Category Archives: Demographics

Would the Suicide Epidemic Get More Attention if the Victims Were Women and Minorities?

Graphic credit: Wall Street Journal

Graphic credit: Wall Street Journal

When does a suicide epidemic become a national crisis? When women and minorities end their lives in greater numbers than men and whites, thus confirming the dominant narrative of a racist, patriarchal society that discriminates against all manner of oppressed groups… Until that time, the rising suicide rate will get only passing attention. Depending on the age group, according to new data, American men kill themselves at a rate four to ten times the rate of women. Whites also end their lives at three times the rates of African-Americans, Latinos and Asians.

In a society stained by “white privilege,” and “male privilege,” a strikingly large number of white males seem to think otherwise. Rather than basking in their advantages, they’re checking out in ever greater numbers. Reviled in the dominant narrative of our age as the oppressor, white males are the one group whose cultural mores reject the idea of victimhood and grievance mongering. Rather than interpreting the inevitable setbacks and vicissitudes of life through the lens of race, class, gender, the so-called “angry white male” is far more likely to direct his anger inward by means of suicide or outward in explosive, mass shootings and death-by-cop incidents.

Graphic credit: American Foundation for Suicide Prevention

Graphic credit: American Foundation for Suicide Prevention

While the right-thinking people are all caught up in the latest victimization drama — the trauma of transgendered people unable to use the bathroom of their choosing — the suicide epidemic receives very little notice. Sure, the problems of transgendered people are real, but c’mon, so are the problems of people whose lives suck so badly that they kill themselves.

According to the American Foundation for Suicide Prevention, Virginia’s suicide profile matches that of the nation (which should come as no surprise, because our demographic profile matches that of the nation). There were 1,122 suicides in Virginia in 2015 — 12.86 per 100,000 population, a hair below the national average. Suicide is the 11th leading cause of death in the state; more than three times as many people die by suicide here than by homicide.

Does anybody care?

— JAB

Explaining Fairfax County by Way of New York City

domestic_outmigration

Graphic credit: StatChat

by James A. Bacon

National population migration surveys invariably show Fairfax County to be a big loser. The county experienced a net domestic out-migration of 16,800 in 2015 and 46,500 since 2010. When viewed in isolation the numbers make Virginia’s largest locality look like a war zone — call it Little Aleppo. Yet somehow the population continues to increase, and somehow the county manages to support one of the highest per capita incomes of any jurisdiction in the United States.

Writing at the StatChat blog, Luke Juday explores the seeming contradiction by taking a look at New York City, which shows a similar profile of massive domestic out-migration and increasing population. By way of explanation, he points to two trends: foreign in-migration and natural increase. In New York, a wave of immigrants more than replaced the native-born Americans who were leaving. Furthermore, the demographic profile skews younger than for the nation — and people in their 20s and 30s have more children than people in their 50s and 60s.

New York is not turning into another Detroit as its native-born population moves away. Sky-high real estate prices may drive out the middle class, but unaffordable real estate is a sure sign of high demand. As Juday points out: “Its population continues to climb despite an astronomical cost of living that suggests even more people would live there if they could.”

That New York is a gateway for immigration is a secret to no one. But the idea that it is a young city is less widely recognized. Writes Juday:

New York is a young city compared to the nation as a whole. Like most cities, it has a disproportionate share of young adults in their 20’s and early 30’s. Young adults are important in demographics for two reasons. First is what they don’t do: die. A population of 20-somethings will have far fewer deaths in any given year than a population of 60-somethings. Second is what they do: have babies. Women between the ages of 20 and 35 are in their prime childbearing years. Unsurprisingly, places that have a lot of women in their prime childbearing years tend to have a lot of births as well.

The people moving to New York are younger than those who are leaving. Think college graduates seeking the bright-lights-big-city in Wall Street, Madison Avenue or Broadway while snow birds retire to Boca Raton. (The numbers also suggest that native-born households in the child-raising years, along with their children, also leave the city — presumably to a less hectic life outside the urban core.) The end result is a city with a high proportion of young, creative, entrepreneurially vital people in their 20s and 30s.

Unfortunately, Juday does not close the loop in his blog post. Is what’s happening in New York also happening in Fairfax County? Well, after accounting for foreign immigration, Fairfax County has actually experienced a net in-migration of 9,200 since 2010, so at least one of New York City’s demographic drivers is the same. Juday does not tell us whether Fairfax has a similar population profile heavily weighted by people in their 20s and 30s. But he promises to reveal more in a later post.

What Went Wrong with Long-Term Care Insurance?

Long-term care insurance information, form, Folders and stethoscope.

Long-term care insurance information, form, Folders and stethoscope.

by James A. Bacon

I am one of those schlubs who takes out insurance policies to protect against bad things happening. One eventuality I worry about is the need for long-term care. The longer you live and the more chronic conditions you develop, the greater the odds – about 50/50 for a 60-year-old today — that you’ll wind up bed-ridden at home or in a nursing facility. Feeling strong and fit at 53 when I took out a policy ten years ago, I was betting that I’d live longer than the average Joe and be more likely than not at some point in my life to benefit from having insurance. Signing up at a relatively young age would lock me in at an affordable rate. Or so I thought.

About two months ago I received a letter from my insurer, New York Life Insurance Company, informing me that my long-term care policy, which had remained stable ten years, was scheduled to increase 20%, costing me, in rough numbers, an extra $300 per year after a three-year phase-in. Three hundred bucks won’t bust the Bacon bank, but I was miffed — it was the principle of the thing. I had not been led to understand that my insurance rate would go up. And I bet there were other policy holders for whom $300 per year would cause real hardship.

Well, a look at my insurance policy indicated that, sure enough, New York Life was entitled to raise my fees. My bad. I should have read the fine print. Even so, any rate increase had to be approved by Virginia’s Bureau of Insurance, and I wondered — as I suppose an estimated 80,000 other long-term care insurance policy holders are wondering — what is the justification for jacking up our rates?

The letter referred vaguely to “longer life expectancies and an increased need for long-term care benefits.” Did the insurer mean to tell me that the people who are the world’s experts in demographic trends failed to anticipate that life expectancies would increase? And they miscalculated what percentage of the population would need long-term care? Really? That sounded lame to me, and I wondered if there was more to the story. In particular, I wondered if years of Quantitative Easing and low interest rates had depressed New York Life returns on insurance premiums below what the company had anticipated when it formulated the rates ten years ago. Could my higher insurance fee represent another $300 a year in tribute to Uncle Sam, just one of many ways in which low interest rates are invisibly transferring wealth from American citizens to its grotesquely swollen and indebted government?

One of the advantages of being a blogger is the ability to pick up the phone and call anyone with a decent chance that someone actually will answer. When I called New York Life to find out what the heck was going on, company spokesperson Terri Wolcott put me in touch with Aaron Ball, vice president and head of the Long Term Care business, who, as coincidence had it, lives in good ol’ Richmond, Va.

Low interest rates were a factor in the rate increases, Ball says, but not a decisive one. He candidly admits that the industry screwed up key underwriting assumptions.

We Underpriced the Policy. Sorry about That.

“When you apply for coverage, it can be 20, 30 or 40 years before you make a claim,” says Ball. “We set up reserves to pay claims 20 to 40 years in the future. We’re earning interest on those investments, and we assume what those interest rates will be.” Ten years ago, carriers were assuming earnings in the 5% to 6% range (conservative assumptions that were lower than what most pension funds were assuming at the time). “Today, they’re assuming in the 3% to 4% range. The low interest rates have put pressure on the portfolios.”

Higher returns on the company’s investment portfolios might have offset the negative experience, tempering the need for a rate increase, Ball says, but the bulk of the blame goes to actuarial miscalculations regarding other key variables.

Morbidity. The first the key variables is morbidity — how sick will policy holders get, and what will be the appropriate venue for treating them? When projecting 40 years into the future, getting this assumption correct can be harder than it looks. The things that put people into long-term care change over time. Ten years ago, frailty issues predominated — hip fractures, cardiovascular problems, and the like. Today, the driver is cognitive claims — Alzheimers and other forms of dementia. Also hard to predict is the setting in which people will be given long-term care. “Back in 1988, there was no such thing as an assisted living facility,” says Ball. As it turned out, New York Life’s morbidity assumptions were close to the mark. Other insurers got these assumptions wrong, and they’ve had to make upward adjustments in their premiums.

Voluntary lapse. When people buy policies, some continue to own the policy and eventually collect benefits, while others let their policies lapse voluntarily. The “lapsers” pay premiums that don’t get refunded, effectively underwriting the cost of the policy for others. When long-term insurance was getting off the ground about 20 years ago, there was no basis for determining how many policy holders would let their policies lapse, so carriers made the best guess they could. In most cases, those guesses were wrong.

New York Life assumed in pricing its premiums that policies would lapse at an annual rate of 2% after four years, but actual experience showed that the rate trended downward to about 0.5%. More people hung onto their long-term care insurance policies than the company expected.

Mortality. The rate at which policies lapse due to the policy holder’s death is another major variable. “We now expect twice as many people to be alive at age 90 compared to what was assumed when the product was priced,” says Wolcott. “Longer life expectancies generally result in additional claims because more people utilize long-term care services at older ages.”

The explanation made sense. I didn’t like it, but it made sense.  New York Life blew two of its key assumptions (though not as badly as many other insurers did) and low interest rates depressed investment turns. Accordingly, to maintain the actuarial viability of the policies, the company had to jack up rates.

But the explanation raises a new set of questions. If policy holders sign a contract with an insurance carrier to provide a certain set of benefits for a certain price, why isn’t the carrier obligated to eat the difference when they make bad decisions? I’ve never heard of carriers filing to reduce premiums if their assumptions turn out to be too optimistic. Maybe it happens, but I haven’t heard of it. No, they keep the profit. Given the way the incentives are structured, aren’t insurance companies encouraged to low ball premiums, knowing that they can come back later and jack up rates? Continue reading

How Many Millions Have Died from This Failed Scientific Orthodoxy?

fat_hypothesis_chart

Graphic credit: Washington Post

One of the most rigorous scientific experiments on the effects of fatty foods in the diet took some 40 years to complete, but the results are now in. Reports the Washington Post:

Collectively, the fuller results undermine the conventional wisdom regarding dietary fat that has persisted for decades and is currently enshrined in influential publications such as the U.S. government’s Dietary Guidelines for Americans. And the long-belated story of the Minnesota Coronary Experiment suggests just how difficult it can be for new evidence to see the light of day when it contradicts widely held theories.

The special diet given to mental patients in Minnesota did succeed in its intent to reduce cholesterol levels. What no one anticipated was that participants were more likely than patients on a conventional diet to die earlier.

Bacon’s bottom line. First question: By regulating and brow-beating food processors to reformulate their packaged foods and by pushing Americans into embracing the new nutritional guidelines, social engineers succeeded in altering the American diet. How many millions of Americans have died as a result?

As an aside, given the obsession with race and class today, one is tempted to ask also if minorities and the poor were disproportionately impacted. Did the nutritional social engineering of the 1970s lead to more obesity, more hypertension, more coronary blockage, and more diabetes than would have occurred otherwise? How many millions suffered death and disability as a result?

Second question: Will the social engineers ever own up to this calamitous public health failure and their complicity, however well intended, in the premature death of millions of Americans? Will Black Lives Matter point an accusing finger at the nutritional policies that arguably have snuffed out a thousand times more African-Americans lives than unjustified police killings?

Third question: What can we learn about what happens when science, politics and scientific funding intersect? As the WaPo summarizes why early results of the study were buried when they conflicted with orthodoxy:

The Minnesota investigators had a theory that they believed in — that reducing blood cholesterol would make people healthier. Indeed, the idea was widespread and would soon be adopted by the federal government in the first dietary recommendations. So when the data they collected from the mental patients conflicted with this theory, the scientists may have been reluctant to believe what their experiment had turned up.

Could the same thing be happening in some other sphere of public policy? Could contradictory scientific evidence be ignored or suppressed? Just asking.

— JAB

A Demographic Mystery

Black poverty rate by county. Source StatChat

Black poverty rate by county. Source: StatChat

The highest median income for African-Americans is highest in Maryland, second highest in Delaware and third highest in Virginia. The flip side of the coin is that the counties with the lowest African-American poverty rates are overwhelmingly clustered in the Mid-Atlantic, as can be seen in the map above. What’s going on? What’s so special about the Mid-Atlantic?

One obvious explanation is that the high median African-American incomes are centered on the Washington metropolitan area. That may be a factor but there’s more. African-American prosperity extends southward well past the Richmond metropolitan area and north into Delaware.

The data comes from Hamilton Lombard with the Demographics Research Group at the University of Virginia, who published on the StatChat blog. In a future post, he says, he will examine how that came to be. I eagerly await his analysis.

(Hat tip: Frank Muraca, Nutshell.)

— JAB

Fat Virginia: Better than Average but a Few Pounds to Trim

Source: WalletHub

fatness_and_health

Rankings based on composite metrics of fat prevalence, fat-related health issues, active lifestyle and healthy food in 100 largest U.S. metropolitan regions. Memphis ranks as the No. 1 metro with fat-related issues, Honolulu as No. 100.

— JAB

Sorry, College Kids, Living off Beer and Pizza Does Not Make You Poor

Source: Demographics Work Group, UVa

Source: Demographics Work Group, UVa

As readers know, I am a data junkie, and I post a lot of maps and charts highlighting, among other things, the variations of wealth and poverty in Virginia. The data is useful in helping us understand social, economic and political dynamics of the state, but they usually come with an asterisk. Poverty rates tend to be abnormally high in college towns. Judging by the statistics, one would expect places like Blacksburg, Charlottesville, Harrisonburg, Radford and Lexington to be havens for housing projects and trailer parks. Yet to all outward appearances, they seem to be fairly prosperous places.

Clearly, the presence of large numbers of college students who are studying (and partying)  rather than working full-time to earn a living are skewing the figures. As a new census brief from the Demographics Working Group at the University of Virginia observes:

Most college students report very low incomes, putting them below their respective poverty thresholds and—especially in cases of large off-campus student populations—raising the rate of poverty in the towns where they live. Yet, intuitively, we recognize that college or graduate student “poverty” means something different than poverty among the unemployed, families with children, or the persistently needy.

In calculating the official poverty rate, number crunchers make some adjustments to minimize the student amplification of poverty by excluding people living in group quarters such as college students in dormitories, older adults in nursing homes, prisoners and inmates, and military personnel in barracks. Even so college students living off-campus inflate the poverty numbers.

By removing all students involved in undergraduate or graduate education, the Demographics Work Group provides a clearer picture of what most of us would think of as “real” poverty. In the aforementioned college towns, making this adjustment cuts poverty rates in half. Even in most populous jurisdictions, such as Richmond, the adjustment creates a noticeable drop.

— JAB