Fast Food and Peru

Statement by the Economist is off by up to 1000%

Statement by the Economist is off by up to 1000%

In a slowly progressing series of global media blunders news agencies like the Economist, Bloomberg* and our own beloved Peru this Week†† made outlandish claims about Peru having the highest density of fast-food restaurants in the world. Thankfully for Peru, all such claims were and still are untrue. However, the real story that the Fast-Food Industry has their sights set on Peru and has targeted large subsets of its wealthier population in ways never seen before never made it to light.

Bloomberg news organizations sensationalizes its own data causing a worldwide misinformation creep.

Bloomberg news organizations sensationalizes its own data causing a worldwide misinformation creep.

Last year Bloomberg, a news organization, put out what it calls a “Ranking” of “Fast-Food Density” which is a table entitled: “FAST-FOOD FIXES PER SQUARE MILE”  showing the following columns:

  • Rank
  • Country
  • Total fast-food establishments
  • Stores per urban agglomeration square mile
  • Distance to travel for fast food (miles)
  • Stores per 100,000 urban agglomeration population, adjusted for urban poverty

In this Ranking it names Peru as #1. So what is Peru #1 at? What does this table mean? In truth, unless you are an industry insider not much! It is all about marketing, not consumer demand, consumption or popularity! The so-called ranking is neither a scientific study nor a non-scientific grouping of useful outside the industry data. Frankly it is a poorly constructed list that is faulty in nearly every way and can’t even be considered useful or raw data for the industry. Peru is in no way first in anything related to do with fast-food, except that in certain pools, of certain customers living close to certain stores the industry has targeted those specific Peruvians in a greater way than in other similar places. Very conditional data, extremely, complex (and inaccurate at that) only intended to be used to say a specific thing about industry marketing and store proximity. Unfortunately, news agencies (including Bloomberg) either did not read the table carefully, misunderstood what is fundamentally faulty and misleading or just wanted to sensationalize assuming because it comes from Bloomberg that it must be OK.

bad data and sensationalism

Quoting bad info, “Peru this Week” furthers unfounded claims about Peru’s love for fast-food.

I’m all for stopping the spread of fast-food everywhere, especially in my backyard. I doubt that Bloomberg, a news agency for Wall Street is interested in curbing fast-food consumption. If the data were true I’d be all over it. Bloomberg has put out some industry related data and then sensationalized it to mean something it is not. Too bad they did because many other news agencies have picked up the so-called story and added their own inaccuracies to it. To further exacerbate, even more news agencies pickup the stories by the secondary news agencies and repeated the now twisted false conclusions and yet twisted them again!

Just because fast-food is bad for you, the environment, the economy, culture, and the planet doesn’t mean that shoddy reporting is acceptable. So shame on Bloomberg for putting out sensationalized data, then further sensationalizing it in their own media, and shame on the other news agencies like the Economist and anyone else that didn’t check their facts. The Economist falsely claimed that “Peru has the highest density of fast-food joints in the world.” (sic) which is a complete and utter fabrication of facts. One could say that Peru has the highest density of fast-food joints, in certain areas, targeted to very specific markets compared to only 34 other specifically targeted countries.

Sloppy reporting misleads readers and many other news agencies.

Sloppy reporting misleads readers and many other news agencies.

A Look at the So-Called Ranking

Though if carefully read, and if not taken very seriously, the original report, which is largely a table and some dense disclaimer text, is not incredibly inaccurate. The problem is that report headlines and table headings do not clearly state that the data only reflects rich people living in poor countries. Further when told by an arm of the agency that authored the report very sensationalized and misleading statements were included opening a window of opportunity for others to pick up and propagate the misunderstood data.

  1. Neither a study nor scientific, the ranking only includes what it calls emerging consumer market countries and it fails, miserably, to readily give a full basis and clear description for what that criteria is. The criteria is paramount to understanding the data set. Rather, Bloomberg relies on a complex subset of what the industry seems to have named as emerging markets. This alone would make any claim that any of the countries listed rank anywhere specifically when compared to the rest of the world, untrue. Nonetheless, news report after news report all across the globe have included headlines and statements to the effect that Peru leads the world in some way related to fast-food and they directly or indirectly rely on this ranking. Assuming the information published in the ranking is correct, which you will see it is not, one could only (falsely) draw the conclusion that of the countries in the data sample Peru was the leader (of what exactly?). Why aren’t the massive fast-food consuming countries even listed? Because this list includes only a subset of nations that are NOT developed, and with further limitations only 34 countries qualify for this sample. So claims about the world can hardly be drawn from this small target!
  2. The Ranking does measure how close stores are to consumers and specifically only those customers that they think have enough money to buy their fast-food. They say: “To best measure the impact of fast foods on these markets, the population data were adjusted to exclude urban poverty.” which means if you included the poorer people, the ranking stores would be much further from everyone. Peru is still a third world country, though they are considered by fast-food to be an emerging market. So again, a higher density of certain people (with money) are closer to fast-food in Peru compared to 34 other countries. But if you compared all Peruvians or all countries in the world, such as the United States, Peru would be nowhere near the top, they would be near the bottom. Probably, less than 31% of Peru’s population qualifies to be listed in their urban agglomeration (I can’t even say that) and then the target population would be further reduced by poverty. As one can quickly see, the targeted population is a small percentage of the total population. Claims than most Peruvians live within a kilometer of a KFC doesn’t hold water. I don’t, I live five times that distance and it is the only KFC for hundreds of kilometers (in Huancayo, Peru). Yet, for my income I’m pretty close to one and I am well targeted. But Huancayo has five of their target Fast-Food restaurants which are roughly one per 100,000 people. This is very low. In the USA there are places where there are more than five McDonald’s per 100,000 people. Huancayo Peru could never support 25 McDonald’s restaurants (it doesn’t even have one), let alone all the other chains combined, so claims made by reporters are so obviously off the mark I wonder how anyone would believe them?
  3. The Ranking uses multiple sources for differing countries to pool stores into urban agglomeration population clusters. This is a big NO, NO** as the differing sources use differing criteria. This alone sets this data apart from science. It is by this standard that the data must be flawed. They say: “SOURCES: “The 2011 Urban Blue Book” published by the Chinese Academy of Social Sciences; 2010 Census of China; Demographia World Urban Areas, 8th Annual Edition, 2012; PBS Frontline report on Iran (2011); The World Bank; “World Urbanization Prospects, The 2011 Revision” published by the U.N. Department of Economic & Social Affairs”. Sure for marketing purposes it might fly, but to make definitive claims–absolutely not. No reporter should touch this except in the context of a marketing report.
bloomberg methodology misinterpreted

Report includes only rich citizens in poor countries and excludes nearly all the worlds population.

So What is this Ranking About and What Does is Really Say?

The Ranking was intended for the fast-food industry only, and has been unfortunately sensationalized in the media, a trend started by Bloomberg itself. The data points to the degree of ‘agglomeration’ that has been used (to likely achieve efficiencies in target marketing and distribution) by fast-food firms in locating near each other within pools of highly dense areas of targeted customers only. When you strip away all of that target marketing speak, the data says ZERO about the amount of fast-food Peruvians consume, ZERO about how close all Peruvians are to fast-food (even in cities), ZERO about how much Peruvians spend on fast food, ZERO about how often they consume it, ZERO about the degree to which fast-food restaurants are located in Peru compared to other places, cities or markets, ZERO about the health impacts of fast-food consumption in Peru, or anything else that many of the various news reports claim.

To sum it up, the ranking should not be used to show that Peru, or its cities are any more or less affected or centered around fast-food except to say they have certain people in certain places that are being marketed too more densely (on a location basis) than a group of other countries of high growth interest.

So yes Peru, be afraid, be very, very afraid because you are the leader in target marketing. Big horrible businesses that want to infect your culture and your citizen’s bodies have set you squarely in their sights and you are willingly welcoming them!

I don’t ever eat at fast food chains. Ever! I can’t stand the horrible food and the health scourge that they represent to the whole world. Sure, I do not always eat healthily, but I do my best to minimize the consumption of junk food, fast food, prepared food, and restaurant food. I want to control what goes into my body as closely as possible and as often as practical. So if I’m going to eat fried food, fat laden food, sugary sensations, I’m going to do so infrequently and in personally meaningful ways. Sitting in a KFC is never meaningful to me.

 *     Quigley, John & Altstedter, Ari Altstedter, et al. (2012, October 14). Re: BLOOMBERG RANKINGS, FAST-FOOD DENSITY [Marketing report Ranking prepred by Bloomberg/BLOOMBERG L.P.]. Retrieved from
†     The Economist, et al. (2013, July 27). Re: The Economist: and Mexico City print edition [Print and online story about obesity and fast food]. Retrieved from
**     Wikipedia, et al. (2013, July 28). Re: Wikipedia: Urban agglomeration […it can be problematic to compare different agglomerations around the world.]. Retrieved from
††     Ortiz, Diego M., et al. (2013, July 25). Re: Peru: malnutrition and obesity side by side: Peru this Week [Online story about obesity and fast food]. Retrieved from