Privatization of Public Services in Leipzig: A Balancing Act between Efficiency and Legitimacy

Since 1992, the budget deficits of larger German cities increased from less than one billion Euros to more than nine billion in 2005. The reasons for this fiscal collapse were a worsening economic situation partly caused by lower government revenue and higher costs of unemployment benefits. In order to reduce their deficits, the municipalities started privatizing and outsourcing large sectors of public administration. Leipzig rejected the path of privatization and became the pioneer in organizing large-scale municipal companies. This approach brought essential social benefits, most needed revenues, greater efficiency in delivering services and allowed the city to reduce almost by 50% the number of its administrative staff.     

Hedonic price indices for the Paris housing market

Summary: In this paper, we calculate a transaction–based price index for apartments in Paris (France). The heterogeneous character of real estate is taken into account using an hedonic model. The functional form is specified using a general Box–Cox function. The data basis covers 84 686 transactions of the housing market in 1990:01–1999:12, which is one of the largest samples ever used in comparable studies. Low correlations of the price index with stock and bond indices (first differences) indicate diversification benefits from the inclusion of real estate in a mixed asset portfolio.*This paper has been developed at the Research Program Real Estate Finance at Goethe-University of Frankfurt/Main (Germany). We gratefully acknowledge iii Internationales Immobilien Institut, IVG Holding AG and Stiftung Rheinische Hypothekenbank for their financial support. For further information, see www.real-estate-finance.de. We thank the participants of the 8th Conference of the European Real Estate Society (ERES), the International Conference of the American Real Estate and Urban Economics Association (AREUEA), the 10th Global Finance Conference 2003 and the anonymous referees for helpful comments, which improved the paper substantially.     

The Infant Orienting With Attention Task: Assessing the Neural Basis of Spatial Attention in Infancy

Infant visual attention develops rapidly over the first year of life, significantly altering the way infants respond to peripheral visual events. Here, we present data from 5‐, 7‐, and 10‐month‐old infants using the Infant Orienting With Attention (IOWA) task, designed to capture developmental changes in visual spatial attention and saccade planning. Results indicate rapid development of spatial attention and visual response competition between 5 and 10 months. We use a dynamic neural field (DNF) model to link behavioral findings to neural population activity, providing a possible mechanistic explanation for observed developmental changes. Together, the behavioral and model simulation results provide new insights into the specific mechanisms that underlie spatial cueing effects, visual competition, and visual interference in infancy.     

Clustering of electrical transmission systems based on network topology and stability

A proper understanding and modelling of the behaviour of heavily loaded large-scale electrical transmission systems is essential for a secure and uninterrupted operation. In this paper, we present methods to cluster electrical power networks based on different criteria into regions. These regions are useful for the efficient modelling of large transcontinental electricity networks, switching operation decisions or placement of redundant parts of the monitoring and control system. In alternating current electricity networks, power oscillations are normal, but they can become dangerous if they build up. The first approach uses the correlation between results of a stability assessment for these oscillations at every node for the cluster criterion. The second method concentrates on the network topology and uses spectral clustering on the network graph to create clusters where all nodes are interconnected. In this work, we also discuss the problem how to choose the right number of clusters and how the discussed clustering methods can be used for an efficient modelling of large electricity networks or in protection and control systems.     

The Impact of Privatization on the Earnings of Restructured Workers: Evidence From the Oil Industry

We study the long-term impact of job displacement from a big state owned enterprise as a result of its privatization in a developing country. Our results suggest large reductions in earnings, which persist throughout the years. However, we also find that the displaced worker’s post-displacement earnings are in line with competitive market wages, and unrelated to sector of employment or to tenure losses, indicating that the long-term reduction in earnings as a result of displacement because of privatization can be traced to the loss of wage rents. Our results indicate that job displacement in SOEs may have very large redistributive implications for the workers involved but that this loss does not necessarily reflect the loss of specific human capital associated to these jobs.

Predicting above-median and below-median growth rates

Multiannual periods of consecutive above-median or below-median growth rates in operating performance, called runs, have a substantial influence on firm valuations. For estimating the probability of an above-median or below-median run and utilizing information efficiently, we employ a stepwise regression to automatically identify the parsimonious indicator-specific set of economically and empirically meaningful variables. Our novel approach uses logit models to distinguish firms that will persistently grow above or below the median over a period of up to 6 years. The predictive power for sales growth rates is highest to discriminate between above-median and below-median growth rates, while the future behaviour of operating income and net income growth rates can partially be explained for below-median growth rates.     

Contextual movement models based on normalizing flows

AStA Advances in Statistical Analysis - Movement models predict positions of players (or objects in general) over time and are thus key to analyzing spatiotemporal data as it is often used in...