The demographic behaviors of one generation, including marriage, divorce, fertility, and survival, create the population of families in which the next generation of children is raised. Assortative mating between men and women with varying socioeconomic characteristics is a key mechanism in establishing the families of the next generation, but differential fertility, child and parent survival, marital disruption, and parents socioeconomic mobility modify these marriage patterns. This article examines the demographic mechanisms through which family backgrounds are created. It presents the mathematical links between marriage patterns and the joint distribution of parents characteristics when their children are born and later in their lives. It illustrates these relationships using data on educational assortative mating, fertility, and mortality in the United States. Although the educational attainments of husbands and wives are strongly associated, patterns of differential fertility reinforce this relationship, resulting in an even stronger association between the educational attainments of mothers and fathers.

In this study examining data from 1850 to 1860, the most important variables explaining emigration and persistence in Peacham are wealth, church membership, and, to a lesser extent, occupation. For Albany, the most important variables are age, length of residence, presence in the agricultural census, and kinship. This tells us a lot about each of these communities, and perhaps suggests some of the effects of growing old.

The employment rate of black men, and particularly of low-skill black men, fell precipitously from 1960 to 2000. At the same time, the incarceration rate of black men rose markedly. This paper examines the relation between immigration and these trends in black employment and incarceration. Using data drawn from the 1960-2000 U.S. Censuses, we find a strong correlation between immigration, black wages, black employment rates, and black incarceration rates. As immigrants disproportionately increased the supply of workers in a particular skill group, the wage of black workers in that group fell, the employment rate declined, and the incarceration rate rose. Our analysis suggests that a 10-percent immigrant-induced increase in the supply of a particular skill group reduced the black wage by 3.6 percent, lowered the employment rate of black men by 2.4 percentage points, and increased the incarceration rate of blacks by almost a full percentage point.

Change detection on spatial data is important in many applications, such as environmental monitoring. Given a set of snapshots of spatial objects at various temporal instants, a user may want to derive the changing regions between any two snapshots. Most of the existing methods have to use at least one of the original data sets to detect changing regions. However, in some important applications, due to data access constraints such as privacy concerns and limited data online availability, original data may not be available for change analysis. In this paper, we tackle the problem by proposing a simple yet effective model-based approach. In the model construction phase, data snapshots are summarized using the novel cluster-embedded decision trees as concise models. Once the models are built, the original data snapshots will not be access anymore. In the change detection phase, to mine changing regions between any two instants, we compare the two corresponding cluster-embedded decision trees. Our systematic experimental results on both real and synthetic data sets show that tour approach can detect changes accurately and effectively.

We present four facts and a model explaining the rise of the service economy. First, the rising share of services in output is a recent phenomenon, starting around the mid-20th century. Second, it re ‡ects increases in both the relative price and relative quantity of services to commodities. Third, this rising share is entirely explained by the surge of skill-intensive services, and is contemporaneous with the increases in the relative quantity of skilled labor and the skill premium. Finally, individual services follow a distinct product cycle as an economy grows. They start being provided as market services, but are later produced at home with the purchase of manufactured intermediate inputs and durable goods. In our model, agents make decisions between the market and home provision over a continuum of wants that are satiated sequentially. The disutility of public consumption and economies of scale (in the use of specialized capital and skills) are the key elements explaining the rich dynamics of the service economy. If skilled labor has a comparative advantage in the production of newer services, the theory explains the late rise in the service economy characterized by rising relative prices and quantities of services, and growth in the relative quantity of skilled labor and the skill premium.

Over the past three decades, the average socioeconomic status of African American males has deteriorated, absolutely and relative to men from other racial and ethnic groups. Despite gains in relative earnings immediately following passage of the Civil Rights Act, the relative earnings of black men . . .

In recent years, the weakness of the canonical support-confidence framework for associations mining has been widely studied. One of the difficulties in applying association rules mining is the setting of support constraints. A high-support constraint avoids the combinatorial explosion in discovering frequent itemsets, but at the expense of missing interesting patterns of low support. Instead of seeking a way to set the appropriate support constraints, all current approaches leave the users in charge of the support setting, which, however, puts the users in a dilemma. This paper is an effort to answer this long-standing open question. According to the notion of confidence and lift measures, we propose an automatic support specification for efficiently mining high-confidence and positive-lift associations without consulting the users. Experimental results show that the proposed method is not only good at discovering high-confidence and positive-lift associations, but also effective in reducing spurious frequent itemsets.

Educational dollars dont go quite as far in some parts of the country as they do in others. Therefore, there is considerable interest in developing a measure of the cost of education that can facilitate cross-state comparisons of school district expenditures and be used to adjust school finance formula. One attractive mechanism for measuring geographic variations in cost is the Comparable Wage index (CWI). A CWI reflects systematic, regional variations in the salaries of workers who are not educators. Provided that those non-educators are similar to educators in terms of age, educational background and tastes for local amenities, a Comparable Wage index can be used to measure uncontrollable variations in the wages paid to educators. Together, the 2000 Census and the Occupational Employment Statistics (OES) survey support the construction of just such an index. The resulting panel of index values measures the wage level for college graduates in all parts of the US for the years 1997 through 2003 and reveals substantial variation in purchasing power both across districts and across time. Interestingly the index suggests that in California, New York, Texas, Virginia, Illinois and New Mexico, the educational dollar can stretch at least 40 percent farther in one part of the state than in another. Such inequalities in purchasing power undermine the equity and adequacy goals of school finance formulas. If states were successfully directing additional resources to school districts in high cost environments, then measured inequality within states should fall when differences in purchasing power are taken into account. Instead, cost adjustment widens the spending gap in all but a handful of states

Womens increased involvement in the economy was the most significant change in labor markets during the past century. Their modern economic role emerged in the United States in four distinct phases. The first three were evolutionary; the last was revolutionary. The revolution was a quiet one, not the big-bang type. The evolutionary phases led, slowly, tothe revolutionary phase. First, I will discuss the three evolutionary phases and how they led to the revolutionary phase. I will then describe the changes that occurred during the revolutionary phase and end with whether the revolution, as some have claimed, is stalled or being reversed...

An analysis of nearly two centuries of homicide data that stretch back to the Mexican period for the city and county of Los Angeles reveal a long history of violence in the region, one in which the homicide rate has consistently been higher than that of other major cities. Such factors as national culture, regional differences, demographics, economics, and political structure help to account for the persistence of this pattern. Does this traditional tolerance for violence and homicide in Los Angeles signify a local articulation of what is deemed normal, and could long-term efforts be devised to counter it?

Change detection on spatial data is important in many applications, such as environmental monitoring. Given a set of snapshots of spatial objects at various temporal instants, a user may want to derive the changing regions between any two snapshots. Most of the existing methods have to use at least one of the original data sets to detect changing regions. However, in some important applications, due to data access constraints such as privacy concerns and limited data online availability, original data may not be available for change analysis. In this paper, we tackle the problem by proposing a simple yet effective model-based approach. In the model construction phase, data snapshots are summarized using the novel cluster-embedded decision trees as concise models. Once the models are built, the original data snapshots will not be accessed anymore. In the change detection phase, to mine changing regions between any two instants, we compare the two corresponding cluster-embedded decision trees. Our systematic experimental results on both real and synthetic data sets show that our approach can detect changes accurately and effectively.

Change detection is important in many applications. Most of the existing methods have to use at least one of the original data sets to detect changing regions. However, in some important applications, due to data access constraints such as privacy concerns and limited data online availability, the original data may not be available for change detection.In this work, we tackle the problem by proposing a simple yet effective model-based approach. In the model construction phase, original data sets are summarized using the novel cluster-embedded decision trees as concise models. Once the models are built, the original data will not be accessed anymore. In the change detection phase, to compare any two data sets, we compare the two corresponding cluster-embedded decision trees. Our systematic experimental results on both real and synthetic data sets show that our approach can detect changes accurately and effectively.

The Dominickers were a group of biracial and triracial families who originated before the Civil War and were concentrated in the Florida Panhandle county of Holmes, in a corner of the southern part of the county west of the Choctawhatchee River, near the town of Ponce de Leon. The group was classified as one of the “reputed Indian-White-Negro racial isolates of the Eastern United States” by the United States Census Bureau in 1950 (Beale, “Estimated”). A few scholarly articles have also from time to time made brief mention of their existence (Beale, “American”; also identified on the map accompanying the Price article). The Dominickers are noteworthy because of their persistence from before the Civil War until the 1960s as a group distinct from both the white and black populations around them. However, since descendants of these families have frequently married outside the group and retain few, if any, physical or cultural differences from the surrounding population, it may be debatable whether the descendants still constitute an ethnic group at all. Few facts are known . . .

1821 Guadalajara, Mexico exhibited surprising mobility within its population. Using a set of data from the back-to-back censuses of 1821 and 1822, this study argues that mobility affected almost every individual who lived in Guadalajara during that time period. The methodology used traces individuals who persisted from one year to the next to determine overall rates of mobility. It is my contention that an analysis of short-term stability and change within this set of historically identifiable individuals, families and households reveals a process of mobility that not only has been neglected by studies based on aggregate data, but that is often at variance with the findings of those studies. The evidence shows that a significant portion of the extensive movement of individuals to and from the wards is short term and often cyclical, rather than long term and permanent. Additionally, data sets from 1811-1813 and 1839-1842 are used as control groups to conclude that the mobility in 1821-1822 was not a unique historical event based on circumstances, but an overarching trend throughout the 19th century.

This report examines possible causes of gender segregation and its link to skills shortages in the UK labour market, by investigating young people’s perceptions about work and their preferences for jobs. In particular, the aim is to identify ways of ensuring that young people’s occupational choices are not determined by their gender or stereotypical views about whether females or males can do particular jobs. (i.e. only women can become nursery nurses and only men can become plumbers). Interviews, document analysis, surveys and an intervention were used to collect evidence from young people aged 14 to 19 years. The research focused on particular areas of work - nursery nursing/child-care, elderly care assistants, hairdressing, travel agency, plumbing, mechanics, building and carpentry, being a chef, and telesales, as well as jobs which young people identified as being of interest to them.

The data in this report indicate that theLatino population dispersion across New York Statecan, but does not necessarily, result in good socioeconomicoutcomes. The highest incomes and lowestunemployment rates are found among Latinos inNassau County, while their neighbors in New York Cityfare poorly. Similarly, residence in Albany is associatedwith higher education, a large managerial-professionalwork force, and among the highest incomes for Latinosin the state, although unemployment rates are high andstate agencies generally employ low rates of Latinos. Incontrast, the residents of Western New York are notdoing as well as those living elsewherebut there isalso tremendous variation and some groups, such asCentral and South Americans, seem to have foundgood economic niches in those cities. This report alsoshows that the employment and income benefits ofchanging labor markets do not affect men and womenor immigrants and U.S. born Latinos in the same way.

This paper adjudicates between competing accounts of recent trends in the amount and patterning of occupational age segregation. These accounts rely on narratives about (1) the decline of age-graded mobility, (2) the rise of occupational volatility, and (3) the existence of dual labor markets, in particular increasingly bimodal age distributions in low-skill occupations. Using new log-multiplicative models and related methods, the findings show that overall age segregation declined between 1950 and 1990, which is consistent with the decline of age-graded mobility. Among women, though not among men, the findings show increasingly bimodal age distributions in particular low-skill occupations, which is consistent with a dual labor market. Starting in 1990, age segregation increased among men and may have increased among women, which is consistent with the occupational volatility narrative.