Political determinants of fiscal transparency: a panel data empirical investigation

The investigation of the determinants of fiscal transparency has been mostly performed on cross-sectional data, and it has produced mixed results. This paper improves the existing literature by performing a static and dynamic panel analysis of the effect of a set of political variables on the level of fiscal transparency in 36 democratic countries. By using a recent measure of fiscal transparency based on IMF Governance Finance Statistics and available from 2003 to 2013, we find strong links between political environments and the dynamics of fiscal disclosure. Our results show that government control over the legislature exerts some negative effect on fiscal transparency, while the effect of government ideology is shown to be at least fragile. Furthermore we find that legislature fragmentation exerts a negative effect on fiscal transparency, which suggests that competition within the parliaments does not increase fiscal transparency, but instead it induces governments to react by reducing accessibility to information.     

E-Index for measuring concentration

The goal of measuring the existing concentration of an economic magnitude among the elements in which it is distributed has been over the past years one of the major worries in the economic statistics arena. Among all the concentration indexes, the Gini index has been applied to a greater extent. This study considers that the traditional Gini index definition, according to a wide array of introductory statistics textbooks, is only applicable in the case of unitary frequency distributions. When dealing with nonunitary frequency distributions, it is possible to convert them into unitary ones, allowing for the application of the Gini index. Nevertheless, it can be appreciated how laborious such a task will be in those cases where the number of elements of the distribution is of considerable magnitude. The aim of this paper is to avoid this handicap by presenting an alternative index, the E-index.     

The decision-influencing use of performance measurement systems in relationships between headquarters and subsidiaries

Although theoretical frameworks assume that performance measurement systems (PMS) can be employed for different uses, there is a lack of prior empirical research examining the use of PMS. In addition, recent International Business studies reveal many unresolved issues about the use of PMS to manage relationships between headquarters and subsidiaries. After summarizing the evolution of the use of PMS over three International Business eras, we focus on the decision-influencing use of PMS, operationalized as the influence of the PMS implemented by headquarters on subsidiaries’ decisions. Based on International Business literature and Management Accounting research, we hypothesize that the subsidiary participation in PMS design, measurement diversity in PMS structure, the linking of PMS to reward, as well as headquarters’ national culture, subsidiary size, and global pressure affect the influence of PMS on subsidiaries’ decisions. We collected data through questionnaires emailed to 100 subsidiaries. Findings show that PMS have a greater influence on decisions in cases of higher subsidiary's participation in PMS design, headquarters’ cultural tolerance for uncertainty, subsidiary size, and global pressure. Contrary to what is contended by advocates of multidimensional approaches to PMS, measurement diversity and the linking of PMS to reward mechanisms do not have a significant impact on the decision-influencing use. We discuss the empirical evidence providing qualitative arguments derived from a focus group, which reveals the existence of a PMS decoupling and helped us to describe four situations with different levels of decision-influencing use of PMS and different economic performance results. The presented quantitative and qualitative empirical evidence offers several insights for research on PMS within multinational companies.     

Modeling current and emerging threats in the airport domain through adversarial risk analysis

Airports are critical infrastructures entailing intense human, commercial and economic activity. As such, they are preferred targets for criminal and terrorist groups, who are attracted by the promisingly high revenues they might get from an attack. Every year, airport authorities worldwide have to face, with limited resources, attacks arising from different adversaries. There are several sensible areas within an airport organization that are especially vulnerable to the terrorist threat, including, among others: (1) those related to human lives (of passengers or staff); (2) airport infrastructure (airport perimeter, main terminal, Air Traffic Control Tower, runways, hangars, etc.); (3) aircrafts and other ground vehicles; and (4) IT systems and services. Besides the more traditional ones, we are particularly concerned with attacks launched against the last type of targets, an emerging and increasingly worrisome threat. Specifically, we analyze the impact of cyber-attacks launched by organized groups whose main goal is to take hold of airport operations. In some cases, in order to have more chances to achieve their purpose (and take advantage of its eventual success), cyber attackers may be backed up by a terrorist group who will try to interfere with the Air Traffic Management network. In this paper, we aim at supporting airport authorities in their fight against both threats, by devising a security allocation plan. We provide an adversarial risk analysis model to address the problem, and apply it to obtain the optimal portfolio of preventive measures in an illustrative case study. The model is open to extensions, as e.g. larger and more complex technical infrastructures, new threats, or additional recovery measures deployed by different defensive agents.     

Multiperiod analysis of a levered portfolio

The notion of Internal Financial Law for an investment is introduced. Through this generalization of the IRR a general notion of outstanding capital is obtained. After the introduction of a generalized version of NPV a decomposition of this parameter is offered which is strictly connected to the notion of ROE.Some applications to yield averaging for portfolios is provided.     

A note on symmetry breaking in a non linear marketing model

Decisions in Economics and Finance - In this paper, we consider the nonlinear discrete-time dynamic model proposed by Bischi and Baiardi (Chaos Solitons Fractals 79:145-156, 2015a). The model...     

Insights on Tax Evasion Using a Monetary Circuit Model

The paper analyses the impact of tax evasion on government budgets, using a monetary circuit model. It is possible to show that, under specific parameter values, evading taxes on profit becomes an attractive proposition when government policies tax capital and employment in different ways and on different timescales. Without adequate tax enforcement, government is reduced to acting as a financial backer for companies. Furthermore, a lower tax rate on labour income enabling money to flow from households to companies through private consumption reduces incentives for tax evasion. These results hold true even if the government decides to implement a balanced budget fiscal policy.     

Business model shifts: a case study on firms that apply high technology to cultural goods

Business models (BMs) in high-tech industries are analysed from a dynamic point of view in the context of cultural goods. Despite the increased interest in BMs, there is still a gap in terms of the generally accepted definition and of how BMs are analysed within an integrated framework. The aim of this paper is twofold: to provide a theoretical framework to understand BMs and how they change over time, and an empirical investigation of the characteristics of firms that apply high technology to cultural goods (HTCGs). Having defined what an HTCG firm is, within- and cross-case analyses are carried out on 30 firms that operate in the field of the cultural goods in Tuscany (Italy). The results showed interesting patterns of evolution of BMs, thus providing useful insights into the unexplored field of HTCGs.     

Forecasting economic activity by Bayesian bridge model averaging

This paper proposes the use of Bayesian model averaging (BMA) as an alternative tool to forecast GDP relative to simple bridge models and factor models. BMA is a computationally feasible method that allows us to explore the model space even in the presence of a large set of candidate predictors. We test the performance of BMA in now-casting by means of a recursive experiment for the euro area and the three largest countries. This method allows flexibility in selecting the information set month by month. We find that BMA-based forecasts produce smaller forecast errors than standard bridge model when forecasting GDP in Germany, France and Italy. At the same time, it also performs as well as medium-scale factor models when forecasting Eurozone GDP.