Giovanni Trovato

Income, relational goods and happiness

Our empirical analysis on the determinants of self-declared happiness on more than 100 000 individuals from representative samples in 82 world countries does not reject the hypothesis that the time spent for relationships has a significant and positive impact on happiness. This basic nexus helps to understand new unexplored paths in the so-called ‘happiness-income paradox’. To illustrate them we show that personal income has two main effects on happiness. The first is a positive effect which depends on individuals ranking within domestic income quintiles. The second is determined by the relationship between income and relational goods. In principle, more productive individuals may substitute (if the income effect prevails over the substitution effect) worked hours with the nonworking time made free for enjoying relationships, when they have strong preferences for them. The problem is that these individuals tend to have ties with their income class peers who share with them a high opportunity cost for the time spent for relationships. Hence, a coordination failure may reduce the joint investment in relational goods (local public goods which need to be co-produced in order to be enjoyed together) and, through this effect, individuals in the highest income quintiles may end up with poorer relational goods. The indirect impact of personal income on happiness through this channel is therefore expected to be negative.

Credit Rationing and Credit View: Empirical Evidence from Loan Data

The empirical literature tries to identify credit rationing and its determinants by using balance sheet data or evidence from corporate surveys. Observational equivalence, identification problems, and interview biases are serious problems in these studies. We analyse the determinants of credit rationing directly on credit files by looking at the difference between the amount demanded and supplied to each borrower from official bank records. Our findings provide microeconomic evidence in support of the credit view hypothesis showing that the European Central Bank refinancing rate is significantly and positively related to partial (but not total) credit rationing. This finding is consistent with the hypothesis that such variable affects the total volume of commercial bank loans.

A finite mixture model for multivariate counts under endogenous selectivity

We describe a selection model for multivariate counts, where association between the primary outcomes and the endogenous selection source is modeled through outcome-specific latent effects which are assumed to be dependent across equations. Parametric specifications of this model already exist in the literature; in this paper, we show how model parameters can be estimated in a finite mixture context. This approach helps us to consider overdispersed counts, while allowing for multivariate association and endogeneity of the selection variable. In this context, attention is focused both on bias in estimated effects when exogeneity of selection (treatment) variable is assumed, as well as on consistent estimation of the association between the random effects in the primary and in the treatment effect models, when the latter is assumed endogeneous. The model behavior is investigated through a large scale simulation experiment. An empirical example on health care utilization data is provided.

Credit rationing and the financial structure of Italian small and medium enterprises

Our aim is to analyze the effect of public subsidies on the development path of Italian small and medium enterprises (SMEs). Public subsidies to SMEs have been often used with the aim of favoring economic growth in less developed regions. The main theoretical arguments justifying this intervention are related to the idea that public subsidies can solve lack-of-capital problems deriving from asymmetric information. According to Stiglitz and Weiss (1981), public subsidies to rationed firms can reduce the informational gap, leading subsidized firms to reduce their financial constraints and to increase their investment levels. Results obtained modelling leverage, performance and investment behaviour in a panel of around 1,900 enterprises over the years 1989 to 1994 seem to confirm the working hypotheses. However, they can not be considered as conclusive and further research is needed in this context.

Structural breaks, price and income elasticity and forecast of the monthly italian electricity demand

Insights about electricity demand dynamics is fundamental for investment capacity, optimal energy policies, and balanced electricity system. This paper presents an empirical analysis of the monthly Italian electricity demand since January 2001 to June 2012. In the first section we conduct a deep analysis of the structural breaks in the electricity demand finding that the series has two structural breaks in August 2002 and August 2004. In the second part of the paper we estimate the price elasticity of demand both for residential and industrial sector. As expected from the electricity economics literature, we find that the long run price and income elasticities are more price elastic than the short run both in the industrial and residential consumption. In the third and last section, we compare two different forecasting models: the Hidden Markov Models (HMM) and the Holt Winters (H-W) seasonal smoothing method. Considering the Mean Absolute Percentage Error (MAPE), the HMM approach seems to show a superiority in forecasting the monthly electricity demand compared to the H-W methodology in the horizon forecast.

Do domestic and cross-border M&As differ? Cross-country evidence from the banking sector

Are the drivers of domestic and cross-border M&As in the banking sector different? Despite the intense research on bank M&As in the last decade, the attention paid to this issue is surprisingly limited. We fill this gap studying the ex-ante determinants of national and international acquisitions in the banking sector in an unbalanced panel of nearly 1,000 banks from 50 world countries, from 1992 to 2007. Our results show that size and profitability have a stronger impact on the probability that a bank is a bidder in a cross-border deal than in a domestic deal. Consistent with the findings of the literature on the determinants of the internationalization of manufacturing firms, international expansion in the banking sector is therefore easier for countries with a number of large “national champions�?, that are more capable to overcome the fixed costs of internationalization and have a stronger incentive to diversify the idiosyncratic risks of their domestic activities.

Estimating an Energy-Social Accounting Matrix for Italy

This chapter describes an application of the Social Accounting Matrix (SAM) to the analysis of the economic impact of energy policies in Italy. An Energy Social Accounting Matrix (ESAM) is estimated for the year 2010 using as an input a general purpose technology-oriented model (of the MARKAL—TIMES family) representing the evolution of the Italian energy environment and a variant of the Wolsky procedure.

Extending Logistic Approach to Risk Modelling ThroughSemiparametric Mixing

The New Proposal of Basel Committee on banking regulation issued in January 2001 allows banks to use Internal Rating Systems to classify firms. Within this context, the main problem is to find a model that fits data as better as possible, providing at the same time good prediction and explicative capabilities. In this paper, our aim is to compare two kind of classification models applied to credit worthiness using weighted classification error as performance function: the standard logistic model and a mixed logistic model, adopting respectively a parametric and a semiparametric approach. As it is well known, the main problem of the former is related to the assumption of i.i.d. hypothesis, while it often turns out necessary to consider the possible presence of unobservable heterogeneity, that characterizes microeconomic data. To better consider this phenomenon we defined and applied a random effect logistic model, avoiding parametric assumptions upon the random effect distribution. This leads to a likelihood which is defined as the integral of the kernel density with respect to the mixing density which has no analytical solution. This problem can be obviated by approximating the integral with a finite sum of kernel densities, each one characterized by a different set of model parameters. This discrete nature helps us in detecting non-overlapping clusters characterized by homogeneous values of insolvency risk, and in classifying firms to one of these clusters by means of estimated posterior probabilities of component membership.