Sales tax enforcement: An empirical analysis of compliance enforcement methodologies and pathologies

Most research on tax evasion has focused on the income tax. Sales tax evasion has been largely ignored and dismissed as immaterial. This paper explored the differences between income tax and sales tax evasion and demonstrated that sales tax enforcement is deserving of and requires the use of different tools to achieve compliance. Specifically, the major enforcement problem with sales tax is not evasion: it is theft perpetrated by companies that act as collection agents for the state. Companies engage in a principal-agent relationship with the state and many retain funds collected as an agent of the state for private use. As such, the act of sales tax theft bears more resemblance to embezzlement than to income tax evasion. It has long been assumed that the sales tax is nearly evasion free, and state revenue departments report voluntary compliance in a manner that perpetuates this myth. Current sales tax compliance enforcement methodologies are similar in form to income tax compliance enforcement methodologies and are based largely on trust. The primary focus is on delinquent filers with a very small percentage of businesses subject to audit. As a result, there is a very large group of noncompliant businesses who file on time and fly below the radar while stealing millions of taxpayer dollars. ^ The author utilized a variety of statistical methods with actual field data derived from operations of the Southern Region Criminal Investigations Unit of the Florida Department of Revenue to evaluate current and proposed sales tax compliance enforcement methodologies in a quasi-experimental, time series research design and to set forth a typology of sales tax evaders. This study showed that current estimates of voluntary compliance in sales tax systems are seriously and significantly overstated and that current enforcement methodologies are inadequate to identify the majority of violators and enforce compliance. Sales tax evasion is modeled using the theory of planned behavior and Cressey’s fraud triangle and it is demonstrated that proactive enforcement activities, characterized by substantial contact with non-delinquent taxpayers, results in superior ability to identify noncompliance and provides a structure through which noncompliant businesses can be rehabilitated.^

The impact of CEO compensation and CEO horizon on a firm's discretionary research and development expenditures

Agency costs are said to arise as a result of the separation of ownership from control inherent in the corporate form of ownership. One such agency problem concerns the potential variance between the time horizons of principal shareholders and agent managers. Agency theory suggests that these costs can be alleviated or controlled through performance-based Chief Executive Officer (CEO) contracting. However, components of a CEO's compensation contract can exacerbate or mitigate agency-related problems (Antle and Smith, 1985). According to the horizon hypothesis, a self-serving CEO reduces discretionary research and development (R&D) expenditures to increase earnings and earnings-based bonus compensation. Agency theorists contend that a CEO's market-based compensation component can mitigate horizon problems. This study seeks to determine whether there is a relationship between CEO earnings- and market-based compensation components and R&D expenditures in the largest United States industrial firms from 1987 to 1993.^ Consistent with the horizon hypothesis, results provide evidence of a negative and statistically significant relationship between CEO cash compensation (i.e., salary and bonus) and the firm's R&D expenditures. Consistent with the expectations of agency theory, results provide evidence of a positive and statistically significant relationship between market-based CEO compensation and R&D.^ Further results of this study provide evidence of a positive and statistically significant relationship between CEO tenure and the firm's R&D expenditures. Although there is a negative relationship between CEO age and the firm's R&D, it was not statistically significant at the 0.5 level. ^

Internet computing with distributed software agent

The phenomenonal growth of the Internet has connected us to a vast amount of computation and information resources around the world. However, making use of these resources is difficult due to the unparalleled massiveness, high communication latency, share-nothing architecture and unreliable connection of the Internet. In this dissertation, we present a distributed software agent approach, which brings a new distributed problem-solving paradigm to the Internet computing researches with enhanced client-server scheme, inherent scalability and heterogeneity. Our study discusses the role of a distributed software agent in Internet computing and classifies it into three major categories by the objects it interacts with: computation agent, information agent and interface agent. The discussion of the problem domain and the deployment of the computation agent and the information agent are presented with the analysis, design and implementation of the experimental systems in high performance Internet computing and in scalable Web searching. ^ In the computation agent study, high performance Internet computing can be achieved with our proposed Java massive computation agent (JAM) model. We analyzed the JAM computing scheme and built a brutal force cipher text decryption prototype. In the information agent study, we discuss the scalability problem of the existing Web search engines and designed the approach of Web searching with distributed collaborative index agent. This approach can be used for constructing a more accurate, reusable and scalable solution to deal with the growth of the Web and of the information on the Web. ^ Our research reveals that with the deployment of the distributed software agent in Internet computing, we can have a more cost effective approach to make better use of the gigantic scale network of computation and information resources on the Internet. The case studies in our research show that we are now able to solve many practically hard or previously unsolvable problems caused by the inherent difficulties of Internet computing. ^

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^