DOES IDIOSYNCRATIC BUSINESS RISK MATTER FOR GROWTH?

Several imperfections can prevent entrepreneurs from diversifying away the idiosyncratic risk of their business. As a result idiosyncratic risk discourages entrepreneurial activity and hinders growth, with the effects being stronger in economies with lower risk diversification opportunities. In accordance with this prediction, we find that OECD countries with low levels of risk diversification opportunities (as measured by the relevance of family firms or of widely held companies) perform relatively worse (in terms of productivity, investment, and business creation) in sectors characterized by high idiosyncratic risk. Differently from previous literature, we allow risk to be country specific. Since risk is endogenous to risk diversification opportunities, we instrument its value using sectoral risk in the United States, a country where idiosyncratic business risk can be more easily diversified away. Tackling the endogeneity of risk and recognizing that it varies by country magnifies the estimated effects of risk on growth.     

Theorizing notions of the participation of people in poverty in social policymaking: Policing,politics and subjectification

Since the 1990s, a paradigm of participation has gained prominence and become a dominant policy rhetoric in anti-poverty policymaking in Europe, embracing the key idea that people in poverty should participate as equal citizens in political decision-making processes. Based on a historical case study of the production process of a Belgian white paper, the General Report on Poverty (1994), we investigate who participated in the GRP production process, and whether the underlying participatory and democratic mechanisms produced a shift in power and might have led to a more socially just and equal society. We rely on a central set of ideas of the French philosopher Jacques Rancière to theorize different notions of participation, and tease out whether the processing of equality had the capacity to move from policing towards politics through subjectification. Our research study demonstrates how social change and a reconfiguration of the democratic order can appear as a moment of democracy, yet inevitably leads to a new police.     

The extraordinary claim of praxeology

The author states first praxeology's dilemma: if its theoremes are a priori in the unidimensional sense in which praxeology seems to be intended, then the theory as represented in the theorem is inapplicable. If it is not a priori in that sense, then praxeology is already defeated. In a concrete analysis of a part of a praxeological system the author shows that the contention which sees economic theory simply as the result of formal deduction starting from an a priori axiom is unfounded. As a language, praxeology is not water-tight; its line of reasoning must always draw from the inarticulate background of professional knowledge. Praxeology can be explained away as a process of dialectical redefinition of concepts, ultimately and inevitably dependent upon empirical hypotheses.     

The Other Side of Ownership in Nonprofit Organizations: an Economic Rationale for Democracy

Going beyond the deeply examined non-distribution constraint, which refers to the right to residual income, the paper investigates the other side of ownership, i.e. the right to residual control, to discover a general economic rationale for what we call “democracy”: a collective decision-making method based on the principles of equality and inclusiveness. The main result of the analysis is to point to the concept of perfect democracy as an efficient solution for the provision of public goods where other allocative mechanisms, such as the marketplace, fail.     

A Bernoulli autoregressive moving average model applied to rainfall occurrence

Abstract

In this article, we propose a new model for binary time series involving an autoregressive moving average structure. The proposed model, which is an extension of the GARMA model, can be used for calculating the forecast probability of an occurrence of an event of interest in cases where these probabilities are dependent on previous observations in the near term. The proposed model is used to analyze a real dataset involving a series that contains only data 0 and 1, indicating the absence or presence of rain in a city located in the central region of São Paulo state, Brazil.     

Censored Exponential Data: Large Deviations for MLEs and Posterior Distributions

In this article, we consider several statistical models for censored exponential data. We prove a large deviation result for the maximum likelihood estimators (MLEs) of each model, and a unique result for the posterior distributions which works well for all the cases. Finally, comparing the large deviation rate functions for MLEs and posterior distributions, we show that a typical feature fails for one model; moreover, we illustrate the relation between this fact and a well-known result for curved exponential models.     

Weighted likelihood latent class linear regression

Statistical Methods & Applications - A weighted likelihood approach for robust fitting of a finite mixture of linear regression models is proposed. An EM type algorithm and its variant based on...     

The unusual genetics of human longevity

In no species other than humans do cultural, social, and biological factors interact with each other in modulating complex phenotypes. Thus, the identification of genetic factors that affect human longevity is a true challenge. The model of centenarians provides us a unique opportunity to tackle this challenge. In this Perspective, we discuss some recent findings (the impact of geography and demography on the longevity phenotype, the relationship between longevity and homozygosity, the role of the nuclear-mitochondrial genome cross-talk) by which new ideas are suggested, such as the concept of a complex allele timing as a pivotal process in modulating the probability of achieving longevity.     

Well-Being and Affective Commitment among Ambulance Volunteers: A Mediational Model of Job Burnout

ABSTRACT

Burnout represents a relevant risk for ambulance volunteers. According to the Job Demands-Resources model, role conflict and social support can be considered as antecedents of burnout which, in turn, may lower affective commitment. This study aims to investigate the relationship between social support, role conflict, and affective commitment, as mediated by job burnout. Survey data were collected from 352 Italian volunteers and analyzed using structural equation modeling methods. Social support was confirmed as a protective factor, and role conflict as an antecedent of burnout which, in turn, decreases affective commitment. The relationship between role conflict and affective commitment is mediated by burnout which, on the contrary, does not mediate the effect of social support on affective commitment. Social support may protect against burnout. Because of its positive effects on a personal and an organizational level, affective commitment may be increased raising levels of supervisor support and reshaping role expectations. It could be useful to carry out future longitudinal studies, in order to confirm the antecedents and effects of job burnout; furthermore, this research should be extended to other ambulance organizations, in order to generalize the results; finally, multi-group analyses would allow to bring out possible differences between paid-staff and volunteers.     

Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions

Insurance and economic data are frequently characterized by positivity, skewness, leptokurtosis, and multi-modality; although many parametric models have been used in the literature, often these peculiarities call for more flexible approaches. Here, we propose a finite mixture of contaminated gamma distributions that provides a better characterization of data. It is placed in between parametric and non-parametric density estimation and strikes a balance between these alternatives, as a large class of densities can be implemented. We adopt a maximum likelihood approach to estimate the model parameters, providing the likelihood and the expected-maximization algorithm implemented to estimate all unknown parameters. We apply our approach to an artificial dataset and to two well-known datasets as the workers compensation data and the healthcare expenditure data taken from the medical expenditure panel survey. The Value-at-Risk is evaluated and comparisons with other benchmark models are provided.