Sickness and disability benefit programmes: What is driving policy convergence?

In this paper we explore the reasons for the apparent convergence in sickness and disability policies across the OECD, asking whether and to what extent policy convergence should be seen as a product of policy learning. We conclude that convergence is the result of policy imitation more than policy learning and that learning (from past mistakes) is more likely within countries than across borders. Given limited evidence on what really "works", when it comes to designing policies that both provide adequate income security and still encourage labour-force participation, governments look abroad or to bodies like the OECD for possible models and ideas to underlie a reform. However, translating those ideas into workable policies requires great sensitivity to the institutional and political-economic context — especially the role of the social partners and the nature of policies in existence. When it comes to policy implementation, such contextual learning may be crucial.     

Communication with parents in child welfare: skills, language and interaction

This paper reviews the literature on communication between professionals and parents/carers in health and social care. It notes that there is considerable research on doctor–patient communication and counselling, but much less on professionals who visit families in their homes. While there is a large literature on good practice and communication skills, there is little research on the actual encounters themselves. What counts as 'communication' in such publications is explored. The paper suggests that there is much to be gained by considering research in discourse analysis and sociolinguistics on professional–client encounters. Examples of some of the key concepts are discussed and an engagement with this literature is recommended.     

Joint Ordering and Pricing Strategies for Managing Substitutable Products

Starr and Rubinson (1978) develop a model to establish the relationship between product demand and relative prices. The notion of relative prices motivates us to consider a situation in which a retailer would either charge the same retail price for all products if he adopts a ‘fixed’ pricing strategy or charge different prices for different products if he adopts a ‘variable’ pricing strategy. In this paper, we develop a base model with deterministic demand that is intended to examine how a retailer should jointly determine the order quantity and the retail price of two substitutable products under the fixed and variable pricing strategies. Our analysis indicates that the optimal retail price under the variable pricing strategy is equal to the optimal retail price under the fixed pricing strategy plus or minus an adjustment term. This adjustment term depends on product substitutability and price sensitivity. We also present two different extensions of our base model. In the first extension, our analysis indicates that the underlying structure of the optimal retail price and order quantity is preserved when there is a limit on the total order quantity. The second extension deals with the issue of retail competition. Relative to the base case, we show that the underlying structure of the optimal retail price and order quantity is preserved in a duopolistic environment. Moreover, our analysis suggests that both retailers would adopt the variable pricing strategy at the equilibrium.     

A flexible particle Markov chain Monte Carlo method

Particle Markov Chain Monte Carlo methods are used to carry out inference in nonlinear and non-Gaussian state space models, where the posterior density of the states is approximated using particles. Current approaches usually perform Bayesian inference using either a particle marginal Metropolis–Hastings (PMMH) algorithm or a particle Gibbs (PG) sampler. This paper shows how the two ways of generating variables mentioned above can be combined in a flexible manner to give sampling schemes that converge to a desired target distribution. The advantage of our approach is that the sampling scheme can be tailored to obtain good results for different applications. For example, when some parameters and the states are highly correlated, such parameters can be generated using PMMH, while all other parameters are generated using PG because it is easier to obtain good proposals for the parameters within the PG framework. We derive some convergence properties of our sampling scheme and also investigate its performance empirically by applying it to univariate and multivariate stochastic volatility models and comparing it to other PMCMC methods proposed in the literature.