Tuesday, February 22, 2011

Chart of the Week: gender equity in the United States

I recently spoke at the 2011 Fair Housing Conference in San Diego, on the topic of gender equity. While I am quite familiar with the topic, even I was surprised by a few of the statistics I dug up for my talk. One that stood out clearly, despite substantial gains in gender equity (including the fact that women's educational attainment is now at parity with men's), is a sharp and continuing disparity in unpaid household labor.

According to the most recent statistics (from the American Time Use Survey), women in the United States do 70 percent more housework than men, before childcare is counted. After including childcare, women put in 75 percent more time on unpaid household activity than men. While 85 percent of women do some form of housework on a typical day, only 67 percent of men do.

The obvious next question is, what about paid work? Women today are almost as likely to hold a job outside the home as men are (72 percent of men and 60 percent of women are in the labor force). But men, on average, do still work more outside the home. When we add in the number of hours of paid work, the amount of work done by either sex is about even (average of 5.4 hours each on a typical day, any day of the week).

So the actual amount of work done each day by the sexes may not be terribly skewed, but the amount of work done for pay still shows substantial gender differences.

(For more current data, see December 2011 update.)

Thursday, February 17, 2011

Fascinating tool for visualizing demographic change over time

I had to share this fascinating visualization tool from NPR on the changing demographics of the United States (and each state) over the past 100 years.

Just keep in mind that the means of collecting race/ethnic data, and even the definitions of race have changed dramatically over the decades. For example, Census questions about Hispanic origin have not been asked consistently over time. Today's Census forms are self-reported, but prior census counts relied on a person's surname to determine Hispanic origin (so if your last name "sounded Hispanic" to a Census worker, you would be classified as such, whether your ancestors were Latino or not).

To link to the tool on NPR's website:

Happy graphing!

Tuesday, February 15, 2011

(Ir)rational Economics

(Ir)rational economics

Most branches of the field of economics presume that people are rational actors in the marketplace. Homo economicus, the “economic person” that is dear to so many concepts we have covered, has full knowledge of all of the facts, is completely capable of analyzing all options, and always makes rational, self-interested decisions that maximize his (or her) utility.

But how well does that assumption hold up in reality? Do people really always make utility-maximizing, rational decisions?

In truth, we can probably all recall situations in which we did not make a “rational” decision (at least from the perspective of classical or neoclassical economics). For example, how many hours has the average customer spent in line waiting for free giveaways? (Think of Black Friday lines at the mall, the annual free cone day at Ben & Jerry’s, and the KFC debacle in 2009 to name just a few examples.) The product might be free, but that time spent in line has a value. So if we spend one hour waiting in line to get a $3 cup of coffee for “free,” that implies that we value our time at less than $3 per hour. Otherwise we would have gone to the coffee shop next door, waited only a minute or two, paid $3 for the beverage, and used the other 58 minutes to do something more productive, right? Homo economicus would not wait in line, so why do we?

The relatively new field of Behavioral Economics is trying to untangle these concepts to understand why and how psychological reactions influence our economic behavior. Rather than relying on simplified models and assumptions to understand the economic world, Behavioral Economics uses research techniques from psychology and sociology to study what people actually do. The results are sometimes astounding, and help answer questions like:
• Why do people gamble, when they know the odds are that they will lose money, not win?
• Why are black pearls more expensive than white ones?
• Why is it so hard to save money today, when we know it will make us better off in the long run?
• Why do we pay so much to avoid risk (rental car insurance and flat-rate phone plans, for example)?

We will explore these fascinating topics in a series of discussions in coming weeks. Stay tuned!