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Tuesday, May 27, 2014

I just love contradictory news...

According to this article, non-college graduates are making income gains relative to college graduates. As a college professor, that's bad news for my profession. In a perfect world, people would want to "become educated" just for the sake of being educated. However, in the real world, most people go to college in hopes of increasing their income. Any research that says college isn't paying off is bad news for colleges.

Therefore, I'm really happy to see this article because it claims that, not only do the college-educated make more money than non-college graduates, the wage gap is growing in favor of the college-educated.

What should we conclude about these contradictory claims? Can they both be correct?

Actually, they can both be correct (and they can both be wrong).

First of all, the first link is about Canada and the claims are based on Canadian data while the second article is about the US. There could be differences in US and Canadian labor markets that narrow the college/non-college wage gap on one side of the border and increase it on the other.

Second, one needs to look at time frames, ages of the workers being considered, means versus medians, etc. to make sure that both claims are really about the same thing. I didn't take time to chase down details on the Canadian article, but I did a little digging on the US article. It refers to a study by the Economic Policy Institute in Washington DC. I found the EPI but could not find this particular study. Instead, I found the following studies:

These two articles seem to agree with the Canadian claim rather than the US claim and this was the agency referenced for the US Claim! Weird.

To make it more confusing, this article is about an MIT economist who says that, at a median income level, college graduates are doing much better than non-college graduates. This article also refers to the EPI study that I can't find.

More contradictory claims, possibly from the same research group. I leave it to readers to do their own research and figure out what's going on, but here are some things to consider:
  • The current college/non-college income gap between 25-year-olds is a completely different question than the current college/non-college income gap between 50-year-olds or the lifetime earnings gap at any age.
  • Historical data on income gaps (current gap or lifetime gap for any age group) are different questions than predicted income gaps.
Read some of the links and see if you can figure out what data and what questions these "contradictory" claims are addressing.

Thursday, May 1, 2014

What's your message?

Good post over at HBR. I've certainly made errors in this regard.

Analyzing data requires a lot of work that no one other than the analyst should ever see. In order to get a handle on the data you might create many exploratory graphs and compute multiple statistics. After doing all that work, it's understandable that you want to show it to someone.  But first ask yourself "why?".

Why use a particular graph? Why a particular computation? Presentation time is when you cut everything down to your essential message and conclusions. Showing extra stuff will just confuse your audience and obscure your message.

Imagine you're at a movie and after every scene they stop the plot to explain how they selected the camera angles and lighting. Then they show you all the footage they edited out and explain why they didn't need that footage in the final film. It wouldn't make any sense. The few people who care buy the DVD with the "director's extras".  The rest of us just want to see the movie.

It's the same with data. Determine your message and present just that message as clearly and simply as possible.

Another HBR post covering the same issue.