A New Statistic: The US Census Bureau’s Supplemental Poverty Measure

This article examines the dynamic relationship between macroeconomic performance and measures of poverty in the United States. The article is organized as follows. Section 2 presents insights on the relationship between poverty and macroeconomic performance that emerge from the literature. The emphasis is on empirical studies from 1986 to 2011. Section 3 provides a snapshot of the change in poverty over National Bureau of Economic Research-dated recessions for a variety of poverty measures. Section 4 uses vector autoregressions (VARs) to characterize the response of poverty to innovations in various social indicators and measures of macroeconomic performance. Section 5 expands the empirical analysis to include alternative measures of poverty—a consumption-based poverty rate constructed by Meyer and Sullivan (2010) and an income-based poverty rate constructed by Broda and colleagues (2009) by using a consumer price index that has been adjusted for substitution and quality bias. Section 6 conducts a forecasting exercise for income poverty and consumption poverty. Section 7 concludes and offers suggestions for future research

Deriving The GLS Transformation Parameter In Elementary Panel Data Models

The Generalized Least Squares (GLS) transformation that eliminates serial correlation in the error terms is central to a complete understanding of the relationship between the pooled OLS, random effects, and fixed effects estimators. A significant hurdle to attainment of that understanding is the calculation of the parameter that delivers the desired transformation. This paper derives this critical parameter in the benchmark case typically used to introduce these estimators using nothing more than elementary statistics (mean, variance, and covariance) and the quadratic formula

Poverty: A Very Short Introduction

No one wants to live in poverty. Few people would want others to do so. Yet, we find ourselves in a situation where millions of people worldwide live in poverty. According to the World Bank in 2010, 1.2 billion people lived below the extreme poverty line with an income of US $1.25 or less a day and 2.4 billion lived on less than US$2 a day. Why is that? What has been done about it in the past? And what is being done about it now? In this Very Short Introduction Philip N. Jefferson explores how the answers to these questions lie in the social, political, economic, educational, and technological processes that impact all of us throughout our lives. The degree of vulnerability is all that differentiates us. He shows how a person\u27s level of vulnerability to adverse changes in their life is very much dependent on the circumstances of their birth, including where their family lived, the schools they attended, whether it was peacetime or wartime, whether they had access to clean water, and whether they are male or female. Arguing that while poverty is ancient and enduring, the conversation about it is always new and evolving, Jefferson looks at the history of poverty, and the practical and analytical efforts we have made to eradicate it, and the prospects for further poverty alleviation in the future

DataWarp: Building Applications which Make Progress in an Inconsistent World

The usual approach to dealing with imperfections in data is to attempt to eliminate them. However, the nature of modern systems means this is often futile. This paper describes an approach which permits applications to operate notwithstanding inconsistent data. Instead of attempting to extract a single, correct view of the world from its data, a DataWarp application constructs a collection of interpretations. It adopts one of these and continues work. Since it acts on assumptions, the DataWarp application considers its recent work to be provisional, expecting eventually most of these actions will become definitive. Should the application decide to adopt an alternative data view, it may then need to void provisional actions before resuming work. We describe the DataWarp architecture, discuss its implementation and describe an experiment in which a DataWarp application in an environment containing inconsistent data achieves better results than its conventional counterpart

Early-life neighborhood context, perceived stress, and preterm birth in African American Women.

Stressors from multiple sources, across the life-course, may have independent and joint associations with preterm birth (PTB) risk in African American women. Using data from the Life-course Influences on Fetal Environments Study (LIFE; 2009-2011) of post-partum African American women from Metropolitan Detroit, Michigan (n=1365), we examined the association between perceived stress and PTB, and effect modification by perceptions of early-life neighborhood social control and disorder. We defined PTB as birth before 37 completed weeks of gestation. We used Cohen's Perceived Stress scale, and valid and reliable scales of early-life (age 10) neighborhood social control and social disorder to quantify exposures. We estimated prevalence ratios (PR) and 95% confidence intervals (CI) with log binomial regression models- with separate interaction terms for perceived stress and each early-life neighborhood scale. We considered p < 0.10 significant for interaction terms. PTB occurred in 16.4% (n=224) of the study participants. In the total sample, perceived stress was not associated with PTB rates. However, there was suggestive evidence of a joint association between perceived stress and early-life neighborhood social disorder (p for interaction = 0.06), such that among women who reported high early-life neighborhood social disorder (n=660), perceived stress was positively associated with PTB (adjusted PR: 1.31; 95% CI: 1.05, 1.63). There was no association between perceived stress and PTB for women in the low early-life neighborhood social disorder strata (n=651) (adjusted PR: 0.95, 95% CI: 0.75, 1.21). There was no evidence that early-life neighborhood social control modified the association between perceived stress and PTB. Our results suggest that early-life neighborhood stressors may magnify the association between current perceived stress and PTB rates, in African American women. More research to confirm and explicate the biologic and/or psychosocial mechanisms of the reported association is warranted

Protective wrapping of off-the-shelf components

System designers using off-the-shelf components (OTSCs), whose internals they cannot change, often use add-on “wrappers” to adapt the OTSCs’ behaviour as required. In most cases, wrappers are used to change “functional” properties of the components they wrap. In this paper we discuss instead protective wrapping, the use of wrappers to improve the dependability – i.e., “non-functional” properties like availability, reliability, security, and/or safety – of a component and thus of a system. Wrappers can improve dependability by adding fault tolerance, e.g. graceful degradation, or error recovery mechanisms. We discuss the rational specification of such protective wrappers in view of system dependability requirements, and highlight some of the design trade-offs and uncertainties that affect system design with OTSCs and wrappers, and that differentiate it from other forms of fault-tolerant design