The economic psychology of value added tax compliance

VAT is a tax on consumer expenditure, collected on business transactions and assessed on the value added to goods and services. It applies, with some exceptions (for example, to young children’s clothes and shoes in the UK), to all goods and services that are bought and sold. VAT is a general tax (as it applies, in principle, to all commercial activities) and a consumption tax (as it is paid ultimately by the final consumer). It is not actually a tax on business, though some business owners do see it that way. In fact, whilst VAT is paid to the tax authorities by the seller of the goods or services, the tax is paid by the buyer to the seller as part of the tax and so, in essence, businesses are acting as unpaid tax collectors. VAT was first introduced in France in 1954, and subsequently has been extended, through a series of directives, to cover the whole of the European Union (EU). The system in the EU is now reasonably standardized, although different rates of VAT apply in different EU member states. The minimum standard rate in the EU is 15 percent, though lower rates are applied to certain services. Some goods and services are exempt from VAT throughout the EU (e.g., postal services, insurance, betting). In addition to spreading throughout Europe (member states are required to introduce VAT, so the increase in membership of the EU has inevitably increased the number of countries that use this system), VAT has also been introduced in a large number of other countries, notably China (Yeh, 1997), and India (after many delays) in 2005, so that now over 130 countries world-wide operate VAT. In the Caribbean, for example, Belize, Dominica, Guyana, and Antigua have all introduced VAT in the past two years. Other countries have introduced taxes that are classified as value added taxes, such as Australia, which now operates a General Sales Tax (GST). The introduction of VAT has been the major tax reform around the world in the past 25 years, and VAT is now of global significance and impact (Ebrill et al., 2001)

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

An Open Framework for Extensible Multi-Stage Bioinformatics Software

In research labs, there is often a need to customise software at every step in a given bioinformatics workflow, but traditionally it has been difficult to obtain both a high degree of customisability and good performance. Performance-sensitive tools are often highly monolithic, which can make research difficult. We present a novel set of software development principles and a bioinformatics framework, Friedrich, which is currently in early development. Friedrich applications support both early stage experimentation and late stage batch processing, since they simultaneously allow for good performance and a high degree of flexibility and customisability. These benefits are obtained in large part by basing Friedrich on the multiparadigm programming language Scala. We present a case study in the form of a basic genome assembler and its extension with new functionality. Our architecture has the potential to greatly increase the overall productivity of software developers and researchers in bioinformatics.Comment: 12 pages, 1 figure, to appear in proceedings of PRIB 201

A Comparison of Big Data Frameworks on a Layered Dataflow Model

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models, for which only informal (and often confusing) semantics is generally provided, all share a common underlying model, namely, the Dataflow model. The Dataflow model we propose shows how various tools share the same expressiveness at different levels of abstraction. The contribution of this work is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to understand high-level data-processing applications written in such frameworks. Second, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.Comment: 19 pages, 6 figures, 2 tables, In Proc. of the 9th Intl Symposium on High-Level Parallel Programming and Applications (HLPP), July 4-5 2016, Muenster, German