Flow and stock analysis of Polish unemployment: January 1990-June 1991*

Abstract. Unemployment is and will be for the foreseeable future one of the major issues of economies in transition. Not onIy is unemployment in post-communist economies a socially explosive problem; what happens on the unemployment front allows us to also make inferences about the state of the transition process in general. In this paper we look at Polish unemployment as it evolved from January 1990 to June 1991. A framework is developed which allows us to discuss flows into and out of unemployment. In analyzing the determinants of the various flows we discuss the macroeconomic environment brought about by the reforms and the role of prices and wages. However, we stress above all institutional and structural factors which have an impact on labour flows in the Polish post-communist economy. The stocks of several variables are also analyzed. We relate the trends of some unemployment stocks (especially the stocks of school leavers and group layoffs) to the trend of the overall stock of unemployment. We also look at the trends of vacancies and of various UIV ratios and touch upon the short run and long run trends of employment by sector. The main conclusion of the paper is that the high level of unemployment in Poland is neither due to the elimination of hidden unemployment (it actually increased in 1990!) nor a result of restructuring. The level of unemployment is high because of large inflows from outside the labour force and because hirings have been much fewer than in previous years while separations have only been slightly greater.     

Approximation Algorithms for Certain Network Improvement Problems

We study budget constrained network upgrading problems. Such problems aim at finding optimal strategies for improving a network under some cost measure subject to certain budget constraints. Given an edge weighted graph G = (V, E), in the edge based upgrading model, it is assumed that each edge e of the given network also has an associated function ce (t) that specifies the cost of upgrading the edge by an amount t. A reduction strategy specifies for each edge e the amount by which the length (e) is to be reduced. In the node based upgrading model, a node v can be upgraded at an expense of c(v). Such an upgrade reduces the delay of each edge incident on v. For a given budget B, the goal is to find an improvement strategy such that the total cost of reduction is at most the given budget B and the cost of a subgraph (e.g. minimum spanning tree) under the modified edge lengths is the best over all possible strategies which obey the budget constraint.After providing a brief overview of the models and definitions of the various problems considered, we present several new results on the complexity and approximability of network improvement problems.     

Evaluation strategies for case series: is Cox regression an alternative to the self controlled case series method for terminal events?

In this paper, we deal with the analysis of case series. The self-controlled case series method (SCCS) was developed to analyse the temporal association between time-varying exposure and an outcome event. We apply the SCCS method to the vaccination data of the German Examination Survey for Children and Adolescents (KiGGS). We illustrate that the standard SCCS method cannot be applied to terminal events such as death. In this situation, an extension of SCCS adjusted for terminal events gives unbiased point estimators. The key question of this paper is whether the general Cox regression model for time-dependent covariates may be an alternative to the adjusted SCCS method for terminal events. In contrast to the SCCS method, Cox regression is included in most software packages (SPSS, SAS, STATA, R, …) and it is easy to use. We can show that Cox regression is applicable to test the null hypothesis. In our KiGGS example without censored data, the Cox regression and the adjusted SCCS method yield point estimates almost identical to the standard SCCS method. We have conducted several simulation studies to complete the comparison of the two methods. The Cox regression shows a tendency to underestimate the true effect with prolonged risk periods and strong effects (Relative Incidence >2). If risk of the event is strongly affected by the age, the adjusted SCCS method slightly overestimates the predefined exposure effect. Cox regression has the same efficiency as the adjusted SCCS method in the simulation.     

Volatility and GMM — Monte Carlo studies and empirical estimations

In this article we examine small sample properties of a generalized method of moments (GMM) estimation using Monte Carlo simulations. We assume that the generated time series describe the stochastic variance rate of a stock index. we use a mean reverting square-root process to simulate the dynamics of this instantaneous variance rate. The time series obtained are used to estimate the parameters of the assumed variance rate process by applying GMM. Our results are described and compared to estimates from empirical data which consist of volatility as well as daily volume data of the German stock market. One of our main findings is that estimates of the mean reverting parameter that are not significantly different from zero do not necessarily imply a rejection of the hypothesis of a mean reverting behavior of the underlying stochastic process.     

Resilience and Climate Change Adaptation: The Importance of Framing

In the Australian policy context, there has recently been a discernible shift in the discourse used when considering responses to the impacts of current weather extremes and future climate change. Commonly used terminology, such as climate change impacts and vulnerability, is now being increasingly replaced by a preference for language with more positive connotations as represented by resilience and a focus on the ‘strengthening’ of local communities. However, although this contemporary shift in emphasis has largely political roots, the scientific conceptual underpinning for resilience, and its relationship with climate change action, remains contested. To contribute to this debate, the authors argue that how adaptation is framed—in this case by the notion of resilience—can have an important influence on agenda setting, on the subsequent adaptation pathways that are pursued and on eventual adaptation outcomes. Drawing from multi-disciplinary adaptation research carried out in three urban case studies in the State of Victoria, Australia (‘Framing multi-level and multi-actor adaptation responses in the Victorian context’, funded by the Victorian Centre for Climate Change Adaptation Research (2010–2012)), this article is structured according to three main discussion points. Firstly, the importance of being explicit when framing adaptation; secondly, this study reflects on how resilience is emerging as part of adaptation discourse and narratives in different scientific, research and policy-making communities; and finally, the authors reflect on the implications of resilience framing for evolving adaptation policy and practice.     

AN OPERATIONAL PLANNING CONCEPT FOR DEEP LANE STORAGE SYSTEMS

The main advantage of deep lane storage systems compared with conventional high bay warehouses is seen in a better space utilization, because products are stored in channels one pallet behind the other. However, for deep lane storage systems the last-in-first-out principle holds and direct access to pallets is lost apart from the last pallet entering a channel. To operate deep lane storage systems effectively, namely, providing high throughput rates even at times of high storage rack utilization requires a sophisticated operational planning system. We will describe a totally new concept consisting of five modules for storage and retrieval assignments, as well as for a reorganization of storage location occupations.     

Zum Tod von Alphons Silbermann (12.8.1909–4.3.2000)

Ohne Zusammenfassung     

Trend and seasonal decomposition in discrete time

A time series is decomposed into a trend-cyclical plus seasonal component by minimizing the sum of a smoothness and a goodness of fit criterion. The smoothness criterion is such that polynomials (trend-cyclical component) and trigonometric functions (seasonal component) are rated with the ideal value zero. The residual sum of squares serves as goodness of fit criterion. The solution is then decomposed into the two components in a natural way.