# Poverty and Statistics

I am repairing a gap in my education by reading Thomas Sowell’s classic, Vision of the Anointed, which was written in 1992 but is still, unfortunately, as valid a critique of leftist thought as it was then. As an example of his methods, he constructs an experiment in statistics. This concerns poverty and inequality and, in particular, the poverty of leftist thinking.

He imagines an artificial population that has absolute equality in income. Each individual begins his (or her) working career at age 20 with an income of \$10,000 per year. For simplicity’s sake, we must imagine that each of these workers remains equal in income and at age 30, receives a \$10,000 raise. They remain exactly equal through the subsequent decades until age 70 with each receiving a \$10,000 raise each decade. He (or she) then retires at age 70 with income returning to zero.

All these individuals have identical savings patterns. They each spend \$5,000 per year on subsistence needs and save 10% of earnings above subsistence. The rest they use to improve their current standard of living. What statistical measures of income and wealth would emerge from such a perfectly equal pattern of income, savings and wealth?

 td {text-align:right; font-family: verdana,arial,sans-serif;} [s~="l"] {text-align:left} Age Annual Income Subsistence Annual Savings Lifetime Savings 20 \$10,000 \$5,000 \$500 \$0 30 \$20,000 \$5,000 \$1,500 \$5,000 40 \$30,000 \$5,000 \$2,500 \$20,000 50 \$40,000 \$5,000 \$3,500 \$45,000 60 \$50,000 \$5,000 \$4,500 \$80,000 70 \$0 \$5,000 \$0 \$125,000

Unfortunately, even with an Excel spreadsheet, I cannot get these numbers to line up properly.

[Jonathan adds: Many thanks to Andrew Garland for providing html code to display these numbers clearly.]

Now, let us look at the inequities created by this perfectly equal income distribution. The top 17% of income earners has five times the income of the bottom 17% and the top 17% of savers has 25 times the savings of the bottom 17%. That is ignoring those with zero in each category. If the data were aggregated and considered in “class” terms, we find that 17% of the people have 45% of the all the accumulated savings for the whole society. Taxes are, of course, ignored.

What about a real world example ? Stanford California, in the 1990 census, had one of the highest poverty rates in the Bay Area, the largely wealthy region surrounding San Francisco Bay. Stanford, as a community, has a higher poverty rate than East Palo Alto, a low income minority community nearby. Why ? While undergraduate students living in dormitories are not counted as residents in census data, graduate students living in campus housing are counted. During the time I was a medical student, and even during part of my internship and residency training, my family was eligible for food stamps. The census data describing the Stanford area does not include all the amenities provided for students and their families, making the comparison even less accurate. This quintile of low income students will move to a high quintile, if not the highest within a few years of completion of graduate school, A few, like the Google founders, will acquire great wealth rather quickly. None of this is evident in the statistics.

Statistics on poverty and income equality are fraught with anomalies like those described by Professor Sowell. That does not prevent their use in furthering the ambitions of the “anointed.”

### 8 thoughts on “Poverty and Statistics”

1. A post/discussion at Stuart Schneiderman’s blog also raises some questions about the commonly made assertion that “a very high % of all medical expenditures are made in the last year(s) of life.” As one commenter pointed out, you don’t always know in advance whether a particular condition is actually going to be fatal…when it IS, then it counts as “last year(s) of life.”

And depending how these numbers are actually measured, it seems likely that if a 22-year-old is in a bad car wreck and requires extensive emergency room and critical care before finally succumbing, then he will be counted as having been in his “last year of life” at the time the expenses were incurred.

People who report various statistics, economic, health-related, etc, are usually remarkably incurious about the details of how those statistics were compiled and what the numbers actually mean.

2. I would also imagine that a lot of the statistics for certain programs such as poverty figures are arrived at in a manner to advance the agenda of the poverty pimps and get more money to fight poverty in this case. What’s the old saying: “there are three kinds of lies–lies, damn lies, and statistics.”

3. We currently have rationed care. It has been since 1978 but the level of coercion is steadily increasing.

The last year of life story is an old one and, as you suspect, a phony statistic. I did a lot of research in the 90s on care of the frail elderly. Health costs peak at about age 70. Thereafter they decline as many elderly people will not consent to procedures after age 80 or so. I tried to set up a study at UC, Irvine on this issue but could not get cooperation from the administration. The docs were all in favor. Joe Scherger, who was then associate dean for primary care (or something like that) was very enthusiastic.

The idea was to provide primary care at assisted living homes. We would send a doctor or nurse practitioner with a laptop (this was about 1997) to the home. The administrators were all on board as they have to provide transportation, a big cost plus a lot of trouble with elderly, and were going to give us a room as a treatment center. The records would all be back at UCI and accessed by internet.

There was good funding available but the administration wouldn’t sign off. They were convinced it would be a money loser. The VA has done studies showing that getting elderly into good shape by adjusting meds and fixing minor chronic issues results in decreased costs from then on. We could never get the study done. Everyone assumes that good quality is more expensive. I’m sure the Obama brain trust thinks so, too. It isn’t but rationing will be substituted for quality.

4. When we were just starting out after college, the unexpected arrival of our second child caused a real financial strain so my spouse and children went to live with relatives while I stayed to work.

My spouse and children lived with her grandparents in a sprawling (it’s Texas) upper middle-home while I slepted comfortably on friend’s couch. Turned out in a medical benefits interview that a government worker had classified my spouse and children as “underhoused” because they had to share her grandmothers massive kitchen and that I was classified as “homeless” because I didn’t have my own bedroom.

After hearing that, I had a hard time taking a lot of government statistics seriously. Living comfortably with upper-middle class kin is not being underhoused as any sane person would define and sleeping on a friends couch, which most of do at some point in our lives is not being “homeless”.

A lot of the nonsense that enters into these statistics comes from the way they label the things they count. When you take statistics all the source data is treated as perfectly obvious e.g. fifty white balls and fifty black balls in an urn, with no possibility for ambiguity. In the real-world you end up with situations like an urn filled 10 unambiguously white balls, 10 unambiguously black balls, and 80 balls in various shades of grey. The studies authors will classify the subjectively darker grey balls as black and the subjectively lighter balls as white. If black balls are considered a problem then the study will trumpet that 50% of the balls were black and that we need a big government program to reduce the number of black balls.

5. Just to reinforce my point about labeling in statistics. In the newsstory I based my last post on there is this paragraph:

But police say identifying the number of deaths similar to Duran’s is more difficult. Because he was killed in a parking lot, and thus on private property, Duran was counted as an accidental death, and not a traffic fatality.

Most people probably think that “traffic fatality” statistics covers all deaths caused by moving vehicles but they don’t. They just cover deaths on public roads. That makes it easy to distort statistics by labeling the data in such a way as to make people think a certain phenomena is being measured when it isn’t.

6. “We currently have rationed care. It has been since 1978 but the level of coercion is steadily increasing.”

My greatest fear regarding Health Care Reform is not that I will wake up someday in the year 2013 in a hospital waiting on the decision of some Death Panel or that it is Socialized Medicine in a way that what we have right now is not. Rather, my worry is that Health Care Reform is one more contributor to the long slippery slope of the trends you have commented upon and that are already taking place with Medicare and with employment-based health insurance.

7. Health Care Reform is one more contributor to the long slippery slope of the trends you have commented upon and that are already taking place with Medicare and with employment-based health insurance.

The trend that is going to kill us is the expansion of insurance into pre-paid care. Moral Hazard becomes a major factor once the principle of insurance is gone. Jack Wennberg’s studies at Dartmouth have shown that there is great variation in the treatment of some conditions while others are treated pretty much in a standard fashion and at steady rates geographically. The difference between the two groups is the fact that the non-variation group is made up of conditions that are unexpected, occur at statistically predictable rates and are severe in consequences. Examples are heart attacks and broken hips.

The group with a lot of variation in incidence and treatment consists of conditions that are rather vague in onset, subject to definition as to whether they are present or not and have mild to moderate severity. Examples include heart failure, anemia, prostate obstructive conditions and the like.

We used to have health insurance that covered the first group but most of the care of the second was in the doctor’s office and paid for in cash, aside from insurance. Wennberg and the other command economy types want to ration the care of the second group but another way is to simply place the responsibility on the patient. Some people will go to the doctor weekly when others with the same condition will see the doctor twice a year. The most ominous trend, which was present in the Clinton Plan, is outlawing private care outside the government plan. There is no rational reason to do so except to prevent comparison between government and private care.