This work provides an overview of standard social science data sources that now allow some systematic study of the gay and lesbian population in the United States. For each data source, we consider how sexual orientation can be defined, and we note the potential sample sizes. We give special attention to the important problem of measurement error, especially the extent to which individuals recorded as gay and lesbian are indeed recorded correctly. Our concern is that because gays and lesbians constitute a relatively small fraction of the population, modest measurement problems could lead to serious errors in inference. In examining gays and lesbians in multiple data sets we also achieve a second objective: We provide a set of statistics about this population that is relevant to several current policy debates.

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This investigation of the effect of sexual orientation on earnings employs General Social Survey data from 1989–96. Depending largely on the definition of sexual orientation used, earnings are estimated as having been between 14% and 16% lower for gay men than for heterosexual men, and between 20% and 34% higher for lesbian women than for heterosexual women. This evidence, the authors suggest, is consistent with either of two complementary constructions: Gary Becker's argument that male/female earnings differentials are rooted in specialization within households and in optimal human capital accumulation decisions individuals make when they are young; and Claudia Goldin's observations about marriage-based gender discrimination, according to which the paternalistic “protection” of wives and mothers from the world of work has tended to overlook lesbians.

In this paper, we examine the impact of the coal boom in the 1970s and the subsequent coal bust in the 1980s on local labour markets in Kentucky, Ohio, Pennsylvania, and West Virginia. We address two main questions in our analysis. How were non-mining sectors affected by the shocks to the mining sector? How did these effects differ between sectors producing local goods and those producing traded goods? We find evidence of modest employment spillovers into sectors with locally traded goods but not into sectors with nationally traded goods.Assumptions about the effects of shocks on local labour markets strongly influence local economic policies. Communities often bitterly oppose plant closures believing that closures will create devastating ripple effects throughout the local economy. Similarly, local and state governments often provide a variety of incentives, such as tax breaks and loans, to encourage businesses to locate in their area, hoping that in addition to the direct economic benefits of a new facility, existing local businesses will also benefit from the additional economic activity generated by the new employment. Indeed, the business press trumpets these ÔspilloverÕ effects as an important benefit of a firm's location decision. Despite these widespread beliefs and government actions, relatively little is known about the indirect impact of local economic shocks. It is difficult to quantify the effect of a shock to the local labour market because the counterfactual (what would have happened in the absence of the shock) is missing.In this paper we take advantage of an economic shock that induced a substantial exogenous shift in the demand for labour in certain local labour markets. We examine the impact of the coal boom in the 1970s and the subsequent coal bust in the 1980s on local economies in the four-state region of Kentucky, Ohio, Pennsylvania, and West Virginia. During the 1970s, regulatory changes and the Organisation of Petroleum Exporting Countries (OPEC) oil embargo drove up the price of coal and generated an enormous boom in the coal economy. There was a tremendous long-term infusion of mining jobs into areas with coal reserves as new mines were opened and existing ones were expanded. The coal boom lasted for more than a decade. By 1983, however, oil prices had declined, alternative mines had opened in the western US, and improvements in mining technology had reduced the demand for coal workers. The coal boom collapsed into a bust.The coal boom and bust primarily affected counties that had large coal industries. By comparing counties in this region that have large coal industries to counties that have no coal to mine, we measure the effect of the coal boom and