Voluntary Disclosure and Political Sensitivity: The Case of Executive Remuneration

This study investigates the relation between firm political sensitivity and the quality and quantity of voluntary disclosures, with special reference to the case of executive remuneration disclosures. I study the relation between firm political sensitivity and the quality and quantity of annual bonus plan disclosures, applying two competing (but not mutually exclusive) theories: political cost theory and managerial power theory. Political sensitivity is proxied using the magnitude of the annual bonus rather than firm size, which while popular in existing literature, is not a perfect proxy for political sensitivity (Ball and Foster, 1982, Meek et al, 1995; Cormier et al, 2005). Results reveal a significant positive relation between disclosure quantity and political sensitivity measures, and a significant negative relation between disclosure quality and political sensitivity proxies. This indicates that managers who are more susceptible to political sanctions related to their remuneration tend to disclose higher volumes of lower quality information. Consistent with earlier studies, the results confirm that firm size is related to voluntary disclosure, and the results also reveal that the use of remuneration consultants have a significant positive effect on disclosure quantity but no impact on disclosure quality

FastPay: High-Performance Byzantine Fault Tolerant Settlement

FastPay allows a set of distributed authorities, some of which are Byzantine, to maintain a high-integrity and availability settlement system for pre-funded payments. It can be used to settle payments in a native unit of value (crypto-currency), or as a financial side-infrastructure to support retail payments in fiat currencies. FastPay is based on Byzantine Consistent Broadcast as its core primitive, foregoing the expenses of full atomic commit channels (consensus). The resulting system has low-latency for both confirmation and payment finality. Remarkably, each authority can be sharded across many machines to allow unbounded horizontal scalability. Our experiments demonstrate intra-continental confirmation latency of less than 100ms, making FastPay applicable to point of sale payments. In laboratory environments, we achieve over 80,000 transactions per second with 20 authorities---surpassing the requirements of current retail card payment networks, while significantly increasing their robustness

The Social Cost of Blackmail

Despite the fact that blackmail constitutes a voluntary transaction between two parties, it is deemed to be a criminal offence in most legal systems. Traditional economic approach to this so-called ‘paradox of blackmail’ emphasizes welfare loss generated by the costly rentseeking activities of potential blackmailers as the primary justification for its criminalization. This argument however does not extend to cases in which potentially damaging information about the victim was acquired by the blackmailer at no cost. It also does not seem to shed light on a related puzzle: why is it legal for a potential victim to bribe the other party with the purpose of achieving the same final outcome (suppression of information) as in the case of blackmail? This paper addresses these questions in a simple model of bargaining under asymmetric information which is used as a unified framework for studying both blackmail and bribery. Under asymmetric information the bargaining outcome is not efficient regardless of the distribution of the bargaining power. However, when the blackmailer is the monopolist seller of the information inefficiency results from his demands being too high relative to the social optimum, providing justification for the practice of penalizing blackmail. On the other hand, when a victim is the monopolist buyer of the information the equilibrium offer is inefficiently low implying that its punishment would be counterproductive. These arguments provide further support for the claim that under reasonable assumptions criminalization of blackmail can be justified on efficiency grounds.blackmail, bribery, bargaining

The Anatomy and Facets of Dynamic Policies

Information flow policies are often dynamic; the security concerns of a program will typically change during execution to reflect security-relevant events. A key challenge is how to best specify, and give proper meaning to, such dynamic policies. A large number of approaches exist that tackle that challenge, each yielding some important, but unconnected, insight. In this work we synthesise existing knowledge on dynamic policies, with an aim to establish a common terminology, best practices, and frameworks for reasoning about them. We introduce the concept of facets to illuminate subtleties in the semantics of policies, and closely examine the anatomy of policies and the expressiveness of policy specification mechanisms. We further explore the relation between dynamic policies and the concept of declassification.Comment: Technical Report of publication under the same name in Computer Security Foundations (CSF) 201

The Sum of Its Parts: The Lawyer-Client Relationship in Initial Public Offerings

This Article examines the impact of the quality of a lawyer\u27s working relationship with his or her client on one of the most important types of capital markets deal in a company\u27s existence: its initial public offering (IPO). Drawing on data from interviews with equity capital markets lawyers at major law firms, and analyzing data from IPOs in the United States registered with the Securities and Exchange Commission between June 1996 and December 2010, this study finds a strong association between several measures of IPO performance and the familiarity between the lead underwriter and its counsel, as measured by the number of times a particular law firm serves as counsel to a managing underwriter within a relatively short time period. Performance is gauged according to a stock\u27s opening day returns, price performance over thirty, sixty, and ninety trading days, correct price revision, litigation rates, and the speed at which deals are completed. I also analyze the relationships between the lawyers for the lead underwriter and the lawyers for the issuer. The analysis shows some benefits from familiarity, albeit generally smaller than those associated with the underwriter-lawyer relationship. In all cases, the positive effects of repeated interaction diminish the further back in time the previous collaborations occurred. To rule out selection and reverse causality, I perform a number of tests using smaller subsets of the data to remove observations that are plausibly selection driven. I also show that the relationships between familiarity and deal quality occur independently of the level of the lawyers\u27 experience. These findings support the conclusion that lawyers\u27 relational skill can positively influence deal outcomes, independent even of substance and process knowledge. I hypothesize that the core advantage of repeated interaction is the formation of more effective lawyer-client team dynamics

Strategic Positioning and the Financing of Nonprofit Organizations: Is Efficiency Rewarded in the Contributions Marketplace?

This article addresses the question of whether operational efficiency is recognized and rewarded by the private funders that support nonprofit organizations in fields ranging from education to social service to arts and beyond. Looking at the administrative efficiency and fundraising results of a large sample of nonprofit organizations over an 11 year period, we find that nonprofits that position themselves as cost efficient reporting low administrative to total expense ratios fared no better over time than less efficient appearing organizations in the market for individuals, foundations, and corporate contributions. From this analysis, we suggest that economizing may not always be the best strategy in the nonprofit sector. This publication is Hauser Center Working Paper No. 2. The Hauser Center Working Paper Series was launched during the summer of 2000. The Series enables the Hauser Center to share with a broad audience important works-in-progress written by Hauser Center scholars and researchers

EUA and sCER Phase II Price Drivers: Unveiling the reasons for the existence of the EUA-sCER spread.

This article studies the price relationships between EU emissions allowances (EUAs) - valid under the EU Emissions Trading Scheme (EU ETS) - and secondary Certified Emissions Reductions (sCERs) - established from primary CERs generated through the Kyoto Protocol's Clean Development Mechanism (CDM). Given the price differences between EUAs and sCERs, financial and industrial operators may benefit from arbitrage strategies by buying sCERs and selling EUAs (i.e. selling the EUAsCER spread) to cover their compliance position as industrial operators are allowed to use sCERs towards compliance with their emissions cap within the European system up to 13.4%. Our central results show that the spread is mainly driven by EUA prices and market microstructure variables and less importantly, as we would expect, by emissions-related fundamental drivers. This might be justified by the fact that the EU ETS remains the greatest source of CER demand to date.Emissions Markets; Arbitrage; EUA-sCER Spread;

Differentially Private Model Selection with Penalized and Constrained Likelihood

In statistical disclosure control, the goal of data analysis is twofold: The released information must provide accurate and useful statistics about the underlying population of interest, while minimizing the potential for an individual record to be identified. In recent years, the notion of differential privacy has received much attention in theoretical computer science, machine learning, and statistics. It provides a rigorous and strong notion of protection for individuals' sensitive information. A fundamental question is how to incorporate differential privacy into traditional statistical inference procedures. In this paper we study model selection in multivariate linear regression under the constraint of differential privacy. We show that model selection procedures based on penalized least squares or likelihood can be made differentially private by a combination of regularization and randomization, and propose two algorithms to do so. We show that our private procedures are consistent under essentially the same conditions as the corresponding non-private procedures. We also find that under differential privacy, the procedure becomes more sensitive to the tuning parameters. We illustrate and evaluate our method using simulation studies and two real data examples

Safeguarding health data with enhanced accountability and patient awareness

Several factors are driving the transition from paper-based health records to electronic health record systems. In the United States, the adoption rate of electronic health record systems significantly increased after "Meaningful Use" incentive program was started in 2009. While increased use of electronic health record systems could improve the efficiency and quality of healthcare services, it can also lead to a number of security and privacy issues, such as identity theft and healthcare fraud. Such incidents could have negative impact on trustworthiness of electronic health record technology itself and thereby could limit its benefits. In this dissertation, we tackle three challenges that we believe are important to improve the security and privacy in electronic health record systems. Our approach is based on an analysis of real-world incidents, namely theft and misuse of patient identity, unauthorized usage and update of electronic health records, and threats from insiders in healthcare organizations. Our contributions include design and development of a user-centric monitoring agent system that works on behalf of a patient (i.e., an end user) and securely monitors usage of the patient's identity credentials as well as access to her electronic health records. Such a monitoring agent can enhance patient's awareness and control and improve accountability for health records even in a distributed, multi-domain environment, which is typical in an e-healthcare setting. This will reduce the risk and loss caused by misuse of stolen data. In addition to the solution from a patient's perspective, we also propose a secure system architecture that can be used in healthcare organizations to enable robust auditing and management over client devices. This helps us further enhance patients' confidence in secure use of their health data.PhDCommittee Chair: Mustaque Ahamad; Committee Member: Douglas M. Blough; Committee Member: Ling Liu; Committee Member: Mark Braunstein; Committee Member: Wenke Le

Scaling Distributed Ledgers and Privacy-Preserving Applications

This thesis proposes techniques aiming to make blockchain technologies and smart contract platforms practical by improving their scalability, latency, and privacy. This thesis starts by presenting the design and implementation of Chainspace, a distributed ledger that supports user defined smart contracts and execute user-supplied transactions on their objects. The correct execution of smart contract transactions is publicly verifiable. Chainspace is scalable by sharding state; it is secure against subsets of nodes trying to compromise its integrity or availability properties through Byzantine Fault Tolerance (BFT). This thesis also introduces a family of replay attacks against sharded distributed ledgers targeting cross-shard consensus protocols; they allow an attacker, with network access only, to double-spend resources with minimal efforts. We then build Byzcuit, a new cross-shard consensus protocol that is immune to those attacks and that is tailored to run at the heart of Chainspace. Next, we propose FastPay, a high-integrity settlement system for pre-funded payments that can be used as a financial side-infrastructure for Chainspace to support low-latency retail payments. This settlement system is based on Byzantine Consistent Broadcast as its core primitive, foregoing the expenses of full atomic commit channels (consensus). The resulting system has extremely low-latency for both confirmation and payment finality. Finally, this thesis proposes Coconut, a selective disclosure credential scheme supporting distributed threshold issuance, public and private attributes, re-randomization, and multiple unlinkable selective attribute revelations. It ensures authenticity and availability even when a subset of credential issuing authorities are malicious or offline, and natively integrates with Chainspace to enable a number of scalable privacy-preserving applications