Suchergebnisse

The aim of this thesis is to analyse empirically, proposing new methods to tackle disputed questions in the literature of political and labour economics, the Italian institutional setting both in a political competition context and in the occupational structure. The first paper explores the relationship between transfers from central state to political aligned municipalities and the effect of these transfers on local electoral consensus. This study contributes to the empirical literature of the political determinants of spikes in central transfers in pre-electoral periods and of the electoral benefits of pork barrel measures for incumbent politicians. Despite several findings of strong evidence that intergovernmental fiscal transfers rise during election years, in the Italian case researchers investigated little the political incentives that lay behind these increases or the success of these transfers in attracting votes. We focus on the so called swing municipalities, defined as those in which the probability of winning is close to one-half, analysing data of Italian comuni with more than 15 000 inhabitants, in the period 2007-2014. From an empirical perspective, every attempt to estimate the causal impact of political alignment on the amount of federal transfers is clearly complicated by endogeneity issues. Without a credible source of exogenous variation in political alignment, the empirical correlation between alignment and transfers (if any) can be completely driven by socio-economic factors influencing both dimensions. We propose a new model specification to account for the endogeneity issue arising when estimating the causal impact of political alignment on transfers: the unpredicted change in the government occurred in 2011 after the resignation of Silvio Berlusconi and the following appointment of Mario Monti as prime minister. We perform our empirical estimation in two steps: first, we apply the close-race RDD setup (Lee 2008) to assess the impact of political alignment on transfers. Results from the close-race RDD show that aligned municipalities receive more grants, with this effect being stronger before elections. At a second empirical stage, we perform a local linear regression of the re-election probability of the local incumbent on transfers, including the first stage error term to have our coefficient of interest measuring only the effect of politically-driven transfers on electoral outcomes, and we conclude that this probability increases as grants increase. The second paper stems from the observation of the most recent phenomena in the domestic and foreign labour market: technological progress has been associated to a crowding-out of cognitive-skill intensive jobs in favour of jobs requiring soft skills, such as social intelligence, flexibility and creativity. Soft skills can be defined as interpersonal, human, people or behavioural skills necessary for applying technical skills and knowledge in the workplace. The nature of the soft skills make them hardly replaceable by machine work, and Among soft skills, creativity is one of the hardest to define and to codify, therefore, creativity-intensive occupations have been shielded from automation. In our work, we focus on creativity, starting from its definition in order to get significant insights on which occupational profiles in Italy can be considered creative and to explore their dynamics in the labour market. A possible analytical definition of creativity comes from the seminal work of Edward De Bono. According to his pioneering research in the field, lateral thinking is strictly related to creativity and it can be described along four dimensions: 1) fluidity, as the ability of a subject to give the highest possible number of answers to a certain question; 2) flexibility, as the number of categories to which we can bring back these questions; 3) originality: ability of expressing new and innovative ideas; 4) processing: ability of realizing concretely one's ideas. We apply this definition to a uniquely detailed occupational dataset on tasks, skills, work attitudes, and working conditions regarding all Italian occupations: the Inapp-Istat Survey on Occupations (Indagine Campionaria sulle Professioni, ICP hereafter), an O*NET-type dataset developed by the Italian National Institute for Public Policy Analysis. The Survey on Occupations, in fact, presents a list of skills and competences and workers are asked to identify those they make use of in performing their job. Inside this list, we identify 25 skills associated to creativity and we formulate a Matrix Completion (MC) optimization problem, as discussed theoretically in Mazumder (2010). Matrix Completion is the task of filling in the missing entries of a partially observed matrix, which we generate by obscuring randomly 10%, 25% and 50% of the entries in the columns associated with the creative skills, given a fixed row (occupation). In our analysis, we use a formulation of the problem known as Nuclear Norm Minimization and we solve it with the Soft Impute Algorithm. We conclude our analysis on social skills in our third paper where we analyse the effects of Covid-19 pandemic on soft skills in the context of Italian occupations, operating in about 100 economic sectors. We leverage detailed information from ICP, the Italian O*Net, and we simulate the impact of Covid-19 on those workplace characteristics and working style that were more seriously hit by the lockdown measures and the new sanitary dispositions (physical proximity, face-to-face discussions, working remotely, ecc.). We simulate three possible scenarios based on the intensity of the effects of COVID-19 on some working conditions, such as working from home, keeping physical distance and so on. We then apply matrix completion, a machine learning technique used in recommendation systems, in order to predict the levels of soft skills required for each occupation when working conditions change, as these changes might be persistent in the near future. Professions showing a lower intensity in the use of soft skills, with respect to the predicted one, are exposed to a deficit in their soft-skill endowment, which might ultimately lead to lower productivity or higher unemployment, thus enhancing the negative effects of the pandemic.

AbstractTechnological progress has been recently associated with a crowding-out of cognitive-skill intensive jobs in favour of jobs requiring soft skills, such as ones related to social intelligence, flexibility and creativity. The nature of soft skills makes them hardly replaceable by machine work and among subsets of soft skills, creativity is one of the hardest to define and codify. Therefore, creativity-intensive occupations have been shielded from automation. Given this framework, our study contributes to a nascent field on interdisciplinary research to predict the impact of artificial intelligence on work activities and future jobs using machine learning. In our work, we focus on creativity, starting from its possible definitions, then we get significant insights on creativity patterns and dynamics in the Italian labour market, using a machine learning approach. We make use of the INAPP-ISTAT Survey on Occupations (ICP), where we identify 25 skills associated with creativity. Then, we apply matrix completion—a machine learning technique which is often used by recommender systems—to predict the average importance levels of various creative skills for each profession, showing its excellent prediction capability for the specific problem. We also find that matrix completion typically underestimates the average importance levels of soft skills associated with creativity, especially in the case of professions belonging to the major group of legislators, senior officials and managers, as well as intellectual professionals. Conversely, overestimates are typically obtained for other professions, which may be associated with a higher risk of being automated.