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on Accounting and Auditing |
| By: | Derks, Koen (Nyenrode Business University); Mensink, Lotte; Smid, Wiert; de swart, jacques; wetzels, ruud |
| Abstract: | When establishing an overall audit strategy, auditors must assess the risk of material misstatement and determine the nature, timing and extent of further audit procedures. Among other things, the nature, timing and extent of further audit procedures is affected by the nature and extent of misstatements identified in previous audits and thereby the auditor’s expectations in relation to misstatements in the current audit. Unfortunately, deciding and justifying the extent to which these historical data should be considered is challenging. Consequently, auditors often incorporate these data in a non-statistical manner. However, there are practical benefits to doing this statistically, such as increased transparency and justifiability. In this article, we introduce a statistical approach to incorporate historical data in the current audit based on the normalized power prior. This approach eliminates the need for auditors to decide how much to discount the historical data and enables them to learn this using the current data. We demonstrate that the normalized power prior improves audit efficiency over time. |
| Date: | 2025–02–12 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:56wpj_v1 |
| By: | Astudillo-Estévez, Pablo; Bacilieri, Andrea |
| Abstract: | There is a large consensus on the fundamental role of firm-level supply chain networks in macroeconomics. However, data on supply chains at the fine-grained, firm level are scarce and frequently incomplete. For listed firms, some commercial datasets exist but only contain information about the existence of a trade relationship between two companies, not the value of the monetary transaction. We use a recently developed maximum entropy method to reconstruct the values of the transactions based on information about their existence and aggregate information disclosed by firms in financial statements. We test the method on the administrative dataset of Ecuador and reconstruct a commercial dataset (FactSet). We test the method's performance on the weights, the technical and allocation coecients (microscale quantities), two measures of firms' systemic importance and GDP volatility. The method reconstructs the distribution of microscale quantities reasonably well but shows diverging results for the measures of firms' systemic importance. Due to the network structure of supply chains and the sampling process of firms and links, quantities relying on the number of customers firms have (out-degrees) are harder to reconstruct. We also reconstruct the input-output table of globally listed firms and merge it with a global input-output table at the sector level (the WIOD). Differences in accounting standards between national accounts and firms' financial statements significantly reduce the quality of the reconstruction. |
| Keywords: | Network reconstruction, supply chain, production network, input-output table, maximum entropy, missing information |
| JEL: | C80 D57 E32 L14 F12 |
| Date: | 2023–05 |
| URL: | https://d.repec.org/n?u=RePEc:amz:wpaper:2023-05 |
| By: | Pinto, David Pineda; Bermúdez, Jose Carlo; Thiago De Gouvea Scot de Arruda |
| Abstract: | Late or unreliable refunds of credits undermine the best traits of value-added tax (VAT) systems and might affect firms' growth and investment opportunities. This paper uses administrative tax records in Honduras to study a tax reform that decreased the withholding rate of value-added tax liabilities by credit and debit card (DCC) providers, aiming to curb unrefunded credits. Using a difference-in-differences approach, exploiting differential exposure to the reform, the paper documents that it caused a decrease in excessive withholding and was equivalent to a cut of 1.1 percentage points in effective tax rates faced by treated firms. The paper then evaluates the effects on firms' economic performance and estimate null effects on several indicators of economic growth and investment. The results challenge the premise that unrefunded VAT credits are an important constraint to firm growth in certain settings. Keywords: VAT refunds, withholding, firms’ performance. |
| Date: | 2024–12–11 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10996 |
| By: | KOUAKOU, Thiédjé Gaudens-Omer |
| Abstract: | This paper analyzes the effect of Basel III adapted to WAEMU on the behavior of banks in the zone (intermediation and market activities). After having developed a model for optimizing the return on bank equity, under various constraints (balance sheet constraints, Basel III regulatory constraints), we resort to linear programming via the Danzig simplex algorithm and to a structure of reasonable rates to obtain the optimal values of the various bank balance sheet items. The results, obtained by comparing these theoretical values with the values observed before Basel III (before January 1, 2018), show an increase in the supply of loans, obtained not only from deposits and bank refinancing but also via resources from the financial markets. We can also observe the intuitive result of an increase of bank reserves in line with the constraint that Basel III imposes on banks to increase their liquidity. In short, Basel III tends to strengthen bank financing in the zone, while improving the soundness of banks through the constitution of larger reserves. |
| Keywords: | prudential regulation, calibration, credit supply, linear programming |
| JEL: | C44 E50 E58 |
| Date: | 2025–01–31 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:123515 |
| By: | Jesús López-Rodríguez; Diego Martínez-López; Brais Pociña-Sánchez |
| Abstract: | The foot-loose capital (FC) models predict that agglomeration forces create rents for the mobile factor (capital), which can be easily taxed, and thus higher equilibrium tax rates are expected. This paper uses a highly flexible econometric specification (P-Spline spatial autoregressive model, PS-SAR) to look at the relationship between tax rates and agglomeration economies in Spain over the period 2013-2020. Our results show the existence of a minimum level of agglomeration economies that are required to find taxable agglomeration rents. This outcome calls for a reassessment of the linear FC models to disentangle which mechanisms might lead to these phenomena. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:fda:fdaddt:2025-02 |
| By: | Picciotto, Sol |
| Abstract: | Protracted debates and negotiations have led to a new approach to taxation of multinationals: apportionment of their global profits based on their real presence in each country. A concerted initiative by willing states could implement this approach using standards now agreed, facilitated through the UN Framework Convention now under negotiation. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:colfdi:311080 |
| By: | Yarygina, Anastasiya; Martínez, André |
| Abstract: | In recent years, tax administrations around the globe have leveraged digital transformation to enhance processes and services to improve tax compliance. Massive self-regularization platforms, which identify noncompliant taxpayers, notify them about the detected inconsistencies, and allow them to amend the situation with the tax authority, are prominent examples of the digital transformation of tax administrations. This study presents the results of the randomized controlled trial evaluating the effectiveness of such a self-regularization platform in the Brazilian State of Para. The results show that the platform increased the amount of the taxes paid by 12.78 times and the probability of tax compliance by 236 percent. Overall, the effectiveness of self-regularization in recovering the evaded tax is 60 percent higher than that of the traditional audit-based approach. The amount of the correction in the declared tax increased by 2.33 times, and the probability of correction by 300 percent. Given the low marginal cost of self-regularization, the results suggest that these platforms are a remarkable opportunity for tax administrations to leverage digital transformation effectively and efficiently, improving tax compliance and increasing tax revenue. |
| Keywords: | digitalization;Tax compliance;Taxpayer support |
| JEL: | H26 H30 H32 O38 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:13975 |
| By: | Antonacci, Paulo; Muhammad Khudadad Chattha |
| Abstract: | This paper presents an evaluation of a tax enforcement program conducted in Indonesia where officials from the tax authority visited properties to engage directly with owners about their property tax obligations. Through these visits, auditors explained outstanding debts and payment processes, aiming to improve tax compliance and revenue collection. The paper uses an administrative data set and a new set of machine learning–based techniques to assess the program’s effectiveness. The program was responsible for increasing tax compliance on the extensive margin by 4.3 percent and on the intensive margin by 5.1 percent in the first year it was implemented. These effects are particularly strong as they persist in the following period. The findings show that the visited properties had better compliance history, lower value, smaller area, and were more likely to have some construction on them. A key finding from the analysis is that higher-value properties are less sensitive to the visits. In other words, if a data-driven tax-enforcement strategy is to be applied, then it may focus resources on enforcing taxation at the poorest part of the population in this case. This opens up the discussion of the distributional consequences of an algorithm-based enforcement strategy, which is increasingly important as machine learning techniques are used by tax authorities. |
| Date: | 2024–09–06 |
| URL: | https://d.repec.org/n?u=RePEc:wbk:wbrwps:10901 |
| By: | Yisong Chen; Chuqing Zhao; Yixin Xu; Chuanhao Nie |
| Abstract: | This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were evaluated. Key themes explored include the impact of data privacy frameworks and advancements in feature engineering and data preprocessing. The study emphasizes challenges such as imbalanced datasets, model interpretability, and ethical considerations, alongside opportunities for automation and privacy-preserving techniques such as blockchain integration and Principal Component Analysis. By examining trends over the past five years, this review identifies critical gaps and promising directions for advancing DL applications in financial fraud detection, offering actionable insights for researchers and practitioners. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.00201 |
| By: | O'Connor, Ruth; Sanchez, Emerson; Bice, Sara; Jones, Kirsty; Henderson, Hayley |
| Abstract: | There is a growing need to maximise the social benefits achieved from public investment in infrastructure, particularly with the transition to net zero economies. Project Delivery Models (PDMs)—the contractual agreements that set out roles and responsibilities for infrastructure project partners—can underpin the delivery of social benefits, yet the processes involving their selection are largely opaque. In this paper we explore how social considerations inform PDM selection and how this is facilitated by policy. We interviewed highly experienced procurement professionals from the sector about how social benefits and risks are considered. We also examined how the associated regulatory environment supports social considerations through documentary analysis of auditing guidance documents. We found that not only are social benefits sidelined in early project decisions, but social risks are inadequately considered. An entrenched gap in social expertise contributes to this situation while project compartmentalisation presents challenges for inclusion and transparency in decision-making. Current auditing processes provide little incentive for social benefit consideration and reinforce both risk framing and compartmentalisation of major infrastructure projects. The paper offers new and important insights into this early project stage and distils five recommendations for improving social benefit creation from infrastructure investments, particularly in developed economies. |
| Date: | 2025–01–14 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:6n9qy_v1 |