Corporate Responsibility and Compliance with the Law: A Case Study of Land,Dispossession, and Aftermath at Newmont's Ahafo Project in Ghana1

An important part of responsible business practices is compliance with the law. This article details what actually happens when the laws of the host country fail to ensure adequate protection. The focus here is on land dispossession and loss of livelihood in relation to a gold mine project in central Ghana. How is it that a well‐known international company—Newmont—with its own corporate social responsibility (CSR) statements sets up a project in the year 2003 that displaces subsistence farmers from their land without compensating in cash or with replacement land? The analysis identifies the factors that lead the company to not compensate farmers for their lost land: cost‐cutting, strict adherence to the law, CSR commitment that was new and not internalized, complexities of the Ghanaian land tenure system, peer pressure to preserve the status quo, selection of an “old‐school” CSR manager, and the inadequacy of Ghanaian mining law to account for relatively novel, “open‐pit” mining techniques. However, the specter of famine raised by civil society activism, the involvement of the International Financial Corporation, and a better qualified CSR team constitute another set of factors that lead to a comprehensive package of livelihood improvement measures. There is a contrast between the complexity, long‐term, and advanced type of assistance Newmont currently envisages and the backward, short‐term, formalism, and brutality of denying compensation for land back in 2003. This research is based on the extensive documentation Newmont makes available on its web site, interviews conducted in Ghana, and literature research.     

Hybrid Samplers for Ill-Posed Inverse Problems

Abstract.  In the Bayesian approach to ill-posed inverse problems, regularization is imposed by specifying a prior distribution on the parameters of interest and Markov chain Monte Carlo samplers are used to extract information about its posterior distribution. The aim of this paper is to investigate the convergence properties of the random-scan random-walk Metropolis (RSM) algorithm for posterior distributions in ill-posed inverse problems. We provide an accessible set of sufficient conditions, in terms of the observational model and the prior, to ensure geometric ergodicity of RSM samplers of the posterior distribution. We illustrate how these conditions can be checked in an application to the inversion of oceanographic tracer data.