Public markets tailored for the cartel - Favoritism in procurement auctions -

In this paper, we investigate interaction between two firms, which are engaged in a repeated procurement relationship modelled as a multiple criteria auction, and an auctioneer (a government employee) who has discretion in devising the selection criteria. A first result is that, in a one-shot context, favoritism turns the asymmetric information (private cost) procurement auction into a symmetric information auction (in bribes) for a common value prize. In a repeated setting we show that favoritism substantially facilitates collusion. It increases the gains from collusion and contributes to solving basic implementation problems for a cartel of bidders that operates in a stochastically changing environment. A most simple allocation rule where firms take turn in winning independently of stochastic government preferences and firms'costs achieves full cartel efficiency including price, production and design efficiency. In each period the selection criteria is fine-tailored to the in-turn winner: the "environment" adapts to the cartel. This result holds true when the expected punishment is a fixed cost. When the cost varies with the magnitude of the distortion of the selection criteria (compared with the true government's preferences), favoritism only partially shades the cartel from the environment. We thus find that favoritism generally facilitates collusion at a high cost for society. Our analysis suggests some anti-corruption measures that can be effective to curb favoritism and collusion in public markets. It also shows that the rotation of officials is not one of them.auction ; collusion ; favoritism ; procurement

Environmental Governance in the Carbon Economy: Regulating Greenhouse Gas Emissions in California\u27s Cap-and-Trade Program

Since 2006 California has been pursuing the most ambitious climate change policy in the United States, implementing a suite of greenhouse gas reduction measures ranging from automobile refrigerant disposal rules to clean energy standards for electric power utilities. The most significant of these measures is the creation of a cap‐and‐trade program. Through this program, regulators seek to create a knowable price‐signal to incentivize emissions reductions among polluters. Using a suite of ethnographic methods, this dissertation looks at the people, ideas, and institutions that have been mobilized in the creation of California’s cap‐and‐ trade program. Substantively, the dissertation engages with three key aspects of the program. First, the way that economic theory is deployed in the creation of the rules of exchange, and how that theory is made to take a compromised but still structuring role in light of the political pressures on regulators in writing the rules of exchange in financial representations of greenhouse gases. Second, the dissertation examines the diverse values, economic and non‐economic, in play during the creation of financial representations of greenhouse gases; and third, the environmental and social justice ramifications of structuring an emissions reduction program around the motivation of doing so at the lowest possible cost to polluters. Theoretically, this dissertation is informed by political ecology on the commodification of nature, commodity theory drawn from economic geography and political economy, and sociological theories of economic practice primarily originating from the social studies of finance. The conclusion of the dissertation is that the result of countless hours of work by regulators and their interlocutors is a suite of market‐like mechanisms that ultimately function more like the administrative tool that environmental markets’ early advocates envisioned rather than the full‐blown financialization of the atmosphere, though with potentially detrimental environmental impacts for vulnerable communities

Essays on Market Microstructure

This dissertation studies topics on market microstructure. The first chapter theoretically studies market manipulation in stock markets in a linear equilibrium. The second chapter empirically examines the presence of opportunities for liquidity arbitrage. The last chapter develops and examines a method to capture a co-movement of informed trading. In chapter 1, I study a theory of trade-based price manipulation in markets. I compare two different types of price manipulation studied in previous literature, uninformed and informed manipulation, in the same linear equilibrium model. I show that the presence of positive-feedback traders creates an incentive for the informed trader to bluff, but the opportunity is absent if a sufficient number of uninformed traders behave strategically. Numerical comparable statics results show that informed manipulation is more likely and more profitable when the noise trading is more volatile and that market efficiency could become worse under the presence of manipulation. A financial transaction tax can not prevent informed manipulation, but it reduces the liquidity of the market. Chapter 2 empirically investigates intra-day price manipulation in a stock market. My microstructure model is specifically designed to define the conditions under which a manipulation opportunity arises from the variation in liquidity as measured by price impact. My empirical analysis using data from the Tokyo Stock Exchange suggests that while there is a significant chance of uninformed manipulation across time and stock codes, it is not profitable enough to affect price fluctuations. Analysis of intraday price and trade sizes shows that the opportunity begins to disappear shortly. Chapter 3 studies contagion in a financial market by using a market microstructure model. We extend the Easley, Kiefer, and O'Hara (1997) model to a multiple-asset framework. The model allows us to identify whether the driving forces of informed trading common or idiosyncratic information events are. We apply the method to three groups of stocks listed on the New York Stock Exchange: American Depositary Receipts (ADRs) of developed and emerging countries, and blue chips. We find contagion among emerging-country ADRs during the Asian Financial Crisis of 1997, in the sense that informed trades were mostly driven by common information events

Limit-order completion time in the London stock market

This study develops an econometric model of limit-order completion time using survival analysis. Time-to-completion for both buy and sell limit orders is estimated using tick-by-tick UK order data. The study investigates the explanatory power of variables that measure order characteristics and market conditions, such as the limitorder price, limit-order size, best bid-offer spread, and market volatility. The generic results show that limit-order completion time depends on some variables more than on others. This study also provides an investigation of how the dynamics of the market are incorporated into models of limit-order completion. The empirical results show that time-varying variables capture the state of an order book in a better way than static ones. Moreover, this study provides an examination of the prediction accuracy of the proposed models. In addition, this study provides an investigation of the intra-day pattern of order submission and time-of-day effects on limit-order completion time in the UK market. It provides evidence showing that limit orders placed in the afternoon period are expected to have the shortest completion times while orders placed in the mid-day period are expected to have the longest completion times, and the sensitivities of limit-order completion time to the explanatory variables vary over the trading day

Robust and cheating-resilient power auctioning on Resource Constrained Smart Micro-Grids

The principle of Continuous Double Auctioning (CDA) is known to provide an efficient way of matching supply and demand among distributed selfish participants with limited information. However, the literature indicates that the classic CDA algorithms developed for grid-like applications are centralised and insensitive to the processing resources capacity, which poses a hindrance for their application on resource constrained, smart micro-grids (RCSMG). A RCSMG loosely describes a micro-grid with distributed generators and demand controlled by selfish participants with limited information, power storage capacity and low literacy, communicate over an unreliable infrastructure burdened by limited bandwidth and low computational power of devices. In this thesis, we design and evaluate a CDA algorithm for power allocation in a RCSMG. Specifically, we offer the following contributions towards power auctioning on RCSMGs. First, we extend the original CDA scheme to enable decentralised auctioning. We do this by integrating a token-based, mutual-exclusion (MUTEX) distributive primitive, that ensures the CDA operates at a reasonably efficient time and message complexity of O(N) and O(logN) respectively, per critical section invocation (auction market execution). Our CDA algorithm scales better and avoids the single point of failure problem associated with centralised CDAs (which could be used to adversarially provoke a break-down of the grid marketing mechanism). In addition, the decentralised approach in our algorithm can help eliminate privacy and security concerns associated with centralised CDAs. Second, to handle CDA performance issues due to malfunctioning devices on an unreliable network (such as a lossy network), we extend our proposed CDA scheme to ensure robustness to failure. Using node redundancy, we modify the MUTEX protocol supporting our CDA algorithm to handle fail-stop and some Byzantine type faults of sites. This yields a time complexity of O(N), where N is number of cluster-head nodes; and message complexity of O((logN)+W) time, where W is the number of check-pointing messages. These results indicate that it is possible to add fault tolerance to a decentralised CDA, which guarantees continued participation in the auction while retaining reasonable performance overheads. In addition, we propose a decentralised consumption scheduling scheme that complements the auctioning scheme in guaranteeing successful power allocation within the RCSMG. Third, since grid participants are self-interested we must consider the issue of power theft that is provoked when participants cheat. We propose threat models centred on cheating attacks aimed at foiling the extended CDA scheme. More specifically, we focus on the Victim Strategy Downgrade; Collusion by Dynamic Strategy Change, Profiling with Market Prediction; and Strategy Manipulation cheating attacks, which are carried out by internal adversaries (auction participants). Internal adversaries are participants who want to get more benefits but have no interest in provoking a breakdown of the grid. However, their behaviour is dangerous because it could result in a breakdown of the grid. Fourth, to mitigate these cheating attacks, we propose an exception handling (EH) scheme, where sentinel agents use allocative efficiency and message overheads to detect and mitigate cheating forms. Sentinel agents are tasked to monitor trading agents to detect cheating and reprimand the misbehaving participant. Overall, message complexity expected in light demand is O(nLogN). The detection and resolution algorithm is expected to run in linear time complexity O(M). Overall, the main aim of our study is achieved by designing a resilient and cheating-free CDA algorithm that is scalable and performs well on resource constrained micro-grids. With the growing popularity of the CDA and its resource allocation applications, specifically to low resourced micro-grids, this thesis highlights further avenues for future research. First, we intend to extend the decentralised CDA algorithm to allow for participants’ mobile phones to connect (reconnect) at different shared smart meters. Such mobility should guarantee the desired CDA properties, the reliability and adequate security. Secondly, we seek to develop a simulation of the decentralised CDA based on the formal proofs presented in this thesis. Such a simulation platform can be used for future studies that involve decentralised CDAs. Third, we seek to find an optimal and efficient way in which the decentralised CDA and the scheduling algorithm can be integrated and deployed in a low resourced, smart micro-grid. Such an integration is important for system developers interested in exploiting the benefits of the two schemes while maintaining system efficiency. Forth, we aim to improve on the cheating detection and mitigation mechanism by developing an intrusion tolerance protocol. Such a scheme will allow continued auctioning in the presence of cheating attacks while incurring low performance overheads for applicability in a RCSMG

The role of public procurement policy in driving industrial development

Given the large sums that are spent in public purchasing, the government of every state is itself an actor in the market, and often a relatively large actor. Government activities in the marketplace, the purchasing of goods and services or sometimes the marketing of the same, thereby impact and in some cases shape the market itself. Governments have therefore explored the possibility of using government purchases to promote not only redistribution among different sections of the population, but also industrial strategy, innovation technologies, or environmental protection through products or services that offer a reduced environmental impact – in an economy. Procurement policy is typically viewed as a legitimate tool to stimulate domestic production and consumption. By targeting contracts towards particular sectors or groups in society, the government could push forward policies to redistribute wealth, promote industrial strategies or ensure sustainable development. The following report sets out an overview of different industrial policy objectives commonly pursued through procurement contracts in India, Jamaica, South Africa, Brazil, Republic of Korea and the UK

DRIVE: A Distributed Economic Meta-Scheduler for the Federation of Grid and Cloud Systems

The computational landscape is littered with islands of disjoint resource providers including commercial Clouds, private Clouds, national Grids, institutional Grids, clusters, and data centers. These providers are independent and isolated due to a lack of communication and coordination, they are also often proprietary without standardised interfaces, protocols, or execution environments. The lack of standardisation and global transparency has the effect of binding consumers to individual providers. With the increasing ubiquity of computation providers there is an opportunity to create federated architectures that span both Grid and Cloud computing providers effectively creating a global computing infrastructure. In order to realise this vision, secure and scalable mechanisms to coordinate resource access are required. This thesis proposes a generic meta-scheduling architecture to facilitate federated resource allocation in which users can provision resources from a range of heterogeneous (service) providers. Efficient resource allocation is difficult in large scale distributed environments due to the inherent lack of centralised control. In a Grid model, local resource managers govern access to a pool of resources within a single administrative domain but have only a local view of the Grid and are unable to collaborate when allocating jobs. Meta-schedulers act at a higher level able to submit jobs to multiple resource managers, however they are most often deployed on a per-client basis and are therefore concerned with only their allocations, essentially competing against one another. In a federated environment the widespread adoption of utility computing models seen in commercial Cloud providers has re-motivated the need for economically aware meta-schedulers. Economies provide a way to represent the different goals and strategies that exist in a competitive distributed environment. The use of economic allocation principles effectively creates an open service market that provides efficient allocation and incentives for participation. The major contributions of this thesis are the architecture and prototype implementation of the DRIVE meta-scheduler. DRIVE is a Virtual Organisation (VO) based distributed economic metascheduler in which members of the VO collaboratively allocate services or resources. Providers joining the VO contribute obligation services to the VO. These contributed services are in effect membership “dues” and are used in the running of the VOs operations – for example allocation, advertising, and general management. DRIVE is independent from a particular class of provider (Service, Grid, or Cloud) or specific economic protocol. This independence enables allocation in federated environments composed of heterogeneous providers in vastly different scenarios. Protocol independence facilitates the use of arbitrary protocols based on specific requirements and infrastructural availability. For instance, within a single organisation where internal trust exists, users can achieve maximum allocation performance by choosing a simple economic protocol. In a global utility Grid no such trust exists. The same meta-scheduler architecture can be used with a secure protocol which ensures the allocation is carried out fairly in the absence of trust. DRIVE establishes contracts between participants as the result of allocation. A contract describes individual requirements and obligations of each party. A unique two stage contract negotiation protocol is used to minimise the effect of allocation latency. In addition due to the co-op nature of the architecture and the use of secure privacy preserving protocols, DRIVE can be deployed in a distributed environment without requiring large scale dedicated resources. This thesis presents several other contributions related to meta-scheduling and open service markets. To overcome the perceived performance limitations of economic systems four high utilisation strategies have been developed and evaluated. Each strategy is shown to improve occupancy, utilisation and profit using synthetic workloads based on a production Grid trace. The gRAVI service wrapping toolkit is presented to address the difficulty web enabling existing applications. The gRAVI toolkit has been extended for this thesis such that it creates economically aware (DRIVE-enabled) services that can be transparently traded in a DRIVE market without requiring developer input. The final contribution of this thesis is the definition and architecture of a Social Cloud – a dynamic Cloud computing infrastructure composed of virtualised resources contributed by members of a Social network. The Social Cloud prototype is based on DRIVE and highlights the ease in which dynamic DRIVE markets can be created and used in different domains

The international contractor's decision to invest : a strategic risk management decision model for public private partnership projects in Saudi Arabia

One of the main sources of risks that influence potential project success is the project selection decision, especially for international contractor organisations looking for an opportunity to invest in public private partnership projects in foreign countries. Project selection decision, which involves the bid/no bid decision, is a critical investment decision needs to be made based on concrete project evaluation and risks identifications; where negative-risk is in place if there is an absence of a rational basis at the time of making such a decision. Thus, negative consequences of such a decision might occur. The bid/no bid decision necessitates an effective project evaluation and risk identification from various aspects with consideration of several internal and external factors in order to achieve project success. Bidding for PPP projects overseas without efficiently applying risk management tools and techniques to evaluate both the project and the organisation’s current situation and capability might result either in large losses or consumption of time and resources that could have been avoided. The prime aim of this research is to develop a strategic investment decision model from the perspective of risk management, in order to facilitate the decisions of international contractors who intend to invest in public private partnership projects in the Saudi Arabian construction industry. This aim requires establishing a link between the risk management process and the organisation's strategy and its current situation, and identifying risks involved in the bid/no bid decision, PPP projects, and international investment in order to provide an effective computer-based model that is capable of organising the bid/no bid decision in a rational, logical, flexible, and user-friendly manner. The pragmatic triangulation philosophy approach is adopted as the best research methodology that allows two types of research strategy to be combined in order to accomplish the research aim and objectives. Thus, the methods used are qualitative interviews and a quantitative questionnaire-based survey. The findings of this research identified critical success factors of international contractors’ bidding decisions for PPP projects in the Saudi Arabian construction industry. In particular, seventy-seven factors affecting the bid/no bid decision were used as a foundation for development of a Strategic Risk Management Decision Model (SRMDM), available at www.srmdm.com