Housing Choice in a Regulated Market: A Nested Multinomial Logit Analysis

The paper integrates several important elements of the institutionalized and regulated nature of the housing market and analyzes the relationship between household type and housing choice in Vienna within a nested multinomial logit framework. In particular, the concept of household specific choice sets is used to account for institutional, informational and income-based constraints of the choice process. Government subsidies (such as housing and rent allowances, non-interest-bearing state loans) are explicitly taken into account in the definition of variables. Housing choice is considered in three stages: the choice of a dwelling unit given dwelling type and residential zone, the choice of a dwelling type given residential zone, and the marginal choice of a residential zone. The coefficients are derived by means of a sequential ML-procedure. The empirical results clearly indicate that demographic variables have significant impacts on housing choice behavior. Income as a single explanatory variable as well as its interacting with size measures and the quality of the house are found to be important criteria for dwelling unit choice, as housing cost variables for dwelling type choice behavior. The dwelling unit and dwelling type choice submodels fit reasonably well, whereas the residential zone model is less successful.     

Chubut,Argentina: a contested Welsh ‘first-place’

Abstract

In an attempt to escape British hegemony, the Welsh established a Patagonian colony in 1865, in what is now the Chubut Province of Argentina. The historical struggles the immigrants faced upon settling the land are rooted in the landscape and commemorated in different versions of Patagonian regional history through provincial museum narratives that serve as a method of solidifying Welshness in Chubut. Contemporarily, the local tourism industry constructs the Welsh as the first settlers in the region, while minimally representing predecessor groups like the indigenous communities or Spanish colonials. Curiously, the representation of these other heritage communities throughout heritage displays actually serves to bolster the Welsh ‘first-place’ claims over the region. These tensions are seen throughout community-based museums in the region that assert a locally rooted hybrid identity by acknowledging local historical diversity, while simultaneously recalling and emphasising the [Welsh] homeland heritage. This paper explores how ‘first-places’ can be a source of symbolic conflict, while simultaneously serving as a dynamic, heritage construction mechanism. This research investigates how the Welsh diaspora negotiates its identity through the mobilisation of heritage, to make claims about the Chubut Province as a symbolic Welsh first-place, as well as broader Argentine heritage.     

Bibliographischer hinweis zu s. 146, anm. 74

Ohne Zusammenfassung     

Gedanken zum namen Himālaya

Ohne ZusammenfassungWilhelm Brandenstein zum 60. Geburtstag gewidmet.     

Optimization in an Error Backpropagation Neural Network Environment with a Performance Test on a Spectral Pattern Classification Problem

This paper attempts to develop a mathematically rigid framework for minimizing the cross-entropy function in an error backpropagating framework. In doing so, we derive the backpropagation formulae for evaluating the partial derivatives in a computationally efficient way. Various techniques of optimizing the multiple-class cross-entropy error function to train single hidden layer neural network classifiers with softmax output transfer functions are investigated on a real-world multispectral pixel-by-pixel classification problem that is of fundamental importance in remote sensing. These techniques include epoch-based and batch versions of backpropagation of gradient descent, PR-conjugate gradient, and BPGS quasi-Newton errors. The method of choice depends upon the nature of the learning task and whether one wants to optimize learning for speed or classification performance. It was found that, comparatively considered, gradient descent error backpropagation provided the best and most stable out-of-sample performance results across batch and epoch-based modes of operation. If the goal is to maximize learning speed and a sacrifice in classification accuracy is acceptable, then PR-conjugate gradient error backpropagation tends to be superior. If the training set is very large, stochastic epoch-based versions of local optimizers should be chosen utilizing a larger rather than a smaller epoch size to avoid unacceptable instabilities in the classification results.     

The Return of Zipf: Towards a Further Understanding of the Rank-Size Distribution

We offer a general-equilibrium economic approach to Zipf's Law or, more generally, the rank-size distribution—the striking empirical regularity concerning the size distribution of cities. We provide some further understanding of Zipf's Law by incorporating negative feedbacks (congestion) in a popular model of economic geography and international trade. This model allows the powers of agglomeration and spreading to be in long-run equilibrium, which enhances our understanding of the existence of a rank-size distribution of cities.