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Different Airport Pricing Regimes and<br />
Their Implications for Overall Welfare<br />
Master Thesis<br />
Annika Reinhold<br />
STREEM
Structure<br />
• Weight Based Pricing<br />
• Airport Congestion Pricing<br />
• Analytical Model<br />
• Numerical Model<br />
• Conclusions
Weight Based Pricing<br />
• Mostly applied pricing regime<br />
• Complex charging structure at many airports<br />
• Economic rationale<br />
– Adequate capacity, cover costs, rate of return<br />
– Ramsey pricing<br />
• Criticism<br />
– Application to congested airports<br />
– Under-pricing of facilities<br />
– Over-investment in capacity
(Airport) Congestion Pricing<br />
• Bottleneck situation<br />
– Rationing scarce resources<br />
– Users pay for caused congestion<br />
– Incentives for investment in capacity<br />
• Difficult to calculate marginal costs<br />
• Opposition to congestion tolls
Airport Congestion Pricing<br />
• Theoretical Models<br />
– Queuing bottleneck model<br />
– Internalization of congestion costs<br />
– Effect of market power<br />
– Hub and spoke networks
Airport Congestion Pricing<br />
• Application<br />
– Boston Logan Airport (BOS)<br />
– New York Airports (JFK, LGA, EWR, TEB)<br />
– London (LHR)
Analytical Model<br />
• Assumptions<br />
– 2 airlines<br />
– 1 airport<br />
• Inverse demand function<br />
D<br />
• Average cost function<br />
ci fi<br />
vi<br />
q<br />
1<br />
q<br />
1<br />
q<br />
q<br />
2<br />
2
Analytical Model<br />
First best toll<br />
• Airline profit maximisation<br />
– Fully symmetric setting<br />
– Nash equilibrium output (closed form)<br />
q N<br />
i<br />
• Airport welfare maximisation<br />
max<br />
• Optimal toll<br />
q<br />
i<br />
*<br />
0<br />
q<br />
1<br />
3<br />
1<br />
q<br />
2<br />
(<br />
*<br />
i<br />
v<br />
i<br />
f<br />
x)<br />
dx<br />
q<br />
i<br />
v<br />
q<br />
i<br />
q<br />
f<br />
i<br />
i<br />
v<br />
i<br />
q<br />
1<br />
q<br />
2
Analytical Model<br />
Second best toll<br />
• Maximise welfare subject to constraint<br />
w<br />
• Lagrangian multiplier<br />
i<br />
• Weight based toll<br />
i<br />
0<br />
q<br />
1<br />
q<br />
2<br />
(<br />
2<br />
q<br />
qi<br />
v i q<br />
( 2 vi<br />
v i )<br />
w<br />
i<br />
x)<br />
dx<br />
i<br />
q<br />
i<br />
q<br />
i<br />
f<br />
w<br />
i<br />
f<br />
i<br />
2<br />
v<br />
(<br />
(<br />
v<br />
2<br />
i<br />
v<br />
i<br />
q<br />
1<br />
q<br />
i<br />
q2<br />
2 v<br />
1<br />
2 v<br />
1<br />
1<br />
q<br />
v<br />
2<br />
i<br />
q1<br />
v )<br />
v<br />
2<br />
2<br />
)<br />
q<br />
i<br />
v<br />
(<br />
(<br />
1<br />
w<br />
q1<br />
2 v<br />
1<br />
2 v<br />
2<br />
2<br />
q<br />
v<br />
v<br />
2<br />
1<br />
1<br />
)<br />
)
Numerical Model<br />
Parameters<br />
Demand characteristics Airline cost function parameters<br />
α 200000 f 15000<br />
β 1 v 5000<br />
Ratio: average cost/ marginal cost 0,70<br />
Ratio: mr/mc 1,00<br />
Ratio: equilibrium fare/ average cost 1,86<br />
Ratio: equilibrium fare/ marginal cost 1,30<br />
Ratio: profits/ turnover 0,23<br />
Ratio: profits/ total costs 0,43<br />
Ratio: cong costs/ average costs 0,05<br />
Ratio: cong costs/ marginal costs 0,03<br />
Ratio: cong costs/ total costs 0,05<br />
Demand elasticity in equilibrium - 1,4
Numerical Model<br />
Base case<br />
• A319 and CRJ900<br />
• Calculating aircraft weights<br />
• Outcome<br />
First best Weight based<br />
Output airline 1 9.25 7.59<br />
Output airline 2 9.25 12.00<br />
Total 18.50 19.59<br />
Toll airline 1 46,236.1 49,994.1<br />
Toll airline 2 46,236.1 26,923.9<br />
Welfare level 1,711,078.9 1,704,991.0
Numerical Model<br />
Asymmetry in cost parameters<br />
1 2 3 4 5<br />
f_1 13000 f_1 13000 f_1 10000 f_1 10000 f_1 9000<br />
f_2 15000 f_2 18000 f_2 20000 f_2 20000 f_2 22000<br />
v_1 5000 v_1 5000 v_1 4500 v_1 4500 v_1 4000<br />
v_2 6000 v_2 6000 v_2 6500 v_2 6500 v_2 6500<br />
• Increasing asymmetry in cost parameters (1,2,3)<br />
• Increasing difference in aircraft weight (4,5)<br />
• In the end, only airline 1 stays in the market
Numerical Model<br />
Asymmetry in cost parameters<br />
welfare level<br />
3.000.000,00<br />
2.500.000,00<br />
2.000.000,00<br />
1.500.000,00<br />
1.000.000,00<br />
500.000,00<br />
-<br />
1 2 3 4 5<br />
scenarios<br />
τ*_2<br />
ω*<br />
ω_w<br />
τ_w2<br />
τ*_1<br />
τ_w1<br />
120.000,00<br />
100.000,00<br />
80.000,00<br />
60.000,00<br />
40.000,00<br />
20.000,00<br />
-<br />
-20.000,00<br />
-40.000,00<br />
toll level
Numerical Model<br />
Increasing congestion<br />
1 2 3 4<br />
f_1 15000 f_1 15000 f_1 10000 f_1 10000<br />
f_2 15000 f_2 15000 f_2 20000 f_2 20000<br />
v_1 5000 v_1 6000 v_1 6000 v_1 7000<br />
v_2 5000 v_2 6000 v_2 10000 v_2 12000<br />
• Identical parameters (1,2)<br />
• Introducing asymmetry (3,4)
Numerical Model<br />
Increasing congestion<br />
welfare level<br />
1.800.000,00<br />
1.600.000,00<br />
1.400.000,00<br />
1.200.000,00<br />
1.000.000,00<br />
800.000,00<br />
600.000,00<br />
400.000,00<br />
200.000,00<br />
-<br />
1 2 3 4<br />
scenarios<br />
ω_w<br />
τ_w1<br />
τ_w2<br />
τ*_2<br />
ω*<br />
τ*_1<br />
120.000,00<br />
100.000,00<br />
80.000,00<br />
60.000,00<br />
40.000,00<br />
20.000,00<br />
-<br />
-20.000,00<br />
toll level
Conclusion<br />
• Weight based pricing assumed to be<br />
inefficient<br />
• Theoretical congestion pricing models take<br />
into account different characteristics<br />
apparent at airports<br />
• Derivation of simple analytical model<br />
• Test results in numerical model
Conclusion<br />
• Tolls under the different pricing regimes<br />
move in opposite direction<br />
• Under weight based pricing efficient airline<br />
1 pays more than inefficient airline 2<br />
• Weight based pricing leads to lower<br />
welfare level than first best pricing<br />
• Weight based tolls do not help in allocating<br />
scarce capacity
Thank you!