Australian Taxation Office

"Big Data" as a term has been among the biggest trends of the last three years, leading to an upsurge of research, as well as industry and government applications. Data is deemed a powerful raw material that can impact multidisciplinary research endeavors as well as government and business performance. The goal of this discussion paper is to share the data analytics opinions and perspectives of the authors relating to the new opportunities and challenges brought forth by the big data movement. The authors bring together diverse perspectives, coming from different geographical locations with different core research expertise and different affiliations and work experiences. The aim of this paper is to evoke discussion rather than to provide a comprehensive survey of big data research.

In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, which makes existing data mining techniques, mainly designed to find frequent patterns, ineffective. In this paper, we propose unexpected temporal association rules (UTARs) to describe them. To handle the unexpectedness, we introduce a new interestingness measure, residual-leverage, and develop a novel case-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle the infrequency, we develop a new algorithm MUTARC to find pairwise UTARs. The MUTARC is applied to generate adverse drug reaction (ADR) signals from real-world healthcare administrative databases. It reliably shortlists not only six known ADRs, but also another ADR, flucloxacillin possibly causing hepatitis, which our algorithm designers and experiment runners have not known before the experiments. The MUTARC performs much more effectively than existing techniques. This paper clearly illustrates the great potential along the new direction of ADR signal generation from healthcare administrative databases.

Abstract This book undertakes a fundamental review of the existing international regime for taxing business profit. It steps back from the current political debates on how to combat profit shifting and how taxing rights over the profits of the digitalized economy should be allocated. Instead, it starts from first principles to ask how we should evaluate a tax on business profit—and whether there is any good rationale for such a tax in the first place. It then goes on to evaluate the existing regime and a number of alternatives that have been proposed. It argues that the existing regime is fundamentally flawed, and that there is a need for radical reform. The key conclusion from the analysis is that there would be significant gains from a reform that moved the regime towards taxing profit in the country in which a business made its sales to third parties. That conclusion informs two proposals that are put forward in detail and evaluated: the Residual Profit Allocation by Income (RPAI) and the Destination-based Cash Flow Tax (DBCFT). The book is authored by group of economists and lawyers—the Oxford International Tax Group, chaired by Michael Devereux. It draws insights from both economics and law—including economic theory, empirical evidence on the impact of taxes, and a detailed examination of practical issues of implementation—to assess the existing system and to consider fundamental reforms. This book will be useful to tax policy makers, tax professionals, academics, and anyone interested in tax policy.

One versus all (OVA) decision trees learn k individual binary classifiers, each one to distinguish the instances of a single class from the instances of all other classes. Thus OVA is different from existing data stream classification schemes whose majority use multiclass classifiers, each one to discriminate among all the classes. This paper advocates some outstanding advantages of OVA for data stream classification. First, there is low error correlation and hence high diversity among OVA's component classifiers, which leads to high classification accuracy. Second, OVA is adept at accommodating new class labels that often appear in data streams. However, there also remain many challenges to deploy traditional OVA for classifying data streams. First, as every instance is fed to all component classifiers, OVA is known as an inefficient model. Second, OVA's classification accuracy is adversely affected by the imbalanced class distribution in data streams. This paper addresses those key challenges and consequently proposes a new OVA scheme that is adapted for data stream classification. Theoretical analysis and empirical evidence reveal that the adapted OVA can offer faster training, faster updating and higher classification accuracy than many existing popular data stream classification algorithms.

The corporation tax is arguably the most well-studied tax found throughout the world. Countless numbers of professionals study the impact of corporate tax law on the affairs of the corporation. Yet, despite considerable resources that are expended on compliance, the tax in many countries raises only a small portion of revenue for governments. For example, in the G7 countries, taxes paid by corporations account for less than 8 per cent of tax revenues raised by governments, except for Japan where the corporation tax yields about 15 per cent of government revenue. This low revenue yield and high compliance cost have resulted in some experts and politicians questioning the usefulness of the corporate income tax.

Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.

STUDY QUESTION: Can the equilibration steps prior to embryo vitrification be automated? SUMMARY ANSWER: We have developed the 'Gavi' system which automatically performs equilibration steps before closed system vitrification on up to four embryos at a time and gives in vitro outcomes equivalent to the manual Cryotop method. WHAT IS KNOWN ALREADY: Embryo cryopreservation is an essential component of a successful assisted reproduction clinic, with vitrification providing excellent embryo survival and pregnancy outcomes. However, vitrification is a manual, labour-intensive and highly skilled procedure, and results can vary between embryologists and clinics. A closed system whereby the embryo does not come in direct contact with liquid nitrogen is preferred by many clinics and is a regulatory requirement in some countries. STUDY DESIGN, SIZE, DURATION: The Gavi system, an automation instrument with a novel closed system device, was used to equilibrate embryos prior to vitrification. Outcomes for embryos automatically processed with the Gavi system were compared with those processed with the manual Cryotop method and with fresh (non-vitrified) controls. PARTICIPANTS/MATERIALS, SETTING, METHODS: The efficacy of the Gavi system (Alpha model) was assessed for mouse (Quackenbush Swiss and F1 C57BL/6J x CBA) zygotes, cleavage stage embryos and blastocysts, and for donated human vitrified-warmed blastocysts. The main outcomes assessed included recovery, survival and in vitro embryo development after vitrification-warming. Cooling and warming rates were measured using a thermocouple probe. MAIN RESULTS AND THE ROLE OF CHANCE: Mouse embryos vitrified after processing with the automated Gavi system achieved equivalent in vitro outcomes to that of Cryotop controls. For example, for mouse blastocysts both the Gavi system (n = 176) and manual Cryotop method (n = 172) gave a 99% recovery rate, of which 54 and 50%, respectively, progressed to fully hatched blastocysts 48 h after warming. The outcomes for human blastocysts processed with the Gavi system (n = 23) were also equivalent to Cryotop controls (n = 13) including 100% recovery for both groups, of which 17 and 15%, respectively, progressed to fully hatched blastocysts 48 h after warming. The cooling and warming rates achieved with the Gavi system were 14 136°C/min and 11 239°C/min, respectively. LIMITATIONS, REASONS FOR CAUTION: Testing of the Gavi system described here was limited to in vitro development of embryos from two mouse strains and a limited number of human embryos. Validation of Gavi system advanced production models is now required to confirm the success of semi-automated vitrification, including clinical evaluation of pregnancy outcomes from the transfer of Gavi vitrified-warmed human embryos. WIDER IMPLICATIONS OF THE FINDINGS: The Gavi system has the potential to revolutionize and standardize vitrification of embryos and oocytes. The success of the Gavi system shows that it is possible to semi-automate complicated labour-intensive ART methods and processes, and opens up the possibility for further improvements in clinical outcomes and efficiencies in the ART clinic. STUDY FUNDING/COMPETING INTERESTS: This study was funded by Genea Ltd. S.B., N.M.T., T.T.P., S.J.M., M.C.B. and T.S. are shareholders of Genea Ltd. E.V., C.H., C.L., S.R.L. and S.M.D. are shareholders of Planet Innovation Pty Ltd. The remaining authors are employees of either Genea Ltd. or Planet Innovation Pty Ltd.

PURPOSE: Although dehydration can impair endurance performance, a reduced body mass may benefit uphill cycling by increasing the power-to-mass ratio. This study examined the effects of a reduction in body mass attributable to unreplaced sweat losses on simulated cycling hill-climbing performance in the heat. METHODS: Eight well-trained male cyclists (mean +/- SD: 28.4 +/- 5.7 yr; 71.0 +/- 5.9 kg; 176.7 +/- 4.7 cm; VO2peak: 66.2 +/- 5.8 mL x kg(-1) x min(-1)) completed a maximal graded cycling test on a stationary ergometer to determine maximal aerobic power (MAP). In a randomized crossover design, cyclists performed a 2-h ride at 53% MAP on a stationary ergometer, immediately followed by a cycling hill-climb time-to-exhaustion trial (88% MAP) on their own bicycle on an inclined treadmill (8%) at approximately 30 degrees C. During the 2-h ride, they consumed either 2.4 L of a 7% carbohydrate (CHO) drink (HIGH) or 0.4 L of water (LOW) with sport gels to match for CHO content. RESULTS: After the 2-h ride and before the hill climb, drinking strategies influenced body mass (LOW -2.5 +/- 0.5% vs HIGH 0.3 +/- 0.4%; P < 0.001), HR (LOW 158 +/- 15 vs HIGH 146 +/- 15 bpm; P = 0.03), and rectal temperature (T(re): LOW 38.9 +/- 0.2 vs HIGH 38.3 +/- 0.2 degrees C; P = 0.001). Despite being approximately 1.9 kg lighter, time to exhaustion was significantly reduced by 28.6 +/- 13.8% in the LOW treatment (LOW 13.9 +/- 5.5 vs HIGH 19.5 +/- 6.0 min, P = 0.002), as was the power output for a fixed speed (LOW 308 +/- 28 vs HIGH 313 +/- 28 W, P = 0.003). At exhaustion, T(re) was higher in the LOW treatment (39.5 vs HIGH 39.1 degrees C; P < 0.001), yet peak HR, blood lactate, and glucose were similar. CONCLUSION: Exercise-induced dehydration in a warm environment is detrimental to laboratory cycling hill-climbing performance despite reducing the power output required for a given speed.

Abstract Owing to the urgency of the climate change crisis and the emerging greenwashing issue that companies face, greenwashing in companies' carbon reduction is exceptionally important in the progress of global carbon reduction and the achievement of the Paris Agreement goals. Therefore, this study focused on multinational corporations (MNCs) listed on the 2021 Fortune Global 500 list (2001–2020) and used institutional theory to theoretically discuss and empirically test the relationship between MNCs' environmental scores and carbon emissions/intensity, as well as the moderating effect of national culture. Our results demonstrate that the environmental scores of MNCs positively affect their carbon emissions and intensity, meaning that managers tend to use high scores to hide their poor carbon reduction, showing a typical “talk” more and “act” less (i.e., greenwashing) behavior. Meanwhile, the moderating effects of individualism, masculinity, and uncertainty avoidance were negative. These revealed that the balance of MNCs' “talk” and “act” can be influenced by the domestic national cultural characteristics. Further analysis showed that the Paris Agreement did not prevent MNCs' greenwashing in carbon reduction. Such greenwashing behavior has become even worse in societies with power distance characteristics after the Paris Agreement. To the best of our knowledge, this is the first study to investigate companies' greenwashing in terms of carbon reduction. The goal is to enhance our understanding of why and how greenwashing occurs based on the institutional theory perspective, extend the application of institutional theory, enrich the literature on companies' greenwashing and national culture, and offer insight into interdisciplinary research between national culture and green transition management. Finally, suggestions for policymakers on how to prevent MNCs' greenwashing in carbon reduction, considering national cultural influence, were provided.

We address the problem of data linkage and data extraction across database tables of sensitive information about individuals, in an environment of constraints on organisations' ability to share data and a need to protect individuals' privacy and confidentiality.

Introduction of Salmonella enterica serotype Typhimurium into food products results from its ability to persist in the intestine of healthy livestock by mechanisms that are poorly understood. The non-fimbrial adhesin ShdA is a fibronectin binding protein required for persistent intestinal carriage of S. Typhimurium. We further investigated the molecular mechanism of ShdA-mediated intestinal persistence by determining the binding-site of this receptor in fibronectin. Analysis of ShdA binding to fibronectin proteolytic fragments and to recombinant fibronectin fusion proteins identified the (13)FnIII repeat module of the Hep-2 domain as the primary binding site for this adhesin. The (13)FnIII repeat module of fibronectin contains a cationic cradle formed by six basic residues (R6, R7, R9, R23, K25 and R54) that is a high affinity heparin-binding site conserved among fibronectin sequences from frogs to man. Binding of ShdA to the (13)FnIII repeat module of fibronectin and to a second extracellular matrix protein, Collagen I, could be inhibited by heparin. Furthermore, binding of ShdA to the Hep-2 domain was sensitive to the ionic buffer strength, suggesting that binding involved ionic interactions. We therefore determined whether amino acid substitutions of basic residues in the cationic cradle of the Hep-2 domain that inhibit heparin binding also abrogate binding of ShdA. Combined substitution of R6S and R7S strongly reduced ShdA binding to (13)FnIII. These data suggest that ShdA binds the Hep-2 domain of fibronectin by a mechanism that may mimic binding of the host polysaccharide heparin.

Abstract Judgment and decision making (JDM) research in accounting focuses on the judgments and decisions of preparers, users and auditors/assurance providers of financial and non‐financial information, including sustainability information. However, the environment in which these judgments are made and how groups interact has changed substantially during the COVID‐19 pandemic, and likely post‐pandemic. We focus on the impact of these changes for corporate social responsibility (CSR) reporting, including managers’ decisions on the preparation and use of CSR information, demand for CSR assurance, and the effect of CSR reports and assurance on investors’ judgments. We then outline implications for the research questions addressed and experimental designs used.

We approach the question of how moving to a dividend exemption system would affect the location incentives of U.S. corporations from three different angles. We start by comparing the U.S. allocation of foreign direct investment in manufacturing across low-tax versus high-tax jurisdictions with that of two major dividend exemption countries, Germany and Canada. The second section demonstrates how the effective tax rate on the typical investment in a low-tax affiliate would change under a dividend exemption system. The final approach uses data from the tax returns of U.S. multinationals to gauge how location decisions will be affected. Taken together, the analysis provides no consistent or definitive evidence that location decisions would be significantly changed if dividends were to be exempt from U.S. corporate tax.

With the rapid rise of information transmission via the Internet, efforts have been made to reduce network load to promote efficiency. One such application is semantic computing, which can extract and process semantic communication. Social media has enabled users to share their current emotions, opinions, and life events through their mobile devices. Notably, people suffering from mental health problems are more willing to share their feelings on social networks. Therefore, it is necessary to extract semantic information from social media (vlog data) to identify abnormal emotional states to facilitate early identification and intervention. Most studies have not considered spatio-temporal information when fusing multimodal information to identify abnormal emotional states such as depression. To solve this problem, this paper proposes a spatio-temporal squeeze transformer method for the extraction of semantic features of depression. First, we embed the module with spatio-temporal data into the transformer encoder, which is leveraged to obtain spatio-temporal feature representations. Second, a classifier with a voting mechanism is designed to encourage the model to classify depression and non-depression effectively. Experiments are conducted on the D-Vlog dataset. The results show that the method is effective, and the accuracy rate can reach 70.70%. This work provides scaffolding for future work in the detection of affect recognition in semantic communication based on social media vlog data.

Jeff Larrimore, Jacob Mortenson and David Splinter Jeff Larrimore is a section chief at the Federal Reserve Board of Governors. Jacob Mortenson is an economist at the Joint Committee on Taxation. David Splinter is an economist at the Joint Committee on Taxation.

Abstract We link the reputational incentives of independent directors in the labor market to firms’ corporate social responsibility (CSR) performance. We show that when a larger fraction of a firm's independent directors considers the firm more prominent, the firm has better CSR performance. This association is more pronounced for firms that are more visible and those that face greater external CSR pressure. Such firms are also more likely to (1) issue CSR reports and obtain independent assurance on those reports; (2) include CSR items in annual general meeting agendas; (3) invest in the optimal level of CSR. Finally, we show that an independent director is more likely to gain directorships if the CSR performance of their most prominent board improves. Our results are robust to controlling for firm fixed effects and when using plausibly exogenous shocks to the reputational incentives of independent directors. Overall, our results are consistent with our conjecture that the reputational incentives of independent directors have a positive influence on firms’ CSR performance.

Enhancing the economic resilience of agriculture is essential for promoting sustainable and high-quality agricultural development. The emergence of digital technology has created new opportunities in this field. However, existing research predominantly focuses on traditional agricultural factors and technologies. Therefore, the impact of digital technology on agricultural economic resilience within the broader context of the “production–operation–industry” system in agriculture has not been comprehensively explored. To bridge this gap, this study analyzes panel data from 30 Chinese provinces from 2011 to 2020. It employs the static Van Dorn’s law and a dynamic spatial panel model to examine how digital technology empowers agricultural resilience. The findings indicate a continuous strengthening of digital technology development in China, albeit with significant polarization and spatial imbalances. Moreover, the resilience of the agricultural economy undergoes notable fluctuations, initially narrowing and subsequently displaying an upward trend. Digital technology clearly plays a pivotal role in empowering resilience through agricultural scale operation, industrial transformation, and technological progress. Its impact, particularly on the promotion of resilience in the eastern region and non-grain-producing areas and on high-level agricultural economies, also shows regional and technological variations.

Abstract In both Australia and the UK, programmes are under way to simplify tax legislation by rewriting it. This paper demonstrates that tax simplification is a complicated concept and concludes that sustainable improvement is unlikely to be achieved if reform is limited only to linguistic changes. Tax law is complicated because there are powerful pressures that tend to increase the complexity of modern tax systems and these should also be considered in any simplification programme. In addition, tax simplification may be promoted by the greater use of purposive legislation — that is, legislation drafted in terms of general principles rather than much more comprehensive legislation designed to deal with every likely possibility. The paper examines the progress of the Australian Tax Law Improvement Project and argues that what is needed is a strategy for tax simplification that is incorporated into the process of generating tax policy itself. JEL classification : H20.

ABSTRACT Two decades ago the term “franchising” was not part of the Chinese language. Although in per capita and GDP terms the Chinese franchise sector is still at an early development stage, China is today the most franchised country in the world in terms of number of systems. Franchising as a method of business operation was introduced to China by the United States' fast food pioneers KFC and McDonald's in the late 1980s in an environment which was by then increasingly receptive to foreign investment and western concepts under Deng Xiaoping's economic reforms and open-door policies. A decade later, China's first rudimentary franchise law signaled the government's imprimatur on franchised distribution and led to its wide adoption by both public and private sector enterprises. Regulatory reforms pursuant to China's World Trade Organization accession in 2001 have liberated franchise sector entry for foreign franchisors and facilitated cross-border franchising. This paper outlines the history and the development of franchising in China, addresses the challenges and opportunities for both domestic and international franchise companies, and discusses the future development of the Chinese franchise sector.

For many taxpayers the uncertainty inherent in the tax system makes paying taxes akin to a game of chance. Some people gamble on the ambiguity of the law and intentionally under-report their earnings, whilst at the other end of the spectrum, others overcompensate for any possible misdemeanours and pay more than they owe. There is great variety of taxpayer behaviour patterns in between these extremes. Existing theories have failed to clarify the complexities of taxpayer decision making and thus failed to establish a useful platform for agencies to influence and encourage voluntary compliance. This study investigated the factors influencing business tax payers' decision on whether to report income and deductions correctly in their 2011 income tax return. The proposed model based on Ajzen and Fishbein's reasoned action approach (the Theory of Planned Behaviour -TPB) has genuine applicability in the tax compliance context. The research was conducted in two phases. Firstly, an online pilot survey was used to elicit salient beliefs in order to construct the primary (TPB) questionnaire. The resulting online survey was distributed to taxpayers who were asked to identify whether, in the previous 12 months they had (i) self-initiated contact or received assistance from the Tax Office; (ii) been contacted via an audit or other verification scenario; or (iii) had no personal contact with the Tax Office. The aim was to determine whether the TPB can reliably predict taxpayers' intention to fulfil their tax obligations and if so, whether it can be used to develop intervention strategies to improve voluntary compliance. The results show that intention to comply is not always a strong predictor of compliance behaviour. The majority of taxpayers who wanted to comply, failed. As complexity and difficulty in performance increases, additional factors are required to predict compliance, such as awareness of the rules. Complexity also reduces the predictability of behaviour. Behaviour prediction can be enhanced by quantifying environmental complexity, providing performance support, and eliminating potential obstacles. Intention can only be leveraged for compliance strategies when the tax system creates the optimal environment for taxpayers to successfully comply. The paper reports the findings and discusses their theoretical and practical implications. The results have significant implications for both behaviour prediction and tax compliance strategy development. The study has broad generalisability as it provides a new model for government agencies to assist them to understand and engage effectively with the people they serve. © School of Taxation and Business Law (Atax), Australian School of Business The University of New South Wales.