The Structure of Opportunity: Middle-Class Mobility in England, 1548-1689

Models that allow the decomposition of mobility into its structural and exchange components are used to identify the structure, and consequences, of middle-class intergenerational mobility in preindustrial (1548-1689) Norwich, England. Dramatic shifts in the mobility opportunities of sons over time are seen to yield distinct political outcomes. Political stability is associated with almost universal upward mobility in the period from 1548 to 1589, while from 1590 to 1639 structural processes leading to massive downward mobility are associated with increased radicalization and participation in the English civil war. From 1640 to 1689, strata persistence in an unstable political context prevailed

Blowin' Down the Road: Investigating Bilateral Causality Between Dust Storms and Population in the Great Plains

Recently, the National Academy of Sciences concluded “it is clear thatpopulation and the environment are usually interrelated . . . ”. This paper directlytests the expected interrelationship using annual county-level population estimatesprovided by the U.S. Census Bureau and annual counts of dust storms from the1960s, '70s, and '80s at weather stations situated throughout the U.S. GreatPlains. In doing so, it implements a research design that extends methods (farremoved from conventional demography) for pure time series analysis withmultilevel regression models. The result is a method for causal modeling in paneldata that produces, in this application, evidence of bilateral causality betweenpopulation size and deleterious environmental conditions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43522/1/11113_2004_Article_5144455.pd

Relationships of Polychlorinated Biphenyls and Dichlorodiphenyldichloroethylene (p,p’-DDE) with Testosterone Levels in Adolescent Males

Background: Concern persists over endocrine-disrupting effects of persistent organic pollutants (POPs) on human growth and sexual maturation. Potential effects of toxicant exposures on testosterone levels during puberty are not well characterized. Objectives: In this study we evaluated the relationship between toxicants [polychlorinated biphenyls (PCBs), dichlorodiphenyldichloroethylene (p,p´-DDE), hexachlorobenzene (HCB), and lead] and testosterone levels among 127 Akwesasne Mohawk males 10 to \u3c 17 years of age with documented toxicant exposures. Methods: Data were collected between February 1996 and January 2000. Fasting blood specimens were collected before breakfast by trained Akwesasne Mohawk staff. Multivariable regression models were used to estimates associations between toxicants and serum testosterone, adjusted for other toxicants, Tanner stage, and potential confounders. Results: The sum of 16 PCB congeners (Σ16PCBs) that were detected in ≥ 50% of the population was significantly and negatively associated with serum testosterone levels, such that a 10% change in exposure was associated with a 5.6% decrease in testosterone (95% CI: –10.8, –0.5%). Of the 16 congeners, the more persistent ones (Σ8PerPCBs) were related to testosterone, whereas the less persistent ones, possibly reflecting more recent exposure, were not. When PCB congeners were subgrouped, the association was significant for the sum of eight more persistent PCBs (5.7% decrease; 95% CI: –11, –0.4%), and stronger than the sum of six less persistent congeners (3.1% decrease; 95% CI: –7.2, 0.9%). p,p´-DDE was positively but not significantly associated with serum testosterone (5.2% increase with a 10% increase in exposure; 95% CI: –0.5, 10.9%). Neither lead nor HCB was significantly associated with testosterone levels. Conclusions: Exposure to PCBs, particularly the more highly persistent congeners, may negatively influence testosterone levels among adolescent males. The positive relationship between p,p´-DDE and testosterone indicates that not all POPs act similarly

The Role of Neutrophil Proteins on the Amyloid Beta-RAGE Axis

We would like to thank Dr. Arthur Owora, previously a Research Biostatistician of the Department of Pharmaceutical Sciences, University of Oklahoma Health Sciences Center, for his assistance on the statistical analysis performed in this study. We thank Dr. Sixia Chen of the Department of Biostatistics and Epidemiogy, University of Oklahoma Health Sciences Center, for his additional input on the statistical analysis. We thank the Laboratory for Molecular Biology and Cytometry Research at the University of Oklahoma Health Sciences Center for the use of the Core Facility which allowed us to perform the MALDI-TOF MS and MS/MS experiments. GM-0111 was provided as a gift by Dr. Justin Savage, GlycoMira Therapeutics, Inc.We previously showed an elevated expression of the neutrophil protein, cationic antimicrobial protein of 37kDa (CAP37), in brains of patients with Alzheimer’s disease (AD), suggesting that CAP37 could be involved in AD pathogenesis. The first step in determining how CAP37 might contribute to AD pathogenesis was to identify the receptor through which it induces cell responses. To identify a putative receptor, we performed GAMMA analysis to determine genes that positively correlated with CAP37 in terms of expression. Positive correlations with ligands for the receptor for advanced glycation end products (RAGE) were observed. Additionally, CAP37 expression positively correlated with two other neutrophil proteins, neutrophil elastase and cathepsin G. Enzyme-linked immunosorbent assays (ELISAs) demonstrated an interaction between CAP37, neutrophil elastase, and cathepsin G with RAGE. Amyloid beta 1–42 (Aβ1–42), a known RAGE ligand, accumulates in AD brains and interacts with RAGE, contributing to Aβ1–42 neurotoxicity. We questioned whether the binding of CAP37, neutrophil elastase and/or cathepsin G to RAGE could interfere with Aβ1–42 binding to RAGE. Using ELISAs, we determined that CAP37 and neutrophil elastase inhibited binding of Aβ1–42 to RAGE, and this effect was reversed by protease inhibitors in the case of neutrophil elastase. Since neutrophil elastase and cathepsin G have enzymatic activity, mass spectrometry was performed to determine the proteolytic activity of all three neutrophil proteins on Aβ1–42. All three neutrophil proteins bound to Aβ1–42 with different affinities and cleaved Aβ1–42 with different kinetics and substrate specificities. We posit that these neutrophil proteins could modulate neurotoxicity in AD by cleaving Aβ1–42 and influencing the Aβ1–42 –RAGE interaction. Further studies will be required to determine the biological significance of these effects and their relevance in neurodegenerative diseases such as AD. Our findings identify a novel area of study that underscores the importance of neutrophils and neutrophil proteins in neuroinflammatory diseases such as AD.Yeshttp://www.plosone.org/static/editorial#pee

The Structure of Opportunity: Middle-Class Mobility in England, 1548-1689

Models that allow the decomposition of mobility into its structural and exchange components are used to identify the structure, and consequences, of middle-class intergenerational mobility in preindustrial (1548-1689) Norwich, England. Dramatic shifts in the mobility opportunities of sons over time are seen to yield distinct political outcomes. Political stability is associated with almost universal upward mobility in the period from 1548 to 1589, while from 1590 to 1639 structural processes leading to massive downward mobility are associated with increased radicalization and participation in the English civil war. From 1640 to 1689, strata persistence in an unstable political context prevailed

Model selection procedures in social research: Monte-Carlo simulation results

Model selection strategies play an important, if not explicit, role in quantitative research. The inferential properties of these strategies are largely unknown, therefore, there is little basis for recommending (or avoiding) any particular set of strategies. In this paper, we evaluate several commonly used model selection procedures [Bayesian information criterion (BIC), adjusted R2, Mallows' Cp, Akaike information criteria (AIC), AICc, and stepwise regression] using Monte-Carlo simulation of model selection when the true data generating processes (DGP) are known. We find that the ability of these selection procedures to include important variables and exclude irrelevant variables increases with the size of the sample and decreases with the amount of noise in the model. None of the model selection procedures do well in small samples, even when the true DGP is largely deterministic; thus, data mining in small samples should be avoided entirely. Instead, the implicit uncertainty in model specification should be explicitly discussed. In large samples, BIC is better than the other procedures at correctly identifying most of the generating processes we simulated, and stepwise does almost as well. In the absence of strong theory, both BIC and stepwise appear to be reasonable model selection strategies in large samples. Under the conditions simulated, adjusted R2, Mallows' Cp AIC, and AICc are clearly inferior and should be avoided.model selection, BIC, AIC, stepwise regression,

Demography as a Spatial Social Science Demography as a Spatial Social Science

Abstract Many social scientists have taken note of the re-emerging interest in issues concerning social processes embedded within a spatial context. While some argue that this awakening is refreshing and new and, in fact, long overdue, I demonstrate that spatially focused demographic theories and research agendas clearly predate contemporary interest in these topics. I assert that recent methodological advancements have merely encouraged and brought refinement to the expanding body of spatially oriented population research -research strongly rooted in demographic tradition and practice. Indeed, I make the claim that, until roughly the mid-20 th century, virtually all demography in the United States (and elsewhere, but not specifically examined here) was spatial demography. (I define spatial demography as the formal demographic study of areal aggregates, i.e., of demographic attributes aggregated to some level within a geographic hierarchy.) While some may find the claim overstated, and argue specific exceptions, I develop my theme, in part, through historical narrative. I posit that until around 1950 almost all demographic analysis involved data taken from areal units. Then, shortly after mid-century, a paradigm shift occurred, and the scientific study of population quickly came to be dominated by attention to the individual as the agent of demographic action. Traditional spatial (or macro-level) demography gave way to micro-demography, and, I argue, most demographers simply abandoned the data and approach of spatial demography. This assertion notwithstanding, I then proceed to show how the tradition of spatial demography actually did persist in small corners of our discipline during the latter half of the 20 th century -despite the ascendancy of the micro-demography paradigm -through the contributions primarily of rural demographers and of others working in the new sub-field which appropriated the appellative "applied" demography. In closing the paper I include a brief but necessary discussion of the recent awakening that has come to spatial demographers from developments in other disciplines -principally from geography, regional science and spatial econometrics. Attributes of spatially referenced data generally violate at least one of the assumptions underlying the standard regression model, which necessitates both caution regarding these violations and attention to methods designed to correct for them. These emerging methods are the topics of a large and rapidly expanding literature. I also include mention of the important recent role played by methods of multilevel modeling (hierarchical linear modeling) in bridging the 50-year-old split between micro-level and macrolevel demography by introducing techniques which simultaneously consider individual (family or household) variation in demographic attributes or behavior as well as the broader geographic contexts in which individual demographic action occurs. 2 Demography as a Spatial Social Science Many social scientists have taken note of the re-emerging interest in issues concerning social processes embedded within a spatial context (see, for example, the papers in 1 I argue in the following section that, until roughly the mid-20 th century, virtually all demography in the United States was spatial demography. Here, I define spatial demography as the formal demographic study of areal aggregates, i.e., of demographic attributes aggregated to some level within a geographic hierarchy. As such, spatial demography is viewed as analogous to the "statistical geography" of Duncan, TRADITIONAL DEMOGRAPHY WAS (MOSTLY) SPATIAL DEMOGRAPHY Prior to the advent of public use microdata files from the decennial census, and before the arrival of large public use analytical files from major surveys (e.g., Current Population Survey, Hawley's (1950) highly influential book on the subject was subtitled "A Theory of Community Structure" (emphasis added), and much of the research over ensuing decades that flowed from this disciplinary paradigm examined structure and change of social aggregates. Dozens of studies followed THE SHIFT FROM MACRO-DEMOGRAPHY TO MICRO-DEMOGRAPHY Two forces likely propelled the change of focus from macro-to micro-demography. One was the emergence of large scale microdata files which provided access to detailed individual/household-level data. The initial motivation for such data sets appears to have been a response to the low levels of fertility in the U.S. reached during the 1930s And so they did. The shift toward micro-level analyses established the preeminence of the individual, family or household as the demographic actor, and left but a small proportion of professional demographers continuing the serious scholarly inquiry of population change among demographic aggregates. CONTINUED INTERSEST IN SPATIAL DEMOGRAPHY AMONG SOME DEMOGRAPHERS Despite the shift in emphasis to micro-demography, there remained some demographers for whom ecological analyses continued to hold fascination. This was not done out of disregard to the ecological fallacy but rather in the belief that some interesting and important research questions can (and sometimes only can) be addressed at the aggregate level. 8 Much of this work can be placed into one of two categories: (1) migration and population distribution research, work carried forward predominantly by rural demographers, and (2) population estimation research, work which came to dominate the portfolio of many applied demographers. Rural Demography Despite many earlier publications reporting empirical research on migration patterns, censuses, when it was discovered that patterns of internal migration in the U.S. (and, it should be added, elsewhere) had shifted, and nonmetropolitan counties were growing at higher levels than their metropolitan counterparts (Beale, 1975; 11 Returning for a moment to mid-century, the 1940s and 1950s witnessed another critical thread of research that was to be very important to the development of spatial demography in the U.S. Around this period, migration research began to focus on the migration event, per se, such as how to conceptualize migration, how to compute migration rates, and how to manipulate other variables to derive estimates of net migration for an area. This work was strongly rooted in substantive migration studies of the 1930s and 1940s (for example This research was important to spatial demography because migration is not a reported or registered event in the U.S. Instead, net migration gain or loss among areas must be estimated from aggregated data. Eventually, component models for calculating population estimates and projections required that reasonable estimates of net migration and of net migration rates be made available, and these estimates increasingly were based on the analysis of population change among small geographic areas. Because the Census Bureau was not engaged in population estimation below the state level prior to the 1970s, and because it frequently fell to rural sociologists and agricultural economists at Land Grant colleges of agriculture (due to the mission of such institutions) to respond to the need for substate population estimates, many demographers in rural sociology departments around the country found themselves actively engaged in the production efforts of population estimates for relatively small areal units in the 1950s and 1960s (see Applied Demography This brings us to an important second category of continuing work in spatial demography: population estimation research. In addition to advances in migration research, the 1950s was also a decade of major improvements in the development of population estimation models for application at the substate level (i.e., counties, cities, and even smaller geographic areas). There were three pivotal activities during this period, and each extended the focus on spatial units, thus continuing the role of space in the population sciences, even while many demographers had begun to shift their analytical efforts to the emerging microdata files. First was the model development work that occurred primarily at the U.S. Census Bureau and in selected university settings. Indeed, it was the early 1950s that spawned small-area population estimation models that even today have been improved upon only modestly. Second was the production work (i.e., the production of small-area population estimates) that found its way into state and local agencies rather than the Census Bureau. Third were the few early tests of various estimation methods against the census counts of 1950 (see, for example, Schmitt 1952; Siegel, In the 1970s, the emergence of state and local demography and, somewhat later, the field of business demography within the Population Association of America, brought a fresh perspective to the analysis of spatial units. This group of demographic practitioners appropriated for their work the term "applied demography," the distinguishing feature of which is that it involves almost exclusively the analysis of demographic data or the production of population 13 estimates and forecasts for spatial units (see The decade of the 1980s witnessed yet another boost to the analysis of spatially-arrayed data. The coming together in the late 1980s and early 1990s of five remarkable products radically changed the world of demography, including parts of demographic study not traditionally concerned with spatial variation. These products were (1) the Census Bureau's TIGER files -digital, seamless, block-level geographic databases for the U.S. released as a 1990 Census product, (2) the summary tape files from the 1980 and 1990 censuses, (3) extensive natural resource, crime, and epidemiological databases -all of which were largely outside the scope of traditional demographic interest, (4) incredibly powerful geographic information system (GIS) software for mapping and, importantly, for integrating spatially-arrayed data from diverse and disparate georeferenced systems, and, finally, (5) the awesome, but affordable, computing hardware platforms on which to bring together these various elements. These elements, having converged so forcefully by the early 1990s, began to alter the way in which spatial demographic research was carried out. Together, these forces motivated the formation of new and broadly interdisciplinary collegial relationships on campuses and elsewhere, and began to foster the development of hypotheses and researchable questions in areas where only a few demographers and ecologists had previously ventured. MULTILEVEL MODELING In the first few sections of this chapter, I discussed how the maturing of demographic science in the U.S. witnessed a shift around 1950 from an interest in population change among geographic areas to an interest in individual-level demographic behavior. I also discussed the reemergence in recent years of interest in areal data brought about by growing awareness of the tools and techniques for properly specifying and estimating statistical models based on geospatial data. I now close our chapter with a brief discussion of how these two perspectives, macro-and micro-demography, are presently being bridged by new interest in multilevel modeling techniques. 15 These methods deal with data organized hierarchically (such as individuals within neighborhoods, pupils within schools, or crimes within census tracts) and provide the opportunity to simultaneously study variation at different levels of the hierarchy. Such models acknowledge that individuals are embedded in social units (schools, tracts, neighborhoods, regions, etc.). As such, they blur the artificial boundaries between micro and macro analyses. Many examples of multilevel modeling are found in sociological and demographic research since the 1980s (e.g., As with spatial regression modeling, however, multilevel strategies bring their own distinct set of methodological issues and cannot be analyzed by conventional statistical approaches. Hox and Kreft (1994) provide a useful summary of the problems that arise when applying single level models to multilevel data (see also households residing within the same neighborhood are likely to have more similar characteristics relative to households within another neighborhood. This dependency, and the accompanying error structure, is not accounted for in a single level model. Therefore, the assumption upon which standard errors and variances are determined is violated and the model produces inefficient estimates of standard errors and overall "explained" variance. Such single level model estimates are biased and unreliable for multilevel data structures. Consequently, in recent years, software development has resulted in many statistical packages offering tools to specify and properly model hierarchical data. The most focused and well known is Hierarchical Linear and Non-Linear Modeling (HLM) SUMMARY In this chapter, I have discussed the role of geographic space in quantitative demography