Does Direct Democracy Matters for Political Parties : An Empirical Test in the Swiss Cantons

According to most political scientists and commentators, direct democracy seems to weaken political parties. Our empirical analysis in the 26 Swiss cantons shows that this thesis in its general form cannot be maintained. Political parties in cantons with extensive use of referendums and initiatives are not in all respects weaker than parties in cantons with little use of direct democratic means of participation. On the contrary, direct democracy goes together with more professional and formalized party organizations. Use of direct democracy is associated with more fragmented and volatile party systems, and with greater support for small parties, but causal interpretations of these relationships are difficult.

Systematic review of the empirical evidence of study publication bias and outcome reporting bias - an updated review.

BACKGROUND: The increased use of meta-analysis in systematic reviews of healthcare interventions has highlighted several types of bias that can arise during the completion of a randomised controlled trial. Study publication bias and outcome reporting bias have been recognised as a potential threat to the validity of meta-analysis and can make the readily available evidence unreliable for decision making. METHODOLOGY/PRINCIPAL FINDINGS: In this update, we review and summarise the evidence from cohort studies that have assessed study publication bias or outcome reporting bias in randomised controlled trials. Twenty studies were eligible of which four were newly identified in this update. Only two followed the cohort all the way through from protocol approval to information regarding publication of outcomes. Fifteen of the studies investigated study publication bias and five investigated outcome reporting bias. Three studies have found that statistically significant outcomes had a higher odds of being fully reported compared to non-significant outcomes (range of odds ratios: 2.2 to 4.7). In comparing trial publications to protocols, we found that 40-62% of studies had at least one primary outcome that was changed, introduced, or omitted. We decided not to undertake meta-analysis due to the differences between studies. CONCLUSIONS: This update does not change the conclusions of the review in which 16 studies were included. Direct empirical evidence for the existence of study publication bias and outcome reporting bias is shown. There is strong evidence of an association between significant results and publication; studies that report positive or significant results are more likely to be published and outcomes that are statistically significant have higher odds of being fully reported. Publications have been found to be inconsistent with their protocols. Researchers need to be aware of the problems of both types of bias and efforts should be concentrated on improving the reporting of trials.

Work motivation and pro-social behavior in the delivery of public services : theoretical and empirical insights

This Ph.D. dissertation seeks to study the work motivation of employees in the delivery of public services. The questioning on work motivation in public services in not new but it becomes central for governments which are now facing unprecedented public debts. The objective of this research is twofold : First, we want to see if the work motivation of employees in public services is a continuum (intrinsic and extrinsic motivations cannot coexist) or a bi-dimensional construct (intrinsic and extrinsic motivations coexist simultaneously). The research in public administration literature has focused on the concept of public service motivation, and considered motivation to be uni-dimensional (Perry and Hondeghem 2008). However, no study has yet tackled both types of motivation, the intrinsic and extrinsic ones, in the same time. This dissertation proposes, in Part I, a theoretical assessment and an empirical test of a global work motivational structure, by using a self-constructed Swiss dataset with employees from three public services, the education sector, the security sector and the public administrative services sector. Our findings suggest that work motivation in public services in not uni-dimensional but bi-dimensional, the intrinsic and extrinsic motivations coexist simultaneously and can be positively correlated (Amabile et al. 1994). Our findings show that intrinsic motivation is as important as extrinsic motivation, thus, the assumption that employees in public services are less attracted by extrinsic rewards is not confirmed for this sample. Other important finding concerns the public service motivation concept, which, as theoretically predicted, represents the major motivational dimension of employees in the delivery of public services. Second, the theory of public service motivation makes the assumption that employees in public services engage in activities that go beyond their self-interest, but never uses this construct as a determinant for their pro-social behavior. In the same time, several studies (Gregg et al. 2011 and Georgellis et al. 2011) bring evidence about the pro-social behavior of employees in public services. However, they do not identify which type of motivation is at the origin of this behavior, they only make the assumption of an intrinsically motivated behavior. We analyze the pro-social behavior of employees in public services and use the public service motivation as determinant of their pro-social behavior. We add other determinants highlighted by the theory of pro-social behavior (Bénabou and Tirole 2006), by Le Grand (2003) and by fit theories (Besley and Ghatak 2005). We test these determinants on Part II and identify for each sector of activity the positive or the negative impact on pro-social behavior of Swiss employees. Contrary to expectations, we find, for this sample, that both intrinsic and extrinsic factors have a positive impact on pro-social behavior, no crowding-out effect is identified in this sample. We confirm the hypothesis of Le Grand (2003) about the positive impact of the opportunity cost on pro-social behavior. Our results suggest a mix of action-oriented altruism and out-put oriented altruism of employees in public services. These results are relevant when designing incentives schemes for employees in the delivery of public services.

The contribution of rectangularization to the secular increase of life expectancy: an empirical study.

BACKGROUND: In low-mortality countries, life expectancy is increasing steadily. This increase can be disentangled into two separate components: the delayed incidence of death (i.e. the rectangularization of the survival curve) and the shift of maximal age at death to the right (i.e. the extension of longevity). METHODS: We studied the secular increase of life expectancy at age 50 in nine European countries between 1922 and 2006. The respective contributions of rectangularization and longevity to increasing life expectancy are quantified with a specific tool. RESULTS: For men, an acceleration of rectangularization was observed in the 1980s in all nine countries, whereas a deceleration occurred among women in six countries in the 1960s. These diverging trends are likely to reflect the gender-specific trends in smoking. As for longevity, the extension was steady from 1922 in both genders in almost all countries. The gain of years due to longevity extension exceeded the gain due to rectangularization. This predominance over rectangularization was still observed during the most recent decades. CONCLUSIONS: Disentangling life expectancy into components offers new insights into the underlying mechanisms and possible determinants. Rectangularization mainly reflects the secular changes of the known determinants of early mortality, including smoking. Explaining the increase of maximal age at death is a more complex challenge. It might be related to slow and lifelong changes in the socio-economic environment and lifestyles as well as population composition. The still increasing longevity does not suggest that we are approaching any upper limit of human longevity.

Estimation of jump-diffusion processes via empirical characteristic functions

Preface The starting point for this work and eventually the subject of the whole thesis was the question: how to estimate parameters of the affine stochastic volatility jump-diffusion models. These models are very important for contingent claim pricing. Their major advantage, availability T of analytical solutions for characteristic functions, made them the models of choice for many theoretical constructions and practical applications. At the same time, estimation of parameters of stochastic volatility jump-diffusion models is not a straightforward task. The problem is coming from the variance process, which is non-observable. There are several estimation methodologies that deal with estimation problems of latent variables. One appeared to be particularly interesting. It proposes the estimator that in contrast to the other methods requires neither discretization nor simulation of the process: the Continuous Empirical Characteristic function estimator (EGF) based on the unconditional characteristic function. However, the procedure was derived only for the stochastic volatility models without jumps. Thus, it has become the subject of my research. This thesis consists of three parts. Each one is written as independent and self contained article. At the same time, questions that are answered by the second and third parts of this Work arise naturally from the issues investigated and results obtained in the first one. The first chapter is the theoretical foundation of the thesis. It proposes an estimation procedure for the stochastic volatility models with jumps both in the asset price and variance processes. The estimation procedure is based on the joint unconditional characteristic function for the stochastic process. The major analytical result of this part as well as of the whole thesis is the closed form expression for the joint unconditional characteristic function for the stochastic volatility jump-diffusion models. The empirical part of the chapter suggests that besides a stochastic volatility, jumps both in the mean and the volatility equation are relevant for modelling returns of the S&P500 index, which has been chosen as a general representative of the stock asset class. Hence, the next question is: what jump process to use to model returns of the S&P500. The decision about the jump process in the framework of the affine jump- diffusion models boils down to defining the intensity of the compound Poisson process, a constant or some function of state variables, and to choosing the distribution of the jump size. While the jump in the variance process is usually assumed to be exponential, there are at least three distributions of the jump size which are currently used for the asset log-prices: normal, exponential and double exponential. The second part of this thesis shows that normal jumps in the asset log-returns should be used if we are to model S&P500 index by a stochastic volatility jump-diffusion model. This is a surprising result. Exponential distribution has fatter tails and for this reason either exponential or double exponential jump size was expected to provide the best it of the stochastic volatility jump-diffusion models to the data. The idea of testing the efficiency of the Continuous ECF estimator on the simulated data has already appeared when the first estimation results of the first chapter were obtained. In the absence of a benchmark or any ground for comparison it is unreasonable to be sure that our parameter estimates and the true parameters of the models coincide. The conclusion of the second chapter provides one more reason to do that kind of test. Thus, the third part of this thesis concentrates on the estimation of parameters of stochastic volatility jump- diffusion models on the basis of the asset price time-series simulated from various "true" parameter sets. The goal is to show that the Continuous ECF estimator based on the joint unconditional characteristic function is capable of finding the true parameters. And, the third chapter proves that our estimator indeed has the ability to do so. Once it is clear that the Continuous ECF estimator based on the unconditional characteristic function is working, the next question does not wait to appear. The question is whether the computation effort can be reduced without affecting the efficiency of the estimator, or whether the efficiency of the estimator can be improved without dramatically increasing the computational burden. The efficiency of the Continuous ECF estimator depends on the number of dimensions of the joint unconditional characteristic function which is used for its construction. Theoretically, the more dimensions there are, the more efficient is the estimation procedure. In practice, however, this relationship is not so straightforward due to the increasing computational difficulties. The second chapter, for example, in addition to the choice of the jump process, discusses the possibility of using the marginal, i.e. one-dimensional, unconditional characteristic function in the estimation instead of the joint, bi-dimensional, unconditional characteristic function. As result, the preference for one or the other depends on the model to be estimated. Thus, the computational effort can be reduced in some cases without affecting the efficiency of the estimator. The improvement of the estimator s efficiency by increasing its dimensionality faces more difficulties. The third chapter of this thesis, in addition to what was discussed above, compares the performance of the estimators with bi- and three-dimensional unconditional characteristic functions on the simulated data. It shows that the theoretical efficiency of the Continuous ECF estimator based on the three-dimensional unconditional characteristic function is not attainable in practice, at least for the moment, due to the limitations on the computer power and optimization toolboxes available to the general public. Thus, the Continuous ECF estimator based on the joint, bi-dimensional, unconditional characteristic function has all the reasons to exist and to be used for the estimation of parameters of the stochastic volatility jump-diffusion models.

DEA does not like positive discrimination : a comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of data envelopment analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run themselves their own efficiency analysis. Within DEA, several alternative models allow for an environment adjustment. Five alternative models, each of them easily accessible to and achievable by practitioners and decision makers, are performed using the empirical case of the 90 primary schools of the State of Geneva, Switzerland. As the State of Geneva practices an upstream positive discrimination policy towards schools, this empirical case is particularly appropriate for an environment adjustment. The alternative of the majority of DEA models deliver divergent results. It is a matter of concern for applied researchers and a matter of confusion for practitioners and decision makers. From a political standpoint, these diverging results could lead to potentially opposite decisions. Grâce à l'existence de logiciels en libre accès et de guides pédagogiques, la méthode data envelopment analysis (DEA) s'est démocratisée ces dernières années. Aujourd'hui, il n'est pas rare que les décideurs avec peu ou pas de connaissances en recherche opérationnelle réalisent eux-mêmes leur propre analyse d'efficience. A l'intérieur de la méthode DEA, plusieurs modèles permettent de tenir compte des conditions plus ou moins favorables de l'environnement. Cinq de ces modèles, facilement accessibles et applicables par les décideurs, sont utilisés pour mesurer l'efficience des 90 écoles primaires du canton de Genève, Suisse. Le canton de Genève pratiquant une politique de discrimination positive envers les écoles défavorisées, ce cas pratique est particulièrement adapté pour un ajustement à l'environnement. La majorité des modèles DEA génèrent des résultats divergents. Ce constat est préoccupant pour les chercheurs appliqués et perturbant pour les décideurs. D'un point de vue politique, ces résultats divergents conduisent à des prises de décision différentes selon le modèle sur lequel elles sont fondées.

Empirical antifungal therapy in neutropaenic cancer patients with persistent fever

Invasive fungal infections are frequent and severe complications in leukaemic patients with prolonged neutropaenia. Empirical antifungal therapy has become the standard of care in patients with persistent fever despite treatment with broad-spectrum antibiotics. For decades amphotericin B deoxycholate has been the sole option for empirical antifungal therapy. Recently, several new antifungal agents became available. The choice of the most appropriate drug should be guided by efficacy and safety criteria. The recommendations from the First European Conference on Infections in Leukaemia (ECIL-1) on empirical antifungal therapy in neutropaenic cancer patients with persistent fever have been developed by an expert panel after assessment of clinical practices in Europe and evidence-based review of the literature. Many antifungal regimens can now be recommended for empirical therapy in neutropaenic cancer patients. However, persistent fever lacks specificity for initiation of therapy. Development of empirical and pre-emptive strategies using new clinical parameters, laboratory markers and imaging techniques for early diagnosis of invasive mycoses are needed.

Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.