1. 1 Extreme Events Scott Matthews Courses: 12-706 / 19-702

2. 2 Admin  HW 5 Due Now  Group Project 2 Out today.  Due Monday Nov 24  Next week: 2 case study writeups due  2 PAGES MAX !! DO NOT SUBMIT MORE!

3. Recap of Decision Trees  When thinking about strategies for decisions we could make 2-way sensitivity graphs.  Purpose: if parameters changed, did that affect our intended strategy?  i.e., what would have to happen to change our mind about our strategy? 3

4. 2-way Simple DA sensitivity 4

5. 5 Extreme Events  Low probability, high consequence (cost) events  Natural disasters (e.g., hurricanes)  Catastrophic infrastructure failure  Considered hard to assess..  But can assess with sensitivity analysis:  On risk tolerance / utility  “how risk averse do you need to be for it to matter?”  On probability - “how likely is it to happen?”  On expected losses (consequences)  “how much would you have to lose for it to matter?”

6. 6 Relevant Thoughts  As you (the decision maker) become more risk averse, you tend to worry ONLY about the worst case  As you accept more risk, converge to risk neutral  Example: using exponential utility (similar to Deal or No Deal)  Recall definition of R parameter in function  Equally willing to risk winning R or losing R/2  For individuals, generally R ~ $1000s  Recall goal is to maximize CE

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9. 9 Example: Infrastructure Failure  Probability (P) of happening: About 0.5%  Damage (D) if occurs - $100 million  Typical EMV = P*D = ~$500k  Can pay to remove large potential cost by buying insurance (cost $20 million)

10. 10 A risk neutral decision maker would ride out the risk all the way to p(fail) of about 0.2. What about a risk averse DM?

11. Extreme Events Spreadsheet  Lets look at the same example, and effects from changing R, Loss, p(fail),..  What is our base case decision strategy?  Does the strategy change as the parameters change?  If it does not, then even though the event is “Extreme” we should be comfortable with our decision. 11

12. In-Class Case Study: Clean Air Regulation Scott Matthews Lecture 24 12-706 / 19-702

13. New Type of Problem  Handout of Tables included  What happens when we cannot/will not monetize all aspects of a BCA?  Example: what if we are evaluating policies where a benefit is lives or injuries saved?  How do we place a value on these benefits?  Are there philosophical problems?

14. In-Class Case Study  Consider this ‘my example’ of how to do a project for this class (if relevant)  Topical issue, using course techniques  As we discuss, think about whether you would do it differently, be interested in other things, etc.  Metrics for this case are ugly (literally): morbidity and mortality for human health  Effectively I ‘redo’ a published government report with different data

15. Background of CAA  Enacted in 1970 to protect and improve air quality in the US  EPA was just being born  Had many sources - mobile and stationary  CAA goal : reducing source emissions  Cars have always been a primary target  Acid rain and ozone depletion  Amended in 1977 and 1990  1990 CAAA added need for CBA (retro/pro)

16. History of Lead Emissions  Originally, there was lead in gasoline  Studies found negative health effects  Tailpipe emissions (burning gas) were seen as a primary source of lead  Regulations called for phaseout of lead  We have also attempted to reduce lead/increase awareness in paints, etc.  Today, new cars must run on ‘unleaded’ gasoline (anyone remember both?)

17. Construction of Analyses  Estimate emissions reduced since 1970  For major criteria pollutants (SO2, NOX,…)  Estimated ‘no control’ scenario since 1970  Estimated expected emissions without CAA  Compared to ‘actual emissions’ (measured)  Found ‘net estimated reduced emissions’  Assumed no changes in population distribution, economic structure (hard)  Modeled 1975/80/85/90, interpolated

18. Analyses (cont.)  Estimated costs of CAA compliance  Done partially with PACE data over time  Also run through a macroeconomic model  With reduced emissions, est. health effects  Large sample of health studies linking ‘reduced emissions of x’ with asthma, stroke, death,..  Used ‘value of effects reduced’ as benefits  26 ‘value of life studies’ for reduced deaths  Does a marginal amount of pollution by itself kill?

19. Value of Life Studies Used  Actually should be calling these ‘studies of consumer WTP to avoid premature death’  Five were ‘contingent valuation’ studies  Others estimated wage/risk premiums  Mean of studies = $4.8 million (1990$)  Different than “Miller” from earlier  Standard dev = $3.2 million ($1990)  Min $600k, Max $13.5 million ($1990)

20. Putting everything together  Had Benefits in terms of ‘Value from reducing deaths and disease’ in dollars  Had costs seen from pollution control  Use min/median/max ranges  Convert everything into $1990, get NB  Median estimated at $22 trillion ($1990)!  $2 trillion from reducing lead  75% from particulates  Is this the best/only way to show results?

21. ‘Wish List’ - added analysis  Disaggregate benefits and costs by pollutant (e.g. SO2) and find NB  Could then compare to existing cost- effectiveness studies that find ‘$/ton’  Disaggregate by source- mobile/stationary  Could show more detailed effects of regulating point vs. non-point sources  Has vehicle regulation been cost-effective?  Why did they perhaps NOT do these?

22. My Own Work  I replicated analysis by using only median values, assumed they were exp. Value  Is this a fair/safe assumption?  See Table 3

23. Implied Results

24. Recall Externality Lecture  External / social costs  A measure of the costs borne by society but not reflected in the prices of goods  Can determine externality costs by other methods - how are they found?  Similar to health effects above, but then explicitly done on a $/ton basis

25. Compare to other studies  Large discrepancies between literature and EPA results!  Using numbers above, median NB = $1 T

26. Source Category Analysis  Using ‘our numbers’, mobile and stationary source benefits (not NB) nearly equal ($550B each in $92)  See Tables 12 and 13 for costs and NB  Up to 1982, stationary NB > mobile  After 1982, mobile >> stationary

27. Final Thoughts  EPA was required to do an analysis of effectiveness of the CAA  Their results seem to raise more questions than they answer  The additional measures we showed are interesting and deserve attention  Questions intent of EPA’s analysis

28. Other Uses - Externality “Adders”  Drop in as $$ in the cash flow of a project  Determine whether amended project cash flows / NPV still positive

29. Mutiple Effectiveness Measures  So far, we have considered externality problems in one of 2 ways:  1) By monetizing externality and including it explicitly as part of BCA  2) Finding cost, dividing by measured effectiveness (in non-monetary terms)  While Option 2 is preferred, it is only relevant with a single effectiveness

30. MAIS Table - Used for QALY Conversions Comprehensive Fatality / Injury Values Injury Severity1994 Relative Value MAIS1.0038 MAIS2.0468 MAIS3.1655 MAIS4.4182 MAIS5.8791 Fatality1.0

31. Single vs. Multiple Effectiveness  Recall earlier examples:  Cost per life saved  Cost per ton of pollution  When discussing “500 Interventions” paper, talked about environmental regs  Had mortality and morbidity benefits  Very common to have multiple benefits/effectiveness  Under option 1 above, we would just multiply by $/life and $/injury values..  But recall that we prefer NOT to monetize and instead find CE/EC values to compare to others

32. Multiple Effectiveness  In Option 2, its not relevant to simply divide total costs (TC) by # deaths, # injuries, e.g. CE 1 = TC/death, CE 2 = TC/injury  Why?  Misrepresents costs of each effectiveness  Instead, we need a method to allocate the costs (or to separate the benefits) so that we have CE ratios relevant to each effectiveness measure

33. Options for Better Method  Use “primary target” as effectiveness  Allocate all costs to it (basically what we’ve been doing)  Add effectiveness measures together  E.g., tons of pollution  Is as ridiculous as it sounds (tons not equal, lives not equal to injuries)

34. Improved Method  In absence of more information or knowing better, allocate costs evenly  E.g., if 2 pollutants each gets 1/2 the cost  Easy to make slight variations if new information or insight is available  Could use our monetization values to inform this (e.g., external cost values, $/life values, etc.)

35. Recall from previous lecture

36. Another Option  For each effectiveness, subtract marginal cost/benefit values of all other measures from total cost so that only remaining costs exist for CE ratios  Again could use median $ values on previous slide to do this  Examples..

37. Wrap Up  There is no “accepted theory” on how to do this.  However when we have multiple effectiveness measures, we need to do something so we end up with meaningful results.