Experimenting with Welfare Reform: Emulating Success,Cutting Costs,or Racing to the Bottom?*

This article calls upon the social scientific research community to systematically assess policy devolution within American federalism through the lens of welfare reform. It asks specifically whether the benefits of policy devolution in this area—the opportunity to learn from policy experiments in similar states and the ability to tailor policies to individual state circumstances—outweigh the costs of devolution—possible racial biases across the states, incentives for cutting costs, and fears of a race to the bottom in benefits and eligibility criteria. A scientific research agenda for addressing these issues is developed. The contribution of De Jong et al. (2006) is assessed in terms of its advancement of this agenda. A path toward future contributions to this agenda is suggested.     

Pseudo-likelihood estimation for a class of spatial Markov chains

We consider a class of finite state, two-dimensional Markov chains which can produce a rich variety of patterns and whose simulation is very fast. A parameterization is chosen to make the process nearly spatially homogeneous. We use a form of pseudo-likelihood estimation which results in quick determination of estimate. Parameters associated with boundary cells are estimated separately. We derive the asymptotic distribution of the maximum pseudo-likelihood estimates and show that the usual form of the variance matrix has to be modified to take account of local dependence. Standard error calculations based on the modified asymptotic variance are supported by a simulation study. The procedure is applied to an eight-state permeability pattern from a section of hydrocarbon reservoir rock.     

The probability of an undominated central voter in 2-dimensional spatial majority voting

Place a voter v in the plane and scatter additional voters at random about v. What are v's chances of being undominated? When the number of voters, n, is even, this probability is known to be nonzero [5]. Here we prove that the exact probability is 2^?(n?2).     

Expressing pain: The communication and interpretation of facial pain signals

This article reviews recent theory and research on the origins, nature, and meaning of facial expressions of pain. A general model of pain expression, distinguishing experiential, encoding, and decoding processes involved in pain episodes, is proposed. Variables which are known to or may affect these processes are reviewed. Relationships between elements of the model and clinical phenomena of interest to health-care workers are discussed. The implications of findings in this area for health-care workers are examined. Areas in need of research are identified.The authors' research reported in this paper was supported by research grants to K. M. Prkachin from the Medical Research Council of Canada and to K. D. Craig from the Social Sciences and Humanities Research Council, the Natural Sciences and Engineering Research Council, the Medical Research Council, and the British Columbia Health Care Research Foundation. We gratefully acknowledge the helpful comments on earlier drafts of Mark Costanzo, Sherry Beaumont, Linda LeResche, and Glenda Prkachin, and the issue editor, Val Derlega.