The Social City : Middle-way approaches to housing and sub-urban golvernmentality in southern Stockholm, 1900-1945

This dissertation deals with the period bridging the era of extreme housing shortages in Stockholm on the eve of industrialisation and the much admired programmes of housing provision that followed after the second world war, when Stockholm district Vällingby became an example for underground railway-serviced ”new towns”. It is argued that important changes were made in the housing and town planning policy in Stockholm in this period that paved the way for the successful ensuing period. Foremost among these changes was the uniquely developed practice of municipal leaseholding with the help of site leasehold rights (Erbbaurecht). The study is informed by recent developments in Foucauldian social research, which go under the heading ’governmentality’. Developments within urban planning are understood as different solutions to the problem of urban order. To a large extent, urban and housing policies changed during the period from direct interventions into the lives of inhabitants connected to a liberal understanding of housing provision, to the building of a disciplinary city, and the conduct of ’governmental’ power, building on increased activity on behalf of the local state to provide housing and the integration and co-operation of large collectives. Municipal leaseholding was a fundamental means for the implementation of this policy. When the new policies were introduced, they were limited to the outer parts of the city and administered by special administrative bodies. This administrative and spatial separation was largely upheld throughout the period, and represented as the parallel building of a ’social’ outer city, while things in the inner ’mercantile’ city proceeded more or less as before. This separation was founded in a radical difference in land holding policy: while sites in the inner city were privatised and sold at market values, land in the outer city was mostly leasehold land, distributed according to administrative – and thus politically decided – priorities. These differences were also understood and acknowledged by the inhabitants. Thorough studies of the local press and the organisational life of the southern parts of the outer city reveals that the local identity was tightly connected with the representations connected to the different land holding systems. Inhabitants in the south-western parts of the city, which in this period was still largely built on private sites, displayed a spatial understanding built on the contradictions between centre and periphery. The inhabitants living on leaseholding sites, however, showed a clear understanding of their position as members of model communities, tightly connected to the policy of the municipal administration. The organisations on leaseholding sites also displayed a deep co-operation with the administration. As the analyses of election results show, the inhabitants also seemed to have felt a greater degree of integration with the society at large, than people living in other parts of the city. The leaseholding system in Stockholm has persisted until today and has been one of the strongest in the world, although the local neo-liberal politicians are currently disposing it off.

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.