The economics of long-term care in Ohio

In 1990, the State of Ohio spent more than $1 billion to finance long-term care services to older Ohioans, and as a result, the Medicaid budget is seen as perhaps the greatest challenge facing state policy makers. But Ohio's public expenditures tell only part of the story and must be seen in a context that includes expenditures by other government programs, private insurers, older people and their families, and charitable organizations. The full picture also includes the economic value of uncompensated care provided by the family and friends of disabled elders and the loss of income that results when caregivers must reduce employment. This report estimates the number of older Ohioans receiving long-term care, the economic value of that care, and sources of economic support for that care

To buy or not to buy : considerations in the decision to purchase long-term care insurance

This report presents findings from a survey of a random sample of retired teachers who were given the opportunity to buy long-term care insurance. Characteristics that distinguish purchasers from nonpurchasers are identified

Career patterns of U.S. male academic social scientists

Seventy-four U.S. male academic social scientists provided career stage data. All were born between 1893 and 1903. The subjects were divided into four groups on the basis of their scholarly article productivity after age 59. Spilerman's conceptualization of work history guided the analysis. To a lesser extent, adult development theory (e.g., Hall and Nougaim, 1968) was also examined.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42843/1/10734_2004_Article_BF00139794.pd

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994-2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm(-2)) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.An international team of researchers finds high potential for improving climate projections by a more comprehensive treatment of largely ignored Arctic vegetation types, underscoring the importance of Arctic energy exchange measuring stations.Peer reviewe