Le disuguaglianze regionali nella distribuzione del reddito. Parte seconda: Un'analisi delle famiglie italiane a rischio di povertà
The aim of this paper is the analysis of vulnerability within families in nine regions of Italy and represents the second part of a research on regional inequalities in income distribution. Poverty tends to be a multi-dimensional concept and the use of monetary income as a single measure of welfare can be misleading. The concept of equality of living standards is a more correct definition, but it is far more elusive. Starting from the concepts of lacks of purchasing power, of financial and patrimonial protections and of opportunities to achieve economic resources, we can measure different dimensions of vulnerabilities between regional living standards. Then, we try to identify specific families’ types in which the previous lacks seem more frequent and spread: young couples with little children, families with children and only one parent and single old women are the most vulnerable families in most of the regions and on average for the whole country. Finally, we try some normative conclusions about the direction of welfare measures to reduce such factors of economic vulnerability within families and regional inequalities in Italy.
Market's SINS and the European Welfare State: theory and empirical evidences
We analyze the European Welfare State according to a theoretical framawork based on four main social goals – stability, inequality, need, safety (SINS) – which the market mechanism fail to achieve: they are the market’s SINS. We propose a theoretical framework which is based on the distinction between “private goods”, which are the domain of the market, and “common goods”, which are the the domain of the Welfare State and the other lower layers of social institutions. The main result is that a high level of social protection benefits, properly allocated to these goals, can be effective for achieving economic goals, because it allows to amend market’s shortcoming for SINS, while allowing the flexibility for complying with the public budget constraint, given the positive relationship between GDP per-capita and the share of social protection benefits. A cross-section analysis shows that the goals of SINS are better achieved when the social protection benefit focuses on the functions of family and children and sickness and health care, because they are the most poverty reduction: they allow to better achieve the goal of reducing inequality and providing families needs. We show that for the families with children the better working arrangement – which is associated with higher GDP per-capita and lower inequality – is one parent working full time and one parent part-time. We explain the cross-section variability for the functions of pensions, housing and social exclusions in relationship with the goals of stability and and safety. We analysis a sample of seven major European countries for the period 1995-2009, focusing on the goal of economic stability and the degree of procyciclical or countercyclical relationship between social protection expenditures and GDP per-capita. A number of econometric and numerical results are derived: countries with better economic performances can have both procyclical and countercyclical behavior, but they have in common higher value of social protection benefits on the onset of the cyclical downturn or recession: the flexibility for budget adjustment come from the political leeway of the high starting value of social protection and the higher share of in-kind benefits. The GDP per-capita in the year 2000 is a good predictor of the average primary balances for the European countries over the decade.
Complexity, Network-Decisions And Alternative Risk Premia: Some New Theories Of Structural Change And Portfolio Decisions.
Structural Change is the basis for many decisions such as government fiscal and monetary policies, government interventions, corporate-expenditure decisions, corporate strategic decisions, portfolio management decisions in finance; household allocation decisions, etc.. Structural change in industries and national economies is increasingly more difficult to detect partly because of the growth and dominance of Complex Systems and “Networks” in modern industries and economies – such Networks and Complex Systems include GVCs (global value chains), retail store networks; franchising networks; internet networks; services networks; credit-chains in industries; online social networks; etc.. The relationship between Complexity and Structural Change is well established in the physics, applied math and economic dynamics literatures. Traditional definitions and modeling of structural change have erroneously focused on changes in labor, technological progress, market share, and or capital – which are grossly inadequate in the new economy and post- global financial crisis (as confirmed by changes in the global retailing and housing industries and other industries). Also the modeling of structural change seems to be constrained by the availability of data, and often omits the relationship between structural change on one hand, and globalization, financial stability and international financial contagion. This article: i) introduces the Operations Strategy Model of structural change, explains how it differs from the Lewis Model of Structural Change; and explains the often omitted relationship between operations strategy (of companies and government agencies) and structural change; ii) introduces other new Complexity-based models/theories of Structural Change in industries; iii) reviews the relationships among Operations Research, Complexity and Structural Change; iv) explains the symbiotic relationship between structural change on one hand, and economic growth, financial stability, financial contagion and globalization; v) discusses the significant gap between academic research and practice and explains some issues and factors that should be considered in the development and testing of structural change models; vi) explains how alternative risk premia can be generated from new models of Structural Change.