This proposed SDN surveys the various accounting stratagems which governments have used to meet fiscal targets—thereby sidestepping the need for true adjustment—and suggests remedial actions to limit this type of fiscal non-transparency. Types of creative accounting covered includes, for instance, currency swaps to hide a debt build-up (as in Greece in 2001–07), sale and leaseback of government property (for example, in the United States), assumption of long-term pension obligations in exchange for short-term revenue (Argentina, Hungary, and other Eastern European countries), use of public-private partnerships to defer the recognition of investment spending (for instance, Portugal), and reliance on non-cash compensation (such as pension rights) to reduce measured wage bills (in the United States, United Kingdom, etc.) As is evident from the examples given, these fiscal tricks have recently come under increased international scrutiny, highlighting the importance of good fiscal reporting, accounting, and transparency in general, for avoiding unpleasant surprises, ensuring government accountability, and containing fiscal vulnerabilities.
Cornelia Hammer, Ms. Diane C Kostroch, and Mr. Gabriel Quiros-Romero
Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.