Conditional intensity: A powerful tool for modelling and analysing point process data

The conditional intensity function of a spatial point process describes how the probability that a point of the process occurs ‘at’ a particular point in its carrier space depends on the realisation of the process in the remainder of the carrier space. Provided that the point process is simple, the conditional intensity determines all of the properties of the process, in particular its likelihood function. In this paper, we review the use of the conditional intensity function in the formulation of point process models and in making inferences from point process data, giving separate consideration to temporal, spatial and spatiotemporal settings. We argue that the conditional intensity function should take centre-stage in spatiotemporal point process modelling and analysis.     

Gender diversity among ‘boys’ in Papua New Guinea: Memories of sameness and difference in early childhood

Childhood is a time when children begin to constitute themselves as gendered subjects largely according to social norms that are rigidly framed within dominant discourses of heteronormative binary gender. This paper is based on the life histories of 42 sexuality and gender diverse adult men and transgender women conducted in PNG. Findings offer insight into the ways in which boys transgressed gender norms through dress, play, work within the home and in dance. We argue that gender transgression in childhood is not a contemporary phenomena and such childhood experiences of boys in PNG should be recognised as part of the country's rich cultural diversity.     

Reforming pensions: Principles, analytical errors and policy directions

This article, sets out a series of principles for pension design rooted in economic theory: pension systems have multiple objectives, analysis should consider the pension system as a whole, analysis should be framed in a second-best context, different systems share risks differently, and systems have different effects by generation and by gender. That discussion is reinforced by identification of a series of widespread analytical errors — errors that appear in World Bank work, but by no means only in World Bank work: tunnel vision, improper use of first-best analysis, improper use of steady-state analysis, incomplete analysis of implicit pension debt, incomplete analysis of the impact of funding (including excessive focus on financial flows, failure to consider how funding is generated, and improper focus on the type of asset in trust funds), and ignoring distributional effects. The second part of the article considers implications for policy: there is no single best pension design, earlier retirement does little or nothing to reduce unemployment, unsustainable pension promises need to be addressed directly, a move from pay-as-you-go towards funding in a mandatory system may or may not be welfare improving, and implementation matters — policy design that exceeds a country's capacity to implement it is bad policy design. We illustrate the ranges of designs of pension systems that fit the fiscal and institutional capacity constraints typical at different levels of economic development. The potential gains from simplicity imply that a country capable of implementing an administratively demanding plan does not necessarily gain from doing so. New Zealand has a simple pension system through choice, not constraint.     

Rural restructuring in the American West: land use, family and class discourses

Migration patterns, technological developments, and altered human–land relationships are combining to precipitate tremendous changes in rural communities across the western US. These processes of restructuring, however, have been quite contentious and divisive for many of the region's small towns. While we are beginning to understand the causes of recent growth and development trends, the consequences of contemporary forces of restructuring on communities remain unstudied. This paper explores the reactions of residents to forces of restructuring in Pagosa Springs, Colorado, the Methow Valley, Washington, Kane County, Utah, and Teton Valley, Idaho. In depth interviews and survey responses give voice to community members by allowing them to articulate their perceptions and interpretations of recent events. The analyses demonstrate tremendous diversity in the ways in which individuals interpret the changes taking place around them. These diverse interpretations challenge singular conceptions of community and highlight the ways in which notions of land use, family, and class are negotiated in the context of contemporary rural restructuring. The analysis further demonstrates the need for a more synthesized field of rural studies spanning disciplinary and national divides.     

Three Likelihood-Based Methods For Mean and Covariance Structure Analysis With Nonnormal Missing Data

Survey and longitudinal studies in the social and behavioral sciences generally contain missing data. Mean and covariance structure models play an important role in analyzing such data. Two promising methods for dealing with missing data are a direct maximum-likelihood and a two-stage approach based on the unstructured mean and covariance estimates obtained by the EM-algorithm. Typical assumptions under these two methods are ignorable nonresponse and normality of data. However, data sets in social and behavioral sciences are seldom normal, and experience with these procedures indicates that normal theory based methods for nonnormal data very often lead to incorrect model evaluations. By dropping the normal distribution assumption, we develop more accurate procedures for model inference. Based on the theory of generalized estimating equations, a way to obtain consistent standard errors of the two-stage estimates is given. The asymptotic efficiencies of different estimators are compared under various assumptions. We also propose a minimum chi-square approach and show that the estimator obtained by this approach is asymptotically at least as efficient as the two likelihood-based estimators for either normal or nonnormal data. The major contribution of this paper is that for each estimator, we give a test statistic whose asymptotic distribution is chi-square as long as the underlying sampling distribution enjoys finite fourth-order moments. We also give a characterization for each of the two likelihood ratio test statistics when the underlying distribution is nonnormal. Modifications to the likelihood ratio statistics are also given. Our working assumption is that the missing data mechanism is missing completely at random. Examples and Monte Carlo studies indicate that, for commonly encountered nonnormal distributions, the procedures developed in this paper are quite reliable even for samples with missing data that are missing at random.     

Affirmative Action in Higher Education: How Do Admission and Financial Aid Rules Affect Future Earnings?

This paper addresses how changing the admission and financial aid rules at colleges affects future earnings. I estimate a structural model of the following decisions by individuals: where to submit applications, which school to attend, and what field to study. The model also includes decisions by schools as to which students to accept and how much financial aid to offer. Simulating how black educational choices would change were they to face the white admission and aid rules shows that race‐based advantages had little effect on earnings. However, removing race‐based advantages does affect black educational outcomes. In particular, removing advantages in admissions substantially decreases the number of black students at top‐tier schools, while removing advantages in financial aid causes a decrease in the number of blacks who attend college.