Lies, Damned Lies…
By Leslie Macmillan
Statistics cited by media and governments are often based on politics, not facts, says Tufts expert
“We’re just saying be careful when you consume these numbers, because there are concrete policy implications to choosing one set of numbers over another,” says Kelly M. Greenhill. Photo: Tia Chapman
If you believe the numbers you read in the headlines, it would seem that global crime, drug use and pornography are growing at alarming rates. One story says Internet child porn is a $20-billion-a-year industry. Another suggests the number of war dead in Iraq has topped 650,000. But are these statistics accurate?
In a new book, Sex, Drugs and Body Counts: The Politics of Numbers in Global Crime and Conflict (Cornell University Press), Kelly M. Greenhill, an assistant professor of political science in the School of Arts and Sciences, and co-editor Peter Andreas from Brown University argue that statistics cited by the media and governments are usually based on politics, not facts.
In a series of essays by political scientists, sociologists and policy analysts, the book deconstructs the metrics used to influence everything from how we combat terrorism and drug trafficking to what wars we choose to fight.
The media, the government and nonprofit organizations all play a part in inflating or deflating numbers to suit their agendas, the authors say, whether it’s to justify their budgets, intervene in a conflict or boost readership. The consequences are serious: public policy is often based on these questionable figures.
Tufts Journal: Your book deals specifically with global crime and armed conflict. Are these the realms where you see the worst examples of inflated numbers?
Kelly M. Greenhill: The phenomenon of bad statistics is ubiquitous, but crime and conflict are two issues for which it’s really hard to get good numbers. Crime happens in the shadows, and conflict happens in dangerous conditions, where it can be really difficult for people like journalists to get in and get the facts. In both these areas, the motivation to politicize the numbers can be profound.
The book says that bad statistics inform public policy. Can you give an example?
A few years ago, Bill Clinton cited the figure that there were 250,000 people killed in the Bosnian civil war in the 1990s as a rationale to continue the war in Iraq. But the 250,000 number had by that point been conclusively discredited. One can muster all kinds of reasons to be in Iraq—just as one can identify a wide variety of reasons not to be there. But to continue to use a bad number from an old war to justify participation in a current conflict is just bad policy.
Another example is U.S. policy towards Colombia. Politicized drug numbers can help support active U.S. intervention there and support an end to it—it all depends what numbers one chooses and what one does with those statistics. Of course, numbers are all about politics: if you want to show you’re making progress, you want a big number before action is taken, and you want a small number after.
Why don’t we question the numbers we read or hear about?
Psychologically, the things we will take on board without question are those that fit with our worldview. “Serbs are bad: of course they must have killed that many Bosnians!” I call that “cognitively congruent”—it fits with what we like to believe.
And if you can’t use a bad number, you can simply say, “He’s just like Hitler.” It’s a different kind of inflation, but it’s the same kind of shorthand. There is a tendency to turn every potential adversary into a Pol Pot or Hitler.
Why are numbers important? Do people have an innate desire to quantify everything?
I don’t know that it is innate, but for some, numbers have a fetishistic quality. Numbers cause many people to view things as more “scientific” and therefore “true.” My field of political science, for instance, is growing more quantitatively dominated and history is increasingly viewed—in a derogatory way—in some circles as just a series of anecdotes.
How did you go about investigating suspect figures, like the one you gave of the 250,000 people killed in Bosnia?
Someone who was in the room when that 250,000 number was first bandied about told me directly that some Bosnian officials were sitting around having coffee with some internationals, and someone asked how many people they thought had been killed. One official thought about it for a moment and then responded with the rather dramatic 250,000 figure. Essentially, it was just pulled out of the air, but it stuck. There was political intent there: it was all about getting the U.S. and the Europeans to step up and more actively intervene. We now know that number is probably closer to 97,000 because a research center in Sarajevo undertook a multi-year study, looking at death records. This is one of those relatively rare occasions where we actually now have a pretty good number.
How much dissemination of bad numbers is deliberate distortion, how much is lazy reporting and how much stems from a legitimate difficulty in measuring or even defining the scope of a problem—for example, counting refugees?
It’s hard to quantify that. I spend a fair amount of time in my chapter about statistics and armed conflict talking about what I call “the good intentions crowd.” I think we are all in favor of saving peoples’ lives, but it’s not unambiguously the right thing to do to inflate the number of, say, refugees, in any given humanitarian emergency, because doing so can have serious consequences. Resources may be devoted to conflicts where they’re not needed instead of being sent places where the demands are real and acute.
Your chapter on numbers in war deals with conflicting counts of refugees after the Rwandan genocide: the U.S. argued that they were fleeing militiamen, while the UN labeled them refugees. Was that a legitimate disagreement over definition of terms that produces two sets of numbers?
The thrust of the book is that conflicts over numbers are quite often simply battles over policy choices. In the realm of armed conflict, the numbers cited usually pertain to body counts and refugee numbers. If you think it’s time to intervene, you pick a big number; if you don’t want to intervene, you pick a small number. There are almost always going to be competing numbers. We’re just saying be careful when you consume these numbers, because there are concrete policy implications to choosing one set of numbers over another.
What impact do you hope the book will have?
We want to encourage a greater measure of skepticism, a greater willingness to raise questions, and a little bit of humility when consuming and using statistics. We need to ask, are there reasons to question these statistics? The answer is probably “yes” under most circumstances.
To be clear, there are times when numbers don’t matter. There are times when, at the end of the day, we’re going to have to fight a war [based on] whether the number of people killed is “X” or “10 times X.” To decide to spend money or to send our troops to one place and not to another is to make choices, and if these choices are predicated on bad numbers, such decisions can be enormously consequential.
Leslie Macmillan can be reached at leslie.macmillan@tufts.edu.