A new composite index for measuring corruption risk at the Italian municipality level

In the previous working paper a set of keywords were identified by finding the most recurrent associated terms that occurred alongside the word ‘corruption’ from a sample pool of academic articles within the Scopus academic database. These recurring keywords or ‘socio-economic indicators’, retrieved through social network analysis techniques (D.F. Iezzi et al. 2020, A.F. Colladon, 2018), are therefore useful to be used as evidence of connections between social factors in studies, debates, and discussions and can be used by policymakers to make good decisions by evaluating specific programs, developing budgets, and setting goals and priorities. The socio-economic indicators (i.e. a value, mostly empirical, with which we want to measure, in a given situation, significant changes in behaviour and social condition) are derived from our research and most of them are placed in the political and economic sphere (sustainable development, underground economy, economic strategies in developing countries, transparency as an anti-corruption mechanism, public awareness) or in the social sphere (education, civilization, amoral familism, nepotism) or technological (digitization process to reduce burdens and improve performance and governance control, dissemination of information). This study aims to utilise these indicators for the purpose of understand ‘corruption’ as a measurable phenomenon after a procedure consising in collecting data from public databases (and open-data datasets) and examining it thereafter by using statistical analysis. The goal of this research is to calculate a composite indicator at the municipal level by collecting data and analysing it to understand how this connection between social factors is distributed in Italy and help to better identify where corruption exists. Three pillars, namely, the dimensions that better represent the concept of the theoretical framework (bad administration, territorial economy, education and civilization) are identified and for each of them, a set of measurable elementary indicators that can be quantified and compared against each other. The choice of each dimension derives from studies, empirical evidence, surveys, and opinions present in the scientific literature on the links between them and corruption. Each pillar becomes a matrix (A. Marradi, 2007) in datasets selected from institutional and certified datasets available on the Internet and combined in a larger matrix following the construction phases of the composite indicator with a non-compensatory approach (Mazziotta, Pareto, 2016). We calculate composite indicators of each pillar and at the end also the composite indicator of all the composite indicators of the pillars. They are calculated, represented, and explained in order to have a unidimensional measure that can help policymakers to understand the complex reality.