Changes in Workplace Segregation in the United States between 1990 and 2000: Evidence from Matched Employer-Employee Data

We present evidence on changes in workplace segregation by education, race, ethnicity, and sex, from 1990 to 2000. The evidence indicates that racial and ethnic segregation at the workplace level remained quite pervasive in 2000. At the same time, there was fairly substantial segregation by skill, as measured by education. Putting together the 1990 and 2000 data, we find no evidence of declines in workplace segregation by race and ethnicity; indeed, black-white segregation increased. Over this decade, segregation by education also increased. In contrast, workplace segregation by sex fell over the decade, and would have fallen by more had the services industry - a heavily female industry in which sex segregation is relatively high - not experienced rapid employment growth.

Spatial Mismatch or Racial Mismatch?

We contrast the spatial mismatch hypothesis with what we term the racial mismatch hypothesis – that the problem is not a lack of jobs, per se, where blacks live, but a lack of jobs where blacks live into which blacks are hired. We first report new evidence on the spatial mismatch hypothesis, using data from Census Long-Form respondents. We construct direct measures of the presence of jobs in detailed geographic areas, and find that these job density measures are related to employment of black male residents in ways that would be predicted by the spatial mismatch hypothesis – in particular that spatial mismatch is primarily an issue for low-skilled black male workers. We then look at mismatch along not only spatial lines but racial lines as well, by estimating the effects of job density measures that are disaggregated by race. We find that it is primarily black job density that influences black male employment, whereas white job density has little if any influence on their employment. The evidence implies that space alone plays a relatively minor role in low black male employment rates.

Measuring the Importance of Labor Market Networks

We specify and implement a test for the importance of network effects in determining the establishments at which people work, using recently-constructed matched employer-employee data at the establishment level. We explicitly measure the importance of network effects for groups broken out by race, ethnicity, and various measures of skill, for networks generated by residential proximity. The evidence indicates that labor market networks play an important role in hiring, more so for minorities and the less-skilled, especially among Hispanics, and that labor market networks appear to be race-based.networks, race, ethnicity, immigrants

A triangular coastal element developed for use in finite difference tidal models

In numerical models of environmental flows it is often necessary to implement impermeable boundaries of complicated shape. For example, when modelling the spread of pollutants in streams, the dispersion of contaminants in lakes and estuaries or the final coastal destination of an oil spill, the land-water boundary is not simply defined. Currently such boundaries are best represented using Finite Element (FE) techniques. However FE techniques are both computationally expensive and difficult to implement. As a result Finite Difference Methods (FDM) on rectangular grids have traditionally been used to model environmental flows. In this paper a triangular boundary element for finite difference models of tidal flows in coastal regions, which improves boundary resolution while maintaining computational efficiency, is developed and tested. Numerical predictions using the new approach are compared with predictions obtained using the traditional stepped boundary and an analytic solution for depth-integrated flow in an idealised estuary

Estimating changes in temperature extremes from millennial scale climate simulations using generalized extreme value (GEV) distributions

Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the CCSM3 in equilibrated pre-industrial and possible future conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that the magnitude of warm extremes largely shifts in accordance with mean shifts in summertime temperatures. In contrast, cold extremes warm more than mean shifts in wintertime temperatures, but changes in GEV location parameters are largely explainable by mean shifts combined with reduced wintertime temperature variability. In addition, changes in the spread and shape of the GEV distributions of cold extremes at inland locations can lead to discernible changes in tail behavior. We then examine uncertainties that result from using shorter model runs. In principle, the GEV distribution provides theoretical justification to predict infrequent events using time series shorter than the recurrence frequency of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series that are of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.Comment: 33 pages, 22 figures, 1 tabl

Spatial Mismatch, Immigrant Networks, and Hispanic Employment in the United States

We study the relationship between Hispanic employment and location-specific measures of the distribution of jobs. We find that it is only the local density of jobs held by Hispanics that matters for Hispanic employment, that measures of local job density defined for Hispanic poor English speakers or immigrants are more important, and that the density of jobs held by Hispanic poor English speakers are most important for the employment of these less-skilled Hispanics than for other Hispanics. This evidence is consistent with labor market networks being an important influence on the employment of less-skilled Hispanics, as is evidence from other sources. We also find that in MSAs where the growth rates of the Hispanic immigrant population have been highest, which are also MSAs with historically low Hispanic populations, localized job density for low-skilled jobs is even more important for Hispanic employment than in the full sample. We interpret this evidence as consistent with the importance of labor market networks, as strong labor market networks are likely to have been especially important in inducing Hispanics to migrate, and because of these networks employment in these “new immigrant” cities is especially strongly tied to the local availability of jobs.