Statistical Journal of the IAOS

The paper provides a summary of the history of the IAOS from 1985 to 2017. Using archive material and recollections of former Presidents it charts the beginnings of the Association in the mid-1980s, reminding us of its original purpose. The paper discusses the successes of setting up the Journal, the Young Statisticians Prize and the web site. It covers the Malaguerra report which reviewed its purpose in 1999 as well as the ‘revitalising’ discussions of 2003. The paper gives useful tables of IAOS conferences, presidents and membership figures, and discusses some of the challenges it has faced, some of which remain relevant today.

The production of official statistics should not be carried out by one of the branches of government whose performance is being assessed on the basis of these statistics. Given that official statistics are tasked with providing the branches of government and the public with the information that enables checking on the performance of various parts of government, the optimal institutional setup for statistical production has to avoid conflict of interest. To do so and to fully and sustainably meet the standard of professional independence, the production of statistics should not be part of the executive branch of government, i.e., it should have institutional independence. The paper presents behaviors and practices consistent with professional independence and discusses the capacity to implement them given the modalities of dependence when statistics production is part of the executive, taking into account safeguards. It concludes that significant risks remain for abuse of the currently existing dependencies of statistics production on the executive and the distinct possibility persists for external pressure or self-censorship, while trust in statistics is not maximized. The paper outlines elements of the needed paradigm shift of institutional independence for official statistics and discusses the costs and benefits of such a shift.

The independence, impartiality and objectivity of the production of official statistics is the most important issue in official statistics. In order to ensure this independence, does the production of official statistics need to be a separate branch of government or should it remain within the executive branch of government? Insee has always been an executive branch of government, while the other 16 main producers of official statistics in France form part of various French ministries. This does not prevent the French statistical system from being reliable and independent from political interference and considered by the public as such. This independence is mostly de facto rather than de jure, knowing that the legal framework is progressively being strengthened. In the French context, this institutional set up presents more advantages than disadvantages.

The paper will discuss the strengths and weaknesses of having an official statistical agency that is independent of Government. It will explain why the strengths far outweigh the weaknesses. There will be some discussion of the institutional place of the official statistical agency and the key principles of enabling legislation. The paper will then discuss what it means in practice to be an independent official statistical agency. What are the main principles and practices that need to be followed? It will finish with a number of scenarios that challenge the realisation of being an independent official statistical agency.

The U.S. Census Bureau is researching uses of administrative records and third party data in survey and decennial census operations. One potential use of administrative records is to utilize these data when race and Hispanic origin responses are missing. When federal and third party administrative records are compiled, race and Hispanic origin responses are not always the same for an individual across sources. We explore different methods to assign one race and one Hispanic response when these responses are discrepant. We also describe the characteristics of individuals with matching, non-matching, and missing race and Hispanic origin data by demographic, household, and contextual variables. We find that minorities, especially Hispanics, are more likely to have non-matching Hispanic origin and race responses in administrative records and third party data compared to the 2010 Census. Minority groups and individuals ages 0-17 are more likely to have missing race or Hispanic origin data in administrative records and third party data. Larger households tend to have more missing race data in administrative records and third party data than smaller households.

The U.S. Census Bureau is researching ways to incorporate administrative data in decennial census and survey operations. Critical to this work is an understanding of the coverage of the population by administrative records. Using federal and third party administrative data linked to the American Community Survey (ACS), we evaluate the extent to which administrative records provide data on foreign-born individuals in the ACS and employ multinomial logistic regression techniques to evaluate characteristics of those who are in administrative records relative to those who are not. We find that overall, administrative records provide high coverage of foreign-born individuals in our sample for whom a match can be determined. The odds of being in administrative records are found to be tied to the processes of immigrant assimilation – naturalization, higher English proficiency, educational attainment, and full-time employment are associated with greater odds of being in administrative records. These findings suggest that as immigrants adapt and integrate into U.S. society, they are more likely to be involved in government and commercial processes and programs for which we are including data.

International migration is of major relevance for the development of countries of origin, transit and destination. While the proportion of the world population who live outside their countries of birth has stayed relatively stable over time, the absolute number of international migrants reached 244 million in 2015. International migration has been recognized as a driver of development in both countries of origin and destination, and in turn, patterns of global development both strongly impact, and are impacted by, migration.

Data sources to inform the issue of international migration include decennial population and housing censuses, population registers, civil registration and other administrative data, residence permits and various household surveys. While providing a valuable source of data on international migration, censuses are sometimes conducted infrequently or the data from census are not fully released, or remain under-analyzed. This paper reports on the availability of migration data from all national population censuses by countries that participated in the 2010 census round, and highlights the potential of these data for the generation of migration profiles and analysis on the character of international migration especially within the context of monitoring and reporting the sustainable development goals.

This paper describes efforts made in Australia in the use of data linkage to enhance Indigenous mortality statistics. The extent of inadequacies of statistics sourced from death registration is discussed and the improvements made by data linkage are presented. Conceptual, methodological and data issues that may give rise to error and bias in such data linkage are discussed.

When several frequency tables need to be produced from multiple data sources, there is a risk of numerically inconsistent results. This means that different estimates are produced for the same cells or marginal totals in multiple tables. As inconsistencies of this kind are often not tolerated, there is a clear need for compilation methods for achieving numerically consistent output. Statistics Netherlands developed a Repeated Weighing (RW) method for this purpose. The scope of applicability of this method is however limited by several known estimation problems. This paper presents two new Divide-and-Conquer (D&C) methods, based on quadratic programming (QP) that avoid many of the problems experienced with RW.

After the global financial crisis of 2008–2009, many advanced economies are suffering from a dearth of domestic investment opportunities. It has been said that lowering real interest rate is the best policy to boost the capital investment. The problem is what inflation rate they have in their mind when the entrepreneurs make investment decisions. Not only the output prices, but also the composition of inputs differ from one industry to another. Therefore, the value added deflator or even the operating surplus deflator for each industry are better alternative to calculate the real interest rate. In the first half of the paper, we examine the theoretical meaning of the value added deflators using a highly simplified symmetric input output table. In the latter half, we will use so-called SNA-IO, the input-output table published as a part of Japanese SNA, to experimentally estimate both value added and operating surplus deflators. The study reveals that if lowering interest rate depreciate the local currency, it will depress value added deflators, and in turn, will discourage capital investments. In this sense, lowering interest rate is a double-edged sword; the governments and central banks should think twice before taking such a policy.

The official statistics generated by government offices and ministries are fundamental to guide actions in the decision-making process within a country. For example, official statistics could be used to promote and allocate public goods like local, regional and national budgets. Moreover, due to the high quality standard required to generate official data, such information is ideal to promote a better understanding about the important facts in economic, sociodemographic, geographic, environmental and governmental management issues. However, it has been observed that many undergraduate students do not use such data in an extensive manner, and they reveal limitations in accessing, analyzing and extrapolating such information to their benefit, both in academic and personal settings. The aim of this work is to describe to what extent Mexican undergraduate students use official statistics as part of their academic assignments, and to identify the main obstacles they encounter when trying to access and use official statistics.

The problem of balancing economic accounts has been recognized for a long time. In 1942, Richard Stone et al. proposed a weighted least squares approach (hereafter SCM approach) to balance small economic accounts. This approach has been extended to accommodate reconciliation of large-scale national accounts (NA) systems. The main challenge turned out to be the estimates of the uncertainties of initial NA aggregates. In this study, we try the SCM approach for automatically balancing a large-scale supply-use framework in the Swedish NA. Efforts are made to estimate the uncertainties not only from sampling errors but also from non-sampling errors. The error estimates are used as weights in the balancing procedure. The approach is evaluated through a test run in parallel with a real compilation of the Swedish annual NA. Our study shows that the automatic balancing procedure is feasible to implement in the production environment of Statistics Sweden. Compared with the current mainly manual balancing process, the automatic procedure is faster, cheaper and requires less time from the NA experts. Above all, the method is transparent and new information can easily be accounted for.

Official Statistics commonly conducts sample surveys to produce estimates of aggregate statistics with a desired level of precision. For this purpose, design-based methods are used which are suitable for the estimation of finite population quantities such as totals or means. In most cases, however, model-based analyses are applied to the survey data as well. Examples include small area estimation techniques that allow for reliable estimates of finite population quantities in the presence of small sample sizes and socio-econometric models used in academia to test scientific hypotheses. This may cause problems as model-based methods frequently assume a non-informative sampling design and a violation of this assumption can lead to erroneous statistical inferences. We argue in this work that if the application of model-based methods can be anticipated before the sample is drawn, then this knowledge should be incorporated in the survey design. We propose a method called antithetic clustering that enables precise estimates for aggregate figures using design-based estimation methods and does automatically lead to non-informative sampling designs. Our method is compared against other sampling plans designed to achieve precise design-based estimates for aggregates in a simulation study.