Public policy in Italy: an empirical analysis on local governments and occupation

Research on public policy has been a fast developing fields among social sciences over manydecades. In this rapid development, policy analysis reached levels of higher understandingof the policy making process together with the capability of supplying decision makers withreliable and relevant knowledge about urgent economic and social problems. FollowingDunn (1981) policy analysis is “an applied social science discipline which uses multiple methodsof inquiry and arguments to produce and transform policy-relevant information that may be utilizedin political settings to resolve policy problems.” And although policy advice is as old as government,modern society faces an increasing complexity that seriously reinforce the decisionmakers’ need for information.The institutional setting in Italy is ideal for studying public policy, as both the politicalenvironment and the labour market undergone dramatic changes over the last 30 years.After almost fifty years of proportional electoral system, in 1991 Italy enter into a period ofelectoral law reformation. Nevertheless, the debate is ongoing and intense in the attemptto answer the question on how to design an electoral system that is able to express thedesired political outcomes in contrast with the rigid structure of the Constitution. Similarly,Italian labour market went through a process of liberalisation since the end of last centurywhen a series of reforms were promoted aiming at fixed-term employment relationships,in the beginning and then moving to targeting open-ended contracts. Alongside, the latestproduction technologies are characterised by an increased digitalization (Brynjolfsson andMcAfee, 2014) that is often included in the political debate with the name of ‘Industry 4.0’and has been related to a significant shift towards the so-called ‘smart factory’ of the future.The difficult health crisis, with all its economic and social consequences, has impactedon the country with a wide range of pre-existing structural problems, which it is crucialto maintain the focus on, aside the new paths drawn by the effects of the pandemic. Theaim of this thesis is to analyse empirically the Italian institutional setting both in a politicalcompetition context and in the occupational structure. In the following chapters, we proposenew methods to tackle disputed questions in the literature of political and labour economics:we make use of the traditional Regression Discontinuity Design, as framed by D. S. Lee(2008) with a new randomization tool to address endogeneity; we expand the applicationof Matrix Completion, a recently developed machine learning techniques to assess deficit insoft skills in the labor market; lastly, we adapt this new Matrix Completion formulation tomake predictions on the future trends in the job market and job conditions after the Covid-19pandemic.The first paper explores the relationship between transfers from central state to politicalaligned municipalities and the effect of these transfers on local electoral consensus. Thisstudy contributes to the empirical literature of the political determinants of spikes in centraltransfers in pre-electoral periods and of the electoral benefits of pork barrel measures forincumbent politicians. Despite several findings of strong evidence that intergovernmentalfiscal transfers rise during election years, in the Italian case researchers investigated littlethe 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 whichthe probability of winning is close to one-half, analysing data of Italian comuni with morethan 15 000 inhabitants, in the period 2007-2014.From an empirical perspective, every attempt to estimate the causal impact of politicalalignment on the amount of federal transfers is clearly complicated by endogeneity issues.Without a credible source of exogenous variation in political alignment, the empirical correlationbetween alignment and transfers (if any) can be completely driven by socio-economicfactors influencing both dimensions. We propose a new model specification to account forthe endogeneity issue arising when estimating the causal impact of political alignment ontransfers: the unpredicted change in the government occurred in 2011 after the resignationof Silvio Berlusconi and the following appointment of Mario Monti as prime minister. Weperform our empirical estimation in two steps: first, we apply the close-race RDD setup (Lee2008) to assess the impact of political alignment on transfers. Results from the close-raceRDD show that aligned municipalities receive more grants, with this effect being strongerbefore elections. At a second empirical stage, we perform a local linear regression of the reelectionprobability of the local incumbent on transfers, including the first stage error termto have our coefficient of interest measuring only the effect of politically-driven transfers onelectoral 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 domesticand foreign labour market: technological progress has been associated to a crowdingoutof cognitive-skill intensive jobs in favour of jobs requiring soft skills, such as socialintelligence, flexibility and creativity. Soft skills can be defined as interpersonal, human,people or behavioural skills necessary for applying technical skills and knowledge in theworkplace. 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 significantinsights on which occupational profiles in Italy can be considered creative and to exploretheir dynamics in the labour market. A possible analytical definition of creativity comesfrom the seminal work of Edward De Bono. According to his pioneering research in thefield, 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 answersto a certain question; 2) flexibility, as the number of categories to which we can bring backthese questions; 3) originality: ability of expressing new and innovative ideas; 4) processing:ability of realizing concretely one’s ideas. We apply this definition to the Survey on Occupations(Indagine Campionaria sulle Professioni, ICP hereafter), run by ISTAT and INAPPin 2007 and 2013, the Italian twin of the US O*NET dataset. The Survey on Occupations,in fact, presents a list of skills and competences and workers are asked to identify thosethey make use of in performing their job. Inside this list, we identify 25 skills associated tocreativity and we formulate a Matrix Completion (MC) optimization problem, as discussedtheoretically in Mazumder (2010). Matrix Completion is the exercise of reconstructing themissing entries of a matrix, which we generate by obscuring randomly 10%, 25% and 50% ofthe 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 Minimizationand we solve it with the Soft Impute Algorithm.We conclude our analysis on social skills in our third paper where we analyse the effectsof Covid-19 pandemic on soft skills in the context of Italian occupations, operating in about100 economic sectors. We make use of the information included in the ICP, the Italian O*Net,and we simulate the impact of Covid-19 on those workplace characteristics and workingstyle that were more seriously hit by the lockdown measures and the new sanitary dispositions (physical proximity, face-to-face discussions, working remotely, ecc.). We simulatethree possible scenarios based on the intensity of the effects of COVID-19 on some workingconditions, such as working from home, keeping physical distance and so on. We then applymatrix completion, a machine learning technique used in recommendation systems, inorder to predict the levels of soft skills required for each occupation when working conditionschange, as these changes might be persistent in the near future. Professions showinga lower intensity in the use of soft skills, with respect to the predicted one, are exposed to adeficit in their soft-skill endowment, which might ultimately lead to lower productivity orhigher unemployment, thus enhancing the negative effects of the pandemic.