a responsibility to take into account the external effects of its decisions. From this perspective, firms should consider how their decisions could achieve socially-preferred outcomes, considering external costs and benefits even when there are no regulatory or community pressures to do so. Many discussions of CSR describe firms as accepting lower profits in return for social acceptance, but Porter and Kramer () argue that socially responsible decisions can also benefit the firm's long-run profitability, especially if the firm focuses on providing benefits to society using its areas of expertise.
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Whatever decision-making process is being followed, one key element in the calculation of the optimal decision is the risk of an accident. This can be complicated, especially when the risk of a substantial release affecting the area outside the facility involves the simultaneous failure of multiple layers of protection. Calculations of such risks often assume that the probability of each layer's failure is independent, so that three layers of protection, each with a 1-in-10,000 risk, would provide an overall risk of 1-in-a-trillion. One lesson from the Bhopal accident is that, at least in that institutional setting, failures to manage risk were correlated across the layers of protection, greatly increasing the risk of an accident. Regulatory decisions under BCA can depend heavily on the calculations of the risk of low-probability events, such as the probability of a large accident at a chemical plant, or the probability of a given person dying of lung cancer after being exposed to air pollutants. A key difference between these two examples lies in the frequency of their observations. Millions of people are exposed to air pollution every year, and the (very small) fraction of individuals who die after the exposure can be calculated, which allows one to determine reasonably precise estimates of the risk's probability. For large industrial accidents, which fortunately rarely occur, calculations of the probabilities involved depend on engineering models of the effectiveness of the different layers of protection. It is not that such calculations cannot be made'they are done regularly as part of both applications of BCA and profit maximizing decisions by firms'but as noted in Box 4.1, there are inherent uncertainties and biases involved.
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There is also the sensitive issue of assigning values to the illnesses or deaths of people that could result from a major accident. For profit-maximizing decisions by the firm, these values can be related back to the potential liability costs of an accident, as discussed earlier. For BCA applications, the most common tool is the 'value of a statistical life' (VSL). Suppose that a typical worker requires $5,000 extra wages per year in order to accept a risky job that has a 1-in-1,000 chance of a fatal accident during the year. A group of 1,000 such workers would, on average, have suffered one extra death per year'and would have accepted a total of $5 million to bear the risk of that death'so the VSL would be $5 million. Considerable effort has been expended by both academics and regulatory agencies to refine their VSL estimates, as well as to consider whether and how VSL values might vary within the population (see Aldy and Viscusi, for a recent example). Despite the common use of VSL in these calculations, many
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