nep-acc New Economics Papers
on Accounting and Auditing
Issue of 2021‒04‒26
five papers chosen by
Alexander Harin
Modern University for the Humanities

  1. Improving nature’s visibility in financial accounting. By C. Feger; Alexandre Rambaud
  2. Analisis Pengaruh Rasio Keuangan Terhadap Penerimaan Opini Audit Going Concern By , Aminah; Pransiska, Deta
  3. A Machine Learning Approach to Analyze and Support Anti-Corruption Policy By Elliott Ash; Sergio Galletta; Tommaso Giommoni
  4. Towards common GHG inventory reporting tables for Biennial Transparency Reports: Experiences with tools for generating and using reporting tables under the UNFCCC By Chiara Falduto; Sina Wartmann
  5. From “Table 29” to the actuarial balance sheet: is it really that big a leap? By Anne M. Garvey; Juan Manuel Pérez-Salamero González; Manuel Ventura-Marco; Carlos Vidal-Meliá

  1. By: C. Feger (AgroParisTech, MRM - Montpellier Research in Management - UM - Université de Montpellier - Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School - UM1 - Université Montpellier 1 - UPVD - Université de Perpignan Via Domitia - UM2 - Université Montpellier 2 - Sciences et Techniques - UPVM - Université Paul-Valéry - Montpellier 3); Alexandre Rambaud (CIRED - Centre International de Recherche sur l'Environnement et le Développement - Université Paris-Saclay - AgroParisTech - EHESS - École des hautes études en sciences sociales - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique)
    Abstract: pas de résumé
    Date: 2020–04–15
    URL: http://d.repec.org/n?u=RePEc:hal:ciredw:hal-02976915&r=
  2. By: , Aminah; Pransiska, Deta
    Abstract: Seorang auditor bertanggungjawab memberikan peringatan maupun sanksi kepada para pengguna laporan keuangan dalam mempertahankan going concern dimasa yang akan datang yang diatur dalam Pernyataan Standar Akuntansi. Penelitian ini digunakan untuk melihat pengaruh dari rasio likuidity, solvenvy, aktivity dan laverage terhadap penerimaan opini audit going concern. Manufacturing company yang tedaftar di Bursa Efek Indonesia pada tahun 2017-2019 merupakan populasi dalam penelitian ini. Metode purposive sampling merupakan sampel yang digunakan dalam penelitian ini. Jumlah sampel yang digunakan dalam penelitian ini sebanyak 69 perusahaan. Analisis statistik deskriftif dan analisis regresi logistik merupakan teknik analisis yang digunakan dalam penelitian ini. Hasil penelitian ini mengemukakan bahwa rasio aktivitytidakberpengaruh terhadap penerimaan opini audit going concern, sedangkan rasio likuidity, solvency dan leverage berpengaruh terhadap penerimaan opini audit going concern.
    Date: 2021–04–20
    URL: http://d.repec.org/n?u=RePEc:osf:osfxxx:u6bj8&r=
  3. By: Elliott Ash; Sergio Galletta; Tommaso Giommoni
    Abstract: Can machine learning support better governance? In the context of Brazilian municipalities, 2001-2012, we have access to detailed accounts of local budgets and audit data on the associated fiscal corruption. Using the budget variables as predictors, we train a tree-based gradient-boosted classifier to predict the presence of corruption in held-out test data. The trained model, when applied to new data, provides a prediction-based measure of corruption that can be used for new empirical analysis or to support policy responses. We validate the empirical usefulness of this measure by replicating and extending some previous empirical evidence on corruption issues in Brazil. We then explore how the predictions can be used to support policies toward corruption. Our policy simulations show that, relative to the status quo policy of random audits, a targeted policy guided by the machine predictions could detect almost twice as many corrupt municipalities for the same audit rate. Similar gains can be achieved for a politically neutral targeting policy that equalizes audit rates across political parties.
    Keywords: algorithmic decision-making, corruption policy, local public finance
    JEL: D73 E62 K14 K42
    Date: 2021
    URL: http://d.repec.org/n?u=RePEc:ces:ceswps:_9015&r=
  4. By: Chiara Falduto (OECD); Sina Wartmann
    Abstract: Under the Enhanced Transparency Framework (ETF) of the Paris Agreement, Parties will be required to report information on national GHG inventories using a set of Common Reporting Tables (CRTs). The CRTs can provide an important source of data at the international and national levels. While a final set of tables has not yet been agreed upon, there is an emerging convergence around the view that the Common Reporting Format (CRF) tables that Annex I Parties currently use to report national GHG inventories could serve as a starting point for the development of CRTs. To support ongoing discussions, this paper provides details on the structure and functions of the existing CRF tables and the CRF Reporter software used to generate the tables, as well as some countries’ experiences with using this current system. To facilitate the transition towards reporting using CRTs, the paper also provides an overview of other tools that could support countries in reporting GHG inventories through CRTs and outlines a set of key issues that could be considered in the transparency negotiations. The paper concludes that the use of CRF tables and a CRF Reporter reduces the reporting burden on Parties – and that this could also be a significant benefit of CRTs and a CRT reporter. The paper also highlights that countries’ experience shows that effective IT arrangements can facilitate the reporting process but that as developing countries have no prior experience with the use of CRF tables and the CRF Reporter, the transition to a new CRT system may need capacity-building support, including for setting up suitable IT arrangements.
    Keywords: Enhanced Transparency Framework, GHG inventories, Paris Agreement, reporting, UNFCCC
    JEL: F53 Q54 Q56 Q58
    Date: 2021–04–23
    URL: http://d.repec.org/n?u=RePEc:oec:envaab:2021/01-en&r=
  5. By: Anne M. Garvey (Department of Economics and Management Sciences, University of Alcalá, Madrid (Spain).); Juan Manuel Pérez-Salamero González (Department of Financial Economics and Actuarial Science, University of Valencia, Valencia. (Spain).); Manuel Ventura-Marco (Department of Financial Economics and Actuarial Science, University of Valencia, Valencia. (Spain).); Carlos Vidal-Meliá (Department of Financial Economics and Actuarial Science, University of Valencia (Spain).; research affiliate with the Instituto Complutense de Análisis Económico (ICAE), Complutense University of Madrid (Spain).)
    Abstract: EU regulations since 2017 have required all Member States to disclose their accrued-to-date pension liabilities (ADL) using a standard actuarial cost method and some common assumptions. This applies to both Social Security (SS) schemes and unfunded defined benefit (DB) schemes covering civil servants. These pension liabilities have to be disclosed in a supplementary table referred to as Table 29. An actuarial balance sheet (ABS) can be defined as a financial statement that lists a pension system's obligations to contributors and pensioners at a particular date, together with the amounts of the assets (financial and in particular those from contributions) that underwrite those commitments. The ABS can be used to assess the solvency of SS schemes, whereas Table 29 cannot. This paper develops a methodology to (easily) transform Table 29 into an ABS and compile its associated income statement (IS). To enable policymakers to better understand how the model would function, the paper also contains a country case study based on data from the most recently published Table 29 for Spain. According to our best estimate assumptions, it can be said that the Spanish pension system is partially insolvent because only part of the pension entitlements is backed up by assets, and that the system's sustainability has markedly deteriorated over the period 2015-2018.
    Keywords: Accountability; Actuarial Balance Sheet; Pension Liabilities; Social Security; Spain; Table 29; Useful Information.
    JEL: G22 H55 H83
    Date: 2021–03
    URL: http://d.repec.org/n?u=RePEc:ucm:doicae:2105&r=

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