDP data between the census years and the regular years has been discussed in Chap. 5, so we’ll not explore it here.

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approach. While for some industrial sectors, such as some of the service industry, the added value are subject to the calculation of the income approach. Currently, when published, only two types of data, i.e. GDP calculated by production approach and GDP by expenditure approach, are released. The GDP calculated by the three methods reflects the production results of the same economy in the same period. In theory, the three results should be consistent. However, in the accounting practice, the results obtained by the three calculation methods are not equal due to various reasons. In the following part, we will briefly review the evolution of the difference between China’s GDP data obtained by production approach and those by expenditure approach. On this basis, we will analyze the causes of the error and make suggestions on how to further reduce the error and improve its connectivity.

6.3.2 The Evolution of the GDP Data Gap Between Production Approach and Expenditure Approach in China In the past 30 years, China’s GDP accounting method has been continuously improved, but there is still considerable error between the GDP by the production approach and the GDP by the expenditure approach. See Table 6.9 for details. It can be seen in Table 6.9 that since the GDP accounting system of the expenditure approach was formally established in 1989, except in 2000 and 2001 when the GDP value calculated by the production approach was slightly higher than the GDP value calculated by expenditure approach, in other years, however, the GDP value calculated by the production approach was less than the GDP by the expenditure approach, that is, the balance between the two was negative.

6.3.3 Analysis of the Reasons for the Gap Between GDP Data Calculated by Expenditure Approach and Production Approach From the above analysis, we know that in most of the years in the past, the GDP calculated by production approach was less than the GDP calculated by the expenditure approach. It should be noted that the relevant data in the Statistical Yearbook 2013 on which Table 6.9 is based have been revised necessarily after the two economic censuses. According to the revision of the two types of GDP data by the two economic censuses, the GDP by the production approach always increased more than the GDP of the expenditure approach did after the revision. Therefore, it can be judged that before the revision, the gap of China’s GDPs by production approach and expenditure approach

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Table 6.9 Differences between China’s GDP data by production approach and by expenditure approach Year

GDP by production approach

GDP by expenditure approach

Balance

1989

16992.30

17311.30

−319.00

1990

18667.80

19347.80

−680.00

1991

21781.50

22577.40

−795.90

1992

26923.50

27565.20

−641.70

1993

35333.90

36938.10

−1604.20

1994

48197.90

50217.40

−2019.50

1995

60793.70

63216.90

−2423.20

1996

71176.60

74163.60

−2987.00

1997

78973.00

81658.50

−2685.50

1998

84402.30

86531.60

−2129.30

1999

89677.10

91125.00

−1447.90

2000

99214.60

98749.00

465.60

2001

109655.20

109027.99

627.21

2002

120332.70

120475.62

−142.92

2003

135822.80

136613.43

−790.63

2004

159878.30

160956.59

−1078.29

2005

184937.40

187423.42

−2486.02

2006

216314.40

222712.53

−6398.13

2007

265810.30

266599.17

−788.87

2008

314045.40

315974.57

−1929.17

2009

340902.80

348775.07

−7872.27

2010

401512.80

402816.47

−1303.67

Note ➀ Data is compiled according to the China Statistical Yearbook 2013. ➁ Difference = GDP by production approach — GDP by expenditure approach, unit: 100 million yuan

will be larger than what is shown in the above table. In other words, China’s GDP by production approach may be underestimated. In addition, China’s GDP accounting by expenditure approach itself can also be further improved (Tables 6.10 and 6.11). Table 6.10 Revision of GDP of production Approach by two economic censuses Revision of annual GDP data in the first economic census

Revision of annual GDP data in the second economic census

Increase in total volume(billion yuan)

Revised ratio (%)

Increase in total volume (billion yuan)

Revised ratio (%)

2300.000

16.80

1340.000

4.50

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Table 6.11 Revision of GDP of the expenditure approach by two economic censuses Revision of annual GDP data in the first economic census

Revision of annual GDP data in the second economic census

Increase in total volume (billion yuan)

Revised ratio (%)

Increase in total volume (billion yuan)

Revised ratio (%)

1788.600

12.60

804.150

2.00

Note The data is compiled from economic census data

6.4 Analysis of the Reasons Why the GDP of Production Approach Is Underestimated In the current practice of national accounting, the GDP data of production approach published in China is actually calculated together by the production approach and the income approach. Therefore, the following analysis of the calculation method also includes that of the income approach. (1)

(2)

Industry. For a long time, there have been the problem of inadequate data sources for the added value accounting of industrial enterprises below designated size and individual industrial enterprises. In the accounting of regular years, the data for industrial enterprises below designated size were mainly from the following information provided by the Department of Industry: table of total output indicators of industrial enterprises below designated size, sample questionnaire of industrial enterprises below designated size and sample questionnaire of individual industry. Due to the incomplete data sources, the added value of industrial enterprises below designated size and individual enterprises was calculated by indirect estimation. For the former, the calculation of relevant data was mainly based on sampling survey data, while the calculation of data of the latter was based on the data of the previous economic census year, which could not fully reflect the development and changes of these enterprises. Construction industry. Due to the lack of necessary basic information, in the regular years, the added value of non-accredited construction enterprises, enterprises with construction activities affiliated to other industries and individual operators of construction industry was calculated by hind casting with the information of accredited construction enterprises and the proportion structure of the data of the previous census year. The specific calculation method is as follows:

Added Value of Construction Industry = Added Value of Accredited Construction Enterprises ÷ Proportion of Accredited Construction Enterprises in the Previous Census Added Value of Non-Accredited Construction Companies = Added Value of Construction Industry − Added Value of Accredited Construction Enterprises

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If this method is used only one year after the economic census, the estimation result is more credible. However, the cycle of economic census is five years, and the economic structure may change within these years. The farther away from the census year, the greater the error of estimation. (3)

Transportation, warehousing and postal services. In this industry, due to the lack of financial information for individuals and other road and water transport businesses, their total output is estimated by the business tax information of road transport industry. The specific calculation is: total output of individuals and other road (water) transport industry = total output of individuals and other road (water) transportation enterprises in the previous year × the development speed of business tax of road (water) transportation industry

In addition, there is the added value of the express delivery services companies calculated by the income approach. The source of the data is also from the investigation data of the State Post Bureau, but not the comprehensive financial information of the industry, so there may be errors. (4)

(5)

(6)

(7)

Information communication, computer services and software industry. In the practice of accounting, a large part of the added value of the income approach in this industry is calculated by using the survey data of the Department of Services and the Department of Accounting as well as the economic census data, which is, therefore, not very accurate for the rapidly changing market of service industry. Wholesale and retail trade. The industry includes two major parts: above and below designated size. Due to the lack of basic information, both the total output and added value of wholesale and retail enterprises below designated size and the individual businesses cannot be directly calculated, but can only be estimated by using the information of wholesale and retail enterprises above designated size of the year and the relationship of relevant indicators in the census year between the enterprises above and below designated size as well as the individual businesses. Accommodation and catering industry. The accounting of accommodation and catering industry also covers two major parts, the above and below designated size. The relevant information of accommodation and catering enterprises below designated size and the individual businesses are also quite inadequate, so the total output and added value of this part are calculated based on the total output of the enterprises above designated size and the ratio of the two parts in census year. Real estate industry. The accounting of China’s real estate industry covers activities of real estate development and management industry, property management industry, real estate intermediary service industry, private housing service industry and other real estate activities. Due to the lack of information, house lease activities undertaken by other industries who have not got a business license from the industrial and commercial sector are not calculated separately. The added value of the current price of the

6.4 Analysis of the Reasons Why the GDP of Production Approach …

(8)

(9)

(10)

(11)

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real estate industry is calculated by the production approach and the income approach, but subject to the result of the income approach. Within the scope of accounting, the data sources in the regular years for the private housing services, including rental services and virtual services, are limited, so the total output is calculated with cost price. In accounting by the income approach, only the depreciation of fixed assets is calculated for the added value of the private housing services. In this case, the net production tax, operating surplus and labor remuneration are zero. Business service industry and leasing industry. At present, the source of added value statistics of the industry is only from the sample survey data of part of the service industry and the annual salary report of the Department of Population as well as the economic census data. It is not complete for the accounting of added value of various leasing and business service enterprises which is also likely to be underestimated. Scientific research, technical services and geological surveying industry. The accounting data of the industry come from some sample survey data of service industry provided by the Department of Service Industry, the survey data of input and output provided by the Department of Accounting, the annual salary report of the Department of Population, the economic census data and the statements of financial position of the research and experimental development industry of the Ministry of Science and Technology. The accounting of added value is subject to the result of the income approach. And the added value of research and experimental development is calculated by using the statements of financial position of the research and experimental development industry from the Ministry of Science and Technology as well as the economic census data, so it is less likely to have undercounts for this part. However, the added value of industry of professional technical service, science and technology exchange and promotion service and geological surveying are calculated by using economic census data and survey data, so it is quite possible to have undercounts. Resident services and other service industries. There have been many problems in the statistics of China’s service industry. Due to the existence of large number of private enterprises and individual businesses, it is very difficult to obtain complete financial information. Therefore, the annual accounting of added value of residents service and other service industries in the regular years is estimated based on the investigation data from the Department of Service Industry, Department of Cities and Department of Population as well as the economic census data. It is very likely to have undercounts. Education, culture, sports and entertainment industry. In the accounting for these industries, the main problem is the poor access to data of production activities of some private and individual enterprises. However, compared with other industries, the added value of this part is not large, so the result of the estimation by using the survey data and economic census data has little effect on the added value of the industry.

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All in all, due to the financial data of the enterprises below designated size, the nonaccredited enterprises and the private and self-employed enterprises are inadequate or missing, the calculation of the added value has to adopt the indirect estimation method which may bring large errors.

6.5 Problems in the GDP Accounting by the Expenditure Approach The GDP accounting by the expenditure approach includes the final consumption expenditure, the total value of capital formation and the net export of goods and services. From the overall perspective of the national economy, it reflects the total size, structure and growth rate of a country’s final demand during the accounting period. Due to restrictions on data sources and other various reasons, there are still many problems in the accounting of China’s GDP by the expenditure approach. (1)

Household consumption. In China’s GDP accounting, there are two ways to calculate household consumption: one method is to calculate by combining the retail sales of consumer goods with household survey data; the other method is to calculate based completely on household survey data. However, for a long time, in accounting practice, the second method had only been used as a trial method for comparison and inspection. It was not until 2006 when the Annual GDP Accounting Methodology for Non-Economic-Census Years was formulated by the National Bureau of Statistics that it was confirmed that the household consumption should be calculated by household survey data and adjusted according to the GDP data by the production approach (income approach) [2]. The basic information on the retail sales of household consumer goods is mainly from the retail sales of social consumer goods which refer to the retail sales of consumer goods to urban and rural residents and social groups in various economic types of wholesale and retail trade, catering, manufacturing and other industries, and the retail sales of non-agricultural consumer goods to farmers. Among them, the retail sales of non-agricultural consumer goods to urban and rural residents and farmers reflect the sales of goods to residents for living consumption, which is the main basis for calculating the commodity consumption of residents.

However, there is a clear distinction between the retail sales of social consumer goods and final consumption, so it is not suitable to replace the latter simply with the former. First of all, the retail sales of social consumer goods is not entirely an indicator of consumption. It includes the construction materials sold to residents to build houses, that is, it contains part of the investment. In addition, it also includes the amount of retail sales to social groups. It is difficult to judge whether the goods sold are used for consumption or for intermediate consumption or investment. Secondly, non-material service consumption such as medical care, culture and art, and education, etc. are not included in the total retail sales of social consumer goods. Finally, the retail sales do

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not cover agricultural products produced and consumed by farmers themselves. In the actual accounting, there is no clear and standardized regulations on how to adjust the above differences, and many of them are only estimates, so there are certain errors in the calculation of household consumption. (2)

(3)

Government consumption. The data source of government consumption has always been the “Financial Accounting Data of Administrative Units” of the Ministry of Finance. Government consumption covers the the regular business expenditure of operating expenses in and off the financial budget, the depreciation of fixed assets of the administrative units and the non-profit institutions, the balance of total output of urban neighborhood committees and rural village committees after deducting their revenues. Among them, the scope of regular business expenditure is not clearly defined. In the actual calculation, some parts that are not of the regular business expenditure are not deducted from the relevant business expenses. In addition, the physical consumption to urban residents provided by governments should be deducted from government consumption expenditure, but due to the lack of relevant data, they are often not deducted in the actual calculations. Total value of fixed capital formation. There are two types of total value of fixed capital formation: tangible and intangible. In the accounting practice in China, when calculating the total value of intangible fixed capital, due to the difficulty in access of data, the cultivated asset and the intangible asset of entertainment and literary works were not included. The part of computer software in intangible asset refers to the income earned by enterprises from development, research and sales of software products. The data are also only from the data of computer software sales provided by the Ministry of Industry and Information Technology. Some computer softwares developed by some enterprises independently for their own use but not for sales are also intangible asset, but it is not included in accounting. The data of mineral exploration fees in intangible assets are from the final accounts of the state financial revenue and expenditure of the Ministry of Finance, which only includes the expenditures of geological exploration by the administrative units in the budget, and the statistical scales and scope are incomplete. Since 2002, the expenditure data of national geological exploration have been provided in the “Table of Geological Exploration Input” of the “China Land and Resources Statistical Yearbook” issued by the Ministry of Land and Resources, but the data were adopted as stipulated in the Annual GDP Accounting Methodology for NonEconomic-Census Years revised in 2010. In addition, regarding the accounting of expenditures for land improvement, the fixed assets formed by the officially approved land improvement expenditures are included in the total completed investment of fixed assets of the whole society through investment projects, while it is difficult to obtain the land improvement costs in the investment projects that are not formally approved, so it is not included in the total amount of fixed capital formation.

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In the accounting of the total value of fixed capital formation, currently, the fixed asset investment of the whole society is generally used for calculation, but in fact there is a clear distinction between the two indicators. Firstly, the fixed investment in rural area and by urban non-agricultural households with a value of less than 500,000 yuan is excluded from the social fixed assets investment, but this part should be included in the calculation of total fixed capital formation. And the sporadic fixed asset investment of less than 50,000 yuan are not included in the present statistical scope, either, because the data are small, and, therefore, difficult to collect. Secondly, in the social fixed assets investment, the value appreciation of commercial housing sales is excluded. The current statistics of fixed asset investment only include the investment in real estate development, but exclude the new added value in the process of real estate transactions, that is, the balance between the sales value of commercial property and the corresponding construction cost. Thirdly, the whole society’s fixed asset investment includes the purchase of old buildings, the value of old equipment and land acquisition fees. However, the old equipment and old buildings are fixed assets formed in the previous period, not the result of the production activities in the current period. The land acquisition fee is only the fee paid for the right to use the land, but without new fixed assets formed. Therefore, it should be deducted when calculating the formation of fixed capital. How to make necessary adjustments to the above indicators when calculating the total fixed capital formation? Where to get the necessary data? In this regard, there is also a lack of transparent and standardized regulations and methods. (4)

(5)

Increase in inventory. The main data used to calculate the increase in inventory in the past was from the state-owned enterprises. Due to the difficulty in obtaining data, the calculation of the increase in inventory of enterprises below designated size and non-accredited enterprises is very inaccurate. Net exports of goods and services. When it comes to China’s GDP accounting by expenditure approach, the data of import and export of goods and services mainly come from the annual Balance of International Payments of the State Administration of Foreign Exchange and the total volume of import and export of the General Administration of Customs. It is worth noting that there are many differences between the trade balance of the customs statistics and the net export of goods and services in GDP calculated by the expenditure approach. First of all, the trade balance in customs statistics only covers that of goods trade but not of service trade; secondly, the import of goods by the customs is calculated based on the CIF price, while the net export of goods and services is calculated based on FOB price, so there is a gap in insurance and shipment costs by using these two different calculating methods. Therefore, in the actual accounting, the customs statistics cannot be simply taken as substitute when calculating the net export of services and goods. The CIF price of goods in the customs statistics should be adjusted to the FOB price before it can be used to calculate the import of goods. Although it is clearly regulated in various versions of the GDP accounting scheme issued in China, in the real accounting, it is still a practice that occurs from time to time to simply use the trade balance in

6.5 Problems in the GDP Accounting by the Expenditure Approach

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customs statistics to replace the net export of goods and services calculated by the expenditure approach. All these may cause errors in the accounting of net exports by the expenditure approach.

6.5.1 Conclusions and Suggestions Based on the above analysis, we can draw the following conclusions: First, the gap between China’s GDP data by production approach and by expenditure approach has been narrowing in recent years. Second, the main error is that the GDP calculated by production approach is less than the GDP calculated by the expenditure approach. Third, the cause of this error mainly lies in the incomplete data sources, which may lead to some undercounts of GDP by the production approach. In addition, there are also some aspects that can be improved in the accounting of GDP by the expenditure approach. In response to the above questions, we make the following suggestions: 1.

To continue to improve the specific schemes for GDP accounting by two methods.

The production approach and the expenditure approach are to calculate the GDP from two perspectives. It is inevitable that there are some errors in calculation results. As long as the errors can be controlled within a certain range, the quality of the GDP data will not be affected. To further reduce the calculation error, the accuracy of the GDP must be improved by either the production approach or the expenditure approach. Only in this way can the connectivity of the two data be ensured fundamentally. 2.

To further improve basic statistics so as to provide reliable basic information for GDP accounting.

In the regular year, it is not possible to obtain comprehensive survey data as in the economic census year, but it is necessary to establish a regular sampling survey system for enterprises below designated size, non-accredited enterprises, individual businesses, and various service enterprises. 3.

To give due consideration to the changes of the actual situation when making indirect estimates based on the data of the economic census.

For example, due to the inadequate regular data, the relevant proportion in census is often used as the foundation for indirect calculation. Some adjustments can be made to this proportion in accordance with the actual situation. For example, the degree of variation can be calculated by referring to the change of the proportion in two census years, and according to this variation rate, the proportion in census years can be modified appropriately which then can be used in indirect calculation in the following years.

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To further improve the specific scheme for the GDP accounting by the expenditure approach.

It is necessary to accurately understand and grasp the definition and scope of indicators included in the GDP by the expenditure approach, and gradually abandon the practice of simply adopting similar indicators as substitute (such as directly using fixed assets investment to replace fixed capital formation). 5.

To further strengthen coordination within the statistical agencies and between the comprehensive statistical agencies and the statistical departments.

In terms of the data source for the GDP by the production approach and the expenditure approach, in addition to the data from statistical system, the data from various national sectors are also important sources for accounting. Therefore, the coordination of statistical agencies is of great significance for ensuring the uniformity of scales and improving the connectivity of data. When designing various survey schemes and questionnaires, the relevant institutions should discuss the scales and scope of the indicators together so as to take into account the needs of various parties and clarify their differences. Meanwhile the database technology can be used to realize the sharing of basic data among departments to avoid the situation of different data for the same indicator.

References 1. Shengping D et al (2006) Application of economic census data in GDP accounting. Statist Res (5) 2. Xianchun X (2006) The annual GDP accounting scheme for non-economic census Years. Statist Res (10)

Chapter 7

Evaluation and Analysis of CPI Data Quality in China

CPI, the consumer price index, is one of the most important macroeconomic indicators which can comprehensively reflect the fluctuations of consumer prices, and can be used to measure the degree of inflation, and, on this basis, to measure the real income level of residents. The quality of CPI data affects not only the effectiveness of macroeconomic policies, but also the behavior of the public and their trust in government statistics. Therefore, it is of great theoretical and practical significance to make research on the quality of CPI data. In order to study the quality of CPI data, it is necessary to evaluate and judge their quality first. In this chapter, the author will first introduce the compilation of current CPI in China briefly. On this basis, the quality of CPI data will be assessed and analyzed from the dimension of accuracy and multi-dimensions.

7.1 CPI and Its Compilation in China 7.1.1 Principles of Design and Compilation of CPI in China According to the definition given by the National Bureau of Statistics [1], CPI is used to reflect the fluctuations of the price of goods and services purchased by residents. At present, China’s official statistics include three categories: urban CPI, rural CPI and CPI for all residents. Before 2001, the weighted arithmetic mean method was mainly used in the compilation of CPI in China. The corresponding monthly and annual calculation methods are as follows: It =



W

Pt Pt−12

© Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_7

(7.1)

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It =



W

Pt Pt−1

(7.2)

In the above formula, I t is the consumer price index, refers Pt to the price of various commodities in t-period, and W is the weight of various commodities. According to the above methods, the National Statistical Bureau has not set a fixed base period in the compilation of specific indexes. The base period varies with the change of reporting period. The calculation of MOM index and YOY index of corresponding reporting period is based on the previous month and the same month of the previous year, respectively. For annual CPI, the calculation is based on the simple average of the year-on-year index. Since the ordinary Laspeyres price index can not measure the impact of substitution effect caused by fluctuation of commodity price on changes of consumer expenditure, since 2001, the National Statistical Bureau has started to use the chain Laspeyres index to compile CPI. Besides, it updated and adjusted the survey commodity basket, and implemented a unified “survey system, commodity catalogue, compilation method and data processing software” nationwide. Since January 1, 2001, the base period of CPI has been fixed at the year of 2000 and adjusted every five years. The basic formula of the chain Laspeyres index can be written as follows: C−L · PtL PtC−L = Pt−1

(7.3)

C−L In the formula, PtC−L and Pt−1 represent the chain Laspeyres price index of and PtL is the Laspeyres Price Index, which can t and t–1 period respectively. P it  pit qit−1 i be written as  pit−1 qit−1 , and pit −1 represent the prices of commodity i in t and t–1 i period, respectively. And qit −1 represents the quantity of commodity i in the t–1 period. Because the chain Laspeyres formula takes into account the influence of the previous index, it can measure the changes of consumption expenditure caused by the substitution effect in the commodity category at the low-level aggregation stage.

7.1.2 Statistical Investigation of CPI in China To compile CPI, we need to carry out corresponding price surveys according to the unified scheme formulated by the National Bureau of Statistics. At present, the work is undertaken by the investigation teams based in 31 provincial regional units (including provinces, autonomous regions and municipalities directly under the Central Government).

7.1 CPI and Its Compilation in China

1. (1)

129

Survey Design Selection of Areas for Price Survey

The price survey areas are selected by combining the stratified sampling method and systematic sampling method, with necessary adjustments made according to the actual situation. Among them, the choice of cities is mainly made according to the rank of the average annual wages of all cities in the region, and with systematic sampling conducted for the number of cities to be investigated combining with the cumulative data of the urban permanent population. For the selection of counties, the same method as that of cities is adopted, but the corresponding per capita net income is chosen as a symbol instead of ranking according to wages. Since 2001, China’s CPI has been investigated in nationally selected survey areas. At present, the regions participating in the national survey include about 500 cities and counties, with a total number of more than 50,000 price survey sites. (2)

Selection of Price Survey Sites

After the price survey areas are determined, the systematic sampling method is mainly used to select specific price survey sites. The selection methods can be summarized as follows: (1) to rank all types of commercial networks in the survey area according to the index like business scale; (2) to select the survey sites through systematic sampling of the cumulative volumes of the indicator of business scale. For the of same specification products, the number of survey sites selected in big cities is 324, and that in medium and small cities (including counties) is 23 and 1–2, respectively. (3)

Classification of Survey Products and Selection of Quantity of Survey Specification Products

At the present stage, the survey specification products of CPI statistics in China are generally classified into eight categories, and each category is further divided into several secondary classes and more basic items. The basic items are composed of a certain number of representative specification products. According to the interpretation of the CPI by the National Bureau of Statistics, there are altogether 262 basic items selected mainly based on the consumption volume and consumption habits of residents. The selection of representative specification products is mainly based on the criterion of large consumption, typical trend and degree of price fluctuation and the strong heterogeneity between products. At present, there are about 600 kinds of routine representative specification products in CPI statistical survey in China, and the catalogue of some products can be adjusted according to the specific situation every year. In each provincial administrative unit, there is no strict unified requirement for the annual small-scale replacement of representative specification products, and corresponding annual adjustment measures should be taken according to the regional conditions. However, in general, the replaced products have certain similarities, that is, change in quality (representativeness lowered), quicker withdrawal of specific product types from the market, etc. This kind of specification products can be replaced in time as long as the comparability can be ensured for the homogeneous items.

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Design of Weighted Data Survey

The calculation weight of CPI in China is calculated based on the survey data of consumption expenditure of 130,000 households in urban and rural areas. In terms of weight adjustment, the weights of items other than fresh vegetables and fruits are fixed every month in each year and adjusted appropriately at the beginning of the year, but the weights of fresh vegetables and fruits have to be adjusted every month. The relevant data come from urban and rural household surveys. Although there are some differences in sampling methods between urban and rural areas, the quality requirements of weight survey data are consistent. 2.

Implementation of Survey

Generally speaking, in the survey of consumer price data, the current CPI compilation in our country adopts “three fixed” methods for price survey, that is “fixed time”, “fixed site” and “fixed person”. Using this method to collect price data can not only help to avoid survey errors caused by frequent changes of price collectors, but also help to improve the convenience and effectiveness in obtaining relevant price data. These measures are conducive to ensuring the stability and comparability of survey data sources to a large extent. The main purpose of the requirement of times and date of investigation is to ensure the comparability of investigation data in time. The survey of the data needed for calculating the weights mainly focus on the survey of consumption expenditures of the urban and rural households (the cash expenditure survey in rural areas). The specific implementation of survey is designed as follows: (1)

Price Data Collection

Generally speaking, the price data of CPI statistics in China are mainly collected directly by the price collectors. Within a certain period of time, the special price collector will go to the selected price survey area to collect the price according to the fixed date and time. In the specific process of collecting prices, the price collectors will go to the fixed site at the specified time to conduct price data survey. As regulated, they need to collect the price data of more than three (including three) representative specification products for each type of survey products, while for the prices of various types of surveyed products, the arithmetic mean of the corresponding collected prices of several representative specification products need to be calculated. In addition, for commodities such as vegetables, meat and eggs, because they are closely related to people’s lives and their prices fluctuate very easily, it is necessary to increase the frequency of investigation in the actual price collection programs. The current requirement is to collect prices at least once every five days. Since 2010, the National Bureau of Statistics has started the pilot work on the use of hand-held data collectors for CPI price surveys in 50 cities, which has played a very good role in improving the quality of CPI statistical source data. Since 2012, hand-held data collection has been carried out in an all-round way, which not only greatly improves the timeliness of data transmission, but also facilitates the real-time audit of data, so it is of positive and practical significance for improving the quality of price data of specification products.

7.1 CPI and Its Compilation in China

(2) A.

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Weighted Data Acquisition Selection of Survey Units

There are some differences in the selection of survey units between in urban and rural areas. Generally speaking, the method of multi-stage, two-phase and random systematic sampling is adopted in the selection of survey units in urban areas, while the method of three-stage and symmetrical systematic sampling is adopted in rural areas. The steps for selecting survey units in urban areas can be summarized as follows: (1) To determine the survey cities and counties. With the provincial administrative unit as a whole, all cities and counties in the jurisdiction are ranked according to the average wages of workers from high to low, and the systematic sampling is carried out with the assistance of the accumulated population data. (2) To determine the survey households. On the basis of the determined cities and counties, the survey households are selected and the sample set of the survey households is constructed accordingly. (3) For the constructed sample set of households, two-phase samples are selected according to the ranking of the per capita income of each sample household, and on this basis, the regular investigations for two-phase samples are carried out. The steps for selecting survey units in rural areas are similar to those in urban areas. The main difference is that when selecting survey counties, the sorting is based on the data of per capita income (grain output). B.

Data records of survey households

For the selected survey households, their data are mainly recorded by means of continuous bookkeeping. At present, about 130,000 urban and rural survey households in China participate in the data records. They record all income and expenditure data in daily life piece by piece, which will be collected for report by the price collectors.

7.1.3 Compilation, Release and Revision of CPI 1.

Weight calculation method

For the weight of CPI, it involves the weight of commodities (services), the weight of cities and counties, the weight of provinces and that of the nation. The calculation of various weights is mainly based on urban and rural household survey data and GDP accounting data. As far as the weight of classified items is concerned, the calculated weight is evaluated and revised rationally every year in China. Assessment methods include convening expert evaluation meetings, checking through typical surveys and related data, and comparing weights with previous years. 2.

Specific method of compiling index

According to the relevant instructions of the National Bureau of Statistics, China adopts the aggregate method to compile the CPI at the national level.

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Calculation of MOM price index

For the MOM price index of the basic categorized items in the period t, the compilation formula can be written as follows: Kt =

 n

G t1 · G t2 · G t3 · · · G tn × 100%

(7.4)

In the formula, Gt1 , Gt2 , Gt3 , …, Gtn represents the relative ratio of the price of the first to the nth representative specification products in t and t-1 period respectively. (2)

Calculation of Fixed Base Index

The compilation formula of the fixed base index I t for the t period is as follows: It = K 1 × K 2 × K 3 × · · · × K t

(7.5)

Among them, K 1 , K 2 , K 3 , …, K t represents the MOM index of each basic classified item from the base period to the reporting period. (3)

Index Calculation in Advanced Aggregation Phase

The compilation formula of weighted aggregation stage is chain Laspeyres formula: Lt =



 Pt × L t−1 Wt−1 Pt−1

(7.6)

qt−1 Among them, W t-1 is the weight which can be expressed as pt−1 , Pt and Pt-1 pt−1 qt−1 represent the price of the t and t−1 periods respectively, qt and qt −1 represent the consumption volume in the t and t−1 periods respectively, and L t and L t −1 represent the chain Laspeyres price index of t and t−1 period respectively.

(4)

Calculation of regional aggregate index

The compilation of regional aggregate index here mainly refers to the weighted aggregation of urban index and rural index within administrative units at all levels. The aggregate weight is calculated according to the survey data of urban and rural consumption expenditure (cash consumption expenditure for rural area). (5)

Index Conversion Method

CPI MOM Index =

Fixed Base Index of Reporting Period Fixed Base Index of Previous Period

(7.7)

CPI YOY Index =

Fixed Base Index of Reporting Period (7.8) Fixed Base Index of the Same Period of Previous Year

CPI Annual Index =

Mean of Cumulative Fixed Base Index of the Year Mean of Cumulative Fixed Base Index of the Previous Year (7.9)

7.1 CPI and Its Compilation in China

3. (1)

133

Release and revision of data Release of CPI data

According to the latest release scheme, China’s monthly CPI data are mainly released on the website of the Statistical Bureau, while the quarterly and annual CPI data are released on the website of the Statistical Bureau and at the specific press conferences. Of course, relevant data will also be issued in the publications like Monthly Macroeconomic Indicators and Statistical Yearbook, etc. Specific content released includes: CPI data of nation, provincial administrative units and 36 large and medium-sized cities. In terms of release time, the monthly CPI is scheduled to be released around the 9th of the following month, while quarterly and annual data are released around the 18th of the first month of the next quarter and the next year. (2)

Revision of CPI data

At present, the released historical data have not been revised in China’s official statistics. The relevant revisions are mainly reflected in the updating of weight data and the improvement of compilation methods. In 2001, the CPI compilation was greatly reformed. In addition to the adjustment of compilation methods and statistical surveys, price indices of fixed base period and comparative base period were added.

7.2 Analysis of CPI Bias 7.2.1 Question Raised For CPI, the current public queries are more manifested in the bias from subjective experience. Figure 7.1 shows the trend of the price satisfaction index of urban depositors given by the People’s Bank of China. It can be seen that the overall satisfaction of urban depositors with current prices has been declining year by year (except for small increases in a few years), which, to some extent, also puts forward higher requirements for government’s statistics of CPI in China. In view of the accuracy of CPI data in China, this section will use several different methods to evaluate and analyze the possible bias. The CPI bias, in short, is the bias between CPI and the real changes of price of living cost of consumers. The reasons for CPI bias are complex, which may be caused by such factors as sample representativeness, weight data, compilation methods, or registration errors and accidental errors in the compilation process. The existence of CPI bias will exert a negative impact on people’s behaviors and feelings and policy making. Therefore, it is very necessary to analyze CPI bias, which is an important aspect of correctly evaluating the quality of CPI data in China. The existing methods for estimating CPI bias mainly include: the bias estimation based on Engel curve [2], the estimation based on quality adjustment [3], and the bias estimation based on time series analysis [4].

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Fig. 7.1 Urban depositors’ current price satisfaction index (Quarterly). Data Source The questionnaire survey data of urban depositors by the People’s Bank of China

7.2.2 Analysis of CPI Bias Based on Time Series 1.

Model of Bias Analysis Based on Time Series

Firstly, the bias estimation based on time series is used for analysis. We choose to use the state space model for empirical analysis. The state space model used in this section is defined as follows: Measurement Equation : ln cpi t = α + βt ln cgpi t + γt ln m2t + εt State Equation: βt = η1 βt−1 + δ1t γt = η2 γt−1 + δ2t

(7.10)

(7.11)

Among them, ln cpi, ln cgpi i ln m2t represent the total CPI index, Corporate Goods Price Index (CGPI) and the broad money supply (M2) after logarithmization, respectively. α is the constant term, t and γt are state variables, and they are unobservable variables varying with time δ2t ∼ N (0, Q 2t )δ1t N (0, Q 1t ) and are subject to AR (1) process; εt N 0, σ 2 ;, t = 1, 2, … T. As for the choice of explanatory variables, we mainly consider its economic implications while taking into account the availability of data. Among them, M2 represents the potential purchasing power, which is an important basis for the central bank’s monetary policy and an important indicator to measure inflation; and the Corporate Goods Price Index (CGPI) takes domestic enterprises as the statistical object to reflect the fluctuations of commodity price in centralized trading among enterprises. This kind of data also plays an active role in judging price situation and macroeconomic monitoring. The data of CGPI and M2 are both from the People’s

7.2 Analysis of CPI Bias

135

Table 7.1 Unit root test of variables Variable

Small P value

Variable

1.3434

0.9988

lncpi

ln cgpi

−0.1121

0.9454

ln m2

−1.2762

0.6406

ln cpi

PP value

PP value

Small P value

−9.9242

0.0000

lncgpi

−5.7532

0.0000

lnm2

−14.6730

0.0000

Notes represents first order difference

Bank of China. In theory, there should be an obvious co-integration relationship between the real CPI and CGPI and M2, so we can use CGPI and M2 to estimate the real CPI. 2.

Results of Empirical Analysis

In the specific empirical analysis, we select the monthly CPI MOM data from January 1997 to December 2013, CGPI fixed base data (December 1993 = 100) and M2 data, and use January 1997 as the base period to carry out the base conversion of CPI and CGPI. Generally, the analysis of state space model needs to satisfy the smoothness of variables or the co-integration relationship between variables [2], so as to avoid possible spurious regression problems. Table 7.1 is the result of unit root test. None of the three groups of variables after logarithmic processing satisfies the smoothness, but they are all stationary at 1% level after first-order differencing, which meets the needs of co-integration analysis. On the basis of unit root test, Johansen co-integration test of three variablesln cpi, ln cgpi i ln m2t is carried out. Among them, the co-integration lag order is determined to be 7 according to the selection criterion of the optimal lag order based on the vector autoregressive model (VAR). The results of co-integration are listed in Table 7.2. It can be seen that at 5% significance level, both the tests based on trace statistics and λ statistics reject the original hypothesis of “null co-integration vector”, and there exists an exclusive co-integration relationship among them. The existence of co-integration shows that no spurious regression problem exists in the state space model consisting of formula (7.10) and formula (7.11). The estimation results of the state space model by Kalman filtering are listed in Table 7.3. As can be seen in Table 7.3, the statistical performance of the estimated state space model is good. Based on the estimation results of the state space model, we further calculated the real CPI fixed-base data fitted by the model. After converting the series into MOM data, we compared them with the CPI MOM indices issued by the National Bureau Table 7.2 Johansen cointegration test for variable sequences Hypothesized no. of cointegration vector

Eigenvalue

Trace statistic (P)

λ statistic (P)

None

0.1115

36.8576(0.0065)

23.1759(0.0254)

At least 1

0.0529

13.6817(0.0921)

10. 6534(0.1725)

At least 2

0.0153

3.0282(0.0818)

3.8415(0.0818)

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7 Evaluation and Analysis of CPI Data Quality in China

Table 7.3 Estimation results of state space model α

Coefficient estimates

Standard bias

Z value

P

1.0047

0.1122

8.9526

0.0000

Final coefficient

Root mean square error

Z value

p

βt

0.7277

0.0070

104.0063

0.0000

γt

0.0271

0.0032

8.5546

0.0000

Log Likelihood: 462.9066; AIC: −4.5187: SC: −4.4862

Fig. 7.2 Changes in CPI bias

of Statistics, and calculated the bias of the corresponding time points by subtracting the estimated CPI ‘real’ value from the official CPI data. The results are listed in Fig. 7.2. From Fig. 7.2 it can be seen that, except for a few years, the CPI bias in China has been decreasing gradually since 2001. This is largely due to the reform of China’s CPI statistics. Since 2001, China’s CPI compilation technology has been greatly improved, with new calculation formulas adopted, and new items of goods and services added. The number of investigation items of goods and services has been increased from 325 to about 550 (more than 600 in large and medium-sized cities). The first base period of comparison was set as 2000, which will be replaced every five years thereafter. A new price index series has been added. These measures have further improved the scientificity and accuracy of compilation of CPI, thus closer to the ‘real’ CPI. As for the increase of bias around 2008, our explanation is that under the influence of the international financial crisis, China’s domestic economy showed a downward trend; small and medium-sized enterprises were severely impacted, and inflation was on a rising track. Under such a macro background, residents needed to spend more on buying the same number of goods or services, but their consumption can often be substituted and they would choose to buy alternative goods (services)

7.2 Analysis of CPI Bias

137

to realize the same life utility. In this case, the bias between the price index of fixed basket commodity (service) calculated by the chain Laspeyres index and the “real” index may also be widened.

7.2.3 Analysis of CPI Bias Based on Engel Coefficient 1.

Basic Principles of Hamilton-Costa Method

Costa (2001) and Hamilton (2001) used Engel curve to estimate CPI bias, which provided a new framework for CPI bias research [5, 6]. Considered to be the economic theorem which is most consistent with economic reality, Engel’s theorem holds that the proportion of food expenditure in total household expenditure will decrease with the increase of total expenditure. Costa and Hamilton believe that, according to Engel’s theorem, for two families with similar family structure and the same total expenditure, their difference of Engel’s coefficients in different periods can be attributed to the bias of their real income and expenditure estimates, and the bias of real income and expenditure may be due to the wrong estimation of prices. On this basis, they put forward a method of estimating the total CPI bias using Engel’s theorem, and made an empirical study on the CPI bias in the United States. Compared with the traditional methods, Hamilton-Costa method has many advantages: firstly, it can estimate the total bias of CPI; secondly, it is flexible to use, and can estimate the CPI bias of specific population of different regions and different income classes; thirdly, the method requires less on data, and the method is relatively simple and operable. The basic idea of Hamilton-Costa method is as follows:  θx · X i, j,t + μi, j,t ωi, j,t = φ + γ (ln PF, j,t − ln PN , j,t ) + β(ln Yi, j,t − ln P jt ) + x

(7.12) The above formula is a rational demand system, in which ω represents the proportion of food expenditure to total household expenditure; PF , PN and P represent the actual price index of non-observable food, non-food and all commodities, respectively; Y represents the level of expenditure; X represents a group of variables about family characteristic; λ represents residuals; the subscript t represents the year, j represents the region (city), i represents family. The actual cost of living Pj,t is a weighted mean of food and non-food prices: ln P jt = α ln PF, j,t + (1 − α) ln PN , j,t

(7.13)

There is bias between the statistical values and the real values of food price P j,t index PF , non-food price index PN and total price index in the form of:

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7 Evaluation and Analysis of CPI Data Quality in China

ln P jt = ln P j,0 + ln(1 + Π j,t ) + ln(1 + E j,t )

(7.14)

P0 denotes the actual price of the base period; Π denotes the percentage of cumulative change (fixed-base CPI) of the observed price of the period from 0 to t; and E denotes the observed bias of cumulative price of the period from 0 to t. The bias of food and non-food is similar to it. The Eq. (7.14) is substituted into the rational demand system Eq. (7.12). The logarithmic form of income Y and price P is replaced by lowercase letters p and y, and the logarithmic form of (1 + Π) and (1+ E) is replaced by and . Then the new form of ideal demand system is obtained as follows: ωi, j,t = φ + γ (π F, j,t − π N , j,t ) + β(yi, j,t − π jt ) +



θx · X i, j,t +

x

x −μ σ

γ (ε F,t − εn,t ) − βεt + γ ( p F, j,0 − p N , j,0 ) − βp N , j,0 + μi, j,t

(7.15)

Assuming that different regions (cities) have the same price bias, then the core equation of Hamilton-Costa’s estimation for CPI bias can be obtained by rewriting the Eq. (7.15): ωi, j,t =φ + γ (π F, j,t − π N , j,t ) + β(yi, j,t − π jt ) +



θx · X i, j,t

x

+

T 

δt · D t +



t=1

δ j · D j + μi, j,t

(7.16)

j

Dt and Dj are virtual variables of time and region respectively. For two families with similar family structure and the same total expenditure, the difference of Engel coefficient in different periods can be attributed to the bias of their real income estimates, so any form of CPI bias will be captured by Dt . The expressions of coefficients of the virtual variable of time Dt and the virtual variable of region Dj are as follows: δ j = γ ( p F, j,0 − p N , j,0 ) − βp N , j,0

(7.17)

δt = γ · (ε F,t − εn,t ) − βεt

(7.18)

Assuming that for any year, the relationship between food bias F,t and non-food bias n,t is a fixed proportion (r), i.e. ε F,t = r εn,t

(7.19)

Then the CPI bias can be expressed as: εt =

δt −β −

γ (1−r ) 1−α(1−r ) t

(7.20)

7.2 Analysis of CPI Bias

139

When r = 1, εt =

δt −β

(7.21)

Therefore, during the t period, the accumulated CPI bias is as follows: δt 1 − exp( ) β 2.

(7.22)

Explanation of data and variables

Empirical data used by Hamilton and Costa are panel data from household surveys, which, however, are difficult to obtain in China. In order to carry out empirical research and analysis, in this study, the statistical data of urban survey are used instead of the panel data of household survey. It is mainly based on the following three factors: (1) The data sequence of urban survey in China is more complete than that of household survey, and the data connectivity is better. After the Fifth National Population Census in 2000, the basic data of the family structure in the country changed greatly, but the data of the urban family structure did not change significantly, therefore, the connectivity of urban data was higher than that of rural and national data; (2) The statistical data of urban survey reflect the basic situation of a city, essentially the equalization of the data of urban families, and such processing will not change the distribution characteristics of urban household data, nor the intrinsic law of data. Therefore, for Engel’s Law which is the law of social statistics, the aggregate data have the same law, and, the more aggregated the data are, the more obvious the law of data is; (3) Hamilton-Costa method is flexible and can estimate the CPI bias of different people in different time, different regions and different strata. It is methodologically feasible to study the CPI bias of cities by using urban data. In the following part, we’ll make an empirical analysis by using the statistics of China’s urban survey, that is, to virtualize each city as a “standard family”, and use the panel data from 1997 to 2011 to estimate the bias of urban CPI. The data we use are all from the China Urban Life and Price Yearbook (1998– 2013) and the China Statistical Yearbook of Survey on Price and Household Income and Expenditure of Urban Residents (1998–2013). Engel coefficient is calculated by the ratio of household expenditure on food consumption and total household consumption expenditure published in the yearbook. Food price index and CPI are obtained by adjusting the initial data with 1997 as the base period. Since it is difficult to obtain the continuous complete data of non-food price index and the weight coefficient of food price index in CPI statistics in the published yearbooks, this study uses the relationship between food and non-food price index and CPI to calculate roughly the non-food price index with the current weight coefficient of 34% as standard. The data of Tibet Autonomous Region in 1997 and 1998 were incomplete, and its Engel coefficient differed greatly from other provinces and cities in the country, so the data of Tibet were excluded. The CPI and food price index of

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7 Evaluation and Analysis of CPI Data Quality in China

Beijing, Tianjin, Shanghai and Chongqing in 2006 were missing in the yearbook, so they were supplemented by the corresponding data of the city published in the 2007 statistical bulletin. To estimate the core Eq. (7.16) of Hamilton-Costa method, we need the variables of basic family characteristics. We choose the average population of family (NUMBERS), the proportion of the family population with income (NUMWITHIN) and the proportion of the family employment (NUMEMPLOYE) as the variables of family characteristics. In addition, in the empirical analysis, we found that there may be statistical errors in the investigation of Engel coefficient both horizontally and vertically. For this reason, we add dummy variables to adjust, as follows. Vertically, it is found that, before 2000, the annual decline of the simple average of Engel coefficient of cities in China was more than twice that after 2000 (including 2000), but there was no significant difference in the variation of variables like family characteristics, expenditure level and price. Therefore, a dummy variable DT was set up to capture and eliminate the possible differences of statistical data before and after 2000 (including 2000). Horizontally, we found, when comparing each two, that the Engel coefficient and per capita expenditure of some cities are higher than those of their counterpart. For example, the historical data of Beijing reflect that its Engel coefficient and per capita expenditure of urban residents are higher than those of Shanxi Province. This difference may be caused by the bias of the statistical scale of consumption expenditure and food expenditure between the two places. In order to adjust this kind of bias, we divide cities with different statistical scales into two categories, as shown in Table 7.4, and set up virtual variables DA . 3.

Results of Empirical Analysis

Tables 7.5 and 7.6 are the results of OLS estimation of Eq. (7.16) by SPSS based on 1998-2011 data (regression 1) and 2000–2011 data (regression 2), respectively. From the coefficients of the regression equation in Tables 7.5 and 7.6, we can see that the dummy variables DT and DA can enter the regression equation at the same time, and the regression coefficients pass the significance test, which means that the actual data support the hypothesis of statistical bias of the data in this study. According to the results of empirical analysis, the following conclusions can be drawn: First, there are statistical scale differences horizontally and vertically between household expenditure and food expenditure in China. The significance of DA in the equation indicates that the hypothesis of the difference of statistical scale between cities in China is valid. Horizontally, there are significant differences in the statistical scales of household expenditure and household food expenditure between cities in China, that is, there are at least two differences as shown in Table 7.4. The significance of DT in the equation shows that the Engel coefficients of families with similar family structure (city), the same price and expenditure level are significantly different around 2000. Vertically, there may be differences between the

1

1

1

1

1

1

1

1

Beijing

Tianjin

Shanghai

Jiangsu

Zhejiang

Shandong

Liaoning

Anhui

Ningxia

Yunnan

Guizhou

Sichuan

Hainan

Guangxi

Hubei

Jiangxi

Province & cities

2

1

1

1

1

1

1

1

Classification

Gansu

Shaanxi

Hunan

Henan

Heilongjiang

Shanxi

Hebei

Xinjiang

Province & cities

2

2

2

2

2

2

2

2

Classification

Qinghai

Chongqing

Guangdong

Fujian

Jilin

Inner Mongolia

Province & cities

Notes In category, 1 means that Engel coefficient and per capita expenditure are both below or above the average, while the others are 2

Classification

Province & cities

Table 7.4 Classification of cities by statistical scales

2

2

2

2

2

2

Classification

7.2 Analysis of CPI Bias 141

142 Table 7.5 Table of regression coefficient

7 Evaluation and Analysis of CPI Data Quality in China Variable Constant

0.268 (0.06)

P 0.000

NUMBERS

0.057 (0.012)

0.000

NUMWITHIN

0.089 (0.048)

0.068

−0.212 (0.048)

0.000

NUMEMPLOYE Real price

Table 7.6 Table of regression coefficient

Coefficient (standard deviation)

0.076 (0.031)

0.015

Real expenditure

−0.006 (0.009)

0.514

DT

−0.069 (0.032)

0.029

DA

−0.009(0.005)

0.073

DA1

0.047 (0.005)

0.000

DA2

0.023 (0.006)

0.000

DT 2

0.049 (0.008)

0.000

DT 3

0.029 (0.007)

0.000

Coefficient of determination R2

0.661

Adjustable coefficient of determination R2

0.642

Standard bias of regression

0.0271

Variable

Coefficient (standard deviation)

P

Constant

0.324 (0.064)

NUMBERS

0.048 (0.013)

0.000

NUMWITHIN

0.052 (0.051)

0.310

−0.219(0.052)

0.000

NUMEMPLOYE Real price

0.000

0.073(0.031)

0.020

Real expenditure

−0.005 (0.010)

0.658

DT

−0.069 (0.031)

0.027

DA

−0.010(0.006)

0.090

DA1

0.050(0.006)

0.000

DA2

0.023(0.006)

0.000

Coefficient of determination R2

0.590

Adjustable coefficient of determination R2

0.566

Standard bias of regression

0.0262

7.2 Analysis of CPI Bias

143

Table 7.7 CPI and Real Cost-of-living Index 1997–2011 1997 CPI

1998

1999

2000

2001

2002

2003

2004

2005

2006

1.0000 0.9940 0.9811 0.9889 0.9958 0.9879 0.9968 1.0297 1.0461 1.0618

Cost-of-living 1.0000 1.0283 1.0319 0.9889 0.9958 0.9879 0.9968 1.0297 1.0461 1.0618 Index

statistical scales of household expenditure and household food expenditure before and after 2000. The variable Real price is the real price in Eq. (7.16), and the variable Real expenditure is the real expenditure level in Eq. (7.16). Second, there is no CPI bias in the fixed-base CPI of Chinese cities from 2000 to 2011. The results of Table 7.5 show that only the virtual time variables DT2 and DT3 of 1998 and 1999 enter the equation by using the fitting results of 1998-2011 data. The statistical properties of other virtual time variables are not significant, and they can not enter the regression equation. It shows that there is no bias between the fixed-base CPI data and the cost-of-living index in the 12 years from 2000 to 2011. Table 7.7 lists the real cost-of-living indexes calculated based on the results of regression analysis and CPI over the years. The CPI in years with no bias represents the change in real cost-of-living. In years with bias, the estimated bias is used to adjust the CPI. Third, whether in Tables 7.5 and 7.6, the elastic relationship between expenditure and price coefficient supports the economic law, and there are positive and negative correlations between Engel coefficient and relative price and real consumption expenditure, respectively. Through the calculation by the regression coefficient table, we can obtain that the Engel coefficient and the coefficients of elasticity of consumption expenditure and food price are −0.1856 and 0.2357 respectively, so if the nominal expenditure increases by 1%, the Engel coefficient will decrease by 0.1856%, while if the food price increases by 1%, the Engel coefficient will increase by 0.2357%. The coefficient of elasticity of relative price is 1.27 times that of expenditure, which shows that the effect of relative price on Engel coefficient is 1.27 times that of expenditure on Engel coefficient. Fourth, the Engel coefficient of China rebounded in 2007. In this year, the change rate of urban relative price is 110.82%, and the change rate of consumption expenditure is 110.01%. According to the relationship in coefficient of elasticity, the positive change of consumption expenditure will decrease the Engel coefficient, while the positive change of relative price will increase the Engel coefficient. But the effect of relative price is 1.27 times that of expenditure effect. The comprehensive effect is that the Engel coefficient grew by 0.69% in 2007. But the Engel coefficient of urban residents actually increased by 0.78%, and the bias of empirical analysis results is only 0.09%. It shows that the excessive rise of relative price of food to non-food caused the abnormality of Engel coefficient of urban residents in 2007.

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7 Evaluation and Analysis of CPI Data Quality in China

7.3 Aggregate and Connectivity Analysis of CPI The aggregating of CPI and the connectivity of data are important for measuring the accuracy of CPI. The matching of the national total index with the aggregate values of urban and rural indices, and the conversion of YOY and MOM data are also one of the most direct criteria for evaluating the quality of CPI data.

7.3.1 Assessment of the Aggregate Scheme of CPI Total Index China’s total CPI is calculated by a weighted average of urban and rural CPI according to the urban and rural consumption expenditure. In his article “Review and Comment on CPI”, Xu Qiyuan put forward the evaluation method of aggregate scheme of CPI total index: to record CPI total index, urban CPI and rural CPI as CPI t , CPI(1)t and CPI(2)t , respectively, and perform unconstrained regression through formula (7.23): C P It = α0 + α1 C P I (1)t + α2 C P I (2)t + εt

(7.23)

and to carry out the following tests: (1) H0: α0 = 0; (2) H0: α1 + α2 = 1. If two original hypotheses are satisfied, it can be verified that the total CPI in China are really calculated accurately from the urban CPI and rural CPI. In the same way, we analyzed the data from January 1996 to January 2014. Considering that Chin’s CPI statistics have undergone major adjustments since 2001, and that 2006 and 2011 are the beginning years of the five-year adjustment of CPI weights, we divide the research interval into four stages, namely, January 1996 to December 2000, January 2001 to December 2005, January 2006 to December 2010 and January 2011 to January 2014. The specific analysis results are shown in Table 7.8. From Table 7.8, it can be seen that the aggregation of urban and rural CPI shows different characteristics in different periods. In the sample interval from January 1996 to December 2000, the analysis results show that the original hypothesis of 0 = 0 and 1 + 2 = 1 is rejected and the aggregate scheme can not be verified from the perspective of model analysis. From January 2001 to December 2005, if judged by significance level of 1%, the analysis results are still not satisfactory, but at significance level of 5%, the two original hypotheses can be accepted. In the two stages starting from January 2006, the above model analysis accepted the original hypothesis of 0 = 0 and 1 + 2 = 1 at the significance level of 1%, which verified the judgment that “the total CPI are really calculated accurately from urban and rural CPI”. It can be seen from the evolution of the above analysis results that the aggregate quality of China’s total CPI is a gradual evolution, and the aggregate accuracy is constantly improving. At the current time point, the aggregate quality of the CPI is guaranteed.

7.3 Aggregate and Connectivity Analysis of CPI

145

Table 7.8 Diagnostic analysis of the aggregate scheme of national CPI total index 1996.1–2000.12

Variable

Coefficient

Standard deviation

t

p

CPI(1)t

0.5317

0.0113

46.95

0.000

CPI(2)t

0.4622

0.0119

38.67

0.000

C

0.6187

0.1250

4.95

0.000

R-squared = 0.9999, AdjR-squared = 0.9999, DW = 1.9483 HO: α1 + α2 = 1,F(1,57) = 24.06,Prob>F = 0.0000 2001.1–2005.12

Variable

Coefficient

Standard Deviation

t

p

CPI(1)t

0.6519

0.0099

65.93

0.000

CPI(2)t

0.3546

0.0082

43.04

0.000

C

−0.6508

0.3111

−2.09

0.041

R-squared = 0.9996, AdjR-squared = 0.9996, DW = 1.7844 HO: α1 + α2 = 1,F(1,57) = 4.31,Prob>F = 0.0425 2006.1–2010.12

Variable

Coefficient

Standard Deviation

t

p

CPI(1)t

0.7034

0.0126

55.64

0.000

CPI(2)t

0.2983

0.0117

25.60

0.000

C

−0.1588

0.1745

−0.91

0.367

R-squared = 0.9999, AdjR-squared = 0.9999, DW = 2.1789 HO: α1 + α2 = 1,F(1,57) = 0.88,Prob>F = 0.3524 2011.1–2014.1

Variable

Coefficient

Standard Deviation

t

p

CPI(1)t

0.7227

0.03576

20.21

0.000

CPI(2)t

0.2771

0.0292

9.49

0.000

C

0.0135

0.7808

0.02

0.986

R-squared = 0.9995, AdjR-squared = 0.9994, DW = 1.6801 HO: α1 + α2 = 1,F(1,34) = 0.00,Prob>F = 0.9821

7.3.2 Connectivity Test of MOM Index and YOY Index At present, China’s CPI mainly includes MOM index, YOY index and base index. Among them, the annual indices available through open channels include the fixed base index (1978 = 100) and the YOY index (the previous year = 100); the monthly data available are mainly the MOM index and the YOY index. For the annual index, the fixed-base index converted from the year-on-year index almost coincides with the fixed-base index released officially. Therefore, the research here mainly focuses on monthly MOM and YOY index of CPI. Usually, the CPI MOM and YOY index are the measurement of price fluctuations in a certain period of time, and the conversion between them should be consistent. Figure 7.3 shows the comparison between the YOY index converted from CPI MOM index and the YOY index issued by national statistics from January 1996 to January 2014.

146

7 Evaluation and Analysis of CPI Data Quality in China

Fig. 7.3 Comparisons between the CPI YOY index converted from MOM index and the YOY index released by the Bureau of Statistic (the same month in the previous year = 100)

It can be seen clearly from the graph that, before 2001, there was a gap between the YOY index converted from CPI MOM index and the YOY index published by the National Bureau of Statistics, and the effect of one-way conversion is not good. However, since June 2001, there has been a close coincidence between the converted year-on-year index and that published by the Bureau of Statistics, except for a slight gap in a few months. Therefore, since the adjustment of China’s CPI statistics in 2001, the statistical quality of China’s CPI has been greatly improved from the perspective of index conversion.

7.4 Multidimensional Quality Assessment of CPI Data in China CPI is a composite statistical index. It is not enough to evaluate data quality only from the perspective of accuracy. This section will evaluate and analyze the quality of China’s CPI data from the perspective of international comparison.

7.4.1 International Comparing Basis for CPI Quality Evaluation At present, the main guiding text for the compilation of CPI in the international statistical community is “Consumer Price Index Manual: Theory and Practice” (hereinafter referred to as “Manual”) [7]. The IMF has also developed a framework(DQAF) for quality assessment of consumer price index.

7.4 Multidimensional Quality Assessment of CPI Data in China

1. (1)

147

Consumer Price Index Manual Calculating method and coverage of CPI

There are two theoretical frameworks for CPI compilation in the Manual, namely the Fixed Basket Index Framework and the Cost of Living Index (COLI) Framework. The former is mainly calculated by chain Laspeyres index, while the latter emphasizes to measure the impact of price fluctuations on the cost of a certain standard of living in a certain period of time from the perspective of effect level, which makes consumers adjust the “commodity basket” in order to keep the effect unchanged when the price of consumer goods changes. Based on economic theory, COLI better reflects consumers’ substitution behavior and makes it closer to economic reality. The advantages of this framework in dealing with the substitution effect between categories and the choice of price formula endow it with a great reference significance in CPI compilation practice. According to the instructions in the Manual, the reference population, geographical scope and commodity (service) coverage of CPI should be defined as widely as possible so that CPI can fully reflect consumption expenditure. Among them, in terms of geographical scope, there may be differences in expenditure between different residential groups and geographical scope, which means the indexes need to be calculated independently for different groups and geographical scope. In terms of the coverage of commodities (services), we should consider consumer goods (services) of the reference population as widely as possible, but to truly reflect inflation, we also need to exclude some controlled commodities. When compiling the total CPI by aggregating the classification index, the index by category should be consistent with the category of consumption expenditure used to calculate the weight so as to ensure that the price index by categories matches the corresponding weight. In addition, for the need of international comparison, CPI statistical categories should be as consistent as possible with the categories of the international Classification of Individual Consumption by Purpose,1 at least to some degree at the departmental level. For the items of basic categories, the Manual provides basic principles for selecting specification products: (1) they should be sufficiently representative; (2) the sales volume should be large enough to meet the reliability of the index statistics; (3) the specification products should be stable in a certain period of time to ensure the comparability of data. (2)

Survey design and implementation

The requirements in the Manual for CPI survey design cover the geographical scope of index application, selection of survey area and survey site, survey commodities (services) and their description, and specific survey methods. The 1 Classification of Individual Consumption by Purpose(COICOP) divides consumption expenditure into 12 categories: food and non-alcoholic beverages; Alcoholic beverages, tobacco and narcotics; Clothing and footwear; Housing, water, electricity, gas and other fuels; Daily maintenance of appliances, household equipment and housing; Health care; Traffic; Communication; Entertainment and culture; Education; Restaurants and hotels; Other goods and services.

148

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Manual advocates the priority of probability sampling method, but in practice, most countries use a variety of methods integratedly, combining probability sampling with non-probability sampling. In addition, a number of specific requirements for sampling methods are put forward, focusing on the determination of sample size, the distribution of price collection, product homogeneity, sample frame and so on. In the implementation of specific investigations, the methods like household surveys, telephone interviews and network-assisted surveys can be adopted. In addition, the Manual also specifies the frequency of price collection, types of investigation sites, seasonal commodity processing, collection channels, product specifications, etc. The basic data for calculating the weight include not only the relevant consumption expenditure data in GDP accounting but also the relevant consumer expenditure survey. In addition, indirect supporting information on consumption expenditure can also be obtained through other means, such as producer (seller) surveys, trade data and administrative records. In addition, another very important link in the implementation of the survey is the quality adjustment. When the representative specification products in some catalogues disappear in the market (or the sales volume is very small), while some goods (services) not included in the catalogue have gained the characteristics of specification products, or when the quality of specification products changes significantly, quality adjustment must be made. There are two situations for the specific adjustment: one is that the features of specification products have changed, and in this case, the sample matching method can be used simply for adjustment; the other is that the quality of specification products changes, so specifically, the price fluctuations caused by quality differences should be eliminated through price adjustment of new specification products which are comparable with the old specification products. (3)

Calculation of Price Index

The CPI calculation methods given in the Manual involves two steps. The first step is to calculate the basic classification index. In the second step, based on the basic category index, the advanced index is calculated with the influence of consumption weight being considered. For the calculation of weights, the weights of categories and items mainly indicate their proportion in the total consumption expenditure of the reference population, which are usually obtained through household expenditure survey. Regional weight reflects the proportion of consumption expenditure of a given type of region in the whole country. Regional weights can be obtained from household expenditure surveys, or from retail or demographic data. Whether the regional weight is introduced into CPI depends on the size and structure of the country and the availability of data. The weights of basic composite indexes are hierarchical weights obtained according to class or sub-class, region and sellers type. The reference period for weight should be set to meet the requirement of long-term and stable adjustment, that is, it should be long enough to contain a seasonal cycle, but should not be with a too long interval to reflect current consumption effectively. For countries (regions) experiencing major economic changes and rapid changes in

7.4 Multidimensional Quality Assessment of CPI Data in China

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consumption patterns, the weight should be updated more frequently, and the basic classification index may need to be revised more frequently. 2.

DQAF Framework for Quality Assessment of CPI Data

The DQAF framework for quality assessment of CPI data is based on the precondition of quality and the five dimensions of ensuring integrity, method soundness, accuracy and reliability, applicability and availability. With more focuses on qualitative assessment of data quality, this framework mainly uses the “Relative Semantic Scale” and “Expert Opinion Method” for specific qualitative analysis. In practical application, the preconditions of data quality and detailed evaluation indicators of five dimensions can be specifically processed through comparing the data to be evaluated with the selected criteria one by one, and evaluated according to the set level. Qualitative evaluation of the operation of the relevant indicators in each dimension can be divided into four grades, namely: meet the requirements, most meet the requirements, most do not meet the requirements, do not meet the requirements. After one-to-one comparative evaluation, the evaluation conclusions of each piece of data are summarized and evaluated.

7.4.2 Evaluation of CPI Data Quality in China Based on International Comparison 1.

Referring to CPI Manual

The purpose of evaluation and analysis is to find out the gap between China’s CPI statistics and international standards, with focus on the main factors of improving the quality of China’s official CPI statistics. Therefore, the analysis of this part will highlight the shortcomings of China’s CPI statistics. (1)

Preconditions for quality

The preconditions of quality, including some preconditions or institutional preconditions, highlight the statistical institutions and their responsibilities. In China, in terms of regulations and laws for statistics, with only the “Statistics Law” as well as some statistical regulations, there are very few official laws and regulations on the quality of statistical data. In terms of data sharing and coordination of data producing institutions, the functions of statistical institutions of Chinese government are relatively decentralized; the coordination ability between comprehensive statistics and department statistics is inadequate; the relationship between national and local statistical systems is not smooth enough; the system of data transmission and auditing is not well-established; the phenomenon of repeated statistics and simultaneous release of the same data by multiple institutions occurs from time to time. All of these have negative impacts on the quality of CPI data.

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7 Evaluation and Analysis of CPI Data Quality in China

Evaluation in the dimension of ensuring integrity

Ensuring integrity involves adhering to the principle of objectivity in the collection, compilation and publication of statistical data, including institutional arrangements to ensure professionalism, transparency and ethical standards in statistical policies and practices. China’s CPI data published every month are relatively rough. Only the total index and classification index are published, but without detailed description, and the current CPI weight information is not transparent. In addition, “ensuring integrity” requires that the public be informed of the data obtained by the government before publication. However, there are many problems in the current disclosure of statistical data in China, such as the disclosure of relevant processes is insufficient, and the transparency and fairness of data need to be further improved. (3)

Evaluation in the Dimension of Method Soundness

At present, there is no clear theoretical introduction for CPI in China, which is, however, similar to the theoretical framework of fixed basket index in compiling practice. Under this framework, CPI bias mainly includes bias of new product factors, bias of quality adjustment factors and formula bias. At present, no special theoretical framework has been put forward in China to measure and adjust such bias. (4)

Evaluation in the Dimension of Accuracy and Reliability

The factors affecting the accuracy and reliability of CPI data come from many aspects, including the bias caused by the emergence of new products and the bias caused by the change of product quality, as well as the bias caused by weight adjustment, missing price and the change of statistical coverage. The improvement of the compilation method of basic index and the quality of aggregation are the two ways to reduce the adverse impact of bias on data quality. Lacking in flexibility and pertinence, the compilation of CPI in China lags behind in the construction of quality adjustment methods and weight updating. With the rapid development of China’s consumption structure currently in particular, the major adjustment of CPI weight data every five year is not conducive to fully revealing the changes in consumer prices, which will affect the improvement of CPI data quality. In terms of data quality assessment, although the government has attached more importance to the work in recent years, in general, the research of data quality assessment in China is still in its infancy, and no official system and methods for it have been formed. In the aspect of data revision, usually only the final results of the revision will be published in the data revision work in China, and the specific methods and processes are generally not disclosed to the public. It can be said that the research on data revision in the country has just started, and more work need to be done for institutionalization and standardization. (5)

Evaluation in the Dimension of Applicability

Under the evaluation element “policies and practices on revision”, there is still a certain gap in the explanation and corresponding interpretation of statistical data

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revision in China’s CPI statistics. Taking CPI weights in China as an example, at present, the official CPI weights can not be obtained from public channels, and there is no effective way to learn the corresponding information about weights revision. In addition, in release of China’s monthly CPI data, the relevant information on index data is relatively inadequate. (6)

Evaluation in the Dimension of Availability

At present, China’s CPI data are mainly released officially through the China Statistical Yearbook, China Monthly Economic Indicators, China Information and the website of the National Bureau of Statistics. With relatively single release form, there is no differentiated form for specific different users. In addition, the CPI statistics are released in a relatively rough way. The monthly CPI launched on the website of the Bureau of Statistics is mainly composed of eight major categories indexes and several sub-categories indexes under the category of food. More detailed classification index data can only be obtained through publications like relevant yearbooks. There is a long lag in the data obtained in this way, and the relevant methods and technical means in the above channels needs to be given more complete interpretation. In addition, the controversial classification weight data of CPI have not yet been officially published. As for the richness of data, the CPI data at present in China are mainly urban CPI, rural CPI and CPI total index. Since 2005, non-food CPI, CPI deducting fresh vegetables and fruits, service item CPI and consumer goods CPI have also been started to issue. Since 2006, CPI deducting food and energy and industrial product CPI have been issued. Although the richness of CPI data has been improved, the specific calculation methods and processes have not yet been published, and the applicability of the above indicators in policy-making needs further verification. In addition, the CPI data which are seasonally adjusted has not been released officially in China. In the future, it is necessary to continue to strengthen the research and implementation of seasonal adjustment of CPI, which is also one of the medium-term plans made by China in response to GDDS in 2003.

References 1. Interpretation of major statistical indicators in China, edited by national bureau of statistics. China Statistics Press (2010) 2. Zeng W, Xu Y (2009) Correlation between CPI deviation and engel coefficient anomaly. Reform (7) 3. U.S. Bureau of Labor Statistics, BLS Handbook of Methods (2007) 4. Chen D (2012) Analysis of Index bias based on state-space models and countermeasures. Master’s thesis of Northwest Normal University 5. Hamilton, Bruce W (2001) Using Engel’s law to estimate CPI Bias. Amer Econ Rev (91–3) 6. Moulton B, Timothy L, Karin M (1999) Research on improved quality adjustment in the CPI:the case of televisions. Proceedings of the fourth meeting of the international working group on price indices, U.S. Dept. of Labor

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7. International Monetary Fund et al (2008) Consumer price index manual: theory and practice, translated by international monetary fund. China financial and Economic Publishing House

Chapter 8

Research on CPI Data Quality in China

On the basis of the previous chapter, this chapter will summarize the main problems affecting the quality of China’s CPI data in the main links of CPI generation, and propose relevant countermeasures and suggestions.

8.1 Quality Problems in China’s CPI Data 8.1.1 The Problems in the CPI Design 8.1.1.1

Problems in Classification Settings

China’s CPI statistics are officially divided into 8 categories according to their purposes, including: food; tobacco, alcohol and supplies; clothing; household equipment and maintenance services; health care and personal products; transportation and communication; entertainment, education, cultural products and services; housing. This classification is not consistent with the Classification of Individual Consumption by Purpose(COICOP), nor the classification of categories of consumer expenditure items in China’s national economic accounting. Therefore, China’s CPI statistics have some shortcomings in international comparability and accessibility of relevant data.

8.1.1.2

Problems with Specific Methods of Compilation

At present, China’s national CPI is compiled through level-to-level aggregation. First, the local consumer price index is calculated by cities and counties, and then the provincial consumer price index is weighted by city and county indexes. Finally, © Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_8

153

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the national price index is calculated by the provincial index weighted by the national consumption structure. Although this method satisfies the needs of hierarchical management of statistics and simplifies the compilation of national and provincial CPI to a certain extent, it impairs the representativeness of the national index to some extent due to the multi-layer weighting. Besides, compiling weighting data from level to level has also increased the workload for the local statistical department, making it more difficult to control the data quality.

8.1.2 The Problems in the CPI Source Data Survey 8.1.2.1

Problems with Survey Methods

In China, considering the differences of development between different economic regions, the survey of CPI data combines the methods of key surveys and sample surveys. Specifically, the method of comprehensive survey is used in collecting the commodity prices in large cities, while for the commodity prices in small and medium-sized cities (including counties), the sample survey method is adopted, which is classified sampling rather than pure random sampling, so the representativeness of the samples is questionable. As for the choices of price collection sites, whether in large cities or megacities, in small and medium-sized cities or counties, the sites are determined by key surveys or classified samplings, but not the random sampling, either. Compared with probability sampling, this method has more subjective factors in the selection of price collection areas, collection sites and the corresponding representative specification products, which is likely to have an adverse effect on representativeness of samples.

8.1.2.2

Problems in Sample Rotation

In the design of China’s CPI survey, relevant sample rotation schemes have been set up, and a certain amount of representative specification products are also rotated every year. However, the rotation of existing commodities is not very timely, and there is no uniform standard for the number of rotations. Even if the rotation of the goods is carried out, the products selected as samples are only simple replacements of the original goods, but do not reflect their true quality effect. Therefore, on many occasions, the current consumer price index does not take into account the actual increase of welfare for the consumers brought about by the improvement of the performance of goods (services).

8.1 Quality Problems in China’s CPI Data

8.1.2.3

155

Problems in the Price Collection Areas and Sites

At present, China has selected more than 500 cities and counties for price collection. However, using the data of more than 500 sample cities and counties to smooth out the consumer price gap through averaging method between the eastern, central and western regions and between urban and rural areas will inevitably lead to statistical noises in CPI data. This problem also occurs in the selection of collection sites. The prices of the same kind of specification products collected in shopping malls, supermarkets, and bazaars may vary. If the representativeness of the selection of collection sites cannot be guaranteed, statistical noises may also occur. In addition, with the urbanization speeding up in China, the urban size is expanding, and the corresponding price collection sites should be adjusted to enhance the representativeness of the collection sites.

8.1.2.4

Selection and Treatment of Representative Specification Products

Consumption patterns have a greater correlation with people’s income levels. Consumers with different income levels may also have different priorities when choosing the same kind of products. In particular, with the rapid development of the social economy today, the level of subdivision of goods (services) is increasing, and more and more subdivided products have emerged. Taking vinegar as an example, you can find various categories in stores currently, such as salad vinegar and dumpling vinegar. Due to the increasing expansion of categories of goods (services), we cannot simply use one or several brands and specifications of goods (services) to reflect changes in market prices.

8.1.3 The Problems in the Compilation Methods, Release and Revision of CPI 8.1.3.1 (1)

Calculation Method of Weight

The Adjustment Frequency of Weight Data

In real life, some scholars have questioned the policy of “one major adjustment every five years and one small adjustment per year” in compiling the weight of the CPI. To this end, the National Bureau of Statistics has given an explanation that “the ‘commodity basket’ is adjusted every five years in the compilation of China’s price index, and the weight is adjusted every year according to the survey data of urban and rural residents’ consumption and related data”. In this way, the commodity basket is separated from weight. In his analysis, He Xinhua speculated that the weight of basic classification of China’s CPI will remain unchanged within five years, while the

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weight of each major category of CPI will be adjusted according to the consumption expenditure of urban and rural residents in the previous year [1]. Considering that the current consumption structure and consumption pattern of Chinese people are in a period of rapid change, it may be difficult to fully reveal the changes in the consumption structure of residents by the five-year frequency of weight adjustment. (2)

Specific Calculation of Weights

At present, China’s official statistics have not officially released the weight data of CPI, and the specific data and calculation details are not accessible. For the current type of researches, specific methods are used to make estimation. According to the relevant instructions of the National Bureau of Statistics, He Xinhua calculated the weight of classification of China’s CPI on the basis of the per capita consumption expenditure of urban residents and the per capita cash expenditure of rural residents. His specific idea of analysis is: there are slight differences in the names of categories corresponding to the per capita consumption expenditure of urban and rural residents and the consumer price index; the miscellaneous goods and service items in the consumption expenditure data do not match the tobacco, alcohol and supplies items in the consumer price index. Therefore, he suggested that half of the “miscellaneous goods and services” in consumption expenditure be classified as tobacco, alcohol and supplies, while the remaining half be classified as health care, and the weights corresponding to each sub-item be calculated accordingly and used to calculate the next year’s urban and rural consumer price index. He Xinhua’s approach was adopted initially in this chapter to estimate the classification weights of CPI. However, by analyzing the consumption expenditure data of China’s household survey, we find that there are some problems in his method. Taking China’s urban household survey as an example, the sub-categories of tobacco and alcohol and beverages have been included in the existing food items in the household consumption expenditures. If neglected, it will inevitably lead to bias in the weight measurement. In this regard, we removed the tobacco item and 1/2 of the wine and beverage items from the food category and attributed it to the items of tobacco, alcohol and supplies. The classification weights recalculated are shown in Table 8.1. In addition to consumer expenditure data in household surveys, the household consumption expenditure data in GDP accounting can also be used to calculate the CPI weight data. Taking the category of urban residence as an example, the weight data calculated based on the GDP accounting data by expenditure approach are shown in Table 8.2. Comparing Tables 8.1 and 8.2, we can see that there are certain differences in the weight of CPI housing category calculated by different data. The possible reason is that the category of housing in the existing household surveys in China does not include expenditures of self-owned houses, which, however, are included in the consumption expenditure of GDP accounting.

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157

Table 8.1 Classification weights of China’s CPI calculated on the basis of urban consumption expenditure data from household surveys unit: % Year Food Tobacco, Clothing Household Medical Traffic and alcohol equipment health communication and supplies care supplies and services

Education Housing culture and entertainment services

1998 43.12 5.51

12.45

7.57

6.51

5.56

10.71

8.57

1999 41.27 5.49

11.10

8.24

7.01

5.94

11.53

9.43

2000 38.77 5.57

10.45

8.57

7.80

6.73

12.28

9.84

2001 36.39 4.77

10.01

7.49

8.08

8.54

13.40

11.31

2002 35.27 4.69

10.05

7.09

8.22

9.30

13.88

11.50

2003 34.87 4.43

9.80

6.45

8.76

10.38

14.96

10.35

2004 34.32 4.45

9.79

6.30

8.96

11.08

14.35

10.74

2005 35.00 4.40

9.56

5.67

9.03

11.75

14.38

10.21

2006 34.01 4.43

10.08

5.62

9.31

12.55

13.82

10.18

2007 33.04 4.53

10.37

5.73

8.91

13.19

13.83

10.40

2008 33.51 4.57

10.42

6.02

8.78

13.58

13.30

9.83

2009 35.21 4.54

10.37

6.15

8.85

12.60

12.08

10.19

2010 33.75 4.70

10.47

6.42

8.92

13.72

12.01

10.02

2011 33.09 4.43

10.72

6.74

8.32

14.73

12.08

9.89

2012 33.82 4.42

11.05

6.75

8.31

14.18

12.21

9.27

Table 8.2 Weights of urban residents’ housing calculated using the GDP data by consumption expenditure approach unit: % Year

2005

2006

2007

2008

2009

2010

2011

2012

Weight

14.20

14.65

17.70

17.06

16.89

16.80

17.62

16.93

(3)

Questioning the Weight of China’s CPI

At present, the public’s questioning of the CPI is mainly reflected in the inconsistency between the index variation and their own perceptions. Quite a number of scholars assumed that the weight setting is unreasonable. Since China’s official statistics do not fully disclose the information about CPI classification weight, people have raised many questions on the accuracy and persuasiveness of the weight data. At present, the questioning of the weight of CPI is mainly focused on the weight of the housing category. Take the United States as an example. In 2004, the weight of the housing category in the CPI was 42% (the proportion after removing the household equipment still reached 38%). The weight of the housing category calculated in the above analysis is only about half of that of the United States, which is obviously unreasonable. From the perspective of the internal structure of the housing category, it includes four items: self-owned housing, rental housing, building materials and water, electricity and gas expenses. The weights of the four in China are 21.1%,

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11.1%, 27%, and 40.8%, respectively, according to the report of the Xinhua.net.1 While as for the structure of the residential category in the United States, the selfowned housing alone accounted for 55.55% of the residential category in 2004. And in 2003, the rate was 68.3%. According to the research report of “China Household Finance Survey” issued by Southwestern University of Finance and Economics and the People’s Bank of China, the rate of urban self-owned housing obtained in China’s 2011 sample survey was 85.39%, which is much higher than the proportion in the United States. From this perspective, the main reason for the disparity in the weight of the housing category between China and the United States lies in the estimation of the expenditure on self-owned housing. In the past ten years, China’s housing prices have risen sharply, and the increase of housing expenditure is an indisputable fact. Therefore, the study of optimization of China’s CPI classification weight with self-owned housing expenditure as a breakthrough point will be of great significance both in theory and in statistical practice.

8.1.3.2

Statistical Processing of Quality Adjustment

At present, the quality adjustment of China’s CPI statistics mainly involves some simple processing measures, such as discount for product packaging conversion and subjective substitution of similar products when types of product are changed, or even when withdrawing from the market. The lack of systematic and clear quality adjustment schemes, especially the relatively inadequacy of application of some popular quality adjustment methods in other countries, is not conducive to reducing the bias of data quality caused by quality adjustment.

8.1.3.3

Seasonal Adjustment of CPI

The monthly CPI data obtained from the website of the National Bureau of Statistics of China include one year-on-year index. Although the influence of some seasonal factors can be reduced to some extent, the removal of them through simple index transformation is very limited. The existence of these seasonal components is not conducive to full understanding of the objective laws and trends of general fluctuation of price levels. Therefore, to carry out seasonal adjustment work and release seasonally adjusted CPI data is an important direction for the future development of CPI statistics in China.

1

The CPI weighting is more instructive in changing the underestimation of “restructuring”. xinhua.net, January 20, 2011. http://news.xinhuanet.com/fortune/2011–01/20/c_121005492_2. htm.

8.1 Quality Problems in China’s CPI Data

8.1.3.4

159

Release and Revision of CPI

At present, China’s CPI data has basically met the requirements of GDDS in terms of release time and frequency. However, there are still many problems in the richness of CPI and the diversity of releasing forms, especially in the construction of CPI system. How to formulate a price index that is more applicable to different policies should be one of the key issues in the future. In the revision of historical data, the public’s questioning should be given active response and revisions and researches on historical data should be carried out when conditions permit. To sum up, according to the DQAF framework for the CPI quality evaluation proposed by the International Monetary Fund, the main quality problems of China’s CPI data at the current stage can be summarized as follows (see Table 8.3).

8.2 Suggestions on Further Improving the Quality of CPI Data 8.2.1 Suggestions on Optimization of Statistical Design 8.2.1.1

(1)

Optimization of Theoretical Framework and Compilation Principles

To Clarify the Theoretical Framework of China’s CPI Statistics

Although some scholars believe that China’s CPI statistics are similar to the fixed basket theory, the official statistics do not clearly state the theoretical framework of CPI compilation. To clarify the theoretical framework of CPI statistics, the first thing to consider is the main goal of the indicator measurement. In terms of the specific connotation, the “fixed basket theory” that simply reflects the average price fluctuation goes for measuring inflation, while the “cost of living theory” that measures the change of utility price can be applied to reflect changes in social welfare and living expenses and adjust wages and income. In China, according to the relevant interpretation of the National Bureau of Statistics [2], the basic uses of the CPI include: “to measure inflation”, “to account national economy”, “to adjust index”, “to calculate real consumption and income”, but the primary measuring objective of the index is not indicated. On the whole, there is no complete “fixed basket theory” and “cost of living theory”, and statistical practice often shows a compromise between the two theoretical frameworks. Even so, it is very important to clarify the theoretical framework of China’s consumer price index as early as possible, which will help to provide direction of optimization and improvement for China’s CPI statistics.

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Table 8.3 Quality problems of CPI data in China and their classification No

Problems

Corresponding quality dimension under the DQAF framework

1

1.1 The lack of effective coordination between the National Bureau of Statistics and the People’s Bank of China on the production and dissemination of relevant data; 1.2 Statistical resource of CPI needs to be further strengthened; 1.3 Lack of inspection on whether the existing data can meet the users’ needs or not;

Prerequisites for quality

2

2.1 The transparency of the CPI compilation Guarantee integrity needs to be further improved, especially in the calculation of weight data;

3

3.1 The current official statistics of China have not clearly stated the theoretical framework for the compilation of CPI; 3.2 The existing classification standards of CPI are not consistent with the international standards and the classification standards of China’s national economic accounting;

Method soundness

4

4.1 The development and application of statistical techniques and methods are relatively deficient, mainly because the internationally popular core CPI and seasonal adjustment techniques are not applied to official statistics; 4.2 Lack of quality assessment and corresponding revision work for CPI data; 4.3 The unreasonable setting of CPI classification weight;

Accuracy and reliability

5

5.1 The existing frequency of weight Applicability adjustment is insufficient in effectively reflecting the changes in residents’ consumption patterns; 5.2 In terms of data consistency, there are discrepancies between the CPI data released by the Statistics Bureau and the relevant survey data of the central bank;

6

6.1 The current form of CPI data available in Availability China is relatively simple, and there are also shortcomings in the interpretation of detailed data

8.2 Suggestions on Further Improving the Quality of CPI Data

(2)

161

Research and Improvement of CPI Compilation Methods

At present, China’s CPI is quite different from the public perception. Although this difference partly derives from the nature of the index itself and the consumers’ misunderstanding of the index’s connotation, there are also quality problems of CPI data caused by insufficient considerations for alternative bias caused by price fluctuations. The substitution effect between categories is only one of the sources of bias of the CPI, but because it involves the issue of consumer substitution behavior and the cost of living of the residents, and such substitution effect needs to be considered in formulating fiscal and monetary policies, the in-depth research on it should be one of the key concerns of scholars and practitioners. In the research, the clarification of the conditions and possible impacts of such work, and especially the understanding of the issue of applicability in China, are of practical significance for further deepening the reform of China’s CPI statistics and clarifying the theoretical framework for CPI formulation in China.

8.2.1.2

Research on the Construction of CPI System

At present, since the construction of CPI system in China is relatively lagging behind with weak directivity of policies, it is difficult to balance the different needs of policy formulation in various aspects. Therefore, it is necessary to formulate price indices selectively that are applicable to different policy objectives based on the needs of economic regulation and macro analysis. (1)

To Improve the Policy Relevance of the CPI

China’s current CPI classification catalogue has not involved unpaid services provided by governments and non-profit institutions. Such fiscal expenditures as social security and the policies on individual income tax are not linked to the CPI. The CPI has weak correlation with adjustment of residents’ wages and variations in social security expenditures. How to enhance policy orientation in the compilation of CPI or to compile consumer price index for different policy objectives is a very difficult task. In the future, we can learn from the practical experience of statistically developed countries in the treatment of substitution effects between categories, and carry out the research of application of relevant indexes, especially the in-depth and detailed researches on the applicability of various consumer price indices in China. (2)

To Compile a Stratified Consumer Price Index for Different Income Groups

Currently, some of the reasons of the difference between China’s CPI and the public perception lie in the different reflections of different income groups to the fluctuation of the price level. In China, the complexity of the consumption structure of residents is not only reflected in the income gap between urban and rural areas, but also in the income gaps and structural differences in consumption expenditure among urban

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residents and rural residents and between different industries and regions, as well as the significant differences in the consumption structure among people of different ages. China’s existing CPI is compiled using a unified classification catalogue which can not reflect the differences in consumption structure of different groups but is likely to arouse public questions about the existing CPI. One of the feasible solutions for the above problems is to compile a stratified CPI for different income groups and different age groups. In the compilation of China’s stratified CPI, special attention should be paid to the division of income standards and the division of different age groups. It is necessary to accord with the real situation of China and to fully reveal the differences in consumption structure of different groups of people. At the same time, it is necessary to make effective use of the existing CPI survey data to maximize statistical efficiency. (3)

Compilation of Core CPI

Inflation usually refers to a sustained and significant increase of the general price level, and in reality people are accustomed to using CPI to measure inflation. However, due to the existence of transient and non-monetary price factors in the traditional CPI data, sometimes it does not reflect the economic content of inflation. If the change in CPI data is mainly caused by such factors, the effect of monetary policy on CPI will be weakened and even have an adverse impact [3]. There are government control items such as gasoline, natural gas, water and electricity and short-term price factors in the category of commodities (services) included in the CPI. The existing processing methods are not conducive to fully reflecting the fluctuation of consumer price levels. The overseas experience in compiling core price index (such as core CPI) can be learned to effectively eliminate such items or to set specific index weights so as to improve the processing in the existing statistics.

8.2.1.3

Optimization of Quality Adjustment Scheme

In today’s society, the technology is advancing with each passing day, and the upgrading of goods (services) is significantly accelerating. Neglecting the impact of quality adjustment on consumer expenditure is bound to expand the bias between the CPI and the reality. At present, the CPI compilation in China has not yet established a complete system and method on quality adjustment, so the research and practical exploration on the quality adjustment of price index should be strengthened in the coming period. In brief, the following details of quality control require special attention: First, it’s necessary to study systematically the possible bias in the current CPI compilation in China, and fully understand the types of bias in China’s CPI, especially the statistical bias caused by the quality adjustment. Second, we need to study the relevant overseas practical experience and quality adjustment schemes and focus on exploration of the application feasibility of relevant adjustment methods (especially some popular quality adjustment methods abroad) in China. The research

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work should precede official statistical practice to provide more theoretical support and method guidance for the quality adjustment in China’s CPI statistics. Third, we should establish a set of CPI quality adjustment schemes in line with the actual official statistics in China, focus on training a group of professionally skilled statisticians, and strengthen quality control in compiling and updating sub-categories, price collection, etc., thus reducing as much as possible the bias of the CPI quality.

8.2.1.4

Optimization of Survey Design

The CPI compilation involves many sub-items and commodities (services). Relying on an excessively single data source is not conducive to improving the CPI quality from the data sources. The special investigation of commodities (services) for consumer expenditure should be carried out conditionally to improve the rationality and effectiveness of the survey design, in which attention should be paid to the effective connection of monthly, quarterly and annual surveys. In compiling the CPI, the available basic data, especially the business survey data and the producer price index (PPI) data, should be fully utilized to control the quality of compilation process of the CPI. For the optimization of survey design related to CPI statistics, we propose the following suggestions and measures: (1)

To Improve Survey Methods

Considering the vastness of China’s territory and the great differences in cultural and natural conditions, there are considerable difficulties in conducting complete probability sampling. For the current survey method of CPI basic data, moderate and reasonable reform should be carried out on the basis of taking into account the scientific nature and practical feasibility of the method. For the selection of the price collection area, its representativeness should be improved as much as possible. In terms of the selection of the price collection sites, on the basis of the reasonable selection of the collection area, the clustering characteristics of urban and rural areas and different income groups should all be taken into account, and, if the sample size allows, random sampling methods should be adopted as much as possible to enhance the representativeness of the collection sites. The same is true for the household survey of consumer expenditure data. Based on the consideration of both the different income groups and different age groups, stratified sampling should be carried out according to the scale of consumption expenditure and the number of population in the survey area. With the increasing popularity of e-commerce, we should also start to study how to better use the e-commerce platform to obtain relevant data in time. (2)

To Improve Techniques of Specification Products Selection and Sample Rotation

For the selection of representative specification products, reasonable classification should be carried out according to the characteristics and types of goods (services) and

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the consumption quantity. For the selection of specification products in each major category, a comprehensive analysis should be made on determining the quantity of selection so that the representativeness of the sample can be ensured, but without ignoring the cost of the investigation. Based on the current situation of China’s government statistical construction and the actual CPI survey, the recent work should focus on the improvement of sample representativeness to ensure the randomness of the survey sample. At the same time, on the basis of ensuring the consistency of the basic classification catalogue, the autonomy of local statistics in the selection of specification products should be enhanced to fully reflect the consumption habits and characteristics of consumption structure of each place. In terms of the sample rotation technique, in order to solve the problems that the existing specification products are not rotated in time and that there is no uniform standard for the number of rotations based on the principle of fully reflecting the quality utility (homogeneously comparable), the data interval for the sample rotation should be clarified, and the specific rotation ratio should be adjusted in time annually in accordance with the actual market changes. The improvement of the quality of the specification products after the rotation and the resulting price fluctuations can be investigated and evaluated through the links of production and sales. (3)

To Optimize Survey Sample Structure

The compilation of CPI involves many major, medium and minor categories and their representative specification products. For the selection of representative specification products in each basic category, in addition to the consumption expenditure, it is also necessary to take into full account of seasonality and periodicity, combining with the effects of real economic operation and consumption patterns, thus determining scientifically the sample structure. In order to further improve the quality of statistical data of China’s CPI survey, the National Bureau of Statistics began to collect CPI handheld data in full scale in 2012, but due to the short implementation time, there were various problems in specific operations. In the future, we should pay enough attention to the improvement of the quality of handheld data collection with focus on data format specifications, auditing and other issues, and besides, we should strengthen organizational management and business training. In the overseas statistical practice of CPI, scanning data has become a new basic data source. However, there are quite a lot of basic conditions for the data to be used for CPI compilation, so it is necessary to clarify the collection and sifting of data and the related aggregation methods. But in the existing application abroad, the research and practical discussion on such issues are mostly based on the reality of a country, and the research on common problems needs to be expanded. In terms of the reality of our country, to carry out the exploration and research of such problems, the first step is to solve the series of problems on price information collection, including collecting, sorting and classifying the supermarket scanning data, etc., especially the merger of different product types.

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8.2.2 The Suggestions on the Quality Optimization of Statistical Data Production Process 8.2.2.1

Optimization Measures in the Implementation of Statistical Surveys

In collecting the price of the specification products, in addition to the specification products whose quality have changed, the issues of promotions, discounts and tie-in sales all exert higher requirements on the collection process for investigators. For the price calculation of such abnormal situations, in addition to explicit regulations (for example, the discounted price should be adopted), the investigator is required to accurately determine the types of specification products that should be included in the price survey. From the perspective of quality control, the key is how to take appropriate control measures to ensure that the price data of specification products can be used to compile the CPI accurately, which requires full consideration of fluctuation of price and elastic changes of quotation. An effective management system for investigators should be formulated to make specific and clear regulations on the investigator’s responsibilities, task requirements, survey content, and assessment criteria, etc., and make specific arrangements for the investigators’ areas of responsibility. Strict supervision and inspection should be carried out in the selection of specification products, price collection and commodity rotation. And the final aim of investigator management is to strengthen good communication between investigators and respondents and the investigator’s effective understanding of the survey feedback, and, meanwhile, to highlight the specificity of the task which can facilitate the supervision and management of the investigators. In terms of urban and rural household surveys, in addition to strict implementation of the household visit system,2 it is also necessary to study new survey methods for consumer expenditure data. It can be transferred to sample surveys to pilot community and village sampling in the price collection areas, on which basis to design the housing frame to select households for questionnaire survey. In this way, the collection subject of original data is transferred to the investigator again. We hold that price collectors in the same district can also serve as the investigators. It is because that the price collectors are familiar with the market situation of the main consumer goods in the area and can communicate with the residents more easily; besides, the communication between the price collectors and the residents also contributes to the collection of effective price information.

2

According to the current “Regulation on Foundation Work of Household Investigation”, in principle, it’s necessary to visit the households no less than two times every month. To the investigation households with poor bookkeeping quality, guidance and assistance should be offered, helping them to record clearly the income and expenses account. The visiting to the households should be recorded.

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8.2.3 Optimization of Control During Data Entry, Review and Pre-processing (1)

Data Entry and Review

A. The Entry and Review of Price Data Since 2012, handheld data collection has been started to implement in China’s CPI statistics. This real-time data collection method facilitates the collection of the price and the integrity of the record, and also puts forward higher requirements. In addition to the quality of price collection, the review also includes whether the price data are true, whether the time of the price collection is consistent, whether the representative specification products and the survey prices are corresponding, whether the number of collections are in compliance with the regulations, and whether the data is entered accurately or not. Therefore, the design of the automatic review process should be enhanced to monitor effectively some of the links with obvious problems. At the same time, random inspections of quality should be strictly carried out on the price collectors every month (quarter or year). B. The Input and Review of Consumption Expenditure Data Currently, in China’s urban and rural household surveys, bookkeeping of sample households is still used to obtain consumption expenditure data. The heavy and tedious workload of this method is likely to lead to a decrease of willingness to cope with bookkeeping, and meanwhile, the problems like memory bias will affect the quality of recorded data. In the short term, this survey method is still the main source of consumption expenditure data, but on this basis, we should explore a series of control measures to ensure data quality, and launch some timely measures, such as rewards to the reporting households. On the basis of the data table of the bookkeeping households, we should strengthen the data review of the statistical department, and communicate with the bookkeeping households as soon as possible to confirm the data that are ambiguous or may have problems. Besides, we should also establish a sound preservation mechanism for basic data and encourage bookkeeping households to retain consumer receipts. C. To Establish a Sound Management System for Basic Data The effective preservation and scientific management of various basic materials can provide sound support for timely verification and revision of data calculation. The specific management system includes: to establish a complete system of household information ledger so that the quality of household bookkeeping can be examined through the analysis of the ledger data and the problems detected can be timely checked and corrected; to improve the construction of ledger, including personnel management, survey sites management, changes of specification products, original price collection and monthly price.

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(2)

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Pre-processing of Data

The pre-processing of data mainly refers to a series of processes aimed at providing available data for CPI compilation after verifying the entry of basic data. A. Data Sorting In the CPI statistics, there are various representative specification products, many of which have high substitutability, and the same specification products may be produced by different manufacturers. These distinctions will lead to the difference in their prices. Therefore, in the pre-processing stage, the data should be sorted first to ensure the comparability and usability of the price data used in index compilation. Specifically, the adoption of price data should be based on population and consumption amounts. Under the premise of ensuring the homogeneous comparability of the products, the prices of the most representative commodities with relatively stable and largest consumption are used as the basic data for the compilation of the CPI. In terms of consumption expenditure data, the recorded data of the bookkeeping households should be classified and aggregated, and the preliminary detection and processing of the expenditure data should be carried out by means of descriptive statistics and simple contingency table analysis. B. Processing of Missing Values and Suspicious Prices For the CPI, the price data included in the index calculation can only be those quotations that have both the current price and the derivative price of the previous period. The survey data that is unacceptable and abnormally fluctuated can be considered suspicious and verified with the initial price record. If it cannot be confirmed, it is considered a missing value. In addition, for the specification products in the survey, the representative ones that have no sales records in the current period and will not be replenished in the short term may be regarded as missing values. For the processing of missing values, in the CPI statistics, the price ratio indicator of the monthly indicator area can be used for processing. After the indexes are calculated, the estimated prices can be treated as a derivative prices. And then the data can be put into storage for future index calculation. However, for commodities that may not be sold in the future market, substitutes need to be selected according to the principle of homogeneous comparability. In addition, in the case of refusal to answer, unavailable data and unable to estimate quality adjustments effectively, relevant methods are also needed for estimation. Second, the scanning data problems. Although China has not yet fully carried out the collection of scanning data, considering their important role in improving the quality of the CPI, it is necessary to conduct a brief analysis of price processing of these data. As the scanning data cover a wide range, it is usually assumed that there is no price missing of specification products (substitutes). Of course, just due to its wide coverage, the scanning data may be found more frequently in the replacement of old products with new ones than in the traditional survey mode. In addition, there are also special cases for the price of goods sold in supermarkets, such as trial sales. The pricing at the beginning will change with the observation of sales volume. If there is

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a long time lag in this process, the bias effect on the final CPI will be increased. In the fixed period of the weight of the specification products (referring to the interval between the two adjustments of the weight), the long-term effect exerted by the specification products on and then off the shelves in the supermarkets on the CPI can be measured by the ratio of the first calculated prices of the specification products to the last prices. The resulting impact can be estimated by increasing the acceptable prices. (3)

Application of Statistical Method in Data Production

In addition to the routine outlier identification and processing, missing value filling, etc., the statistical methods in the compilation of CPI also involve some calculations of the expenditure values that may not be directly represented by the market prices and the compilation method of some derivative indexes (e.g. the seasonally adjusted CPI). A. Calculation of Consumption Expenditure Values without Direct Representation of Market Prices It is difficult to measure the specific expenditure amount of some products in the consumer goods (services) covered by the CPI since there is no direct market price. Taking the calculation of self-owned housing expenditure as an example, the National Bureau of Statistics adopts a cost sharing method similar to virtual rent, which mainly involves factors like housing price, area and depreciation rate; while the main methods adopted by the statistical departments abroad in measuring the selfowned housing expenditures include the virtual rent method and the consumption cost method. Compared with China’s methods, these methods are relatively more mature. The calculation of self-owned housing expenditure is not limited to the simple housing price and area, but includes the discount rate and housing insurance, the reflection of which is closer to reality. Of course, for the processing of the above aspects, we should find out the appropriate calculation method based on the actual situation in China. B. Seasonal Adjustment of the CPI Regarding the seasonal adjustment of the CPI, the initial starting point and the final goal are to eliminate the seasonal components in the CPI sequence and fully reflect the general law of sequence variation. The research methods for this include ratio-tomoving average method, smoothing method, filtering method, etc., and thereafter the X-11 method, the X-11-ARIMA method, the X-12-ARIMA method, and the Tramo Seats method were successively produced. At present, the most common adjustment methods used internationally are the X-12-ARIMA method and the Tramo Seats method, whose advantages lie in sequence extreme value processing, asymmetric moving average, trading day effect processing, and selection and diagnosis of related estimation and inference methods. In recent years, China’s central bank has also introduced the X-12-ARIMA method, and, combining with the characteristics of China’s CPI, made some improvement

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including the introduction of the Spring Festival effect. In the application of the method, the central bank uses seasonally adjusted month-on-month data to track and analyze China’s economy, thus achieving good results in reflecting China’s short-term economic changes. Despite this, China’s official statistics have not yet released the seasonally adjusted CPI data, and the application of mature seasonal adjustment methods of other countries needs to be improved. In addition, in the field of statistics in other countries, the development of seasonal adjustment software and the improvement and upgrading of methods have also made progress in recent years. Domestic scholars, research institutions and practical departments should keep up with the trends abroad and, combining with China’s reality, learn and introduce their techniques and methods and develop a better seasonal adjustment method suitable for China’s actual situation.

8.2.4 Suggestions for Improvement in the Evaluation and Revision of Statistical Data 8.2.4.1

Evaluation of Report Data

The compilation of the CPI involves a variety of data, such as the total index, the sub-index, and the consumption expenditure data, etc. The quality assessment of the report data can also be carried out from analysis of various relevant data. The releasing frequency and method of relevant data, as well as the availability of data and the applicability of indicators are all important aspects of the assessment. (1)

Accuracy and Reliability Assessment

The annual (quarterly) index is obtained on the basis of the monthly index, so the evaluation of the monthly index is the focus of the research. The key to the assessment of the total index lies in two points: one is to evaluate it by searching for the economic significance of its structural changes and outliers, and the other is to test the variation trend, fitting degree and consistency among the statistical indicators through comparison with the related indicators. For the structural changes, we can make a preliminary judgment by comparing CPI trends with changes of China’s monetary policy, or conduct tests by using random variance expansion model and make comprehensive analysis based on real economic conditions. The same is true for the diagnosis of outliers. The time series method (or the distribution-based statistical test) can be used for diagnosis and analysis. It is necessary to find out the actual economic basis for the detected outliers, which will be used to judge whether there exist the quality problems. There are many measuring methods that can be used to analyze the correlation between indicators. But the key to doing such research is to really clarify the actual relationship between the variables. Only in this way can we ensure the credibility of the assessment based on correlation.

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B. Evaluation of Consumer Expenditure Data Currently, China’s official statistics have not officially announced the weight of the CPI. As far as the existing research is concerned, most of the criticisms of domestic CPI weights start from the estimation of self-owned housing expenditures. Various methods are used to measure and evaluate the estimated statistics of the National Bureau of Statistics and the general view is that the National Bureau of Statistics has an underestimation in the category of housing. For the assessment of accuracy of weight data, the most urgent task at present is that the official statistics can release weight data and the corresponding processing methods in time. Only in this way can the evaluation of relevant data be truly comparable. (2)

Evaluation of Other Quality Dimensions

In addition to the dimensions of accuracy and reliability, the quality of statistical data also includes quality preconditions, guarantees of integrity, method soundness, applicability, and availability. China’s CPI data can be evaluated from the following aspects, as shown in Table 8.4. Table 8.4 Highlights on assessments of other quality dimensions No Quality dimension (premise) Evaluation points 1

Prerequisites for quality

Statistical functions, institutional settings, coordination between integrated statistics and departmental statistics, relationships between National Bureau of Statistics and local bureaus of statistics, data transfer and audit systems

2

Guarantee integrity

Whether the principle of objectivity is adhered to in the collection of basic data, the compilation of indicators and the release of data; whether the requirements of specialization and transparency are met in statistical policies and practices; and whether statisticians meet the standards of professional ethics

3

Method soundness

Whether the concept and definition of the CPI follow the internationally accepted standards, guidelines or good practices; whether the compilation of index has a clear theoretical framework; and whether the index bias can be better identified and processed

4

Applicability

Whether frequency and timeliness comply with the international standards; statistical consistency (including consistency within groups, consistency of different arrays, consistency of different data sources), and whether the indicators for public interpretation and clarification of data corrections are sufficient and available

5

Availability

Whether the form and channel for the public release of data and data types are rich; whether it can meet the public’s needs for variation; whether the data is publicly available; whether the data has been sufficiently interpreted; and whether there are timely and professional support services

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8.2.4.2

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Revision of Data

The revision of CPI data is also an indispensable part of quality control. To provide a comparable data sequence through revision is of positive significance to the formulation of monetary policy, adjustment of salary, and revision of individual income tax policy. For the academic research, its positive impact is self-evident, and it can better reveal the general change law of price levels through analysis of historical situations. The revision of the CPI can be divided into two categories: First, the content specified in the CPI statistical scheme, including weight update, sample rotation, price adjustment, etc., namely the “routine adjustment”; Second, the revisions required for the errors in historical data and derived from the improvement of the compilation method, namely the “historical data revision”. (1)

Routine Adjustment

At present, for the weight of China’s CPI, “a major adjustment is carried out every five years, and a minor one every year”. From the perspective of the changes in the consumption structure of China’s residents, the adjustment cycle of weight should be shortened, and a full consideration should also be given on the adjustment of weights. To improve sample rotation, it is necessary to solve the problem that there is no uniform standard for the number of commodity rotations as soon as possible, and to improve the timeliness of existing commodity rotations to reflect the true quality effect. In the selection of price collection areas, price collection sites and specification products, the most fundamental direction is to ensure representativeness under the premise of meeting the quantitative requirements. We should not only pay attention to the consumption differences between eastern, central and western regions and between the urban and rural areas, but also pay attention to the price differences of same kinds of specification products collected in shopping malls, supermarkets and bazaars. At the same time, with the urbanization in China, we should also attach importance to the adjustment of the price collection areas and the price collection sites. In addition, for an increasing number of subdivided products, the characteristics of consumers’ demands for different quality of representative specification products should be fully considered to fully reflect the different demands of consumers at different income levels. (2)

Revision of Historical Data

The revision of historical data is carried out to meet the needs of policy formulation, academic research, and investment analysis. It is a revision of historical data from the perspective of meeting users’ needs. Drawing on the ideas of the United States, the revision of historical data can be further divided into two parts: one is the revision of the data errors, and the other is the backward revision corresponding to the improvement of the compilation methods. For the revision of data errors, it is more urgent to improve the accuracy and reliability of CPI data and to narrow the gap between the index and the public perception.

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Taking into account the demands for continuous improvement of government statistics and the statistical reality of China’s CPI, the research conducted needs to focus on the adaptability of the improved methods and support of statistical survey. (3)

Increase in the Transparency of the CPI Statistics

At present, China has a high degree of transparency in the compilation of CPI data. The calculation methods of the index, statistical statements and their specifications, catalogues and other information can be obtained through open channels. However, compared with some countries, such as the United States, the transparency of China’s CPI official statistics needs to be improved, and the integrity of the information available is insufficient. In the United States, for example, we can obtain almost all materials for price index compilation on the websites of the US Bureau of Labor Statistics and the Bureau of Economic Analysis of the Department of Commerce, including updates of technical methods, detailed statistics, and even the related research reports. However, China’s official statistics lack systematic disclosure of data, and some key information, such as the weight of CPI, has not been systematically disclosed. Therefore, in the future, we will continue to strengthen the transparency of CPI statistics, so that people can fully understand and apply the statistical information, which has an important impact on improving data quality and further enhancing the credibility of official statistics.

References 1. He X (2011) Several key concepts in the Argument for accurate understanding of CPI. Macroeconomics (3) 2. Lu L (2012) Research on the bias of CPI statistics and residents’ perceptions. Master’s thesis of southwestern University of finance and economics 3. Fan Y, Feng W (2005) Core inflation measurement and effectiveness of macro-regulation: an empirical analysis of China from 1995 to 2004. J Manage World (5)

Chapter 9

Research on the Quality of China’s Real Estate Price Index

The real estate price index is a comprehensive indicator reflecting the fluctuations of real estate prices in a country or region. The high-quality real estate price index is of great significance for correctly guiding the supply and demand of real estate and effectively carrying out the macro-control of the real estate market. In recent years, the quality of China’s real estate price index has become a hot issue for the public. To this end, further in-depth research on this issue will be conducted in this chapter from the following three aspects. The first section introduces the practice of compilation of China’s real estate price index and analyzes the existing problems. The second section establishes an evaluation model for the real estate price index based on the transaction price data of the real estate authorities. The third section, based on the theory of resale model, explores the new methods to compile real estate price indexes to further improve the quality of China’s real estate price index.

9.1 Compilation of China’s Real Estate Price Index and Its Problems 9.1.1 China’s Current Real Estate Price Index At present, the index related to the real estate price released in China mainly includes “the housing sales price index of the 70 large and medium-sized cities” and “China Real Estate Index System (CREIS)”. 1.

The housing sales price index of the 70 large and medium-sized cities

The housing sales price index is a relative number that comprehensively reflects the overall trend and range of housing prices.

© Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_9

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In October 1997, the former State Development Planning Commission and the National Bureau of Statistics of China jointly issued the “Notice on the Compilation of Real Estate Price Indexes”, deciding to start the compilation of real estate price indexes in 35 large and medium-sized cities in 1998. The policy helped to improve the government’s macro-control of the real estate market and played a positive role in forming rational market prices. In order to meet the needs of the development of the real estate market and government’s macro-control and to further reflect the fluctuations of prices in the real estate market comprehensively, accurately and in time, the National Development and Reform Commission and the National Bureau of Statistics jointly issued the “Notice on Improvement of Compilation of Real Estate Price Indexes” in July 2004, deciding to expand the compilation of the real estate price index gradually from the original 35 large and medium-sized cities to about 70 cities from 2005. Since then, in the economic life, “the housing sales price index of the 70 large and medium-sized cities”(hereinafter referred to as “housing price index of the large and medium-sized cities”) has become the alternative name of the housing price index announced by the National Bureau of Statistics. The housing sales price index of 70 large and medium-sized cities is released uniformly by the National Bureau of Statistics once a month. The content includes the month-on-month, year-on-year and fixed-based price index of new and secondhand houses in different cities. Among them, the newly built houses are divided into two categories: affordable housing and new commercial housing. In the category of new commercial housing, there are three basic categories: 90 m2 and below houses, 90–144 m2 houses and over 144 m2 houses. For the second-hand houses, there are also the same three basic categories as the above. According to the new scheme for real estate statistical survey released by the National Bureau of Statistics on February 16, 2011, the basic data required for the sales price index of the newly built houses were obtained from the online sign data of the real estate management department in the 53 municipal cities, provincial capitals, the capital cities of the autonomous regions (excluding Lhasa), the planned separate cities and some prefecture-level cities; the other 17 cities temporarily used the relevant data in the statistical statements of the real estate development of the statistical bureaus. The basic data required for the second-hand housing sales price index were collected by the national investigation teams from the real estate management departments and real estate brokers of the relevant cities. The “housing price index of the large and medium-sized cities” is compiled once a month with the method of chain Laspeyres index model. The calculation steps for the new housing sales price index are as follows: (1)

Month-on-month price index for basic classification of cities.

Calculation steps and methods: First, to calculate the month-on-month indices of three basic categories of 90 m2 and below houses, 90–144 m2 houses and 144 m2 houses for a new housing project; second, to calculate the month-on-month index of three basic categories of the city by double weighting, that is, to calculate the price indices by using the sales area and sum of money of the month as the weights

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respectively, and then the two price indices are simply averaged; third, to calculate the month-on-month price indices of affordable housing by using the same calculation method as the basic categories of commercial housing. (2)

Price indices of different categories in different cities

A. Formula for the fixed-base price index (Note: taking 2010 as the base period, that is, the average price in 2010 as the base period price, and the sales area in 2010 as the sales area of base period).  Pt Q2010  Lt = Lt − 1 × Pt − 1Q2010

(9.1)

In the formula, represents the average price of each category in the current month, Q2010 represents the sales area of each category in 2010, Lt and L t −1 are the fixed-base Pt Q2010 is the month-onprice index of this and last month, respectively, and  Pt−1Q2010 month index.

Lt L t −1

B. The formula for the month-on-month price index. The Month-on-Month Price Index =

Fixed-base price index of the month × 100 Fixed-base price index of the previous month

(9.2) (3)

The formula for year-on-year price index of the

Year-on-Year Price Index of the Month =

Lt L t −1

month.

Fixed-base price index of the month × 100 Fixed-base price index of the same month the previous year

(9.3) In view of some problems to be solved in the aggregate of the national real estate price indices, the National Bureau of Statistics has no longer released the nationwide real estate price index since 2011, and the urban price index has been dominated by the month-on-month index. 2.

China Real Estate Index System (CREIS)

China real estate index system (CREIS) [1] is a set of index system and analysis methods that reflects the development track of the real estate market and the current market conditions in major cities in China in the form of price indices. In 1993, the Development Research Center of the State Council of China, China Real Estate Association, and China National Real Estate Development Group Corp. jointly initiated the system which started to release the real estate indices of Beijing in January 1995, and gradually expanded to other cities. In September 1995, the CREIS passed the national certification. Since 2004, China Index Academy, as the specific implementation agency of CREIS, has implemented a comprehensive technical improvement

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of the system. Based on the original core theory and calculation method, the original system was revised in the three aspects of base period, price and season, and the real estate index curves of each city were reconstructed. At the same time, the Hedonic Price Model, which is currently widely applied in the international real estate market, was introduced. In 2005, the new CREIS passed the academic appraisal of an appraisal committee composed of experts from the Development Research Center of the State Council, the Ministry of Construction, and the Ministry of Land and Resources. At present, the CREIS has covered 17 major cities, including Beijing, Shanghai, Tianjin, Guangzhou, Wuhan, Shenzhen, Chongqing, Hangzhou, Chengdu and Nanjing, etc. and regularly releases the real estate price indices of major cities in China.

9.1.2 The Current Problems of China’s Real Estate Price Index 1.

The compilation of the real estate price index does not have a clear purpose, but the index covers too much

At present, the National Bureau of Statistics, scientific research institutions, real estate agencies, etc. have compiled and published a number of real estate price indices. These indices cover a wide range of markets, including the land market, the commodity housing market, the commercial property market and the industrial property market. The wide coverage of indices can reflect comprehensively the fluctuations in the price of various types of real estate, which is its advantage. However, various kinds of properties have their own characteristics, and there are big differences in product form, pricing and price fluctuation mechanism. It is difficult to use a single real estate index with too wide coverage and too strong comprehensiveness to reflect the characteristics of all kinds of property and to meet the needs of all kinds of users. In terms of the experience of other countries, the real estate price indices of the United States, Canada, the United Kingdom, Japan and other countries (regions) are compiled based on the housing price index. In the future, China’s real estate price index should also be compiled with the housing price index as the core. The other needs can be satisfied by compiling separately different types of property price indices. 2.

The source of basic data of the real estate index needs to be improved

In general, there are three sources for the housing price index data, namely: the transaction registration data of the housing property management center, the evaluation data of the intermediary agencies and the survey data of the real estate site. The transaction registration data of the housing property management center are the most objective reflection of the housing market price. Except for the non-market products, such as the affordable housing, most of the transaction data are the authentic reflection of the consensus of buyers and sellers in bargaining with optimal conditions

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for each party. Determined by the willingness of both parties and the forces of supply and demand in the market, the data are the most realistic reflection of market supply and demand. Although there are a small number of non-market transactions, such as tax avoidance, transfer of assets, and intra-family transactions, non-market behaviors can usually be filtered by regulations and eliminated so as to obtain the most authentic data of market supply and demand. Since the data involve various aspects of privacy of developers and housing traders, the transaction subjects usually don’t want their data to be disclosed. Although held by the government registration department, the housing transaction data are not within the government information disclosure project, and the government and related subjects are sensitive to the data in this respect, so the housing transaction data, although relatively objective and reliable, are not easy to obtain. The evaluation data of the intermediary agencies refer to the evaluation data of real estate brokerage agencies, banks, and professional assessment agencies on the value of property in operation. The valuation of the intermediary agencies are usually given by professional market involvers who are familiar with the market, and the valuation price is usually a reasonable assessment of the theoretical value of housing. Value is the benchmark of price fluctuation, but in most cases, value and price are not strictly equal, especially in the real estate market where there is much controversy over the “bubble”, the valuation of the institutions is usually lower than the market transaction price. In other countries, the index based on evaluation data of real estate is thought to have artificially smoothed the price index, thus erroneously estimating the risk level of portfolios based on the index, and therefore, the difference between the valuation and the market price exist widely. In addition, when applying valuation data to the compilation of housing price index, there are other problems. First, the evaluators of the data usually have a real interest relationship with the houses, therefore, the evaluators usually tend to evaluate “in favor of themselves” and the objectivity of the data is questionable. Second, the evaluation data are very subjective. The differences in evaluators’ professional level, their optimistic attitude about the market prospects and their mastery of the market information may cause the great difference between the valuation prices, so that it is difficult to determine a recognized reasonable valuation. Third, the evaluation agencies usually only evaluate some but not all the properties in the entire market, and the evaluated properties often have some common factors to attract valuation, for example, the properties that attract the brokers’ valuations are mostly the “second-hand new houses” with frequent transactions; the banks usually only evaluate the houses that apply for loans; while the professional evaluation agencies usually serve the more valuable properties. The houses that are not “attractive” to the evaluation agencies are rarely evaluated by the institutions. The existence of many drawbacks makes it difficult for the evaluation data to objectively reflect the overall picture of the price of the entire housing market. The real estate survey data refers to the housing price data obtained by the investigators through questionnaires or on-site visits, etc., for the need of compilation of index or other purposes. The reliability of the survey data is influenced by both the investigators and the respondents. In terms of investigators, such factors as the professionalism of the questionnaire design, the rationality of the questionnaire survey

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process, the attitude of the surveyors, the control of the investigation process and the entry of the survey data will affect the authenticity and accuracy of the survey data. As for the respondents, the real estate developers tend to be reticent about the price. Even to the potential buyers, marketers usually only inform them of the public quotation “in an ambiguous manner”. The actual price and discount are often determined after a bargain between the buyers and sellers. It is difficult to get cooperation from the other party in the interview survey. If the self-administered questionnaire is used, the marketers often fill it out casually and quickly. Therefore, it is difficult to guarantee the reliability of the survey data. In 2010, the National Bureau of Statistics took the lead in compiling indexes using online sign data in 35 cities across the country, and made it clear that, in the future, the use of online sign data would be extended to the statistics of real estate price of 70 large and medium-sized cities. Replacing the “unreliable” survey data in statements with online data is an important improvement in China’s statistics of real estate price. However, in order to further promote the online sign data in practice, clear regulations should be made to prevent the prevarication of relevant departments, and meanwhile, to better protect the privacy of the parties while using the online sign data. At the same time, it is necessary to conduct further research on the specific processing and identification methods of the online sign data. 3.

The method of compiling the real estate index needs to be improved

Most of the compilation of China’s real estate price index choose the chain Laspeyres index model. The precondition for applying this method is that the products traded at the base period and the reporting period are of the same quality or substantially the same. However, unlike the ordinary consumer goods market, the housing market is more like a non-homogenous market. The two properties traded in different periods often have differences in location, community planning, building quality, apartment type and decoration; even for the same property, due to depreciation and changes in external environment and internal decoration, the quality may be greatly different in different periods. Therefore, it is difficult for the housing market to meet the requirements of homogeneity. In addition to the homogeneous differences, the huge differences in product structure between transactions in different periods are also major challenges for the traditional index from the housing market. In the traditional index model, the changes in the product structure of the market transaction (or consumption) in the base period and the reporting period are small, that is, the proportion of most products in transactions of the base period and the reporting period (or consumption) varies slightly, so the trading volume of the base period or the reporting period can be used as a weight to calculate the average index. However, it is not the case in the housing market. Especially in the first-hand housing market, when new properties are introduced to the market, the developers usually increase promotion efforts to speed up de-stocking of products. In marketing, this period is called “vigorous marketing period”. The trading volume of real estate in the “vigorous marketing period” usually accounts for a higher share of the current market transactions. The product structure of the property greatly affects the product structure of the current market transactions. The

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frequent emergence of new properties will cause great fluctuations in the product structure of market transactions. When using the traditional index to compile the housing price index, the selection of weights becomes another challenging task. In order to solve the above problems, scholars in other countries have proposed two solutions. One is to use the hedonic price theory to decompose the goods into several “utility-generating” characteristics. When calculating the price index, the sample of the base period and the reporting period will be adjusted to “products with the same characteristics” to estimate the price index under the traditional index framework or by using a regression model with dummy variables. The other is to limit the sample to data of repeated transaction that has occurred more than twice and to control the heterogeneity between the two samples through repeated transactions of the same sample. After eliminating the factors such as depreciation, the price index of the housing market can be estimated using the sample data of repeated transactions. Based on these two ideas, the scholars have established the Hedonic Price Index [2], the Repeat Sales Model [3], and the Pooled Model combining the two ideas [4]. All the three models use the regression method to calculate the price index. While solving the homogeneity problem, the price index estimated by the regression method avoids the choice of the same measure factors, thus ingeniously avoiding the influence of structural differences on the price index. In order to further improve the compilation method of China’s real estate price index, the research on these models should not just stay in theory, but it is necessary to further explore the feasibility of practical application of these models in China through empirical research. In the third section of this chapter, we will have more discussions on this issue. 4.

The aggregation method of the annual index of real estate prices needs to be further explained to the public

On February 25, 2010, the National Bureau of Statistics released the data of sales price of houses in 70 large and medium-sized cities in China in 2009, which was 1.5% higher than the previous year. Once this result was announced, it was immediately questioned by the public. In response to the question, the relevant personnel of the National Bureau of Statistics explained that, in 2009, China’s real estate prices showed a trend of increasing from low to high, bleak at the beginning of the year and prosperous at the end. The annual price index is the weighted average of the monthly price indices. Therefore, “to synthesize the annual data, the real estate price index is not very high, and 1.5% refers to the average annual increase.” However, in the real economic life, people are generally accustomed to comparing the house price at the end of the year with the house price at the beginning of the year to get the annual increase. In 2009, the year with special fluctuations in housing prices, there was a very obvious difference between the statistical data obtained by the above method and the public’s personal perceptions about the housing prices. As shown in Fig. 9.1, the housing prices in 2008 were high at the beginning and then lowered down. While in 2009, the housing prices were in a trend of increase from low to high. The vertical distance from p¯ 0 to p¯ 1 indicates the average increase of

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Fig. 9.1 Analysis of the bias between the current real estate annual price index and public perception. Notes Since the National Bureau of Statistics does not publish a quarterly aggregation of real estate price index of the cities, this figure is not based on actual statistics, but just a schematic diagram

housing price in 2009 calculated by the chain Laspeyres index model. The vertical distance from p0 to p1 is the increase in housing prices perceived by residents in the year. There is a considerable gap between the two. In addition, the above real estate price index published by the National Bureau of Statistics is a weighted average of the price indices of cities across the country, reflecting the average increase in the real estate price index of cities across the country. In reality, what the public directly perceives is generally the increase in local real estate prices. In China, the real estate prices of the first, second and third tier cities are quite different. The news media are more concerned about the increase of real estate prices in first- and second-tier cities, which often causes a big gap between people’s perception and the real estate price indices released by the National Bureau of Statistics. In order to better solve the above problems, we must further improve the transparency of statistical work, and explain to the public that the real estate price index reflects the fluctuation in average price. Besides, it is necessary to make further research on the aggregate method of the current index.

9.2 Assessment of Accuracy of Housing Price Index 9.2.1 Theoretical Model for Assessing the Accuracy of Housing Price Index Up to now, although there are many disputes and criticisms on the real estate price index, most of them are based on perceptual experience and subjective judgment. There is no systematic and scientific quantitative evaluation method. Therefore, in this section, based on the hedonic price theory, a theoretical model of the relationship between the housing price index and the average transaction price is constructed with

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the average transaction price data available in daily administration. The accuracy of the disputed price index will be tested with the relatively reliable average transaction price. The basic characteristics of a house are classified into housing structure and location features. For the same set of house, the price change can be reflected as: Pt = (1 + e) pt−1 It St L t .

(9.4)

In the Eq. (9.4), e represents the estimation bias of the price index, I represents the price index. And the price of the house in t period is equal to the product of the price in t−1 period and the price increase, the location change, the quality change and the index bias, with S representing the internal attributes of the house, that is, decoration, community greening, housing orientation, etc. and L referring to the location factors, that is, the public and commercial facilities, such as transportation, education, recreation, etc. Similarly, for the entire market, P t = (1 + E)P t−1 I t S t L t

(9.5)

That is, the average transaction price in the t period is the product of the average transaction price in the t−1 period and the price index, the location change, the quality change, and the index bias. If the index can accurately reflect the price change of the entire market, then E is equal to 0, and the average transaction price of the whole market is affected by price rise, location change and quality change. If a longer period of time is considered, the Eq. (9.5) can be transformed into: PT = P0

T  

(1 + et )I t S t L t



(9.6)

t=1

For the convenience of calculation, we perform logarithmic transformation on Eq. (9.6) and get: ln P T = ln P 0 +

T  t=1

ln(1 + et ) +

T  t=1

It+

T  t=1

ln S t +

T 

ln L t

(9.7)

t=1

For the Eq. (9.7), if we can find accurate proxy indicators for location and quality changes, we can use the average transaction price changes in the two consecutive periods to calculate the index to determine whether there is a bias in the price index released by the government. In real life, the average transaction price in different periods and the price index released by the government are available. But it is difficult to directly measure the quality change and location change, so it is difficult to directly estimate whether the index bias exists by using the above formula. In order to solve similar problems, we try to select some indicators that are closely related to location

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and housing quality. These indicators may not represent the level of change in location and quality directly, but they can be used as proxy indicators of location or quality changes. If the proxy indicators can represent the trend and level of quality and location change, then E can also capture the possibility of price index bias and reflect its trend. Further, we can transform the Eq. (9.7) into: 

T T T T      ln P T − ln P 0 − It = ln(1 + et ) + ln S t + ln L t t=1

t=1

t=1

(9.8)

t=1

  In the equation, ln P T − ln P 0 is the logarithmic form of the year-on-year index of the average transaction price. The left side of the equation is the difference between the logarithm of the year-on-year index of the average transaction price and the year-on-year logarithm of the housing price index, that is, the logarithmic form of the ratio of the year-on-year average transaction price index to the year-on-year price index, which can be described by the cumulative changes in house quality, location, and index bias.  If the index accurately measures the pure price change in T ln(1 + et ) is zero, that is, there is no constant term in the housing market, then t=1 the econometric model constructed by Eq. (9.8).

9.2.2 Empirical Research 1.

Selection of Indicator

The dependent variable is the year-on-year index of the average transaction price of commercial housing. The average price of commercial housing is calculated by the ratio of the total annual transaction amount to the annual transaction floor area. The data come from the “housing sales price index of the 70 large and medium-sized cities” of the National Bureau of Statistics. The location and quality changes cannot be directly observed, so we can only use proxy indicators. Generally speaking, for the same market, the greater the supply of real estate development, the more the developers tend to improve the construction quality of houses to attract buyers. Therefore, we choose the supply index of the real estate market to reflect indirectly the improvement of quality of the housing market from the perspective of competition. The three proxy indicators selected for housing quality here are the year-on-year index of real estate development investment, the year-on-year index of housing completions and the year-on-year index of construction floor area. In the process of urbanization, there are usually two location factors that will affect the average transaction price of houses on the market. One is the remote effect of housing development brought by urbanization. People usually exploit land with better location first. With the advancement of urbanization and further expansion of

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people’s demand for land, even the land with poor location will also be put into use, and more and more remote areas will be developed. The remote effects brought about by urbanization will cause the decline of average transaction price of houses. The increase in housing supply in remote areas will lower the average transaction price of the entire market. The other type is the location improvement effect brought about by urbanization investment. In the process of urbanization, the increase in investment has led to an increase in public facilities, which has improved the supporting facilities for the location of the old and the new districts of cities, thus improving the living quality of houses in each location. For the remote effect, we choose the year-on-year indexes of urbanization rate and the built-up area as the proxy indicators. For the improvement effect of the location, we choose the year-on-year indexes of fixed asset investment and the proportion of fixed assets investment in GDP to measure it. 2.

Data source and processing

This empirical analysis adopts the annual national data from 1998 to 2010. The commercial housing price indexes are from the “Operation of National Real Estate Market” published by the National Bureau of Statistics over the years. The average price of commercial housing is calculated by annual total transaction amount and annual transaction area. To further verify the rationality of the proposed model, we have also verified the relationship between the average transaction price and the index between cities. For the empirical evidence at the city level, we choose the data of 17 cities of Beijing, Shanghai, Tianjin, Shenzhen, Chongqing, Chengdu, Dalian, Guangzhou, Hangzhou, Hefei, Nanjing, Ningbo, Qingdao, Xiamen, Shenyang, Xi’an and Changsha from 2005 to 2010. The data of urbanization rate is from the “China Urbanization Rate Survey Report” published by the National Bureau of Statistics over the years. The indicator data of built-up area, fixed asset investment, GDP, completed area, started area, and real estate development investment are obtained from the statistical yearbook issued by the National Bureau of Statistics. In terms of data processing, in the empirical analysis at the national level, various indexes are adjusted uniformly to the year-on-year indexes with the year of 1998 as the base period. In the empirical analysis at the city level, various indexes are adjusted uniformly to the year-on-year indexes with the year of 2005 as the base period. 3.

Results of empirical analysis

We’ve selected 3 quality proxy indicators, 2 positive location proxy indicators and 2 negative location proxy indicators. After the combined regression of the three groups of indicators and the trial of all the combinations, it is found that the combination of urbanization rate, the proportion of the fixed assets investment to GDP and monthon-month data of real estate development investment is the most appropriate for both statistical and practical interpretation. Based on the data across the country and from the 17 cities, the results of empirical analysis are shown in Table 9.1.

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Table 9.1 Empirical analysis of the country and 17 cities Items

Nation

City

With constant Without constant With constant Without constant Constant term Urbanization rate index

0.3267 −0.1095**

–

−0.091

–

−0.5128***

−0.317

−0.3901***

Fixed asset investment index

0.4348***

0.5903***

0.463***

0.1123***

Real estate development investment index

0.2076

0.6023***

0.188**

0.3946**

F statistic R2 Adjusted

R2

D-W Statistics

14.4533

22.3628

0.9347

0.9323

56.74 0.06602

61.34 0.8391

0.9095

0.8529

0.4079

0.6894

2.6203

2.2337

2.4983

2.3353

*, **, *** represent respectively that the coefficient passes the test at the significance level of 10%, 5% and 1%

4. (1)

Interpretation of the results of empirical analysis Index bias

The constant term is used to measure the bias of the commercial housing sales price indexes compiled by the National Bureau of Statistics and by the local bureaus of statistics. It can be seen from the model analysis results that the constant term in the model cannot pass the t test. That is to say, there is no sufficient evidence to show that the constant term is significantly non-zero. If the relationship described above exists and its functional form is set up correctly, the month-on-month indexes of the urbanization rate, the proportion of fixed assets investment to GDP, and the real estate development investment can also accurately measure the level of change in location and the quality of urban housing. According to the empirical result, there is no evidence to show that there is a significant index bias between the price indexes published by the National Bureau of Statistics and by the local bureaus of statistics. (2)

Coefficient

The regression results at the national level show that the partial elasticity coefficient of the remote effect of the location is −0.5128, that is, with other conditions unchanged, for every one percent increase in the urbanization rate, the gap between the year-on-year indexes of average price and those of the sales price decreases by about 0.5128 percentage points. The partial elasticity coefficient of the agglomeration effect of location is 0.5903, which indicates that for every one percent increase in the proportion of fixed assets investment to GDP, under the same conditions, the gap between the year-on-year indexes of average price and those of the sales price increases by about 0.5903 percentage points. The partial elastic coefficient of monthon-month index of real estate investment is 0.6023, that is, when the other variables

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Fig. 9.2 Relationship between average transaction price and price index

remain unchanged, for every one percent increase in the month-on-month index of real estate investment, the difference between the year-on-year indexes of average price and those of the sales price increases by about 0.6023 percentage points. Figure 9.2 is a comparison chart of the year-on-year indexes of average price and sales price of commercial housing in China from 1998 to 2010. It can be seen from the figure that there is a trumpet gap between the two. In 1998, China’s urbanization rate was 30.4%. After 13 years development, the number reached 47.5% in 2010, an increase of 56.25%. The proportion of fixed assets investment in GDP increased from 33.66% to 68.97%, an increase of 104.9%. The real estate development investment increased from 1.16 to 1.33 on a month-on-month basis, an increase of 14.66%. The gap between the year-on-year indexes of average price and sales price increases by 34.36% compared with 1998. The regression results show that, although more housing is being developed to the suburbs with the advancement of urbanization, due to the increase in investment and the improvement of housing quality, the trumpet gap between the average transaction price and the price index is growing. At the city level, the improvement of location brought by increased investment and the improvement of housing quality brought by increased competition offset the remote effect of urbanization. However, the investment plays a smaller role in enlarging the price gap at the city level than at the country level.

9.2.3 Summary In this section, the quality evaluation of housing price index is mainly done in the following two aspects. Firstly, by analyzing the characteristics of products that affect the price of commercial housing in China, the research puts forward the idea of assessing the accuracy of the housing price index from the perspective of average transaction price. Based

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on the hedonic price theory, the relationship between price index and average transaction price is decomposed into three aspects: location, house quality and index bias (Eq. (9.5)), and the theoretical model and empirical research equation (Eq. (9.8)) of three relations are constructed. Secondly, this paper evaluates the accuracy of China’s commercial housing price index. The empirical results show that there is no sufficient evidence to prove that there exists a significant index bias in the price index released by the National Bureau of Statistics. The results of empirical analysis also show that the improvement of location brought by fixed asset investment offsets the remote effect; in addition, in the increasingly fierce competition of the housing market, the increase in supply makes developers more concerned to improve the building quality, which also increases the average transaction price in the market. However, in the empirical research, we’ve also found that both the improvement effect of location brought by investment and the improvement effect of quality brought by competition are quantitatively comparable to the remote effect, but in real life, if such quantitative relationship is not established, the price index of commercial housing is likely to be constantly underestimated. It should be noted that the premise of the above empirical research conclusions is that the proxy indicators selected for location and quality can well reflect the impact of changes of quality and location on real estate prices. If the proportion of fixed assets investment in GDP and the month-on-month index of the real estate investment cannot accurately represent the improvement of location and housing quality, the reliability of the above conclusions will be affected. Due to the limitations of the data, the above research needs to be improved in both the indicator selection and the data range. It is expected that there will be more complete data and more convincing proxy indicators in the future to further demonstrate whether there is a bias in China’s real estate price index.

9.3 Research on Housing Price Index Based on Repeat Sales Model 9.3.1 Question Raised As mentioned above, the Hedonic Price Index Model, the Repeat Sales Model and the Pooled Model are more suitable for the compilation of the housing price index. China has been studying the above three index models since the 1990s. The previous achievements mainly focus on two aspects. One is the introductory research, that is, to introduce the theory of the Hedonic Price Index Model, the Repeat Sales Model and the Pooled Model to the country, and to qualitatively discuss what should be paid attention to when applying these models in domestic practice (such as Wang Libin, 1997, etc.). The other is the empirical research. Most of these researches are based on survey data. The Hedonic Price Index Model is used to adjust the samples in the traditional index compilation to comparable samples, and then to carry out the

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indexation (Sun Xianhua, Liu Zhenhui, Zhang Chenxi, 2008, etc.). Since the online sign data of housing transactions are rarely available in China, it is difficult to carry out the empirical research on the Repeat Sales Model and the Pooled Model based on such data. In the following part, we will explore the compilation of China’s housing price index in two aspects. First, to explore how to better use the new data source of online sign data in the compilation of housing price index. In order to deal with the “homogeneity” of housing products, the processing rules on “pseudo” repeat sales data are put forward. Second, based on the repeat sales model, an empirical research on the compilation of housing price index is carried out to discuss the application issues, such as filtering rules for non-market transactions, index estimation methods, calculation of real index and index bias.

9.3.2 The Repeat Sales Model and Its Estimation Method 1.

Basic model

The construction of the Repeat Sales Model is based on the calculation of micro-rate of return. For a single housing transaction, if it is purchased in t−1 period and sold in t period, regardless of depreciation and decoration, the natural logarithmic form of the appreciation of the housing in the t period (or impairment, as the direction of price fluctuation is unknown, hereinafter uniformly referred to as appreciation) can be expressed as:   ri,t = ln Pi,t /Pi,t−1

(9.9)

In it, r i,t is a logarithmic form of the appreciation of housing i in the t period, and Pi,t is the transaction price. Suppose that the price fluctuation of a single set consists of two parts: the price index of overall market and the external impact to the price of a single set of housing, and the relationship is: ri,t = μt + εi,t

(9.10)

And t is the logarithmic form of the price index of the t period housing market, and i,t is the error form. Since the time of buying and selling of a large number of samples is different and the holding periods overlap, we can estimate the price index t of each period by the transaction data within the period of t = 1, 2, …T. With Bi and S i as the prices of a single set of house in the buy-in period bi and the sell-out period S i respectively, the added value of all houses can be expressed as: yi = ln(Si /Bi ) =

si  t=bi +1

ri,t =

si  t=bi +1

μt +

si  t=bi +1

εi,t

(9.11)

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The repeat sales model represented by Eq. (9.11) can be expressed in a matrix: y = Xμ + ε

(9.12)

The design matrix X represents the holding period features of the housing sample. Each row of X represents a transaction sample of repeat sale. The elements of each row represent the time structure of the housing transaction. Usually the first nonzero element represents the purchase behavior, and the last non-zero element represents the selling behavior. The different forms of the design matrix X also represent different perceptions of the added value, usually with geometric and arithmetic design matrices. The geometric form of the design matrix supposes an even appreciation of housing over the holding period, and the difference between the purchasing price and selling price is the result of the even appreciation during the holding period. The form of X can be expressed as […0 −1 0…1 0…], while x ij = −1 and x ik = 1 indicate that the sample i was bought in the period j and sold in the period k. The added value form of the arithmetic form is […0 1 1…1 0…]. When the purchase behavior of the housing occurs, in the following period, the value-added vector of the sample is marked as 1, indicating that the house enters the value-added state. When the sample is sold, the selling period is marked as 1, and the subsequent period is 0. 2. (1)

Estimation of the model Ordinary Least Squares (OLS)

Suppose that the error forms of Eq. (9.4) are independent and identically distributed, and the mean is zero, the variance is constant, then the least squares regression result is expressed as:  −1  X y μˆ O L S = X  X (2)

(9.13)

Generalized Least Squares (GLS)

Webb [5] found that the residual of ordinary least squares regression is positively correlated with the length of housing holding period. It is suggested to use the holding period matrix to eliminate the estimated heteroscedasticity and propose to use the generalized least squares method to estimate the repeat sales model. Its form is:  −1  −1 XΩ y μˆ G L S = X  Ω X

(9.14)

P  , P −1 is the diagonal matrix used for weighting, and the In the case,  = P √ diagonal element is 1/ si − bi − 1. The form of the generalized linear estimator expressed by the Eq. (10.11) is also the form of maximum likelihood estimation of the repeat sales model. (3)

Three-stage generalized least squares (3GLS)

Case and Shiller [4] proposed using three-stage weighted generalized least squares estimation to eliminate the heteroscedasticity of the repeat sales model. The steps are

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as follows: ➀ to estimate the market price index by ordinary least squares method; ➁ based on the result of the first step to calculate the estimated error eˆ = y − X μˆ O L S ; ➂ to establish a functional relationship between the variance and the holding period and calculate the eˆ2 = α − ψβ + τ, eˆ2 = αˆ − ψ βˆ estimated error ➃ to estimate the variance obtained in the second step as the number of periods by generalized least squares:  −1  −1 −1 μˆ 3G L S = X  ΩW X ΩW R S y RS X

(9.15) 2

Ω WRS is a diagonal matrix for weighting whose diagonal element is 1/eˆˆ . 3.

Index modification

The log transformation of price change and the price index of the single sample has operational advantages. Especially when constructing the arithmetic value-added form of the repeat sales model, if the log transformation is not performed, the arithmetic value-added repeat sales model cannot be constructed. However, log transformation also has its own defects. Since the logarithmic function is a concave function, when the price changes of the transaction sample are not exactly equal, the average after log transformation will be less than the log-transformed value of the average. Therefore, the arithmetic average calculated by the log transformation of price changes must be less than the logarithm value of the average of the actual total price changes, thus resulting in an underestimation of the price index. In order to overcome the defect of logarithmic form, the arithmetic design matrix is usually applied in the index compilation, and the variance of the price index parameter ut is used to modify the index in the form of:

ad justet It

1 ≈ exp μt + var(μt ) 2

(9.16)

9.3.3 The Processing of Data 1.

Basic data

The data used in this section are the online sign data of the new housing transaction, covering a total of 5,312 samples of all the new housing transactions from January 1, 2003 to March 31, 2005 in the Lianqian Lot of Xiamen City. Since the price index compiled in this section reflects the price fluctuation of housing affected by the market supply and demand, it is necessary to exclude the transaction data for nonmarket supply and demand, including mainly the following two categories: ➀ products with non-market price formation and fluctuation mechanism, including resettlement housing, affordable housing, unit welfare housing, social security housing,

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unit fund-raising housing, etc., which have restrictions on purchase and transfer; ➁ non-market transactions, mainly including intra-family transactions, transactions between relatives and friends, transactions between families and institutional entities, and transactions for tax avoidance, etc., and transactions in which the transaction price is significantly lower than the market price for unknown reasons. In addition, villas are restricted products and the transaction object and price fluctuation mechanism are different from ordinary housing, so they are not considered, either. Since non-market transactions are “covert” and cannot be eliminated directly, we have set up three filtering rules in the empirical analysis. Rule 1 considers that when the transaction price is lower than 85% of the average price of the building in the month, the transaction data may be generated by non-market transactions, so the data are excluded. The corresponding exclusion criteria for Rule 2 and Rule 3 are 90% and 95%, respectively. 2. (1)

Data processing and “pseudo repeat sales rule” Pseudo repeat trading rule

The repeat sales models abroad are generally based on the second-hand housing transaction data. In this regard, this model does not seem to be directly applicable to the price index study of China’s new housing market. However, unlike the single houses in the European and American countries, China’s housing is dominated by buildings, which are similar to condominiums in other countries. Therefore, by analyzing the building products and their pricing characteristics, we can find The “pseudo repeat” of the new housing market. In Fig. 9.3 there are four buildings, A, B, C and D, belonging to the same project, among which the building A and B are of the same height and have the same layout. If these buildings are sold at the same time, there are usually the following characteristics. ➀ The four buildings enjoy the same exterior environment, municipal supporting facilities and internal community supporting facilities. ➁ With the price of the bottom floor determined, the price disparity will be doubled for each higher floor correspondingly, and the price of the middle floor usually corresponds to the average price of the whole building. Taking Fig. 9.3 as an example, the floor price disparity of A21 is twice as high as that of A11, and the floor price disparity of A31 is double of that of A21; while the price of A31 is the average price of Building A, D61

D62

A51

A52

B51

B52

C51

C52

C53

D51

D52

A41

A42

B41

B42

C41

C42

C43

D41

D42

A31

A32

B31

B32

C31

C32

C33

D31

D32

A21

A22

B21

B22

C21

C22

C23

D21

D22

A11

A12

B11

B12

C11

C12

C13

D11

D12

A

B

Fig. 9.3 Example of a pseudo repeat transaction

C

D

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the average price of Building D is the arithmetic average of the prices of D31 and D41. ➂ The different apartments of each floor of the building are sold at the same unit price, that is, the unit price per square meter of A21 and A22 is the same, which implies that there is no difference in the quality of average each square meter of the same floor. ➃ The housing launched in the same batch with the same layout and the same floor numbers are usually priced the same, that is, the unit price of each floor for building A and B is usually the same as they have the same layout and the same number of floors. Therefore, it can be considered that “the different units of the same floor in the same building (or several buildings with the same number of floors, the same layout and the same price) of the same project launched in the same period of time with the same unit price are considered to be the products with the same quality.” When these products with the same quality are traded in different periods of time, it is considered that the repeat transaction occurred to these “same-quality” products, that is, the transactions for A11, A12, B11 and B12 in different periods of time are considered to be repeat transactions of the same quality products. We call them “pseudo repeat transactions”. (2)

Paired sample rules

Intuitively, we can take the first transaction of the homogeneous A11, A12, B11, B12 as the virtual “purchasing period” and the other transactions as the virtual “selling period”, and use the unit price disparity of the transaction to calculate the price index. However, since the building is priced by the arithmetic relationship between the starting price and the floor difference, there is bias for the calculation of the price index which seems reasonable, though. If the developer adjusts the starting price and the floor price disparity of the building while keeping the average price of the building unchanged, the price level of the two periods calculated by the average is not equal to 1, which is obviously not in line with the actual situation. In order to eliminate the bias of direct comparison, we need to pair the samples and calculate the implied average price of the building in each transaction sample to conduct indexation. The virtual “purchasing period” of the new housing is defined as the first transaction period of the homogeneous housing, which is usually the month when the transaction of the building is opened. The transactions that occur after that period is considered to be a virtual “selling period”, and there are also different pairing rules for the two virtual periods. A. Pairing in the virtual “purchasing period”. For the virtual “purchasing period” we only need to obtain an underlying average price of the building, which requires a full use of the transaction sample of the base period to obtain the pairing unit price. When calculating the price of the base period, first we need to calculate the average number of floors of all transaction samples in the base period. If the average floor number of the transaction sample is larger (less) than the floor where the average price of the building is located, we need to add in the lowest (highest) floor repeatedly until the average number of floors of the adopted sample is equal to the floor where the average price of the building is located, and then the average transaction price is calculated as the comparison price of the base period. If the average floor of the

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base period transaction sample is equal to the floor where the average price of the building is located, the average price is directly calculated as the comparison price of the base period. B. Pairing in the virtual “selling period”. For the virtual “selling period” we need to obtain the underlying average price of the building of each sample. According to the number of samples used for pairing, it can be divided into first-order and secondorder of pairing. The first-order pairing indicates that the sample is located on the floor where the average price of the building is located. Actually, no pairing occurs. The second-order pairing is to look for two samples, and the simple average of the floors is equal to the floor where the average price of the building is located, and then the average transaction price of the two is calculated as the price after pairing. So is for the third order and so on. The higher the order, the less information a single sample contributes to the price after pairing, so the price after pairing of a single sample usually takes the pairing result of the lowest order. If a single sample participates in the same order pairing repeatedly, the average of all the prices after pairing is used as the final paired price. In the actual pairing, too-low-order pairing will lose a large number of samples, while too-high-order pairing will cause excessive use of information, so the pairing order should be determined according to the actual needs. The above rules for finding the pseudo repeat transactions and the sample pairing rules are the pseudo repeat sales rule proposed in this section. After the fourthorder pairing using the pseudo repeat sales rule, 3,154 samples were obtained for compilation of index, while another 1,598 data failed to enter the compilation of repeat sales index, so the sample loss rate caused by pairing was 0.3363. After the sample is processed by the pseudo repeat sales rule, the maximum bias of the structure of the processed data from the structure of the pre-processing data is 0.0259, so the structures of the sample before and after pairing still maintain good consistency.

9.3.4 Empirical Analysis 1.

Sample filtering rules

Table 9.2 lists the fitting effects of various regression methods under different sample filtering rules. Since the repeat sales rule needs to calculate the average virtual “purchasing” price in the opening period of the repeat transaction of this type, the average price as a basis of comparison does not enter the regression, so actually there are 2,345 samples entering the regression. When 85%, 90% and 95% are set as the standard filtering conditions, the sample loss rates are 0.0866, 0.1915, and 0.2537, respectively. Regardless of the estimation method, the stricter the filtration standard, the larger the adjusted R2, and the better the regression fitting effect. For the real estate market, the value of a single set of housing is relatively large, especially in the Xiamen market where the total price of a house is usually over hundreds of thousands of yuan. Therefore, the change of total price caused by a small discount usually reaches tens of thousands of yuan. It is generally not possible

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Table 9.2 Fitting effects of various estimation methods under different sample filtering rules Filtering rules

Sample remaining

Sample loss rate

Adjusted R2 under different estimation methods OLS

GLS

3GLS

All samples

2345

0

0.1785

0.1481

0.1923

Rule 1

2142

0.0866

0.3511

0.3004

0.3771

Rule 2

1896

0.1915

0.4725

0.4350

0.4943

Rule 3

1750

0.2537

0.6101

0.5941

0.6277

for a single buyer to bargain for a “larger” discount with the developer. In addition, the pairing itself has an intrinsic averaging mechanism. If the average price after the second-order or more pairing is significantly 5% lower than the average price of the building after the pairing, it means that the bias of the sample before the pairing is far greater than 5%. On the whole, Rule 3 has advantages in both the fitting effect and the realistic basis. 2.

Selection of estimation method

It can be seen from Table 9.2 that the fitting effect of the OLS estimation method is better than the GLS estimation method, indicating that the heteroscedasticity related to the virtual holding period does not exist. In this section, the F value obtained in the Goldfeld-Guandt Test for the sample based on the virtual holding period is 1.53, rejecting the heteroscedasticity hypothesis related to the virtual holding period, that is, for the “homogeneous” housing of the same project, the price distribution is constant whether transacted in advance or afterwards. In reality, usually every project lasts less than one year from opening to selling out. For the duration of the project, the environment and the supporting facilities around the housing often do not change significantly. The external environment does not support the heteroscedasticity hypothesis related to the holding period. In addition, since all the houses in the sample are semi-finished whose inherent living quality will not change during the sales holding period, the housing quality itself does not support the heteroscedasticity hypothesis related to the holding period, either. The fitting effect of the 3GLS estimation method is slightly better than the OLS estimation method, which shows that the elimination of the heteroscedasticity by the 3GLS estimation method improves the estimation effect. 3GLS decomposes the variance of the error into two parts that are related and unrelated to the holding period. Since the empirical results do not support the heteroscedasticity related to the holding period, the heteroscedasticity is likely to be caused by the non-holding-period factors, such as the region and the project. Since the geometric design matrix and the arithmetic design matrix have intrinsic consistency, the statistical properties of the estimated coefficients and the equation are exactly the same. Without the modification by the Eq. (9.16), the simple fixed basis index estimated by the two design matrixes and the month-on-month indexes are exactly the same in each period. After the modification of the arithmetic design

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Fig. 9.4 Comparison of various indexes

matrix by the Eq. (9.16), the fixed basis estimated by the 3GLS is shown in Fig. 9.4. For comparison purposes, we also calculated the traditional index of the average transaction prices. By comparison, the following conclusions can be drawn. First, compared with the index calculated by the average transaction price without considering homogeneity, the price index calculated by the repeat sales model is more in line with the real housing market. The index calculated by the average transaction price peaked at 139.79% in March 2004 and then declined to 116.79% in March 2005. The price index calculated by the repeat sales model fluctuated upward. The unadjusted index remained at around 130% after July 2004, while the adjusted index went all the way up to 156.02% in March 2005. Obviously, the price trend reflected by the repeat sales model was more in line with the reality of the Xiamen market. During this period, the real estate market in Xiamen and even China was booming, so the price perceived by residents was on the rise, and the estimated results of the repeat sales model were more in line with such market development track and residents’ perceptions. Second, the modified price index is higher than the unmodified price index, but the excessively large variance estimated by some individual coefficients may lead to excessive modification effects. Using the variance to modify the repeat sales model can overcome the underestimation of index caused by log transformation. However, the too large variance of a coefficient may cause excessive modification. This paper attempts to select exponential variables by means of stepwise regression and backward regression, but these methods are apt to cause inconsistency in variable selection and therefore not suitable for practical application. Third, the real index is between the unadjusted and adjusted indexes, which is between 126.85% and 153.95%. Failure to modify the index will underestimate its actual performance, but the existence of some insignificant coefficients will lead to excessive modification of the index. Based on the estimation accuracy of the text, we can only determine that the index is between 126.85% and 153.95%. Even so, the

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minimum of the estimation by the repeat sales model is greater than the price index of 116.79% calculated by the average price and closer to the real market price trend than the traditional index model. 3.

Discussion on index bias

Cho [6] distinguishes the five biases in the repeat sales model [6]: the renovation bias caused by neglecting the renewal of the housing during the holding period, the hedonic bias caused by neglecting the external environmental changes surrounding the housing during the holding period, the sample-selection bias due to the inconsistency of product structure of the second-hand transaction and of the entire market, the trading-frequency bias caused by the fact that some samples that are prone to frequent transactions, such as the second-hand new houses, often enter the index calculation to affect the index, and the aggregation bias caused by the possible loss of trading samples when dividing the investigation periods or by regression (when calculating the current price index), including the non-current samples. Applied to the new housing market, except for the aggregate bias, almost no other biases exist. In the sales process, the housing is generally not renovated or refurbished, so there is no renovation bias. The sales period for new housing is often within one year, so there is very little change in the external environment, hence no hedonic bias. The calculation of price uses transaction data of all new housing, hence no sample-selection bias. All samples are subject to only one substantive transaction, so naturally there is no trading-frequency bias. However, due to the virtual purchasing period set and sample pairing conducted, the pseudo repeat sales rule brings about two new types of bias. (1)

Virtual purchasing period bias

The possible biases come from two sources if the first transaction of the housing with the same quality is used as the virtual purchasing period to estimate index. On the one hand, the sample transacted in the virtual purchasing period contains the information of price fluctuation of the period which should be included in the calculation of the current price index, but in the pseudo repeat transactions, only the transaction price of the housing with the same quality after this transaction is calculated. On the other hand, as the comparison basis of the pseudo repeat transaction prices, the virtual purchasing period should be the result of objective supply and demand. However, the price of the virtual purchasing period is usually a subjective and strategic result. It is usually the result of balance by the developer based on the market information and their own sales strategy. When the developer’s price expectation deviates from the reality of market supply and demand, or when the sales strategies such as “open low and go high” are adopted, the subjectivity of the price in the virtual purchasing period may cause bias in the later price index. (2)

Pairing bias

Pairing the samples may also result in two biases. On the one hand, pairing will result in loss and reuse of samples. Low-order pairing tends to lose samples, while highorder pairing is apt to reuse some samples. In this paper, fourth-order pairing was

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carried out for balancing, but still 25% of the sample were lost, and some samples were reused. On the other hand, after the sample pairing, the sample entering the index calculation may deviate from the actual transaction structure of the market. Although this situation did not occur in this paper, in practice, the sample-selection bias caused by pairing may still exist.

9.3.5 Summary By analyzing the characteristics of product and pricing of new housing in China, we propose a pseudo repeat sales rule, which includes two parts: the pseudo repeat transaction rules and the data pairing rules. With the rule, we can convert the transaction of the new housing market into a specific pseudo repeat transaction, thus compiling the price index of the new housing market by using the repeat sales model. When compiling the housing price index by using the pseudo repeat sales rule and the repeat sales model, the empirical evidence in this section can provide some references as followed. ➀ The empirical research finds that the transaction may include non-market trading behavior when the price after the sample pairing is 5% lower than of the paired average price of the month, it is recommended to be eliminated. ➁ The heteroscedasticity related to the holding period does not exist in the new housing data, and the index can be estimated by OLS or 3GLS. The OLS estimation method is simple, but the estimation effect of 3GLS is slightly better than OLS. ➂ The price index estimated by the repeat sales model is closer to reality than the traditional index model. Since the logarithmic processing may cause underestimation of the index, it is usually recommended to adjust the index. However, when the variance of the regression coefficient is too large, the adjusted index may be over-adjusted and the real index is between the adjusted and unadjusted indices. ➃ Compiling the price index of the new housing market by the repeat sales model can eliminate most of the traditional biases of the model, but will produce two new types of biases, which requires awareness in practice.

References 1. China index academy, China real estate index system: theory and practice. China financial and economic publishing house (2005) 2. Court AT (1939) Hedonic price indexes with Antomotive examples in the dynamics of automobile demand in New York, General Motors 3. Bailey MJ, Muth RF, Nourse HO (1963) A regression method for real estate price index construction. J Amer Statisit Assoc (58) 4. Case KE, Shiller RJ (1987) Prices of Single-Family Homes Since 1970: New Indexes for Four Cities, New England Economic Review 5. Webb, Cary, “The Expected Accuracy of a Real Estate Price Index”, Working Paper, Department of Mathematics, Chicago State University (1981)

References

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6. Cho M (1996) House price dynamics: a survey of theoretical and empirical issues. J Housing Res (7)

Chapter 10

Research on Ecological Environment of Statistics

10.1 Impact of Statistical Ecological Environment on Data Quality I.

The definition of statistical ecological environment in this book

The purpose of studying the statistical ecological environment is to coordinate more effectively the subjects involved in statistical activities and create conditions conducive to the implementation of statistical work, thus building a highly credible statistical system that can provide in time high-quality statistical products and services veritably and effectively. Combined with the status quo of China’s statistical development, the relationship between the entire statistical activity system and the statistical ecological environment in China can be summarized as Fig. 10.1. As can be seen in Fig. 10.1, the main participants of statistical activities in China include statistical agencies and respondents like government departments, enterprises, and the public. The extensive participation and interaction of multiple subjects is the guarantee for the smooth progress of statistical activities in China. As the organizer of statistical activities and the provider of statistical industries and services, the statistical agency, who is at the core of the statistical activity system, undertakes many functions, such as formulating statistical indicators, organizing and coordinating statistical activities, processing statistical data and releasing statistical products, etc. While, as the respondents of statistical activities, the government departments, enterprises, and people are the basic units of economic activities and the providers of basic data for statistical activities. The quality of the basic data they provide affects the operation of the entire statistical system. The statistical ecological environment studied in this chapter is the general term of various external factors that the statistical activities depend on and that have an effect on statistical agencies and various respondents, including the socio-economic environment, political and

© Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2_10

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Fig. 10.1 Organizational mechanism of statistical activities and statistical ecological environment in China

legal environment, institutional environment, software and hardware technical environment, public opinion environment and international statistical environment that are related to statistical activities. In the following part, we will focus on the behaviors and interests of the participants in the statistical activities to analyze the main factors that constitute China’s statistical ecological environment and their impact on the quality of statistical data from different perspectives. II.

The impact of socio-economic environment on data quality

The socio-economic environment, that is, the state of social and economic development in a country and region, has a great impact on the quality of statistical data. First of all, statistical agencies and their products and services are created and developed to meet the needs of social and economic development. Generally speaking, countries and regions with good socio-economic development have relatively developed socio-economic statistics, and the statistics released are significantly better than the less developed countries and regions in terms of completeness, timeliness and connectivity of indicators. Second, countries and regions with better socio-economic development have sufficient financial and material resources to invest in statistics. At the 33rd meeting of the United Nations Statistical Commission in March 2002, it was raised that “the necessary conditions for statistical work such as material and financial resources include office buildings, computers and other office equipment required for statistical operations and the necessary budget; the intangible assets such as software developed for statistics, and statistical libraries with statistical operation manuals and application

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guides; there must be a sufficient number of statistical staff to perform statistical work, and a considerable number of statistical staff must be professional statistical technicians who are regularly trained and have rich practical experience.” In the economically developed countries and regions the above conditions can be better met, so the quality of their statistical data is generally better; while in less developed countries and regions, due to financial constraints, insufficient investment in statistics will affect the quality of statistical data. Third, in areas with better socio-economic development, the government does not need to intervene in statistical activities to dress up political achievements. The requirement for statistical work is to find problems in social and economic development through effective statistical investigation and analysis so as to serve macroeconomic management. In countries with less social and economic development, it is more likely to create “digital achievements” through improper interference. Therefore, a good social and economic development environment is an important prerequisite to ensure the healthy and orderly development of statistical activities in a country or region. III. 1.

The impact of political and legal environment on data quality Political Environment

The impact of differences in the political environment on statistical activities is mainly reflected in the motivation of the government to actively intervene in statistical activities and the direction and intensity of the interventions. The selection of government officials in China is mainly based on top-down appointments. Statistical indicators are used as the most important basis for assessment of their performances and resource allocation. In this case, some government officials may inevitably have improper motivation to intervene in statistical activities. “Digital officials, officials’ figures” is a vivid description of this phenomenon. All these make the objectivity and independence that statistical agencies and statisticians should possess cannot but be greatly disturbed. The fundamental problem affecting the quality of China’s official statistical data lies in the unreasonable official selection and appointment system and the performance evaluation mechanism. Therefore, the construction of statistical ecological environment in China should be coordinated with the reform of the political system and go in line with the reform of China’s economic and political systems. 2.

Legal environment

Statistical laws and regulations are the institutional guarantees to promote the normal conduct of statistical activities, regulate the behavior of all participants in statistical activities, and ensure the accuracy, timeliness, effectiveness and objectivity of statistical data. Building a complete and feasible statistical legal system is an important prerequisite for promoting statistical standardization. Since the founding of the People’s Republic of China, China has gradually formed a relatively complete system of statistical laws and regulations, and the construction of legal system for statistical activities has achieved remarkable results.

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In December 1983, the official promulgation of the “Statistics Law of the People’s Republic of China” (hereinafter referred to as the “Statistics Law”) marked the beginning of China’s statistical work on the legal track. After that, the “Statistics Law” experienced two amendments in May 1996 and June 2009, and the latest “Statistics Law” came into effect on January 1, 2010. The amendments of the “Statistics Law” fully demonstrate the importance that the state attaches to the construction of the statistical legal environment. In order to reduce the interference of government actions in statistical activities, the state has also introduced relevant regulations, mainly the “Notice on Resolutely Opposing and Stopping Statistical Falsification” issued in 1998 and the “Provisions on Punishment of Statistical Violations” that was implemented on May 1, 2009. The introduction of these measures has, to a certain extent, curbed the intervention of government departments in statistical activities and shaped the professionalism of statisticians and their awareness of responsibility, thus playing a positive role in maintaining a good statistical legal environment. In addition, China’s current statistical regulations include: “Provisions on Inspection of Statistical Law Enforcement”, “Notification System of Statistical Illegal Cases”, “Administrative Provisions for Statistical Investigation Projects’ Examination and Approval”, “Interim Measures for the Administration of Statistical Investigation Projects”, etc., and the departmental statistical regulations formulated by other administrative departments, such as the “Regulations on Railway Statistics”, “Regulations on the Administration of Financial Statistics” and “Interim Measures for the Administration of Environmental Statistics”. Some provinces and municipalities have also formulated corresponding statistical regulations, which, as an important part of China’s statistical legislative system, have laid an important foundation for the legalization of China’s statistical undertakings and provided legal support for the effective implementation of statistical activities. However, in general, the legal environment for statistical activities in China is not yet sound, and there are many problems that need to be studied and solved urgently. Taking China’s statistical basic law “Statistics Law” as an example, the main problems are: (1) The punishment of statistical illegal acts is obviously not severe enough. The “Statistics Law” stipulates that the statistical illegal act of “providing false data or concealing statistical data of important facts” shall be imposed a maximum fine of 50,000 yuan, while according to the “Law on Commercial Banks”, the same act shall be imposed a fine of 200,000 to 500,000, and if it constitutes a crime, the person concerned shall be prosecuted for his criminal liability. Because of this, the “Statistics Law” has a low level in the legal hierarchy and the deterrent effect is not strong. (2) The asymmetry of rights and obligations between the organizer of the statistical activity and the respondents. Statistical products carry the nature of public goods. Accurate and timely release of statistical products is conducive to the correct economic decision of the economic entity. As the original data provider, the respondents who fulfill the obligation to accept statistical investigation should enjoy the right to obtain statistical products. Only in this way will the respondents provide authentic data. However, the “Statistics Law” only stipulates the obligation of the respondents to actively cooperate with the investigation of the statistical agencies and

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provide authentic data, but does not mention their right of access to statistical products and services. (3) Insufficient protection of the respondents’ privacy. Although the “Statistics Law” emphasizes that statistical survey activities aim to obtain real socio-economic data, which has nothing to do with enterprise taxation and family planning penalty, there are still quite a few respondents who participate in the survey passively. It is mainly due to the lack of protection for the respondents’ privacy in the current “Statistics Law"and its ambiguity and randomness in the protection of the privacy of survey data. (4) There is no legal guarantee for the stability of statistical indicators and calculation methods, which brings inconvenience to decision making and scientific research based on statistical data. (5) The existing enforcing team for statistical law is not sound enough yet. (6) Because of the lack of a sound management coordination mechanism, there still exist the problems of “multiple data for the same indicator” and inconsistency of local aggregate data and national data. For other statistical regulations, there are also more or less problems. Therefore, it is still an arduous task to strengthen the construction of the statistical legal system in China for a long period of time. IV.

The impact of the institutional environment on data quality

The statistical system mentioned here includes both the statistical management system and the statistical survey system. The statistical management system refers to the management structure and organizational relationship of statistical activities, involving multiple relationships between statistical agencies and the government and within statistical agencies. The neutrality of statistical agencies and their internal synergy are the institutional basis for the objectivity and fairness of statistical products and the efficiency of statistical activities. Its impact on the quality of statistical data is mainly reflected in the institutional constraints and mechanisms of action. The statistical survey system refers to the system of interrelated statistical survey management and statistical methods, which is composed of two subsystems: the management and operation system of statistical survey and the method and institution system [1]. The coordination in management of statistical surveys and the scientific rationality and relevance of survey methods are important factors affecting the quality of statistical data. The coordination in management of statistical surveys directly affects the matching and connection of all aspects of statistical activities; while the scientific rationality and relevance of statistical methods affects the quality of statistical data in the process of collecting, sorting and processing raw data. Since the founding of the People’s Republic of China, the statistical management system and statistical survey system in China have undergone a series of changes with an overall development towards a more rational direction. 1.

Statistical management system

The statistical management system of the People’s Republic of China can be traced back to the Statistics Department (later changed into the General Office of Statistics) of the Planning Bureau, Financial and Economic Commission, the Government Administration Council of the Central People’s Government in October 1949. At that

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time, the national statistical work was organized and promoted by the greater administrative areas. In 1952, the National Bureau of Statistics of China was established as the functional department of the government that unifiedly lead the national statistical work. It was responsible for the unified formulation of the national statistical system and methods. Subsequently, various regions and departments successively established statistical agencies. So far, China had initially established relatively complete statistical management structure. In 1962, in response to the “false report” issues in various areas during the “Great Leap Forward” period, the State Council issued the “Decision on Strengthening Statistical Work”, proposing the objective model of China’s statistical management system at that time, that is, the “One Vertical, Three Unified”1 statistical system. During the Cultural Revolution, China’s statistical management system was severely damaged. After the reform and opening up, China has carried out reforms in the statistical management system in response to a series of new situations in its economic and social development. It has successively set up three general teams: the Rural Social and Economic Investigation Team, the Urban Social and Economic Investigation Team, and the Enterprise Investigation Team.2 In 2005, the National Bureau of Statistics carried out reform on vertical management of the three directly affiliated general teams to form investigation teams at all levels vertically managed by the National Bureau of Statistics, which played a positive role in promoting the reform of China’s statistical management system. At present, China has formed a statistical management system of “unified leadership and hierarchical responsibility”. From the perspective of organization and management of statistical activity, China’s statistical system includes the government’s comprehensive statistical system, the government departments’ statistical system, and the enterprises and institutions statistical system. The government’s comprehensive statistical agencies and departmental statistical agencies, which belong to the government statistical agencies, constitute the two pillars of the government statistical system. The government’s comprehensive statistical system consists of the National Bureau of Statistics, the statistical agencies of provincial, municipal, and county-level local people’s governments, statistics stations in towns, and investigation teams at all levels. The National Bureau of Statistics is responsible for the national statistical work and implements a management model of “double leadership, hierarchical management, integration of departments and regions at different levels and regions-based” for local statistical bureaus. The local statistical bureaus at all levels are under the dual leadership of the government at the same level and the statistical bureau at the higher level. The statistical operation is mainly led by 1

“One Vertical and Three Unified” system means that the national statistical system adopts vertical leadership and unified management in the establishment, officials and funding; the accuracy of the statistics data should be the responsibility of the head of statistical work of each unit. 2 Among them, in 1981, the rural sampling survey team was resumed (renamed the Rural Social and Economic Investigation Team in 1988), and gradually expanded to the national and provincial investigation teams led by the National Bureau of Statistics and 857 county investigation teams of the counties selected as national samples. In 1984, the city sample survey team was established in the national and provincial-level administrative units and the surveyed cities (counties) selected (in 1988, it was renamed the Urban Social and Economic Investigation Team).

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the statistical bureau at the higher level, while the administrative management of staffing, appointment and dismissal of officials is mainly decided by the government at the same level. The funds required by the local statistical bureaus consist of central and local allocations. The National Bureau of Statistics implements “centralized and vertical leadership” for the subordinate investigation teams whose statistical operation, staffing, and funding are vertically led and managed on a unified basis by the National Bureau of Statistics. The statistical system of government departments is composed of statistical agencies and personnel established and appointed by various departments under the State Council and the local governments. The guidance of the National Bureau of Statistics for statistical agencies of other government departments mainly lies in business approval and administrative coordination. The main statistical task of the enterprise and institutions statistics system is to undertake their own statistical tasks while filling in the statistical questionnaires formulated and issued by the state organs in accordance with law [2]. From the perspective of the statistical management system, under the current system, it is still quite difficult to resist the improper intervention of some administrative leaders in statistics and prevent the falsification of statistical data. It is because that, under the current management model, local statistical agencies are mainly led by local governments in the administration, and their funds are largely dependent on local government allocation. If the local statistical agencies insist on statistical supervision and offend the superior administrative leadership, not only the promotion of the statistical officials may be affected, but the local statistical work may also be difficult to carry out normally. Although the investigation teams are managed vertically by the National Bureau of Statistics, in fact, due to their inextricable links with the local statistics bureaus (or local governments) in funding and official appointments, it is hard for the primary level teams not to be influenced by the local statistical bureaus and governments. All of these make the objectivity and independence that statistical agencies and statisticians should have cannot but be greatly disturbed. In addition, within the statistical agencies, the division of responsibilities, rights, and interests between the government’s comprehensive statistics and departmental statistics and between the statistical agencies and other government departments is not clear. The National Bureau of Statistics has limited administrative efforts for local statistical bureaus. The horizontal relationship between the government’s comprehensive statistics and departmental statistics is poorly coordinated due to administrative subordination and overlapping functions, which leads to the phenomenon that the statistical indicators, measurement scales, indicator codes, etc. are decided by the departments themselves, and that many departments release at will to the public the important statistics on national economy and social development. 2.

Statistical survey system

Since the founding of the People’s Republic of China, with the social and economic development and the advancement of the political and economic system reform, a series of changes have taken place in China’s statistical survey system. Before the reform and opening up, influenced by China’s highly centralized planned economic system, its statistical survey mainly collected relevant statistical

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information through the periodic statistical statement system that was reported and aggregated from level to level. At that time, the statistical survey system was basically able to collect and provide the necessary statistical information, thus providing an important basis for the government to prepare and inspect plans and manage the national economy. After the reform and opening up, with the gradual establishment of China’s socialist market economy, the original statistical survey system could no longer meet the needs of social and economic development, therefore, the reform of the statistical survey system has gradually been put on the agenda. In 1994, the State Council approved and issued the “Request for Establishing a National Census System and Reform the Statistical Survey System”, which greatly promoted the reform of China’s statistical survey system. At present, a relatively complete and fully functional statistical survey system based on periodic census has been initially formed, with regular sample surveys and statistical statements as the main body and supplemented by key surveys and scientific calculations. The cycle and year of the implementation of population census, economic census and agricultural census have been determined, and a complete set of sampling methods and systems have been established, including a sample survey system for agricultural production, a sample survey system for urban and rural households, a sample survey system for enterprises, and a sample survey system for prices, etc. However, in the statistical survey system, there are still some problems to be further addressed. The periodic statistical census still has many problems, such as high frequency of census, heavy workload, high survey cost, and different census time points, while the sample survey is far from playing its due role. The main reasons for this situation are as follows: First, under the system where the statistics are used as the main basis for performance assessment and resource allocation, governments at all levels must fully understand and master the statistics of the levels or units under their charge. However, the present sampling survey method, which is characterized by examining selectively some samples to estimate the general result, cannot meet this need. Secondly, under the current statistical system, from the perspective of the whole society, sampling surveys certainly save more manpower, material and financial resources than comprehensive statement surveys; however, for the statistical agencies, the arrangement of periodic statements does not require payment of fees, while the use of sample surveys instead increase their expenses. This cannot but affect the initiative of the statistical departments to promote sample surveys. Third, the original sample surveys were mainly undertaken by the Agricultural and Rural Investigation Team, the Enterprise Investigation Team and the Urban Investigation Team. The three teams who were formed according to their scope of work are lacking in effective coordination. Each of them only consider what was within their scope, which is also unfavorable to the further expansion of the sample survey and extension of its application scope. Fourth, some technical issues related to sample surveys, including design of sampling scheme, construction of sampling frames, and rotation of samples, are yet to be further studied and improved.

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Hardware and software technical conditions and its impact on data quality

Statistical activities are a complex systems engineering which has high requirements for related hardware and software technologies. The advanced technologies and equipment have a positive impact on improving statistical work efficiency, optimizing statistical activity processes, and strengthening the application of statistical results. The popularity of telephones and Internet and the advancement of computer technology in recent years have provided new and powerful tools for statistical surveys. These new technical means have greatly improved the efficiency of statistical surveys, and may also bring about major changes in the way statistical surveys are conducted. In addition, statistical sampling techniques, data analysis and processing techniques, and statistical data adjustment techniques all play an important role in the corresponding statistical activities, and can provide necessary guarantees for the improvement of statistical data quality and the smooth development of statistical activities. It is also of great practical significance to introduce the modern management technology and quality management system into statistical surveys. The modern management technology helps to save statisticians and funds, thus applying the limited statistical resources to the most critical and efficient statistical survey projects. The management system of statistical data quality is helpful for process management and monitoring of statistical surveys so as to discover in time the problems in statistical activities and optimize the statistical investigation process in due time, thus improving the quality of statistical data. VI.

Public opinion environment

The public opinion environment mentioned here refers to the attitude of the public and various media and the atmosphere of public opinion to statistical activities and statistical products and services, which, specifically, includes the opinions of the participants, such as the statistical agencies, government departments, the public, and enterprises on statistical activities. The attention of the public opinion to statistical activities and statistical information can play the function of information transmission and provide the public with timely and effective access to statistical data and related information. The correct supervision by public opinion can disclose the possible problems in statistical work. While promoting the openness and transparency of statistical information, it can also strengthen constantly the public’s awareness of the importance of statistical activities and prompt people to take statistical activities more consciously and seriously, thus improving the statistical awareness of the whole society. The change in the concept of participants will also have an important impact on the statistical activities themselves. If we can establish the idea in the whole society that the high-quality statistical data not only depend on the efforts of the statistical departments but also cannot be separated from the support of the public, it will definitely enable the respondents to improve their understanding of the nature of statistical activities and actively cooperate with the statistical investigation activities, so that the authenticity and objectivity of the statistical data can be guaranteed from the source. At the same time, it also helps to promote the coordination of the rights

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and obligations of the respondents, and effectively explores the positive effects of statistical products on social and economic development. If the statistical departments can change the concept and truly realize that, as the public goods produced by the statistical departments using the data from all walks of life, the statistical results should be widely used by the society, then they will do everything possible to meet the needs of users and develop more applicable statistical products to provide more convenience for the public to use statistical data. Only in this way can people’s daily life and behavioral decisions be closely related to statistical data, and can the public be universally aware of the importance of real data, thus better understanding, supporting, and cooperating with the statistical work. The statistical departments must also attach great importance to improving credibility. Only by establishing good statistical credibility can the public be actively engaged in statistical investigations from concept to action, and only when the respondents provide real and credible raw data can the construction of statistical credibility be fundamentally ensured. This is a complementary process. VII.

The impact of the international statistical environment on data quality

Today, with the developing of economic globalization, statistical exchanges and cooperation among countries are one of the important parts of political and economic exchanges, and it has become an important way for a country to promote the statistical management level, optimize the technical means, and improve the quality of statistical data. At present, a number of transnational statistical organizations, including such important international statistical organizations as the United Nations Statistical Commission, the United Nations Statistics Division, the International Monetary Fund’s statistical agencies, the International Bureau of Labor Statistics, and such regional statistical agencies as the ESCAP Statistics Division and the Eurostat, have been providing various communication platforms for statistical exchanges and cooperation between countries, aiming to promote the update of international statistical methods, the coordination of international statistical projects and the cooperation in the field of statistical technology. An active participation in the exchanges and cooperation of international statistical affairs and improvement of the degree of integration of China’s statistical methods, techniques and management with the other international organizations can provide a good international statistical environment for the development of China’s statistical undertakings. China first attended the 21st session of the United Nations Statistical Commission as an observer in 1981 and officially attended the 23rd session in 1985. As an important member of the United Nations Statistical Commission, China has always played an important role in the international statistical practice. In 2008, China set up a special trust fund project in the United Nations to carry out statistical exchanges and cooperation among developing Asian countries in the form of technical consultation, personnel exchanges and study visits, aimed at improving their statistical capacity, thus providing conditions and platforms for China to actively participate in international statistical cooperation. The improvement of the international statistical environment can exert influence on the quality of statistical data in the following aspects. First, by participating in exchanges and cooperation of international statistical affairs, we can learn from

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the advanced statistical systems and methods of other countries, thereby enhancing the statistical capacity in China. Second, joining the international statistical organization will enhance the connection of China’s statistical affairs with the international community, facilitate the exchange of statistical information and cooperation between countries, and help China get a greater say in the international statistical affairs. Third, the international statistical norms often require member states to abide by specific rules and obligations, submit relevant statistical reports and accept supervision from international community, which will exert a positive impact on the improvement of China’s statistical capacity.

10.2 How to Build a Statistical Ecological Environment that is Conducive to Improving Data Quality The quality of statistical data is an important aspect of the national economy and people’s livelihood. The UN’s Fundamental Principles of Official Statistics states that “Bearing in mind that the quality of official statistics, and thus the quality of the information available to the Government, the economy and the public depends largely on the cooperation of citizens, enterprises, and other respondents in providing appropriate and reliable data needed for necessary statistical compilations and on the cooperation between users and producers of statistics in order to meet users’ needs.” Thus, the key to fundamentally improving the quality of statistical data is to create an environment where the statistical agencies and the respondents can work together effectively, that is, to build a good statistical ecological environment. In the following part, we’ll put forward a few suggestions on how to build a statistical ecological environment that is conducive to improving data quality. I.

To optimize the performance appraisal mechanism and build a good statistical political environment

Under the current political and economic system in China, statistical data still play an important role in reflecting the performance of government officials and allocating the government resources. Therefore, it is difficult to completely avoid the improper intervention of local governments in statistical activities. To this end, it is necessary to further optimize the performance appraisal mechanism and build a good statistical political environment. The following measures are recommended to be taken. First, the role of the market in resource allocation should be improved gradually to weaken the excessive dependence of resource allocation on government statistics. Second, the democratic mechanism for the selection and appointment of officials should be improved further, and the practice of GDP-only hero should be avoided. The assessment methods for officials should be improved with the assessment indicators streamlined further so as to facilitate the inspection and supervision of superiors. The fraudulent behavior must be

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punished more severely. Third, the existing statistical management system and statistical survey system should be reformed to institutionally ensure the independence of statistical work. II.

To strengthen the construction of statistical legal system and improve the level of China’s statistical regulations in the legal hierarchy

For a long time, the backwardness of the statistical legal system has become one of the fundamental problems that constrain the improvement of the quality of statistical data in China. The completeness of the “Statistics Law”, which is the statistical basic law, is important for the construction of statistical legal system. To improve and amend the “Statistics Law”, on the basis of meeting the needs of statistical practice, we should carry forward the construction of future-oriented statistical regulations, and, through continuously promoting the statistical legislative process and exerting more severe penalties on statistical violations, raise the level of statistical laws and regulations in the legal hierarchy. Besides, on the basis of clarifying the legal status of statistical agencies, we should strengthen statistical supervision and improve the efficiency and strength of statistical laws and regulations in combating statistical violations. We should establish necessary supervision and coordination mechanism for statistical work, and formulate a number of laws and regulations on statistical work, including regulations on the reporting obligations of respondents and the confidentiality obligations of investigation subjects, regulations on the publication of statistical data to the public, the specific statistical methods system, and the approval procedures for adding new statistical investigation projects. In the form of statistical laws and regulations, the division of functions between comprehensive statistics and departmental statistics, national statistics bureau and local statistics bureaus shall be clarified to avoid duplication, overlapping and omission of statistical work, thereby achieving data sharing and effective collaboration. In order to reduce and avoid the intervention of local governments in statistical work, it is necessary to further strengthen the construction of relevant regulations on supervision. Taking the implementation of the “Provisions on Punishment of Statistical Violations” (hereinafter referred to as “the Provisions”) as an opportunity, we should strictly investigate the data fraudulent behavior of local statistical departments and the inappropriate administrative intervention of the local governments, and realize its legalization as soon as possible through constant improvement. The acceptance and hearing of statistical illegal cases can also break through the traditional principle of territorial jurisdiction and conduct trials in different places. III.

To further reforming the statistical management system and improve the independence and neutrality of statistics

As for the development of foreign official statistics, the European and American countries rely more on well-established laws, regulations, and institutional arrangements to ensure the independence of government statistical departments. The government statistical agencies in these countries adhere to a fair, neutral, and objective stance and usually only release the data or describe the report data objectively. It shows that

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only by carrying out effective construction of laws and regulations and institutional arrangements can the independence of government statistical work be fundamentally guaranteed and the statistical data quality be continuously promoted. We should be ready to learn from the advanced experience of these countries and innovate in the reform of China’s statistical system and development of the statistical career. We believe that the statistical system of unified and centralized management nationwide should be the target model for future efforts. Currently when the conditions are not fully prepared, the following transition modes can be used. For the provincial statistical bureaus, the management mode of “double leadership, hierarchical management, and integration of departments and regions” is to be still implemented for the time being, but the appointment and removal of the provincial statistical directors should be determined after consultation between the provincial government and the National Bureau of Statistics. The state should establish a direct reporting network for the large and medium-sized enterprises and entrust the provincial statistical bureaus to investigate the sampling survey data in need. The provincial statistical bureaus are to be jointly funded by the state finance and provincial finance. The local government statistical agencies below the provincial level should all adopt the management mode of “centralized and vertical leadership”. The advantages of this mode lie in two aspects: one is that it is conducive to mobilizing the initiative of the central and provincial levels and gives better play to statistics in macro-control and hierarchical decision-making; the other is that it can improve statistical supervision and enhance the independence of the statistical agencies below prefectural levels where administrative interference is more likely to occur. In addition, in the reform of the survey system, we must first make systematic research on the issues such as “why to investigate”, “what to investigate”, and “how to investigate”, especially make more efforts in the design of the indicator system and the investigation project and increase or decrease in time the statistical survey indicators in response to actual needs. We can get to know the relevant requirements for statistical product through strengthening communication with users of various statistical information and increase the necessary investigation items accordingly. Meanwhile we should check up the existing investigation items and resolutely delete some outdated or unnecessary ones. In addition, it should be noted that the survey items should be set in line with the international standards as much as possible so that the definition and statistical classification can be made in accordance with the internationally comparable statistical indicators and relatively consistent statistical standards. Efforts should be made to solve the problems of high frequency of census and non-uniformity of census time in periodic census statistics. The time and frequency of periodic censuses should be determined in the form of institutional regulations, and the selection of census indicators should be optimized. The limited statistical resources should be used for the main indicators that can best reflect the state of national economic and social development. Besides, we should refine the division of labor, and clarify responsibilities, thereby continuously improving the coordination of various departments (personnel) in the census work, and hence, the efficiency of statistical census.

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It is necessary to further promote sample surveys and continuously improve the importance attached by governments at all levels to sample surveys. The promotion of sample surveys should be supported in institutional staffing and financial funding. Besides, the experts and scholars can be organized to strengthen the top-level design of the survey scheme and address the technical issues related to construction of sampling frame, sample rotation, multi-objective and hierarchical sample survey, so that the sample survey can truly become the main body of China’s statistical survey system. IV.

To strengthen statistical education and publicity, and create a good environment for statistical concepts and opinions

The improvement of the quality of statistical data is inseparable from the extensive participation and active collaboration of various subjects of statistical activities. Strengthening statistical education and publicity and shaping good statistical concepts are important ways to improve the quality of statistical data. At present, due to some people’s unawareness of the statistical purposes, there exist negative participation, concealment, and false data in response to statistical surveys. Some scholars and media have misunderstandings about statistical indicators and their calculation methods. Therefore, it is very important to strengthen the statistical education and popularization of statistical knowledge and to properly carry out media public relations campaign and related publicity. To this end, the statistical departments need to do the following work efficiently. First, they must explain to the public that the most important function of statistical work is to provide objective data; the function of statistical supervision should be reflected mainly in the review of statistical data and the supervision and guidance of superior statistical departments to the lower-level statistical departments. According to the Statistics Law, the statistical departments are obliged to protect the privacy and business secrets of the respondents, and the statistical survey data may not be used for other purposes. Second, they must further change their concept and strengthen their service awareness, thus continuously improving the accuracy, applicability and accessibility of statistical data. Third, they should strengthen statistical publicity and education and convey scientific statistical concepts to the public by television, internet, newspapers and other media to improve the transparency of China’s statistical methods and systems. With further popularization of statistical knowledge, people can have a correct understanding of government statistics so that they can actively cooperate with the investigation and provide objective and authentic data. With the help of the media and public opinion, the statistical departments can vigorously publicize relevant statistical laws and regulations to build an honest and trustworthy statistical moral environment, thus forming a conceptual atmosphere in which statistical institutions can interact effectively and co-exist with respondents. For the misunderstanding of statistical work and the improper reporting that damage the credibility of statistics in the media, they should take the initiative and be ready to make a convincing explanation to the public in time and reduce the negative impact caused by these misunderstandings and improper reports.

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To increase investment in statistical activities, update the hardware facilities and improve the qualification of statisticians

It is necessary to continue to increase financial investment in statistical work, further improve the statistical software and hardware technical environment, and strive to improve the professionalism of statisticians. Specifically, it can be started from the following aspects: First, to innovate technical means to improve the efficiency of statistical survey. New survey techniques (such as the emerging survey tools like the Internet) should be vigorously promoted and applied, and major transformations in statistical survey methods should be promoted by carefully studying the applying scope of new technologies and their integration with various traditional survey methods. Second, to further strengthen team building. The education of professional ethics for statisticians should be carried out in the form of publicity, training, selfinspection and mutual inspection, and effective supervision and management systems with clear rewards and punishments should be formulated to integrate professional ethics into performance evaluation. Besides, it’s necessary to establish a system of lifelong education for statisticians to provide vocational training to them so that they can update their knowledge constantly. In the statistical departments, the evaluation system for technical titles should be fully implemented, and only those with professional qualifications can serve as the business leaders of statistical agencies at all levels. VI.

To further strengthen contacts with international statistical organizations

Statistical exchanges and cooperation have become an important part of the development of bilateral (multilateral) relations among countries and an integral part of a country’s statistical undertakings. The future development of statistical undertakings in China needs further connections with international organizations, and through exchanges and cooperation, we can effectively learn from their advanced statistical methods, systems and concepts, thus continuously injecting fresh impetus into the development of China’s statistical undertakings. Specifically, we should further improve the degree of convergence with international standards in terms of indicators, methods, and systems; we should continuously improve the transparency of China’s statistical data by widely accepting the supervision and questioning of the international community, and establish an effective feedback mechanism to expand constantly the channels of dialogue with our international counterparts. At the same time, we must encourage our experts and scholars to participate in more activities of relevant international organizations, and strive to get a greater say for our country in the international statistical community and international statistical agencies.

References 1. Xianzuo Q (2002) Bringing China’s statistics in line with international practice. Stat Res 4 2. Wuyi Z (2009) Research on statistical survey system and survey methods. China Statistics Press

Epilogue

This book is the final work of the key project of the National Social Science Fund of China (Project No. 09AZD045). The original book consists of thirteen chapters, and the first draft was written by members of the research team, arranged as follows: Chap. 1 Introduction, by Zeng Wuyi; Chap. 2 Basic Theories of Statistical Data Quality Management, by Zeng Wuyi, Wang Kaike; Chap. 3Framework of Overall Quality Management System of Statistical Data, by Wang Kaike, Zeng Wuyi; Chap. 4 Basic Methods of Inspection and Assessment of Statistical Data Quality, by Zeng Wuyi, Xue Meilin; Chap. 5 Revision Methods of Statistical Data, by Liu Xiaoer, Xue Meilin; Chap. 6 Research on the Quality of China’s GDP Data, by Xue Meilin, Zeng Wuyi; Chap. 7 Evaluation and Analysis of CPI Data Quality in China, by Wang Kaike; Chap. 8 Research on CPI Data Quality in China, by Wang Kaike; Chap. 9 Research on the Quality of China’s Real Estate Price Index, by Xu Yonghong; Chap. 10 Research on Statistical Ecological Environment, by Zeng Wuyi, Wang Kaike. Yuan Jiajun undertook the organization and arrangement of the manuscript. The publication of this book was strongly supported by the Statistics Section of the National Social Science Fund of China. Xu Xianchun, the former deputy director of the National Bureau of Statistics of China, prefaced the book. Many people have made efforts for the publication and distribution of the book, including President Yun Wei as well as the editor Wang Nannan and Gao Jing of the Social Sciences Academic Press (China), and the translator Li Wenda, an associate professor with Hebei Normal University. On this occasion, on behalf of all the authors of this book, I would like to express my heartfelt thanks to all the people who have contributed to the book! Wuyi Zeng December, 2018

© Social Sciences Academic Press 2022 W. Zeng, A Study of Quality Management of Official Statistics in China, Research Series on the Chinese Dream and China’s Development Path, https://doi.org/10.1007/978-981-33-6602-2

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