Policy instrument affordances: a framework for analysis

The present study uses the concept of technological efficacy derived from ecological psychology and design studies to offer an alternative way of analyzing how policy instruments affect change. Reasoning from this, the paper outlines a framework for analyzing policy instruments in terms of their affordances. We define affordances as the means through which an instrument exerts influence on its intended target audience. Using this approach, we contend that policy instruments may be analyzed as interfaces that organize social relations and create structures of opportunity and/or restrict possibilities for action. We argue that explicating the pathways through which instruments afford or constrain action is a central task for policy analysis. Our proposed framework of analysis builds on the idea that instruments yield effects by facilitating action and learning. We further contend that the actions that an instrument can facilitate or inhibit are determined by specific affordance modalities of the instrument in conjunction with contingencies of the actor and the policy environment. Examples from research policy are used to illustrate some of these effects.     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

Devolution in a ‘Stateless Nation’: Nation‐building and Social Policy in Scotland

The implications of the 2011 Scottish election and the proposed referendum on Scottish independence for the future of social policy across the devolved UK are profound but far from certain. It is crucial to understand not only the historical nature of this conjuncture but to develop an adequate conceptual understanding of the place of social policy in the dialectic between state and nation in Scotland. To this end, we critically examine theories that depict Scotland as an essentially ‘stateless nation’ in the light of recent developments. In so doing, we examine the implications for social policy of the changing character of statehood in Scotland, the nature of civil nationalism, and the problem of legitimacy in Scotland for the UK as a multinational state. As the architecture of statehood is re‐negotiated, strong centrifugal pressures are being created for a more distinct divergence of social policy in Scotland from the rest of the UK regardless of the outcome of the independence referendum. Policy‐making is ensnared in a series of tensions, not just between Westminster and Holyrood but also, more broadly, tensions between competing principles of social justice and territorial justice, and competing demands between welfare nationalism and competitive nationalism.     

Does race matter? Assessing consumer discrimination in the secondary basketball card market

Sociologists continue to observe the ways race permeates America's social institutions, the institution of sport being no exception. Although researchers have explored customer racial discrimination via examinations of the secondary sports card market, only three studies have explored the phenomenon in the context of basketball, a sporting context with a higher proportion of non-White players than the baseball and football leagues that have been the primary focus to date. We explore the unique way race matters on the hardwood by employing a methodological approach that previously has been used to study card collecting in other contexts. Data were obtained for 215 retired players and their rookie cards. Controlling for other factors, to include career performance, position, and card scarcity, the results reveal no direct effect of race on card values, but there is an interaction effect between race and Hall of Fame status that impacts card prices. The potential source and implications of this interaction are discussed as well as suggestions for future research.     

Derrida’s “Indecent Objection”

Derrida’s account of forgiveness appears to oppose politics to ethics, challenging political reconciliation in the name of unconditional forgiveness. Yet at the same time Derrida seems to sacrifice ethics to politics by advancing but refusing to take the side of what he calls an “indecent objection” to reconciliation. This essay seeks to account for Derrida’s strategy and to think through some of the consequences of Derrida’s emphasis on the “impossibility” of forgiveness.     

Predicting infectious disease outbreak risk via migratory waterfowl vectors

The spread of an emerging infectious disease is a major public health threat. Given the uncertainties associated with vector-borne diseases, in terms of vector dynamics and disease transmission, it is critical to develop statistical models that address how and when such an infectious disease could spread throughout a region such as the USA. This paper considers a spatio-temporal statistical model for how an infectious disease could be carried into the USA by migratory waterfowl vectors during their seasonal migration and, ultimately, the risk of transmission of such a disease to domestic fowl. Modeling spatio-temporal data of this type is inherently difficult given the uncertainty associated with observations, complexity of the dynamics, high dimensionality of the underlying process, and the presence of excessive zeros. In particular, the spatio-temporal dynamics of the waterfowl migration are developed by way of a two-tiered functional temporal and spatial dimension reduction procedure that captures spatial and seasonal trends, as well as regional dynamics. Furthermore, the model relates the migration to a population of poultry farms that are known to be susceptible to such diseases, and is one of the possible avenues toward transmission to domestic poultry and humans. The result is a predictive distribution of those counties containing poultry farms that are at the greatest risk of having the infectious disease infiltrate their flocks assuming that the migratory population was infected. The model naturally fits into the hierarchical Bayesian framework.     

Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits

We consider assortment problems under a mixture of multinomial logit models. There is a fixed revenue associated with each product. There are multiple customer types. Customers of different types choose according to different multinomial logit models whose parameters depend on the type of the customer. The goal is to find a set of products to offer so as to maximize the expected revenue obtained over all customer types. This assortment problem under the multinomial logit model with multiple customer types is NP‐complete. Although there are heuristics to find good assortments, it is difficult to verify the optimality gap of the heuristics. In this study, motivated by the difficulty of finding optimal solutions and verifying the optimality gap of heuristics, we develop an approach to construct an upper bound on the optimal expected revenue. Our approach can quickly provide upper bounds and these upper bounds can be quite tight. In our computational experiments, over a large set of randomly generated problem instances, the upper bounds provided by our approach deviate from the optimal expected revenues by 0.15% on average and by less than one percent in the worst case. By using our upper bounds, we are able to verify the optimality gaps of a greedy heuristic accurately, even when optimal solutions are not available.