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C00002 00002	safety[f82,jmc]		When safety kills
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safety[f82,jmc]		When safety kills

	It is a truism that poverty kills, and we shall quantify this
proposition statistically by computing the relation between the income
of a state and its death rate - both taken from the 1979 Statistical
Abstract of the United States.  This probably uncontroversial proposition
has a probably controversial corollary - too much money spent on a safety
measure kills more than it saves by draining money out of the economy
that the people who would otherwise get the money and be more prosperous
would spend to live longer.

	The estimated cost of safety measures in lives is one life
for several million dollars spent on safety or anything else.  The
number, as we shall explain, cannot be computed accurately, but all
estimates are between one million and five million dollars.  This
means that if more than five million dollars are spent on a safety
measure per life saved, net lives are lost.  This relates to social
policy, because many of the required safety measures for nuclear
power plants and other industrial facilities may cost many tens
of million dollars per life saved.  Since the money spent on safety
comes out of utility rates, we shall argue that the public would
be safer if the expenditure were not made and rates kept down
instead.

	The statistical technique is quite simple and even naive,
and the numbers obtained are certainly subject to qualification.  However,
some of the safety expenditures are so far out of line that the
conclusion that they are irrational isn't very sensitive to the
numbers.

	Our data comes from the "Statistical
Abstract of the United States: 1979" (100th edition), U.S. Bureau of Census,
Washington, D.C., 1979.  Table No. 107 on page 74 give data on
death rates for each state and also grouped into nine regions
of the country.  We used the last column which gives the death rate
per thousand of population for the year 1977, the latest included.
For income we used Table No. 730 on page 445 which gives the per capita
income by states and with the states grouped into nine
 regions.  We used the column for 1978, the most
recent given.

	For doing the computation, we used the Hewlett-Packard HP-15
hand-held calculator and its built-in program for linear regression.
We did the computation by regions first and obtained the formula

	<death rate (per thousand)> = 10.46 - 0.00023 <personal income>.

Let's abbreviate this

	d.r = 10.46 - 0.00023 p.i.

Remembering that the death rate is given per thousand, the conclusion
is that for each $4.4 million of personal income in a state in 1978,
one fewer person died in 1977.  We're sorry about the fact that
the years don't agree exactly, but that's what was in the tables
in the Statistical Abstracts.  The reader with access to other
data can probably find data for matching years.

	The correlation co-efficient was -0.2, which indicates that income
accounted for only 20 percent of the variation of death rate among
regions.

	We also did the calculation in three other different ways.  Taking
the five highest and the five lowest states in income, the calculation
gave

	d.r = 13.6 - 0.00064 p.i

with a correlation of -0.63.  An income decrease of  $1.56 million
seems to cost a life.

	The highest income states included Washington D.C. and Alaska.
Washington has the second highest death rate (perhaps because it has
a high income segment of Government employees and a low income
group of poor blacks), and Alaska has the highest income and the
lowest death rate (presumably because its elderly retire and die
elsewhere).  If we eliminate D.C. and Alaska, the equation
becomes

	d.r = 13.3 - .0006 pi,

and a correlation of -0.82, which isn't much different.
An income decrease of $1.6 million seems to cost a life.

	Taking the five highest and five lowest death rates gies

	d.r. = 14.5 - .000846 p.i.

with a correlation of -0.29.  An income decrease of $1.18 million
seems to cost a life.

	Finally, we did the calculation with all 50 states plus
D.C. which gives

	d.r. = 11.8 - .00043 p.i.

with a correlation of -0.35.  This suggests that fully a third of
the variance in death rate among states is accounted for by
variations in personal income.  Dividing the 0.00043 by 1000 and
inverting gives a result suggesting that every reduction in
personal income by $2.3 million costs a life.

Discussion:

	If one wanted an accurate answer, a prospective experiment
would be needed, i.e. one should have an experimental group (given
money) and a control group left alone.  Ideally, the experimental
group shouldn't know they were being subsidized - their employers
might be subsidized to increase their pay and give misleading
reasons for doing so.  Such an experiment is unlikely to be performed
so we are reduced to inferring what we can from the statistics
available.

	The interesting number from the policy point of view is the
amount of decrease in personal income that apparently costs a life.
The estimate ranges from the $1.18 million when we take the states
with highest and lowest death rates to the $4.4 million obtained when
we aggregate by regions.  Asked to guess, I'd go for the number obtained
when all states are taken into account, namely $2.3 million.

	I have consulted professional statisticians, who point out
various possibilities for quantitative error.  Some errors may arise
from the aggregation by states, and other may give spurious
correlations, e.g. a state to which many people retire may be expected
to have a lower income and a higher death rate.  However, no-one has
suggested that another technique or more data is likely to give
qualitatively different results.

	We may speculate about how income saves lives.  People eat better,
value their lives more and avoid some risks, visit doctors more readily,
and pay more taxes for public health measures.  All these factors
combine in producing the effect of average personal income on average
death rate.  For the purpose of the present analysis, it isn't necessary
to distinguish these factors.  This is fortunate, since the information
required to do so probably isn't available.

	There are other ways of comparing money and lives.  First of all,
we may look for ways of saving lives with money.  Many years ago
it was estimated that $100,000 spent judiciously on stoplights and
other automobile safety measures would save a life.  Most likely
live can be saved even more cheaply by public health measures in
underdeveloped countries.  However, the advocates of an expensive
safety measure can point out (if they are inclined to address the
issue at all) that there is no guarantee that money unspent on
one safety measure will be spent on another more cost-effective
measure.

	Yet another way is to estimate the risks people take
voluntarily to get money or to save it.  These also give smaller
estimates of the monetary value of life than the cost of many
safety measures.  The advocates of a particular exprensive safety
measure often point to the difference between risks taken voluntarily
and those imposed by others.  Starr (19xx) discusses these issues.

	All these approaches are relevant to deciding whether
a proposed expenditure is worthwhile.  However, the
present approach has the advantage that it compares
an action (undertaking or requiring an expensive safety measure)
with doing nothing - an option that is often available to a decision
maker, who usually doesn't have the option of an alternate expenditure
on saving lives.  Thus the Nuclear Regulatory Commission cannot
require that a utility build a clinic in Indonesia instead of
making the earthquake proofing stronger, but often it has the the
option of not requiring the expenditure.  So, often, does a judge.

	Considerations of this kind are most important if they affect
policy, and affecting policy means politics, and politics involves
expressing conclusions in politically effective ways.  We offer two
ways of putting the conclusions of this study - mild and sharp.

	The mild way of putting it is that policy makers who consider
a particular expenditure on safety - whether it is their own money
or they are in a postion to require expenditure by others - consider
whether the expenditure will save a life for every $2.3 million spent.
If not the drain they cause on the economy may cost more lives than
the safety measure saves.  Meta-policy makers, i.e. Congress and
state legislatures, might consider requiring impact statements
estimating lives to be saved per dollar spent on specific safety
measures before a regulatory body can impose a new safety measure.
It might be remembered that it isn't easy for a regulator faced with
TV broadcasts, press conferences by environmentalists, and questions
by excited legislators to say that the proposed "tough new regulatons"
won't save as many lives as they will cost, and the best opiion may
be to do nothing.  It is especially difficult if the value of the
regulator's own job may be put in question.  For this reason, a meta-rule
requiring the impact statement with a $2.3 million cost limit per
life saved may be helpful.

	The sharp way of putting the issues is to accuse Ralph Nader,
the Natural Resources Defense Council, the Sierra Club, Musicians
United for Safe Energy, the blockaders of the Seabrook site and Diablo
Canyon, the California Legislature, the Washington, D.C. Court of
Appeals and other busybodies of having killed a large number of people
in furtherance of arbitrary notions of who were the good guys and who
were the bad guys.