Infrastructure Contracts: Trust and Institutional Updating

This paper discusses trust and trust perceptions in infrastructure contracts and supporting institutions. We focus on perceptions of the trustworthiness of the government purchasers of infrastructure services by the supplying companies and by the governments themselves. In particular, we allow for trust updating and trust misalignments, which may give rise to ‘undertrusting’ and ‘overtrusting’. The core of the paper sets out a game theoretic model of contracts with dynamic adjustment of trust perceptions, which we use to explore the impact of trust misalignment both on economic efficiency (measured by expected welfare) and on investment levels. We explore flexible contracts with and without pre-payments, rigid contracts (which do not allow for post-investment renegotiation), and hybrid contracts. We then compare the efficiency of the flexible contracts to that of hybrid contracts using as a criterion the expected welfare implications of each contract. The model is used to shed light on current issues on the sustainability of private investment infrastructure contracts in developed and in developing countries, including the role of regulatory institutions.     

A Guide to Exact Simulation

Markov Chain Monte Carlo (MCMC) methods are used to sample from complicated multivariate distributions with normalizing constants that may not be computable in practice and from which direct sampling is not feasible. A fundamental problem is to determine convergence of the chains. Propp & Wilson (1996) devised a Markov chain algorithm called Coupling From The Past (CFTP) that solves this problem, as it produces exact samples from the target distribution and determines automatically how long it needs to run. Exact sampling by CFTP and other methods is currently a thriving research topic. This paper gives a review of some of these ideas, with emphasis on the CFTP algorithm. The concepts of coupling and monotone CFTP are introduced, and results on the running time of the algorithm presented. The interruptible method of Fill (1998) and the method of Murdoch & Green (1998) for exact sampling for continuous distributions are presented. Novel simulation experiments are reported for exact sampling from the Ising model in the setting of Bayesian image restoration, and the results are compared to standard MCMC. The results show that CFTP works at least as well as standard MCMC, with convergence monitored by the method of Raftery & Lewis (1992, 1996).     

New development: Exploring public service markets

The authors explain why public service markets are fundamentally different from regulated utilities markets by looking at the product characteristics, market structure, funding oversight and legal arrangements in such markets. They highlight the issues which will be important as marketized delivery becomes increasingly mainstream in public services provision.     

Bayesian Premium Rating with Latent Structure

We propose a fully Bayesian approach to non-life risk premium rating, based on hierarchical models with latent variables for both claim frequency and claim size. Inference is based on the joint posterior distribution and is performed by Markov Chain Monte Carlo. Rather than plug-in point estimates of all unknown parameters, we take into account all sources of uncertainty simultaneously when the model is used to predict claims and estimate risk premiums. Several models are fitted to both a simulated dataset and a small portfolio regarding theft from cars. We show that interaction among latent variables can improve predictions significantly. We also investigate when interaction is not necessary. We compare our results with those obtained under a standard generalized linear model and show through numerical simulation that geographically located and spatially interacting latent variables can successfully compensate for missing covariates. However, when applied to the real portfolio data, the proposed models are not better than standard models due to the lack of spatial structure in the data.     

The impact of signal dependence and own ability awareness on herding behaviour: a tale of two managers

This model examines the case of managers whose signals, when informative, are perfectly correlated as in the Scharfstein and Stein model [1990. The American Economic Review 80(3): 465–479]. This has a herd increasing impact as it introduces a positive reputation externality. On the other hand, it is also assumed that managers have perfect knowledge of their own ability, an assumption with herd reducing implications. Combining these two offsetting, in terms of herding, assumptions, it is found that a smart manager who plays first will sometimes, but not always, truthfully announce his/her private information. On the other hand, a smart manager who plays second will always report his/her true signal, while a dumb manager who plays second may herd, either on the first manager's action and/or on the prior. It is also found that the more likely a dumb manager who plays second is to herd on the first manager's action, the less likely is a smart manager who plays first to herd on the prior. Copyright © 1999 John Wiley & Sons, Ltd.