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on Accounting and Auditing |
| By: | Katarzyna A. Bilicka; Michael P. Devereux; İrem Güçeri |
| Abstract: | Many multinational firms (MNEs) pay low or no corporation tax in high-tax countries because they shift taxable income to tax havens. We incorporate nonconvex costs of profit shifting and unobserved heterogeneity in profit-shifting ability in the MNEs' value maximization problem to study responses of firms to tax policies. We estimate our model using UK corporate tax returns data and quantify: (i) the elasticities of tax base and capital stock with respect to tax rates, (ii) the fixed and variable components of profit-shifting costs for different firm types, and (iii) the government's trade-off between raising tax revenue by reducing profit shifting and attracting investment. Accounting for extensive margin profit-reporting decisions, we reconcile most of the discrepancies between previous micro- and macro-level estimates of tax base elasticities. We test the predictions of the model using a quasi-natural experiment that restricted profit-shifting by Italian MNEs that operated in the UK and evaluate two types of tax policies that can be analyzed using our approach. |
| JEL: | H25 H26 H32 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33132 |
| By: | Beuselinck, Christof; Karavitis, Panagiotis; Kazakis, Pantelis; Mouna, Niswatil |
| Abstract: | This study examines the impact of e-government advancements on corporate tax planning activities. We define e-government as the readiness and capacity of national institutions to use information and communications technologies to deliver public services. Using over 82, 000 worldwide firm-level data from 10, 936 unique firms in 56 countries over the period 2008-2021, we observe a negative association between a country’s e-government advancement and the overall tax avoidance practices of firms. Via path analysis we identify the underlying mechanisms through which e-government affects corporate tax avoidance and document that the total tax enforcement budget but also specific technological features such as AI-machine learning, and robotic process automation explains a sizeable fraction of the negative relationship between e-government advancements and corporate tax avoidance. Additionally, our cross-sectional analysis reveals that the impact of e-government on curbing tax planning is particularly pronounced in environments where firms traditionally accrue tax benefits via investments into organizational capital. Our main findings remain robust after implementing an instrumental variables strategy and conducting various robustness tests. Collectively, our findings indicate that e-government investments can help raise a nation’s tax revenue collection, as such investments are linked to reduced corporate tax avoidance activities. |
| Keywords: | tax avoidance, tax planning, digitalization, e-government, digital governments |
| JEL: | G30 G38 H26 L1 M41 M48 |
| Date: | 2024–11–18 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122742 |
| By: | Deaa Al-Deen Al-Sraheen (Accounting Department, School of Business, Al al-Bayt University) |
| Abstract: | The study aimed to investigate the relationship between accounting disclosure and stock returns in its first model. The second model examined the role of ownership concentration as a moderator variable in this relationship, using a sample of Jordanian service companies listed on the Amman Stock Exchange from 2018 to 2021. The final sample consisted of 440 firm observations over four years. The study found a positive and statistically significant relationship between accounting disclosure and stock returns. Moreover, the concentration of ownership was found to have a positive effect on this relationship, indicating that it contributes to improving stock returns by enhancing the effectiveness of accounting disclosure. By introducing the moderated role of ownership concentration, the study made a valuable contribution to the field. The study also provided recommendations to Jordanian companies, urging them to adopt public policies and mechanisms that enhance disclosure and move towards digital disclosure to keep up with evolving financial statement presentation methods. |
| Keywords: | Accounting Disclosure, Ownership Concentration, Stock return, Jordanian Companies |
| JEL: | M41 M40 |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:14716134 |
| By: | Dawei Cheng; Yao Zou; Sheng Xiang; Changjun Jiang |
| Abstract: | The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05815 |
| By: | chen, shuning; Managi, Shunsuke |
| Abstract: | Natural capital defines planetary boundaries and provides a basis for sustainable development. This study reviews previous theoretical developments and confirms that natural capital accounting within the Inclusive Wealth (IW) framework provides a robust link between current capital assets and intergenerational well-being. This study contributes to the literature by combining theoretical advances with practical applications to address criticisms of empirical practice and improve the reliability and scope of cross-country natural capital accounting. An analysis of natural capital levels and changes in 163 economies over the past 30 years reveals significant regional disparities in the decline of global natural capital. In low-income countries, consumption driven by population growth and primary production patterns is severely depleting renewable natural capital. In middle-income countries, urbanization exacerbates natural capital depletion by substituting other forms of capital for natural capital. The wealth status of major G20 economies points to intensive environmental costs and loss of ecosystem services under technological progress, which ignores public ecosystem externalities. This study demonstrates the urgency of natural capital depletion awareness in the management of all economies and highlights the ability of natural capital accounting within the IW framework to inform policy decisions on sustainable growth. |
| Keywords: | Natural capital, Sustainability, Inclusive Wealth, Comprehensive Wealth |
| JEL: | F6 I3 Q5 |
| Date: | 2024–11–05 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122597 |
| By: | Nane Davtyan |
| Abstract: | The rapid advancement of Artificial Intelligence (AI) has revolutionized consumer behavior analysis and digital marketing strategies by enabling personalized and efficient data-driven approaches. AI-driven tools like predictive analytics, natural language processing (NLP), machine learning, and programmatic advertising allow marketers to process vast amounts of real-time consumer data, facilitating optimized campaign performance and precise targeting. This paper explores the integration of AI in marketing, highlighting its role in enhancing predictive analytics, sentiment analysis, and real-time segmentation. Compared to traditional methods, AI-driven insights significantly improve engagement, accuracy, and return on investment (ROI). AI also plays a vital role in marketing automation, allowing dynamic adjustments in campaigns, ad placements, and content creation, improving efficiency and reducing costs. However, AI’s reliance on consumer data raises concerns regarding data privacy and algorithmic bias, especially in targeting. This paper stresses the importance of ensuring transparency, fairness, and regular audits in AI systems to maintain consumer trust and promote ethical AI use. Future research directions are discussed, focusing on enhancing transparency and algorithmic accountability while navigating the ethical challenges of AI in marketing. |
| Keywords: | Artificial Intelligence (AI), Consumer behavior analysis, Digital marketing, Predictive analytics, Natural language processing (NLP) |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:bfv:sbsrec:005 |