and
t>2 = In [0.450 + 0.5τ — τ2 + 0.05p] (22)
It is easily verified that v1 > v2 for all τ ∈ [0, 0.25] and p ∈ [0,1] . In
the social welfare function group 2 should therefore be given at least as large
weight as group 1. Since there are only two groups, it is easier to work with
exogenous welfare weights than the concave function used in Sections 3-4.
The social welfare function is thus
W = υ1 + 7v2
(23)
where 7 ≥ 1.
The optimal value of W is obtained by solving the following equations:
∂W
dp
∂W
∂τ
0.045 0.095 + 0.057 = 0
У1 У1 — P У2
0.9(—0.5 — 2 τ) 0.1(—0.5 — 2 τ) 7 (0.5 — 2 τ)
У1 У1 — P У2
for p ∈ [0,1], τ ∈ [0, 0.25], given 7. A numerical solution for the optimizing
problem for different values of 7 is found using Maple (s.t. τ ∈ [0, 0.25] and
p ∈ [0,1]):
τ p W
7 = 1 .1062872413 .6703546385 -.3575958178
7 = 1.5 .1357928137 .8486112171 -.6707438076
7 = 1.75 .1455947627 .9067929325 -.8227024058
From this table it is clear that as the concern for equity (measured by
7) increases, we get an increase in both the optimal marginal tax and the
optimal co-payment.
18
More intriguing information
1. The name is absent2. INSTITUTIONS AND PRICE TRANSMISSION IN THE VIETNAMESE HOG MARKET
3. Concerns for Equity and the Optimal Co-Payments for Publicly Provided Health Care
4. The name is absent
5. Whatever happened to competition in space agency procurement? The case of NASA
6. Wirkt eine Preisregulierung nur auf den Preis?: Anmerkungen zu den Wirkungen einer Preisregulierung auf das Werbevolumen
7. Bridging Micro- and Macro-Analyses of the EU Sugar Program: Methods and Insights
8. Regional science policy and the growth of knowledge megacentres in bioscience clusters
9. Implementation of Rule Based Algorithm for Sandhi-Vicheda Of Compound Hindi Words
10. The name is absent