This article focuses on settlement trends of immigrants during the periods that bookend the twentieth century, both eras of mass migration. It compares settlement patterns in both periods, describing old and new gateways, the growth of the immigrant population, and geographic concentration and dispersion. Historically, immigrants have been highly concentrated in a few places. Between 1930 and 1990, more than half of all immigrants lived in just five metropolitan areas. Since then, the share of these few destinations has declined, as immigrants have made their way to new metro areas, particularly in the South and West. During the same period, immigrants began to choose the suburbs over cities, following the decentralization of jobs and the movement of opportunities to suburban areas. There are now more immigrants in U.S. suburban areas than cities.

...As a natural extension to this research, this paper provides what we believe is the first empirical analysis of the extent to which people in different occupations locate near one another, or what we term occupational coagglomeration.1 To do so, we present and examine measures of the coagglomeration of U.S. occupations at the state and metropolitan area levels. Importantly, our analysis covers the full spectrum of the U.S. economy. By contrast, most empirical studies of industry agglomeration focus on the manufacturing sector (Barrios et al., 2004; Devereux, Griffith, and Simpson, 2004; Duranton and Overman, 2005; Ellison, Glaeser, and Kerr, 2010), which accounts for less than ten percent of U.S. employment. Kolko (2010) broadened the analysis of industry concentration to include the service sector, which revealed some interesting differences in the patterns of industry location. Thus, to fully capture the geographic concentration of economic activity, it is important to move beyond specific sectors of the economy.We then turn our attention to identifying factors underlying the patterns of occupational coagglomeration that are identified, with a focus on the importance of the similarity of knowledge required to perform a job. Reorienting the focus from industries to occupations changes the way that we think about the determinants of agglomeration. Industries are defined along the basis of what firms make (e.g., good or service produced), while occupations are organized by what people do (e.g., skills and knowledge requirements) in their jobs (Feser, 2003). This means that, for example, whereas input-output relationshipscharacterizing the amount of one good needed to produce anothermight influence the settlement patterns of some firms, this determinant of industry agglomeration is less relevant to the study of occupations. Less constrained by production relationships that dictate how things are made, people are apt to locate around others involved in the same types of work activities (e.g., computer programming), thinking less about whether their peers are employed by companies making similar or different types of goods and services. Thus, we expect occupations with similar knowledge profiles to exhibit strong patterns of coagglomeration....

I estimate the effect of schooling on the propensity to migrate by exploiting variation in schooling due to compulsory schooling laws (CSLs) in the United States. I obtain negative estimates of this effect among those with relatively little schooling. In contrast, previous research estimates positive schooling effects on migration at higher levels of schooling. I speculate that additional schooling at low levels enhances local labor market contacts and thereby increases the opportunity cost of migration (leaving those contacts behind).

The United States is home to about 1.7 million Filipino immigrants, making them the second-largest immigrant group in the United States after Mexican immigrants. The Filipino immigrant population grew rapidly during the 1970s and 1980s and has continued to grow (although at a slightly slower pace) since then. In addition, the United States is home to about 1.4 million native-born US citizens who claim Filipino ancestry. Heavily concentrated in the western United States, the Filipino born account for almost half of all immigrants in Hawaii (for more information on immigrants by state, please see the ACS/Census Data tool on the MPI Data Hub). Compared to other immigrant groups, Filipinos are better educated than the immigrant population overall, and Filipino immigrant women are more likely than other immigrant women to participate in the civilian labor force. This spotlight focuses on Filipino immigrants residing in the United States, examining the population's size, geographic distribution, and socioeconomic characteristics using data from the US Census Bureau's 2008 American Community Survey (ACS) and 2000 Decennial Census, and the Department of Homeland Security's Office of Immigration

It is a well-known fact that the drug-related violence in Mexico has seen an upsurge in recent years. This increase in violent crimes has been attributed to the so-called "war against drug trafficking," which was declared when President Felipe Calderon took office in 2006. According to a report from . . .

This study introduces a new measure of urban centrality. The proposed urban centrality index (UCI) constitutes an extension to the spatial separation index. Urban structure should be more accurately analyzed when considering a centrality scale (varying from extreme monocentricity to extreme polycentricity) than when considering a binary variable (mono- centric or polycentric). The proposed index controls for differences in size and shape of the geographic areas for which data are available, and can be calculated using different variables such as employment and population densities, or trip generation rates. The properties of the index are illustrated with simulated artificial data sets and are compared with other similar measures proposed in the existing literature. The index is then applied to the urban structure of four metropolitan areas: Pittsburgh and Los Angeles in the United States; São Paulo, Brazil; and Paris, France. The index is compared with other traditional spatialagglomerationmeasures,suchasglobalandlocalMoran’sI,anddensitygradient estimations.

In recent decades, the prevalence of obesity in America has increased dramatically. Though it has attracted less attention, the demographic composition of the American population also changed during this period. We decompose the increase in the average body mass index of the American population over 30 years and show that demographic changes explain a statistically significant but economically marginal amount of the change. Instead, the rise in average obesity is best explained by increases in BMI within demographic groups. Furthermore, our results indicate that groups' experiences have been heterogeneous with younger women experiencing especially large gains in weight. We uncover some evidence consistent with the hypothesis that this can be at least partially attributed to increased labor force participation.

Children, particularly when young, require care. Parents often outsource childcare but childcare shocks, such as illness, can return childcare responsibilities back to parents unexpectedly and may cause income loss. Teachers' strikes are an exogenous childcare shock that shift childcare to parents. A teachers' strike-day reduces family income by at least one quarter of a day's income suggesting families are unable to insure perfectly against childcare shocks. For the average and median family, fathers face higher income costs from teachers' strikes than mothers. Finally, fathers and mothers are also more likely to face income costs when sons are affected than daughters.

It is impossible to directly visualize all of the items of a large dataset at once. Often, the number of items exceeds the number of pixels. Since direct representation is not a reliable option, a variety of methods have been developed for dealing with indirect representation. Such methods include clustering and intelligent filtering to reduce the number of items being considered in the first place. However, these techniques impose a high computational and interpretation costs. The alternative is to employ techniques to directly deal with the over-plotting that occurs. that occurs when there are too many items to display without overlapping. Over-plotting techniques include alpha composition, color weaving and selective plotting. Each of these has variants that yield different cognitive or computational optimizations. Unfortunately, most advanced over-plotting techniques are wrapped up in specific libraries. Experimenting with different techniques is cumbersome because they have not been provided with uniform interfaces or in a single runtime. This paper presents Abstract Rendering, a recasting of the rendering process that enables concise expression of many over-plotting techniques. Furthermore, the Abstract Rendering formulation yields efficient execution strategies. Combined, it is practical to explore different overplotting techniques for large data without requiring significant alteration to existing pipelines.

This study applies principles from the theory of household life cycles to the study of early childhood mortality in the population of the Northern Orkney Islands, Scotland. The primary hypothesis is that unfavorable household economic conditions resulting from changes in household demographic composition increase the risk of death for children under the age of 5 years because of limited resources and intra-household competition. We apply Cox proportional hazards models to nearly 5,000 linked birth and death records from the Northern Orkney Islands, Scotland, from the period 1855 to 2001. The dependent variable is the child's risk of death before age 5. Findings suggest that children in households with unfavorable age compositions face higher risk of death. This elevated risk of death continues once heterogeneity among children, islands, and households is controlled. Results also show differential risk of death for male children, children of higher birth orders, and twin births. The analyses present evidence for intra-household competition in this historic setting. The most convincing evidence of competition is found in the effects of household consumer/producer ratios and twinning on child mortality risks.

Using 2000 U.S. Census data we illustrate the importance of accounting for household specialization in lesbian couples when examining labor supply differences between heterosexual married and partnered lesbian women. Specifically, we find the labor supply gap is substantially larger between married women and partnered lesbian women who specialize in market production (primary earners) than between married women and partnered lesbian women who specialize in household production (secondary earners). Applying a semi-parametric decomposition approach we show that controlling for children significantly reduces the gap between married women and secondary lesbian earners both in terms of the decision to remain attached to the labor market (the extensive margin) and annual hours of work conditional on working (the intensive margin). Further, the effect of controlling for children primarily reduces the percentage of secondary lesbian earners working extremely high annual hours.

There are a significant number of H-1B immigrants who are employed through specific outsourcing companies upon which United States’ companies then contract with for contract labor. According to Thibodeau & Machlis (2012), the top United States companies that employ H-1B immigrants are “offshore outsourcing companies”, including Cognizant and Infosys, which each employ approximately 5000 H-1B immigrants who are then contracted out to other United States companies for specific jobs. The fact that most H-1B immigrants are not employed directly by the United States companies for which they are working, implies that there will be some discrepancy between the temporary immigrant workers and the native workers in terms of: types of jobs, wage rate, and job security. Therefore, this paper aims to study how the recession of 2008 affected the market for workers in the IT industry, specifically focusing on the number of jobs lost or retained by temporary immigrant workers compared with those of native workers.

Information flooding may occur during an OLAP session when the user drills down her cube up to a very fine-grained level, because the huge number of facts returned makes it very hard to analyze them using a pivot table. To overcome this problem we propose a novel OLAP operation, called shrink, aimed at balancing data precision with data size in cube visualization via pivot tables. The shrink operation fuses slices of similar data and replaces them with a single representative slice, respecting the constraints posed by dimension hierarchies, until the result is smaller than a given threshold. We present a greedy agglomerative clustering algorithm that at each step fuses the two slices yielding the minimum increase in the total approximation, and discuss some experimental results that show its efficiency and effectiveness.

We provide new estimates of migrant flows into and out of America during the Age of Mass Migration at the turn of the twentieth century. Our analysis is based on a novel data set of administrative records covering the universe of 24 million migrants who entered Ellis Island, New York between 1892 and 1924. We use these records to measure inflows into New York, and then scale-up these figures to estimate migrant inflows into America as a whole. Combining these flow estimates with census data on the stock of foreign-born in America in 1900, 1910 and 1920, we conduct a demographic accounting exercise to estimate out-migration rates in aggregate and for each nationality-age-gender cohort This exercise overturns common wisdom on two fronts. First, we estimate flows into the US to be 20% and 170% higher than stated in official statistics for the 1900-10 and 1910-20 decades, respectively. Second, once mortality is accounted for, we estimate out-migration rates from the US to be around .6 for the 1900-10 decade and around .75 for the 1910-20. These figures are over twice as high as official estimates for each decade. That migration was effectively a two-way flow between the US and the sending countries has major implications for understanding the potential selection of immigrants that chose to permanently reside in the US, their impact on Americans in labor markets, and institutional change in America and sending countries.

U.S. unemployment still lingers above recent historical averages, but some areas are doing much better than others. In particular, areas with higher average education levels have higher employment rates and lower unemployment rates, and this is not just true for highly educated individuals. By examining the ‘externalities’ of education, John Winters finds that less educated workers especially benefit from the education received by their neighbors and co-workers.

Climate change and metropolitan areas are mutually connected. On the one hand, metropolitan areas contribute to climate change through production of greenhouse gases emissions. On the other hand, it is likely that climate-generated stressors will affect many aspects of life in metropolitan areas. Such impact-feedback interactions can change residential mobility patterns, and thereby alter spatial patterns in metropolitan areas. This paper focuses on climate impacts and corresponding transformations in household mobility and housing consumption behaviors at three spatial scales.

Almost universally, self-reported ethnicity and race are treated as exogenously given traits that are not subject to change. But in cases where ethnicity and race are subjective, does self-identification respond to economic incentives? This paper provides a first examination of this question by linking data on ethnic and racial self-identification with changes in state-level affirmative action policies in higher education, contracting, and employment. Consistent with a diminished incentive to identify as an ethnic and racial minority, we find evidence that individuals from underrepresented groups are less likely to identify once affirmative action policies have been banned. To our knowledge, this is the first study to investigate whether ethnic and racial self-identification responds to local economic and social conditions in the United States. As such, it has broad implications for understanding the impact of affirmative action policies and the emerging literature on the construction of race and ethnicity.

Ordinary least squares linear regression models are frequently used to analyze and model spatial phenomena. These models are useful and easily interpreted, and the assumptions, strengths, and weaknesses of these models are well studied and understood. Regression models applied to spatial data frequently contain spatially autocorrelated residuals, however, indicating a misspecification error. This problem is limited to spatial data (although similar problems occur with time series data), so it has received less attention than more frequently encountered problems. A method called spatial filtering with eigenvectors has been proposed to account for this problem. We apply this method to ten real-world data sets and a series of simulated data sets to begin to understand the conditions under which the method can be most usefully applied. We find that spatial filtering with eigenvectors reduces spatial misspecification errors, increases the strength of the model fit, frequently increases the normality of model residuals, and can increase the homoscedasticity of model residuals. We provide a sample script showing how to apply the method in the R statistical environment. Spatial filtering with eigenvectors is a powerful geographic method that should be applied to many regression models that use geographic data.

The static elasticity of substitution (SES) between housing and non-housing consumption is important not only in understanding housing demand, but also in asset pricing because housing consumption influences the marginal utility of numeraire consumption. Previous estimates of the elasticity are low when micro data are used but high when macro data are used. We use aggregate time-series data to estimate SES by allowing for non-homotheticity in preferences. We obtain lower estimates of SES ranging from 0.4 to 0.9 when we allow for non-homotheticity than when we maintain homotheticity assumption. Homotheticity is rejected in the way that consumption share of housing decreases as income grows and as income is derived more from employment than from investments. We also obtain low IES ranging from 0.05 to 0.14. JEL classification: G12, D12, R21, R30.