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Penn state runs auctions to license its intellectual property. For each license on the block there is a brief description of what the relevant technology is and an opening bid which I interpret as a reserve price. It also notes whether the license is exclusive or not. Thus, the license is sold for a single upfront fee. No royalties or other form of contingent payment. As far as I can tell the design is an open ascending auction.

In an earlier pair of posts I discussed a class of combinatorial auctions when agents have binary quadratic valuations. To formulate the problem of finding a welfare maximizing allocation let {x^k_j = 1} if object {j \in M} is given to agent {k \in N} and zero otherwise. Denote the utility of agent {k \in N} from consuming bundle {S \subseteq M} by

\displaystyle u_k(S) = \sum_{i \in S}u^k_i + \sum_{i, j \in S}w^k_{ij}.

The problem of maximizing total welfare is

\displaystyle \max \sum_{k \in N}\sum_{i \in M}u^k_ix^k_i + \sum_{k \in N}\sum_{i \neq j}w^k_{ij}x^k_ix^k_j

subject to

\displaystyle \sum_{k \in N}x^k_i \leq 1\,\, \forall i \in M

\displaystyle x^k_i \in \{0,1\}\,\, \forall i \in M, k \in N

I remarked that Candogan, Ozdaglar and Parrilo (2013) identified a solvable instance of the welfare maximization problem. They impose two conditions. The first is called sign consistency. For each {i,j \in M}, the sign of {w^k_{ij} } and {w^r_{ij}} for any {k, r \in N} is the same. Furthermore, this applies to all pairs {i, j \in M}.

Let {G^w} be a graph with vertex set {M} and for any {i,j \in M} such that {w^k_{ij} \neq 0} introduce an edge {(i,j)}. Because of the sign consistency condition we can label the edges of {G^w} as being positive or negative depending on the sign of {w^k_{ij}}. Let {E^+ = \{(i,j): w^k_{ij} \geq 0\}} and {E^- = \{(i,j): w^k_{ij} < 0\}}. The second condition is that {G^w} be a tree.

The following is the relaxation that they consider:

\displaystyle \max \sum_{k \in N}\sum_{i \in M}u^k_ix^k_i + \sum_{k \in N}\sum_{(i,j) \in E^+ \cup E^-}w^k_{ij}z^k_{ij}

subject to

\displaystyle \sum_{k \in N}x^k_i \leq 1\,\, \forall i \in M

\displaystyle z^k_{ij} \leq x^k_i, x^k_j\,\, \forall k \in N, (i,j) \in E^+

\displaystyle z^k_{ij} \geq x^k_i + x^k_j - 1\,\, \forall k \in N, (i,j) \in E^-

\displaystyle x^k_i, z^k_{ij} \in \{0,1\}\,\, \forall i \in M, k \in N

Denote by {P} the polyhedron of feasible solutions to the last program. I give a new proof of the fact that the extreme points of {P} are integral. My thanks to Ozan Candogan for (1) patiently going through a number of failed proofs and (2) being kind enough not to say :“why the bleep don’t you just read the proof we have.”

Let {{\cal C}} be the maximal connected components of {G^w} after deletion of the edges in {E^-} (call this {G^+}). The proof will be by induction on {|{\cal C}|}. The case {|{\cal C}| = 1} follows from total unimodularity. I prove this later.

Suppose {|{\cal C}| > 1}. Let {({\bar z}, {\bar x})} be an optimal solution to our linear program. We can choose {({\bar z}, {\bar x})} to be an extreme point of {P}. As {G^w} is a tree, there must exist a {C \in {\cal C}} incident to exactly one negative edge, say {(p,q)}. Denote by {P'} the polyhedron {P} restricted to just the vertices of {C} and by {Q} the polyhedron {P} restricted to just the vertices in the complement of {C}. By the induction hypothesis, both {P'} and {Q} are integral polyhedrons. Each extreme point of {P'} ({Q}) assigns a vertex of {C} (the complement of {C}) to a particular agent. Let {X_1, X_2, \ldots, X_a} be the set of extreme points of {P'}. If in extreme point {X_r}, vertex {p} is assigned to agent {k} we write {X_{rk} = 1} and zero otherwise. Similarly with the extreme points {Y_1, Y_2, \ldots, Y_b} of {Q}. Thus, {Y_{rk} = 1} is {Y_r} assigns vertex {q} to agent {k}. Let {v(X_{r})} be the objective function value of the assignment {X_r}, similarly with {v(Y_{rk})}.

Now {({\bar z}, {\bar x})} restricted to {P'} can be expressed as {\sum_r\lambda_{r}X_{r}}. Similarly, {({\bar z}, {\bar x})} restricted to {Q} can be expressed as {\sum_r\mu_{r}Y_{r}}. We can now reformulate our linear program as follows:

\displaystyle \max \sum_r\lambda_{r}v(X_{r}) + \sum_r\mu_{r}v(Y_{r}) -\sum_{k \in N} |w^k_{pq}|y^k_{pq}

subject to

\displaystyle -\sum_r\lambda_{rk} = -1

\displaystyle -\sum_r\mu_{rk} = -1

\displaystyle \sum_{r: X_{rk} = 1}\lambda_{r}X_{r} + \sum_{r: Y_{rk} = 1}\mu_{r}Y_{r} \leq y^k_{pq} + 1\,\, \forall k \in N

\displaystyle \lambda_{r}, \mu_{r}, y^k_{pq} \geq 0\,\, \forall r, k

The constraint matrix of this last program is totally unimodular. This follows from the fact that each variable appears in at most two constraints with coefficients of opposite sign and absolute value 1 (this is because {X_{rk}} and {X_{rk'}} cannot both be 1, similarly with the {Y}‘s). Total unimodularity implies that the last program has integral optimal solution and we are done. In fact, I believe the argument can be easily modified to to the case where in {G^w} every cycle must contain a positive even number of negative edges.

Return to the case {|{\cal C}| = 1}. Consider the polyhedron {P} restricted to just one {C \in {\cal C}}. It will have the form:

\displaystyle \sum_{k \in N}x^k_i \leq 1\,\, \forall i \in C

\displaystyle z^k_{ij} \leq x^k_i, x^k_j\,\, \forall k \in N, (i,j) \in E^+ \cap C

\displaystyle x^k_i, z^k_{ij} \in \{0,1\}\,\, \forall i \in C, k \in N

Notice the absence of negative edges. To establish total unimodularity we use the Ghouila-Houri (GH) theorem. Fix any subset, {S}, of rows/constraints. The goal is to partition them into two sets {L} and {R} so that column by column the difference in the sum of the non-zero entries in {L} and and the sum of the nonzero entries in {R} differ by at most one.

Observe that the rows associated with constraints {\sum_{k \in N}x^k_i \leq 1} are disjoint, so we are free to partition them in any way we like. Fix a partition of these rows. We must show to partition the remaining rows to satisfy the GH theorem. If {y^k_{ij} - x^k_i \leq 0} is present in {S} but {y^k_{ij} -x^k_j \leq 0} is absent (or vice-versa), we are free to assign the row associated with {y^k_{ij} - x^k_i \leq 0} in any way to satisfy the GH theorem. The difficulty will arise when both {y^k_{ij}}, {x^k_i} and {x^k_j} are present in {S}. To ensure that the GH theorem is satisfied we may have to ensure that the rows associated with {y^k_{ij} - x^k_i \leq 0} and {y^k_{ij} -x^k_j \leq 0} be separated.

When {S} is the set of all constraints we show how to find a partition that satisfies the GH theorem. We build this partition by sequentially assigning rows to {L} and {R} making sure that after each assignment the conditions of the GH theorem are satisfied for the rows that have been assigned. It will be clear that this procedure can also be applied when only a subset of constraints are present (indeed, satisfying the GH theorem will be easier in this case).

Fix an agent {k \in N}. The following procedure will be repeated for each agent in turn. Pick an arbitrary vertex in {C} (which is a tree) to be a root and direct all edges `away’ from the root (when {S} is a subset of the constraints we delete from {C} any edge {(i,j)} in which at most one from the pair {y^k_{ij} - x^k_i \leq 0} and {y^k_{ij} -x^k_j \leq 0} appears in {S}) . Label the root {L}. Label all its neighbors {R}, label the neighbors of the neighbors {L} and so on. If vertex {i \in C} was labeled {L} assign the row {\sum_{k \in N}x^k_i \leq 1} to the set {L} otherwise to the row {R}. This produces a partition of the constraints of the form {\sum_{k \in N}x^k_i \leq 1} satisfying GH.

Initially, all leaves and edges of {C} are unmarked. Trace out a path from the root to one of the leaves of {C} and mark that leaf. Each unmarked directed edge {(i,j)} on this path corresponds to the pair {y^k_{ij} - x^k_i \leq 0} and {y^k_{ij} -x^k_j \leq 0}. Assign {y^k_{ij} - x^k_i \leq 0} to the same set that is the label of {i}. Assign {y^k_{ij} - x^k_j \leq 0} to the same set that is the label of vertex {j}. Notice that in making this assignment the conditions of the GH theorem continues to satisfied. Mark the edge {(i,j)}. If we repeat this procedure again with another path from the root to an unmarked leaf, we will violate the GH theorem. To see why suppose the tree contains edge {(i,j)} as well as {(i,t)}. Suppose {i} was labeled {L} on the first iteration and {(i,j)} was marked. This means {y^k_{ij} - x^k_{i} \leq 0} was assigned to {L}. Subsequently {y^k_{it} - x^k_i \leq 0} will also be assigned to {L} which will produce a partition that violates the GH theorem. We can avoid this problem by flipping the labels on all the vertices before repeating the path tracing procedure.

What is the institutional detail that makes electricity special? Its in the physics that I will summarize with a model of DC current in a resistive network. Note that other sources, like Wikipedia give other reasons, for why electricity is special:

Electricity is by its nature difficult to store and has to be available on demand. Consequently, unlike other products, it is not possible, under normal operating conditions, to keep it in stock, ration it or have customers queue for it. Furthermore, demand and supply vary continuously. There is therefore a physical requirement for a controlling agency, the transmission system operator, to coordinate the dispatch of generating units to meet the expected demand of the system across the transmission grid.

I’m skeptical. To see why, replace electricity by air travel.

Let {V} be the set of vertices and {E^*} the set of edges a the network. It will be convenient in what follows to assign (arbitrarily) an orientation to each edge in {E^*}. Let {E} be the set of directed arcs that result. Hence, {(i,j) \in E} mens that the edge {(i,j)} is directed from {i} to {j}. Notice, if {(i,j) \in E}, then {(i,j) \not \in E}.

Associated with each {(i,j) \in E} is a number {x_{ij}} that we interpret as a flow of electricity. If {x_{ij} > 0} we interpret this to be a flow from {i} to {j}. If {x_{ij} < 0} we interpret this as a flow from {j} to {i}.

  1. Let {\rho_{ij}} is the resistance on link {(i,j)}.
  2. {c_i} unit cost of injecting current into node {i}.
  3. {v_i} marginal value of current consumed at node {i}.
  4. {d_i} amount of current consumed at node {i}.
  5. {s_i} amount of current injected at node {i}.
  6. {K_{ij}} capacity of link {(i,j)}.

Current must satisfy two conditions. The first is conservation of flow at each node:

\displaystyle s_i + \sum_{k: (k,i) \in E}x_{ji} = \sum_{j: (i,j) \in E}x_{ij} + d_i\,\, \forall i \in V

The second is Ohm’s law. There exist node potentials {\{\phi_i\}_{i \in V}} such that

\displaystyle \rho_{ij}x_{ij} = \phi_i - \phi_j\,\, \forall (i,j) \in E.

Using this systems equations one can derive the school boy rules for computing the resistance of a network (add them in series, add the reciprocals in parallel). At the end of this post is a digression that shows how to formulate the problem of finding a flow that satisfies Ohm’s law as an optimization problem. Its not relevant for the economics, but charming nonetheless.

At each node {i \in V} there is a power supplier with constant marginal cost of production of {c_i} upto {S_i} units. At each {i \in V} there is a consumer with constant marginal value of {v_i} upto {D_i} units. A natural optimization problem to consider is

\displaystyle \max \sum_{i \in V}[v_id_i - c_is_i]

subject to

\displaystyle \sum_{j: (i,j) \in E}x_{ij} -\sum_{j: (j,i) \in E}x_{ji} - s_i + d_i= 0\,\, \forall i \in V

\displaystyle \rho_{ij}x_{ij} = \mu_i - \mu_j\,\, \forall (i,j) \in E

\displaystyle -K_{ij} \leq x_{ij} \leq K_{ij}\,\, \forall (i,j) \in E

\displaystyle 0 \leq s_i \leq S_i\,\, \forall i \in V

\displaystyle 0 \leq d_i \leq D_i\,\, \forall i \in V

This is the problem of finding a flow that maximizes surplus.

Let {{\cal C}} be the set of cycles in {(V, E^*)}. Observe that each {C \in {\cal C}} corresponds to a cycle in {(V, E)} if we ignore the orientation of the edges. For each cycle {C \in {\cal C}}, let {C^+} denote the edges in {E} that are traversed in accordance with their orientation. Let {C^-} be the set of edges in {C} that are traversed in the opposing orientation.

We can project out the {\phi} variables and reformulate as

\displaystyle \max \sum_{i \in V}[v_id_i - c_is_i]

subject to

\displaystyle \sum_{j: (i,j) \in E}x_{ij} -\sum_{j: (j,i) \in E}x_{ji} - s_i + d_i= 0\,\, \forall i \in V

\displaystyle \sum_{(i,j) \in C^+}\rho_{ij}x_{ij} - \sum_{(i,j) \in C^-}\rho_{ij}x_{ij} = 0\,\, \forall \,\, C \in {\cal C}

\displaystyle -K_{ij} \leq x_{ij} \leq K_{ij}\,\, \forall (i,j) \in E

\displaystyle 0 \leq s_i \leq S_i\,\, \forall i \in V

\displaystyle 0 \leq d_i \leq D_i\,\, \forall i \in V

Recall the scenario we ended with in part 1. Let {V = \{1, 2, 3\}}, {E = \{(1,3), (1,2), (2,3)\}} and in addition suppose {\rho_{ij} =1} for all {(i,j)}. Only {(1,3)} has a capacity constraint of 600. Let {D_1 = D_2 = 0} and {S_3 = 0}. Also {c_1 = 20} and {c_2 = 40} and each have unlimited capacity. At node 3, the marginal value is {V > 40} upto 1500 units and zero thereafter. The optimization problem is

\displaystyle \max Vd_3 - 20s_1 - 40 s_2

subject to

\displaystyle x_{12} + x_{13} - s_1 = 0

\displaystyle x_{23} - s_2 - x_{12} = 0

\displaystyle d_3 - x_{13} - x_{23} = 0

\displaystyle x_{13} - x_{23} - x_{12} = 0

\displaystyle -600 \leq x_{13} \leq 600

\displaystyle 0 \leq d_3 \leq 1500

Notice, for every unit of flow sent along {(1,3)}, half a unit of flow must be sent along {(1,2)} and {(2,3)} as well to satisfy the cycle flow constraint.

The solution to this problem is {x_{13} = 600}, {x_{12} = -300}, {x_{23} = 900}, {s_1 = 300}, {s_2 = 1200} and {d_3 = 1500}. What is remarkable about this not all of customer 3′s demand is met by the lowest cost producer even though that producer has unlimited capacity. Why is this? The intuitive solution would have been send 600 units along {(1,3)} and 900 units along {(1,2) \rightarrow (2,3)}. This flow violates the cycle constraint.

In this example, when generator 1 injects electricity into the network to serve customer 3′s demand, a positive amount of that electricity must flow along every path from 1 to 3 in specific proportions. The same is true for generator 2. Thus, generator 1 is unable to supply all of customer 3′s demands. However, to accommodate generator 2, it must actually reduce its flow! Hence, customer 3 cannot contract with generators 1 and 2 independently to supply power. The shared infrastructure requires that they co-ordinate what they inject into the system. This need for coordination is the argument for a clearing house not just to manage the network but to match supply with demand. This is the argument for why electricity markets must be designed.

The externalities caused by electricity flows is not a proof that a clearing house is needed. After all, we know that if we price the externalities properly we should be able to implement the efficient outcome. Let us examine what prices might be needed by looking at the dual to the surplus maximization problem.

Let {y_i} be the dual variable associated with the flow balance constraint. Let {\lambda_C} be associated with the cycle constraints. Let {\nu_i} and {\theta_i} be associated with link capacity constraints. Let {\mu_i} and {\sigma_i} be associated with the remaining tow constraints. These can be interpreted as the profit of supplier {i} and the surplus of customer {i} respectively. For completeness the dual would be:

\displaystyle \min \sum_{(i,j) \in E}[\nu_{ij} + \theta_{ij}]K_{ij} + \sum_{i \in V}[S_i \mu_i + D_i \sigma_i]

subject to

\displaystyle -\theta_{ij} + \nu_{ij} + \rho_{ij}\sum_{C^+ \ni (i,j)}\lambda_C - \rho_{ij}\sum_{C^- \ni (i,j)}\lambda_C + y_i - y_j = 0\,\, \forall (i,j) \in E

\displaystyle \mu_i - y_i \geq -c_i\,\, \forall i \in V

\displaystyle \sigma_i + y_i \geq v_i\,\, \forall i \in V

\displaystyle \nu_{ij}, \theta_{ij}, \mu_i, \sigma_i \geq 0\,\, \forall i \in V,\,\,\forall (i,j) \in E

Now {y_i} has a natural interpretation as a price to be paid for consumption at node {i} for supply injected at node {i}. {\mu_i} and {\nu_i} can be interpreted as the price of capacity. However, {\lambda_C} is trickier, price for flow around a cycle? It would seem that one would have to assign ownership of each link as well as ownership of cycles in order to have a market to generate these prices.

In this, the second lecture, I focus on electricity markets. I’ll divide the summary of that lecture into two parts.

Until the 1980s electricity markets around the world operated as regulated monopolists. Generation (power plants) and distribution (the wires) were combined into a single entity. Beginning with Chile, a variety of Latin American countries started to privatize their electricity markets. So, imagine you were a bright young thing in the early 1980s, freshly baptised in the waters of Lake Michigan off Hyde Park. The General approaches you and says I want a free market in electricity, make it so (Quiero un mercado libre de la electricidad, que asi­ sea.) What would you reccomend?

Obviously, privatize the generators by selling them off, perhaps at auction (or given one’s pedigree, allocate them at random and allow the owners to trade among themeselves). What about the wire’s that carry electricity from one place to another. Tricky. Owner of the wire will have monopoly power, unless there are multiple parrallell wires. However, that would lead to inefficient duplication of resources. As a first pass, lets leave the wires in Government hands. Not obviously wrong. We do that with the road network. The Government owns and mainatins it and for a fee grants access to all.

So, competition to supply power but central control of the wires. Assuming an indifferent and benign authority controlling the wires, what will the market for generation look like? To fix ideas, consider a simple case. Two generators {\{1, 2\}} and a customer {3}.

Generator has unlimited supply and a constant marginal cost of production of $20 a unit. Generator 2 has an unlimited supply and a constant marginal cost of production of $40 a unit. Customer 3 has a constant marginal value of {V} upto 1500 units and zero thereafter. Assume {U} to be sufficiently large to make all subsequent statements true. Initially there are only two wires, one from generator 1 to customer 3 and the other from generator 2 to customer 3. Suppose {\{1,2,3\}} are all price takers. Then, the Walrasian price for this economy will be $20. For customer 3 this clearly a better outcome than unregulated monopoly, where the price would be {U}. What if the price taking assumption is not valid? An alternative model would be Bertrand competition between 1 and 2. So, the outcome would be a `hairs breadth’ below $40. Worse than the Walrasian outcome but still better than unregulated monopoly. It would seem that deregulation would be a good idea and as the analysis above suggest, there is no necessity for a market to be designed. There is a catch. Is unregulated monopolist the right benchmark? Surely, a regulated monopolist would be better. Its not clear that one does better than the regulated monopolist.

Now lets add a wrinkle. Suppose the wire between 1 and 3 has capacity 600 units. There are two ways to think of this capacity constraint. The first is a capacity constraint on generator 1 that we have chosen to model as a constraint on the wire {(1,3)}. The second is that it is indeed a constraint on the wire {(1,3)}. The difference is not cosmetic as we shall see in a moment.

Suppose its a constraint on generator 1′s capacity. Then, under the price taking assumption, the Walrasian price in this economy will be $40. An alternative model of competition would be Bertrand-Edgeworth. In general equilibria are mixed, but whatever the mixture, the expected price per unit customer 3 will pay cannot exceed $40 a unit. In both cases, the outcome is better for customer 3 than unregulated monopolist.

Assume now the capacity constraint is on the wire instead. Under the price taking assumption, at a price of $20 unit, generator 1 is indifferent between supplying any non-negative amount. Generator 3′s supply correspondence is the empty set. However there is no way for supply to meet demand. Why is this? In the usal Walrasian set up each agent reports their supply and demand correspondence based on posted prices and their own information only. To obtain a sensible answer in this case, generator 1 must be aware of the capacity of the network into which its supply will be injected. As the next scenario we consider shows, this is not easy when it comes to electricity.

Suppose there is now a link joining generator 1 and 2 with no capacity constraint. There is still a 600 unit capacity constraint on the link between 1 and 3. One might think, that in this scenario, customer 3 can receive all its demand from generator 1. It turns out that this is not possible because of the way electricity flows in networks.

A recent paper by Bergeman, Brooks and Morris (BBM) supposes a monopolist free to segment the market in any way she can (without worrying about arbitrage), and asks what is the achievable set of pairs of producer and consumer surplus? BBM gives a simple and satisfying answer to this question. This post attempts a short proof of their characterization.

A monopolist faces a market consisting of buyers with valuations {\{v_1, v_2, \ldots, v_n\}}. Order them so that {v_1 < v_2 < \ldots < v_n}. The number of buyers with valuation {v_i} is {d_i} and assume the buyers are divisble. A segmentation of the market is a partition of the buyers into upto {n} markets with the property that the profit maximizing price in market {j = 1, \ldots, n} is {v_j}. If we let {x_{ij}} be the number of buyers with valuation {v_i} in market {j}, then any segmentation is characterized by the following:

\displaystyle \sum_{j=1}^{n}x_{ij} = d_i\,\, \forall i = 1, \ldots, n

\displaystyle v_j\sum_{r \geq j}x_{rj} \geq v_t \sum_{r \geq t}x_{rj}\,\, \forall j = 1, \ldots, n, \,\, \forall t \neq j

\displaystyle x_{ij} \geq 0\,\, \forall i,j

Denote by {X(d)} the set of feasible segmentations. Let {P = \sum_{j=1}^{n}v_j\sum_{r= j}^nx_{rj}} be the profit earned by the monopolist under the segmentation {x \in X(d)}. The consumer surplus of buyers under the segmentation {x \in X(d)} is {C = \sum_{j=1}^{n}\sum_{r = j}^n[v_r-v_j]x_{rj}.}

It is easy to see that {P \leq \sum_{j=1}^nv_jd_j = \Pi_1(d)}. The upper bound follows from the segmentation that assigns all buyers with valuation {v_j} to the {j^{th}} market and no others. This corresponds to first degree price discrimination. It is also easy to see that {P \geq \max_{1 \leq j \leq n} v_j\sum_{r \geq j}d_j = \Pi_0(d)}. The lower bound comes from the segmentation that assigns all customers to market {k}, where {v_k} is the profit maximizing monopoly price without discrimination. BBM show the following:

Theorem {(P,C)} is feasible iff {\Pi_0(d) \leq P \leq \Pi_1(d)} and {P + C \leq \sum_jv_jd_j}.

That {\Pi_0(d) \leq P \leq \Pi_1(d)}, is straightforward. The hard part is to show two things.

1) For any {P} such that {\Pi_0(d) \leq P \leq \Pi_1(d)} there is a {C} such that {P + C = \sum_{j=1}^nv_jd_j}.

2)There exists an {x \in X(d)} such that {P = \Pi_0(d)} and {C = 0}.

To prove the first item (which BBM note in the paper is easy) on this list, call a segmentation {x \in X(d)} upper triangular if {x_{ij} = 0} for all {i < j}. Note {v_k > v_t}.

Let {r = \arg \max_{1 \leq q \leq t}v_q\sum_{i=q}^tx_{ik}}. We construct a new segmentation {y \in X(d)} from {x} by shifting the buyers in market {k} with values below {v_k} into market {r}. As the profit maximizing price just to this portion of buyers is {v_r}, moving them into market {r} leaves the profit maximizing price in market {r} unchanged. Formally:

{y_{ij} = x_{ij}} for all {i} and {j \neq k, r}.
{y_{ik} = x_{ik}} for all {i \geq k}.
{y_{ik} = 0} for all {i t}.
{y_{ir} = x_{ir} +x_{ik}} for all {i \leq t}.

Under segmentation {y}, both {P} and {C} increased in value, contradicting the initial choice of {x}.

To prove the second item on the list, among all feasible segmentations such that {P = \Pi_0(d)}, choose one that minimizes {C}, say {x}. Call {x} lower triangular if {x_{ij} = 0} for all {i > j}. I show that {x} must be lower triangular from which it follows that {C = 0}. The proof will be by induction on {n} the number of distinct valuations.

The case {n=2} is straightforward. Suppose first that {\Pi_0(d) = v_1(d_1 + d_2)}. The following segmentation, as can be verified, does the trick:

{x_{22} = d_2}
{ x_{12} = \alpha} where {v_1[\alpha + d_2] = v_2d_2}
{x_{21} = 0}
{x_{11} = d_1 - \alpha}

If {\Pi_0(d) = v_2d_2}, the segmentation that assigns all buyers to market 2 will have the requiste property.

Now consider the case of arbitrary {n} and suppose first that {\Pi_0(d) > v_1\sum_{i=1}^nd_i}. Given an instance {d} on {n} valuations construct an instance {d'} on {n-1} valuations {\{v'_2, \ldots, v'_{n}\}} by setting {d'_i = d_i} for all {i \geq 2}. It is easy to see that {\Pi_0(d') = \Pi_0(d)}, i.e., the optimal monopoly profit with no discrimination remains unchanged. By the induction hypothesis there is a segmentation {y \in X(d')} that is lower triangular. To conclude the argument we must show how to convert {y} into a legitimate segmentation for {d}.

{x_{ij} = y_{ij}} for {i,j \geq 2}.
{x_{1i} = \mu_i} where {v_1[\mu_i + \sum_{r=2}^nx_{ri}] \leq v_{ii}x_{ii}} for all {i \geq 1} and {\sum_{i=1}^n\mu_i = d_1}.
If the {\mu_i}‘s can indeed be chosen as specified, then, {x \in X(d)} is lower triangular and the corresponding {P = \Pi_0(d)} and {C = 0}. To verify that appropriate {\mu_i}‘s exist, it is enough to check that
\displaystyle d_1 \leq \sum_{i=1}^n\frac{v_{ii}}{v_1}x_{ii} - \sum_{i=1}^n \sum_{r=2}^nx_{ri} = \frac{1}{v_1}\Pi_0(d') - \sum_{r=2}^nd_r

which follows from the hypothesis that {v_1\sum_{r=1}^nd_r \leq \Pi_0(d)}.

To conclude, suppose now that {\Pi_0(d) = v_1\sum_{i=1}^nd_i}. Construct a new instance {d'} on {n-1} valuations {\{v'_1, v'_3 \ldots, v'_{n}\}} by setting {d'_i = d_i} for all {i \geq 3} and {d'_1 = d_1 + d_2}. Notice, {\Pi_0(d') = \Pi_0(d)}. By the induction hypothesis there is a segmentation {y \in X(d')} that is lower triangular. To conclude the argument we must show how to convert {y} into a legitimate segmentation for {d}.

{x_{ij} = y_{ij}} for {i,j \geq 3}.
{x_{2i} = \mu_i} for all {i \geq 1} where {v_2[\mu_i + \sum_{r \geq 3}x_{ri}] \leq v_{ii}x_{ii}}, {\mu_i \leq y_{1i}} and {\sum_{r=1}^n\mu_i = d_2}.
{x_{1i} = y_{1i} - \mu_i} for all {i \geq 1}.

If the {\mu_i}‘s can be chosen as specified then, {x} is lower triangular, in {X(d)} and the corresponding {P = \Pi_0} and {C = 0}. Verifying that the appropriate {\mu_i}‘s exist, can be done in the same way as the previous case.

In part two, as promised, I turn to the welfare maximization problem. To formulate the problem of finding a welfare maximizing allocation let {x^k_j = 1} if object {j \in M} is given to agent {k \in N} and zero otherwise. Denote the utility of agent {k \in N} from consuming bundle {S \subseteq M} by

\displaystyle u_k(S) = \sum_{i \in S}u^k_i + \sum_{i, j \in S}w^k_{ij}.

The problem of maximizing total welfare is

\displaystyle \max \sum_{k \in N}\sum_{i \in M}u^k_ix^k_i + \sum_{k \in N}\sum_{i \neq j}w^k_{ij}x^k_ix^k_j

subject to

\displaystyle \sum_{k \in N}x^k_i \leq 1\,\, \forall i \in M

\displaystyle x^k_i \in \{0,1\}\,\, \forall i \in M, k \in N

Welfare maximization with BQP preferences is in general NP-hard. One proof relies on a reduction to the multi-way cut problem. Given a graph {G=(V,E)} with edge weight {c_{ij}} for each {(i,j) \in E}, and a set of terminal vertices {T = \{v_1, v_2, \ldots, v_n\}}, a multiway cut is a set of edges whose removal disconnects every pair of terminal vertices. The problem of finding the multiway cut of minimum total weight is called the multiway cut problem. When {T} consists of only two terminals ({k = 2}) the problem reduces to the well known minimum cut problem. For {k \geq 3}, it is known that the problem is {NP-}hard even on planar graphs.

We can obtain the multiway cut problem by setting {u^k_i = 0} for all {k \in N} and {i \in M} and {w^k_{ij} = c_{ij}} for all {k \in N} and {(i,j) \in E}. Any pair {(i,j)} such that {x^k_ix^r_j = 1} for {k \neq r} will be part of a multi-way cut. This reduction implies that welfare maximization when {w^k_{ij} \geq 0} for all {k \in N} and {i,j \in M} is NP-hard. This is in contrast to the case of surplus maximization.

,Candogan, Ozdaglar and Parrilo (2013), the paper that prompted this post, identify a solvable instance of the welfare maximization problem. They impose two conditions. The first is called sign consistency. For each {i,j \in M} the sign of {w^k_{ij} } and {w^r_{ij}} for any {k, r \in N} is the same. Furthermore, this applies to all pairs {i, j \in M}. Sign consistency by itself is not sufficient to obtain a solvable instance. Another condition is needed. Let {G^w} be a graph with vertex set {M} and for any {i,j \in M} such that {w^k_{ij} \neq 0} introduce an edge {(i,j)}. The second condition is that {G^w} be a tree. Interestingly, Erdos and Sz\’{e}kely (1995) show that a generalization of the multiway cut problem which corresponds to welfare maximization under sign consistency and {u^k_i = 0} for all {k \in N} and {i \in M}, is polynomially solvable when the underlying graph {G} is a tree. The Candogan, Ozdaglar and Parrilo (COP) proof is based on a dynamic programming argument similar to the one used in Erdos and Sz\’{e}kely (1994).

The key result in COP is the following natural linearization of the welfare maximization problem admits an integral optimal solution.

\displaystyle \max \sum_{k \in N}\sum_{i \in M}u^k_ix^k_i + \sum_{k \in N}\sum_{i \neq j}w^k_{ij}y^k_{ij}

subject to

\displaystyle \sum_{k \in N}x^k_i \leq 1\,\, \forall i \in M

\displaystyle y^k_{ij} \leq \{x^k_i, x^k_j\}^-\,\, \forall w^k_{ij} > 0

\displaystyle y^k_{ij} \geq x^k_i + x^k_j -1\,\, \forall w^k_{ij} < 0

\displaystyle x^k_i \in \{0,1\}\,\, \forall i \in M, k \in N

There is a connection between the welfare maximization problem and packing subtrees into trees that I want to highlight. It suggests a possible avenue by which one might enlarge the class of preferences COP consider.

Because of the sign consistency condition we can label the edges of {G^w} as being positive or negative depending on the sign of {w^k_{ij}}. Let {E^+ = \{(i,j): w^k_{ij} \geq 0\}} and {E^- = \{(i,j): w^k_{ij} < 0\}}. Let {{\cal C}} be the maximal connected components of {G^w} after deletion of the edges in {E^-} (call this {G^+}). For any {C \in {\cal C}} and {S \subseteq C} let

\displaystyle v_k(S) = \sum_{i \in S}u^k_i + \sum_{i,j \in S}w^k_{ij}.

By the way, for each {C \in {\cal C}} and {k \in N}, {v_k} is supermodular over the subsets of {C}. Let {z_k(S) = 1} if {S \subseteq C} is assigned to agent {k} and zero otherwise. The problem of finding a welfare maximizing allocation can be expressed as follows:

\displaystyle \max \sum_{k \in N}\sum_{C \in {\cal C}}\sum_{S \subseteq C}v_k(S)z_k(S) - \sum_{k \in N}\sum_{(i,j) \in E^-}|w^k_{ij}|y^k_{ij}

subject to

\displaystyle \sum_{S \ni i}\sum_{k \in N}z_k(S) \leq 1\,\, \forall i \in M,\,\, S \subseteq C \in {\cal C}

\displaystyle y^k_{ij} \geq \sum_{S \ni i}z_k(S) + \sum_{S \ni j}z_k(S) - 1\,\, \forall k \in N, (i,j) \in E^-

\displaystyle z_k(S) \in \{0,1\}\,\, \forall k \in N, S \subseteq C \in {\cal C}

\displaystyle y^k_{ij} \in \{0,1\}\,\, \forall k \in N, (i,j) \in E^-

In the above program, we can, without loss, restrict attention to subsets {S} that a subtrees (connected subgraphs) of {E^+}. To see why, suppose in that in some optimal solution to the above integer program, {z_k(S) = 1} where {S} is not a subtree. Then, we can write {S = A \cup B} where both {A} and {B} are in the same component of {C} as {S} is. Furthermore, it must be the the case that there is no edge {(i,j) \in E^+} such that {i \in A} and {j \in B}. Therefore, {v_k(S) = v_k(A) + v_k(B)}. That means we can construct a new optimal solution by setting {z_k(S) = 0} and raising {z_k(A)} and {z_k(B)} to 1. Note, in the original solution {z_k(A), z_k(B) =0} by virtue of the first constraint. As long as {S} is not a subtree we can repeat this argument.

Hence, if we let {{\cal T}} be the set of subtrees of {G^+}, the welfare maximization problem can be expressed as follows:

\displaystyle \max \sum_{k \in N}\sum_{T \in {\cal T}}v_k(T)z_k(T) - \sum_{k \in N}\sum_{(i,j) \in E^-}|w^k_{ij}|y^k_{ij}

subject to

\displaystyle \sum_{T \in {\cal T}}\sum_{T \ni i}\sum_{k \in N}z_k(T) \leq 1\,\, \forall i \in M,

\displaystyle y^k_{ij} \geq \sum_{T \ni i}z_k(T) + \sum_{S \ni j}z_k(S) - 1\,\, \forall k \in N, (i,j) \in E^-

\displaystyle z_k(S) \in \{0,1\}\,\, \forall k \in N, T \in {\cal T}

\displaystyle y^k_{ij} \in \{0,1\}\,\, \forall k \in N, (i,j) \in E^-

Its important to emphasize that no {T \in {\cal T}} contains a negative edge.

Were it not for the second set of the constraints, integrality would follow from Barany, Edmonds and Wolsey (1986). I’m not aware of this variation of the tree packing problem having been considered. A follow up paper by Aghezzaf and Wolsey (1994) comes close in the sense of allowing for a piecewise linear concave objective function.

In 1961, Clarence Earl Gideon was charged with breaking and entering with intent to commit petty larceny. Appearing without counsel, Gideon invoked reverent authority:

The United States Supreme Court says I am entitled to be represented by Counsel.

Those words set in train a chain of events that confirmed, three years later, the right of indigent defendants in criminal proceedings, upon request, to have counsel appointed both during trial and on appeal. Qualified counsel, however, is scarce and there is an abundance of indigent defendants. Over the years the state has chosen to solve the problem of matching counsel to indigent defendant by fiat. Judge Posner in US vs Ely justifies this as follows:

There are practical reasons for not giving indigent criminal defendants their choice of counsel. Appointed counsel are not paid at munificent rates under the Criminal Justice Act, 18 U.S.C. § 3006A(d); in the Central District of Illinois, in the most recent year for which data are available (1980), the average fee per case under the Act was only $426.31. Director of Adm. Off. of U.S. Cts., 1982 Ann.Rep. 511 (Exh. C-1). The best criminal lawyers who accept appointments therefore limit the amount of time they are willing to devote to this relatively unremunerative type of work; some criminal lawyers, indeed, only reluctantly agree to serve as appointed counsel, under pressure by district judges to whom they feel a sense of professional obligation. The services of the criminal defense bar cannot be auctioned to the highest bidder among the indigent accused — by definition, indigents are not bidders. But these services must be allocated somehow; indigent defendants cannot be allowed to paralyze the system by all flocking to one lawyer.

Time to sharpen pencils and put on the thinking cap. For a graduate student looking for a topic in market design, I cannot think of a more interesting question than how to match counsel to indigent defendants. One has: moral hazard (on the part of attorney), asymmetry of information (how does one distinguish between a good lawyer and a bad one), informed third parties with divided interests (Judges who appoint counsel, but may be more interested in a speedy trial than a vigorous defense), budget constraints (on the part of defendants) and competing objectives (speedy resolution vs. correct adjudication). For a description of the institution as it is currently structured and a proposal to revise it based on vouchers see Friedman and Schulhofer (yes Friedman fils).

A paper by Azevdo, Weyl and White in a recent issue of Theoretical Economics caught my eye. It establishes existence of Walrasian prices in an economy with indivisible goods, a continuum of agents and quasilinear utility. The proof uses Kakutani’s theorem. Here is an argument based on an observation about extreme points of linear programs. It shows that there is a way to scale up the number of agents and goods, so that in the scaled up economy a Walrasian equilibrium exists.
First, the observation. Consider {\max \{cx: Ax = b, x \geq 0\}}. The matrix {A} and the RHS vector {b} are all rational. Let {x^*} be an optimal extreme point solution and {\Delta} the absolute value of the determinant of the optimal basis. Then, {\Delta x^*} must be an integral vector. Equivalently, if in our original linear program we scale the constraints by {\Delta}, the new linear program has an optimal solution that is integral.

Now, apply this to the existence question. Let {N} be a set of agents, {G} a set of distinct goods and {u_i(S)}the utility that agent {i} enjoys from consuming the bundle {S \subseteq G}. Note, no restrictions on {u} beyond non-negativity and quasi-linearity.

As utilities are quasi-linear we can formulate the problem of finding the efficient allocation of goods to agents as an integer program. Let {x_i(S) = 1} if the bundle {S} is assigned to agent {i} and 0 otherwise. The program is

\displaystyle \max \sum_{i \in N}\sum_{S \subseteq G}u_i(S)x_i(S)

subject to
\displaystyle  \sum_{S \subseteq G}x_i(S) \leq 1\,\, \forall i \in N

\displaystyle \sum_{i \in N}\sum_{S \ni g} x_i(S) \leq 1 \forall g \in G

\displaystyle x_i(S) \in \{0,1\}\,\, \forall i \in N, S \subseteq G

If we drop the integer constraints we have an LP. Let {x^*} be an optimal solution to that LP. Complementary slackness allows us to interpret the dual variables associated with the second constraint as Walrasian prices for the goods. Also, any bundle {S} such that {x_i^*(S) > 0} must be in agent {i}‘s demand correspondence.
Let {\Delta} be the absolute value of the determinant of the optimal basis. We can write {x_i^*(S) = \frac{z_i^*(S)}{\Delta}} for all {i \in N} and {S \subseteq G} where {z_i^*(S)} is an integer. Now construct an enlarged economy as follows.

Scale up the supply of each {g \in G} by a factor of {\Delta}. Replace each agent {i \in N} by {N_i = \sum_{S \subseteq G}z_i^*(S)} clones. It should be clear now where this is going, but lets dot the i’s. To formulate the problem of finding an efficient allocation in this enlarged economy let {y_{ij}(S) = 1} if bundle {S} is allocated the {j^{th}} clone of agent {i} and zero otherwise. Let {u_{ij}(S)} be the utility function of the {j^{th}} clone of agent {i}. Here is the corresponding integer program.

\displaystyle \max \sum_{i \in N}\sum_{j \in N_i}\sum_{S \subseteq G}u_{ij}(S)y_{ij}(S)

subject to
\displaystyle  \sum_{S \subseteq G}y_{ij}(S) \leq 1\,\, \forall i \in N, j \in N_i

\displaystyle \sum_{i \in N}\sum_{j \in N_i}\sum_{S \ni g} y_{ij}(S) \leq \Delta \forall g \in G

\displaystyle y_{ij}(S) \in \{0,1\}\,\, \forall i \in N, j \in N_i, S \subseteq G

Its easy to see a feasible solution is to give for each {i} and {S} such that {z_i^*(S) > 0}, the {z_i^*(S)} clones in {N_i} a bundle {S}. The optimal dual variables from the relaxation of the first program complements this solution which verifies optimality. Thus, Walrasian prices that support the efficient allocation in the augmented economy exist.

In this post I describe an alternative proof of a nifty result that appears in a forthcoming paper by Goeree, Kushnir, Moldovanu and Xi. They show (under private values) given any Bayesian incentive compatible mechanism, M, there is a dominant strategy mechanism that gives each agent the same expected surplus as M provides.

For economy of exposition only, suppose 2 agents and a finite set of possible outcomes, {A}. Suppose, also the same type space, {\{1, \ldots, m\}} for both. Let {f(\cdot)} be the density function over types. To avoid clutter, assume the uniform distribution, i.e., {f(\cdot) = \frac{1}{m}}. Nothing in the subsequent analysis relies on this.

When agent 1 reports type {s} and agent 2 reports type {t}, denote by {z_r(s,t) } the probability that outcome {r \in A} is selected. The {z}‘s must be non-negative and satisfy {\sum_{r \in A}z_r(s,t) = 1.}

Associated with each agent {i} is a vector {\{a_{ir}\}_{r \in A}} that determines, along with her type, the utility she enjoys from a given allocation. In particular, given the allocation rule {z}, the utility that agent {1} of type {t} enjoys when the other agent reports type {s} is {\sum_{r \in A}ta_{1r}z_r(t,s).} A similar expression holds agent 2.

Let {q_i(s,t) = \sum_{r \in A}a_{ir}z_r(s,t).} Interpret the {q}‘s as the `quantity’ of goods that each agent receives. Dominant strategy implies that {q_1(s,t)} should be monotone increasing in {s} for each fixed {t} and {q_2(s,t)} should be monotone increasing in {t} for each fixed {s}. The interim `quantities will be: {mQ_i(s) = \sum_t\sum_{r \in A}a_{ir}z_r(s,t).} Bayesian incentive compatibility (BIC) implies that the {Q_i}‘s should be monotone. To determine if given BIC interim `quantities’ {Q_i}‘s can be implemented via dominant strategies, we need to know if the following system is feasible

\displaystyle \sum_{r \in A}a_{1r}[z_r(i,j) - z_r(i-1,j)] \geq 0\,\, \forall \,\, i = 2, \ldots, m \ \ \ \ \ (1)

\displaystyle \sum_{r \in A}a_{2r}[z_r(i,j) - z_r(i,j-1)] \geq 0\,\, \forall \,\, j = 2, \ldots, m \ \ \ \ \ (2)

\displaystyle \sum_t\sum_{r \in A}a_{1r}z_r(s,t) = mQ_1(s) \ \ \ \ \ (3)

\displaystyle \sum_s\sum_{r \in A}a_{2r}z_r(s,t) = mQ_2(t) \ \ \ \ \ (4)

\displaystyle \sum_{r \in A}z_r(s,t) = 1\,\, \forall s,t \ \ \ \ \ (5)

\displaystyle z_r(i,j) \geq 0\,\, \forall i,j,r \ \ \ \ \ (6)

System (1-6) is feasible iff the optimal objective function value of the following program is zero:

\displaystyle \min \sum_{i,j}w_{ij} + \sum_{i,j}h_{ij}

subject to
\displaystyle \sum_{r \in A}a_{1r}[z_r(i,j) - z_r(i-1,j)] + w_{ij} - u_{ij} = 0\,\, \forall \,\, i = 2, \ldots, m\,\, (\mu_{ij}) \ \ \ \ \ (7)

\displaystyle \sum_{r \in A}a_{2r}[z_r(i,j) - z_r(i,j-1)] + h_{ij} - v_{ij} = 0\,\, \forall \,\, j = 2, \ldots, m\,\, (\nu_{ij}) \ \ \ \ \ (8)

\displaystyle \sum_j\sum_{r \in A}a_{1r}z_r(i,j) = mQ_1(i)\,\, (\alpha_i) \ \ \ \ \ (9)

\displaystyle \sum_i\sum_{r \in A}a_{2r}z_r(i,j) = mQ_2(j)\,\, (\beta_j) \ \ \ \ \ (10)

\displaystyle \sum_{r \in A}z_r(i,j) = 1\,\, \forall i,j\,\, (\lambda(i,j)) \ \ \ \ \ (11)

\displaystyle w_{ij},\,\, h_{ij}\,\,z_r(i,j) \geq 0\,\, \forall i,j,r \ \ \ \ \ (12)

Let {(z^*, w^*, h^*, u^*, v^*)} be an optimal solution to this program.
Suppose, for a contradiction there is a pair {(p,q)} such that {w^*_{pq} > 0}. I will argue that there must exist an {s} such that {u^*_{ps} > 0}. Suppose not, then for each {j \neq q}, either {w^*_{pj} > 0} or {w^*_{pj} = 0} and {u^*_{pj} = 0} (at optimality, it cannot be that {w^*_{pq}} and {u^*_{pq}} are both non-zero). In this case

\displaystyle m[Q_1(p)-Q_1(p-1)] = \sum_j\sum_{r \in A}a_{1r}z^*_r(p,j) - \sum_j\sum_{r \in A}a_{1r}z^*_r(p-1,j)

= \sum_{j: w^*_{pj} > 0}a_{1r}[z^*_r(p,j) - z^*_r(p-1,j)] = -\sum_j w^*_{pj}. This last term is negative, a contradiction. Therefore, there is a s such that w^*_{ps} = 0 but u^*_{ps} > 0.

Let {Z_1(i,j) = \sum_{r \in A}a_{1r}z^*_r(i,j)}, {Z_2(i,j) = \sum_{r \in A}a_{2r}z^*_r(i,j)} and denote by {Z(i,j)} the point {(Z_1(i,j), Z_2(i,j))}. Observe that {Z(i,j)} is in the convex hull, {K} of {\{(a_{1r}, a_{2r}\}_{r \in A}} for all {i,j}. Thus choosing {\{z_r(i,j)\}_{r \in A}}‘s amounts to choosing a point {Z(i,j) \in K}. Equivalently, choosing a point {Z(i,j) \in K} gives rise to a set of {\{z_r(i,j)\}_{r \in A}}‘s. For convenience assume that {Z(i,j)} is in the strict interior of {K} for all {(i,j)} and that K is full dimensional. This avoids having to deal with secondary arguments that obscure the main idea.

Recall, {w^*_{pq} > 0} implies {Z_1(p,q)  0} implies that {Z_1(p,s) > Z_1(p,s)}. Take all points {\{Z(i,q)\}_{i \geq p}} and shift them horizontally to the right by {\delta}. Call these new points {\{Z'(i,q)\}_{i \geq p}}. Observe that {Z'(i,q) \in K} for all {i \geq p}. Next, take all points {\{Z(i,s)\}_{i \geq p}} and shift them horizontally to the left by {\delta} to form new points {\{Z'(i,s)\}_{i \geq p}}. These points are also in {K}. Leave all other points {\{Z(p,j)\}_{j \neq, q,s}} unchanged.

Because the vertical coordinates of all points were left unchanged, (8) and (10) are satisfied by this choice of points. Because {\{Z(i,q)\}_{i \geq p}} and {\{Z(i,s)\}_{i \geq p}} were shifted in opposite directions along the horizontal, (9) is still true. Finally, because all points in {\{Z(i,q)\}_{i \geq p}} and {\{Z(i,s)\}_{i \geq p}} were shifted by the same amount, (7) continues to hold.

The shift leftwards of {Z(p,s)} reduces {u_{ps}} while the rightward shift of {Z(p,q)} reduces {w_{pq}}. Thus, we get a new solution with higher objective function value, a contradiction.

If {Z(p,q)} and {Z(p,s)} are not the interior of {K} but on the boundary, then horizontal shifts alone may place them outside of {K}. In the case of {Z(p,q)} this can only happen if {Z_2(p,q) > Z_2(p-1,q)}. In this case, shift {Z(p,q)} across and to the right by {\delta} as well and then downwards by the same amount. This would have to be matched by a corresponding upward shift by some point {Z_2(h,q)}. Similarly with {Z(p,s)}.

Thanks to Alexey Kushnir and Ahmad Peivandi for comments.

Tayfun Sonmez was at Northwestern recently to give a mini-course on market design. Naturally he discussed Kidneys. Tayfun pointed out that the use of money to procure organs was illegal in the US. What is more, physicians would show you the door should you mention its use. I think Tayfun used the stronger `throw you out.’

The opposition of (many?) physicians to the use of money to procure organs attracted my curiosity. Yes, they object (as do others) on ethical grounds. Should one accept that at face value? Could they object because it is in their incentive to do so? A transplant requires both a kidney and a team of physicians. Trade in kidneys is outlawed so depressing the supply of kidneys. This makes transplant opportunities scarce so allowing physicians to raise the price of a transplant. In other words physicians get to capture the rents from scarcity. Not just from transplants but from dialysis (a substitute for kidneys) as well.

So much for the theory. What about the evidence? I suppose one would look at the profitability of dialysis and transplant centers as well as the rate of entry. I would guess there have been large increases in dialysis centers. The Economist magazine in August 2010 describes the US dialysis market as

…….. the world’s most lucrative dialysis market, with the government spending $24 billion a year, or $71,000 a year per patient, on dialysis, and private insurers paying yet more.

As an aside, a recent paper  claims that patients at for-profit dialysis centers are 20% less likely to be informed about transplant options and referred for operations than those at nonprofit centers.

The average hospital admission fee for a kidney transplant in the US is $91,200. However, the profitability of transplant centers is more difficult to determine because of how a hospital can choose to allocate its costs among its different divisions. I suspect  that they must be profitable for the incumbents because of the 2002 fight between transplant centers over the geographical distribution of organs.

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