Classification of longitudinal career paths

The aim of the present article is to classify, in terms of contractual stability, the careers of the workers in a specified territorial context (Province of Milan-Italy), utilizing large administrative archives. The final goal is a synthetic clustering that identifies individuals in homogeneous groups regarding the longitudinal sequences of contractual typologies occurring in the evolution of vocational experiences during their career, identifying, on the one hand, the worker profiles that remain stable in each contractual typology and on the other hand, the profiles that improve or worsen contractual stability over time. Methodologically, our approach uses a combination of scaling methods to estimate stability scores of each contractual typology and Latent mixture models to cluster similar trajectories. Specifically, the scores of contractual stability were performed by Multidimensional Scaling with individual preferences, taking into account the ordinal nature of distances among contractual typologies and the heterogeneity factors of the subjects. Further, Latent Growth Mixture models, capitalizing the longitudinal property of data sequences, were proposed to identify distinctive, prototypical developmental trajectories of contractual stability within the analyzed population.     

Standard costs of regional public rail passenger transport: evidence from Italy

ABSTRACT

The paper estimates the standard cost in Italian regional public rail passenger transport services (LPTR), depending on service characteristics. The results highlight the crucial role of: number of seats per ride, commercial speed, service size and length of rail tracks. The model also shows the positive link between investment in rolling stock and the unit cost of the service. Finally, based on the empirical evidence, we propose regulatory adjustments to accomplish policy targets regarding the fair allocation of public LPTR funds to Regions and Local Authorities and a more efficient use of (scarce) local and national public resources.     

Innovation,finance, and economic growth: an agent-based approach

This paper extends the endogenous growth agent-based model in Fagiolo and Dosi (Struct Change Econ Dyn 14(3):237–273, 2003) to study the finance–growth nexus. We explore industries where firms produce a homogeneous good using existing technologies, perform R&D activities to introduce new techniques, and imitate the most productive practices. Unlike the original model, we assume that both exploration and imitation require resources provided by banks, which pool agent savings and finance new projects via loans. We find that banking activity has a positive impact on growth. However, excessive financialization can hamper growth. Indeed, we find a significant and robust inverted U-shaped relation between financial depth and growth. Overall, our results stress the fundamental (and still poorly understood) role played by innovation in the finance–growth nexus.

     

Solving optimal growth models with vintage capital: The dynamic programming approach

This paper deals with an endogenous growth model with vintage capital and, more precisely, with the AK model proposed in [R. Boucekkine, O. Licandro, L.A. Puch, F. del Rio, Vintage capital and the dynamics of the AK model, J. Econ. Theory 120 (1) (2005) 39-72]. In endogenous growth models the introduction of vintage capital allows to explain some growth facts but strongly increases the mathematical difficulties. So far, in this approach, the model is studied by the Maximum Principle; here we develop the Dynamic Programming approach to the same problem by obtaining sharper results and we provide more insight about the economic implications of the model. We explicitly find the value function, the closed loop formula that relates capital and investment, the optimal consumption paths and the long run equilibrium. The short run fluctuations of capital and investment and the relations with the standard AK model are analyzed. Finally the applicability to other models is also discussed.     

The distribution of household consumption-expenditure budget shares

This paper explores the statistical properties of household consumption-expenditure budget share distributions – defined as the share of household total expenditure spent for purchasing a specific category of commodities – for a large sample of Italian households in the period 1989–2004. We find that household budget share distributions are fairly stable over time for each specific category, but profoundly heterogeneous across commodity categories. We then derive a parametric density that is able to satisfactorily characterize (from a univariate perspective) household budget share distributions and: (i) is consistent with the observed statistical properties of the underlying levels of household consumption-expenditure distributions; (ii) can accommodate the observed across-category heterogeneity in household budget-share distributions. Finally, we taxonomize commodity categories according to the estimated parameters of the proposed density. We show that the resulting classification is consistent with the traditional economic scheme that labels commodities as necessary, luxury or inferior.     

Training and economic density: Some evidence form Italian provinces

In this paper we use a search and matching model to investigate the economic relationship between training and local economic conditions. We identify two aspects of this relationship going in opposite directions: on the one hand, the complementarity between local knowledge spillovers and training generates a positive correlation between training and local density; on the other hand, higher wages and labor turnover in denser areas reduce training. Overall the relationship can be either positive or negative, depending on the relative strength of these two effects. Our empirical analysis, based on a sample of Italian firms, shows that training is lower in provinces with higher labor market density, measured as the number of employees per squared kilometer.     

Path-dependent scenario trees for multistage stochastic programmes in finance

The formulation of dynamic stochastic programmes for financial applications generally requires the definition of a risk–reward objective function and a financial stochastic model to represent the uncertainty underlying the decision problem. The solution of the optimization problem and the quality of the resulting strategy will depend critically on the adopted financial model and its consistency with observed market dynamics. We present a recursive scenario approximation approach suitable for financial management problems, leading to a minimal yet sufficient representation of the randomness underlying the decision problem. The method relies on the definition of a benchmark probability space generated through Monte Carlo simulation and the implementation of a scenario reduction scheme. The procedure is tested on an interest rate vector process capturing market and credit risk dynamics in the fixed income market. The collected results show that a limited number of scenarios is sufficient to capture the exposure of the decision maker to interest rate and default risk.     

Emerging markets’ spreads and global financial conditions

In this article, we analyze how much of the reduction in emerging markets’ spreads can be ascribed to specific factors—linked to the improvement in a given country's fundamentals, rather than to common factors—linked to global liquidity conditions and agents’ risk aversion. By means of factor analysis, we find that a single common factor is able to explain a large part of the co-variation in emerging market economies’ (EMEs) spreads observed in the last 4 years; in turn, this common factor can be traced back mainly to financial market volatility. Due to the particularly benign global financial conditions of recent years, spreads seem to have declined to below the levels warranted by improved fundamentals. As a consequence, EMEs do remain vulnerable to sudden shifts in financial market conditions.     

Exchange rates and fundamentals: evidence on the economic value of predictability

A major puzzle in international finance is the well-documented inability of models based on monetary fundamentals to produce better out-of-sample forecasts of the nominal exchange rate than a naive random walk. While this literature has generally employed statistical measures of forecast accuracy, we investigate whether there is any economic value to the predictive power of monetary fundamentals for the exchange rate. We find that, in the context of a simple asset allocation problem, the economic value of exchange rate forecasts from a fundamentals model can be greater than the economic value of random walk forecasts across a range of horizons.