Civil Systems Planning Benefit/Cost Analysis

1. Civil Systems PlanningBenefit/Cost Analysis Scott Matthews 12-706/19-702 / 73-359 Lecture 10

2. Sorta Timely Analysis • How sensitive is gasoline demand to price changes? • Historically, we have seen relatively little change in demand. Recently? • New AAA report: higher gasoline prices have caused a 3 percent reduction in demand from a year ago. • What was p? q? ? • What does that tell us about gasoline? 12-706 and 73-359

3. Distorted Market - Vouchers • Example: rodent control vouchers • Give residents vouchers worth $v of cost • Producers subtract $v - and gov’t pays them • Likely have spillover effects • Neighbors receive benefits since less rodents nearby means less for them too • Thus ‘social demand’ for rodent control is higher than ‘market demand’ 12-706 and 73-359

4. Distortion : p0,q0 too low What is NSB? What are CS, PS? S Social WTP P S-v P0 P1 DS: represents higher WTP for rodent control DM Q Q0 Q1 12-706 and 73-359

5. Social Surplus - locals Make decisions based on S-v, Dm What about others in society, e.g. neighbors? P P S S-v P1+v C A P0 B E P1 DS Because of vouchers, Residents buy Q1 DM Q Q0 Q1 12-706 and 73-359

6. Nearby Residents Added benefits are area between demand above consumption increase What is cost voucher program? P P S S-v F P1+v C A G P0 B E P1 DS DM Q Q0 Q1 12-706 and 73-359

7. Voucher Market Benefits • Program cost (vouchers):A+B+C+G+E ---- • Gain (CS) from target pop: B+E • Gain (CS) in nearby: C+G+F • Producers (PS): A+C • --------- • Net: C+F 12-706 and 73-359

8. Opportunity Cost: Land • Case of inelastic supply • Government decides to buy Q acres of land, pays P per acre • Alternative is parceling of land to private homebuyers • What is total cost of project? Price Can assume quantity of land is fixed (Q) S b P D Q 12-706 and 73-359

9. Opportunity Cost: Land Government pays PbQ0, but society ‘loses’ CS that they would have had if government had not bought land. This lost CS is the ‘opportunity cost’ of other people using/buying land. • Total cost is entire area under demand up to Q (colored) Price S b P D 0 Q 12-706 and 73-359

10. Example: Change in Demand for Concrete Dam Project • If Q high enough, could effect market • Shifts demand -> price higher for all buyers • Moves from (P0,Q0) to (P1,Q1).. Then?? Price D+q’ D S P1 P0 a Q1 Q0 Quantity 12-706 and 73-359

11. Another Example: Change in Demand • Original buyers: look at D, buy Q2 • Total purchases still increase by q’ • What is net cost/benefit to society? Price D+q’ D S P1 P0 a Q1 Q2 Q0 Quantity 12-706 and 73-359

12. Another Example: Change in Demand • Project spends B+C+E+F+G on q’ units • Project causes change in social surplus! • Rule: consider expenditure and social surplus change Price D D+q’ S P1 C A F B P0 G E G G Q1 Q2 Q0 Quantity 12-706 and 73-359

13. Dam Example: Change in Demand • Decrease in CS: A+B (negative) • Increase in PS: A+B+C (positive) • Net social benefit of project is B+G+E+F Price D D+q’ S P1 C A F B P0 G E G G Q1 Q2 Q0 Quantity 12-706 and 73-359

14. Final Thoughts: Change in Demand • When prices change, budgetary outlay does not equal the total social cost • Unless rise in prices high, C negligible • So project outlays ~ social cost usually • Opp. Cost equals direct expenditures adjusted by social surplus changes Quantity 12-706 and 73-359

15. Secondary Markets • When secondary markets affected • Can and should ignore impacts as long as primary effects measured and undistorted secondary market prices unchanged • Measuring both usually leads to double counting (since primary markets tend to show all effects) • Don’t forget that benefit changes are a function of price changes (Campbell pp. 167) 12-706 and 73-359

16. Decision Analysis Clemen - Chapter 3 (and a little reminder from Chapter 2)

17. Structuring Decisions • All about the objectives (what you want to achieve) • Decision context: setting for the decision • Decision: choice between options (there is always an option, including status quo) • Waiting for more information also an option • Uncertainty: as we’ve seen, always exists • Outcomes: possible results of uncertain events • Many uncertain events lead to complexity • Next week we’ll play with models for that 12-706 and 73-359

18. Structuring Decisions (2) • But this week, we’ll start simple • Steps: • Identifying objectives • Structuring elements into framework • Refining/precisely defining all elements 12-706 and 73-359

19. Example: Who to Nominate as a Supreme Court Justice • Objectives? • Categories? • Means/fundamentals? Hierarchy? 12-706 and 73-359

20. Influence Diagrams /Decision Trees • Probably cause confusion. If one confuses you, do the other. • Important parts: Decisions Calculation/constant Chance Events Consequence/payoff 12-706 and 73-359

21. Other Notes • Chance node branches need to be mutually exclusive/exhaustive • Only one can happen, all covered • “One and only one can occur” • Timing of decisions along the way influences how trees are drawn (left to right) • As with NPV, sensitivity analysis, etc, should be able to do these by hand before resorting to software tools. 12-706 and 73-359

22. Solving Decision Trees • We read/write them left to right, but “solve” them right to left. • Because we need to know expected values of options before choosing. • Calculate values for chance nodes • Picking best option at decision nodes • We typically make trees with “expected value” or NPV or profit as our consequence • Thus, as with BCA, we choose highest value. 12-706 and 73-359

23. For Next Class • Be able to solve by hand the Texaco decision tree (Figure 4.2) • Ideally also the same one with PrecisionTree (@RISK) or Treeplan (on course website) 12-706 and 73-359