This study introduces a new measure of urban centrality. It identifies distinct urban structures from different spatial patterns of jobs and resident population. The proposed urban centrality index constitutes an extension to the spatial separation index (MIDELFART-KNARVIK et al., 2000). It is suggested that urban structure should be more accurately analyzed when considering a centrality scale (varying from extreme monocentricity to extreme polycentricity) rather than being considered as a binary variable (monocentric or polycentric). The proposed index controls for differences in size and shape of the geographic areas for which data is 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 in simulated artificial data sets. Simulation results for hypothesized urban forms are compared to other similar measures proposed by previous literature. The index is then applied to the urban structure of four different Metropolitan Areas: Pittsburgh and Los Angeles in the United States, São Paulo in Brazil and Paris, France, comparing to other traditional spatial agglomeration measures such as global and local Moran’s I, and density gradients estimations.

Research on college-educated women suggests that their strategies in navigating work and family and resulting life course outcomes have changed during the course of the 20th century. However, data and studies on the life course of women with postgraduate degrees are sparse. We use the CPS June supplement on Marriage and Fertility to understand how the timing of first birth, levels of childlessness and parity outcomes of highly educated women have changed for female birth cohorts 1921-1975. We also examine trends in median age of graduate students and the proportion of graduate students living with children. We find that among women with postgraduate education, the median age at first birth increased over cohorts and leveled out at age 34 for birth cohort 1956-60. Compared to other educational groups, childlessness is highest among highly educated women and peaks for the same birth cohort at ~ 35%, but decreases again in cohorts born after 1960.

This paper takes the risk of college participation into context when evaluating the return to college education. College dropout and a higher permanent income shock for those who graduate from college accounts for 51% of the excess return to college education. Using a simple risk premium approach, I reconcile the observed high average returns to schooling with relatively low attendance rates. A high dropout risk has two important effects on the estimated average returns to college education: via selection bias and via risk premium.

This study examined stay-at-home father households and their members. Different definitions of stay-at-home father households were constructed based on the income and the distribution of spouses working hours. Using social exchange theory, gender roles theory, and feminist perspective, definitions were tested regarding the likelihood that the husband was the primary caregiver and the wife was the primary breadwinner. To answer this question, data from the Current Population Survey of 1968 to 2008 was used. By examining 41 years of data, I was able to follow trends in the characteristics of stay-at-home father households over time. Using descriptive statistics and logistic regression, results showed the probability of a household becoming a stay at home father household increased when: (a) there were fewer and older children in the household, as compared to stay-at-home mother households; (b) the husband suffered from some disability that limited his ability to work; and, (c) the wife had greater earnings or earning potential than her husband. Results also indicate that stay-at-home father households are growing in their relevance and that fathers are more likely to become stay-at-home fathers only when the family demands are lower in terms on number and age of children. In sum, stay-at-home father households experienced tremendous change in the last 41 years. This change was threefold. First, stay-at-home father households almost tripled in their frequency within the U.S. population. Second, stay-at-home father households shifted from being low-income, low-education and old parental-age households that were forced into such a household paid-work structure because of inability of the father to work or find work, to households that, in greater numbers, were choosing to become stay-at-home father households. The third and related shift was a change in the characteristics of stay-at-homeivfather households. Stay-at-home father households, and especially those who chose to become such households, became more similar in all characteristics (income, number of children, age of children, number of children 5 years of age and under, age of spouses and education of spouses) to dual-earner households and especially to stay-at-home mother households.

The preservation of riparian zones and other environmentally sensitive areas has long been recognized as one of the most cost-effective methods of managing stormwater and providing a broad range of ecosystem services. In this research, a cellular automata (CA)Markov chain model of land cover change was developed to integrate protection of environmentally sensitive areas into urban growth projections at a regional scale. The baseline scenario is a continuation of the current trends and involves only limited constraints on development. The green infrastructure (GI) conservation scenario incorporates an open space conservation network based on the functional boundaries of environmentally sensitive areas. It includes variable buffer widths for impaired streams (as identified on the USEPA 303d list for stream impairment), 100-year floodplain, wetlands, urban open space and steep slopes. Comparative analysis of each scenario with landscape metrics indicated that under the GI conservation scenario, the number of urban patches decreased while the extent of interspersion of urban land with green infrastructure patches increased leading to improved connectivity among open space features. The analysis provides a quantitative illustration of how our process contributes towards achieving urban planning objectives while incorporating green infrastructure.

The health status of Mexican Immigrants has important repercussions on their economic well-being and that of their families and the areas they live. There is extensive scientific literature documenting the health status of Mexican immigrants but not its causes. Acculturation, assimilation to the labor market, and changes in the composition of the household generate new behaviors that might have a direct effect on the immigrant’s health status. In this study, I analyze the determinants of the health status of this large segment of the US population from a time use perspective. In particular, I examine how their eating and physical activity habits vary with time since arrival in the U.S.

The Personal Responsibility and Work Opportunity Act (PRWORA) of 1996 restricted federal funding of health insurance for legal immigrant children that have lived in the United States for five years or less. The State Children's Health Insurance Program (SCHIP), enacted in 1997, was subject to this restriction. This paper explores health insurance coverage for children in immigrant families compared to those in native-born families prior to and after major policy changes. A difference-in-differences method was used to compare changes in health insurance coverage using data from the Current Population Survey Annual Social and Economic Supplement (CPS-ASEC). Samples were compared based on nativity and whether the state of residence opted to use state funds to provide health insurance for immigrant children ineligible for federal funding. Policies that differentially target immigrant populations can create major barriers to health care access for an already vulnerable population.

This paper presents a new picture of the labor market effects of technological change in pre-WWII United States. I show that, similar to the recent computerization episode, the electrification of the manufacturing sector led to a"hollowing out" of the skill distribution whereby workers in the middle of the distribution lost out to those at the extremes. To conduct this analysis, a new dataset detailing the task composition of occupations in the United States for the period 1880-1940 was constructed using information about the task content of over 4,000 occupations from the Dictionary of Occupational Titles (1949). This unique data was used to measure the skill content of electrification in U.S. manufacturing. OLS estimates show that electrification increased the demand for clerical, numerical, planning and people skills relative to manual skills while simultaneously reducing relative demand for the dexterity-intensive jobs which comprised the middle of the skill distribution. Thus, early twentieth century technological change was unskill-biased for blue collar tasks but skill-biased on aggregate. These results are in line with the downward trend in wage differentials within U.S. manufacturing up to 1950. To overcome any threat to the exogeneity of the electricity measure, due for example to endogenous technological change, 2 instrumental variable strategies were developed. The first uses cross-state differences in the timing of adoption of state-level utilityregulation while the second exploits differences in state-level geography that encouraged the development of hydro-power generation and thus made electricity cheaper. The results from these regressions support the mainconclusions of the paper.

There is a long-standing debate among academics about the effect of immigration on native internal migration decisions. If immigrants displace natives this may indicate a direct cost of immigration in the form of decreased employment opportunity for native workers. Moreover, displacement would also imply that cross-region analyses of wage effects systematically underestimate the consequences of immigration. The widespread use of such area studies for the US and other countries makes it especially important to know whether a native internal response to immigration truly occurs. This paper introduces a microsimulation methodology to test for inherent bias in regression models that have been used in the literature. We show that some specifications have built biases into their models, thereby casting doubt on the validity of their results. We then provide a brief empirical analysis with a panel of observed US state-by-skill data. Together, our evidence argues against the existence of native displacement. This implies that cross-region analyses of immigrations effect on wages are still informative.

This paper offers a theoretical framework to help study the effects of offshoring on industrial productivity, employment and wage inequality. I then test the implication of the model, using U.S. industry trade data that is matched to individual worker data from the Current Population Survey. I extend a multi-sector version of Bernard, Redding and Schott (2007) and explicitly model vertical fragmentation of production tasks across countries as motivated by Grossman and Rossi-Hansberg (2008). Wage inequality across and within industries arises due to an imperfect labor market with a notion of fairness, where wages depend on the productivity of the firm they work in. A fall in offshoring cost increases domestic aggregate productivity and changes the labor composition across and within industries. Labor demand and relative wages decrease for tasks that are easier to offshore. The empirical findings are consistent with models predictions. Offshoring, especially to low-wage countries, significantly increases the probability of workers switching occupations and industries. Offshoring also raises the risk of becoming unemployed and the duration of the unemployment. For occupations with higher offshorability, these switchingand unemployment pressures are stronger. This reallocation also affects wages. One standard deviation increase in offshoring decreases the average weekly wage about 5 percent (or $37) for an occupation with average offshorability, and offshoring can explain about a quarter of the increasing in wage inequality from 1989 to 2005.

Demography’s task is to document, analyse and theorize population dynamics, the ways in which spatially-defined populations grow, shrink or redistribute themselves over time. Historical demographers, in particular, strive to model population processes that spread across space and unfold over time, and this volume explores innovative ways in which demographers are doing so. The eight chapters that follow utilize newly-available sources of historical data and implement novel methods to assess the role of time and space and their intersection in core demographic, historical and sociological theories.

Historically, the Mexican origin population in the United States was highly geographically concentrated in a small number of states. In the 1990s, a significant proportion of the total Mexican origin population began migrating outside of these states, to new destinations of immigrant settlement. This research examines how different types of immigrant destinations influence the school non-enrollment outcomes of Mexican origin adolescents. I use the 2005-2007 American Community Survey to compare the rates of school non-enrollment of Mexican origin 15-17 year-olds in new and traditional destination states with those of non-Hispanic whites. I evaluate whether differences in non-enrollment between Mexican origin adolescents in these destinationsand non-Hispanic whites can be attributed to variation in individual and household characteristics related to destination selection and the process of assimilation. I show that Mexican origin adolescents in new destinations have higher rates of school nonenrollment than both their peers in traditional destinations and non-Hispanic whites.However, there is no evidence of a net new destination effect on Mexican origin nonenrollment after immigrant generation and parental educational attainment are controlled. Contrary to the predictions of the segmented assimilation theory, the native-born Mexican origin population in traditional destinations does not exhibit higher than average rates of school non-enrollment. In fact, native-born Mexican origin adolescents in traditional destinations experience an enrollment advantage over non-Hispanic whites at comparable levels of parental education. Mexican origin adolescents with the leastamount of exposure to the United States, the 1.25 generation, is the most at risk of nonenrollmentrelative to non-Hispanic whites, regardless of the destination where they reside. Many of these adolescents, however, are likely teenage labor migrants who never enroll in schools in the United States.

The labor movement in the United States entered into crisis in the mid-1970s as the economy moved from a manufacturing to a service base. The decline of the manufacturing sector has led to a significant reduction in union participation and living standards from their peak in the 1960s to their current low point. The current economic crisis may produce changes in union structures. The future of the U.S. labor movement depends on its capacity to recruit new members, particularly among the women, young people, and ethnic groups that have recently entered the labor market.

In a recent paper in this Journal, Dynarski (2008) used data from the 1-percent 2000 Census Public Use Microdata Sample (PUMS) files to demonstrate that merit scholarship programs in Georgia and Arkansas increased the stock of college-educated individuals in those states. This paper replicates the results in Dynarski (2008) but we also find important differences in the results between the 1-percent and 5-percent PUMS, especially for women. We also demonstrate that the authors use of clustered standard errors, given the small number of clusters and only two policy changes, severely understates confidence intervals.

This short article introduces two techniques of generating cross-tabulations in small areas (for example, block groups or tracts) for which only univariate distributions are available. These techniques require either a microsample or a cross-tabulation from a larger geographic area (for example, a Public Use Microdata Area). One technique uses hill-climbing algonthms, and the other is based on iterated proportional fitting. In this article, we identify the general charactenstics of both techniques. We present and evaluate an example (generating cross-tabulations of households by housing value and number of children enrolled in public school), bñefiy discuss extensions of both techniques to synthetic population construction, and test the synthetic populations by comparing the estimated microsamples with the actual population.

The conclusions drawn by those who study gentrification have shifted in recent years; in the 1980s it was assumed that gentrification was harmful to low-income residents of up-and- coming neighborhoods (Marcuse 1986, Ley 1981). More recently, several studies have shown that gentrification does not harm the original residents of a neighborhood and, in fact, might improve their economic well-being (Freeman 2004, Freeman & Braconi 2005, Vigdor 2002). This thesis expands upon that work by using data from the 2000 Census and 2005-2009 American Community Survey (ACS), as well as the March Supplement of the Current Population Survey (CPS) to determine whether gentrification had a positive effect on the labor market outcomes of low-skilled residents. Using probit and OLS models, I find the relationship between gentrification and several labor market outcomes for low-educated adults to be negative and significant. The magnitudes of these effects, however, are very small; although living in a gentrified neighborhood is associated with a small decrease in labor supply, as measured by labor force participation, hours worked per week, weeks worked in the last year, estimates may be biased by the Great Recession. Further research is necessary to determine whether gentrification per se was responsible for these outcomes.

The large and significant relationship between city population and wages has been well established in the agglomeration literature, and the influence of selection effects on this wage premium is important. This paper contributes new evidence to the understanding of this premium by using two different data sets of siblings in order to estimate the agglomeration premium while controlling for unobserved heterogeneity with a family-specific fixed effect. The inclusion of a familial fixed effect into the regression framework makes the city-size wage premium insignificant, and there is a large return to a variable representing the correlation between familial ability and residence in an urban area in all of the data sets used in the analysis. The results are discussed in the context of the existing literature, and they demonstrate the importance of family background and selection effects for interpreting the agglomeration premium, which is small in the fixed effects regression.