Transcript Civil Systems Planning Benefit/Cost Analysis

Civil Systems Planning
Benefit/Cost Analysis
Scott Matthews
12-706/19-702 / 73-359
Lecture 10
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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?
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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’
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Distortion : p0,q0 too low
What is NSB? What are CS, PS?
S
P
Social
WTP
S-v
P0
P1
DM
Q0
Q1
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DS: represents
higher WTP
for rodent control
Q
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Social Surplus - locals
P
Make decisions based on S-v, Dm
What about others in society,
S
e.g. neighbors?
P
S-v
P1+v
P0
A
B
C
E
P1
DS
Because of vouchers,
Residents buy Q1
DM
Q0
Q1
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Q
5
Nearby Residents
P
Added benefits are area between demand
above consumption increase
S
What is cost voucher program?
P
S-v
F
P1+v
P0
A
B
C
E
G
P1
DS
DM
Q0
Q1
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Q
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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
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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
S
P
Can assume quantity
of land is fixed (Q)
b
D
Q
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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
P
b
D
0
Q
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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
D+q’
S
P1
P0
a
Q0
Q1
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Quantity
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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
D+q’
S
P1
P0
a
Q2
Q0
Q1
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Quantity
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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+q’
D
S
P1
P0
A
B
C
E
G
G
Q2
F
G
Q0
Q1
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Quantity
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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+q’
D
S
P1
P0
A
B
C
E
G
G
Q2
F
G
Q0
Q1
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Quantity
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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
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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)
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Decision Analysis
Clemen - Chapter 3
(and a little reminder from
Chapter 2)
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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
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Structuring Decisions (2)
But this week, we’ll start simple
Steps:
Identifying objectives
Structuring elements into framework
Refining/precisely defining all elements
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Example: Who to Nominate as a
Supreme Court Justice
Objectives?
Categories?
Means/fundamentals? Hierarchy?
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Influence Diagrams /Decision
Trees
Probably cause confusion. If one
confuses you, do the other.
Important parts:
Decisions
Calculation/constant
Chance Events
Consequence/payoff
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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.
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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.
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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)
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