Tagged: benford’s law

When data goes bad

Bad data on sex trafficking: flow chart
Image by Lauren York on the Data Journalism Blog

Data is so central to the decision-making that shapes our countries, jobs and even personal lives that an increasing amount of data journalism involves scrutinising the problems with the very data itself. Here’s an illustrative list of when bad data becomes the story – and the lessons they can teach data journalists:

Deaths in police custody unrecorded

This investigation by the Bureau of Investigative Journalism demonstrates an important question to ask about data: who decides what gets recorded?

In this case, the BIJ identified “a number of cases not included in the official tally of 16 ‘restraint-related’ deaths in the decade to 2009 … Some cases were not included because the person has not been officially arrested or detained.” Continue reading

Statistics as journalism redux: Benford’s Law used to question company accounts

A year and a day ago (which is slightly eerie) I wrote about how one Mexican blogger had used Benford’s Law to spot some unreliable data on drug-related murders being used by the UN and Mexican police.

On Sunday Jialan Wang used the same technique to look at US accounting data on over 20,000 firms – and found that over the last few decades the data has become increasingly unreliable.

Deviation from Benford's Law over time

“According to Benford’s law,” she wrote, “accounting statements are getting less and less representative of what’s really going on inside of companies. The major reform that was passed after Enron and other major accounting standards barely made a dent.”

She then drilled down into three industries: finance, information technology, and manufacturing, and here’s where it gets even more interesting.

“The finance industry showed a huge surge in the deviation from Benford’s from 1981-82, coincident with two major deregulatory acts that sparked the beginnings of that other big mortgage debacle, the Savings and Loan Crisis.  The deviation from Benford’s in the finance industry reached a peak in 1988 and then decreased starting in 1993 at the tail end of the S&L fraud wave, not matching its 1988 level until … 2008.”

Benford's law, by industry

She continues:

“The time series for information technology is similarly tied to that industry’s big debacle, the dotcom bubble.  Neither manufacturing nor IT showed the huge increase and decline of the deviation from Benford’s that finance experienced in the 1980s and early 1990s, further validating the measure since neither industry experienced major fraud scandals during that period.  The deviation for IT streaked up between 1998-2002 exactly during the dotcom bubble, and manufacturing experienced a more muted increase during the same period.”
The correlation and comparison adds a compelling level to the work, as Benford’s Law is a method of detecting fraud rather than proving it. As Wang writes herself:
“Deviations from Benford’s law are [here] compellingly correlated with known financial crises, bubbles, and fraud waves.  And overall, the picture looks grim.  Accounting data seem to be less and less related to the natural data-generating process that governs everything from rivers to molecules to cities.  Since these data form the basis of most of our research in finance, Benford’s law casts serious doubt on the reliability of our results.  And it’s just one more reason for investors to beware.”

I love this sort of stuff, because it highlights how important it is for us to question data just as much as we question any other source, while showing just how that can be done.

It also highlights just how central that data often is to key decisions that we and our governments make. Indeed, you might suggest that financial journalists should be doing this sort of stuff routinely if they want to avoid being caught out by the next financial crisis. Oh, as well as environment reporters and crime correspondents.

Statistical analysis as journalism – Benford's law

drug-related murder map

I’m always on the lookout for practical applications of statistical analysis for doing journalism, so this piece of work by Diego Valle-Jones, on drug-related murders, made me very happy.

I’ve heard of the first-digit law (also known as Benford’s law) before – it’s a way of spotting dodgy data.

What Diego Valle-Jones has done is use the method to highlight discrepancies in information on drug-delated murders in Mexico. Or, as Pete Warden explains:

“With the help of just Benford’s law and data sets to compare he’s able to demonstrate how the police are systematically hiding over a thousand murders a year in a single state, and that’s just in one small part of the article.”

Diego takes up the story:

“The police records and the vital statistics records are collected using different methodologies: vital statistics from the INEGI [the statistical agency of the Mexican government] are collected from death certificates and the police records from the SNSP are the number of police reports (“averiguaciones previas”) for the crime of murder—not the number of victims. For example, if there happened to occur a particular heinous crime in which 15 teens were massacred, but only one police report were filed, all the murders would be recorded in the database as one. But even taking this into account, the difference is too high.

“You could also argue that the data are provisional—at least for 2008—but missing over a thousand murders in Chihuahua makes the data useless at the state level. I could understand it if it was an undercount by 10%–15%, or if they had added a disclaimer saying the data for Chihuahua was from July, but none of that happened and it just looks like a clumsy way to lie. It’s a pity several media outlets and the UN homicide statistics used this data to report the homicide rate in Mexico is lower than it really is.”

But what brings the data alive is Diego’s knowledge of the issue. In one passage he checks against large massacres since 1994 to see if they were recorded in the database. One of them – the Acteal Massacre (“45 dead, December 22, 1997″)is not there. This, he says, was “committed by paramilitary units with government backing against 45 Tzotzil Indians … According to the INEGI there were only 2 deaths during December 1997 in the municipality of Chenalho, where the massacre occurred. What a silly way to avoid recording homicides! Now it is just a question of which data is less corrupt.”

The post as a whole is well worth reading in full, both as a fascinating piece of journalism, and a fascinating use of a range of statistical methods. As Pete says, it is a wonder this guy doesn’t get more publicity for his work.