In this paper we try to understand how racial segregation of the geographic spaces of three major US cities (New York, Los Angeles and Chicago) affect the mobility patterns of people living in them. Collecting over 75 million geo-tagged tweets from these cities during a period of one year beginning October 2012 we identified home locations for over 30,000 distinct users, and prepared models of travel patterns for each of them. Dividing the cities geographic boundary into census tracts and grouping them according to racial segregation information we try to understand how the mobility of users living within an area of a particular predominant race correlate to those living in areas of similar race, and to those of a different race. While these cities still remain to be vastly segregated in the 2010 census data, we observe a compelling amount of deviation in travel patterns when compared to artificially generated ideal mobility. A common trend for all races is to visit areas populated by similar race more often. Also, blacks, Asians and Hispanics tend to travel less often to predominantly white census tracts, and similarly predominantly black tracts are less visited by other races.

The determinants of public opinion toward public transit is a little-researched topic, though a better understanding of what makes consumers willing to support transit may reveal which attributes of transit consumers value most. One determinant of peoples will-ingness to support investments in mass transit may be the price of fuel for transits principal competition, the private automobile. In this paper, I examine the relationship between the cost of gasoline and stated willingness to invest public money in mass transit improvements. I hypothesize that fuel price volatilityin addition to price itselfis a determinant of support for more mass transit funding, controlling for other factors. As the price of gasoline becomes more uncertain, the public should, all else equal, support investment in mass transportation, a form of transportation that may provide some measure of protection from the price of fuel. Results suggest a strong effect of price volatility on consumers willingness to support transit expenditures.

By capturing the actual costs associated with meeting basic needs, a new tool can measure the economic security of grandparents raising grandchildren. In 2011, 7 million U.S. grandparent heads of households had a grandchild living with them. Approximately 3 million had primary responsibility for meeting their grandchildren's basic needs. In New England alone, 237,000 grandparents had grandchildren living with them, and 77,000 were the primary caregivers.[1] But grandparents over 65 often face financial challenges supporting an additional dependent on a retirement income without financial help from the child's parents. Financial hardships can have an impact on the emotional, mental, and physical well-being of both grandparent and grandchildren.[2] Nationally, more than one in four grandparent caregivers lives in overcrowded conditions, more than one in six pays over half their income in rent, and 60 percent who qualify for rent subsidies do not receive any.[3] As for the grandchildren, although 48 percent of those living with grandparents experience some food insecurity, only about 43 percent receive food stamps.

Individual outcomes are highly correlated with group average outcomes, a fact often interpreted as a causal peer effect. Without covariates, however, outcome-on-outcome peer effects are vacuous, either unity or, if the average is defined as a leave-out mean, determined by a generic intraclass correlation coefficient. When pre-determined peer characteristics are introduced as covariates in a model linking individual outcomes with group averages, the question of whether peer effects or social spillovers exist is econometrically identical to that of whether a 2SLS estimator using group dummies to instrument individual characteristics differs from OLS estimates of the effect of these characteristics. The interpretation of results from models that rely solely on chance variation in peer groups is therefore complicated by bias from weak instruments. With systematic variation in group composition, the weak IV issue falls away, but the resulting 2SLS estimates can be expected to exceed the corresponding OLS estimates as a result of measurement error and for other reasons unrelated to social effects. Research designs that manipulate peer characteristics in a manner unrelated to individual characteristics provide the most compelling evidence on the nature of social spillovers. As an empirical matter, designs of this sort have mostly uncovered little in the way of socially significant causal effects.

Globally, the twenty-fi rst century has witnessed tremendous progress in the application of demographic methods to the study of labor force dynamics. Changes in population structure have also led to changes in labor force trends, sizes, and compositions both in the African continent and in the other regions of the world. Ruzicka (1973) wrote that the growth in the labor force sizes, together with changes in labor force age-sex structure, is closely linked with the demographic characteristics of population in any country. Changing dynamics and patterns of labor force participation in Africa, including changing demographic structures, are a source of concern for the present and future sizes and structures of the labor force in the continent. Population characteristics such as age structure, age-sex composition, population’s socioeconomic characteristics-educational attainments, population well-being, nutritional status, and so on-have enormous infl uence on labor force sizes, structures, and levels. Other important issues, such as changes relating to transition to prime age, quantum and proportion, female labor force, women as primary breadwinners, underage labor, elderly labor, and disruptions to prime ages (because of HIV/AIDS and migration), have great implications for the labor force sizes and structures.

This report presents new research on the scope of federally-supported employment in the private economy and shows how, using our over $1.3 trillion dollars in federal purchasing, the President of the United States can place over twenty million Americans on a pathway to the middle class.

Following the reauthorization of the Childrens Health Insurance Program (CHIP) in 2009, fifteen states raised their CHIP income eligibility thresholds to further reduce uninsurance among children. We examined the impact of these expansions on uninsurance, public insurance, and private insurance among children who became newly eligible for CHIP after the expansions. Using a difference-in-differences approach, we estimated that the expansions reduced uninsurance by 1.1 percentage points among the newly eligible, cutting their uninsurance rate by nearly 15 percent. Public coverage increased by 2.9 percentage points, with variations in take-up among the states. A better understanding of these state-level differences in take-up could inform efforts to enroll children who remain uninsured but are eligible for CHIP. CHIP is up for reauthorization in 2015, and further funding will be needed to maintain the program, which provides insurance to children who might not have access to affordable private coverage.

Population aging, growing racial/ethnic diversity, changing family structure, and regional population shifts are changing the U.S. demographic landscape and exacerbating differences between the haves and the have-nots.

Using 2006–2010 American Community Survey data and a multilevel research design, this study examines how the spatial concentration of African Americans within local labor markets is associated with the probability of self-employment and job earnings from business ownership. We find a negative relationship between the spatial concentration of African Americans and their business ownership in Miami, but not in Atlanta. In Detroit, a higher percentage of US-born African Americans in the local labor market is actually associated with a higher probability of business ownership. The relationship between the percentage of African Americans and job earnings also differs across the three study areas. These results suggest that the relationship between racial/ethnic concentration and African American entrepreneurship is highly contingent on the economic conditions and demographic composition of local labor markets.

Food desert studies attempt to identify geographic areas in which people lack access to sources of healthy food. While academic and policy research often defines access to food stores using simple Euclidean distance or road network metrics, dense urban areas with large public transit systems call for more sophisticated methods of determining access. It is particularly important to understand the level of access the transit-dependent population has to healthy food vendors, as their mobility is largely constrained by the routes and scheduling of their local public transportation system. In this study, we analyze public transit access to supermarkets in Cincinnati, Ohio. Using General Transit Feed Specification (GTFS) data from the Southwest Ohio Regional Transit Authority (SORTA) and the Transit Authority of Northern Kentucky (TANK) and custom ArcGIS tools, we calculate the time it takes to travel from each Cincinnati census block to its nearest supermarkets at different times of the day. This transit-travel-time matrix allows us to investigate food deserts that change shape based on the time of day considered and to calculate the temporal variability in block-level access. Also, by combining this time-dependent analysis with census data on race, income, and age, we search for disparities in average levels of accessibility. The results of this analysis identify the areas and subpopulations in Cincinnati with the greatest need for improved access to healthy food stores and will demonstrate how schedule-dependent transportation can be factored into measures of accessibility. Ultimately, this study paints a more complete and realistic picture of food deserts in Cincinnati and helps policy-makers better target interventions aimed at mitigating their effects.

Attracting highly educated immigrants- especially scientists and engineers- is a potentially effective economic growth-promoting strategy. This paper evaluates the contribution of foreign-born scientists and engineers to the wage and employment growth of native-born workers. First, we analyze the effects of an increase in the number of foreign H-1B workers across U.S. cities and Federal Skilled Worker Program immigrants across Canadian cities. Second, we compare the effects of attracting scientists and engineers as a growth strategy against alternatives such as pursuing fast-growing industries and expanding tertiary education institutions. We conclude by arguing that the H-1B program can explain a quarter of the wage growth of U.S. college-educated workers from 1990 to 2010, and that the currently proposed enlargement of the program could generate an additional 2 percentage points of wage growth for highly educated natives over the next 20 years.

Objective. This study aimed to evaluate the incidence of cervical cancer by nativity [United States (US) versus non-US], neighborhood socioeconomic status and ethnic enclave among Hispanics and Asians in California. Methods. Using data from the California Cancer Registry, information on all primary invasive cervical cancer (Cca) patients diagnosed in California from January 1, 1990 through December 31, 2004 was obtained. We analyzed the influence of enclave, socioeconomic status and nativity on Cca incidence. Results. Among the 22,189 Cca cases diagnosed between 1990 and 2004, 50% were non-Hispanic white, 39% Hispanic and 11% Asian women, and 63% US-born. Seventy percent of the Cca cases were squamous cell carcinoma, 19% adenocarcinoma and 11% other histologies. Higher incidence of Cca was observed in high enclave (76%) and low socioeconomic status (70%) neighborhoods. By ethnic group, US-born women showed lower rates of squamous cell carcinoma compared to foreign-born women. Hispanics living in low socioeconomic and high enclave had 12.7 times higher rate of Cca than those living in high socioeconomic, low enclave neighborhoods. For Asian women incidence rates were 6 times higher in the low socioeconomic, high enclave neighborhoods compared to those living in high socioeconomic, low enclave neighborhoods. Conclusion. More targeted outreach to increase Pap smear screening and human papilloma virus vaccination for women living in high enclave neighborhoods can help decrease the incidence of Cca in these groups of women.

Objective: Previous authors have answered "how many children in immigrant families are uninsured"; we do not know the inverse: "how many uninsured children live in immigrant families." This paper will show the total contribution of having an immigrant parent to the uninsured rate for children in the United States. Data Source: Secondary data from the 2008-2010 American Community Survey. Study Design: Descriptive analyses and a multinomial probit model illustrate the relationship between immigration history and insurance status. Principal Findings: In 2010, almost half (42%) of uninsured children lived in an immigrant family. State-level estimates range from a low of 4% in Maine to a high of 69% in California. Two thirds (69%) of these uninsured children are citizens; furthermore, 39% are Medicaid eligible, 39% are not eligible for Medicaid, and eligibility is unknown for the 21% that are low-income, noncitizens. Conclusions: In 2000, a third of all uninsured children lived in immigrant families. In 2010, 42% of all uninsured children lived in immigrant families. Initiatives to expand coverage or increase Medicaid and CHIP uptake will require decision makers to develop new policy and outreach approaches to enroll these children so they do not fall further behind.

No veteran who has risked his or her life to protect our homes should return to find that they are not able have their own. For their sacrifice, it is imperative that we ensure our veterans have access to safe, affordable, and secure housing. This can be particularly challenging in rural America due to vast geographies, limited resources, and less social service infrastructure. The overall demographic picture of veterans will undergo major shifts in the coming years. As two wars overseas wind down, more veterans will be coming home. Returning to all corners of our nation, they will have housing needs to be addressed. The demographic changes associated with the baby boom generation and the overall graying of America will also shape veterans housing needs. The aging veteran population will have its own unique challenges. Ensuring that their housing needs are met is the least we can do to thank them for their service to this country. Veterans Are More Prevalent in Rural America With more veterans per capita than either suburban . . .

This paper documents a little-noticed feature of US labor markets—very large variation in the labor supply of married women across cities. We focus on cross-city differences in commuting times as a potential explanation for this variation. We start with a model in which commuting times introduce non-convexities into the budget set. Empirical evidence is consistent with the model’s predictions: Labor force participation rates of married women are negatively correlated with the metropolitan area commuting time. Also, metropolitan areas with larger increases in average commuting time in 1980–2000 had slower growth in the labor force participation of married women.

The U.S. Census Bureau's American Community Survey (ACS) is the foundation of social science research, much federal resource allocation and the development of public policy and private sector decisions. However, the high uncertainty associated with some of the ACS's most frequently used estimates can jeopardize the accuracy of inferences based on these data. While there is high level understanding in the research community that problems exist in the data, the sources and implications of these problems have been largely overlooked. Using 2006{2010 ACS median household income at the census tract scale as the test case (where a third of small-area estimates have higher than recommend errors), we explore the patterns in the uncertainty of ACS data. We consider various potential sources of uncertainty in the data, ranging from response level to geographic location to characteristics of the place. We nd that there exist systematic patterns in the uncertainty in both the spatial and attribute dimensions. Using a regression framework, we identify the factors that are most frequently correlated with the error at national, regional and metropolitan area scales, and find these correlates are not consistent across the various locations tested. The implication is that data quality varies in dierent places, making cross-sectional analysis both within andacross regions less reliable. We also present general advice for data users and potential solutions to the challenges identi ed.

Extensive research has demonstrated that the period from birth through age three is critical to a childs development. Depending on circumstances, children can begin with a great start, or they can begin to fall behind, and some of the children who fall behind early will never catch up. Early investment can help to ensure that all children get off to a great start. An extensive research base demonstrates that early investment is far more effective at improving outcomes for at-risk children than later remediation. Early investment can provide lifelong benefits to children, including improving their health and increasing their education attainment and future earnings. In addition, research has demonstrated that early investment pays substantial returns to taxpayers through lower expenditures on special education, grade retention, and welfare programs, through increased tax payments, and through a reduction in crime. This report identifies places where targeted investments are most likely to produce the best outcomes for Michigans children and for the state as a whole.