Governments have created a broad ecosystem of knowledge as a public good in the double meaning of a good that is non-rivalrous in use and at least partly non-excludable, but also a good that is originally produced by public policies. Examples are policies towards universities, research infrastructures, and support for business R&D. Knowledge is born as a good in principle available to all, but then along the way, still partly due to some public policies, it is transformed into wealth for private investors. This is not an entirely new phenomenon, but what is new is the scale and pervasiveness of the process. Which channels of privatization of knowledge are the most widespread today, and how through them social inequalities are generated, is clearest in the case of digital content and information production. In markets dominated by tech giants (Apple, Alphabet, Microsoft, Facebook, Tencent, Alibaba, Amazon), enclosures (as Polanyi puts it) concern intangible assets such as digital information. What are the corrective policies? This paper, discusses the fact that in the face of the ineffectiveness of income redistributive mechanisms, Sraffa's thought (production of knowledge by means of knowledge) should be taken up in order to rethink a new economic subject that combines some traits of research infrastructures and the commons (Ostrom 1990). The social value produced would return through the internalization of missions defined from below, the collective and civic use of resources, democratic governance, self-financing, and the relationship between scientific research and technological development objectives and innovative public service objectives.

The goal of this study is a comparative analysis of the first and second wave of the Coronavirus disease 2019 (COVID-19) to assess the impact on health of people for designing effective policy responses to constrain negative effects of future infectious diseases similar to COVID-19 in society. The research here focuses on a case study of Italy, one of the first countries to experience a rapid increase in numbers of COVID-19 related infected individuals and deaths. Statistical analyses, based on daily data from February 2020 to February 2021, suggest that the first wave of COVID-19 pandemic in Italy had a high negative impact on health of people over February-May 2020 that declined from June 2020 onwards. Second wave of COVID-19 pandemic started in August 2020, to February 2021, has a growing incidence of confirmed cases, whereas admissions to Intensive Care Units and total deaths have lower levels compared to first wave. Lessons learned from a comparative analysis between first and second wave of the COVID-19 pandemic can be generalized in similar geo-economic regions to support effective policy responses of crisis management to constrain the impact of recurring waves of COVID-19 pandemic and future infectious diseases on health of people.

Howis the relation between duration of lockdownand numbers of infected people and deaths of Coronavirus disease 2019 (COVID-19), and growth level of Gross Domestic Product (GDP) in countries? Results here suggest that, during the first wave of COVID-19 pandemic, countries with a shorter period of lockdown (about 15 days: Austria, Portugal and Sweden) have average confirmed cases divided by population higher than countries with a longer period of lockdown (about 60 days, i.e., 2 months: France, Italy and Spain); moreover, countries with a shorter period of lockdown have average fatality rate (5.45%) lower than countries with a longer length of lockdown (12.70%), whereas average variation of fatality rate from March to August 2020 (first pandemicwave of COVID-19) suggests a higher reduction in countries with a longer period of lockdown than countries with a shorter duration (-1.9% vs. -0.72%). Independent Samples Test reveals that average fatality rate of countries with a shorter period of lockdown was significantly lower than countries with a longer period of lockdown (5.4% vs. 12.7%, p-value<.05). TheMann-Whitney Test confirms that average fatality rate of countries with a shorter period of lockdown is significantly lower than countries having a longer period of lockdown (U=0, pvalue = .005). In addition, results show that lockdowns of longer duration have generated negative effects on GDP growth: average contraction of GDP (index 2010 = 100) from second quarter 2019 to second quarter of 2020 in countries applying a longer period of lockdown (i.e., about two months) is about -21%, whereas it is -13% in countries applying a shorter period of lockdown of about 15 days (significant difference with Independent Samples Test: t4=-2.274, p-value < .085). This finding shows a systematic deterioration of economic system because of containment policies based on a longer duration of lockdown in society. Another novel finding here reveals that countries with higher investments in healthcare (as percentage of GDP) have alleviated fatality rate of COVID-19 and simultaneously have applied a shorter period of lockdown, reducing negative effects on economic system in terms of contraction of economic growth. Overall, then, using lessons learned of the first wave of COVID-19 pandemic crisis, this study must conclude that a strategy to reduce the negative impact of future epidemics similar to COVID-19 has to be based on a reinforcement of healthcare sector to have efficient health organizations to copewith pandemics of newviral agents by minimizing fatality rates; finally, high investments in health sector create the social conditions to apply lockdowns of short run with lower negative effects on socioeconomic systems.

The word Happiness, in widest sense, is used for all that is good, and often considered interchangeable with Wellbeing or Quality of Life (QoL), indicating both individual and social prosperity (worlddatabaseofhappiness.eur.nl/). In recent years economists have returned to happiness (Bruni, Porta, 2007), to explore why the increase of wealth does not correspond to greater social and individual well-being (Easterlin, 1974). Therefore the assessment of society progress need a change of paradigm, which does not mean reducing living standard but changing the model, trying to enhance possibility of genuine interpersonal meeting and relationships opportunities (Bartolini, 2010). A new, interdisciplinary, approach to happiness is developed since then to give an answer to the modest correlation between income and happiness. Further, in the last decades, that persons are able to assess their happiness or their satisfaction with their own lives has become clear. In particular the use of objective indicators of economic-social type is accompanied by subjective indicators on the perceived QoL. Quantitative indicators are, for example, the economic status, the housing system and the environment, the education system and the healthcare, the education level and the quality of the health of individuals; qualitative indicators are made up of satisfaction for life in general and in different environments and contexts such as family, work, sports, friendship, sex, healthcare, pension, social and cultural environment, etc. The subjective indicators are certainly not straightly observable and measurable. Recently the analysis of happiness, wellbeing and QoL was studied at the urban level (Florida et al., 2013; Ballas, 2013; Marans, 2012; Marans, RW & Stimson, RJ Eds., 2011) and in particular the use of wellbeing measures to direct the policy development, to monitor policy progress and evaluate their results (Dolan et al., 2011). Some authors (Florida et al, 2013) have analysed the factors that outline the happiness of cities, finding that beyond income, also human capital plays a significant role in the urban happiness. Starting from this premise, the paper intends to give a contribution while examining in depth the connections between urban happiness and policies, through the exploratory analysis on the approach used in Bristol, UK.

The paper investigates factors affecting inequalities between HEIs performance because of their localization in more or less developed regions; for this aim it tests the relationships existing between public policies, market investment and inequalities in HEIs performance, according to three hypotheses: 1.Good scientific performance is related to the wealth of the economic context; 2.Public policies aimed at reducing inequalities may rather increase them, as they do not intervene on the real causes of inequality; 3.Market forces are correlated with inequalities: i.e. disciplines with higher share of private investment and more variance of private investment are those with stronger qualitative inequalities.