Welch College

The purpose of this discussion is to assess the ability of the Documentary Hypothesis to provide a compelling reading of Genesis 37. Is the literary reconstruction of the J/E source divisions able to offer a compelling, internally consistent text? As a counter proposal, I consider the framework of coherence theory as a means of evaluating the central claim of documentarians that Genesis 37 lacks compositional cohesion and coherence. The method of this discussion takes the following course. First, I sketch out a brief overview of the various compositional approaches to Genesis 37 and its place in the Joseph story. Second, I consider the nature of narrative cohesion and coherence and how these concepts inform one’s reading of Genesis 37, arguing that the markers of cohesion embedded in the narrative provide a means of guiding the reader coherently through the twists and turns of the plot line. Third, I highlight several narrative links that cross proposed source boundaries, functioning as linguistic signals for cohesion, and that suggest literary coherence. Fourth, I re-assess the so-called Ishmaelite/Midianite problem that has served a central role in most source divisions of Genesis 37.

ABSTRACT This paper analyzes if the wage premium associated with higher education is different for schooling received online than for schooling received on-ground using two separate years of data on nurses in the United States. Combined, the 2018 and 2022 National Sample Survey of Registered Nurses (NSSRN) contain over 74,000 nurses employed full-time. For the nurses who receive additional education beyond their initial nursing education, the NSSRN asks whether the coursework or courses were online. We find a wage penalty for online education. The result appears to be robust, occurring in both years, with and without controls for regional and occupational fixed effects and using controls for potential omitted variables. The extent of the wage penalty varies depending on the model’s specification and year being examined.

This study investigates the occurrence and persistence of abnormal stock returns surrounding corporate earnings announcements, particularly emphasizing how varying frequencies of financial reporting influence market behavior. Specifically, this research examines the effects of the timing and frequency of disclosures on market reactions and stock price volatility during critical earnings announcement periods. By analyzing firms within the Dow Jones Industrial Average (DJIA) from 2014 to 2024, this study evaluates the interplay between financial reporting schedules and market responses to stock prices. Furthermore, it considers the impact of peer firms’ reporting practices on the assimilation of firm-specific information into stock prices. Using econometric models, including Vector Auto Regression (VAR), Impulse Response Functions (IRFs), and Self-Exciting Threshold Autoregressive (SETAR) models, causal relationships between reporting frequency, stock price volatility, and abnormal return patterns across different volatility regimes are identified. The findings highlight that quarterly reporting practices intensify market responses and contribute to significant variations in stock price behavior in high-volatility periods. These insights provide a deeper understanding of the role of financial disclosure practices and forward-looking guidance in shaping market efficiency. This study contributes to ongoing discussions about balancing the transparency benefits of frequent reporting with its potential to amplify market volatility and sector-specific risks, offering valuable implications for policymakers, investors, and corporate managers.

This paper aims to provide a comprehensive review of the integration of artificial intelligence (AI) and blockchain technology in U.S. tax administration. It explores how these technologies are revolutionizing tax compliance and fraud detection, offering a comparative analysis with traditional methods. The paper highlights the potential benefits of these technologies in enhancing efficiency, accuracy, and transparency in tax administration, aligning with the U.S. government's objectives of ensuring fiscal integrity and public trust. The review also examines international best practices and proposes how the U.S. can leverage these technologies to maintain its global leadership in financial governance and innovation. The study is structured around four key objectives: assessing the current integration of AI and blockchain in tax administration, evaluating their effectiveness in enhancing tax compliance, identifying implementation challenges, and developing strategic recommendations. Employing a comprehensive literature review approach, the study synthesizes findings from various sources to provide an in-depth understanding of the role and impact of these technologies in modern tax systems. The results reveal that AI and blockchain significantly improve tax compliance and administration efficiency but also introduce challenges such as data privacy concerns and the need for robust regulatory frameworks. In conclusion, the study underscores the transformative potential of AI and blockchain in tax administration, recommending continuous research and development, coupled with stakeholder education and engagement. These efforts are crucial for overcoming operational challenges and fully harnessing the benefits of these technologies in modernizing tax systems. The paper concludes with strategic recommendations for policymakers, tax authorities, and researchers, emphasizing the importance of a balanced approach that fosters technological innovation while maintaining legal compliance and adherence to fundamental principles. Keywords: Artificial Intelligence, Blockchain, Tax Administration, Tax Compliance, Digital Transformation, Financial Governance.

Purpose Guilt appeals are increasingly being used in road safety campaigns, despite recent research that has raised doubts about their effectiveness and the potential for triggering defensive responses. Building on the extended parallel process model, this study aims to add to this growing body of research by evaluating whether combining a hope message with guilt appeals can solve this problem. Design/methodology/approach An online experiment with a 2 (Appeal type: Guilt vs Guilt+hope) × 2 (Language intensity: Low vs High) between-subjects design was conducted. A total of 399 participants recruited from Amazon’s Mechanical Turk were randomized to view one of the four advertisements discouraging texting while driving (TWD). Their affective responses, perceptions about the advertisements and intentions to not TWD were measured. Findings The results showed that compared to guilt-only appeals, guilt+hope appeals directly reduced defensive responses (i.e. perceived manipulative intent and anger) across varying levels of language intensity. In addition, guilt+hope appeals mitigated the negative impacts of manipulative intent on intended emotions and intentions to not TWD. Originality/value Findings of this study mark the first to support the idea that communicating hope within guilt appeals is a promising social marketing strategy to discourage TWD.

This paper models the dynamics of Chinese yuan-denominated long-term interest rate swap yields. It shows that the short-term interest rate exerts a decisive influence on the long-term swap yield after controlling for various macrofinancial variables, such as core inflation, the growth of industrial production, the percent change in the equity price index, and the percentage change in the Chinese yuan exchange rate. The autoregressive distributed lag approach is applied to model the dynamics of the long-term swap yield. The findings reinforce and extend John Maynard Keynes's conjecture that in advanced countries, as well as emerging market economies such as China, the central bank's actions have a decisive role in setting the long-term interest rate on government bonds and over-the-counter financial instruments, such as swaps.

PurposeIn this paper, the author attempts to answer an important question upon founder-CEOs' exiting: How do they sell their remaining ownership shares? The literature has largely been silent on this question, and therefore is missing an important piece of the puzzle on the final stage of the founding entrepreneurs' involvement in their companies.Design/methodology/approachThe author uses both theoretical models and empirical methods to examine how founder-CEOs sell their remaining ownership shares.FindingsThe author finds that founder-CEOs of high-growth firms and those with high managerial ability are more likely to sell remaining ownership shares gradually rather than suddenly. Moreover, if either the growth or the managerial ability is high, founder-CEOs managing firms with high volatility tend to sell gradually.Originality/valueThis paper provides insights into the final stage of founding entrepreneurs' involvement in companies. The methodology of pattern recognition also helps investors and regulators in tracking and monitoring stock trading of founders and other company insiders.