Application of Benford’s Law to the Argentine whitemouth croaker (Micropogonias furnieri) fishery
DOI:
https://doi.org/10.47193/mafis.3742024010710Keywords:
Mean Absolute Deviation (MAD), fisheries management, fisheries resourcesAbstract
In the field of statistical data, both in the natural and social sciences, it has been observed that the distribution of the first, second and first two digits in real data frequently follows a pattern known as ‘Benford’s law’. This law has recently been used as a tool to identify anomalies in different databases, suggesting in some cases the possibility of fraud. It was observed that ‘genuine’ data digits tend to follow the law, while manipulated data digits do not. In this work, we explore their applicability beyond the financial sphere, investigating whether they can detect irregularities in scientific data, specifically in the official catch statistics of the Argentine whitemouth croaker (Micropogonias furnieri) fishery. To this end, we compare the frequency of the first, second and first pair of digits of the capture with the expected distribution using the mean absolute deviation (MAD). We implemented a methodology based on Monte Carlo simulations and the Kolmogorov-Smirnov test to calculate critical values of the MAD conformance test, addressing the unique nature of data and the variability in sample size. The conducted analysis suggested the presence of anomalies that could denote unusual patterns warranting further detailed investigation. In the field of assessment, management/administration, and conservation of fishing resources, the reliability of catch data is essential. The use of Benford’s law could optimize the selection of information used to develop indicators and reduce uncertainty in estimating the population status of resources.
Downloads
Metrics
References
Badal-Valero E, Alvarez-Jareño JA, Pavía JM. 2018. Combining Benford’s law and machine learning to detect money laundering. An actual Spanish court case. Forensic Sci Int. 282: 24-34. DOI: https://doi.org/10.1016/j.forsciint.2017.11.008
Barney B, Schulzke K. 2016. Moderating “cry wolf” events with excess MAD on Benford’s law research and practice. J Forensic Accounting Res. 1 (1): 66-90.
Benford F. 1938. The law of anomalous numbers. Proc Am Philos Soc. 78: 551-572.
Blitzstein JK, Hwang J. 2020. Benford’s Law: theory, applications, and limitations. En: Lee C-F, Lee JC, editores. Handbook of financial econometrics and statistics. Nueva York: Springer. 1-27.
Cabeza García PM. 2019. Aplicación de la ley de Benford en la detección de fraudes. Univ Soc. 11 (5): 421-427.
Carozza C, Ruarte CO, Rico MR, Lagos AN, García S, Riestra C, Lorenzo MI. 2019. La pesquería del variado costero. Evolución de los desembarques y recomendación de la Captura Biológicamente Aceptable efectuadas a la CTMFM para las principales especies costeras óseas. Año 2018. Inf Téc INIDEP Nº 5/2019. 62 p.
Carozza C, Navarro L, Jaureguizar A, Lasta C, Bertolotti MB. 2001. Asociación íctica costera bonaerense “variado costero”. Inf Téc Int DNI-INIDEP Nº 38/2001.
Cerqueti R, Lupi C. 2021. Some new tests of conformity with Benford’s Law. Stats. 4 (3): 745-761. DOI: https://doi.org/10.3390/stats4030044
Cerri J. 2018. A fish rots from the head down: how to use the leading 2 digits of ecological data to detect their falsification. DOI: https://doi.org/10.1101/368951
Cinelli C. 2014. Benford. Analysis: Benford analysis for data validation and forensic analytics. R package version 0.1. 1.
Diekmann A. 2007. Not the first digit! Using Benford’s Law to detect fraudulent scientific data. J Appl Stat. 34: 321-329.
García S. 2023. La pesquería comercial argentina de corvina rubia (Micropogonias furnieri) entre los 34° S y los 39° S. Año 2021. Inf Invest INIDEP Nº 4/2023. 11 p.
García S, Martinez Puljak G, Hernández, D. 2018. Uso del monitoreo satelital como indicador del esfuerzo pesquero de la flota comercial argentina. Inf Invest INIDEP Nº 26/2018. 19 p.
Kössler W, Lenz HJ, Wang XD. 2021. Is the Benford law useful for data quality assessment? En: Knoth S, Schmid W, editores. Frontiers in statistical quality control 13. ISQC 2019. Cham: Springer. DOI: https://doi.org/10.1007/978-3-030-67856-2_22
Maunder M, Punt A. 2004. Standardizing catch and effort data: a review of recent approaches. Fish Res. 70. 141-159
Mebane W. 2008. Election forensics: the second digit Benford’s Law test and recent American presidential elections. Detecting and deterring electoral manipulation. Washington: Brooking Press.
Mochty L. 2002. Die Aufdeckung von Manipulationen im Rechnungswesen - was leistet das Benford’s Law. Die Wirtschaftsprufung. 55 (14): 725-736.
Morrow J. 2014. Benford’s Law, families of distributions and a test basis. CEP Discussion Papers Nº 1291. Londres: Centre for Economic Performance.
Newcomb S. 1881. Note on the frequency of use of different digits in natural numbers. Am J Math. 4 (1): 39-40.
Nigrini M. 1999. I’ve got your number. J Account. 187: 79-83.
Nigrini M. 2012. Benford’s Law: applications for forensic accounting, auditing, and fraud detection. Vol. 586. John Wiley Sons.
Noleto-Filho EM, Carvalho AR, Thomé-Souza MJF, Angelini R. 2022. Reporting the accuracy of small-scale fishing data by simply applying Benford’s law. Front Mar Sci. 9: 947503. DOI: https://doi.org/10.3389/fmars.2022.947503
Pauly D, Belhabib D, Blomeyer R, Cheung WL, Cisneros-Montemayor AM, Copeland D, Harper S. 2014. China’s distant-water fisheries in the 21st Century. Fish Fish. 15: 474-488.
Pearson K. 1900. On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philos Mag Ser. (5) 50: 157-175.
Pinkham, R. 1961. On the distribution of the first significant digits. Ann Math Stat. 32: 1223-1230.
R Development Core Team. 2019. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
Schisterman EF, Perkins NJ, LIU A, Bondell H. 2005. Optimal cut-point and its corresponding Youden index to discriminate individuals using pooled blood samples. Epidemiology. (16) 1: 73-81. DOI: https://doi.org/10.1097/01.ede.0000147512.81966.ba
Schräpler Jörg P. 2010. Benford’s Law as an instrument for fraud detection in surveys using the data of the socio-economic panel (SOEP). SOEPpapers. 273. 56 p. DOI: https://doi.org/10.2139/ssrn.1562574
Silva L, Filho DF. 2021. Using Benford’s Law to assess the quality of COVID-19 register data in Brazil. J Pubic Health. 43: 107-110.
Scott P, Fasli M. 2001. Benford’s Law: an empirical investigation and a novel explanation. CSM Technical Report. 349. Department of Computer Science, University Essex.
Tsagbey S, De Carvalho M, Page GL. 2017. All data are wrong, but some are useful? Advocating the need for data auditing. Am Stat. 71 (3): 231-235.
Tesfamichael D, Pauly D. 2011. Learning from the past for future policy: approaches to time series catch data reconstruction. West Indian Ocean J Mar Sci. 10 (2): 99-106.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Sebastián García, Julieta S. Rodríguez, Bruno V. Menna
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors of articles published in Marine and Fishery Sciences retain copyright on their articles, except for any third-party images and other materials added by Marine and Fishery Sciences, which are subject to copyright of their respective owners. Authors are therefore free to disseminate and re-publish their articles, subject to any requirements of third-party copyright owners and subject to the original publication being fully cited. Visitors may also download and forward articles subject to the citation requirements. The ability to copy, download, forward or otherwise distribute any materials is always subject to any copyright notices displayed. Copyright notices must be displayed prominently and may not be obliterated, deleted or hidden, totally or partially.
This journal offers authors an Open Access policy. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other legal purpose within the Creative Commons 4.0 license (BY-NC-SA), without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of Open Access.
