The L2H2 Auction: Efficiency and Equity in the Assemblage of Land for Public Use

The burden of redevelopment projects, whether or not they ultimately benefit the communities in which they are undertaken, is borne disproportionately by those displaced. Neighborhoods are destroyed and residents are made to leave a home they love, compensated only by its market value. The benefits and costs of redevelopment can only be estimated since there are no direct market tests. Here a mechanism, developed as an extension of two recent papers, by Lehavi and Lichts (L2) and by Heller and Hill (H2), provides a market-based efficiency test for a proposed project and a compensation rule that alleviates the disproportionate burden on displaced residents. Assembled property is sold at an auction. The reserve price (the lowest price at which the assembled property will be sold) is set so that all displaced residents receive at least their personal value of their property. A successful bid, one that claims the assembled property, is sufficient proof of efficiency.

Trust among Internet Traders: A Behavioral Economics Approach

Standard economic theory does not capture trust among anonymous Internet traders. But when traders are allowed to have social preferences, uncertainty about a seller's morals opens the door for trust, reward, exploitation and reputation building. We report experiments suggesting that sellers' intrinsic motivations to be trustworthy are not sufficient to sustain trade when not complemented by a feedback system. We demonstrate that it is the interaction of social preferences and cleverly designed reputation mechanisms that solves to a large extent the trust problem on Internet market platforms. However, economic theory and social preference models tend to underestimate the difficulties of promoting trust in anonymous online trading communities.

The Complexity Dilemma in Policy Market Design

Regulators are increasingly pursuing their policy objectives by creating markets. To create a policy market, regulators require firms to procure a product that is socially useful but that confers little direct private benefit to the acquiring party. Examples of policy markets include pollutant emissions trading programs, renewable energy credit markets, and electricity capacity markets. Existing scholarship has tended to analyze policy markets simply as market-based regulation. Although not inaccurate, such inquiries are necessarily incomplete because they do not focus on the distinctive traits of policy markets. Policy markets are neither typical regulations nor typical markets. Concentrating on policy markets as a distinctive type of market brings to light common characteristics of such markets, which in turn generates insights into how they can be used more effectively to implement policy. In particular, this Article focuses on a recurring fundamental challenge in policy market design: managing complexity. Typical markets manage complexity through market forces. As a regulatory creation, however, policy markets require regulators to manage their complexity. This poses what we call the complexity dilemma, which requires regulators to balance strong pressures both toward and away from complexity. The central argument of this Article is that although policy markets are an important part of a regulator’s toolkit, they are also subject to complexity that limits their usefulness. Understanding the complexity dilemma and its crucial role in policy market design forms an essential step toward progress in improving the design and function of these markets

Carbon Pricing in New York ISO Markets: Federal and State Issues

New York’s Clean Energy Standard (“CES”), adopted in August 2016, aims to steer the state’s electricity sector away from carbon-intensive generation sources. It supports low-carbon alternatives by requiring retail electricity suppliers to purchase credits, the proceeds from which are paid to renewable and nuclear generators. Recognizing that this will affect the operation of wholesale electricity markets, New York’s electric transmission grid operator (the “New York Independent System Operator” or “NYISO”) has commenced a review to assess possible means of incorporating the cost of carbon emissions into market prices. This Article explores two approaches to carbon pricing in NYISO markets: the first would involve NYISO adopting a carbon price of its own initiative with a view to improving the operation of wholesale electricity markets (“Approach 1”), while the second would involve adoption of a carbon price designed to reflect and harmonize state-level policies aimed at reducing electricity sector emissions (“Approach 2”). Under either approach, NYISO would adopt a per megawatt hour carbon price and use it to establish a fee for each generating unit, consistent with its emissions profile. This fee would be added to the prices generators bid into the wholesale electricity market and those adjusted prices used by NYISO to determine the dispatch order. The result would likely be a re-ordering of dispatch, with high-emitting generators dispatched (and paid) less frequently, and cleaner alternatives more frequently. Our proposal, while conceptually simple, is likely to be difficult to implement

Resource and Bandwidth Allocation in Hybrid Wireless Mobile Networks

In the lead up to the implementation of 802.16 and 4G wireless networks, there have been many proposals for addition of multi-hop MANET zones or relay stations in order to cut the cost of building a new backbone infrastructure from the ground up. These types of Hybrid Wireless Networks will certainly be a part of wireless network architecture in the future, and as such, simple problems such as resource allocation must be explored to maximize their potential. This study explores the resource allocation problem in three distinct ways. First, this study highlights two existing backbone architectures: max-coverage and max-resource, and how hybridization will affect bandwidth allocation, with special emphasis on OFDM-TMA wireless networks. Secondly, because of the different goals of these types of networks, the addition of relay stations or MANET zones will affect resource availability differently, and I will show how the addition of relay stations impacts the backbone network. Finally, I will discuss specific allocation algorithms and policies such as top-down, bottom-up, and auction-based allocation, and how each kind of allocation will maximize the revenue of both the backbone network as well as the mobile subscribers while maintaining a minimum Quality of Service (or fairness). Each of these approaches has merit in different hybrid wireless systems, and I will summarize the benefits of each in a study of a network system with a combination of the elements discussed in the previous chapters