Agency Problems and Airport Security: Quantitative and Qualitative Evidence on the Impact of Security Training

We analyze the issue of agency costs in aviation security by combining results from a quantitative economic model with a qualitative study based on semi‐structured interviews. Our model extends previous principal‐agent models by combining the traditional fixed and varying monetary responses to physical and cognitive effort with nonmonetary welfare and potentially transferable value of employees' own human capital. To provide empirical evidence for the tradeoffs identified in the quantitative model, we have undertaken an extensive interview process with regulators, airport managers, security personnel, and those tasked with training security personnel from an airport operating in a relatively high‐risk state, Turkey. Our results indicate that the effectiveness of additional training depends on the mix of “transferable skills” and “emotional” buy‐in of the security agents. Principals need to identify on which side of a critical tipping point their agents are to ensure that additional training, with attached expectations of the burden of work, aligns the incentives of employees with the principals' own objectives.     

Illegal Immigrants and the Informal Economy in Italy

ABSTRACT: The present work is divided into three parts. In the first part the (limited) empirical data relevant to the phenomenon in question is presented, that is, the growing illegality of Italian immigration and the illegal work that immigrants are destined to take up. Obviously the illegality of their presence and the íllicitness of the work are, by definition, phenomena which are difficult to quantify. Some estimates, albeit limited to certain regions, are nevertheless available and the picture that emerges is coherent with the present hypothesis. The hypothesis is then explained at a theoretical level, in the second part, where a simple model of general equilibrium is presented, referring to a two-sector economy with a single good. The model has the sole aim of illustrating the plausible characteristics of an economy characterised by the phenomenon that this present paper attempts to clarify. In the third part, to give an example, a simulation is put forward, utilising, in the model, values not incoherent with estimates based on Italy as a whole.     

Robust estimation in long-memory processes under additive outliers

In this paper, we introduce an alternative semiparametric estimator of the fractional differencing parameter in ARFIMA models which is robust against additive outliers. The proposed estimator is a variant of the GPH estimator [Geweke, J., Porter-Hudak, S., 1983. The estimation and application of long memory time series model. Journal of Time Series Analysis 4, 221–238]. In particular, we use the robust sample autocorrelations of Ma, Y. and Genton, M. [2000. Highly robust estimation of the autocovariance function. Journal of Time Series Analysis 21, 663–684] to obtain an estimator for the spectral density of the process. Numerical results show that the estimator we propose for the differencing parameter is robust when the data contain additive outliers.     

An automatic classification and robust segmentation procedure of spatial objects

This paper proposes a statistical procedure for the automatic volumetric primitives classification and segmentation of 3D objects surveyed with high density laser scanning range measurements. The procedure is carried out in three main phases: first, a Taylor’s expansion nonparametric model is applied to study the differential local properties of the surface so to classify and identify homogeneous point clusters. Classification is based on the study of the surface Gaussian and mean curvature, computed for each point from estimated differential parameters of the Taylor’s formula extended to second order terms. The geometrical primitives are classified into the following basic types: elliptic, hyperbolic, parabolic and planar. The last phase corresponds to a parametric regression applied to perform a robust segmentation of the various primitives. A Simultaneous AutoRegressive model is applied to define the trend surface for each geometric feature, and a Forward Search procedure puts in evidence outliers or clusters of non stationary data. An erratum to this article can be found at