1. 1 Civil Systems Planning Benefit/Cost Analysis Scott Matthews Courses: 12-706 and 73-359 Lecture 17 - 11/6/2002

2. 12-706 and 73-3592 Specifics on Saving Lives  Cost-Utility Analysis  Quantity and quality of lives important  Just like discounting, lives are not equal  Back to the developing/developed example  But also: YEARS are not equal  Young lives “more important” than old  Cutting short a year of life for us vs  Cutting short a year of life for 85-year-old  Often look at ‘life years’ rather than ‘lives’ saved.. These values also get discounted

3. 12-706 and 73-3593 Economic valuations of life  Miller (n=29) $3 M in 1999 USD, surveyed  Wage risk premium method  WTP for safety measures  Behavioral decisions (e.g. seat belt use)  Foregone future earnings  Contingent valuation

4. 12-706 and 73-3594 Contingent Valuation  Analysis method used when there is no observable market  Example: water quality at national parks  Asks questions to population  Is a last resort option!  Called ‘contingent’ since you never really pay  Valuing use non-controversial  Valuing ‘non-use’ VERY controversial

5. 12-706 and 73-3595 Example  Asked for valuations of a certain good  Then estimate overall WTP for it - similar to travel time demand functions  Extrapolated to entire population  Assumes random sample!

6. 12-706 and 73-3596 Criticisms of CV  Extrapolation of ‘all CV studies’ to average consumer would take over their budget  Normal statistical problems (sampling, non-response, biases, etc.)  Surveying opinions is imprecise  Problems tend to be complicated

7. 12-706 and 73-3597 WTP versus WTA  Economics implies that WTP should be equal to ‘willingness to accept’ loss  Turns out people want MUCH MORE in compensation for losing something  WTA is factor of 4-15 higher than WTP!  Also see discrepancy shrink with experience  WTP formats should be used in CVs  Only can compare amongst individuals

8. 12-706 and 73-3598 Measuring Lives Saved  Life years (prevented fatalities) not equal  Qualitative and quantitative issue  Need to consider tradeoffs  Simple example from text  Status quo: no newborns survive a condition  Alt. A: 5 live, but with permanent disability  Alt. B: 2 live, but with low levels of disability  Which option (SQ, A, B) is preferable?  Assume Y increasing, H increasing

9. 12-706 and 73-3599 Simple Example

10. 12-706 and 73-35910 The Quality/Quantity Game  Assume “preference” for  Increased number of years lived  Increased level of health  Would your preferences be the same?  If so, SQ “dominated” by A and B  But which of A or B is better?  We all understand difference in years  Come up with an index of health status

11. 12-706 and 73-35911 Health Status Index Death 0 Severely Disabled Minimally Disabled HealthModerately Disabled 0.150.470.921  Derived from asking experts  But this says nothing about tradeoff!  Can perform tradeoff survey  Value of “shorter Y, higher H” vs. opposite

12. 12-706 and 73-35912 Methods  Health Rating method (see above)  Time tradeoff method  Standard gamble method  Discounting life years

13. 12-706 and 73-35913 Risk Analysis  Study of the interactions between decision making, judgment, and nature  Evidence : cost-effectiveness of risk reduction opportunities varied widely - orders of magnitude  Economic efficiency problems

14. 12-706 and 73-35914 Cost-Effectiveness of Life-Saving Interventions  From “500 Life-saving Interventions and Their Cost-Effectiveness”, Risk Analysis, Vol. 15, No. 3, 1995.  ‘References’ (eg #1127) are all other studies  Model:  Estimate costs of intervention vs. a baseline  Discount all costs  Estimate lives and life-years saved  Discount life years saved  CE = C I -C B /E I -E B

15. 12-706 and 73-35915 Specific (Sample) Example  From p.373 - Ref no. 1127  Rear outboard lap/shoulder belts in all (vs. 96%) of cars  Intervention: require all cars made after 9/1/90 to have belts - 96% already had them  Thus costs only apply to remaining 4% of cars  Target population: occupants over age 4  Others would be in child safety seats  What would costs be?

16. 12-706 and 73-35916 Example (cont)  1986 Costs (from study): $6 cost per seat  Plus added fuel costs (due to increased weight) = total $791,000 over life of cars  Effectiveness: expect 23 lives saved during 8.4 year lifetime of cars  But 95.8% already exist, thus only 0.966 lives  Or 0.115 lives per year (of use of car)  But these lives saved do not occur all in year 0 - they are spread out over 8.4 years. We thus need to discount the effectiveness of lives saved per year into ‘year 0’ lives..

17. 12-706 and 73-35917 Cost per life saved  With a 5% discount rate, the ‘present value’ of 0.115 lives for 9 years = 0.817  This is basically an annuity factor  So cost/life saved = $791,000/0.817  Or $967,700 per life (in “$1986/1986 lives”)  Using CPI: 145.8/109.6 -> $1,287,326 in $1993  But this tells us only the cost per life saved  We realistically care more about quality of life, which suggests using a quality index, e.g. life- years saved.

18. 12-706 and 73-35918 Cost per life-year saved  Assume average age of fatality in car accident was 35 years  Life expectancy tables suggest a 35 year old person would on average live to age 77  Thus ‘42’ life years saved per fatality avoided  1 life year for 42 years @5%= 17.42 years  $1993 cost/life-year = $1,287,326/17.42  2 sig. figures: ~$74,000 as in paper

19. 12-706 and 73-35919 Overall Results  Some had $10B  Median $42k per life year saved  Some policies implemented, some only studied  Variation of 11 orders of magnitude!  Some maximums - $20 billion for benzene emissions control at tire factories  $100 billion for chloroform standards at paper mills

20. 12-706 and 73-35920 Comparisons

21. 12-706 and 73-35921 Agency Comparisons  $1993 Costs per life year saved for agencies:  FAA (Aviation): $23,000  CPSC (Consumer Products): $68,000  NHTSA (Highways): $78,000  OSHA (Worker Safety): $88,000  EPA (Environment): $7,600,000!  Are there underlying causes for range? Hint: are we comparing apples and oranges?