COMMENT ⊗   VALID 00003 PAGES
C REC  PAGE   DESCRIPTION
C00001 00001
C00002 00002	safety[f82,jmc] When safety kills
C00018 00003		Here are some of the ways in which the conclusions might
C00019 ENDMK
C⊗;
safety[f82,jmc] When safety kills

	It is a truism that poverty kills, and we can try to 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.   Namely it drains 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.  The number, as
we shall explain, cannot be computed accurately, but all our 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, it seems to follow
that the public would be safer if the expenditure were not made and rates
kept down instead.

	The same appears to be true of the tamper-proof packaging being
introduced following the 1982 poisoning of Tylenol capsules.

	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 gives 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.  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 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,

with 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 and safety 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 organization's
money or whether 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 activists, 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 option may be to do nothing.  It is especially
difficult if doing nothing puts the value of the regulator's own job 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 issue 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.
	Here are some of the ways in which the conclusions might
be attacked:

	1. The most drastic attack is to suggest that the causality is
reversed.  Perhaps states are healthy or unhealthy for reasons unrelated
to income, but healthy states have higher income, because sick
people don't earn as much money.  The only reason to disbelieve this
is that plausible reasons why specific states are healthier than
others are hard to think up, whereas the economic reasons why some
the presence of specific economic opportunities make certain states
wealthier readily come to mind.