scholarly journals Benford's Law and the Detection of Election Fraud

2011 ◽  
Vol 19 (3) ◽  
pp. 245-268 ◽  
Author(s):  
Joseph Deckert ◽  
Mikhail Myagkov ◽  
Peter C. Ordeshook
Keyword(s):  
Success Rate ◽  
Benford’S Law ◽  
Official Statistics ◽  
Benford's Law ◽  
The Law ◽  
Election Fraud ◽  
Common Application

The proliferation of elections in even those states that are arguably anything but democratic has given rise to a focused interest on developing methods for detecting fraud in the official statistics of a state's election returns. Among these efforts are those that employ Benford's Law, with the most common application being an attempt to proclaim some election or another fraud free or replete with fraud. This essay, however, argues that, despite its apparent utility in looking at other phenomena, Benford's Law is problematical at best as a forensic tool when applied to elections. Looking at simulations designed to model both fair and fraudulent contests as well as data drawn from elections we know, on the basis of other investigations, were either permeated by fraud or unlikely to have experienced any measurable malfeasance, we find that conformity with and deviations from Benford's Law follow no pattern. It is not simply that the Law occasionally judges a fraudulent election fair or a fair election fraudulent. Its “success rate” either way is essentially equivalent to a toss of a coin, thereby rendering it problematical at best as a forensic tool and wholly misleading at worst.

scholarly journals Comment on “Benford's Law and the Detection of Election Fraud”

2011 ◽  
Vol 19 (3) ◽  
pp. 269-272 ◽  
Author(s):  
Walter R. Mebane
Keyword(s):  
Careful Analysis ◽  
Benford’S Law ◽  
Simulation Exercise ◽  
Strategic Actions ◽  
Significant Digit ◽  
Benford's Law ◽  
The Mean ◽  
Election Fraud ◽  
Open Question

“Benford's Law and the Detection of Election Fraud” raises doubts about whether a test based on the mean of the second significant digit of vote counts equals 4.187 is useful as a test for the occurrence of election fraud. The paper mistakenly associates such a test with Benford's Law, considers a simulation exercise that has no apparent relevance for any actual election, applies the test to inappropriate levels of aggregation, and ignores existing analysis of recent elections in Russia. If tests based on the second significant digit of precinct-level vote counts are diagnostic of election fraud, the tests need to use expectations that take into account the features of ordinary elections, such as strategic actions. Whether the tests are useful for detecting fraud remains an open question, but approaching this question requires an approach more nuanced and tied to careful analysis of real election data than one sees in the discussed paper.

scholarly journals Using Benford’s Law to detect data error and fraud: An examination of companies listed on the Johannesburg Stock Exchange

2014 ◽  
Vol 9 (3) ◽  
pp. 341-354 ◽  
Author(s):  
A Saville
Keyword(s):  
Stock Exchange ◽  
Financial Analysts ◽  
Benford’S Law ◽  
Accounting Data ◽  
Johannesburg Stock Exchange ◽  
Accounting Numbers ◽  
Benford's Law ◽  
Data Error ◽  
The Law ◽  
Private Investors

Accounting numbers generally obey a mathematical law called Benford’s Law, and this outcome is so unexpected that manipulators of information generally fail to observe the law. Armed with this knowledge, it becomes possible to detect the occurrence of accounting data that are presented fraudulently. However, the law also allows for the possibility of detecting instances where data are presented containing errors. Given this backdrop, this paper uses data drawn from companies listed on the Johannesburg Stock Exchange to test the hypothesis that Benford’s Law can be used to identify false or fraudulent reporting of accounting data. The results support the argument that Benford’s Law can be used effectively to detect accounting error and fraud. Accordingly, the findings are of particular relevance to auditors, shareholders, financial analysts, investment managers, private investors and other users of publicly reported accounting data, such as the revenue services

scholarly journals Benford’s Law Application: Case of Elections in Sri Lanka

2020 ◽  
Author(s):  
Richmond Sam Quarm ◽  
Richmond Sam-Quarm
Keyword(s):  
Sri Lanka ◽  
Political Parties ◽  
Presidential Election ◽  
Benford’S Law ◽  
Third Place ◽  
Benford's Law ◽  
Data Points ◽  
The Mean ◽  
Election Fraud ◽  
Election Results

Sri Lanka, like many developing countries has been involved in a circle of allegations of election fraud. Usually these claims are pronounced more by losing parties. This study uses Benford’s law, a law of probability distribution of digits, to investigate whether the election fraud claims might have merit. A sample in this study is made of 808 election data. This data comes from the 2010 Presidential election for representatives from three major political parties and from 2010 General Election data. All of the data points were obtained through reliable government sources, two of which are, the Department of Elections website of Sri Lanka, and the National News Paper statistics (2010). The study contrasts the distribution of the first digit of election results against the Benford’s Law benchmark. After obtaining the results, we organize the data and find median, mean, mode and standard deviation. The preliminary results showe that the data does not align with Benford’s law predictions. In other words, it shows that the data does not follow the law where the mean is larger than the median and there is a positive skewness then it likely follows a Benford’s distribution. The distribution of the first digit of actual data for three parties disagrees with Benford's law. This misalignment is more pronounced for the winning party than for the second and third place parties, respectively. We, therefore, look forward to run the data through several critical analyses and observing if there shall be any fraud or manipulation in numbers.

scholarly journals Benford’s Law Application: Case of Elections in Sri Lanka

2020 ◽  
Vol 3 (4) ◽  
Author(s):  
Agim Kukeli ◽  
◽  
Hettiyadura Shehan Karunaratne ◽  
Keyword(s):  
Sri Lanka ◽  
Political Parties ◽  
Presidential Election ◽  
Benford’S Law ◽  
Third Place ◽  
Benford's Law ◽  
Data Points ◽  
The Mean ◽  
Election Fraud ◽  
Election Results

Sri Lanka, like many developing countries has been involved in a circle of allegations of election fraud. Usually these claims are pronounced more by losing parties. This study uses Benford’s law, a law of probability distribution of digits, to investigate whether the election fraud claims might have merit. A sample in this study is made of 808 election data. This data comes from the 2010 Presidential election for representatives from three major political parties and from 2010 General Election data. All of the data points were obtained through reliable government sources, two of which are, the Department of Elections website of Sri Lanka, and the National News Paper statistics (2010). The study contrasts the distribution of the first digit of election results against the Benford’s Law benchmark. After obtaining the results, we organize the data and find median, mean, mode and standard deviation. The preliminary results showe that the data does not align with Benford’s law predictions. In other words, it shows that the data does not follow the law where the mean is larger than the median and there is a positive skewness then it likely follows a Benford’s distribution. The distribution of the first digit of actual data for three parties disagrees with Benford's law. This misalignment is more pronounced for the winning party than for the second and third place parties, respectively. We, therefore, look forward to run the data through several critical analyses and observing if there shall be any fraud or manipulation in numbers.

scholarly journals Analyzing Big Data with Benford’s Law: A Lesson for the Classroom

2019 ◽  
Vol 12 (2) ◽  
pp. 33-42
Author(s):  
Susan W. Lanham
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Information Resources ◽  
Benford’S Law ◽  
Introductory Statistics ◽  
Instructional Approaches ◽  
Practical Application ◽  
Mathematical Concepts ◽  
Benford's Law ◽  
The Law

Most literature related to Benford’s Law discusses what the law can be used for and how it works but fails to address effective methods and procedures for teaching the law to students. This article examines existing information resources to determine the most effective methods and procedures used to explain this Law to those who have no experience with it. A contribution to knowledge is made by providing step by step instructional approaches for teaching Benford’s Law to students that are tied to existing literature. Benford’s Law is a fascinating lesson for students who have been exposed to statistical and mathematical concepts for as long as they can remember yet know nothing of the law’s existence. This lesson is suitable for any introductory statistics or mathematics course where students are learning about probability. The Law has a practical application in the field of business and can also be taught as part of a fraud examination, data analytics, or auditing course.

Can Vote Countsʼ Digits and Benfordʼs Law Diagnose Elections?

Benford's Law ◽  
2015 ◽  
Author(s):  
Walter R. Mebane,
Keyword(s):  
United States ◽  
Political Parties ◽  
Strategic Behavior ◽  
The United States ◽  
Benford’S Law ◽  
Conditional Mean ◽  
Benford's Law ◽  
Election Fraud

This chapter illustrates how the conditional mean of precinct vote counts' second digits can respond to strategic behavior by voters in response to the presence of a coalition among political parties. The digits in vote counts can help diagnose both the strategies voters use in elections and nonstrategic special mobilizations affecting votes for some candidates. The digits can also sometimes help diagnose some kinds of election fraud. The claim that deviations in vote counts' second digits from the distribution implied by Benford's law is an indicator for election fraud, generally fails for precinct vote counts. This chapter shows that such tests routinely fail in data from elections in the United States, Germany, Canada and Mexico, countries where it is usually thought that there is negligible fraud.

scholarly journals Was there any widespread fraud in 2020 presidential election? What does Benford's Law say?

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Deeya Datta ◽  
David Banks
Keyword(s):  
Presidential Election ◽  
Forensic Analysis ◽  
Benford’S Law ◽  
Large Set ◽  
Donald Trump ◽  
Comprehensive Investigation ◽  
Electoral Fraud ◽  
Benford's Law ◽  
Election Fraud ◽  
Basic Statistical Analysis

Fair elections free of any interference are integral tenets of any functioning democracy, and widespread election fraud is undoubtedly a serious threat to a free republic. While instances of electoral fraud are much more prevalent in countries with illiberal democracies, the U.S has recently faced such an accusation. Although he was unable to provide any concrete evidence, the former U.S. President Donald Trump accused his opponent, Joe Biden, now president, of electoral fraud after the presidential election. Fortunately, election forensics are often successful in investigating the validity of such fraud allegations. In this paper, I applied Benford’s law, a rule that should stand up to any large set of natural numbers, such as un-tampered electoral data. Using this law and basic statistical analysis of votes of U.S. counties for candidates of the two major parties, I completed a forensic analysis to investigate Mr. Trump’s allegation. My comprehensive investigation does not find any evidence supporting his allegation.

When Does the Second-Digit Benford’s Law-Test Signal an Election Fraud?

2011 ◽  
Vol 231 (5-6) ◽  
Author(s):  
Susumu Shikano ◽  
Verena Mack
Keyword(s):  
Statistical Method ◽  
Specific Form ◽  
Benford’S Law ◽  
Parliamentary Election ◽  
Test Results ◽  
Electoral Fraud ◽  
Wide Range ◽  
Benford's Law ◽  
Election Fraud ◽  
Minimal Information

SummaryDetecting election fraud with a simple statistical method and minimal information makes the application of Benford’s Law quite promising for a wide range of researchers. Whilst its specific form, the Second-Digit Benford’s Law (2BL)-test, is increasingly applied to fraud suspected elections, concerns about the validity of its test results have been raised. One important caveat of this kind of research is that the 2BL-test has been applied mostly to fraud suspected elections. Therefore, this article will apply the test to the 2009 German Federal Parliamentary Election against which no serious allegation of fraud has been raised. Surprisingly, the test results indicate that there should be electoral fraud in a number of constituencies. These counter intuitive results might be due to the naive application of the 2BL-test which is based on the conventional χ

scholarly journals Benford’s Law Application: Case of Elections in Sri Lanka

2020 ◽  
Author(s):  
Richmond Sam Quarm ◽  
Mohamed Osman Elamin Busharads
Keyword(s):  
Sri Lanka ◽  
Political Parties ◽  
Presidential Election ◽  
Benford’S Law ◽  
Third Place ◽  
Benford's Law ◽  
Data Points ◽  
The Mean ◽  
Election Fraud ◽  
Election Results

Sri Lanka, like many developing countries has been involved in a circle of allegations of election fraud. Usually these claims are pronounced more by losing parties. This study uses Benford’s law, a law of probability distribution of digits, to investigate whether the election fraud claims might have merit. A sample in this study is made of 808 election data. This data comes from the 2010 Presidential election for representatives from three major political parties and from 2010 General Election data. All of the data points were obtained through reliable government sources, two of which are, the Department of Elections website of Sri Lanka, and the National News Paper statistics (2010). The study contrasts the distribution of the first digit of election results against the Benford’s Law benchmark. After obtaining the results, we organize the data and find median, mean, mode and standard deviation. The preliminary results showe that the data does not align with Benford’s law predictions. In other words, it shows that the data does not follow the law where the mean is larger than the median and there is a positive skewness then it likely follows a Benford’s distribution. The distribution of the first digit of actual data for three parties disagrees with Benford's law. This misalignment is more pronounced for the winning party than for the second and third place parties, respectively. We, therefore, look forward to run the data through several critical analyses and observing if there shall be any fraud or manipulation in numbers.

Sign in / Sign up

Export Citation Format

Share Document