On Friday, it was learned that UPMC is spending $51 million on a new corporate jet plane, a Bombardier
Global Express, described as “a luxury, ultra-long range business
jet with twin Rolls Royce engines.” For security reasons, the
flight plans of this airplane are to remain hidden from the public.
I'm sure Pittsburghers who are
struggling to pay their medical bills will be thrilled to hear that
UPMC is able to afford $51 million for a new stealth jetliner. But
who is going to ride around on this luxury aircraft, since, as we now
know, UPMC has no employees?
We caught Lou Reed at the Radio City
Music Hall around the time his New York
album came out (1989). His guest was Little Jimmy Scott. Here's
everyone's favorite side from that album:
In case you didn't catch the last
verse:
Well Americans don't care much for
anything,
land and water the least
And animal life is so low on the totem
pole
with human life not worth more than
infected yeast
Americans don't care too much for
beauty
They'll shit in a river, dump battery
acid in a stream
They'll watch dead rats wash up on a
beach
and complain if they can't swim
They say things are done for the
majority
Don't believe half of what you see and
none of what you hear
It's like my painter friend Donald
said to me
“Stick a fork in their ass and turn
them over, they're done”
In
this day and age, we hear and read on an almost weekly basis,
instances of hackers penetrating many government and industry
websites to install malicious malware and viruses. So I'm kind of
surprised to see no one inquiring whether the Affordable Care Act
website has likewise been attacked by individuals intent on
installing similar defects in the website software to negatively
impact its successful rollout, and to further embarrass and smear the
president. Just asking.
Steve
Siskind
Canonsburg
I
have been wondering the same thing myself. I can understand why, if
this is true, the Obama administration might not want it to be known.
But shouldn't the news media be inquiring about this?
Right now, the corporate media are
focused like a laser on the “scandal” of the malfunctioning of
the national health insurance exchange website. Although these
glitches reflect negatively on the Obama administration, they are
technical problems that will be fixed. They have nothing to do with
the substance of the law. A more important failure is the number of
people, particularly poor people, who will remain uninsured after the
Affordable Care Act (ACA) is implemented. This is not the fault of
the act as written, but is the result of the Supreme Court decision
on its constitutionality and intractable opposition from Republican
politicians.
An October study by the Kaiser Family Foundation (KFF) estimates that 5.2 million non-elderly adults living
below the federal poverty level (FPL) will remain uninsured in 2014
because they live in the 26 states that—at the time of the
study—had declined to participate in the Medicaid expansion. These
states are concentrated in the South and West and are controlled
largely by Republican governors and legislatures. About half of
Americans, but 58% of America's uninsured working poor, live in those
states. Pennsylvania is among them. Our Medicaid expansion status is uncertain, but even if the Corbett administration reaches
agreement with the federal government on an expansion plan, it is
unlikely to be implemented in 2014.
To review, of the approximately 30
million people who were expected to be insured for the first time
under the ACA, fully half of them—the poorest half—were to be
insured through Medicaid expansion. Traditional Medicaid is
jointly administered by the state and federal governments. Federal
law requires that all children be covered if their family makes less
than the FPL. Children under six are covered up to 133% of FPL. The
eligibility rules for adults are determined by the states. In most
states, adults without children don't qualify no matter how poor they
are. The income level at which parents with dependent children
qualify for Medicaid varies from a low of about 20% of FPL in the
least generous states to a high of 133% is the most generous states.
(In Pennsylvania, it is 46%.)
The ACA expanded Medicaid by making
everyone—children and adults—eligible for Medicaid if their
family income is 138% of FPL or less. Since this is expensive, the
feds agreed to pay most of the cost: 100% in 2014, dropping to 95%
in 2017 and 90% in 2020. The ACA required states to implement
Medicaid expansion. If they refused, the federal government
threatened to withhold its contribution to traditional Medicare—about
57% of the cost. However, this clashed with the conservative
majority of the Supreme Court's long-term goal of rolling back
federal regulation of the states. In National Federation of
Independent Business v. Sibelius, the Supremes decided that the Medicaid expansion rules were coercive and that states may opt out.
This denies medical care to many of the Americans who need it most,
people who fall into the coverage gap.
The ACA provides subsidies, on a
sliding scale, for people individuals and families whose income is
between 100% and 400% of FPL. The coverage gap consists of those
people, living in states that do not expand Medicaid, who are not
poor enough to qualify for Medicaid in their state, but whose income
is below 100% of FPL, the level at which the subsidies kick in. This
is illustrated in the chart below.
As noted, poor and uninsured Americans
tend to be concentrated in the “red states” that are not
expanding Medicaid. Twenty percent of the people in the coverage gap
live in Texas, and another 15% live in Florida, followed by Georgia
with 8% and North Carolina with 6%. At present, 6.8% of the
residents of states that are participating Medicaid expansion are
poor and uninsured, but 9.1% of the residents of the refusing states
are poor and uninsured. The chart below shows the breakdown of the
poor and uninsured by race, and shows that Medicaid expansion has a
discriminatory impact.
Live in States Expanding
Live in States Not Expanding
White
40%
60%
Black
32%
68%
Hispanic
51%
49%
Asian
70%
30%
Total
42%
58%
These are America's working poor. By occupation, the folks most likely to be poor and uninsured are (1)
cashiers, (2) construction laborers, (3) housekeepers, (4) cooks, and
(5) waiters and waitresses.
At the time the ACA was passed, single
payer advocates noted that the ACA provided less than universal
coverage. The largest excluded group is undocumented immigrants, but
the ACA also excludes native Americans and people who are
incarcerated, have a religious objection, or can prove financial
hardship. To that we must now add 5.2 million working poor
Americans, a target group that the ACA was clearly intended to help.
These people are being left to die for lack of health care. It will
be interesting to see whether they respond at the ballot box when they
realize what their state politicians have done to them.
Single payer health insurance is needed now more than ever.
To make sense of this post, you must first read Part 1 and Part 2.
Kahan and his colleagues did not discuss differences between the responses of
liberals and conservatives to his four contingency problems, in spite
of the fact that political ideology interacted with his other
variables in a way that was statistically significant.
These curves
represent the distributions of the responses in each condition. The
high point of each distribution is the mean. To help us keep these
findings straight, Kahan has drawn the liberals in blue and the
conservatives in red. The top two figures are of little interest.
They merely show that liberals and conservatives are equally likely
to answer the questions about the skin cream incorrectly.
However, the bottom
two graphs show ideological polarization. Specifically, the
conservatives are more likely than the liberals to answer the
question correctly when the correct answer corresponds with their
expectations, and they are more likely than the liberals to answer
incorrectly when the correct answer conflict with their ideology.
This is true of both the low numeracy participants (on the left) and
the high numeracy participants (on the right). Stated simply, the
two red distributions are farther apart than the two blue ones.
Of course, since
this study involves only a single issue, another way of interpreting
these data is that conservatives care more about gun control than
liberals do.
Table 6
You can also see
this ideological asymmetry in the bottom figure of Table 3, repeated
above, where, at most levels of numeracy, the two red lines are
farther apart than the two blue lines. In a blog post, Kahan acknowledges this evidence of asymmetry, but contends that it less
interesting than the fact that both liberals and conservatives show
confirmatory bias at high levels of numeracy. He points out that the
greatest evidence of asymmetry occurs at low to moderate levels of
numeracy (inside the solid circle). At the very highest levels of
numeracy (inside the broken circle), the two conservative groups
appear to be converging, while the two liberal groups are diverging.
He's right about
that. Committed liberals must acknowledge the disappointing
performance of the highly numerate liberals. The only reasonable
explanation is that bright, highly educated liberals are biased too.
But that doesn't change the fact that, in this experiment, conservatives show
more evidence of ideological bias than liberals do.
You must read Part 1 before continuing or none of this will make
sense.
A study was conducted by Dan Kahan of Yale University and three colleagues
involving a demographically representative sample of 1111 American
adults recruited by Polimetrix/YouGov, an online survey research
firm. Preliminary questions were used to categorize the participants
along two dimensions:
Liberalism
v. Conservatism: They rated themselves as very liberal to very
conservative on a 5-point scale, and strong Democrat to strong
Republican on a 7-point scale. These scales were combined, and the
participants were divided at the midpoint into liberal Democrats and
conservative Republicans.
Numeracy:
Participants' numeracy was measured using nine real world
mathematical problems. They received a numeracy score of 0 to 9.
Each participant
was then asked to answer one of the four judgment of contingency
problems shown in Table 2 of Part 1. These are labeled by the
correct answer to the problem:
Rash
increases: The rash got worse when the skin cream was used.
Rash
decreases: The rash got better when the skin cream was used.
Crime
increases: There was more crime with the ban on concealed
weapons.
Crime
decreases: The ban on concealed weapons reduced crime.
Table 3
The top graph presents the results for the skin cream questions. On the vertical
dimension, the higher the line, the more people answered the question
correctly. Horizontally, as you go from left to right, numeracy
increases. The four groups all show almost the same results. As you
would expect with content-neutral problems, as numeracy increases,
more people answer correctly.
The bottom graph shows the results for the gun control questions. There is a tendency
for scores to improve with numeracy, but the results are not uniform.
The most striking trend is evidence of confirmatory bias.
Participants are more likely to give the correct answer in the two
conditions where it is consistent with their political views—the
liberals when crime decreases and the conservatives when crime
increases. When the correct conclusion is inconsistent with the
participant's partisan ideology, the lines are almost flat, which
means that the high numeracy people are not successfully
correcting their biases. The numerate liberals claim that gun
control is effective, even when the evidence shows it is not. The
numerate conservatives claim that concealed weapons discourage crime,
even when the evidence shows they do not. These labels may help to
make the graph more clear.
Table 4
What is going on in
the minds of these participants? It's likely that participants in
all four groups initially engage in System 1 thinking. They look at
the data and immediately believe what their political ideology leads
them to expect. For the two groups in which the data are consistent
with their prior beliefs, this is no problem. If they use System 2
to do the math, they will become more confident of the correct
conclusion. But the two groups in which the data are inconsistent
with their prior beliefs are faced with a conflict if they engage in
System 2 thought. The results suggest that either they don't bother,
or if they do, they manage to reinterpret the data in a way that
confirms their expectations.
These
data support the identity-protective cognition thesis
rather than the science-comprehension thesis.
It seems unlikely that additional math and science training would
help these folks to draw the correct conclusion when that conclusion
conflicts with their ideology. One commentator referred to these results as “the most depressing discovery about the brain, ever.”
(Journalists have a depressing tendency to refer to the results of
psychological research as discoveries about “the brain,” falsely implying that they are physiologically determined. I would say these
results demonstrate the effectiveness of political socialization.)
They are consistent with findings showing that giving people information which corrects common misconceptions sometimes backfires
and causes them to believe them more strongly. It also supplements
Kahan's previous finding that the most scientifically literate Americans are not more
convinced that climate change is a serious threat, but are more
ideologically polarized than those who are less scientifically
literate.
Despite the fact
that the authors proposed that such results would raise doubts about
the possibility of rational self-government, they do not recommend that we curtail participatory democracy and turn over our more
important decisions to Big Brother. Kahan sees the problem as a
failure of scientific communication. He proposes that scientific
issues be reframed so as to reaffirm the ideological beliefs of those
who might otherwise be skeptical. For example, in another article he suggests that global warming be reframed as a technical problem to be
address by corporations rather than a political problem to be
addressed by government:
They [conservatives] would probably look at the evidence more
favorably, however, if made aware that possible responses to climate
change include nuclear power and geoengineering. . . Similarily,
[liberals] are less likely to reflexively dismiss the safety of
nanotechnology if they are made aware of the part that nanotechnology
might play in environmental protection, and not just its usefulness
in the manufacture of consumer goods.
Here is a video of
Kahan discussing the results of his global warming studies and his
suggestions for improving scientific communication.
Kahan seems to be
suggesting that our ideological disagreements can be solved through
clever marketing, but this seems an unlikely solution. For example,
any serious attempt to curtail climate change through either nuclear
power or geoengineering would encounter strong and well-founded
objections from important members of the scientific community, and
consensus would quickly disappear.
Before we throw up
our hands in despair, Kahan's results need to be replicated with a
broader range of issues. However, to be fair, global warming is such
an important issue that failure to reach consensus on a solution will
make all the other issues which we think we care about seem like the
equivalent of rearranging the deck chairs on the Titanic.
However, there are
some research findings that seem to offer greater hope of overcoming
ideological polarization. I will discuss them in future posts. But
first, for those that are interested, an appendix about the differences between liberals and conservatives.
Let's not be naïve. Obviously, the
political system we have now is more a plutocracy than a democracy.
Our government has been captured by corporations and the people who
own them. But suppose, by some miracle, we were able to enact
effective campaign reform laws and politicians became responsive to
public opinion. Are American citizens capable of making rational
decisions based on scientific information?
Before we go any further, please take a
look at this problem.
Table 1
The problem requires correctly
interpreting a contingency table. Even bright college students often
get it wrong. When deciding whether the skin cream works, some
people only compare the numbers in the top row—the number of people
who used the skin cream who get better or worse. But this ignores
important information from the control group that didn't take the
skin cream. Other people only compare the numbers in the left
column—the number of people getting better who took the skin cream
or didn't. But this ignores the fact that many more people took the
skin cream than didn't. To correctly answer this question, you have
to consider all four cells of the table. You must compare the
percentage of people who used the skin cream who got better to the
percentage of people who didn't use the skin cream who got better.
Of the 298 people who used the skin cream, 223, or 75% of them, got
better. Of the 128 people who didn't use it, 107, or 84% of them,
got better. The correct answer is that the skin cream is
ineffective.
Why is this problem so difficult? In
part, it's because Dan Kahan and his colleagues—who did the study I'm about to report—put their thumb on the scales. They chose data
such that, if you use either of the two shortcuts I
mentioned—comparing only the numbers in the top row or the left
column—you'll get the wrong answer. Fifty-nine percent of the
participants in this study answered the problem incorrectly.
This problem illustrates the difference
between System 1 and System 2 thinking, the central metaphor of this blog. According to Daniel Kahnemann, in his book Thinking, Fast and Slow, we have two cognitive
systems. System 1 is automatic and effortless. We simply say and do
what feels right. The System 1 response to this problem is to say
that the skin cream works, because that's what it looks like at first
glance. System 2 is deliberate and effortful. It comes into play
when we take the time to analyze a situation carefully. It can be
used to correct the errors that System 1 is prone to making. In this
case, System 2 requires that you do the math.
As
difficult as this problem is, it has one thing going for it. We
don't have any emotional investment in whether this skin cream works.
But what if the issue were one about which we had a prior
hypothesis? In this case, System 1 could lead us to a second source
of error, confirmatory bias—the
tendency the interpret new information in a way that is consistent
with our prior beliefs and ideological biases. Compare the problem
in Table 1 to these three other problems.
Table 2
Does a city-wide
ban on concealed weapons increase or decrease crime? Conservatives
would probably expect crime to increase (since people can no longer
use their concealed weapons to shoot bad guys), and would be
resistant to information suggesting that gun control actually works.
Of course, the opposite is true for liberals. Kahan expected
political ideology to affect participants' responses to the gun
control questions, but not the skin cream questions.
Now
let's introduce another variable into the mix—numeracy.
(I realize this is getting complicated, but please bear with me.
It's worth it.) Numeracy—analogous to literacy—refers to
mathematical competence and a tendency to use quantitative reasoning
in appropriate ways. You would expect numerate people to do better
when making judgments of contingency, at least when evaluating skin
creams. But what happens when numerate people encounter data
contradict that their political views?
We
know that liberals and conservatives clash over scientific issues
such as gun control and global warming. Kahan suggests two theories
to explain these political impasses. The science-comprehension
thesis suggests that people
don't have enough training in science and math. With better
education, people would be capable of correctly interpreting the
results of empirical studies. The identity-protective
cognition thesis is more
pessimistic. It argues that our ideological polarization is so great
that it cancels out even the ability of well-educated people to
utilize their math and science skills. If the confirmatory bias
trumps numeracy, Kahan believes that this raises serious questions
about whether Americans are capable of enlightened self-government.
We may have to turn over important decisions to committees of
experts. (But who will choose the experts?)
What do you think?
The answer provided by Kahan's research comes in Part 2.
The reminiscence bump is not a dance step, but a characteristic of autobiographical memory—our recall of our own life history. While you might expect memories to fade gradually with time, previous studies of older adults show better recall for events during their youth, between the ages of 10 and 30. One way to test this is with popular music. In studies in which participants listen to hit songs from before they were born to the present, they are more likely to recognize and prefer songs from their teens and twenties, with the bump peaking at age 23.5. Reminiscence bumps have also been found for films, books, sports figures, current events and personal memories.
It won't be a surprise to music fans that we show a lifelong preference for the songs of our youth. The audience for oldies shows consists mostly of people who were in high school or college at the time the music was originally popular.
Several explanations have been suggested for the bump. One possibility is that we had many vivid first-time experiences during late adolescence and young adulthood, which were encoded in memory more strongly due to their emotional content. The first experience of a given type may also become a prototype, which is more easily recalled than other members of the category. It has also been suggested that hormonal and neurobiological changes play a role.
In a new study by Krumhansl and Zupnick, 62 Cornell University students with an average age of 20 were played clips from the two top hits of every year from 1955 to 2009 in random order. They were asked whether they recognized each song, whether they liked it, and to rate its quality. They indicated whether they had personal memories of each song, and if so when and with whom they heard it. Here are the results for recognition, quality, liking and personal memories.
These students are too young to test for the reminiscence bump. Their better recall for songs released after 2000 has a trivial alternative explanation—that more recent songs are better remembered. However, there were “cascading reminiscence bumps”—two earlier, smaller bumps, which are seen most clearly in the recognition and personal memories data. One occurs at from 1980-1984, about the time their parents were 20, and the other in the '60s, the decade when most of their parents were born.
The obvious explanation for the 1980-84 bump is that as children and adolescents, we are exposed to out parents' favorite music and wind up liking some of it. (My parents were fans of big band music. I didn't care much for big bands when I lived at home, but I've gradually come to like them much better—a sleeper effect?) The authors suggest that the '60s bump could reflect the musical taste of the students' grandparents. They also entertain the hypothesis that '60s music is generally better known and of higher quality than the music of other decades—a claim I regard as suspect.
Krumhansl and Zupnick also asked their participants what genres of music they listened to while growing up and now. Of course, their sample was neither large nor representative of college students generally, but here are the results.
Neither jazz nor blues did well—a problem for the future of both genres. I was prepared to see them overshadowed by pop, rock and hip-hop, but they also did worse than classical, country and soundtracks! (Of course, in recent years, the more popular soundtracks have been collections of recent hits, rather than original music composed for the film.)
