Patterns and Drivers of Emigration of the Turkish Second Generation in the Netherlands

European Journal of Population - Using unique longitudinal data from the Dutch population registers, this study investigates the patterns and drivers of emigration of the Turkish second generation...     

Institutional Fleecing: The Slow Death of Dutch Provinces

Many theories on organizational change depart from the assumption of change for the better, that is, change in order to gain a stronger position in relation to its competitors, to become more effective or efficient, or at least to improve the organization. It is our idea that the theories developed on this basis are biased. Contrasting to this dominant approach, this article gives a longitudinal analysis of the development in an organization in decline. It is directed at explaining the downfall of a Dutch province. In the medieval empire of Charlemagne it was one of the most powerful duchies of Europe. Later it became one of the seven provinces in the powerful Dutch Republic, and nowadays it has lost nearly all its tasks and functions. Departing from neo-institutional theory this paper seeks the major causes for this downfall and specifies some of the relations in the theoretical model of punctuated equilibrium. It concludes that the historical institutional dynamics can be seen as a self-enforcing process in which punctuations can be explained by the increasing distance between individual preferences within the organization, and the pressures originating from developments in the environment.Dr. M.S. de Vries is professor in Public Administration at the Radboud University Nijmegen. Address: Thomas van Aquinostraat 5 (room 5.1.56). P.O. Box 9108, 6500 HK Nijmegen, The Netherlands, tel;++31 24 3615691.     

民族精神决定汉语的价值取向--洪堡特语言学评介

本文根据洪堡特的《论人类语言结构的差异性及其对人类精神发展的影响》,从语言的界定与民族精神、语言的认识与世界观、语言的系统性与思维方式、语言的分类与创新等方面对汉语的形成和发展进行了剖析,并由此阐明了汉语在今后发展以及创新的见解。     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location‐Specific Premiums in the Netherlands

As flood risks grow worldwide, a well‐designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood‐loss‐sharing program involving private insurance based on location‐specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS‐based flood model and a stochastic optimization procedure with respect to location‐specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile‐related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures.     

The Impact of Macro- and Micro-Economic Uncertainty on Family Formation in The Netherlands

In this paper, we study the impact of macro- and micro-economic uncertainty on family formation between 1970 and 2000 in The Netherlands. Using data of the Family Survey Dutch Population, we analysed the monthly hazard rates of experiencing the transition into first union, first marriage and parenthood after the start of the relationship of 365 male and 364 female partners by applying piecewise-constant exponential models. The results show that macro-economic uncertainties, i.e. high unemployment rates, lead to postponement of the first union and marriage, but not of the first child. In addition, we found that this relationship is not interpreted by individual-level employment insecurity, i.e. temporary employment or unemployment, which does not seem to prevent people from making long-term family commitments. Although hypothesized, we did not find that the negative effects of macro- and micro-level insecurities on family formation reinforce each other or that they vary between individuals with different educational qualifications.     

An Ecological Model for Quantitative Risk Assessment for Schistosomiasis: The Case of a Patchy Environment in the Coastal Tropical Area of Northeastern Brazil

We developed a stochastic model for quantitative risk assessment for the Schistosoma mansoni (SM) parasite, which causes an endemic disease of public concern. The model provides answers in a useful format for public health decisions, uses data and expert opinion, and can be applied to any landscape where the snail Biomphalaria glabrata is the main intermediate host (South and Central America, the Caribbean, and Africa). It incorporates several realistic and case‐specific features: stage‐structured parasite populations, periodic praziquantel (PZQ) drug treatment for humans, density dependence, extreme events (prolonged rainfall), site‐specific sanitation quality, environmental stochasticity, monthly rainfall variation, uncertainty in parameters, and spatial dynamics. We parameterize the model through a real‐world application in the district of Porto de Galinhas (PG), one of the main touristic destinations in Brazil, where previous studies identified four parasite populations within the metapopulation. The results provide a good approximation of the dynamics of the system and are in agreement with our field observations, i.e., the lack of basic infrastructure (sanitation level and health programs) makes PG a suitable habitat for the persistence and growth of a parasite metapopulation. We quantify the risk of SM metapopulation explosion and quasi‐extinction and the time to metapopulation explosion and quasi‐extinction. We evaluate the sensitivity of the results under varying scenarios of future periodic PZQ treatment (based on the Brazilian Ministry of Health's plan) and sanitation quality. We conclude that the plan might be useful to slow SM metapopulation growth but not to control it. Additional investments in better sanitation are necessary.     

Unraveling quantity discounts

We consider the situation in which a buying organization deals with a discrete quantity discount schedule offered by a selling organization. Furthermore, the buying organization can negotiate with the selling organization about the lot size and purchase price, but does not know the underlying function that was used by the selling organization to determine the quantity discount schedule. In this paper, we provide an analytical and empirical basis for one general quantity discount function (QDF) that can be used to describe the underlying function of almost all different quantity discount types. We first develop such a QDF analytically. Among other things, this QDF enables buying organizations to calculate detailed prices for a large number of quantities. We subsequently show that the QDF fits very well with 66 discount schedules found in practice. We discuss that the QDF and related indicators can be a useful tool in supplier selection and negotiation processes. It can also be used for competitive analyses, multiple sourcing decisions, and allocating savings for purchasing groups. Additionally, the QDF can be included in research models incorporating quantity discounts. We conclude the paper with an outlook on further QDF research regarding the characterization of commodity markets from a demand elasticity point of view.