Archive: May 2011

What works

As an image, this picture does an excellent job of supporting the argument made in the accompanying article, which is basically that merging two large companies, each with their own deeply embedded systems for handling passengers, planes, workers, and baggage as well as their own attitudes about how things should be done is a task nobody can understand until they attempt it. And then it becomes tedious almost immediately. The New York Times often saves clinchers for the end of the article and this one was a good one. Peter Wilander, an executive at Delta responsible for in-flight services (talk to this guy if you have a problem with the peanuts), cannot hide his frustration,

“The amount of work is boring beyond belief,” Mr. Wilander said. “It is also critical to the airline.”

What needs work

Is there anyone else out there who feels that if the PhD in applied mathematics is resorting to a merger by post-it, that there are real shortcomings in the system’s management abilities at Delta? Theresa Wise is Delta’s Chief Information Officer and the creator of this lovely Post-It art. While the post-its are both aesthetically pleasing and instantly graspable, I could not square the idea that a bunch of post-its stuck to a wall would really be the right answer to a problem like this:

A major switch happened when the new airline canceled all Northwest’s bookings and transferred them to newly created Delta flights in January 2010. It required computer engineers to perform 8,856 separate steps stretched out over several days.

Here’s hoping that my experience with Delta later today does not involve making seat assignments with Post-Its. For all of my snarkiness, I generally find Delta to be a good airline, better than the old Northwest.

References

Mouawad, Jad. (19 May 2011) Delta-Northwest Merger’s Long and Complex Path in The New York Times, Business Day Section. [Graphic How to Merge Two Airlines]

Kids and happiness

Thanks to my twitter feed I landed on Philip Cohen’s blog post “Children beget happiness, eventually” on his blog Family Inequality. In the post, Cohen discusses A Global Perspective on Happiness and Fertility which appeared in Population and Development Review last March.

Margolis and Myrskyla used the World Values Survey from 1981 – 2005 for a total of 201,988 responses across 86 countries to perform their inquiry into the relationship between having kids and being happy. They measured happiness by asking people “taking all things together, would you say you are very happy, quite happy, somewhat happy, or not at all happy?”. They controlled for all sorts of things that probably matter like socioeconomic status, country level effects, and state welfare regimes. This is global evidence, folks, not US-only.

Cohen included the graph below and discussed the author’s findings which, in summary, are as follows:

1. Having kids does not lead to happiness when parents are actively involved in raising said children.
2. Older parents consistently report being happier than their childless counterparts. [My editorial comment: It is reasonable to believe that, for the most part, the children are no longer living with their parents by the time their parents start to report increases in happiness. At the very least, the kids are at least spending more time out of the home by the time mom and dad are between ages 40 and 49. The majority of kids are almost surely out of the house by the time their parents are 50+ which is the ‘happiest’ time to be a parent. Perhaps it’s because parents are proud of their kids’ accomplishments, perhaps it’s because the parents are no longer anxiously worrying about their kids well-being on a day-to-day basis. Who can say.]
3. Results in the 15 – 19 age cohort have fewer data points and are thus somewhat less representative. It’s hard to have three or four kids while in that age cohort.

An experiment

I used the exact same evidence to create the graph at the top of the blog because I wasn’t satisfied that the results were being clearly communicated by the graph above. Instead of plotting the happiness of age cohorts, I flipped it around and looked at happiness by number of children. Since I used the exact same information – pulling it directly from the graph because I couldn’t find a corresponding table in the paper – I do not have distinctly different findings to report. Duh. However, this is an excellent example of why visualizations are meaningful. It’s the same information, plotted in two different ways.

In my version, it is clearer to see that having 1 – 3 children represents extremely similar patterns of happiness across the life course. I discount the results at the very low age range because we know that the data at that end is less-than-representative. If we just look from the 20-29 cohort through to the 50+ cohort, we see that having more kids eventually represents more happiness for parents but that they are about equally unhappy during the most active years of child-rearing.

Having four or more kids breaks the pattern. This is evident in both graphic representations. In my opinion, it is more evident in the first version of the graph than the second version, as they appear in this post. I used a similar sensibility for the colors of 1, 2, and 3 children trends and a different kind of color for the 4+ kids scenario.

The graphs do not explain why having four (or more) kids would be so different than having, say, three kids. More study is needed.

My #1 take-away: do not have four or more children if you value your happiness.
My #2 take-away: Think twice about having any children at all if you would prefer to be happy for the twenty or so years it’s going to take those kids to move out.
My #3 take-away: Thanks, mom and dad. I hope you’re happy now.

References

Cohen, Philip. (14 May 2011) Children beget happiness, eventually [blog post] on Family Inequality.

Conley, Dalton. (2005) The Pecking Order: A Bold New Look at How Family and Society Determine Who We Are, New York: Vintage.

Margolis, Rachel and Mikko Myrskyla. (9 March 2011) A Global Perspective on Happiness and Fertility in Population and Development Review, Vol.37(1): 29-56.

What works

It’s a fun Friday post. Pull the slider to the right and watch the center of the US population move to the left (west) and south. What I like best about this is the interactivity. If it were just a static connect-the-dots it wouldn’t stay in your mind the way it does when you are the one doing the pulling of the slider. Getting those muscles involved, however minor their involvement might be, works more of your brain than if only your eyes were doing the work. My second favorite thing is more brain than brawn – the Census people found a way to remind us that in the beginning of our nation, we had fewer states. We added them as we went – almost always adding states to the west – so that can help explain why the center of the population originally started sliding leftwards. It has continued to slide leftwards (and towards the south) because those newer states have some lovely living conditions to offer. Not everyone loves the snow and ice of New England winters or the hurricanes of the southeastern seaboard.

What needs work

The center of population is a composite ‘score’ which tells us virtually nothing about why people are moving or where they are moving to…clearly, Missouri is not now a hotspot for internal migration. But if you were a school kid trying to grok the concept of the center of population, you might easily conclude that Missouri is a populous state.

I might have added some indicator of population sizes across US regions (state by state would be too confusing, but lumping by region would be fine).

References

2010 US Census. Center of Population

Drug cartels cause social ills

Unless you’ve had your head in a bucket since 2007, you are at least vaguely aware that Mexican drug cartels trafficking their goods into the US have caused significant social illness in Mexico, especially in areas close to the US border. Social illness here can be measured in cartel-driven murders, but that captures only the most gruesome, sensational branches of the drug virus. Besides the deaths are fear, anxiety, mistrustfulness as well as poverty, corruption, and vast inequality.

Is mapping the right way to understand Mexico’s drug trafficking problem?

The graphics here try to pack all of the complexity and destruction of those social ills into maps. Maps are rational. They allow us to feel we have a handle on the components that make up a problem. In this case, I am sure they are not explaining the whole story. I’m also not sure they are trying to explain the whole story.

What I like about the first map is that the map makers lay out the obvious: which cartels are where. Then they go one step further and highlight the contested territory. In case the colors aren’t coming through clearly, the white areas are the disputed areas. There are a lot of white areas.

And yet…

One would expect most of the violence in a situation like this to be in the disputed areas. But that isn’t the case. Most of the violence is near the US border. The border is another kind of contested territory, one that is much more important than white areas as far as violence prevention is concerned. In fact, those areas aren’t governed by one cartel or another because those areas are not critically important to drug trafficking. None of the cartels much care.

So let’s take a look at another map because I’m thinking the first one implies that we should find violence in the middle of the country.

Drugs and deaths in Mexico

This graphic shows not only traffic patterns – where do the drugs go? – but also maps of where the deaths have been. It quickly becomes clear that the drug-related deaths are up near the US border, not in the ‘disputed areas’ highlighted in the previous map. In this map, (thanks unnamed National Post graphic designer) that undisputed area is left unclaimed and unlabeled. That’s a more accurate way to understand those regions and the inset series of maps below the main map do a good job of visually locating cartel-related violence.

The other thing I love about this map is that it specifies *which* drugs are being trafficked. Call me crazy, but I have found it odd that there is a great deal of talk about ‘drugs’ in Mexico as if there is no good reason to talk about which drugs are being moved where. Why is it useful to know which drugs are going where? First, it’s nice to know which drugs because different drugs have different price points per volume and weight. Economics matter. If one drug has a higher profit margin than another because it retails for more per ounce but doesn’t cost much more to produce/transport, one could assume that it will become more popular. Then again, demand matters, too. Even if pot is easy to produce, doesn’t mean you can convince cocaine users to try weed. They probably already tried it and moved on.

Another reason it matters which drugs we’re talking about is that detection and apprehension vary from drug to drug. An easy example: a pot sniffing dog probably won’t lead authorities to a stash of ephedra. What’s more, being able to tell where things are coming from and going to means that it is easier for authorities to target weak points in the routes. We know from news stories (I recommend looking at the LATimes, see references below), we know that drug runners pour much energy into protecting the drug routes right at the US border. But they aren’t digging tunnels under all of Mexico. There are points in the chain of drug traffic that are more vulnerable. Some of those points are deep within Mexico where it might be difficult to get well-trained, cooperative authorities with the necessary tools and manpower to perform raids.

My main gripe about these graphics is that they display this problem as a Mexican problem. This is not a Mexican problem. It is a Mexico-US problem. The demand in the US is pulling all those drugs up from south of the border. Looking at it this way helps introduce conversations about economic imbalances. I imagine that one of the reasons drugs come from Mexico is the same reason that many large companies choose not to have large labor forces in the US: labor is cheaper in Mexico. Various instantiations of poverty also tend to encourage corruption; encouraging local police to fight the cartels is hard when they are out-gunned and out-manned by cartels who can afford to pay off whoever they want including witnesses, other cops, border agents, and whoever else is likely to become cooperative after the application of a bit of grease.

Conclusion

The drug-related social illness in Mexico is an unfolding problem, one that has been discussed with more complexity elsewhere. I hope to illustrate that while the rationality of mapping patterns is appealing, it also tends to obscure complexity. It’s easier to misinform than inform with a map. They are deceivingly neat, these maps.

References

LATimes “Mexico Under Siege” special section, US News.

Llana, Sara Miller. (13 January 2011) “Mexico drug war death toll up 60 percent in 2010. Why?” in The Christian Science Monitor, World/Americas section.

National Post Staff. (31 December 2010) Graphic: Drug Terror Just South of the Mexico Border in The National Post. [Graphic].

How is the scoring system determined?

British researchers affiliated with the Independent Scientific Committee on Drugs met for a one day workshop and constructed a composite scoring system to determine which drugs are most harmful both to individuals and to society collectively. Scores can range from 0 – 100. Authors David Nutt, Leslie King and Lawrence Phillips found that,

heroin, crack cocaine, and metamfetamine were the most harmful drugs to individuals (part scores 34, 37, and 32, respectively), whereas alcohol, heroin, and crack cocaine were the most harmful to others (46, 21, and 17, respectively). Overall, alcohol was the most harmful drug (overall harm score 72), with heroin (55) and crack cocaine (54) in second and third places.

The full list of factors that were included in the composite score are here:

• Mortality
• Damage
• Dependence
• Impairment of mental functioning
• Loss of tangibles
• Loss of relationships
• Injuries to others
• Crime increase
• Environmental degradation
• Family breakdowns
• International turmoil
• Economic cost
• Loss of community cohesion and reputation

Though it is possible to go into an explanation of how each of these was measured and subsequently combined to produce the composite scores, I am going to leave that discussion to the authors of the original study. There’s an overview graph below and the full article Drug Harms in the UK: A multi-criteria decision analysis is at the Lancet.

What can be done?

I found it interesting that there was no attempt made to distinguish between legal and illegal drugs. Yes, of course, some drugs are not clearly legal or illegal. They are legal when prescribed and supervised by a doctor but illegal when used off-label or outside the medical authority system (like anabolic steroids, methadone, and marijuana in California). I assumed that most methadone users are under some kind of supervision but that most anabolic steroid users are using the steroids off-label (ie illegally). You can quibble with my choices below. The point here is that I found the graph to have more context if the legality issue was visually inscribed into it.

There are age limits and places where it’s illegal to smoke or drink, but for the most part everyone will be able to use alcohol and tobacco legally for most of their lives. Methadone is probably being used legally in most cases. That’s why I shaded those bars grey. I am not expert on methadone, but I see that it is much less harmful to users and to society than heroin, the drug it stands in for, so I guess if this were the only data I had to make a decision about continuing methadone treatment programs, I would keep them going. I would also call for close scrutiny of methadone programs. Something is clearly not working as well as it could be.

As for alcohol and tobacco…well…it’s hard to argue *for* the continuing legality of alcohol. How large do detriments to society have to be to trigger additional control mechanisms? The authors of the study noted that alcohol is part of society and it isn’t going anywhere. I agree. Prohibition was a failed experiment in this country and I’m not suggested we try it again. However, I would like to reopen the debate about how the negative impacts of alcohol can be alleviated. I recommend that all new cars must have breathalyzers in them. If the driver cannot blow a legal sample, the car won’t start. Yes, people could game that system by having their friends blow for them, but often one’s friends are also drunk. And hopefully, friends really wouldn’t let their friends drive drunk. Once upon a time, seatbelts were considered extraneous and seatbelt laws were considered constraints upon American’s rights to freedom and the pursuit of happiness. Well, when a drunk driver kills one of your family members, you might decide that the sudden loss of your mother or son or niece puts a much bigger crimp in your pursuit of happiness than a breathalyzer in your car ever would have. Will breathalyzers make cars cost more? Probably. But the cost of dealing with car accidents caused by drunken driving, even when they aren’t fatal, is absorbed by random individuals who happened to be in the wrong place/time as well as tax payers who pay to repair guard rails, subsidize public hospitals and EMTs, pay cops’ salaries, and so on.

References

Nutt, David J, Leslie A King, and Lawrence D Phillips. (6 November 2010) “Drug harms in the UK: a multicriteria decision analysis” The Lancet, Vol 376(9752): 1558 – 1565.

Excellent use of a map

I spend a lot of time explaining which uses of maps are bad. In this case, the use of a map is spot-on. Nothing could better display this information than a map. So here’s what you are seeing. Due to the mechanism that determines flight pricing, some non-stop flights from City A to City B are cheaper than multi-leg flights that take passengers from City A to City C with a layover in City B. Figuring out where these curiously expensive cities are and then booking tickets through them (instead of to them) is called hidden-city ticketing. It’s technically forbidden by the airlines because it messes up their profit-making abilities, more on that later.

There are some markets – Atlanta, Cleveland, Salt Lake City, Charlotte, Detroit, Cincinnati or Chicago O’Hare – where prices are too high compared to the rest of the airfare market. If you want the longer version of why this is true, there is an excellent, lengthy, FiveThirtyEight/Nate Silver blog post, Which Airports Have the Most Unfair Fares?, on the vagaries of airfare pricing. Suffice it to say, if you happen to need to fly into one of these expensive cities, especially if you do it often, you are interested in figuring out how to avoid feeling like you are getting ripped off.

As a visual representation of this simple-but-hard-to-explain Point A to Point C via Point B scenario, a map is the best way to clarify the concept. Just look at how the visual works. A person starts in Fargo and wants to get to Chicago. If they crank that request through kayak, they end up with a direct flight to Chicago for $586 [ouch]. But if, instead, they tell kayak that they want to go from Fargo to New York with a layover in Chicago they end up paying only $213. Kayak let’s you tell it where you’d like to have a layover. (Detroit’s airport is surprisingly nice, for instance, and if I have to layover in the summer, I’ll go through Detroit.)

How can airlines charge less to fly a person a greater distance? Not all airline pricing is driven by fuel, snacks, and human capital costs. A good bit of it is driven by demand and supply – the classic economics story from your undergrad days. Some markets are not well served creating mini-monopolies for service in and out of those airports. Other markets, like New York, have a great deal of service provision forcing airlines to pull their prices into a lower, more competitive range.

Is it legal?

Perhaps you have read somewhere in your ticket’s fine print that the airline prohibits you from bailing out of your scheduled travel halfway through the trip. The New York Times asked a lawyer whether or not it’s even legal for the airlines to penalize people this way and how far they can go to punish someone caught doing this. It turns out, there are penalties the airlines can impose, but most of them can be side-stepped by savvy travelers. The Times presented recommendations, summarized here:

Making a habit of this certainly won’t endear you to the airlines. Most of them — the major exception being free-spirited Southwest Airlines — expressly forbid it in their ticketing rules. But those rules don’t carry the force of law, and most travel lawyers say that their recourse is limited. They could probably preclude you from flying with them in the future, but their case for demanding penalties is weak, and the risk of detection is low if you don’t book these kinds of routes more often than a couple of times per carrier per year.

Also, do not end up checking bags. They will end up at your final destination. Get to the gate early enough to ensure yourself space in the overhead bins.

Book your itinerary as two one-way flights. This should be logically obvious. If you are going from Fargo to Chicago but you book your ticket through to New York, you clearly won’t be wanting a return flight from New York because you never intended to actually see the Big Apple in the first place. The other kicker is that if you fail to report for part of your ticket, the airline will probably cancel whatever remains on the ticket. So book one-ways.

Don’t lie if the airlines catch you; lying increases your likelihood of being found guilty of fraud. Honesty is the best policy.

References

Silver, Nate. (6 April 2011) Which Airports Have the Most Unfair Fares? [blog post] The New York Times, FiveThirtyEight blog.

Silver, Nate. (4 May 2011) How to beat high airfares. Sunday Times Magazine. [Graphic The Art of Hidden-City Ticketing]

Voluntary Pricing

I put this simple bar graph together to illustrate the following text that I got from Yochai Benkler’s paper and he got from a paper about Magnatune pricing,

In the recent paper on Magnatune, the data revealed that over a five year period, 48% of users paid $8 per album where $5 was the minimum, and only 16% paid the minimum. Another 15% paid $10, 7.3% $12, etc., up to 2.6% who paid $18 per album. Payments were highly anchored around coordination focal points — for example, the drop down menu called “$8” the “typical” donation. While 48.05% of fans paid $8, only 2.93% paid $7.50 and 0.34% paid 8.50.

I wanted to see how these numbers looked as a graphic because it was a little hard to make sense of what was happening just reading about them. What concerned me was that Benkler seemed to have crafted his text to imply – but not state directly – that voluntary music pricing schemes lead people to pay more, not less, for their music. This would make a fantastic story, but for some reason I wasn’t entirely comfortable just going ahead with that implication tucked into my subconscious mind.

When I graphed it, I added a block on the lower end of the scale to help illustrate the fact that Magnatune will not sell albums below $5. So, if we were expecting a bell curve of payment choices, all of the people who might have paid less than $5 were bunched up at the $5 mark or priced out altogether. Maybe they grumbled and agreed to pay $5 when they would have chosen $2 or $3 or perhaps they just didn’t buy the album at all. It’s hard to say.

Of course, I wouldn’t really expect people to distribute their payments for an album along a bell curve. I would have expected more clustering around the lower numbers – why would people pay more if they could pay less? Especially because they may not have taken the time to listen to the whole album for one reason or another…so they are paying for something that is not completely known. We’ve all been there before – some songs on albums just aren’t as good as others.

On the other end of the spectrum are the people who not only have taken time to get so familiar with the music that they aren’t worried about the dreadfulness of the unknown. Benkler’s paper indicates that people who develop close relationships with the musicians through collaborative efforts or fansites might be willing to pay more as a sign of respect and admiration.

Getting back to the graphic as a mechanism for making sense of the information, the point is that there are actually FEWER people in the lower range than in the higher range. Nearly half of people paid the requested amount ($8) but where they deviated from the requested amount, more people paid decided to give more, rather than less.

How can we explain that irrational behavior? I’m guessing that it has something to do with the free riders, the people who aren’t paying anything at all. These are not people who are getting their music from Magnatune, these are the friends of those paying people who are sharing iTunes accounts and getting their new music for free. There are other ways to get music for free besides sharing iTunes accounts but I’m not trying to get into all that. My point is that, after having graphed this information, I feel reasonably assured that there are quite a few people who are listening without paying a thing. It doesn’t really matter to me how they are doing that.

What matters is that the shape of the graph and the distribution of payments that we can see leads me to believe that there ought to be a substantial proportion of people – at least 14% – who are free riders. That’s a very rough estimate, but it complicates the happy story that if musicians pursued voluntary pricing they might stand to make more. It’s hard to say if that’s true or not. I guess it’s nice to allow your biggest fans to ‘vote with their dollars’ and just shrug off the free-rider problem as being outside the pricing structure. If people don’t want to pay, they are going to find ways not to pay no matter how the pricing structure is set up. But if people DO want to pay more, they can only do so under a voluntary pricing scheme. If the prices are set, they cannot opt to ‘vote’ with their dollars and pay more.

*I stick ‘vote’ in scare quotes when I am linking it up to economic activity because I like to reserve the term voting for direct political participation rather than for political participation that is supposedly possible by participating in capitalist exchanges. I hardly think that consumer behavior is as critically important as electoral behavior. Not everyone agrees with me, but that’s not a topic for this post.

References

Benkler, Yochai. (2011) “Voluntary Payment Models” in Rethinking Music. Cambridge, MA: Berkman Center for the Internet and Society at Harvard University.

Vanity sizing = planned obsolescence

Vanity sizing is the fashion industry’s particular take on planned obsolescence, especially for women’s clothing. By incrementally expanding the measurements keyed to each size, people will continually wear smaller and smaller sizes as the year’s progress (assuming the people stay the same size). This means that if you were a size 8 last season, by next season you may not have lost a pound or toned an ab, but you will miraculously fit into a size 6 because the size 6 will now have the dimensions that the size 8 had last season. People, and women in particular, seem to get a feel-good bump out of wearing smaller sizes and will therefore buy more items in the new smaller size than they would have if their size hadn’t changed.

Goldilocks and the three dresses

Vanity sizing turns us all into Goldilocks. And you know what? I don’t care which fairy tale character is being dragged out to describe the situation of femininity today, neither I nor anyone else is trying to be a fairy tale character (exception granted to Kate Middleton). No more Cinderella, no Alice in Wonderland (falling through a looking glass is no fun at all, even if lawn bowling with flamingos and evil queens makes a good spectator sport), and Goldilocks spent most of that story lonely, frustrated, and displeased.

The chart above uses empirical evidence to *prove* that shopping will surely frustrate all women. I don’t know if men have the same problem, though I would imagine they are somewhat better off because their sizes are not just keyed to measurements, they ARE measurements. A 30×32 pair of pants is supposedly the same from one brand to the next. Maybe that’s true in mens clothes. As you can see above, women’s dress sizes certainly do not adhere to any agreed upon standard. A size 8 has a huge range of variability. However, even when women’s clothes do use measurements to describe their sizes – like jeans, which are sized not by the 0,2,4,6,8 system but by the waist measurement – a size 26 in one brand is not the same as a size 26 in the next brand. I learned that the hard way last week. And yes, I can hear fashion designers pointing out that different cuts fit differently – some are meant to be loose, others slim fitting. Maybe I’m just not fashion-aware and I’m mistaking fit differences for vanity sizing when any true fashionista would see that there is simply a different fit implied by each cut. Well. Here’s what I have to say to that: if the jeans are supposed to be 26″ in the waist, they better be 26″ in the waist. The rest of them can fit like jeggings or flare like early 1970’s bell bottoms or, heck, they can poof out like MC Hammer pants. But the waist needs to be 26″ if it is sold as a size 26″.

The graphic

This graphic is great for three reasons:

1. These folks did their homework. There are many brands represented here, from fashion labels like Marc Jacobs and Dolce and Gabbana to more affordable clothing from Old Navy and the Gap (which I always thought was the worst offender in the vanity sizing race to the biggest clothing labeled with the smallest sizes). The sheer volume of the comparison is extremely helpful.

2. The small inset of a woman’s hourglass torso acts like a site plan to the more detailed drawing. I love this. Perhaps that’s because my first drawings were architectural in nature and I like the orienting function of the relatively small overview.

3. They included three measurements – bust, waist, and hip – all three are critical to a good fit. And not all brands feel the same way about the ideal ratio of bust to waist to hip. I don’t buy button down shirts because what fits in the waist never fits in the bust.

Overall, this graphic confirms my angry fears that one day I will not be able to buy anything off the rack. Both my svelte best friend and my advisor (males) struggle to find off-the-shelf items that fit well. The smallest sizes are often too big to fit well and when they aren’t too big, they sell out very quickly. My advisor occasionally wears a shirt he inherited from his grandmother when she died. It’s a nice way to remember his grandma but it also fits better than many of his other options.

Letter to fashion world

Dear fashion world:

Please continue to make clothing for small people. And please find a way for women who are small to wear something other than ‘0’ or ’00’. Psychologically, it is bad to be called a zero; being a double zero is worse. Zeros don’t count for anything. Most people want to count for something. And since women’s identities and dress sizes are far too often conflated, wearing a size zero is like being a zero. That is an existentially dubious position to occupy.

Also, I realize that people might be inclined to buy clothing if it is a smaller size – they feel gratified that they have lost weight and are happy to buy new, ‘smaller’ clothes – but if you keep slowly enlarging the dimensions on all of the sizes, there won’t be clothing left for the small people. And at some point, losing the market share from the smaller people will trump the market share gained by getting slightly larger people to buy a little more simply because whatever they are buying is a smaller size than they thought they were. I think the solution is to put a lock on the smallest sizes and only muck around with the larger sizes – add more sizes to the top if Americans need more accommodations on that end.

Please stop expanding the dimensions of the smallest sizes. Small people need clothes that fit well, too.

– Not a zero

References

Clifford, Stephanie. (April 24, 2011) One Size Fits Nobody: Seeking a Steady 4 or a 10 [Graphic] in the New York Times, Business Day Section.