Risk Mapping of Groundwater‐Drawdown‐Induced Land Subsidence in Heterogeneous Soils on Large Areas

Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large‐scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics‐based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from low‐risk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk‐reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Gender differences in labour market integration trajectories of recently arrived migrants in the Netherlands

This study investigates gender differences in recently arrived migrants’ labour market activity and occupational status both shortly after arrival and with increasing length of stay. We examine the role of education, household composition and traditional gender role values by estimating multi-group multilevel models based on three waves of the New Immigrants to the Netherlands Survey. In line with findings regarding gender gaps in labour market behaviour, recent female migrants are less active on labour market than their male counterparts, and we observe a clear motherhood penalty and fatherhood premium on the number of hours worked. Men and women show only marginal differences in their occupational statuses. Changes over time do not differ between men and women, indicating persistent gender inequality in labour market attainment. Moreover, interesting differences between the nationalities were found. Polish migrants show the highest activity levels and lowest occupational status, also when compared to Bulgarians. Spanish migrants hold the highest occupational statuses. Recent Turkish migrants seem to be better integrated and show fewer gender differences than the more established Turkish minority in the Netherlands.     

Toward an emergent Asian behavioural model of perceived managerial and leadership effectiveness: a cross-nation comparative analysis of effective and ineffective managerial behaviour of private sector managers in India and South Korea

ABSTRACT

This Type 4 (emic-and-etic) indigenous cross-case/cross-nation comparative study compares the results of two Type 3 (emic-as-emic) indigenous replication studies of effective and ineffective managerial behaviour carried out within private companies in India and South Korea respectively. The method used was ‘realist qualitative content analysis’ involving inductive open and axial coding. Of the Indian findings 100% were found to be convergent in meaning with 94.43% of the equivalent South Korean findings. This has led to the identification of a two-factor emergent Asian behavioural model of perceived managerial and leadership effectiveness comprised of 16 positive (effective) and 6 negative (ineffective) generic behavioural criteria. These criteria could be used in both countries to critically review and improve extant, or develop new, competency-based management/leadership development programmes. The research findings lend no support to claims that national culture has a major impact on managerial and leadership practices, styles, and effectiveness.     

Dynamic inference for non-Markov transition probabilities under random right censoring

The main contribution of this article is the verification of weak convergence of a general non-Markov (NM) state transition probability estimator by Titman, which has not yet been done for any other general NM estimator. A similar theorem is shown for the bootstrap, yielding resampling-based inference methods for statistical functionals. Formulas of the involved covariance functions are presented in detail. Particular applications include the conditional expected length of stay in a specific state, given occupation of another state in the past, and the construction of time-simultaneous confidence bands for the transition probabilities. The expected lengths of stay in a two-sample liver cirrhosis dataset are compared and confidence intervals for their difference are constructed. With borderline significance and in comparison to the placebo group, the treatment group has an elevated expected length of stay in the healthy state given an earlier disease state occupation. In contrast, the Aalen-Johansen (AJ) estimator-based confidence interval, which relies on a Markov assumption, leads to a drastically different conclusion. Also, graphical illustrations of confidence bands for the transition probabilities demonstrate the biasedness of the AJ estimator in this data example. The reliability of these results is assessed in a simulation study.     

Negotiating Accessibility for Electronic Resources

Abstract

Accessibility of library electronic resources is a must. Its importance derives from professional ethics of librarianship, rising total costs of acquisition, and mounting legal challenges to colleges and universities that fail to provide resources accessible to users with disabilities. Library staff are responsible for ensuring the accessibility of vendor-licensed eresources. This column reviews the accessibility clauses of nine model license agreements for electronic resources. It describes terms that should go into an optimal accessibility clause and creates a composite model clause. It also provides guidance for library staff seeking to negotiate stronger accessibility language into vendor license agreements. Finally, it addresses the impact of accommodation requests on the total cost of acquiring library eresources, concluding with a call to redouble efforts to advocate for greater accessibility and educate both vendors and library staff about its importance.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

农用承包地流转意愿的实证研究--基于福建省六地市农户问卷调查

从农户意愿角度，以福建省农民的农用承包地为研究对象，对影响农户承包地流转的因素进行分析，通过实地调查和问卷调查的形式收集到影响农户流转承包地的样本数据，并对通过建立二元Logistic模型对各个因素的显著性进行检验。通过实证研究发现，户主受教育程度、户主的职业、农户家庭的人口、家庭的收入主要来源、承包地面积和农户流转承包地的租金水平是农户流转承包地的显著性影响因素，其中户主受教育程度、家庭的收入主要来源、承包地面积和租金水平是正向影响，户主的职业和农户家庭的人口是负向影响。根据研究的结论提出相应的政策建议。     

Tests for time series of counts based on the probability-generating function

We propose testing procedures for the hypothesis that a given set of discrete observations may be formulated as a particular time series of counts with a specific conditional law. The new test statistics incorporate the empirical probability-generating function computed from the observations. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is included as well as real-data examples.     

Research note: The consequences of different methods for handling missing network data in stochastic actor based models

Although stochastic actor-based models (e.g., as implemented in the SIENA software program) are growing in popularity as a technique for estimating longitudinal network data, a relatively understudied issue is the consequence of missing network data for longitudinal analysis. We explore this issue in our research note by utilizing data from four schools in an existing dataset (the AddHealth dataset) over three time points, assessing the substantive consequences of using four different strategies for addressing missing network data. The results indicate that whereas some measures in such models are estimated relatively robustly regardless of the strategy chosen for addressing missing network data, some of the substantive conclusions will differ based on the missing data strategy chosen. These results have important implications for this burgeoning applied research area, implying that researchers should more carefully consider how they address missing data when estimating such models.