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Abstract The industrialization of shoemaking occurred in small towns in antebellum New England in which capital formation was severely hampered by currency and credit problems prior to the Legal Tender Act of 1862. In a comparison of two communities, it was discovered that the construction of a railroad had different results depending on the structural ties between the community and the railroad. In one community, the railroad drew external capital to the community that provided the basis for a crucial expansion of scale of shoe production prior to the rise of the factory system. In the other, it depleted local capital, ending the shoe industry and damaging most other local economic activities as well.

A population's long‐term exposure distribution for a specified compound is typically estimated from short‐term measurements of a sample of individuals from the population of interest. In this situation, estimates of a population's long‐term exposure parameters contain two sources of sampling error: the typical sampling error associated with taking a sample from the population and the sampling error from estimating individual long‐term exposure. These components are not separable in the data collected, i.e., the value observed is due partly to the individual sampled and partly to the time at which the individual was sampled. Hence, the distribution of the data collected is not the same as the population exposure distribution. Monte Carlo simulations are used to compare the distribution of the observed data with the population exposure distribution for a simple additive model. A simple adjustment to standard estimates of percentiles and quantils is shown to be effective in reducing bias particularly for the upper percentiles and quantils of the population distribution.