ISA 570: Italian Auditors’ and Academics’ Perceptions of the Going Concern Opinion

This study aims to identify which of the indicators used to assess whether an entity may not be able to continue as a going concern are regarded by Italian partners in auditing firms and Italian accounting and finance academics as being the most important. The indicators and ratios are those pursuant to auditing standard ISA 570 Going Concern, the literature and professional practice. ISA 570 does not suggest a hierarchy of these indicators, because they are only examples of events or conditions that, individually or collectively, may cast significant doubt on the entity's ability to continue as a going concern (GC), nor does it suggest specific ratios. To this end, an online survey was administered to partners in auditing firms and to the university faculty of Accounting and Finance. The paper contributes to enriching previous literature by providing insights into how Italian auditors and Italian financial reporting scholars prioritise GC indicators suggested by ISA 570. The results have the potential to help auditors and directors focus on the most relevant indicators in a GC assessment.     

Lessons From Italy: A Good Pension System Needs an Effective Broader Social Policy Framework

Intereconomics - Italy, which is sometimes considered a laggard in terms of social and economic reforms, can boast a pension system that is, by and large, functioning sufficiently well in terms of...     

Trade shocks,product mix adjustment and productivity growth in Italian manufacturing

In this paper, we use firm-level data on the universe of Italian manufacturing multi-product exporters to test whether demand shocks in export markets lead multi-product exporters to increase their productivity. The main mechanism behind the documented productivity gains is the reallocation of resources across products within firms (American Economic Review, 104, 2014 and 495; National Bureau of Economic Research Working Paper Series No. 22433, 2016). Intuitively, the increased demand stemming from foreign markets will induce firms to adjust their product mix by moving inputs from low to high productive/profitable uses. We find that these productivity gains are significant and can explain between 1/10 and 1/2 of aggregate productivity growth in the manufacturing sector.     

Social Security and Old Age Unemployment Risk with Efficient Wage Bargaining

I analyze a life‐cycle economy with old age productivity risk where wages, employment, and severance payments are set through efficient bargaining between risk averse unions and risk neutral firms. Allocations with limited union membership are second‐best inefficient as they generate too little labor supply in young age, too much consumption before retirement, too little employment of older workers (early retirement), and too little insurance against old age unemployment. Providing public transfers to early retirees (disability benefits or early pensions) might help to increase the degree of risk sharing at the cost of lower old age employment. Depending on whether absolute risk aversion is increasing or decreasing in consumption, these policies might or might not produce efficiency gains at equilibrium.     

Reinforced urn processes for credit risk models

We propose a Bayesian nonparametric model to estimate rating migration matrices and default probabilities using the reinforced urn processes (RUP) introduced in Muliere et al. (2000). The estimated default probability becomes our prior information in a parametric model for the prediction of the number of bankruptcies, with the only assumption of exchangeability within rating classes. The Polya urn construction of the transition matrix justifies a Beta distributed de Finetti measure. Dependence among the processes is introduced through the dependence among the default probabilities, with the Bivariate Beta Distribution proposed in Olkin and Liu (2003) and its multivariate generalization.     

HOW TO MEASURE THE ECONOMIC IMPACT OF VECTOR‐BORNE DISEASES AT COUNTRY LEVEL

Vector‐borne diseases (VBDs) are widespread in less developed countries and reemerging in developed ones. Available economic studies agree that VBDs have significant effects on countries' economic outcomes, and affirm that a systematic evaluation of such effects is crucial for the efficient allocation of resources to health‐related priorities. This paper provides a comparative assessment of available methodologies for measuring the economic impact of VBDs at national level. We review both macroeconometric and micro‐based approaches, and examine advantages and disadvantages of current methods. We conclude by suggesting possible areas for future research.     

Reverse mortgages through artificial intelligence: new opportunities for the actuaries

Decisions in Economics and Finance - In its basic structure, the reverse mortgage (RM) is a contract where a home owner borrows a part or the totality of the future liquidation value of his home at...     

CHOOSING THE VARIABLES TO ESTIMATE SINGULAR DSGE MODELS

We propose two methods to choose the variables to be used in the estimation of the structural parameters of a singular DSGE model. The first selects the vector of observables that optimizes parameter identification; the second selects the vector that minimizes the informational discrepancy between the singular and non‐singular model. An application to a standard model is discussed and the estimation properties of different setups compared. Practical suggestions for applied researchers are provided. Copyright © 2014 John Wiley & Sons, Ltd.     

Assessment of meaningful change in routine outcome measurement (ROM) with a combination of a longitudinal and a ‘classify and count’ approach

To assess significant changes of health status in people receiving health care, distribution-based and anchor-based methods have been proposed. However, there is no real consensus on what method is the best for evaluating clinically meaningful change. To maximize the internal and external validity of outcome assessment, we propose combining two approaches as recommended by recent practical guidelines on this field. Specifically, we suggest applying longitudinal hierarchical linear models on subgroups of patients showing reliable change and reliable and clinically significant change. This combined approach improved the model’s ability (1) to quantify the magnitude of changes to be reliable and clinically meaningful and (2) to select significant predictors of changes. An empirical application on a prevalence sample of Italian outpatients attending four community mental health services was done. A cross-sectional model and three longitudinal models were applied on the entire study sample and reliable and clinically meaningful change subsamples to investigate the magnitude of change and the predictive effect on outcomes of clinical, socio-demographic and process variables on different patients’ subgroups. Differences were found suggesting that both the statistical method and the sample used to calculate individual changes affect the estimates. The main conclusion is that ignoring the longitudinal data structure or including patients with unreliable change at the follow-up might result in misleading inferences that can alter the real magnitude of changes and the contributions of predictors. The approach proposed provides robust feedback to clinicians on clinically significant change and can be recommended in outcome studies and research.