Monday, December 19, 2011

Festive facts for the holidays

The winter holiday season often means travel, shopping, and eating...
Here are the numbers behind the holidays:

Types of Christmas trees
purchased in 2011, based on data
from the Christmas Tree Association
and author's calculations
Americans will purchase 34.5 million Christmas trees this year. More than one third of those trees will be artificial, according to a survey conducted by Nielsen for the American Christmas Tree Association.

The survey "also found 11 percent of U.S. households who will display a real tree will also display an artificial tree, recognizing a growing trend toward celebrating Christmas with more than one Christmas tree."

The U.S. also produces $1.5 billion worth of candles each year. Those candles come in handy for Hanukkan, Kwanzaa, and Christmas celebrations.

New Orleans Holiday Travel (author's photo)
91.9 million Americans (about one third of the total US population) will travel at least 50 miles from home over the winter holiday season (Dec 23 - Jan 2). This represents a 1.4 percent increase in total holiday travel, compared with 2010, according to an annual report released by AAA.

Auto travel accounts for the lion's share (90 percent) of those trips. Despite notoriously long lines at the airport, only 8 percent of holiday travelers plan to fly, and the remaining 2 percent will travel via train, bus, or other mode.

About half of long-distance travelers (those going 50 miles or more) will make a day trip of it. Travelers who plan to stay overnight at their destinations will spend an average of four nights away from home.

Most retail stores do the bulk of their yearly business in December. For retail stores overall, 14 percent of annual sales occur in the last month of the year. For jewelry stores, December makes up 20 percent of annual sales.

Total retail sales for the holiday season are expected to reach nearly $470 billion. And for those who want to avoid the malls, $34 billion of December 2010 retail sales were online or mail-order.

Photo courtesy of State Library and Archives of Florida
$2.5 billion worth of toys (including stuffed toys, dolls, puzzles, and electric trains) were imported to the U.S. from China between January and September 2011. China also leads the pack in U.S. imports of ice skates ($17.7 million) and basketballs ($38.9 million).

Within the U.S. there are about 8,000 workers across 579 locations that primarily manufacture toys and games. Is it any surprise that Santa needs 8,000 helpers?

If you're making latkes for Hanukkah, chances are the potatoes come from Idaho or Washington. 50 percent of the nation's 'taters were grown in those two states, according to the U.S. Census Bureau.

U.S. farmers produced 2.01 billion bushels of wheat - crucial for making Christmas cookies - in 2011. Kansas, Montana and North Dakota accounted for about a third of the nation's wheat production.

But while Americans eat plenty of cookies, you might as well hold the eggnog. Nationwide consumption averages only half a cup per capita, according to figures from Indiana University.

Candy canes might be a holiday staple, but chocolate is a nearly universal gift. The winter holidays represent the biggest boxed chocolate selling season as 70 percent of adults in the U.S. give or receive a box of chocolates during the holidays.

Saturday, December 10, 2011

More Americans staying put

In addition to rising unemployment and declining retail sales, the recession also has more people staying put in their current residence. According to statistics from the 2010 American Community Survey, only 15 percent of Americans lived in a different home a year ago, down from more than 16 percent of the population moving in 2005. According to the Brookings Institution fewer U.S. residents moved in 2010 than in any year since 1948.*

The map shows the proportion of population that lived in a different house the prior year (data 2006-2010), with the darkest colors representing areas with the highest proportion of people who stayed in the same house, and lightest colors representing areas with the most movers. Not surprisingly, given the housing bubble, Arizona, Nevada had relatively high rates of movers, while rural areas in Louisiana, Mississippi, and Alabama had few movers.
Percent of the population who reported living in the "same house" the prior year (2008-2010).
Source: U.S. Census Bureau, American Community Survey, author's calculations
This changing migration dynamic has important repercussions for states like California, which historically were high in-migration states. Despite California's reputation as being a state of newcomers, in 2010 a majority (54 percent) of residents were California-born. (U.S. Census Bureau, Lifetime Mobility in the United States). As a result of this change, Dowell Myers asks in his article "The New Homegrown Majority in California"
"How is the stance taken by voters with regard to taxation and services different if California is growing because of migration by outsiders rather than growing from California-born residents? What does the shift from a reliance on high migration to a homegrown majority mean for today's taxpayers?"
California is not yet approaching the level of "homegrown" population seen in states like Louisiana (79 percent) or Michigan (77 percent). But neither is it like Florida, Alaska, Arizona, or Washington DC, all of which have 60 percent or more of their population born out-of-state.

*Note: Brookings puts the number of movers at 35.1 million, but I pulled the figures from the American Community Suvey for 2010 and found more than 45 million, so take the "lowest since 1948" quote with the proverbial grain of salt.
Source: U.S. Census Bureau, American Community Survey (2010),
"Selected Social Characteristics in the United States" - downloaded 12/9/11

Monday, December 5, 2011

Who does the housework?

Earlier this year two researchers, Dr. Kristen Myers and Ilana Demantas, completed a study of the housework roles of unemployed men. Dementas interviewed twenty out of work men, and found that they were likely to have increased their housework responsibilities substantially during their period of unemployment. While Dementas' work may reflect changing gender roles and responsibilities, the sample size on the study is small.

Moreover, despite substantial gains in gender equity (including the fact that women's educational attainment is now at parity with men's) there remains a sharp and continuing disparity in unpaid household labor when analyzed in aggregate at the national level.

Sources: 2010 American Time Use Survey, author's calculations
While 84 percent of women do some form of housework on a typical day, only 67 percent of men do, according to 2010 statistics (from the American Time Use Survey).

Across the population, women do, on average, 50-70 percent more housework than men, before childcare is counted. In childcare, the differences are even more dramatic. Women perform twice as much childcare related work as men, on average.

The 2010 results are consistent with findings from prior years in American Time Use Survey.

Sunday, November 20, 2011

Thanksgiving by the numbers

Holiday data roundup for the fourth Thursday in November.


42.5 million Americans (about 14 percent of the total US population) will travel at least 50 miles from home over Thanksgiving weekend. This represents a 4 percent increase in total holiday weekend trips, compared with 2010, but remains well below the peak in 2005 according to an annual report released by AAA.

Auto travel accounts for the lion's share (90 percent) of those trips. Despite notoriously long lines at the airport, only 8 percent of holiday travelers plan to fly, and the remaining 2 percent will travel via train, bus, or other mode.

About half of long-distance travelers (those going 50 miles or more) will make a day trip of it. The other half will spend an average of three nights away from home.


13.3 pounds of turkey are consumed by the average American each year, according to data from the U.S. Census Bureau.

248 million turkeys were raised in the United States in 2011, up 2 percent from the prior year. U.S. turkey production for 2010 was valued at $4.37 billion. And, according to the Bureau of Labor Statistics, the retail price per pound of turkey is lowest in November.

750 million pounds of cranberries were grown in the U.S. in 2011. Wisconsin grows the most (430 million pounds) followed by Massachusetts (210 million pounds).

2.4 billion pounds of sweet potatoes and 1.1 billion pounds of pumpkins were grown in the United States in 2010. No stats are available on how many are baked into pies.


A poll by Harris Interactive found that more than half of working Americans (59 percent) report checking their work email on major holidays such as Thanksgiving and Christmas.

While many retailers tried to get a jump on the holiday shopping season this year, there is a clear spike in retail jobs each year from November to December. This should come as no surprise since about half of Americans spend time shopping on Thanksgiving weekend.

Saturday, November 5, 2011

The numbers behind the NYC Marathon

   45,000 runners
+ 26.2 miles
+ 5 boroughs
+ 32,000 gallons of gatorade
+ 1 Sunday in November
= 1 NYC Marathon

Occasionally my two loves (running and data) come together in perfect harmony. This weekend is one of those times. In honor of the ING New York City Marathon, here are some eye-opening stats about the race.

Who's running:
In 2011 the race is more than 350 times larger than it was its inaugural year. In 1970, according to marathon organizers, "127 runners paid the $1 entry fee to NYRR to participate in a 26.2-mile race... Fifty-five runners crossed the finish line."

On Nov. 6, 2011 officials expect 45,000 runners to toe the starting line. To put that in perspective, there will be one NYC Marathon runner for every resident of Olympia, WA (or Harrisburg, PA - for those in the eastern time zone).
Of those 45,000 runners:
  • The sex ratio is 1.63:1.00 (62% men / 38% women, which is similar to the 2010 average for marathons nationwide which showed a 59/41 split)
  • One third are between the ages of 40 and 49.
  • The oldest male entrant is 87. The oldest female is 84.
Course info:

  • The race's 26.2 miles traverse all five of New York's boroughs.
  • The highest elevation along the course (approx 260 feet above sea level) occurs in the first mile, on the Verrazano Narrows bridge.

Who's making it happen:
  • More than 150 New York Road Runners staff work year-round on the marathon.
  • There are more than 8,000 volunteers for the event.
  • 130 bands will serenade the runners.
  • The week before the race, 100 people worked to clean up the post-snowstorm debris in Central Park that was blocking the race course.
Hydration and fuel:
According to Wolfram|Alpha "to help energize and hydrate the runners before the race begins" organizers will provide runners with:
  • 42,000 Power Bars
  • 90,000 bottles of water
  • 45,000 cups of coffee
On the course, there will be:
  • 62,370 gallons of water
  • 32,040 gallons of Gatorade
And with all that hydration (plus race-day nerves), expect long bathroom lines:
On Sunday, more than 1,600 portable toilets will be available for the runners. That works out to about one port-o-potty for every 28 runners, but you know the lines will be longer than that!

Data Sources:
ING New York City Marathon website
Running Trip "NYC Marathon by the numbers"
Running USA Annual Marathon Report (2011)
The Weather Channel

Wednesday, November 2, 2011

World population = 7 billion

It just so happens that this year the United Nation's announcement that the world's population reached 7 billion coincides with the lesson I teach on demographics, population growth, and urbanization in Intro to Sociology.

In searching for materials to make this data-intensive lesson more accessible to intro students, I came across this video from National Geographic.

It provides a series of facts about global population growth in a format that is eye-catching and memorable. This video is perfect for introducing the concept of demographic change.

Monday, October 31, 2011

Freaky fun Halloween stats

24.7 pounds: Per capita consumption of candy by Americans in 2010.
Candy consumption, much like home prices, peaked in the middle of the decade and dipped at the start of the recession in 2008. Consumption has been increasing slowly each year since then.
If this candy were entirely Snickers bars, it would work out to nearly candy 4 bars per week, per person.
Source: U.S. Census Bureau, Current Industrial Reports, Confectionery: 2010
393,000 little ghouls and goblins: The estimated number of potential trick-or-treaters (children age 5-14*) in San Diego County in 2010 based on the results of Census 2010. The national estimate is 41 million potential trick-or-treaters in the United States in 2010.
Source: U.S. Census Bureau, Census 2010

1.09 million doorbells to ring: In 2010 San Diego region trick-or-treaters have roughly 1.09 million households** to choose from when asking for candy. The San Diego region accounts for approximately 1 out of every 100 homes in the United States. The U.S. estimate is 116.7 million in 2010.
Source: U.S. Census Bureau, Census 2010

1.1 billion pounds of pumpkins: Pumpkin production in the United States in 2010 totaled more than one billion pounds. California accounted for more than 100 million pounds of pumpkins grown in 2010. San Diego County’s pumpkin growing, however, is relatively low. In 2010 the county grew 5,200 tons of squash (up from 4,400 in 2009), which includes (but is not limited to) pumpkins.
Sources: USDA National Agricultural Statistics and SD County, Annual Crop Report (2010) 

*Of course, many other children - older than 14, and younger than 5 - also go trick-or-treating. **Measured as the number of occupied housing units.

Monday, July 4, 2011

Fourth of July fireworks

Interesting trade balance fact: The U.S. produces about $300 million worth of flags (of all types) in a year, but imports $2.8 million worth of American flags from China

In 2010 the U.S. imported nearly $200 million worth of fireworks, almost all (96 percent) from China. More fireworks, $230 million, are produced in the U.S. than are imported, but the trade balance on fireworks is negative. Only $37 million worth are exported in an average year, mostly to Japan (16 percent).

U.S. Census Bureau, Facts for Features

Friday, July 1, 2011

When good people do bad things with data - wealth and children

I used to think the "Freakonomics" folks had a good thing going. The first book was relatively well researched, and, let's face it, the book was fun to read.

However, in the past five years I have been perpetually frustrated by the authors' repeated mis-application of very basic statistical/logical priciples. Namely the authors blatantly ignore the difference between correlation and causality. If my Intro to Sociology students can pick apart the difference between correlation and causality, you would think trained economists could do the same.

The latest blunder strikes me as particularly egregious: implying that children are an "inferior good" based on oversimplified interpretation of a couple of charts. Now, whatever your take on pint-sized people may be, I suggest that a chart showing a negative correlation between GDP-per-capita and children-per-woman only shows correlation, not causality.

Even the most casual observer can see that the countries with the low income/high fertility combination are still largely agricultural or in the very early stages of industrialization. Conversely, the countries with high income/low fertility are largely industrialized or post-industrial in their development. Basic demographics teaches us that fertility is higher in agricultural societies than in industrial ones, regardless of wealth. Fertility is as much about social and cultural structure as about income.

But even more fundamental to my argument: Something really fascinating happens to those post-industrial countries... At a certain point, the fertility rate starts going back up as income increases.

Let me put this another way. Income has continued to rise in the United States from the mid-1990s to today (recession aside) and through that entire period of time, with rising income fertility has increased.

You can see this trend in the screen show above by following the course of the yellow bubble (U.S.) from the early 1990s to 2008. And note, further, that the screen shot captured by the freakonomists shows only 2008, not the upward trend over the prior 15 years. (Or the drop in birth rate during the recession, which implies a positive, not negative, correlation between income and fertility in the U.S.)

Why is that trend not shown in their analysis?

Perhaps because it would ruin their headline.

(But maybe I'm just seeing a correlation between bad analysis and eye-catching headlines, and assuming causality...)

To read the original post see:
Or a useful summary by the NY Times.
Or build your own Gapminder plot.

Thursday, June 30, 2011

Delaying the wedding bells

As June is the unofficial national wedding month, it seems appropriate to wrap up this month with some concluding thoughts on marriage trends.

Educational attainment levels for women are at an all-time high in the U.S. Today eight percent of the female population age 15-50 has a graduate degree, 18 percent has a bachelor's degree, and nearly 60 percent have at least some college education (up to and including bachelor's and graduate degrees).

The likelihood of being married is considerably lower for lower levels of educational attainment. Less than half of women with "some college" education were married in 2009. And only one quarter of women with a less-than-high-school education are married. On the other hand, nearly 60 percent of women with a bachelor's degree and nearly 70 percent of women with a graduate degree are married.

So why the big difference?

The staff at the U.S. Census Bureau suggest that marriage is becoming a characteristic of the economically advantaged:
As marriage rates have decreased and cohabitation has become more common, marriage has become more selective of adults who are better off socioeconomically and have more education.
However, marriage doesn't "select" people. People select marriage. So I offer a counter-argument that there is larger force at work. Perhaps marriage isn't simply becoming a tradition of the well-to-do. Perhaps as more women choose to go to college, they are delaying marriage, and thus don't enter the "married" category until they achieve at least a bachelor's degree.

Source data: U.S. Census Bureau, American Community Survey, 2005 and 2009

Saturday, June 18, 2011

Are men faster than women, or did they just get a head start?

I am working on the Gender Stratification lesson plan for an Intro to Sociology class I am teaching, and I came across the following piece of gender history:
Not until 1984 in Los Angeles would the women's marathon become a sanctioned Olympic event. Joan Benoit Samuelson, the winner, had attended high school in Maine, where women's track teams were not granted varsity status during her freshman and sophomore years. She won the 1975 state championship in the mile -- the longest distance a woman was allowed to run -- but because she insisted on practicing with the boys to improve her times, she was denied the school's most valuable athlete award. "That's when I said to myself, 'I'll show that coach -- I'm going to win an Olympic medal some day,' " Samuelson says. Nine years later she did.
NY Times (1996)

In the era of Title IX it is difficult to remember that less than a generation ago, women were not grated equal access to sports (not to mention certain educational or occupational opportunities). Improvements in gender equity since the 1960s have been rapid, but the effects of gender discrimination linger.

One of the most common excuses for excluding women from certain endeavors has always been that they are not as ______ as men (insert "strong," "fast," "smart," "tough," or any number of other adjectives here).

But are women really weaker or slower than men?

Or did they just get a late start?

Today's marathon world record, still held by Paula Radcliffe from the 2003 London Marathon, represents a pace that is equivalent to the fastest man on earth in the early 1960s. And while men's marathon times have remained (from a statistical perspective) roughly stable over the last century, dropping from 2:55 in 1909 to 2:03:59 in 2006, women's times have seen rapid improvement.

Comparatively speaking, men dropped about 50 minutes off of their record times (for an improvement of 29 percent over 100 years). During the same period women's record times dropped by more than three hours, for an improvement of 60 percent over the same 100 years.

Today the differential in men's and women's paces can be measured in seconds per mile, not minutes.

In 1980 only 10 percent of marathon runners were female. In 2009 the proportion reached 41 percent (data from Running USA). The share of women running in shorter races has risen even faster. In the half-marathon distance, women became the majority of runners in 2005.

And, perhaps most interestingly, women who enter ultra-marathons (any distance longer than 26.2 miles) have a substantially higher likelihood of completing the race than male entrants do.

So it is entirely possible that women can be just as fast as men, but got a late start in this game. Granted, there are runners who make a strong argument to the contrary by suggesting that over the last couple of decades the disparity in paces between men and women has remained roughly constant both in the marathon and in other distances.

This is one case where only time will tell.

Chart data source:
data compiled by author

Monday, June 13, 2011

Let the wedding bells ring

Anyone who has ever glanced at a bridal magazine or received a wedding invitation knows that June is usually the hottest month for weddings. So in honor of all the happy couples, let's take a look at marriage statistics.

Not surprisingly, destination wedding locations have the highest marriage rates (per 1,000 population). The current rate (40.9) in Nevada is more than five times higher than the national average (6.8), and Hawaii rings in at more than double (17.9) the national average.

Some of the lowest rates, on the other hand, are in and around the nation's capitol. (With a decade of congressional infidelity scandals, the low marriage rate in D.C. is probably no surprise to readers.)

Marriage rates have been falling nationwide for the past decade, from 8.2 in 2000 to 6.8 in 2009 (data from the CDC). Falling marriage rates are part of a long-term trend toward delayed marriage. The average age at first marriage back in 1956 was only age 20 for women (age 22 for men) and has risen to age 26 for women (28 for men) today.

In marriage rates, Nevada declined the most, dropping from 72.2 in 2000 to 40.9 in 2009, and falling by more than 50 percent from a 1990 level of 99 marriages per 1,000 population. (Maybe being married by Elvis at a drive-through chapels is losing it's appeal?)

However, divorce rates are also falling. In fact, the number of "long-lasting" marriages is starting to increase. Divorce rates peaked in the years following mid-1970s changes in divorce laws, but then leveled off and fell slightly. Some of this can be attributed to lower marriage rates (fewer marriages likely lead to fewer divorces), but some is likely a result of people waiting longer to get married in the first place.

Image Credits:
Charts by author, with data from the U.S. Centers for Disease Control and Prevention (CDC)
Photo courtesy of

Tuesday, June 7, 2011

Demographic trends in San Diego County – new data from Census 2010

Population Growth 1910-2010
In the past 100 years, San Diego county has grown from a small city of 60,000 people to a thriving metropolitan area of more than 3 million. In San Diego’s early years Census figures show that the county’s population nearly doubled every ten years from 1910 to 1960. Between 1960 and 1990 growth slowed to an average increase of approximately 33 percent every decade. Growth slowed again in recent years, with less than a 15 percent increase 1990-2000 and only 10 percent growth from 2000-2010.

Migration Patterns
Migration was the primary driver of population growth at the beginning of the last century, but that trend has shifted considerably in recent decades. Until the late 1990s, most growth in Southern California was a result of in-migration from other parts of the United States and from other countries around the world.

However, according to data from the U.S. Census Bureau American Community Survey and a study by the University of Southern California Population Dynamics Research Group, that trend has shifted statewide, with most of the population now being, in the words of the study authors, “homegrown” (i.e. born in California).

Aging Population
As San Diego grows, it is growing older. The very first trickle in the aging wave of the Baby Boomers just turned 65 this past January. Because the Baby Boom is just now reaching this milestone, the proportion of the population age 65 and older had remained constant at 11 percent over the past two decades. However, that will shift dramatically in the next two decades.

On the younger end of the spectrum, the share of San Diego's population under the age of 18 is shrinking, from 26 percent in 2000 to 23 percent in 2010. As a result of these shifts the median age (the point at which half of the population is younger and half older) increased from 31.0 years in 1990 to 34.6 today.

Race and Ethnicity
One of the biggest demographic shifts highlighted by the 2010 Census is confirmation that San Diego is now a “majority-minority” county. This means that no single race or ethnic group accounts for more than half of the region’s population.

The Asian population, which grew fastest, increased by 34 percent (to 328,000 residents) between 2000 and 2010. The Hispanic/Latino population increased by 32 percent over the decade, and now accounts for approximately one third of the county’s total population (991,000 residents).

Conversely, the non-Hispanic White, Black, and American Indian populations all declined slightly during the decade (by 3, 5, and 8 percent, respectively)

Housing - Occupancy and Household Size
While much talk about the Census revolves around the demographic characteristics of the population, the 2010 Census also provides valuable information about neighborhood housing characteristics including occupancy rates and average household size.

In 1950, the average household size in San Diego was more than 3.1 persons per household. This decreased steadily as birth rates and average family size fell during the 1960s and 1970s, so by 1980, the region hit a low of 2.6 persons per household. However, birth rates began to rise starting in the mid-1970s, and a similar upward trend can be seen in average household size.
San Diego's lowest residential vacancy rates, not surprisingly, were during the housing boom in the early 2000s, and have risen considerably – back to rates reminiscent of the 1970s and 1980s – in the past three years. Data from the 2010 Census shows a residential vacancy rate of 6.7 percent for San Diego county.

2010 and Beyond
The demographic characteristics of the San Diego county will continue to evolve over time. San Diego is transitioning from a history of high-volume in-migration, to a more “homegrown” population, and from a relatively "young" area to a metropolitan area with an aging population.

Source data:
U.S. Census Bureau, Census 2010, Redistricting Files
California Department of Finance, Demographic Research Unit

Sunday, June 5, 2011

Aging population and driving

This story in today's San Diego Union Tribune, about a fatal accident involving a 71-year-old driver, prompted me to post a piece I've been working on for a couple of weeks...

Six months ago the first wave of Baby Boomers turned 65, prompting questions about how the nation's transportation system will adapt to an aging population. There are some benefits that may arise from having an older population, and there will undoubtedly be challenges.

As the population ages we may care less about our cars. A Gallup poll in 1991 found that 20 percent of Americans found driving to be a chore. The same response in 2006 got a 40 percent boost to 28 percent. When you drill down into the details of the survey, likelihood of taking a ride “just because it’s fun” decreases substantially with age. In short, an older population is less likely to enjoy driving.

And, contrary to most road-rage induced stereotypes, older Americans also drive more safely. (However, there are some limitations to that trend, as described below.)

In their 2009 study of aggressive driving behavior, AAA found that at age 16 nearly 60 percent of drivers show aggressive behavior. By age 35 aggressive driving falls to 35 percent and by age 60 is below 27 percent.

With an already aging population, safer driving is beginning to show up in accident statistics. According to the U.S. Department of Transportation:

“in 2010 the number of traffic fatalities in America fell to the lowest levels since 1949...despite a sharp increase in the number of miles Americans drove last year - 21 billion additional miles. In addition, the rate of road fatalities in the U.S. has also dropped to its lowest level since 1949. Over the last five years, traffic deaths have declined by 25 percent…And the rate of fatalities per million miles traveled fell to 1.09 from 1.13 in 2009.”

Transportation Secretary Ray LaHood credits this to “the combined efforts of DOT, states, law enforcement, safety organizations, and America's drivers who are taking personal responsibility for their driving habits.” But I also see a demographic shift at play, much as there was a demographic influence on falling crime rates, beginning in the mid-1990s.

However, as opening article implies, there are also substantial health issues that may impair the driving of older Americans.
For example, the likelihood of reporting some form of disability DOUBLES between the age groups 65-74 and 75 and older (from 25 percent at age 65 to fully half the population age 75+). Similarly, according to data from the U.S. Centers for Disease Control and Prevention 10 percent of older men and 15 percent of older women reported cutting back on driving due to a physical problem in the past year. And, for a combination of reasons, older drivers are likely to avoid driving in certain conditions. Older drivers tend to avoid driving at night, driving in bad weather, and (to a lesser extent) driving in heavy traffic.

All of this adds up to some very complicated issues facing the nation's transportation system. Fortunately, research is underway to better understand the implications of age-related health issues on transportation at institutes such as the Age Lab at MIT.

Saturday, June 4, 2011

Data vs. Information (round 2)

Convenient to this week's blog theme on "Data vs. Information", The Economist published a post by Joseph Schumpeter on the issue of rampant growth in data and what mountains of data mean for business decisions. The entire article is worth reading, but the following quote struck me as particularly well-written:

The sheer size of today’s data banks means that companies need to be more careful than ever to treat data as a slave rather than a master. There is no substitute for sound intuition and wise judgment. But if firms can preserve a little scepticism, they can surely squeeze important insights from the ever-growing store of data.

Wednesday, June 1, 2011

Hurricane seasons begins

It's June 1, so the 2011 hurricane season has begun...

Nearly 37 million people live in areas most at risk of hurricanes, an area covering 179,000 square miles along the coastal region stretching from Texas to North Carolina, according to the U.S. Census Bureau. Hurricanes occasionally strike farther north, but such events are rare.

Hurricane History
The Department of Housing and Urban Development estimates that in the summer of 2005 hurricanes Katrina, Rita, and Wilma damaged "more than one million housing units across five states." Of the damaged homes 515,000 were in Louisiana, 220,000 in Mississippi, and nearly 140,000 in Texas.

By 2010, according to the HUD study, three quarters of the 2005 hurricane-damaged properties on "significantly affected" blocks were in good condition (at least on the outside*), but nearly 15 percent of the properties still had substantial visible repair needs, and 11 percent no longer contained a permanent residential structure. Louisiana homes, of the state affected, are most likely to still have unrepaired damage. Mississippi homes were most likely to be either repaired or entirely demolished and left vacant.
*We should note that these estimates do not include homes with mold or other water damage issues that might render the structures uninhabitable.

From a business perspective, in the year following Katrina New Orleans had about 95,000 fewer jobs, with most losses in tourism and port operations. It took nearly two years after Katrina for the number of restaurants in New Orleans to rebound to it's pre-hurricane level (according to restaurant critic Tom Fitzmorris in his book Hungry Town).

Hurricane Demographics
In addition to their physical and economic damage, major hurricanes can cause huge demographic shifts. For example, during hurricane Katrina approximately 1.5 million people over the age of 16 left their homes in Louisiana, Mississippi, and Alabama. And while many have returned, not all have. For example between 2005 and 2010 New Orleans saw its population decline by 25 percent from an estimated 455,000 before the hurricane to 344,829 as of April 1, 2010.

And while New Orleans tends to dominate the news headlines because of the broken levees, Pass Christian, MS actually has sustained a greater proportionate loss in population. Though a small town before Katrina (just under 7,000 according to 2005 Census Bureau estimates) the population had shrunk in half in the year following the hurricane, and many residents did not return. The 2010 Census count shows a resident population of only 4,613 - a sustained decline of 34 percent since the hurricane. Gulfport, MS also declined by an estimated 5,500 residents (-8 percent) between 2005 and 2010.

Less notorious, but just as significant in terms of population shift, was Hurricane Ike in Galveston, TX. Nearly 10,000 residents remained displaced two years after the hurricane. The estimated population before Ike was 57,000 but was only 47,743 at the 2010 Census. The HUD report on housing shows that one quarter of homes in Texas hurricane-affected neighborhoods still showed significant damage in 2010, in part because of the 2005 series of hurricanes and in part because of Ike.

In natural disasters traditional sources of demographic data (building permits, school enrollment records, utility hookups, drivers licenses, etc...) are either no longer available, or provide misleading information about the displaced, remaining, and returning population. Some of the most clever demographic techniques I have seen to date were hurricane-related. At the 2010 Applied Demography conference Mark VanLandingham and Janna Knight presented their techniques for reverse-estimating the post-Katrina population of New Orleans. And Nazrul Hoque, Alelhie Valencia, and Karl Eschbach presented their techniques for filling in the data gaps for post-hurricane Galveston.

Hurricane Preparedness
And in case all this talk of hurricanes has you thinking it's time to update your emergency preparedness kit, the CDC has developed a useful but tongue-in-cheek checklist that will get you through any disaster - even a zombie apocalypse!Get A Kit,    Make A Plan, Be Prepared.
Click on image for more information from the CDC.

Image of Hurricane Katrina in the Gulf of Mexico courtesy of NASA-GSFC, data from NOAA GOES

Tuesday, May 31, 2011

Data vs. information

Today's post from Dr. Groves, Director of the U.S. Census Bureau encapsulates what this blog, data insights, is all about.

What’s the difference between “data” and “information?”

We’re entering a world where data will be the cheapest commodity around, simply because the society has created systems that automatically track transactions of all sorts. For example, internet search engines build data sets with every entry, Twitter generates tweet data continuously, traffic cameras digitally count cars, scanners record purchases, RFID’s signal the presence of packages and equipment, and internet sites capture and store mouse clicks. Collectively, the society is assembling data on massive amounts of its behaviors. Indeed, if you think of these processes as an ecosystem, it is self-measuring in increasingly broad scope. Indeed, we might label these data as “organic,” a now-natural feature of this ecosystem.

Information is produced from data by uses. Data streams have no meaning until they are used. The user finds meaning in data by bringing questions to the data and finding their answers in the data...
To read the entire post, see:

In an era when there are more sources of data available than ever before, we analysts are challenged to use that data well and in innovative ways. In recent years I have also found that simply because lots of data exist, and the public knows that lots of data exist, analysts are expected to HAVE everything and to KNOW everything with an immediacy that is often impractical and sometimes impossible. In other words, expected to synthesize "information" on any given topic simply because data exist.

The challenge for analysts has gone from turning tailored research data into research findings, to taking streams of sometimes incomplete, and clearly not "tailored," data and turning them into useful information. This change, in some ways, is like having an open fire hydrant and being asked to use the geyser to water an orchid. What you need is there, but certainly not in the form you need it.

The ability to use data well requires both strong traditional analytical training and a clever and creative streak. If anything, careful analysis is more important than ever before, but that alone is no longer sufficient. To be able to capitalize on these new waves of data, analysts will need to develop an ability to synthesize statistics from multiple sources, and also to be critical of the data available. (What pieces are missing? How was the data source changed over time? Is the data representative of a whole population or a selected subset? How can, or should, or shouldn't, the data be extrapolated to other groups?)

These questions and others will certainly keep us busy for a long time.

Saturday, May 28, 2011

Crushing obesity

In the early 1990s, half of the U.S. population could proudly state that they were neither overweight nor obese. Today only one third can make that claim. The rate of obesity* has nearly doubled from about 15 percent of the population to more than 26 percent in less than two decades.

But numbers alone seem inadequate to describing the magnitude of the problem. The U.S. Centers for Disease Control and Prevention (CDC) put together a series of maps showing obesity rates from 1985-present.

Obesity's rapid spread across America:
In the maps below the lightest blue represents obesity rates by state of less than 10 percent. The darkest red-orange represents obesity rates of 30 percent or higher. Note the rapid shift over 20 years from no states having reported obesity rates above 15 percent (in 1989) to no states having rates below 15 percent (in 2009).

So why are we getting fat?
There are a variety of factors at play in the rising obesity epidemic. An article in Slate this week, and on my blog last summer, describes the link between obesity and commuting. The Federal Reserve and the USDA suggest that increased food consumption, particularly fast food, is the primary driver expanding America's waistlines.

New research, from Pennington Biomedical Research Center at Louisiana State University, digs into another trend - our economy - to understand the obesity epidemic. Using data on occupational patterns since the 1960s, coupled with weight data, and energy expenditure (i.e. calories burned) per day per occupation, they come to a startling conclusion: modern jobs make us fat. Specifically "In the early 1960's almost half the jobs in private industry in the U.S. required at least moderate intensity physical activity whereas now less than 20% demand this level of energy expenditure. Since 1960 the estimated mean daily energy expenditure due to work related physical activity has dropped by more than 100 calories in both women and men."

Research from the Mayo Clinic, American Cancer Society, and have come to a similar conclusion - sedentary behavior leads to obesity. So the advent of desk jobs that require sitting for long periods of time may be a primary cause of rising rates of obesity.

One conclusion is certain, whatever the cause (or, more likely, causes) of obesity, the cost is too high to be ignored. Obesity increases a person's risk of heart disease, diabetes, high blood pressure, and certain types of cancer, among other ailments (source: CDC). In 2006 the increased rate of obesity resulted in an estimated $40 billion in healthcare costs.

*According to the CDC, obesity is “defined as a Body Mass Index (BMI) of 30 or greater.BMI is calculated from a person's weight and height and provides a reasonable indicator of body fatness and weight categories that may lead to health problems. Obesity is a major risk factor for cardiovascular disease, certain types of cancer, and type 2 diabetes.

Map images courtesy of the U.S. Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance System.

Tuesday, May 24, 2011

Transportation by generation

Yesterday I participated in a demographics forum at the U.S. Department of Transportation to discuss the issues of an aging population on transportation infrastructure and generational differences in transportation use. The video and slides from the forum have been posted by the DOT.

To view the webcast and slides, click here:


Friday, May 20, 2011

Whadja say? I can't heer yooooo.

Thanks to The Economist for pointing me to this fascinating map from Rick Aschmann.

Having lived in each corner of the country, and traveled to more than half of the states, I find accents fascinating, but never considered mapping the data. I just knew, growing up in southern New England, that "systematic r-dropping" led the word "cart" to sound like "cot" (or, more unfortunately "party" to sound like "potty.") I did not know, as Aschmann documents, that this accent type does not exist anywhere else in the world! Aschmann's work provides an interesting visual display of regional dialects, and also provides a wealth of qualitative data, including samples, on many of them.

Despite the incredible wealth of data on accents and dialects, I do think data for non-English-speaking areas are lacking. For example, the map includes Navajo, despite the fact that fewer than 400,000 people (0.13% of the nation's population) speak Navajo or another Native American language. However, the only areas labeled "Spanish Speaking" are in Mexico. Yet more than 12 percent of the U.S. population speaks Spanish. Spanish-speaking populations account for an even higher share in states along the border. Nearly 30 percent of the population age 5 and older speak Spanish as a primary language in California and Texas. Similarly, in California nearly 3 percent of the population speaks Chinese, another 2 percent speak Tagalog, and 1 percent each speak Vietnamese and Korean. (Source: U.S. Census Bureau, 2009 American Community Survey).

Click on the map, or follow the link below, to access the original.
Original map:

Wednesday, May 18, 2011

Small (business) is beautiful

In honor of National Small Business week...

Most U.S. companies are small businesses.

The smallest of the small businesses are known as "non-employers" meaning that the business is run by a single individual. "Most are self-employed persons operating unincorporated businesses, and may or may not be the owner's principal source of income," according to the U.S. Census Bureau. There are 21.7 million of these small businesses, contributing just under one trillion dollars to the U.S. economy.

In the next group are the traditional small businesses, with fewer than 5 employees. These account for 3.6 million firms, and with about 1.6 employees on average, employ 6.1 million workers.

However, most American workers work for very large companies. Nearly 40 million workers work for the fewer than 2,000 American firms that could be considered the nation's giants - with 5,000 or more employees.

(On the chart, the number of companies is shown in red, number of employees in grey, and average payroll per employee in green.)

Of all states, Florida seems to be the home of small business. Less than 10 percent of Florida companies have 20 or more employees, and nearly 70 percent have less than 5 employees. Delaware and D.C. have the largest concentrations of large (500+ workers) companies.

The largest firms tend to pay more, on average, to their employees. The largest companies have payroll per employee of $48,000 while the smallest are closer to $38,000. However, those payroll statistics are skewed greatly by the multi-million dollar salaries paid to the executives of the nation's largest companies. According to a study conducted for the New York Times, the median executive pay was $7.7 million in 2009 and $9.6 million in 2010. The Wall Street Journal, using their own survey, reports similar trends.

While executive pay has shown rapid growth since the 1980s, the trends in business size have remained relatively constant over the past decade.

Tuesday, May 10, 2011

Go West, Americans! (Or maybe South?)

Since the Census Bureau started keeping score in 1790, the nation's population has grown fastest in the western and southern regions, shifting the nation's "mean center of population" in a steady march across the continent.

According to the Census Bureau:

The center is determined as the place where an imaginary, flat, weightless and rigid map of the United States would balance perfectly if all residents were of identical weight.

Tracking the mean center of population tells a story of the nation's growth, conflicts, and social change. This interactive map from the U.S. Census Bureau shows the shifting mean center of population over time:

Today's mean center is in Texas County, Missouri - more than 1,000 miles from the first recorded center in Kent County, Maryland (1790). Some of the biggest shifts over time show the nation's development, and at times, growing pains.

Major shifts over time:
1790: First mean center is calculated as falling about 23 miles east of Baltimore, MD.

1810: The Louisiana Purchase (1803) doubled the land area of the nation, and the mean center shifted into Virginia.

1860: The center shifted by more than 80 miles (biggest shift on record) thanks to rapid growth in the nation's western states, driven in large part by the Gold Rush.

1870: Just ten years later the mean center of population experienced it's biggest shift to the north, as Northeastern and Midwestern cities experienced rapid post-Civil War growth as people fled the war-ravaged South. Also during this time Alaska became a U.S. territory (1867).

1920: The smallest shift on record was between 1910 and 1920. The nation's current territory had already been acquired, slowing the rate of westward expansion. The Northeast and Midwest saw large inflows of international migrants. And last, but certainly not least, there was substantial migration of black/African American population out of the South and into the Northeast and Midwest, precipitated by the intense racism that spawned the Jim Crow laws.

1950: After six decades in Indiana (the longest in any one state), the center finally crossed state lines into Illinois.

2010: The center has its biggest recorded shift to the south, as Georgia, Florida, North Carolina, South Carolina, and Texas record rapid population growth.

How American Productivity is like the Kentucky Derby

Throughout the first three quarters of the Kentucky Derby on May 7, Shackleford was way out front. However, something interesting happened in the final stretch. Other horses started to break away from the pack, and despite the jockey's strenuous whipping of Shackleford's flanks, the horse could go no faster.

This scene reminded me of the productivity news released by the Bureau of Labor Statistics two days earlier. (What can I say, I am an inveterate data geek.)

The past couple of years have seen incredible gains in the productivity of the average American worker, measured as output per hour worked. 2009 saw an increase of 3.7 percent over 2008. 2010 was even stronger, with productivity increasing by 3.9 percent over 2009 (one quarter saw a jump of 6.7 percent). The nation hasn't seen that level of productivity growth since 2002, when people were recovering from the shock of the prior September. In 2002 people were just learning how to use their iPods and Google was just getting it's legs, so rapid productivity growth that year can, at least in part, be linked to technology gains.

So, what is behind this recent productivity boom?
And more importantly:
Why did productivity growth start slowing toward the end of 2010, falling to only 1.6 percent in the first quarter of 2011?

While it is possible that companies merely cut the "dead weight" in their laborforce with layoffs, I think the answer is more basic. This brings me back to the Derby analogy... With unemployment rates hovering between 9 and 10 percent in 2009 and 2010, I strongly suspect that American workers were working harder for fear of losing their jobs. Plus, with companies cutting their workforce, the remaining workers had to work harder (or smarter) to keep up with corporate demands for the same (or higher) level of output.

In addition, the majority of those who lost jobs and have since been re-employed report being overqualified for their current gig, according to Pew Research. This would undoubtedly give another, temporary, bump to productivity levels.

But with no major advances in technology, American workers are like Shackleton in the home stretch: there is only so much more performance to be squeezed out of a worker before he or she has nothing left to give.

So productivity growth is likely to tail off for the near-term. This might be troublesome for the corporate bottom line, but may be an unexpected boon for the 14 million unemployed Americans. As the economy recovers and demand picks back up, companies that have already stretched their existing workforce as far as they can will have to begin hiring to keep up with demand.

Photo courtesy of TheRichBrooks:

Sunday, May 8, 2011

How many moms?

If you tried to go out to brunch today, you probably noticed that there are a LOT of moms.

In the United States there are about 4.1 million babies born each year, despite a decrease during the recession. About 40 percent of those births were first births, meaning an estimated 1.6 million women are celebrating their first Mother's Day this year.

And while many moms have 2 or 3 children, those 4.1 million babies each year add up to a lot of moms over time. Today there are more than 31.7 million families where kids under the age of 18 are still living at home with their mom (excludes father-only families and children being raised by grandparents or others). And that doesn’t count those of us who have gotten old enough to move out, but still want to treat Mom on Mother’s Day, which brings the total number of mothers to more than 85 million moms in the United States.

Speaking of treating mom, the National Restaurant Association reports that Mother’s Day is the most popular day for dining out. More than a third of respondents planning to dine out said that they would go out for breakfast or brunch, and 20 percent report that they will take mom out for more than one meal that day. According to the National Retail Federation that adds up to more than $3 billion spent on meals for moms today.

The NRF also reports that gift-givers will spend $140 on average for mom, and a survey by showed that residents of California, Oregon, New York, and North Dakota spend the most, while residents of Alabama spend the least. Nationwide total spending for Mother's Day (on any type of gift) is expected to exceed $16 billion.

For San Diego specific stats, see "Moms are worth a lot this Mother's Day"

Photo courtesy of: Clevercupcakes -

Thursday, April 7, 2011

Recession is bad for the baby business

For another sign that The Great Recession is taking a toll on the population, look no further than the local maternity ward. The birth rate is falling after many years of slow but steady increase. Research that I presented at the 2010 Applied Demography conference tracked the trend in fertility during recessions, controlling for factors such as female labor force participation, contraceptive technologies, and other social trends. Recent publications from the Pew Research Center arrived at the same conclusion: births fall during recessions.

This trend may be contrary to popular logic. It is common to assume that for two-income families, a layoff means that one member of the family has more time on his (or her) hands for child-rearing and for... to be delicate... the activities that lead to child-rearing. However, research suggests that economically depressed times drag down more than just the stock market.

According to a survey conducted by the Guttmacher Institute, "Sixty-four percent of women agree with the statement, 'With the economy the way it is, I can’t afford to have a baby right now'."

Overall the survey suggests that women want to delay having a child because of financial instability resulting from the recession, and that most (but not all) are being more careful about contraception as a result. Data from the Nielsen market research company confirms that condom sales have increased since the recession started.

The survey responses mirror a trend that is beginning to show up in birth certificates. Nationwide the birth rate fell by 4 percent (from 4.3 million to 4.1 million) between 2007 and 2009. To put that into perspective, 185,000 fewer births is equivalent to the population of Little Rock, AR. Data for the first half of 2010 shows a continuing decline (data from CDC).

Declines were sharpest in the southeast and western states - those hit earliest, and perhaps hardest by the recession (see map). For example, the number of babies born to residents of California fell by nearly 24,800 (4.5%) between 2008 and 2009, and by more than 40,000 (7%) between 2007 and 2009, according to records from the California Department of Health.

The falling number of births can be linked to declining fertility across almost all population groups. This means that across all race and ethnic groups, and across almost all age groups, women are having fewer babies in 2009 than in 2007. Only women over age 40 saw any increase in birth rate over the past two years, and those minor increases were not sufficient to offset declines in all other groups.

So for as long as the economy remains in the doldrums, savvy market watchers should be investing in condoms, not in baby gifts.

Tuesday, March 8, 2011

Census 2010 paints a different picture of San Diego

This afternoon the U.S. Census Bureau released neighborhood-level data for California, to be used in the redistricting process. San Diego county's population grew by 10 percent, with some distinct changes in demographic composition.

Within San Diego, the Hispanic and Asian populations each grew by more than 30 percent, while the non-Hispanic White, Black, and Native American populations actually were smaller in 2010 than in 2000. San Diego joins many California counties that are now "majority minority" (meaning the non-Hispanic White population accounts for less than half of the total).

Wednesday, March 2, 2011

Census Data - as it is released

From the U.S. Census Bureau...
Click on a state to see county-level data detail.

Tuesday, March 1, 2011

(Ir)rational Economics: the placebo effect and wine prices

So far 2011 has been an excellent year for wine market news. The IMF found the price of fine wine tracks almost exactly with the price of oil. The correlation is so perfect (90%) that it would defy logic unless the same factor is influencing both. And it is. According to the study "aggregate demand growth, especially in emerging markets, is the most decisive factor in determining crude oil and fine wine prices."

So, in short, as developing countries get richer, they demand more and better wine. (Makes sense, as I have progressed in my career and made more money, I have demanded more and better wine, too!)

But what about the price range within the wine market?
What makes one wine expensive, and one cheap, in the first place? Or more importantly, is there really a difference between a $9 bottle and a $90 one?

Clearly there is a market for high-priced wines. I once witnessed a Japanese tourist paying $6,000 (plus shipping, taxes, and fees) for a handful of bottles at Opus One, and that wasn't even considered terribly expensive by wine-collector standards.

So does price matter?

Slate's author Coco Krumme did some research to try to answer that question. What Krumme found is that fancy words are strongly associated with high-priced bottles of wine, and "cheap" words with inexpensive ones.

But is that because the expensive wines are actually better? Or because we think they should be better? Don't get me wrong, I do believe there is good wine and not-so-good wine in the world. But can we tell the difference between a decent bottle and an exclusive one? Research suggests that we cannot...

In the Power of Price chapter in Predictably Irrational, Dan Ariely describes is that there is a strong placebo effect of price on our perceptions. If something is more expensive, we expect it to taste better, work better, and to be of higher quality.

And apparently when we know the price, we think that higher-priced wines are better, even when the wine we're tasting is a $9 bottle with a $90 price tag slapped on (based on research from A. Rangel at Caltech).

So the moral of this story is: drink wine you like, especially if you can get it cheap. Just remember to remove the price tag before you serve it to your guests.

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!