The origins of statistics lie in demography, economics, and
public administration or the "translation of politics into the reality
that citizens see every day"(1). Statistics was born at the beginning of
the industrial revolution. With the new world view of science and mathematics
emerging during the enlightenment, government and social application emerged
concurrently. Thus, statistics was born. The industrial revolution needed
standardization for mass production by taking an individual product/people and
comparing it to a sample that is to represent the population. Statistics
supplies the mean (average) and the standard deviation (variability) of a
sample. These two statistics allow us to fully describe an entire population
without ever needing to know the values for a specific individual, and thus, as
Latour puts it (p. 59), “the threshold between local and global can now be
crossed instantaneously.”
Under this enlightenment way of thinking, statistics is a materialist framework of understanding how to mobilize our world into numbers and something we can grasp. Statistics ascribes numbers to things, people, and stuff so we can visualize the attributes of a population. This reduction is what Latour describes the work of science. Reduction gives us compatibility, standardization, text, calculation, circulation, and relative universality (Latour 71). “The sciences do not speak of the world but, rather, construct representations that seem always to push it away, but also to bring it closer” (30). Thus statistics is a model of how we see the world. This is a way of knowing our world.
One application of statistics as a seeing device in modern day is the utilization of the value of a statistical life (VSL) in evaluating public policy changes proposed by the Environmental Protection Agency (EPA).
Under this enlightenment way of thinking, statistics is a materialist framework of understanding how to mobilize our world into numbers and something we can grasp. Statistics ascribes numbers to things, people, and stuff so we can visualize the attributes of a population. This reduction is what Latour describes the work of science. Reduction gives us compatibility, standardization, text, calculation, circulation, and relative universality (Latour 71). “The sciences do not speak of the world but, rather, construct representations that seem always to push it away, but also to bring it closer” (30). Thus statistics is a model of how we see the world. This is a way of knowing our world.
One application of statistics as a seeing device in modern day is the utilization of the value of a statistical life (VSL) in evaluating public policy changes proposed by the Environmental Protection Agency (EPA).
Value of Statistical Life
The value of a statistical life is used by governments all
over the world when doing a cost benefit analysis of a project. This idea of
placing a monetary value on human life is quite pertinent to our reading of
State of Fear because the EPA uses VSL when performing CBA of environmental
regulations. But what exactly is the VSL, well in case of the epa it is 6.9
million dollars. (2) But what does this figure represent? In EPA’s own words
this is what the VSL means:
“ Suppose each person in a sample of 100,000 people were
asked how much he or she would be willing to pay for a reduction in their
individual risk of dying of 1 in 100,000, or 0.001%, over the next year. Since
this reduction in risk would mean that we would expect one fewer death among
the sample of 100,000 people over the next year on average, this is sometimes
described as "one statistical life saved.” Now suppose that the average
response to this hypothetical question was $100. Then the total dollar amount
that the group would be willing to pay to save one statistical life in a year
would be $100 per person × 100,000 people, or $10 million. This is what is
meant by the "value of a statistical life.” Importantly, this is not an
estimate of how much money any single individual or group would be willing to
pay to prevent the certain death of any particular person.”
So really it is how much a we are willing to pay for a one-unit
reduction in risk of death. The EPA uses several different studies to arrive at
their figure of 6.9 million dollars. The way that those studies figure out
their numbers is by examining similar jobs but with different degrees of risk,
or by looking at how much life insurance people buy, pretty much any area where
we can see how much money people place on not wanting to die.
Now what does the EPA do with the actual figure?
Well now that they have the benefit of ~ 1.4 billion they can compare it to the estimated costs
If you look at the two tables you see that in one the total benefits are ~200 million where as in the other they are ~ -300. What causes the discrepancy? In the method that makes the project "worth it" the VSL is the simple value that the EPA used at the time (5.9 million). However for the second case where the project should not be approved under standard CBA the value used is the value of statistical life year. This value adjusts much more for the age of the population involved.
This example of the cost benefit analysis for the section 126 Rule shows how even using the exactly same data, calculations done by the same people, can show completely different results that depend only on how the seeing device of statistics is employed.
The table below show how much of the range of VSL estimates that US government agencies employ when evaluating projects. Pretty much any of the numbers there would be fair game for an evaluation and would allow a person to cite a government or study as the source supporting their conclusions.
We as a group believe that it is highly important for people to have an understanding of the limitations that these estimates present, and the susceptibility of statistics towards the bias of the ones using it as a seeing device. This is not to say that statistics are somehow bad, or that people necessarily use them to cheat but it can very telling when a group would use the upper bound of 12 million to evaluate an environmental study vs a group who would use 3 or 4 million.
It is hard to look at this topic without simply having the emotional repulsion to placing a monetary value on human life. But in a lot of ways this value is arrived at by looking how much people in real life chose to be compensated for the risk to that life. Much in how we are complicit by allowing our government officials to come up with this value we are also responsible for the birth of this value. To show a good example of the public rhetoric on the issue we offer you this
For a less entertaining and but more serious discussion of the rhetoric involved we would like to present you with these quotes form the New York Times:
“The paper cites
the spat, back in 2008, over EPA's decision to adopt a new estimate. That
outcry was prompted by an Associated Press story that ran with the headline
"American life worth less today."
The headline was
wrong, supporters of cost-benefit analysis say, because the government is not
predicting the value of saving people who would otherwise die. Instead,
regulations reduce the risk of death by a tiny amount -- say, by
one-in-a-million -- for millions of people.
Economists devised
the VSL to summarize how much people would pay to avoid those incremental
risks. The studies are usually based on the extra pay that laborers get for
high-risk jobs, or on surveys that ask people what they would give up to avoid
an extra one-in-a-million chance of terminal cancer, a heart attack or a fatal
car crash.”
NYT article- https://www.nytimes.com/gwire/2011/01/18/18greenwire-epa-plans-to-revisit-a-touchy-topic-the-value-75301.html?pagewanted=all
OECD (2012), Mortality Risk Valuation in Environment,
Health and Transport Policies, OECD Publishing. http://dx.doi.org/10.1787/10.1787/9789264130807-en
EPA report -http://www.epa.gov/ttn/caaa/t1/reports/c4suppo3.pdf
No comments:
Post a Comment