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A quantifiable basis for developing design guidelines for pedestrian access to light-rail transit (LRT) stations is provided for planners based on observations in Calgary, Canada. Calgary's LRT system, which began operations in 1981, has been operating for long enough for walking patterns to and from its stations to become established. Interviews were conducted with 1,800 peak-hour LRT users about the origins and destinations of their LRT trips. Those who walked to or from a station were asked to point out on a map their approximate origins or destinations. The distances were then measured off the maps. Walking distance guidelines were developed for central business district (CBD), transfer and local stations. Catchment area maps were produced, and the relationship between reported walking time and measured walking distance was calculated. Also compared are the walking distances at LRT stations and the walking distances at bus stops. The research strongly indicates that people walk farther to reach an LRT station than a bus stop. Using bus walking standards would result in an underestimate of LRT walking distances by about half. For the city of Calgary the average walking distance to suburban stations is 649 m with a 75th-percentile distance of 840 m. At CBD stations the average walking distance is 326 m and the 75th-percentile distance is 419 m.

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While benefit–cost analysis (BCA) is now a permanent part of the regulatory process in the United States, and many other countries around the world as well as the European Union have adopted it or are moving toward it, there have been few empirical attempts to assess either whether its use improves regulations or how BCA interacts with the political environment. We use a unique US database of the costs and benefits of 109 economically significant regulations issued between 2000 and 2009 to examine whether the amount of information provided in the BCA or political factors surrounding the regulation better correlate with the net benefits of the regulation. We find that there is little correlation between the information provided by the analysis and the net benefits. However, we find that regulations that receive few public comments and are not issued at the end of an administration, have the highest net benefits. These are the regulations that are the least politically salient. This interaction between the political environment and the economic performance of a regulation has been under‐examined and deserves further study.

This paper compares the quality and use of regulatory analysis accompanying economically significant regulations proposed by US executive branch agencies in 2008, 2009, and 2010. We find that the quality of regulatory analysis is generally low, but varies widely. Budget regulations, which define how the federal government will spend money or collect revenues, have much lower-quality analysis than other regulations. The Bush administration's "midnight" regulations finalized between Election Day and Inauguration Day, along with other regulations left for the Obama administration to finalize, tended to have lower-quality analysis. Most differences between the Bush and Obama administrations depend on agencies' policy preferences. More conservative agencies tended to produce better analysis in the Obama administration, and more liberal agencies tended to do so in the Bush administration. This suggests that agencies more central to an administration's policy priorities do not have to produce as good an analysis to get their regulations promulgated.

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This article develops a model of the conditions under which risk regulations that are too expensive have net adverse health effects. Two principal components of this relationship are the implicit value of life and the income elasticity of risky behaviors. Using new empirical estimates for the income elasticity of many of the most consequential risk-related behaviors, our results imply that a $15 million decrease in income is associated with the loss of an additional statistical life. Regulations that cost more than $15 million per expected life saved will have counterproductive effects on individual mortality. (JEL L5 1, 112, 517)