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The author discusses the paper Helping reviewers ask the right questions: The Information Quality (InfoQ) framework for reviewing applied research by Ron S. Kenett and Galit Shmueli. Topics discussed include method of review and review being the challenges faced by reviewers, and the data resolution being described as measurement scale and aggregation level of data.

This paper proposes an approach to guide statistical capacity building in developing countries using an analysis based on components of the World Bank's Statistical Performance Indicator on a sample of 215 countries. The approach demonstrates the importance of expanding traditional capacity-building activities to include programs to strengthen and better monitor the ability of National Statistical Systems (NSS) to respond to user data needs. Based on this analysis, the paper recommends a two-step strategy for building and enhancing the statistical capacity of national statistical systems in developing countries. The strategy creates a sustainable trajectory for developing NSSs that meets the growing demands of local and global data users. The paper emphasizes the importance of donor coordination and South-South learning initiatives for international capacity-building efforts. [ABSTRACT FROM AUTHOR]

Almost 2 years have passed since the launching of the 2030 Sustainable Development Goals but no big strides have been made especially on data compilation to track and measure these goals. Uganda, like the rest of the world has to base its development agenda on SDGs. However, the major constraint often lies on measuring the indicators and even compilation of data. The author discusses 7 SDG economic indicators based on the Ugandan context. This paper considered a baseline year of 2014 for the economic indicators of the SDGs in Uganda. A comparative review of data was obtained from UNSD SDG indicator global database and available national data in Uganda to update the Uganda country page constructed by the ACS survey in 2017. Metadata analysis was invoked following a 4-assessment check. Results showed that a lot of data from administrative sources could be used to feed into the Ugandan page given that censuses and surveys are not regularly updated. However, the paper shows some discrepancies in the ACS survey report data. There is need to explore more of the administrative data in government ministries and agencies in order to close the data gaps for the economic indicators of the SDGs. [ABSTRACT FROM AUTHOR]

Seventeen Sustainable Development Goals (SGDs) were adopted by the World Health Organization (WHO) in 2015 for the 2030 Agenda for Sustainable Development. Sustainable Development Goal 3 (SDG3) is 'Better health and well-being by 2030'. According to WHO, good health in the context of SDG3 is assessed with respect to the level and distribution of individuals' and communities' healthy life, conditions that affect health and well-being and risk factors whose presence would affect health and well-being. The overall aim is that each SDG target is achieved by 2030. In 2018 the WHO used statistical methods to assess the state of health in Africa in the context of SDG3. Their analysis revealed successes and shortfalls towards attaining SDG3. Backed by public health and other activities, statistics play an important role in improving the health and well-being of Africa. This paper explains how statistics can be used to help African countries to attain SDG3, in its role in modeling event histories, diagnosis, evidence-based medicine, determination of risk factors of exposures of morbidity and mortality, determination of risk factors of morbidity and mortality, the computation of the level and distribution of vital events, measuring disease frequency and progress, quantification of life expectancy and monitoring and evaluation. [ABSTRACT FROM AUTHOR]

This paper highlights the key characteristics and implications of the strategic and data production frameworks designed and progressively implemented by the United Nations Committee of Experts on Business and Trade Statistics (UNCEBTS) to enhance the relevance, accuracy and coverage of business statistics, according to an internationally comparable, result-oriented and sustainable approach. The strategic framework aims to expand the traditional scope of official business statistics by including all relevant environmental and social related issues. NSOs may achieve relevant improvements by focusing their efforts upon specific global goals consistent with their national ones, and sourcing from knowledge sharing with other countries and international coordination. It also highlights the relevance of an enterprise-centered approach for a better understanding of emerging phenomena by official statisticians, and for priority setting in improving the quality of business statistics. The data production framework is dominated by the crucial role of the Statistical Business Register (SBR) as the backbone of any current and future improvements in the relevance and accuracy of business statistics. Its implications, both in terms of sustainability of production lines, data integration and production of new indicators that exploit the variability dimension of business statistics are further investigated in the paper. [ABSTRACT FROM AUTHOR]

As societies evolve through the data revolution, it is important that National Statistical Offices (NSOs) continue to devote efforts to fine-tune their approaches to maintain the vital trust they need to operate successfully. To do this, they must constantly re-invent themselves to remain relevant to new data needs and keep up with their high-quality standards. With new sources of information surfacing both in the public and private spheres, options are multiplying for NSOs to design new ways to gather and grow the data into information. As this is happening, new practices and new issues are emerging throughout the data life-cycle process. Operating beyond the sample survey paradigm, NSOs see themselves confronted with the need to anchor their new approaches in solidly defined and defendable frameworks. Further, as new data themes, methods and sources are considered, transparency becomes a central issue. Using Statistics Canada's data life-cycle management model, this paper illustrates how the scientific approach can be leveraged to make transparency more explicit both in projects and management. [ABSTRACT FROM AUTHOR]

This introduction offers conceptual reflections to frame the special stream on statistical and data literacy in policy-making. It discusses the relevance of the use of statistics and data in politics and highlights their impact on policy-making. It underlines the need for and identifies key meanings of statistical and data literacy in policy-making. It also highlights how statistical and data literacy in policy-making is specific. Finally, it presents the individual contributions to the special stream that originate from the ISI World Statistics Congress 2021 Invited Paper Session on 'Statistical and Data Literacy in Policy-Making'. The session was co-organised together with the Director of the IASE's International Statistical Literacy Project (ISLP), Reija Helenius, to whom we are extremely grateful for linking our activities to the ISLP. [ABSTRACT FROM AUTHOR]

Operational since 2019, the 50x2030 Initiative to Close the Agricultural Data Gap is working with partner countries to produce and use timely, high-quality agricultural and rural data, with a focus on helping them track their progress toward meeting priority indicators of the Sustainable Development Goals on zero hunger and gender equality, and strengthening agricultural data systems, among others. This paper provides an overview of how 50x2030 works toward its vision of strong national agricultural and rural statistical systems in low- and lower-middle-income African, Middle Eastern, Asia-Pacific and Latin American countries. It explains the Initiative's guiding principles, discusses its approach to robust data production and use, and provides examples of its early impact using the experience of three partner countries, Cambodia, Senegal and Uganda. [ABSTRACT FROM AUTHOR]

Almost every public sector department produces some statistics and accumulates its share in the formulation of National Statistics. The accurate and timely statistics are vital for planning and development, budgeting and evaluation of the implemented programs. It may be reasonable to assume that datasets are being produced at almost all levels of the departments; nonetheless, a substantial number of valuable data-items are left unreported and hence they are unable to play their role in evidence-based planning and decision-making. There is a need to uncover these sources, to explore the reasons behind the non-reporting of data and to devise strategies to utilize these sources in the production of official statistics. In this paper, we present the results of a national-level survey conducted to collect information on data-processing and reporting mechanisms of public-sector organizations in Pakistan. Along with presenting the survey results, the paper discusses the potential sources of unreported data (including Big Data), the reasons for non-reporting at different sectoral levels and the confidentiality, privacy, and data-sharing constraints. Based on the above, the paper ends by proposing the compilation of a Directory of Administrative Data Sources (DADS) in order to establish an improved administrative infrastructure in the country. [ABSTRACT FROM AUTHOR]

This article offers reflections on the use of data as evidence in 21st century policy-making. It discusses the concept of evidence-informed policy-making (EIPM) as well as the governance and knowledge effects of data as evidence. With this focus, it interlinks the analysis of statistics and politics. The paper first introduces the concept of EIPM and the impact of evidence use. Here it focusses on science and knowledge as resources in policy-making, on the institutionalisation of science advice and on the translation of information and knowledge into evidence. The second part of the article reflects on data as evidence. This part concentrates on abstract and concrete functions of data as governance tools in policy-making, on data as a robust form of evidence and on the effects of data on knowledge and governance. The third part highlights challenges for data as evidence in policy-making, among them, politicisation, transparency, and diversity as well as objectivity and contestation. Finally, the last part draws conclusions on the production and use of data as evidence in EIPM. Throughout the second part of the reflections, reference is made to Walter Radermacher’s 2019 matrix of actors and activities related to data, facts, and policy published in this journal. [ABSTRACT FROM AUTHOR]

Recent years have seen dramatic changes in sources of data, amounts of data, availability of data, frequency of data, and types of data. Along with advances in data analytic technology these changes have opened up huge possibilities for improving the information content and timeliness of official statistics. in this paper we characterise such "smart statistics", examining their potential benefits and the obstacles that must be overcome if they are to be trusted and relied upon. In particular, we list eight specific recommendations which we believe producers of smart statistics should adhere to if the full potential for economic and social benefit is to be achieved. [ABSTRACT FROM AUTHOR]

The article discusses various papers presented at the International Statistical Institute's (ISI) World Statistics Congress in 2013. The first paper, by Victoria Stodden from Columbia University, describes standards required for researchers seeking to publish information about computational research. Ron Sandland's paper describes the infrastructure investments for sharing of scientific data sets for collaboration in fields such as bioinformatics, astronomy, and climate research.

The quality of statistical data is essential for effective decision-making. The problem of evaluating the quality of statistical data is not a new paradigm as rapid methodological changes and globalization complicate the generation of high quality statistical data in all areas. The problem is on selecting appropriate criteria to evaluate the quality of statistical data, not just related to the intention of statistical survey, but also to the beliefs held by both statisticians and respondents. As a result, there is a strong need to discuss this topic. In this paper author provides a multi-dimensional approach of measuring the quality of official statistical data and propose the system of indicators to determine statistical data quality. To some of the proposed data quality dimensions and indicators not much attention has been paid previously. [ABSTRACT FROM AUTHOR]

One major criticism against the use of synthetic data has been that the efforts necessary to generate useful synthetic data are so intense that many statistical agencies cannot afford them. We argue many lessons in this evolving field have been learned in the early years of synthetic data generation, and can be used in the development of new synthetic data products, considerably reducing the required investments. The final goal of the project described in this paper will be to evaluate whether synthetic data algorithms developed in the U.S. to generate a synthetic version of the Longitudinal Business Database (LBD) can easily be transferred to generate a similar data product for other countries. We construct a German data product with information comparable to the LBD - the German Longitudinal Business Database (GLBD) - that is generated from different administrative sources at the Institute for Employment Research, Germany. In a future step, the algorithms developed for the synthesis of the LBD will be applied to the GLBD. Extensive evaluations will illustrate whether the algorithms provide useful synthetic data without further adjustment. The ultimate goal of the project is to provide access to multiple synthetic datasets similar to the SynLBD at Cornell to enable comparative studies between countries. The Synthetic GLBD is a first step towards that goal. [ABSTRACT FROM AUTHOR]

In recent years, the random noise method has been gaining wider use in National Statistical Institutes (NSIs) to protect respondent data from disclosure. The random noise method takes a micro-data approach to disclosure limitation: a multiplier (noise factor) is applied to each unit prior to tabulation - thus, guaranteeing that different tabulations, from the lowest to the highest level, are consistent. In this paper we evaluate two different random noise models in an applied context. Our analysis suggests that both the Basic Noise Model (BNM) and the Alternate Noise Model (ANM) are unsatisfactory for protecting smaller units. To overcome this difficulty, we developed a (third) Mixed Perturbation Model (MPM) that combines the use of multiplicative noise to protect large units with the use of synthetic models to protect the smaller units. To accomplish this, we constructed a hybrid model (first logistic and then linear) to generate the synthetic data. Results indicate that the mixed approach performs better than either of the other two models, both in terms of reliability and disclosure limitation, although it too has weaknesses. Hence, areas for future study remain that our research suggests might be tackled next. [ABSTRACT FROM AUTHOR]

If data availability were a simple problem, it would already have been resolved. In this paper, I argue that by viewing data availability as a public good, it is possible to both understand the complexities with which it is fraught and identify a path to a solution. [ABSTRACT FROM AUTHOR]

This paper describes the establishment, development and evolution of national statistical offices worldwide. It is written to commemorate John Koren and other writers who more than a century ago published national statistical histories. We distinguish four broad periods: the establishment of the first statistical offices (1800-1914); the development after World War I and including World War II (1918-1944); the development after World War II including the extraordinary work of the United Nations Statistical Commission (1945-1974); and, finally, the development since 1975. Also, we report on what has been called a "dark side of numbers", i.e. "how data and data systems have been used to assist in planning and carrying out a wide range of serious human rights abuses throughout the world". [ABSTRACT FROM AUTHOR]

As societies evolve through the data revolution, it is important that National Statistical Offices (NSOs) continue to devote efforts to fine-tune their approaches to maintain the vital trust they need to operate successfully. To do this, they must constantly re-invent themselves to remain relevant to new data needs and keep up with their high-quality standards. With new sources of information surfacing both in the public and private spheres, options are multiplying for NSOs to design new ways to gather and grow the data into information. As this is happening, new practices and new issues are emerging throughout the data life-cycle process. Operating beyond the sample survey paradigm, NSOs see themselves confronted with the need to anchor their new approaches in solidly defined and defendable frameworks. Further, as new data themes, methods and sources are considered, transparency becomes a central issue. Using Statistics Canada's data life-cycle management model, this paper illustrates how the scientific approach can be leveraged to make transparency more explicit both in projects and management. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDG) indicator framework represents a major challenge and a unique opportunity for the advancement of the global statistical system, both in terms of methodological development and governance. Over the past three years, the Inter-Agency and Expert Group on SDG indicators (IAEG-SDG) has gradually developed a number of documents providing criteria and guidelines for regulating data flows between countries and custodian agencies needed to inform the global SDG reporting process. The validation of methods and data for SDG indicators, while apparently consisting of two completely separate matters, have been closely linked in the SDG process. When validating country data, National Statistics Offices (NSOs) are effectively also certifying the specific methodology used by the custodian agency for the compilation of the indicator, in particular the data source used and the adjustments made to harmonize national definitions and classifications. This article highlights some of the main challenges in the practical implementation of the guidelines on data flows, identifies areas in need of further guidance from the IAEG-SDG and provides some proposals aimed at improving the global SDG reporting process. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including synthetic data at the U.S. Census Bureau, synthetic longitudinal business database, and the synthetic U.S. business establishment data (SynLBD).

To monitor progress towards global goals such as the Sustainable Development Goals, global statistics are needed. Yet cross-country datasets are rarely truly global, creating a trade-off for producers of global statistics: the lower the data coverage threshold for disseminating global statistics, the more can be made available, but the lower accuracy they will have. We quantify this availability-accuracy trade-off by running more than 10 million simulations on the World Development Indicators. We show that if the fraction of the world's population on which one lacks data is x , then one should expect to be 0.37 * x standard deviations off the true global value, and risk being as much as x standard deviations off. We show the robustness of this result to various assumptions and give recommendations on when there is enough data to create global statistics. Though the decision will be context specific, in a baseline scenario we suggest not to create global statistics when there is data for less than half of the world's population. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has occurred against a sobering global backdrop: national data collection programs and the production of core economic statistics have long been under-funded (by national government as well as the international development community), and data gaps are still significant. The pandemic has highlighted the importance of NSOs and the urgent need to strengthen and modernize core data collection programs as the backbone of national data systems. As the severity of this problem and its damaging implications are becoming more salient, members of the international development and national statistics communities have joined forces in an effort to address it. A collective, high-level effort is now being mobilized by senior leadership of the World Bank and the United Nations, in close collaboration with the Global Partnership for Sustainable Development Data, to join forces to increase global investments in fragile, low-and-middle-income countries' data priorities and to better put data to work for green, resilient, inclusive development. Specifically, two new complementary funds have recently been launched by the World Bank and United Nations to support countries' data systems, data capital, and risk analytics in a coordinated way: the World Bank-hosted Global Data Facility and the UN-hosted Complex Risk Analytics Fund (CRAF'd). [ABSTRACT FROM AUTHOR]

The article discusses various challenges related to data infrastructure needed for collaborative research. It is stated that open data access is the foundation of collaborative research, but achieving it is not easy. Three essential components of collaborative research are infrastructure, a suitable collaborative research culture, and researchers with the necessary skills. In Australia, the importance of research data for collaborative research is being recognized at international levels.

Micro data linking (MDL) has become an important cornerstone in the production of new statistical insights. MDL is now widely acknowledged as a strategic activity to avoid increasing the respondent burden when meeting new user demands on e.g. globalisation. MDL is a strong method in combining micro data on individual entities such as enterprises, people or in a combination of both (Linked Employer-Employee Data). MDL is one of the most powerful methods to answer urgent questions on emerging policy or research topics such as the interconnectedness of the economies and its consequences for jobs, income and growth. Not only for national purposes, where economic behavior and dynamics by enterprises can be expressed in terms of job dynamics, income and welfare for its citizens, but also from an international perspective where consistent and coherent indicators play an important role. Firstly, the article addresses the central role of the Business Register for any MDL approach related to business statistics; secondly the current official statistics based on MDL such as Trade by Enterprise Characteristics (TEC) are described. Thirdly, the most common use of MDL in terms of producing experimental statistics are described, including linked employer-employee data (LEED). [ABSTRACT FROM AUTHOR]

The professional discussion on "The future of economic statistics" has a practical driver: Economic statistics, produced by national statistical offices, face severe difficulties in describing the national and global economic development in a relevant and coherent manner. This is not only our perception as statisticians – there is a growing criticism towards traditional economic statistics among researchers, policymakers and other users. In this article, we reflect on the factors that have caused the current situation and propose solutions to improving the situation by data sharing. One aspect of the solution relates to the role of national statistical offices. Instead of being solely national institutions, dealing with national data only, they should exploit the possibilities of using statistical data, collected by statistical authorities of other countries, to produce better quality economic statistics. The other aspect of the solution is the sharing of innovative practices to understand and correctly record the activities of multinational enterprise groups (MNEs). The proposals we make in this article are not restricted to MNEs but are applicable to any type of economic activity with a cross-border dimension. The observations we make here are based on the work done when preparing the UNECE Guide to Sharing Economic Data. [ABSTRACT FROM AUTHOR]

Administrative data provide an attractive means of obtaining generally cheap and comprehensive data for producing official statistics. However, their limitations need to be understood to ensure the appropriate use of official statistics. The UK Statistics Authority developed a pragmatic and proportionate approach to the quality assurance of administrative data, with three levels of assurance: basic, enhanced and comprehensive. The level selected is dependent on the risk of data quality concerns and degree of public good represented by the statistics. The approach identifies four practice areas that reflect the need for the assurance of administrative data to extend beyond the checks made by statistical producers on the data they receive. Producers should also have a good understanding of how the data are compiled and why; have established effective communication links with data supply partners; and understand their partners' data quality processes and standards. The Authority's guidance is being applied by UK statistical producers, to achieve compliance with the Code of Practice for Official Statistics. [ABSTRACT FROM AUTHOR]

The article offers information on technology, education and statistical system in Algeria. Topics discussed include developing education based on knowledge, science and technology, education programs designed around Western ideals to promote human values keeping pace with technological advancement and encouraging statistical teachers to explore curriculum in new direction.

Data openness is an important issue for statistical systems in many countries, particularly in the developing world. Data do not become ¡°open¡± overnight, even if governments so desire. Openness has several components. First, one has to deal with legal issues of openness. Then there are organizational and technical issues, having to do with compiling and presenting data in open formats. In addition, underlying data quality issues surface when data become more open. Furthermore, there are often conflicting interests advocating for or trying to limit data openness, within the government, in civil society, and in the private sector. Therefore, opening databases cannot be accomplished by a simple act of "good will" on the part of government; it entails a lot of preparation and the balancing of many interests. While large, multilateral organizations have recently become notable advocates of open data and, more broadly, "open government," their interests and practical capacities are often limited by their mandate: some may be interested only in economic statistics, or national-level health statistics, for example. And large international agencies are often unable or unwilling to engage with civil society organizations or other interest groups, who are potential users and producers of data. Still there is a need for their financial support for complex reforms. But these agencies may themselves be limited in their authority or capacity. Furthermore large multilateral organizations may not be able to engage civil society in some countries due to political antagonisms or other circumstances. Therefore there is a niche for non-governmental organizations, bringing international experience adaptable to local conditions to serve as technical resources and trusted advisers to governments and to act as go-betweens with foundations and international agencies that are prepared to support open data reforms. [ABSTRACT FROM AUTHOR]

In most countries, national statistical agencies do not release establishment-level business microdata, because doing so represents too large a risk to establishments' confidentiality. Agencies potentially can manage these risks by releasing synthetic microdata, i.e., individual establishment records simulated from statistical models designed to mimic the joint distribution of the underlying observed data. Previously, we used this approach to generate a public-use version - now available for public use - of the U.S. Census Bureau's Longitudinal Business Database (LBD), a longitudinal census of establishments dating back to 1976. While the synthetic LBD has proven to be a useful product, we now seek to improve and expand it by using new synthesis models and adding features. This article describes our efforts to create the second generation of the SynLBD, including synthesis procedures that we believe could be replicated in other contexts. [ABSTRACT FROM AUTHOR]

National Statistical offices (NSOs) create official statistics from data collected from survey respondents, government administrative records and other sources. The raw source data is usually considered to be confidential. In the case of the U.S. Census Bureau, confidentiality of survey and administrative records microdata is mandated by statute, and this mandate to protect confidentiality is often at odds with the needs of users to extract as much information from the data as possible. Traditional disclosure protection techniques result in official data products that do not fully utilize the information content of the underlying microdata. Typically, these products take the form of simple aggregate tabulations. In a few cases anonymized public-use micro samples are made available, but these face a growing risk of re-identification by the increasing amounts of information about individuals and firms available in the public domain. One approach for overcoming these risks is to release products based on synthetic data where values are simulated from statistical models designed to mimic the (joint) distributions of the underlying microdata. We discuss recent Census Bureau work to develop and deploy such products. We discuss the benefits and challenges involved with extending the scope of synthetic data products in official statistics. [ABSTRACT FROM AUTHOR]