Words versus actions as a means to influence cooperation in social dilemma situations

We use a sequential voluntary contribution game to compare the relative impact of a first-mover’s non-binding announcement versus binding commitment on cooperation. We find that a non-binding announcement and a binding commitment increase individual contributions to a similar extent. Since announced contributions systematically exceed commitments, in sessions with a non-binding announcement, second-movers tend to contribute more to the group activity than in sessions with a binding commitment. Yet, second-movers appear to be more motivated towards achieving a social optimum when the first-mover uses commitment. We also find that a non-binding announcement has a higher impact on individual propensity to cooperate than the ex post contribution of the first-mover. However, the failure to make announced contributions decreases cooperation even though the first-mover is reassigned in every period.     

The Effects of Transformational Leadership on Organizational Performance through Knowledge and Innovation*

Today's information and knowledge society requires new leaders who can confront a reality based on knowledge and foster innovation to achieve improvements in organizational performance. However, organizations sometimes fail to achieve sustainable competitive advantage due to their limited understanding of the relationships between these strategic variables. To date, very little research has analysed the direct and indirect relationships between these variables. Our study seeks to fill this research gap by analysing theoretically and empirically how the leader's perceptions of different intermediate strategic variables related to knowledge (knowledge slack, absorptive capacity, tacitness, organizational learning) and innovation influence the relation between transformational leadership and organizational performance. Based on the literature, we develop a theoretical model that shows the interrelations between these variables. We then test the model using data from 408 Spanish organizations, discuss the findings and provide several implications for business practitioners.     

On the Smoothness of Projections in Statistical Models

Here we examine the existence of the projection of a probability measure in a parametric statistical model. Once, in a general framework, the existence of the projection is established, we consider the problem from a statistical point of view, modeling a parametric statistical model as a convenient manifold.     

Predictors of behavioural problems in foster children: the key role of impulsivity

Although several variables have been reported to be associated with behavioural problems in foster children, few studies have sought to establish more precisely the extent to which these variables may explain problematic behaviour. The main aim of this study is to determine the extent to which certain variables may predict behavioural problems shown by foster children. Participants included 104 foster children and their respective foster families. Multiple linear regression analysis revealed that the following variables predict behavioural problems in foster children: impulsivity/attention deficit in the child, level of burden in the foster carers, rigid or authoritarian parental discipline, and criticism/rejection by the foster parents. The model explained 46% of the variance in behaviour problems, with the greatest predictive power (29%) corresponding to the variable ‘impulsivity/attention deficit’. The results show that impulsivity/attention deficit is the most powerful predictor of behavioural problems in foster children. This is consistent with the findings of various studies that have reported an association between a lack of inhibitory control and problematic behaviour.     

A uniform central limit theorem for neural network-based autoregressive processes with applications to change-point analysis

We consider an autoregressive process with a nonlinear regression function that is modelled by a feedforward neural network. First, we derive a uniform central limit theorem which is useful in the context of change-point analysis. Then, we propose a test for a change in the autoregression function which – by the uniform central limit theorem – has asymptotic power one for a large class of alternatives including local alternatives not restricted to the correctly specified model.     

Accuracy of Power-Divergence Statistics for Testing Independence and Homogeneity in Two-Way Contingency Tables

The small-sample accuracy of seven members of the family of power-divergence statistics for testing independence or homogeneity in contingency tables was studied via simulation. The likelihood ratio statistic G ² and Pearson's X ² statistic are among these seven members, whose behavior was studied at nominal test sizes of.01 and.05 with marginal distributions that could be uniform or skewed and with a set of sample sizes that included sparseness conditions as measured through table density (i.e., the ratio of sample size to number of cells). The likelihood ratio statistic G ² rejected the null hypothesis too often even with large table density, whereas Pearson's X ² was sufficiently accurate and only presented a minor misbehavior when table density was less than two observations/cell. None of the other five statistics outperformed Pearson's X ². A nonasymptotic variant of X ² solved the minor inaccuracies of Pearson's X ² and turned out to be the most accurate statistic for testing independence or homogeneity, even with table densities of one observation/cell. These results clearly advise against the use of the likelihood ratio statistic G ².     

Integration and efficiency convergence in EU banking markets

Evidence of financial integration and convergence are considered of importance in assessing the outcome of EU deregulation policies aimed at improving the efficiency and performance of banking sectors. This paper evaluates the recent dynamics of bank cost efficiency by means of data envelopment analysis (DEA). Borrowing from the growth literature, we apply dynamic panel data models (GMM) to the concepts of β-convergence and σ-convergence to assess the speed at which banking markets are integrating. We also employ a partial adjustment model to evaluate convergence towards best practice. Results seem to provide supporting evidence of convergence of efficiency levels towards an EU average. Nevertheless, there is no evidence of an overall improvement of efficiency levels towards best practice.     

Variance Inflation Factor and Condition Number in multiple linear regression

The Variance Inflation Factor and the Condition Number are measures traditionally applied to detect the presence of collinearity in a multiple linear model. This paper presents the relation and the difference between both measures from theoretical and empirical perspectives by using Monte Carlo simulations and taking special interest in the computational techniques.     

The variance upper bound for a mixed random variable

In this note, we derive upper bounds on the variance of a mixed random variable. Our results are an extension of previous results for unimodal and symmetric random variables. The novelty of our findings is that this mixed random variable does not necessarily need to be symmetric and is multimodal. We also characterize the cases when these bounds are optimal.     

Testing for state dependence in binary panel data with individual covariates by a modified quadratic exponential model

We propose a test for state dependence in binary panel data with individual covariates. For this aim, we rely on a quadratic exponential model in which the association between the response variables is accounted for in a different way with respect to more standard formulations. The level of association is measured by a single parameter that may be estimated by a Conditional Maximum Likelihood (CML) approach. Under the dynamic logit model, the conditional estimator of this parameter converges to zero when the hypothesis of absence of state dependence is true. Therefore, it is possible to implement a t-test for this hypothesis which may be very simply performed and attains the nominal significance level under several structures of the individual covariates. Through an extensive simulation study, we find that our test has good finite sample properties and it is more robust to the presence of (autocorrelated) covariates in the model specification in comparison with other existing testing procedures for state dependence. The proposed approach is illustrated by two empirical applications: the first is based on data coming from the Panel Study of Income Dynamics and concerns employment and fertility; the second is based on the Health and Retirement Study and concerns the self reported health status.