Government agencies and researchers produce endless reams of statistics. While statistics can be valuable, they can be easily misrepresented. A 2017 study on the use of statistics in news characterized the problem as such:
“The constant supply of data produced by think tanks, government agencies, independent researchers, academics and others is a significant and a potentially healthy democratic resource. But the time constraints that characterize modern news production put considerable pressure on journalists, who have to interpret the sometimes highly complex methods and meanings behind statistics, reporting data even-handedly and with clarity.”
Without critical and educated evaluation of statistics, the public could be misled by information that’s misused, incomplete, or manipulated. Unfortunately, it seems that manipulation of statistics to further an agenda is becoming more common throughout society. Bad information propagated by government agencies especially is affecting policy decisions and harming the public when news outlets report it with insufficient examination.
Here are three common ways governments and modern society in general misuse statistics.
1. “Predicting” the Long-Term Effects of Complicated Legislation: Obamacare
When the Congressional Budget Office (CBO) tried to predict the effects of the Affordable Care Act (commonly known as “Obamacare”), it was utterly wrong on numerous points. The CBO released their projections in a 2010 report that became a vital component of how President Obama marketed the bill.
Congressmen Mark Meadows and Jim Jordan published an article in the Washington Examiner in 2017 detailing how wide of the mark CBO’s analysis actually was:
“As the ACA made its way through Congress in 2010, policy decisions were made based on flawed CBO projections, such as the one saying 21 million Americans would enroll in the insurance exchanges by 2016. The actual number was less than half that, around 10 million, and the miscalculation was disastrous for consumers.”
“CBO also miscalculated other economic impacts of the ACA. For example, they projected Medicaid expansion to cost $4,200 per enrollee. The actual cost turned out to be to $6,366 per enrollee.”
In other words, the CBO overestimated how many people would be enrolled in the new insurance exchanges by over 100% while underestimating the cost of the Medicaid expansion by over 50%.
2. Using Bad Measurement Techniques: Child Poverty in America
In December of last year, a United Nations report contended that: “A shockingly high number of children in the US live in poverty.” The report cites a plethora of statistics to support its assertions.
But do the report’s statistics convey an accurate picture of the situation?
Daniel Mitchell, in an article for the Foundation for Economic Education (FEE), looked into the report’s statistics and found some serious problems. For example, the UN’s report argues that: “About 20 per cent of [American] children live in relative income poverty, compared to the OECD average of 13 per cent.” However, Mitchell points out that the OECD defines the poverty threshold income as “50% of the median disposable income” of the relevant country. The problem with the statistics becomes obvious at this point.
Since America has the highest disposable income in the OECD, this measurement of poverty is highly misleading. By the OECD’s definition, the average poor person in America will have a far higher standard of living and real income than a poor person almost anywhere else in the world. This illustrates the problems a faulty measurement technique can create.
3. Drawing Broad Conclusions from Narrow Data: The Unemployment Rate
The Trump administration has repeatedly touted America’s increasingly low unemployment rates as proof of a robust and healthy labor market. However, Trump himself has argued that the measurement is incredibly narrow and even said in 2015 that the “true” unemployment rate at the time could have been be as high as 40% (when it was officially 5.1%).
While numerous outlets have debunked the methods used to get the 40% figure, Trump correctly noted that the unemployment rate doesn’t account for numerous unemployed groups, which can create misleading conclusions about the health of the economy.
To understand the issue, it’s necessary to look at how the Bureau of Labor Statistics (BLS) calculates the unemployment rate.
Simply put, the unemployment rate is the percentage of people classified as “unemployed” who are part of the labor force. The “unemployed,” per the BLS website, are those who “do not have a job, have actively looked for work in the prior 4 weeks, and are currently available for work.”
One main flaw with this definition is that the BLS’s data from May 2018 shows 5.7 million people wanted a job but did not meet the criteria to count as part of the labor force for various reasons. Therefore, a huge number of jobless people willing to work have no bearing on the unemployment rate.
A second main flaw is that because part-time workers are counted as employed, people could be underemployed and struggling even when the unemployment rate is low.
Therefore, even though the unemployment rate can give a general impression of the labor market, there’s an incredible amount of relevant information not taken into account.
One way to deal with these shortcomings is the BLS’s “U-6” measurement of unemployment which accounts for part-time workers, discouraged workers who want jobs but have quit looking for them, and those who have looked for a job in the last 12 months. The U-6 rate is at 7.6% as of May 2018.
What Can you Do About It?
So how can you handle the deluge of questionable statistics in the modern world? The concluding paragraph of a past article on Intellectual Takeout still constitutes good advice:
“Writing in the journal Statistical Science, the sociologist Joel Best argues that we ought to avoid calling statistics ‘lies’, and instead educate ourselves so that we can question how and why statistical data are generated. . . Numbers themselves – unless purposefully falsified – cannot lie, but they can be used to misrepresent the public statements and ranking systems we take seriously. . . When you read a statistic, of any kind, be sure to ask how – and more importantly, why – the statistic was generated, whom it benefits, and whether it can be trusted.”
Have you seen other ways governments and news outlets misuse statistics? Are there methods to evaluate statistical information that you like to use?
[Image Credit: Flickr-U.S. Department of State]