Boundaries of the business model within business groups

Scholars have sought various ways to find out how financial performance of the firm can be affected by its business model (BM). However, to date academic literature has focused attention on the “firm” as a unit of analysis without clearly defining the boundaries of the reporting entity to which the BMs refer. The aim of this paper is to investigate what are the boundaries of the BM of the affiliated-group companies and how the degree of independence of BMs is measured within the business group. The contribution of the paper is in using the BM concept to expound and criticise the assumptions in economic analysis and accounting standards that groups of companies are economic units that optimise economic income of the group as a whole and that the financial statements of individual subsidiaries, sub-groups and the group as a whole report the value generated by the group.     

Robust linear mixed models for Small Area Estimation

Hierarchical models are popular in many applied statistics fields including Small Area Estimation. One well known model employed in this particular field is the Fay–Herriot model, in which unobservable parameters are assumed to be Gaussian. In Hierarchical models assumptions about unobservable quantities are difficult to check. For a special case of the Fay–Herriot model, Sinharay and Stern [2003. Posterior predictive model checking in Hierarchical models. J. Statist. Plann. Inference 111, 209–221] showed that violations of the assumptions about the random effects are difficult to detect using posterior predictive checks. In this present paper we consider two extensions of the Fay–Herriot model in which the random effects are assumed to be distributed according to either an exponential power (EP) distribution or a skewed EP distribution. We aim to explore the robustness of the Fay–Herriot model for the estimation of individual area means as well as the empirical distribution function of their ‘ensemble’. Our findings, which are based on a simulation experiment, are largely consistent with those of Sinharay and Stern as far as the efficient estimation of individual small area parameters is concerned. However, when estimating the empirical distribution function of the ‘ensemble’ of small area parameters, results are more sensitive to the failure of distributional assumptions.     

A Latent Process Model for Temporal Extremes

This paper presents a hierarchical approach to modelling extremes of a stationary time series. The procedure comprises two stages. In the first stage, exceedances over a high threshold are modelled through a generalized Pareto distribution, which is represented as a mixture of an exponential variable with a Gamma distributed rate parameter. In the second stage, a latent Gamma process is embedded inside the exponential distribution in order to induce temporal dependence among exceedances. Unlike other hierarchical extreme‐value models, this version has marginal distributions that belong to the generalized Pareto family, so that the classical extreme‐value paradigm is respected. In addition, analytical developments show that different choices of the underlying Gamma process can lead to different degrees of temporal dependence of extremes, including asymptotic independence. The model is tested through a simulation study in a Markov chain setting and used for the analysis of two datasets, one environmental and one financial. In both cases, a good flexibility in capturing different types of tail behaviour is obtained.     

An SEIR Model with Time-Varying Coefficients for Analyzing the SARS-CoV-2 Epidemic

In this study, we propose a time-dependent susceptible-exposed-infected-recovered (SEIR) model for the analysis of the SARS-CoV-2 epidemic outbreak in three different countries, the United States, Italy, and Iceland using public data inherent the numbers of the epidemic wave. Since several types and grades of actions were adopted by the governments, including travel restrictions, social distancing, or limitation of movement, we want to investigate how these measures can affect the epidemic curve of the infectious population. The parameters of interest for the SEIR model were estimated employing a composite likelihood approach. Moreover, standard errors have been corrected for temporal dependence. The adoption of restrictive measures results in flatten epidemic curves, and the future evolution indicated a decrease in the number of cases.     

博洛尼亚进程中的意大利大学改革

对于欧洲大学而言,“博洛尼亚进程”是近年来居于首位的大事件。作为最早的倡导国和参与国之一,意大利积极融入博洛尼亚进程,并以此为契机改革弊端日益呈现的大学体制。改革既有历史的原因,也是社会经济发展的必然结果。1 861年意大利统一以来,意大利大学呈现出从精英到大众, 从分权到集权的特征。20世纪80年代以来的改革主要围绕“赋予大学更多自主权”和“提升大学质量”展开。1 999年《捷其诺条例》的颁布对意大利大学改革具有里程碑的意义。之后,意大利大学进行了大刀阔斧的改革 :建立基于“欧洲学分互认体系”的学分制,重置本科课程,引入“3+2”学位体系,引入“Y”体系,颁发文凭补充文件等。这些改革对意大利大学体制产生了深远的影响。     

Evaluating the Dynamic Effects of Active Labour Policies in Italy

Abstract. This paper analyses whether active labour market policies (ALMPs) have differing effects on unemployment and employment dynamics according to the particular region in which they are implemented. To this end, it analyses alternative theoretical and econometric models thought to capture the possible effects of active labour market policies on labour force dynamics. The econometric methodologies implemented are the generalized method of moment (GMM) and the panel vector autoregression (P‐VAR). The evidence yielded by the GMM models suggests that the effects of different ALMPs on unemployment are dissimilar across the Italian regions. It follows that some active programmes are likely to have a greater effect in the South than in the North. The results of the P‐VAR models estimated are synthesized by impulse response analysis and forecast error variance decomposition. The impulse response analysis suggests that an increase in total ALMP gives rise to: (i) a decrease in the unemployment rate; and (ii) a significant increase in labour force participation. More interestingly, the results obtained from the error variance decomposition analysis show that unemployment movements are not driven by shocks in the ALMPs and that, especially in the northern regions, atypical contracts shocks account for a substantial portion of unemployment dynamics.     

Gender and migration from Albania

This article examines the dynamics and causes of the shift in the gender composition of migration, and more particularly, in women’s access to migration opportunities and decision-making. Our analysis focuses on Albania, a natural laboratory for studying international migration where out-migration was essentially nonexistent from the end of World War II to the end of the 1980s. Interest in the Albanian case is heightened because of the complex layers of inequality existing at the time when migration began: relatively low levels of inequality within the labor market and educational system—a product of the Communist era—while household relations remained heavily steeped in tradition and patriarchy. We use micro-level data from the Albania 2005 Living Standards Measurement Study, including migration histories for family members since migration began. Based on discrete-time hazard models, the analysis shows a dramatic increase in male migration and a gradual and uneven expansion of the female proportion of this international migration. Female migration, which is shown to be strongly associated with education, wealth, and social capital, appears responsive to economic incentives and constraints. Using information on the dependency of female migration to the household demographic structure as well as the sensitivity of female migration to household-level shocks, we show how household-level constraints and incentives affect male and female migration differently. Throughout this period, however, women’s migration behavior appears more directly aligned with household-level factors, and there is little evidence to suggest that increased female migration signals rising behavioral independence among Albanian women.     

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.     

Alien plant species do have a clear preference for different land uses within urban environments

Since neophytes can become invasive in the future, untangling their ecological preferences is of paramount importance, especially in urban areas where they represent a substantial proportion of the local flora. Studies exploring alien species assemblages in urban environments are however scarce. This study aims to unravel alien plant species preferences for five urban land uses (densely built-up areas, open built-up areas, industrial areas, broadleaved urban forests, and agricultural areas and small landscape elements). We took the city of Brussels as a model, in which we recorded all vascular species growing spontaneously in grid cells of 1 km². We tested two different ways of classifying the 1-km² cells: (1) We simply associated each cell with its dominant land cover; (2) We used a fuzzy approach for which the degree of association of a given cell to a given land cover depended on the proportion of that land cover within the cell. For both classification methods, we calculated the indicator species of the resulting land cover types based on alien species only. The crisp and fuzzy classifications identified 33 and 49 species, respectively, with a clear preference for some urban land use types (from a total of 129 alien plant species analyzed). Results showed that urban land use types having apparently similar environmental conditions can actually harbor different neophyte assemblages. Fine-tuning the categorization of urban environments in future ecological studies is therefore important for understanding spatial patterns of alien species occurrence.