Jani Little

Environmental Hazards, Migration, and Race

This study contributes to our understanding of the association between internal migration patterns and environmentally hazardous facilities, with a focus upon race-specific outmigration at the county-level, nationwide. Among research suggesting inequalities with regard to the social distribution of environmental risk, selective migration is often implied to be a key dynamic leading to differential exposure to proximate environmental hazards. Nonetheless, the models presented here provide no evidence of differential migratory response by race to environmentally hazardous facilities, net of a wide array of socioeconomic controls for labor force opportunity, climate, and demographic structure. Future research should consider these associations at more precise geographies and/or at the individual level.

Sexual Minority Status, Abuse, and Self-Harming Behaviors among Incarcerated Girls

This self-report study of 404 incarcerated youth found extraordinarily high rates of sexual minority status (SMS; i.e., lesbian/gay or bisexual) among the girls, particularly girls of color. Further analyses of the 107 girls 16 and older found that SMS girls reported being the victims of abuse and engaging in self-harming behaviors more than non-SMS (straight) girls. Structural equation models indicated that regardless of sexual identity, abuse was a risk factor for self-harming. This relationship held for physical or sexual abuse and for abuse by family members or people outside the family. Relative to non-SMS girls, SMS girls demonstrated higher rates of sexual abuse, primarily family sexual abuse, which mediated the relationship between SMS and self-harming.

Simplicity and complexity in extrapolatwe population forecasting models

The literature on population forecasting reveals a continuing debate over the relative ex post accuracy of forecasts from simple and complex models. In this paper ex ante projections from simple models are used to evaluate the plausibility of point and interval forecasts from a complex cohort‐component model. This paper compares complex with simple forecasts along two dimensions: simplification of input schedules and of the projection models using those input schedules. Projections of vital rates that are the inputs for a cohort‐component projection may be provided by extrapolations of historical trends in age‐specific rates, relational methods that link future age‐specific rates to a general trend variable and a “standard”; schedule, or time series forecasts of the parameters of a functionally specified model schedule. This paper compares point and interval forecasts of fertility patterns that result from such alternative input specifications. Furthermore, the cohort‐component framework may be replaced b...

Lesbian, Gay, and Bisexual Youth Incarcerated in Delinquent Facilities

The incarcerated population of both youth and adults has long been characterized as disproportionately male, of color, and poor, compared to their numbers in the community (non-incarcerated individuals). When sexual minority status (SMS) or sexual identity has been addressed, it has typically been to sensationalize, demonize, and pathologize incarcerated SMS youth and adults. Our research is the only existing study, of which we are aware, that documents the representation of SMS youth among incarcerated youth. In this state-wide study of 404 girls and boys incarcerated in Ohio, we found it significant that SMS incarcerated youth tended to want more treatment/counseling than their non-SMS counterparts, particularly sexual and physical abuse counseling. There were far fewer differences in these youth based on SMS in terms of their desires for programs that were not treatment/counseling, and in all of these cases, the non-SMS youth wanted the programs more than the SMS youth. The findings stress the need to acknowledge incarcerated youths’ needs that may be accentuated by SMS.

Describing Spatial Structures of Migration

The notion of age structure is a central concept in demography, but the structure of migration, which is inherently spatial, is not commonly presented. The former has been used to develop functional representations of the age patterns of a population or that of a stream of migrants, and it is the basis for the construction of model migration schedules, mathematical expressions such as those that describe the age patterns of migration propensities in Chapter 2. The latter, on the other hand, has no such widely accepted mathematical representation. Yet it clearly exists, as the spatial pattern of the principal U.S. elderly retirement flows depicted in Fig. 3.1 illustrates. We offer such a definition, one that draws on Rogers et al. (2002) and the log-linear specification of the geographer’s spatial interaction model.

Imposing Age and Spatial Patterns

The methods proposed in this chapter are particularly useful when sample sizes are insufficient to provide reliable age-specific migration flows. These procedures rely both on the survey data in question and on known regularities in migration schedules that have been observed within larger geographic regions, within families that exhibit similar migration age patterns, and within the same area over time. We build upon the smoothing methods developed in Chapter 4, and go one step further by proposing procedures that are designed especially to alleviate the diminished reliability in the national survey estimates of migration age structure that are associated with the less populated geographic areas.

Inferring Age and Spatial Patterns

In this chapter, we focus on methods for estimating migration flows in the absence of migration data. To obtain the patterns of interest, we use auxiliary information. Our examples illustrate both current and historical applications of indirect estimation. In Section 6.2, a model for estimating the age composition of out-migration in the United States from aggregate totals of out-migration and population age compositions is presented. This work draws from a recent paper by Little and Rogers (2007). The possibility of using 0–4 year old birthplace-specific population stocks to estimate interregional migration flows is demonstrated in Section 6.3, following work set out in Rogers and Jordan (2004) and Raymer and Rogers (2007). We then apply the methodology to estimate the historical (and completely missing) migration flows for the 1905–1910 and 1915–1920 periods. Finally, in Section 6.4, we focus on the potential for merging migration data obtained from multiple sources. Here, the aim is to follow Frans Willekens’s recommendation that “in order to compile coherent and internally consistent information on migration, data from several sources ought to be combined” (Willekens, 1994, p. 31). Smith, Raymer, and Giulietti (2010), for example, follow this advice by combining census, registration, and survey migration data in England and Wales.

Describing Age Structures of Migration

Empirical schedules of age-specific rates exhibit remarkably persistent regularities in age pattern. Mortality schedules, for example, normally show a moderately high death rate immediately after birth, after which the rates drop to a minimum between ages 10 and 15, then increase slowly until about age 50, and thereafter rise at an increasing pace until the last years of life. Fertility rates generally start to take on nonzero values at about age 15 and attain a maximum somewhere between ages 20 and 30; the curve is unimodal and declines to zero once again at some age close to 50. Similar unimodal profiles may be found in schedules of first marriage, divorce, and remarriage (Rogers, 1986). The most prominent regularity in age-specific schedules of migration is the high concentration of migration among young adults; rates of migration also are high among children, starting with a peak during the first year of life, dropping to a low point during the teenage years, turning sharply upward to a peak near ages 20–22, and then declining regularly thereafter, except for a possible slight hump at the onset of the principal ages of retirement, and/or an upward slope at the oldest ages.

Smoothing Age and Spatial Patterns

A comparison of an observed pattern of age-specific rates or probabilities with the corresponding model schedule fitted pattern identifies idiosyncrasies in the observed data and points to possible data errors or to irregularities created by an insufficiently large sample. Actuaries calculating life insurance policies or annuities, for example, would want to smooth irregular patterns to ensure that age-specific probabilities of dying, do not show, say, that an average 45-year old female had a higher risk of dying within the next year than did an average 46-year old female. Confronting such an irregularity, an actuary is likely to smooth out the suspicious behavior with a model mortality schedule, for example, the eight-parameter Heligman-Pollard (1980) model mortality schedule.