Tradition, Change and Variation : Past and Present Trends in Public Old-age Care

The general aim of this dissertation is to describe and analyse how public old-age care in Sweden has developed and changed during the last century. The study applies a provider perspective on how care has been planned and professionally carried out. A broader social policy perspective, studying old-age care at central/national as well as local/municipal level, is also developed. A special focus is directed at the large local variation in care and services for the elderly. The empirical base is comprised of official documents and other public sources, survey data from interviews with elderly recipients of public old-age care, and official statistics on publicly financed and controlled old-age care and services. Study I addresses the development of old-age care in Sweden during the twentieth century by studying an important occupation in this field – the supervisors and their professional roles, tasks and working conditions. Throughout, the roles of supervisors have followed the prevailing official policy on the proper way to provide care for elderly people in Sweden; from poor relief at the beginning of the 1900s, via a generous level of services in the 1960s and 1970s, to today’s restricted and economy-controlled mode of operation. Study II describes and compares two main forms of public old-age care in Sweden today, home help services and institutional care. The care-load found in home-based care was comparable to and sometimes even larger than in service-homes and other institutions, indicating that large care needs among elderly people in Sweden today can be met in their homes as well as in institutional settings. Studies III and IV analyse the local variation in public old-age care in Sweden. During the last decades there has been an overall decline in home help services. The coverage of home help for elderly people shows large differences between municipalities throughout this period, and the relative variation has increased. The local disparity seems to depend more on historical factors, e.g., previous coverage rates, than on the present municipal situation in levels of need or local economy and politics. In an introductory part the four papers are linked together by an outline of the demographic situation and the social policy model for old-age care in Sweden. Trends that have been apparent over time, e.g. professionalisation and market orientation, are traced and discussed. Conflicts between prevailing ideologies are analysed, in regards to for instance home-based and institution-based care, social and medical culture, and local and central levels of decision-making. ’Welfare municipality’, ‘path dependency’, and ‘decentralisation’ are suggested as a conceptual framework for describing the large and increasing local variations in old-age care. Finally, implications of the four studies with regard to old-age care policy and further research are discussed.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.