Fact and Fiction in EU-Governmental Economic Data

Abstract

To detect manipulations or fraud in accounting data, auditors have successfully used Benford’s law as part of their fraud detection processes. Benford’s law proposes a distribution for first digits of numbers in naturally occurring data. Government accounting and statistics are similar in nature to financial accounting. In the European Union (EU), there is pressure to comply with the Stability and Growth Pact criteria. Therefore, like firms, governments might try to make their economic situation seem better. In this paper, we use a Benford test to investigate the quality of macroeconomic data relevant to the deficit criteria reported to Eurostat by the EU member states. We find that the data reported by Greece shows the greatest deviation from Benford’s law among all euro states.

The impact of fiscal austerity on suicide: On the empirics of a modern Greek tragedy

“…Among them, the Greek government found underreporting of the budget deficit by nearly 50%, shortly after the Greek elections, in November 2009. According to the study of Rauch et al (2011), macroeconomic data, such as the budget deficit and debt, among others, reported by Greece shows the greatest deviation from Benford's law (that is used to detect manipulated data) among all euro member states. In particular, on 5 November 2009, the Greek public sector revised its budget deficit to 12.7% of Gross Domestic Product (GDP) from the 6% originally disclosed, which was then revised once again to the unprecedented 15.6% in November 15,…”

Detection of Anomalies in Accounting Data Using Benford’s Law: Evidence from India

“…ISSN 2329-9150 2017 researchers have verified for themselves that Benford's Law is widely obeyed. In practice, the accuracy of Benford's Law has been experimented in many fields, such as accounting fraud detection (Nigrini, 1996), electricity bills, street addresses, stock prices, macroeconomic data like population numbers, death rates, lengths of rivers (Rauch et al 2011), scientific fraud detection (Diekmann, 2007).…”

Identifying Odometer Fraud: Evidence from the Used Car Market in the Czech Republic

“…Entries include an identifier for the seller, the region where the car is located, the dates when the ad was placed and when it ended, the car's characteristics (type of vehicle, producer, condition, engine displacement 4See also Benford (1938); Cho and Gaines (2007); Diekmann (2007); Jacob and Levitt (2003); Judge and Schechter (2009) ;Nye and Moul (2007) ;Rauch, Göttsche, Brähler, and Engel (2011);Varian (1972).…”

“…First digits tests exploit the fact that first digits of a vector of (count) numbers should follow the Benford distribution (Benford 1938;Fewster 2009;Varian 1972).10 This has been exploited in a number of studies in order to identify fraudulent data (Cho and Gaines 2007;Debreceny and Gray 2010;Judge and Schechter 2009;Mebane 2010;Mebane and Kalinin 2009;Nigrini 1999;Nye and Moul 2007;Rauch et al 2011), but also criticized (Deckert, Myagkov, and Ordeshook 2011;Diekmann 2007). More specifically, this method is problematic because deviations from Benford's law may occur in true data if the data is 10Let D denote a digit and p a probability, then the Benford probability mass function is defined as ppD " dq " log 10 pd`1q´log 10 d, where d " 1, 2, .…”