Junk Charts

Making maps

I love articles that expose the behind-the-scenes of creating complex graphs.  This Wall Street Journal blog post tells us some dirty secrets behind these cartograms that depict the "influence" of different media outlets throughout the world.

(Via Andrew Sullivan; he's dissing NYT again)

Additional links of interest:

Original posting at Paris-based L’Observatoire des Médias blog

Boing Boing

Gawker

Online Journalism Blog (warning: this link is taken over by a rogue script from an advertiser or some other entity that distributes scripts so it wasn't loading when I tried)

The punch line

Mike K submitted this great entry months ago.  It's a map depicting stock market correction across the globe during the summer.  You have to click on the link to the WSJ website in order to see the interactive element.



Here are Mike's comments and mine:

Why it's bad:

First, to see the detail you have to click on the countries one by one. Hard to do a comparison of two countries. This makes it close to FlashJunk.

The color scheme is supposed to help but:

Second, the colors are too close together to allow easy comparisons of, say, Canada and Australia.

In addition, the binning of the colors is uneven and oddly chosen.  In the middle of the scale, each color shift represents 1% but at the edge, it is 5%, or more.

Third, area of these countries, or their geographic location, isn't really that relevant. Market cap might be. Then tiny-but-richly-capitalistic Netherlands wouldn't have to be shown in the middle of the Atlantic, as if the dikes had all burst and Amsterdam had floated out to sea.

Indeed, it begs the question: what were the gold dots suppose to signify? (Hint: it's not location.)

Fourth, why the selectivity? There's stock markets in Turkey, and in Russia, and in Ireland and in Thailand. (Oh, wait, they show the one in Thailand -- except they put it in Myanmar instead.)

Finally, the chart lacks a punch line. 

In the junkart version, I want to test the hypothesis of a global contagion so I plot the data in order of closing times of individual stock markets.  (I just guessed the closing times based on the map.)  Not much here though.

Source: "Global Correction", Wall Street Journal, August 2007.

Mid-week entertainment: dogma

This chart from a Wall Street Journal editorial has been making the rounds lately, being ridiculed left and right.  A number of you have been leaving comments here so I'm putting it up and center as our light entertainment for the week.

The chart is being used to justify this economic concept called the "Laffer Curve" which claims that lowering tax rates can increase total tax receipts (for example, because fewer people will cheat the government.)  As far as I know, it is dogma, and has never been proven empirically.

I also agree with Prof. Gelman's skepticism about using countries as experimental units to inform domestic policy.

Fire away!

Further reading:

Junk Chart readers

Economist's View
Tufte blog
Gelman blog

And more:

Cosmic Variance
Brad DeLong

Baby names and success

While we speak of baby names, David F. nominates this set of 6 charts from WSJ.  Compare this with Wattenberg's names voyager, and the benefit of interactive graphics is immediately evident.

In David's words:

They show graphs of six different names, but the two on the bottom use a dramatically different scale (from 1st to ~20th, instead of from 1st to 1000th). The introductory text notes the difference, but it is still a shock.

We like the use of "small multiples" but their impact is compromised if we don't keep the background material constant so that readers can compare between charts.  By having  different scales, the message was distorted: Mary has had a much larger drop than David, and it's easily missed in these charts.

Lines should take the place of areas which carry scant meaning in this context.

The use of blue and red is a nice touch but dovetailing the male and female charts strikes us as excessive fun.  It would have been clearer to give the sons and the daughters their own columns.

The article itself relates the anguish of modern parents in naming their babies.  Much of this angst can be traced to serious econometric studies that claim to have found cause-and-effect relationships between someone's name and their eventual success in life.  Some of this research was highlighted in Freakonomics, for example.  My stance is that all such studies are dubious, there being innumerable confounding factors (socio-economic, genetic, cultural, luck, etc. etc.).  In addition, the measured response can range from "happiness" to income to many other metrics.  The danger of finding something because one looks hard enough is very real.  We don't currently have tools powerful enough to substantiate this sort of studies.

Source: "The Baby-Name Business", Wall Street Journal, June 22, 2007

Losing the tune

Duncan C. nominates this Wall Street Journal chart.  A sure sign of trouble is when the accompanying article waxes about a new on-line music service, another one that practises "loss-based" pricing, i.e. priced to ensure a loss; the article does not mention anything about this chart.  It just stood on the side, like wallpaper.

Its key message does not seem to connect with the data.  "Growth in digital music downloads has not been rapid enough to offset declining CD sales": but in terms of total units, the chart shows a small dropoff in CD sales coupled with an explosion in digital songs sold.  Besides, the units of discs, songs and iPods are not directly comparable.

Source: "Listen to Music Free But Pay to Carry", Wall Street Journal, June 5, 2007.

The Immigrants' Path

A recent Wall Street Journal editorial used this chart (via the National Foundation for American Policy) to claim success for the "Bracero" guest worker program, initiated in 1942.  Their analysis:

... illegal border crossings subsequently plummeted.  Between 1953 and 1959, they fell by some 95%.  In 1960, mainly in response to complaints from labor unions, the program was scaled back and eventually phased out.

 

 

 

Long-time readers may recall Friedman's Crossover Law of Petropolitics, where the opportune criss-crossing of lines
plotted along double axes was taken as proof of causality.  Friedman's Law lurked here, right in the 1953-1959 range. 

 

The NFAP went one better: in their original version, they blew up the 1953-1959 period to show us the criss-crossing lines!

We see trouble right from the start.  The "subsequent" effect that proved the case occurred in 1953, over 10 years after the program started. During that first decade, the number of apprehensions rose 4388%, in spite of the guest worker program.

A scatter plot (below left) now shows the lack of any meaningful relationship between these two variables.  While high admissions appeared together with low apprehensions, any level of admissions had historically been paired with low apprehensions.

On the right, I connected the dots in chronological order.  Any claim of a negative relationship between admissions and apprehensions has been debunked.  From 1942 on (as we trace the line clockwise from lower left), first the nation experienced stepwise increasing admissions coupled with stepwise increasing apprehensions; then it witnessed sharply dropping apprehensions with relatively stable admissions; and finally it saw plummeting admissions while apprehensions remained low.  Three separate episodes, three distinct patterns.  There was no association, let alone causation.

Source: "Immigration Plan B", Wall Street Journal, June 13 2007.

Illusory disparity

The WSJ published a chart with the cheeky title of "Rich Get Richer" (reminiscent of the Economist).  The underlying data concerned one-, three- and ten-year returns for the buyout fund category.  For each return class, the overall mean and the means for the top and bottom 25% funds were depicted.

I won't go into the relevance of the title as I simply could not figure out how it connected with the data.  The following shows the original chart side by side with the junkart version.



Improvements include:

• Lines show the comparisons with a minimum of fuss compared with colored bars
• The overall mean return is placed in the middle of each line segment where it belongs, instead of being the first column
• The axis label, "annualized return", tells readers what is the performance measure
• Adding the word "funds" to "top quartile" and "bottom quartile" removes the possible confusion that those represent individual returns of the funds ranked at 25th and 75th percentiles, rather than the average returns of the bottom 25% and top 25% of funds
• The linear construct paints the correct picture that individual fund returns fall into a continuum

(Thanks to my students for some of these points.)

Reference: Wall Street Journal, Mar 3-4 2007.

Much data, zero info

The number crunching college football fans at the Wall Street Journal wondered out loud:

One of the biggest developments in college football in recent years was the decision by Virginia Tech and Miami -- perennial top-20 teams -- to leave the Big East conference and join the Atlantic Coast Conference.  How much has that strengthened the ACC?

The data table on the right was ostensibly the answer.  Readers were drawn to the bolded numbers, the almost identical winning percentages of ACC and SEC (averaged over the last decade, as the text explained).

The question is a classic one of cause and effect: did the addition of two strong teams cause the ACC to become stronger?  Startlingly, the data cited was useless, and the analysis conducted irrelevant.

First, the difference in winning percentages between ACC and SEC is the wrong metric.  Something more pertinent is, for example, the change in winning percentage of ACC before and after the team additions.

Second, the observation period is seriously mistaken.  The ACC expansion occurred in 2004 so average winning percentages from 1995-2005 have zilch to say about its effect.

Third, a Web search uncovers that major realignment occurred again in the ACC in 2005, making it very difficult to isolate the effect of adding Virginia Tech and Miami in 2004.

Thus, the data table contains zero information for addressing the stated problem.  How to measure the effect properly seems to me a tall order, and a good discussion topic.

Besides the iffy statistics, it is also impossible to read this table.  The data in the lower left triangle is a reflection of those in the upper right triangle, containing no new information.  Head-to-head conference comparisons seem to serve no purpose.  Actual win-loss numbers create clutter while adding no insight.  (Theoretically, the larger the number of contests between any two conferences, the more reliable are the winning percentages.  Confidence intervals is a much better way to present such information but even those would be over-kill for our purpose.)

Reference: "College Football's Power Struggle", Wall Street Journal, Sept 16-17, 2006.