Growth,unemployment and heterogeneity

The paper analyzes unemployment in a medium-run growth model, where aggregate demand and supply interact, using a top-down approach. The aim of the essay is the study of a nonlinear system where both aggregate demand and supply are endogenous and generate bounded unemployment, followed by a methodological effort direct to identify possible lines of convergence with the agent based models (ABM) approach. This is a by-product of the presence of heterogeneity in the model. Heterogeneity acts through two different channels and operates among class of agents: it comes into the aggregate consumption function where households are assumed employed or unemployed; it changes the learning process of pessimists and optimists. The analysis is carried on through simulations. The resulting system is fairly stable to changes in main structural parameters. On one hand, autonomous demand drives the dynamics of the system, while heterogeneity in the consumption function, due to the presence of unemployment, strengthens the links with supply aspects. On the other hand, both the rate of growth of labor productivity and labor supply are endogenous. Two major results are obtained. First, unemployment allows the so called Harrodian reconciliation between aggregate demand and supply. Second, unemployment remains bounded meaning that the interaction between aggregate demand and supply thwarts instability. These results are in keeping with those obtained by means of a bottom-up approach, typical of ABM. Possible explanations and implications of this convergence are put forward and open the venue to further deepening of complementarities among the two modeling strategies.

     

“Openness” of public governments in smart cities: removing the barriers for innovation and entrepreneurship

International Entrepreneurship and Management Journal - Open innovation practices have been found to positively affect innovation and entrepreneurship due to the complementarities and uniqueness of...     

Who promotes a value‐in‐diversity perspective? A fuzzy set analysis of executives’ individual and organizational characteristics

Diversity perspectives are philosophies of or approaches to diversity held by organizations, groups, or executives. They are important for organizations because they can determine the success or failure of diversity in the workforce. However, little is known about the predictors of diversity perspectives among executives. Using fuzzy set qualitative comparative analysis, we analyzed 50 interviews with top executives in Germany to identify individual and organizational characteristics that predict executives' adoption of a diversity perspective, in particular of a value‐in‐diversity perspective. Specifically, we analyzed gender, age, education level, vocational background, and tenure (individual characteristics), as well as size, sector of organization, and competitive environment (organizational characteristics), as potential predictors. We found single characteristics did not predict adoption, but configurations of characteristics did. Drawing on the person‐situation‐interactionist perspective, we developed specific profiles of executives likely to foster a value‐in‐diversity perspective and identified characteristics of their work environments that support such an approach. Theoretical and practical implications are discussed.     

Gender Inequality and Growth in Europe

In this paper, we will briefly review the existing literature on the growth impacts of gender gaps and assess its relevance for the European situation. We will show that gender gaps in education in Europe are unlikely to play an important role for economic performance, but that gender gaps in employment appear to impose a significant efficiency cost in European countries where the gaps are large.     

The role of tie strength on consumer dissatisfaction responses

This study examines the impact of tie strength and service failure type on consumers’ behavioral intention (voice complaint and negative Word-of-Mouth) in a restaurant context. Overall, the results indicate that tie strength significantly reduces consumers’ intention to spread negative Word-of-Mouth, but it fails to influence their complaint intention. In addition, failure type plays an important role on consumers’ complaint behavior; consumers are more willing to voice a complaint after a core service failure than after an interactional service failure.     

Information Asymmetry Determinants of Sarbanes-Oxley Wealth Effects

We investigate the roles of information asymmetry and governance in the wealth effects associated with passage of the Sarbanes-Oxley Act (SOX) for a sample of 1,158 firms. For events suggesting adoption of stringent reform legislation, we find more (less) favorable abnormal returns (ARs) for firms with high (low) information asymmetry and for firms with weak (strong) governance. More favorable effects could result from expected improvements for firms with high information asymmetry or weak governance. Firms with positive ARs experience information asymmetry reductions post-SOX, indicating the market was able to discern the firms that would most benefit from the legislation's passage.     

Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances

We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, that is, SARAR(1, 1), by exploiting the recent advancements in score‐driven (SD) models typically used in time series econometrics. In particular, we allow for time‐varying spatial autoregressive coefficients as well as time‐varying regressor coefficients and cross‐sectional standard deviations. We report an extensive Monte Carlo simulation study in order to investigate the finite‐sample properties of the maximum likelihood estimator for the new class of models as well as its flexibility in explaining a misspecified dynamic spatial dependence process. The new proposed class of models is found to be economically preferred by rational investors through an application to portfolio optimization.     

Adversarial and Amiable Inference in Medical Diagnosis,Reliability and Survival Analysis

In this paper, we develop a family of bivariate beta distributions that encapsulate both positive and negative correlations, and which can be of general interest for Bayesian inference. We then invoke a use of these bivariate distributions in two contexts. The first is diagnostic testing in medicine, threat detection and signal processing. The second is system survivability assessment, relevant to engineering reliability and to survival analysis in biomedicine. In diagnostic testing, one encounters two parameters that characterize the efficacy of the testing mechanism: test sensitivity and test specificity. These tend to be adversarial when their values are interpreted as utilities. In system survivability, the parameters of interest are the component reliabilities, whose values when interpreted as utilities tend to exhibit co‐operative (amiable) behavior. Besides probability modeling and Bayesian inference, this paper has a foundational import. Specifically, it advocates a conceptual change in how one may think about reliability and survival analysis. The philosophical writings of de Finetti, Kolmogorov, Popper and Savage, when brought to bear on these topics constitute the essence of this change. Its consequence is that we have at hand a defensible framework for invoking Bayesian inferential methods in diagnostics, reliability and survival analysis. Another consequence is a deeper appreciation of the judgment of independent lifetimes. Specifically, we make the important point that independent lifetimes entail at a minimum, a two‐stage hierarchical construction.