Does Benford’s Law hold in economic research and forecasting?

Günnel, Stefan; Tödter, Karl‐Heinz

doi:10.1007/s10663-008-9084-1

Cited by 37 publications

(29 citation statements)

References 23 publications

(10 reference statements)

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“…Mithilfe des Newcomb-Benford-Gesetzes wurde die Verteilung dieser Zahlen berechnet [1,16]. Der χ 2 -Test untersuchte die Differenz der Verteilung des erwarteten gegenüber dem berechneten Auftreten der Einzelzahlen [5,9]. Dies wurde jeweils für die erste Ziffer einer Zahl berechnet.…”

Section: Materials Und Methodeunclassified

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Vom Finanz- zum Wissenschaftsbetrug

et al. 2012

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Fraud is as old as Mankind. There are an enormous number of historical documents which show the interaction between truth and untruth; therefore it is not really surprising that the prevalence of publication discrepancies is increasing. More surprising is that new cases especially in the medical field generate such a huge astonishment. In financial mathematics a statistical tool for detection of fraud is known which uses the knowledge of Newcomb and Benford regarding the distribution of natural numbers. This distribution is not equal and lower numbers are more likely to be detected compared to higher ones. In this investigation all numbers contained in the blinded abstracts of the 2009 annual meeting of the Swiss Society of Anesthesia and Resuscitation (SGAR) were recorded and analyzed regarding the distribution. A manipulated abstract was also included in the investigation. The χ(2)-test was used to determine statistical differences between expected and observed counts of numbers. There was also a faked abstract integrated in the investigation. A p<0.05 was considered significant. The distribution of the 1,800 numbers in the 77 submitted abstracts followed Benford's law. The manipulated abstract was detected by statistical means (difference in expected versus observed p<0.05). Statistics cannot prove whether the content is true or not but can give some serious hints to look into the details in such conspicuous material. These are the first results of a test for the distribution of numbers presented in medical research.

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Section: Materials Und Methodeunclassified

“…Vereinfacht gilt also: Für die erste Ziffer gilt folgende Wahrscheinlichkeit: p(d i )=log 10 (1+1/d); hierbei ist mit d die erste Ziffer einer Zahlenfolge vor dem Komma gemeint, und es gilt i={1,2, …9} [9,17].…”

Section: Materials Und Methodeunclassified

Vom Finanz- zum Wissenschaftsbetrug

et al. 2012

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“…Tällaisia testejä ovat esimerkiksi parametriset khiin neliötesti ja keskiarvotesti tai ei-parametrinen Kuiperin testi, joka perustuu Kolmogorov-Smirnovin testille (Günnel & Tödter 2009). Useissa käytännön sovelluksissa pyritään suorittamaan negatiivinen testi empiirisen jakauman "luonnollisuudelle" (Nigrini 1996).…”

Section: Lain Tilastollinen Testaaminen Ja Empiiriset Sovelluksetunclassified

Näkökulmia tilintarkastukseen ja arviointiin

Leponiemi¹,

Ruohonen²,

Wacker³

et al. 2017

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“…Diekmann (2007), Günnel andTödter (2009), Tödter (2009) Regression coefficients and standard in original research articles…”

Section: Applying Benford's Lawmentioning

confidence: 99%

Applying 'Benford's Law' to the Crosswise Model: Findings from an Online Survey on Tax Evasion

Kundt

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Applying "Benford's law" to the Crosswise Model: Findings from an online survey on tax evasion Terms of use: Documents in THORBEN KUNDT Zusammenfassung/ AbstractMany surveys on sensitive topics such as tax evasion suffer from the reluctance of respondents to provide truthful answers which can cause downward-biased estimates. This paper addresses this problem by making use of a recent survey method (Crosswise Model) designed to provide positive incentives for respondents to answer sensitive questions more truthful. We extend the Crosswise Model by applying the so-called "Benford Illusion" which allows us to increase the precision of the Crosswise Model that is less statistically efficient than other methods. To test the effectiveness of the model in providing privacy protection, we carried out an online survey in which the respondents were randomly allocated into two splits differing only by the questioning technique applied. Our results suggest that the Crosswise Model can help to increase privacy protection compared to a simple direct questioning approach. As a consequence, survey estimates of tax evasion using the Crosswise Model are likely to become more valid. At the same time, we show that were able to obtain an efficient estimator without substantially decreasing privacy protection, even for a relatively small sample size.

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Does Benford’s Law hold in economic research and forecasting?

Cited by 37 publications

References 23 publications

Vom Finanz- zum Wissenschaftsbetrug

Vom Finanz- zum Wissenschaftsbetrug

Näkökulmia tilintarkastukseen ja arviointiin

Applying 'Benford's Law' to the Crosswise Model: Findings from an Online Survey on Tax Evasion

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