An administration, by the numbers

While I am a fervent participant in policy debates, I tend to stay away from partisan politics in all things blog-related.

That said, this was too good to not share.

NPR compiled some key socio-economic stats on the nation when Obama was sworn into the white house 8 years ago, and the (spoiler alert) much better conditions the president elect will inherit.

The nation Obama inherited and the one he left for future generations

What is killing white women?

An article by Monica Potts, "What's Killing Poor White Women?" led sociologist Erin Hoekstra to ask "Why Are Poor White Women Dying Younger than Their Moms?" (It should be noted that while the titles refer to "poor" women, both authors really mean women who lack at least a high school education.)

The authors note that among "white women who don’t graduate from high school... life expectancy has declined dramatically over the past 18 years."

Both authors imply that education and economic opportunity are likely the root of the problem. Potts writes:

Researchers have long known that high-school dropouts like Crystal are unlikely to live as long as people who have gone to college. But why would they be slipping behind the generation before them? James Jackson, a public-health researcher at the University of Michigan, believes it’s because life became more difficult for the least-educated in the 1990s and 2000s. Broad-scale shifts in society increasingly isolate those who don’t finish high school from good jobs, marriageable partners, and healthier communities. “Hope is lowered. If you drop out of school, say, in the last 20 years or so, you just had less hope for ever making it and being anything,” Jackson says. “The opportunities available to you are very different than what they were 20 or 30 years ago. What kind of job are you going to get if you drop out at 16? No job.”

While that may be true, (and indeed, education is linked to longer life expectancy in general) I suspect it is only part of the answer. Some of the decline may be even more basic: selection bias.

Women, particularly white women, have made huge gains in educational attainment and earnings in the past several decades. While the wage gap and other inequalities remain, progress is undeniable for a substantial proportion of women. But perhaps only the healthiest white women are taking advantage of this progress.

Perhaps there is a third factor contributing to both low educational attainment and poor health of white high school dropouts. Perhaps high school dropouts are subject to social, health, or emotional problems that result in both dropping out and poor(er) health.

New data: 2012 American Community Survey

New data released today from the 2012 American Community Survey (and also from Tuesday's release of the Current Population Survey) show no change in median household income, poverty rate, and health insurance coverage in the United States (nor change across the majority of states in the U.S.).

While this may seem like a bad news story, 2012 is the first year since the the beginning of The Great Recession in which income did not fall and poverty did not rise. While the trends do not yet show growth, 2012 may be the inflection point the economy has been waiting for.

One key point of note, older Americans seem to be faring better - or at least recovering from the recession quicker - than younger Americans with substantially lower overall rates of poverty (despite a bit of an increase from last year for those age 65+) and improving homeownership rates.

For a concise summary of the new data:
William Frey, of Brookings, discusses findings from the 2012 ACS on Morning Edition. (Note: I love that reporter/interviewer Steve Inskeep refers to today's data release as "Christmas for demographers.")

Data link roundup (week of June 7, 2013)

The week's top data analysis links...
This week's themes: how jobs affect cities, how technology affects jobs, and how the economy changes everything.


THE CITY GROWS FROM 9-5

Transportation planners and emergency response teams know that daytime population and nighttime population are two very different things. But just how different are they?

New data from the American Community Survey, parsed and published by The Atlantic Cities, shows that Manhattan's population doubles during the typical work day - from commuters alone. (Figures do not include tourists and other travelers.)


FROM HORSE-DRAWN CARTS TO SMARTPHONES
(also BEST CHART OF THE WEEK)

New analysis from the Office for National Statistics summarizes 170 years of industrial change in England and Wales. Highlights:

• At every Census from 1841, the percentage of people working in agriculture and fishing has declined. In 1841, 22% of people worked in this industry and by 2011 this had fallen to less than 1%.
• The service workforce grew from 33% of workers in 1841 to 81% -- dwarfing all other sectors -- in 2011.
• Women outnumber men in service jobs. Men outnumber women in manufacturing and construction.

Source: Office for National Statistics

MIGRATION STORY OF THE RECESSION

The biggest population changes that happened from the Great Recession (aside from the birth dearth): big cities grew and more people stayed put.


IN CASE YOU MISSED IT...

Headline writers think demographers shrank Germany.

Hurricane season started.

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).

Sources:
U.S. Census Bureau, Facts for Features

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: http://www.freakonomics.com/2011/06/10/the-rich-vs-poor-debate-are-kids-normal-or-inferior-goods/
Or a useful summary by the NY Times.
Or build your own Gapminder plot.

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.

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.

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: http://www.flickr.com/photos/therichbrooks/

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 Ebates.com 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 - http://www.flickr.com/photos/clevercupcakes

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.

(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.

(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!

The hefty toll of the obesity epidemic

Obesity rates are going up, up, up.

Today, Colorado has the nation’s lowest rate of adult obesity, at 18.6 percent, followed by Washington DC at 19.7 percent. California ranks 17, tied with Alaska, with an estimated 24.8 percent of adults in the obese category. The nation’s highest rates of obesity are in the southeast, with Louisiana (33%) and Mississippi (34%) topping the charts, according to data released last week by the Centers for Disease Control and Prevention (CDC).

Compare this with data from 1990, and the picture becomes quite alarming. In 1990 ten states had obesity rates below 10 percent. Today none do. In 1990 no states had obesity rates above 15 percent. Today none have rates below 15 percent.

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.”

These obesity-related diseases carry a hefty price tag. Estimates in 2000 put obesity-related health care costs in California at $7.7 billion dollars, and the national cost at $75 billion. According to more recent projections from United Health Foundation, the American Public Health Association and Partnership for Prevention, if current trends continue costs may reach as high $318 billion in the next eight years. Even if rates stay steady at their current high levels the report finds that “the U.S. could save an estimated $820 per adult in health care costs by 2018 ? a savings of almost $200 billion dollars.”

Perhaps not surprisingly, obesity is strongly correlated with physical activity. States where a high proportion of commuters walk, run, or bike to work have some of the nation’s lowest obesity rates. For example, nationwide about 3.4 percent of commuters walk or bike as their primary mode of commute to work. In Colorado, with the nation’s lowest obesity rate, the walk-or-bike rate is 4.2 percent - 25 percent higher than the national average. In Washington DC with the nation’s second lowest obesity rate, the walk-or-bike rate is 13.7 percent. Conversely, commuters in the three states with the highest levels of obesity (Mississippi, Louisiana, Tennessee) are only about half as likely to walk or bike to work as the national average.

For original publication, please see: http://www.examiner.com/x-43439-San-Diego-Economy-Examiner~y2010m8d10-The-hefty-toll-of-the-obesity-epidemic