In-Class Case Study: Clean Air Regulation

1. In-Class Case Study:Clean Air Regulation Scott Matthews Lecture 24 12-706 / 73-359 / 19-702

2. 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?

3. 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

4. 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)

5. 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?)

6. 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

7. 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?

8. 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 = $4.8 million (1990$) • Standard dev = $3.2 million ($1990) • Min $600k, Max $13.5 million ($1990)

9. 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?

10. ‘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?

11. 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

12. Implied Results

13. 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

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

15. 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

16. 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

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

18. 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

19. MAIS Table - Used for QALY Conversions

20. 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

21. Multiple Effectiveness • In Option 2, its not relevant to simply divide total costs (TC) by # deaths, # injuries, e.g. CE1 = TC/death, CE2 = 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

22. 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)

23. 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.)

24. Recall from previous lecture

25. 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..

26. 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.