The impact of successful cross-competencies on a career in tourism in Italy: the meeting point between the students’ perceptions and the requirements for professionals

ABSTRACT

The paper aims to understand how the changes occurring in the tourism sector are affecting the labor market in Italy, with a special focus on the relevance of successful cross competences (SCC). It focuses on comparing the relevance of these competencies in the perception of both students preparing to enter the tourism field and tour operators. The two-step study combined qualitative analysis that put forth specific characteristics of the tourism labor market in Italy through interviews with experts, and quantitative analysis that correlated the requirements of the tour operators to the ideas students have of what competencies tour operators entering the field should have. The results evinced differing perceptions of SCC and their relative importance in professional fields. Students manifest to miss awareness of the importance of SCC for their future careers. Furthermore, organizational ability, self-control and self-esteem were perceived by tour operators as the most important competencies to be acquired.     

Deconstructing the Gains from Trade: Selection of Industries vs Reallocation of Workers

In a Ricardian model with general distributions of industry efficiencies, the welfare gains from trade can be decomposed into a selection and a reallocation effect. The former is the change in average efficiency as a result of the selection of industries that survive international competition. The latter is the rise in the weight of exporting industries in production, owing to the reallocation of workers from non‐exporting industries. This decomposition, which is hard to calculate in the general case, simplifies dramatically with Fréchet‐distributed efficiencies, providing easy‐to‐quantify model‐based measures of these two effects. Selection (reallocation) turns out to matter mostly when welfare gains are small (large).     

Labor productivity growth: disentangling technology and capital accumulation

How much of the convergence in labor productivity that we observe in manufacturing is due to convergence in technology versus convergence in capital-labor ratios? To shed light on this question, we introduce a nonparametric counterfactual decomposition of labor productivity growth into growth of the capital-labor ratio (K/L), technological productivity (TEP) and total factor productivity (TFP). Our nonparametric specification enables us to model technology allowing for heterogeneity across all relevant dimensions (i.e. countries, sectors and time). Using data spanning from the 1960s to the 2000s, covering 42 OECD and non OECD countries across 11 manufacturing sectors, we find TEP and TFP to account for roughly 46 and ?6% of labor productivity growth respectively, on average. While technological growth at the world level is driven primarily by the US and a handful of other OECD countries, we find strong evidence of convergence in both technology and capital-labor ratios. Interestingly, very few of the usual growth determinants are found to enhance the process of technological catching-up.     

Does the Macroeconomy Predict UK Asset Returns in a Nonlinear Fashion? Comprehensive Out‐of‐Sample Evidence

We perform a comprehensive examination of the recursive, comparative predictive performance of linear and nonlinear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR) and smooth transition autoregressive (STR) regime switching models and a range of linear specifications including models with GARCH type specifications. Results demonstrate UK asset returns require nonlinear dynamics to be modelled with strong evidence in favour of Markov switching frameworks. Our results appear robust to the choice of sample period, changes in loss functions and to the methodology employed to test for equal predictive accuracy. The key findings extend to a similar sample of US data.     

Persistent and transient cost efficiency—an application to the Swiss hydropower sector

Electricity prices on the European market have decreased significantly over the past few years, resulting in a deterioration of Swiss hydropower firms’ competitiveness and profitability. One option to improve the sector’s competitiveness is to increase cost efficiency. The goal of this study is to quantify the level of persistent and transient cost efficiency of individual firms by applying the generalized true random effects (GTRE) model introduced by Colombi et al. (Journal of Productivity Analysis 42(2): 123–136, 2014) and Filippini and Greene (Journal of Productivity Analysis 45(2): 187–196, 2016). Applying this newly developed GTRE model to a total cost function, the level of cost efficiency of 65 Swiss hydropower firms is analyzed for the period between 2000 and 2013. A true random effects specification is estimated as a benchmark for the transient level of cost efficiency. The results show the presence of both transient as well as persistent cost inefficiencies. The GTREM predicts the aggregate level of cost inefficiency to amount to 21.8% (8.0% transient, 13.8% persistent) on average between 2000 and 2013. These two components differ in interpretation and implication. From an individual firm’s perspective, the two types of cost inefficiencies might require a firm’s management to respond with different improvement strategies. The existing level of persistent inefficiency could prevent the hydropower firms from adjusting their production processes to new market environments. From a regulatory point of view, the results of this study could be used in the scope and determination of the amount of financial support given to struggling firms.     

University specialization and new firm creation across industries

This article examines how the scientific specialization of universities impacts new firm creation across industries at the local level. In accordance with the Pavitt-Miozzo-Soete taxonomy, we consider eight industry categories, which reflect the characteristics of firms’ innovation patterns and, ultimately, the knowledge inputs that firms require. Using data on new firm creation in Italian provinces (i.e., at the NUTS3 level), we estimate negative binomial regression models separately for each industry category to relate new firm creation to the scientific specialization in basic sciences, applied sciences and engineering, and social sciences and humanities of neighboring universities. We find that universities specialized in applied sciences and engineering have a broad positive effect on new firm creation in a given province, this effect being especially strong in service industries. Conversely, the positive effect of university specialization in basic sciences is confined to new firm creation in science-based manufacturing industries, even if this effect is of large magnitude. Universities specialized in social sciences and humanities have no effect on new firm creation at the local level whatever industry category is considered.     

Modelling a sustainable requalification problem by analytic hierarchy process

Quality & Quantity - Decision problems are often characterized by a great number of alternative actions and by the complexity of relationships between factors involved in the process. This...     

MEASURING PRODUCTIVITY

Abstract Quantifying productivity is a conditio sine qua non for empirical analysis in a number of research fields. The identification of the measure that best fits with the specific goals, as well as being data driven, is currently complicated by the fact that an array of methodologies is available. This paper provides economic researchers with an up‐to‐date overview of issues and relevant solutions associated with this choice. Methods of productivity measurement are surveyed and classified according to three main criteria: (i) macro/micro; (ii) frontier/non‐frontier and (iii) deterministic/econometric.