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This paper examines several measures of poverty and hardship for the United States to illustrate how a single measure of poverty may identify different groups of people as “in need.” Individuals and families may encounter difficulty meeting needs on many dimensions and there are a variety of measures designed to identify those who experience poverty or difficulty making ends meet. In general, there is agreement that all of the approaches capture different pieces of the puzzle while no single indicator can yield a complete picture. To understand this multidimensional aspect of poverty, several measures are examined in this paper: the official U.S. poverty measure, a relative poverty measure, a new supplemental measure that follows recommendations of the U.S. National Academy of Sciences (NAS), an index of material hardship, a measure of household debt, and responses to a question about inability to meet expenses. This study uses the 2008 panel of the Survey of Income and Program Participation (SIPP) and updates a similar analysis that used the 1996 panel of SIPP (Short, 2005). The SIPP is a longitudinal survey that allows us to examine all of these various indicators for the same people over the period from 2009 to 2010. The study uses regression analysis to assess the relationship among several indicators of economic hardship. Results suggest that an understanding of relationships between various indicators can allow only one indicator of poverty alone to be interpreted more appropriately and used more wisely to target the needs of the disadvantaged.

A mathematical model for sustainable optimization is presented. The sustainability parameters are linked to end-of-life considerations in product design and development. The sustainability considerations such as total embodied energy, eco-materials selection, CO₂ emission, cost savings due to recycling and reuse including the water savings are presented for helical compression springs used in mattresses.

We know a great deal about the ways in which routines of news coverage may bias newspaper content, but little about how different article retrieval practices influence newspaper data assembled by scholars. Using the New York Times as a source of data on social movement activity, we compare depictions of protest by the African-American Civil Rights movement over time produced using the two most common article retrieval methods: index versus full-story coding. Full-story coding clearly offers more depth and greater breadth in terms of the events identified. Moreover, many of the same event characteristics associated with selection bias in newspaper reporting (e.g., size and confrontational nature of a protest event, presence of counter-demonstrators or police, and event sponsorship by a recognized social movement organization) are selected upon again when stories are indexed by New York Times staff.