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

This paper uses confidential Census data, specifically the 1990 and 2000 Census Long Form data, to study demographic processes in neighborhoods that gentrified during the 1990’s. In contrast to previous studies, the analysis is conducted at the more refined census-tract level, with a narrower definition of gentrification and more closely matched comparison neighborhoods. Furthermore, our access to individual-level data with census tract identifiers allows us to separately identify recent in-migrants and long-term residents. Our results indicate that, on average, the demographic flows associated with the gentrification of urban neighborhoods during the 1990’s are not consistent with displacement and harm to minority households. In fact, taken as a whole, our results suggest that gentrification of predominantly black neighborhoods creates neighborhoods that are attractive to middle-class black households.

Human capital theory predicts that individuals acquire less schooling when the returns to schooling are small. To test this theory, the authors study the effect of the Appalachian coal boom on high school enrollments. During the 1970s, a boom in the coal industry increased the earnings of high school dropouts relative to those of graduates. During the 1980s, the boom subsided and the earnings of dropouts declined relative to those of graduates. The authors find that high school enrollment rates in Kentucky and Pennsylvania declined considerably in the 1970s and increased in the 1980s in coal-producing counties relative to counties without coal. The estimates indicate that a longterm 10% increase in the earnings of low-skilled workers could decrease high school enrollment rates by as much as 5-7%-a finding with implications for policies aimed at improving low-skilled workers' employment and earnings, such as wage subsidies and minimum wage increases.

This paper uses confidential Census data, specifically the 1990 and 2000 Census Long Form data, to study the demographic processes underlying the gentrification of low-income urban neighborhoods during the 1990's. In contrast to previous studies, the analysis is conducted at the more refined census-tract level with a narrower definition of gentrification and more closely matched comparison neighborhoods. The analysis is also richly disaggregated by demographic characteristic, uncovering differential patterns by race, education, age and family structure that would not have emerged in the more aggregate analysis in previous studies. The results provide no evidence of displacement of low-income non-white households in gentrifying neighborhoods. The bulk of the increase in average family income in gentrifying neighborhoods is attributed to black high school graduates and white college graduates. The disproportionate retention and income gains of the former and the disproportionate in-migration of the latter are distinguishing characteristics of gentrifying U.S. urban neighborhoods in the 1990's.

This paper extends and synthesizes the various approaches used in the recent welfare migration literature to both offer the most comprehensive set of tests to date for welfare migration and to also determine the relative importance of short-distance moves in welfare migration flows. The current study follows on the finding of McKinnish (2005) of welfare migration effects obtained by comparing welfare participation at state borders to state interiors. This identification strategy is extended to micro-data from the 1980 and 1990 Decennial Censuses and combined with the demographic comparisons used elsewhere in the welfare migration literature. The signs and patterns of the estimates are consistent with the presence of welfare migration effects, and the magnitudes of the estimates are consistent with the importance of short-distance moves in welfareinduced migration flows, but most of the estimates are not statistically significant.