Gender and public attitude toward corruption and tax evasion

The topics of corruption and tax evasion have attracted significant attention in the literature in recent years. We build on that literature by investigating empirically: (1) whether attitudes toward corruption and tax evasion vary systematically with gender and (2) whether gender differences decline as men and women face similar opportunities for illicit behavior. We use data on eight Western European countries from the World Values Survey and the European Values Survey. The results reveal significantly greater aversion to corruption and tax evasion among women. This holds across countries and time, and across numerous empirical specifications. (JEL H260, D730, J160, Z130)

Unraveling the complexity of driving while intoxicated: a study into the prevalence of psychiatric and substance abuse comorbidity

Objective Research is beginning to provide an indication of the co-occurring substance abuse and mental health needs for the driving under the influence (DUI) population. This study aimed to examine the extent of such psychiatric problems among a large sample size of DUI offenders entering treatment in Texas. Methods This is a study of 36,373 past year DUI clients and 308,714 non-past year DUI clients admitted to Texas treatment programs between 2005 and 2008. Data were obtained from the State's administrative dataset. Results Analysis indicated that non-past year DUI clients were more likely to present with more severe illicit substance use problems, while past year DUI clients were more likely to have a primary problem with alcohol. Nevertheless, a cannabis use problem was also found to be significantly associated with DUI recidivism in the last year. In regards to mental health status, a major finding was that depression was the most common psychiatric condition reported by DUI clients, including those with more than one DUI offence in the past year. This cohort also reported elevated levels of Bipolar Disorder compared to the general population, and such a diagnosis was also associated with an increased likelihood of not completing treatment. Additionally, female clients were more likely to be diagnosed with mental health problems than males, as well as more likely to be placed on medications at admission and more likely to have problems with methamphetamine, cocaine, and opiates. Conclusions DUI offenders are at an increased risk of experiencing comorbid psychiatric disorders, and thus, corresponding treatment programs need to cater for a range of mental health concerns that are likely to affect recidivism rates.

The impact of tax morale and institutional quality on the shadow economy

This paper uses a multivariate analysis to examine how countries‘ tax morale and institutional quality affect the shadow economy. The literature strongly emphasizes the quantitative importance of these factors in understanding the level of and changes in the shadow economy. Newly available data sources offer the unique opportunity to further illuminate a topic that has received increased attention. After controlling for a variety of potential factors, we find strong support that a higher tax morale and a higher institutional quality lead to a smaller shadow economy.

Local autonomy, tax morale, and the shadow economy

Policymakers often propose strict enforcement strategies to fight the shadow economy and to increase tax morale. However, there is an alternative bottom-up approach that decentralises political power to those who are close to the problems. This paper analyses the relationship with local autonomy. We use data on tax morale at the individual level and macro data on the size of the shadow economy to analyse the relevance of local autonomy and compliance in Switzerland. The findings suggest that there is a positive (negative) relationship between local autonomy and tax morale (size of the shadow economy).

Serious tax non compliance : motivation and guardianship

This article reviews what international evidence exists on the impact of civil and criminal sanctions upon serious tax noncompliance by individuals. This construct lacks sharp definitional boundaries but includes large tax fraud and large-scale evasion that are not dealt with as fraud. Although substantial research and theory have been developed on general tax evasion and compliance, their conclusions might not apply to large-scale intentional fraudsters. No scientifically defensible studies directly compared civil and criminal sanctions for tax fraud, although one U.S. study reported that significantly enhanced criminal sanctions have more effects than enhanced audit levels. Prosecution is public, whereas administrative penalties are confidential, and this fact encourages those caught to pay heavy penalties to avoid publicity, a criminal record, and imprisonment.

On inequality tax evasion and progressive taxes

This paper proposes a simple variation of the Allingham and Sandmo (1972) construct and integrates it to a dynamic general equilibrium framework with heterogeneous agents. We study an overlapping generations framework i n which agents must initially decide whether to evade taxes or not. In the event they decide to evade, they then have to decide the extent of income or wealth they wish to under-report. We find that in comparison with the basic approach, the ‘evade or not’ choice drastically reduced the extent of evasion in the economy. This outcome is the result of an anomaly intrinsic to the basic Allingham and Sandmo version of the model, which makes the evade-or-not extension a more suitable approach to modelling the issue. We also find that the basic model, and the model with and ‘evade-or-not’ choice have strikingly different political economy implications, , which suggest fruitful avenues of empirical research.

Connected Communities and Creative Economy; Clash, cluster, complexity, creativity

After a brief personal orientation, this presentation offers an opening section on „clash, cluster, complexity, cities‟ – making the case that innovation (both creative and economic) proceeds not only from incremental improvements within an expert-pipeline process, but also from the clash of different systems, generations, and cultures. The argument is that cultural complexity arises from such clashes, and that clustering is the solution to problems of complexity. The classic, 10,000-year-old, institutional form taken by such clusters is … cities. Hence, a creative city is one where clashing and competitive complexity is clustered… and, latterly, networked.

Genetic Composition and Complexity of Virus Populations at Tungro-Endemic and Outbreak Rice Sites

We have recently demonstrated the geographic isolation of rice tungro bacilliform virus (RTBV) populations in the tungro-endemic provinces of Isabela and North Cotabato, Philippines. In this study, we examined the genetic structure of the virus populations at the tungro-outbreak sites of Lanao del Norte, a province adjacent to North Cotabato. We also analyzed the virus populations at the tungro-endemic sites of Subang, Indonesia, and Dien Khanh, Vietnam. Total DNA extracts from 274 isolates were digested with EcoRV restriction enzyme and hybridized with a full-length probe of RTBV. In the total population, 22 EcoRV-restricted genome profiles (genotypes) were identified. Although overlapping genotypes could be observed, the outbreak sites of Lanao del Norte had a genotype combination distinct from that of Subang or Dien Khanh but a genotype combination similar to that identified earlier from North Cotabato, the adjacent endemic province. Sequence analysis of the intergenic region and part of the ORF1 RTBV genome from randomly selected genotypes confirms the geographic clustering of RTBV genotypes and, combined with restriction analysis, the results suggest a fragmented spatial distribution of RTBV local populations in the three countries. Because RTBV depends on rice tungro spherical virus (RTSV) for transmission, the population dynamics of both tungro viruses were then examined at the endemic and outbreak sites within the Philippines. The RTBV genotypes and the coat protein RTSV genotypes were used as indicators for virus diversity. A shift in population structure of both viruses was observed at the outbreak sites with a reduced RTBV but increased RTSV gene diversity

Managing process model complexity via concrete syntax modifications

While Business Process Management (BPM) is an established discipline, the increased adoption of BPM technology in recent years has introduced new challenges. One challenge concerns dealing with the ever-growing complexity of business process models. Mechanisms for dealing with this complexity can be classified into two categories: 1) those that are solely concerned with the visual representation of the model and 2) those that change its inner structure. While significant attention is paid to the latter category in the BPM literature, this paper focuses on the former category. It presents a collection of patterns that generalize and conceptualize various existing mechanisms to change the visual representation of a process model. Next, it provides a detailed analysis of the degree of support for these patterns in a number of state-of-the-art languages and tools. This paper concludes with the results of a usability evaluation of the patterns conducted with BPM practitioners.

An examination of tax-deductible donations made by individual Australian taxpayers in 2008-09, ACPNS Working Paper 54

Each year, The Australian Centre for Philanthropy and Nonprofit Studies (CPNS) at Queensland University of Technology (QUT) collects and analyses statistics on the amount and extent of tax-deductible donations made and claimed by Australians in their individual income tax returns to deductible gift recipients (DGRs). The information presented below is based on the amount and type of tax-deductible donations made and claimed by Australian individual taxpayers to DGRs for the period 1 July 2008 to 30 June 2009. This information has been extracted mainly from the Australian Taxation Office's (ATO) publication Taxation Statistics 2008-09. The 2008-09 report is the latest report that has been made publicly available. It represents information in tax returns for the 2008-09 year processed by the ATO as at 31 October 2010.

Controlling chaos? The value and the challenges of applying complexity theory to project management

The literature abounds with descriptions of failures in high-profile projects and a range of initiatives has been generated to enhance project management practice (e.g., Morris, 2006). Estimating from our own research, there are scores of other project failures that are unrecorded. Many of these failures can be explained using existing project management theory; poor risk management, inaccurate estimating, cultures of optimism dominating decision making, stakeholder mismanagement, inadequate timeframes, and so on. Nevertheless, in spite of extensive discussion and analysis of failures and attention to the presumed causes of failure, projects continue to fail in unexpected ways. In the 1990s, three U.S. state departments of motor vehicles (DMV) cancelled major projects due to time and cost overruns and inability to meet project goals (IT-Cortex, 2010). The California DMV failed to revitalize their drivers’ license and registration application process after spending $45 million. The Oregon DMV cancelled their five year, $50 million project to automate their manual, paper-based operation after three years when the estimates grew to $123 million; its duration stretched to eight years or more and the prototype was a complete failure. In 1997, the Washington state DMV cancelled their license application mitigation project because it would have been too big and obsolete by the time it was estimated to be finished. There are countless similar examples of projects that have been abandoned or that have not delivered the requirements.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Overcoming the complexity of diagnostic problems due to sensor network architecture

In fault detection and diagnostics, limitations coming from the sensor network architecture are one of the main challenges in evaluating a system’s health status. Usually the design of the sensor network architecture is not solely based on diagnostic purposes, other factors like controls, financial constraints, and practical limitations are also involved. As a result, it quite common to have one sensor (or one set of sensors) monitoring the behaviour of two or more components. This can significantly extend the complexity of diagnostic problems. In this paper a systematic approach is presented to deal with such complexities. It is shown how the problem can be formulated as a Bayesian network based diagnostic mechanism with latent variables. The developed approach is also applied to the problem of fault diagnosis in HVAC systems, an application area with considerable modeling and measurement constraints.