Approaching an investigation of multi-dimensional inequality through the lenses of variety in models of capitalism

Antonelli, Gilberto; Calia, Pinuccia Pasqualina; Guidetti, Giovanni Working Paper Approaching an investigation of multi-dimensional inequality through the lenses of variety in models of capitalism Quaderni - Working Paper DSE, No. 984 Provided in Cooperation with: University of Bologna, Department of Economics Suggested Citation: Antonelli, Gilberto; Calia, Pinuccia Pasqualina; Guidetti, Giovanni (2014) : Approaching an investigation of multi-dimensional inequality through the lenses of variety in models of capitalism, Quaderni - Working Paper DSE, No. 984, Alma Mater Studiorum - Università di Bologna, Dipartimento di Scienze Economiche (DSE), Bologna, https://doi.org/10.6092/unibo/amsacta/4143 This Version is available at: https://hdl.handle.net/10419/159822 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/3.0/ ISSN 2282-6483 Approaching an investigation of multi-dimensional inequality through the lenses of variety in models of capitalism Gilberto Antonelli Pinuccia Pasqualina Calia Giovanni Guidetti Quaderni - Working Paper DSE N°984 Approaching an investigation of multi-dimensional inequality through the lenses of variety in models of capitalism Gilberto Antonelli a , Pinuccia Pasqualina Calia b , Giovanni Guidetti c Abstract After a synthetic presentation of the state of poverty and inequality in the world and the contradictions incurred by economic theory in this field after decades of globalization and in the midst of a persisting global crisis, in paragraphs 2. and 3. we outline the rational for our theoretical analysis, underlining two main aspects. First of all, in paragraph 2. we recall the reasons which makes inequality a multidimensional phenomenon, while in paragraph 3. we explore the reasons why the models of capitalism theory is relevant for studying multidimensional inequality. These paragraphs emphasise that inequality is a multidimensional and cumulative phenomenon and it should not be conceived only as the result of the processes of personal and functional distribution of income and wealth, which even by themselves are intrinsically multidimensional. The basic idea is that institutions, the cobweb of relations among them and their interaction with the economic structure define the model of capitalism which characterises a specific country and this, in turn, affects the level and the dynamics of inequality. This approach is consistent with the sociological approach by Rehbein and Souza (2014), based on the analytical framework developed by Pierre Bourdieu. In paragraph 4. we outline the rational for our empirical analysis, applying the notion of institutional complementarity and examining the relationship between institutional complementarity, models of capitalism and inequality. Besides, refining Amable’s analysis (2003), we provide empirical evidence on the relationship between inequality in income distribution and models of capitalism. Additionally, basing on cluster analysis, we identify six different models of capitalism in a sample of OECD countries, provide preliminary evidence on the different level of inequality which characterises each model and suggest that no evidence supports of the idea that a single model of capitalism is taking shape in this sphere in EU. In paragraph 5. we give some hints about issues in search for a new interpretation capable to fasten together the process of increasing inequality, the notion of symbolic violence and the models of capitalism theory. In the last paragraph we focus on conclusions useful for carrying on our research agenda. Keywords: multidimensional inequality, poverty, models of capitalism, cluster analysis, institutions, economic structure, complementarity. JEL classification P16 (Political economy), B52 (current heterodox approaches: institutional, evolutionary). a Department of Economics, University of Bologna; School of Development Innovation and Change (SDIC). [email protected] b Department of Statistical Sciences “Paolo Fortunati”, University of Bologna. Pinuccia Pasqualina Calia <[email protected]> c Department of Economics, University of Bologna; School of Development Innovation and Change (SDIC). Corresponding author: Giovanni Guidetti <[email protected]> 1 1. Introduction The state of poverty and inequality in the world after decades of globalization and in the midst of a persisting global crisis is openly disclosed by UNDP (2013, 2014). While some fragile success has been achieved in terms of extreme poverty 1 , relative poverty 2 and inequality looks out of control. At the world level the extreme or absolute poverty rate fell in 2010 to less than half the 1990 rate. The 1.A. target of the Millennium Development Goals was met five years ahead of the 2015 deadline. This implies that 700 million fewer people lived in conditions of extreme poverty in 2010 than in 1990. As suggested in the Report by UNDP (2013, p. 1), this result has been fostered by “impressive average gains against multiple indicators of material prosperity.” 3 Nevertheless, at the global level 1.2 billion people are still living in extreme poverty, with different trajectories in the different world regions. Moreover, if we truly think that poverty is a multi-dimensional and cumulative phenomenon, the overall scenario becomes more fuzzy and alarming. Following UNDP (2014, p. 3) “Those living in extreme poverty and deprivation are among the most vulnerable. Despite recent progress in poverty reduction, more than 2.2 billion people are either near or living in multidimensional poverty. That means more than 15 percent of the world’s people remain vulnerable to multidimensional poverty. At the same time, nearly 80 percent of the global population lack comprehensive social protection. About 12 percent (842 million) suffer from chronic hunger, and nearly half of all workers - more than 1.5 billion - are in informal or precarious employment.” 4 When we come to consider recent trends in inequality the scenario get even worse. “The richest 1 percent of the world population owns about 40 percent of the world’s assets, while the bottom half owns no more than 1 percent. Despite overall declines in maternal mortality, women in rural areas are still up to three times more likely to die while giving birth than women living in urban centres. Social protection has been extended, yet persons with disabilities are up to five times more likely than average to incur catastrophic health expenditures. Women are participating more in the work force, but continue to be disproportionately represented in vulnerable employment. Humanity remains deeply divided.” The optimistic scenario anticipated at the end of last Century by mainstream economists 5 and international organizations 6 has been radically overturned. Globalisation used to be thought good for the poor, the unemployed and the middle-class. But, the impact of the global crisis has deeply challenged this view, fostering new interest for inequality in policy makers, citizens and social scientists. And now the balance is bluntly described as follows. “Over the last two decades, income inequality has been growing on average within and across countries. As a result, a significant majority of the world’s population lives in societies that are more unequal today than 20 years ago. ... In fact, the sharpest increases in income inequality have occurred in those developing countries that were especially successful in pursuing vigorous growth and managed, as a result, to graduate into higher income brackets. Economic progress in these countries has not alleviated disparities, but rather exacerbated them. ... there are clear signs that this situation cannot be sustained for much longer. Inequality has been jeopardizing economic growth and poverty reduction. It has been stalling progress in education, health and nutrition for large swathes of the population, thus undermining the very human capabilities necessary for achieving a 1 In the past, the absolute poverty line at the international level was next to $1 a day. From 2008 it has been revised by World Bank to $1.25 a day at 2005 purchasing-power parity. 2 Measuring relative poverty is akin to measuring income inequality, since it is related to the overall distribution of income or consumption in a country; for example, in OECD and EU the relative poverty line is set at an income level which is at 60% of the median household income. 3 Per capita GDP in lowand middle-income countries has more than doubled in real terms since 1990. In the same period, life expectancy in developing countries has risen from 63.2 years to 68.6 years. 4 Jessé Souza (2011) would probably include these persons in what he calls the “underclass”. 5 See for instance Sala-i-Martin (2002). 6 The reference goes, for instance, to the s.c. ‘Washington consensus’ view and the Davos World Economic Forum, especially in the 1971-2004 editions. 2 good life. It has been limiting opportunities and access to economic, social and political resources. Furthermore, inequality has been driving conflict and destabilizing society. When incomes and opportunities rise for only a few, when inequalities persist over time and space and across generations, then those at the margins, who remain so consistently excluded from the gains of development, will at some point contest the ‘progress’ that has bypassed them. ... But perhaps most important, extreme inequality contradicts the most fundamental principles of social justice, starting from the notion, enshrined in the Universal Declaration of Human Rights, that “all human beings are born free and equal in dignity and rights””. (UNDP, 2013, p. 1) Of course poverty and inequality are very different, even if not unrelated, concepts. The mainstream view oversimplifies the issue: “While it is unanimously agreed that poverty is bad, it is less clear that income inequalities are undesirable. After all, increases in inequality can arise from the worsening of the poor (a situation that is clearly bad) or the improvement of the rich (a situation that is clearly not bad).” (Sala-i-Martin, 2002, p. 1) Following this way of reasoning, it is rather common to distinguish between inequality in opportunities and inequality in outcomes. Much of the debate in development theory has been structured along these lines: the first is primarily concerned with factors that inhibit equitable outcomes, such as unequal access to employment or education; and the second with factors which influence the level achieved in various material dimensions of human success, such as income or education. While the latter is regarded as a standard result of economic and social life, and particularly of the competitive game, the former begets disapproval as an infringement of democratic principles. In any case, every judgement on inequality is extremely diverse for at least four indirect reasons. (a) Economies are going through deep transformations that are affected by outsourcing/unboundling and networks/value chains restructuring. (b) Economic and social classes are at the same time more and more fragmented, but less and less recognizable on account of specific roles performed in economic systems (and society in general). A range of income sources is available for the average worker, but these sources are not anymore necessarily linked to the factors of production the individual is endowed with. (c) Welfare perspectives vary according to the position held by the single agent in the household, in the society and in the networks in which she/he lives. (d) Different social and economic models of capitalism score rather different end results. At the same time, different layers of inequality are altogether relevant: (i) intra and inter generations; (ii) within and between genders; (iii) intra and inter countries (especially among DCs, Emerging Powers, LDCs); (iv) within and between local economic systems; (v) within and between employment categories (e.g., unions, professional associations); (vi) within and between social groups (e.g., economic, ethnic, religious groups); Indeed, we have to recognize, and the more so in the midst of a global crisis, that: (i) income inequality represents only one of the several dimensions of economic inequality; (ii) economic inequality may be determined by non economic inequality and generate further economic and non economic inequality 7 ; (iii) also non economic inequality generates very important negative material and immaterial effects. All this makes less simple to find easy compensations to the economic and non economic costs of inequality. What's more, the global crisis in action is showing us that even basic elements we used to regard as engines for equality in opportunities 8 can be easily transformed over time into “privileges” by the 7 For instance, a decrease in the incentive to invest in physical and human capital and an increase in social tensions and political instability may be driven by severe income equality. 8 For instance, the citizens right to a decent life, a decent work, education and access to information and knowledge; or the refugee's right to be protected against refoulement. 3 adverse circumstances. This leads to blurring the boundaries, which are shaped by economic, social and institutional behaviours and constraints, between what we could define a sustainable or unsustainable threshold of inequality. This can undermine the trade off between the bad and the good side of inequality. The overall scenario could change and, according to some authors 9 , the XXI Century could be characterized by the prevalence of the “patrimonial capitalism” pre-existing in XIX Century. 10 Since nowadays “Many people believe that modern growth naturally favours labour over inheritance and competence over birth” and “... democratic modernity is founded on the belief that inequalities based on individual talent and effort are more justified than other inequalities” (Piketty, 2014, p. 237 and p. 241), this shock could bring about a dramatic change in this popular confidence and consequent expectations. 11 All this amounts to say that the very conception of inequality and its role in society is changing both in developed and developing countries and this change is strictly linked to its multidimensional character. 12 Therefore more than a single prescription derived from a mono-disciplinary perspective what is really crucial is the methodological assumption which is implied in the reconstruction of the nature, determinants and effects of inequality. The assumption should comprise four steps. In the first step, a multi-disciplinary approach is important because it allows to understand more of the different dimensions of social and economic problems, avoiding, in this way, the pitfalls arising from the adoption of a unilateral perspective. Secondly, the focus on the conditions of different countries at the world level (developed, developing, Emerging Powers) is also essential because, apart from helping to grasp the different specific perspectives, allows to understand interactions. In the third step, after the focalization on key research questions and real problems, each discipline 13 can perform his proper task, making use of its specific tools of analysis. In the fourth and last step, multi-disciplinarity recovers a central role when we need to concentrate on policy design. The capability to pursue such an approach is decisive in order to reach original goals in the framework of the integrated cooperation programme we are implementing with the Global Study Programme. After the introduction, in paragraphs 2. and 3. we try to outline the rational for our theoretical analysis, underlining two main aspects. First of all, in paragraph 2. we recall the reasons which makes inequality a multidimensional phenomenon, while in paragraph 3. we explore the reasons why the models of capitalism theory (MCT) 14 is relevant for studying multidimensional inequality. In paragraph 4. we try to outline the rational for our empirical analysis, applying the notion of institutional complementarity and examining the relationship between institutional complementarity, models of capitalism and inequality. Besides, refining Amable’s analysis (2003), we provide empirical evidence on the relationship between inequality in income distribution and models of capitalism. 9 The most prominent is Piketty (2014). 10 In this respect the XX Century could have been the exception. 11 This perspective of study can be relevant also in trying to assess the effects of other global phenomena taking place in the present historical phase, such as “land grabbing”. 12 This means that the definition of inequality cannot be only economic and that it crucially depends among other factors on the actual performance of the social system in which human beings live and interact. 13 In our case, economics, sociology and law. 14 After some early work by Albert (1991) and Prodi (1991), non-mainstream economists like Amable (2000, 2003) and Aoki (2001), as well as sociologists like Crouch (2009, 2010), have developed a theoretical framework based on the notion of variety in models of capitalism, we will refer to as MCT. 4 In paragraph 5. we give some hints about issues in search for a new interpretation capable to fasten together the process of increasing inequality, the notion of symbolic violence and the MCT. In the last paragraph we focus on conclusions useful for carrying on our research agenda. 2. In what respects inequality is multidimensional? Inequality, both at the macro-economic level, which refers to national and supra-national entities, at the meso-economic level, which refers to local communities, and at the micro-economic level, which refers to individuals and generations, is in its essence a multidimensional and cumulative phenomenon. Even when we split income inequality, which depends on the personal and functional distribution of income and wealth, from other sorts of inequality 15 this phenomenon remains multidimensional, meaning that it evokes different types of individual and social background leading to the final outcome. One of the most important reasons is that inequality depends on wealth and income distribution and that “in all societies, income inequality can be decomposed into three terms: inequality in income from labour; inequality in the ownership of capital and the income to which it gives rise; and the interaction between the two terms” (Piketty, 2014, p.238). The relevance of ownership of capital and inheritance, which is strongly linked to the historical tradition, contributes to link economic inequality to the socio-institutional framework and the sociocultural perspective. Talent and effort in this case are less important than inheritance and marriage in determining success and this in turns implies different tastes and behaviours. 16 Just when we confine our research on inequality in income from labour, we have to be conscious that it can be derived from self-employment or wage labour, which imply totally different socioeconomic conditions, in which also the provisional or permanent character of employment contribute to differentiate the socio-economic background. Furthermore, economic inequality is critical, but, the more we explore the extremes of the distribution, the more we note an overlapping and a bumping up of different dimensions of inequality. Low per capita incomes are highly correlated with low quality of life, and therefore with variables like poor health, low education, higher uncertainty and insecurity of employment and low participation to civil society. Therefore, countries with low human development index (HDI) suffer most because they tend to have greater inequality in more dimensions (UNDP, 2013). Besides, following Marmot 17 (2013), we can distinguish between the material deprivation, which entails malnutrition, exposition to infected organisms, low resistance to their effects, exposition to hot and cold weather and to toxic elements, and the processes conditioning adult mortality, which take place even when poverty thresholds are overtaken. In this case human capital and human development are affected by the living situation of the single person both in terms of their direct outcomes (life expectancy, productivity, income) and in terms of impact on their creation and destruction channels (education, healthcare). Another important implication is that a potential reduction in one dimension of inequality does not 15 This practice has been favoured by the hegemony acquired by the neoclassical theoretical agenda in modern labour economics (Teixeira, 2003). This agenda, by the way, has greatly contributed to stress the mono-dimensional character of inequality with the human capital theory. 16 Also illegal accumulation of wealth and post-conflict transition phases can have some impact, in this respect. 17 This epidemiologist is laying the foundations of what can be defined social epidemiology, through the establishment of a systematic link between variance in health and life expectancy, on the one side, and social status, on the other. 5 imply an even reduction in all its dimensions. 18 The multidimensionality issue becomes more and more important as the awareness of the constraints caused by the existence of a “maximum sustainable inequality threshold” (MSIT) for the economy increases. 19 But, in order to pursue our argument in a more systematic way, it is convenient to investigate the different dimensions of inequality singling out its basic characteristics, its determinants and its effects. 2.1. Nature In the last quarter of a century a deep rethinking on the notion of well-being is leading to a rising agreement on the idea that it arises “from a combination of what a person has, what a person can do with what they have, and how they think about what they have and can do” (IDS, 2009). Along this line of reasoning, well-being embraces three basic components: (a) the material and economic one, stressing welfare conditions, standards of living and economic values; (b) the relational one, emphasizing personal and social relations; (c) the subjective one, highlighting, moral values and perceptions, side by side with option and existence values. The three components are merged together and their boundaries are highly fuzzy (McGregor, 2007; Sumner and Mallett, 2013). This, in turn, has induced a multi-layered revision of the notion of inequality, thanks to which nowadays also experience and intuition suggest that inequality is a multidimensional phenomenon: so many are its features and the circumstances in which it can be felt, conditional on culture, gender, ethnicity, religion, race, geographic location, age and other characteristics, relevant for human well-being, both across individuals and across groups. It is important to specify that the multidimensional nature of inequality concerns both each basic component per se and the connections between the three of them. We mean that, even separating the material and economic dimension from the others, and limiting ourselves to consider inequalities in each of the proxies for the standards of living, since this can concern variables such as income, wealth, education, health and nutrition, the multidimensional nature of inequality leaks out. Of course the multidimensionality becomes broader if the three basic components are allowed to interact. Among the non-economic components an essential role is played by ethnicity, gender and religion. In any case, beneath them, the access to many wants is often unevenly distributed and limited by economic constraints. Limiting ourselves only to very immediate examples, we could mention the option to use 20 sophisticated drugs and cures, safe transports, qualified information and knowledge, natural and environmental resources of higher purity, and also a safe neighbourhood in which to rise children. Direct and indirect linkages connect material and immaterial components of inequality. Income constraints can easily bring about fragilities and drive persons to suffer from non-economic dimensions of inequality. The inequality in access to goods and resources and the limits to an inclusive growth process are often augmented by complementarities among goods and the increasing relevance of “network products” 21 which characterize the actual conditions of consumption. Moreover, the increasing diffusion of non private goods, contrary to what could be envisaged, can contribute to increase inequality and decrease inclusiveness. It depends on the multiplicity of 18 Even if this result is partly due to the measures used, a study by UNDP (2013) shows that in the last two decades at a worldwide level there have been much greater reductions in inequality in health and education than in income. 19 We can define MSIT as the maximum level of inequality not inhibiting inclusive growth in a given economic system (Antonelli, 2013). 20 Or even the benefit of knowing that a chance of utilization exists in the future. 21 For a comprehensive study see Shy (2001). 6 economic goods and the prevailing regulation structure for their provision. 22 In both cases the quality of consumption is conditioned by the ease of use of related conditions and externalities. 2.2. Determinants When we come to consider the determinants of inequality we can easily realize how much the social and genetic components are able to influence the economic ones and conversely. Wealth, education, and social privilege are strongly interrelated with psychological temper and genetic privilege. At the personal and family level, a poor environment and natural gifts tend to lower the probability of economic success and to increase income inequality over the lifecycle. In fact, today, the majority of experts believe that behaviour and development are influenced by both “nature” and “nurture” 23 , while a minority take the extreme nativist or extreme empiricist views. However, researchers and experts still debate the degree to which biology and environment influence behaviour and performance. This suggest that the capability to take into account the interactions between the different dimensions of inequality is crucial. At the nationwide and, especially, at the meso-economic level, the welfare infrastructure and public policies can be very important in supplying concrete and timely assistance to disadvantaged individuals and families in local communities. To the extent that microeconomic studies observe critical and sensitive periods in the life cycle of individuals, indicating, for instance, that some skills are more easily acquired during certain stages, for most configurations of disadvantage it is important to socially invest relatively more in the early stages of childhood than in later stages (Cunha, Heckman and Schennach, 2010). 24 Education, health care and social welfare services at the local level are therefore important drivers of the capability of a community to practice cohesion and civic virtues, with significant effects on the distribution of labour market performance and labour income opportunities. In this respect Adelman (2000, p. 18) adds another vital remark which is more appropriate in a meso-economic framework. “Cultural factors play a significant role in shaping institutions and societal responses to new challenges and opportunities. ... Both individualistic and communitarian cultures have advantages and disadvantages. … Individualistic responses foster innovation, dynamism, creative destruction and geographic and social inequality. … Communitarian responses foster social cohesion and the social ability to absorb change, and hence national resilience and malleability. They place a premium on social equity in growth outcomes and foster societal and governmental approaches to development. They also enable societies to more easily absorb short run decreases in personal welfare in the interest of the common long run good (Rodrik 1997, 1998).” At the macroeconomic level, up to now the optimistic prediction by Kuznets (1955) was able to persuade the majority of the economic profession. Kuznets, using only U.S. data for the period 1913-1948, suggested that in every country, over the course of industrialization and economic development, inequality follows a bell-shaped curve. In this theory inequality plays the role of an endogenous variable which is decreasing after a certain mean income threshold has been overtaken. In contrast with this view, Adelman (2000, pp. 14-18) stresses that fifty years of development history show how inequality can play the role of an exogenous variable which is negatively correlated with economic development. In these cases the relationship is reversed. 25 More recently, Piketty (2014, p. 15) adds that “... the magical Kuznets curve theory was formulated in large part for the wrong reasons, and its empirical underpinnings were extremely fragile. The 22 For more details see Antonelli (2011). 23 Even if the two terms are rephrased more exactly. 24 For instance, the capability to timely support drug addicted young woman in the first years of age of their children can make social assistance much more effective in terms of inequality outcomes. 25 Adelman (2000, p. 17) answers: “Is there a Kuznetz curve?” And her answer is “Not in the sense that a U-shaped course of inequality is inevitable.” 13 other institutions 46 . However recent developments in the field of institutionalist analysis have focused on the interaction between two or more institutions and has led to introduce the notion of institutional complementarity. We can have institutional complementarity when two or more institutions interact so that the working and the performance of the institutions involved in this relationship is affected by the working and the operation of the others. 47 Basically, the operation of a single institution reinforces and is reinforced by the functioning of other institutions. Even on the basis of the functionalist interpretation of institutional complementarity, there are two important consequences of institutional complementarity: (ii) possible prevailing of sub-optimal configurations of institutions and (ii) path dependence. (i) Aoki shows that when two institutions are complementary this configuration can be stable even though, if compared to another configuration of complementary institutions, it results to be suboptimal. (ii) the existence of complementary relationships can cause institutional lock-in and path dependence. Applying the notion of path dependency developed in the field of evolutionary economics by Arthur (1994) and David (1994), Kang (2006) shows how institutional complementarity can bring about path dependency since it implies four mechanisms which are at the basis of path dependency: (a) large set-up costs or initial costs for the agents affected by the operation of the institutions; (b) learning effects; (c) coordination effects; (d) adaptive expectations. This entails the absence of a single best institutional combination. In this framework it is very important to understand, first of all, who are the institutional builders. 48 Following Streeck (Crouch, Streeck, Boyer, Amable, Hall and Jackson, 2005), the political élites acts as founders of specific institutions. It is very important to identify the composition of these élites and the mechanisms through which these élites contribute to the process of institutional building. Second, we need to know who are the agents whose action is affected by the operation of institutions. For some agents the institution can be considered as a set of constraints and rules of the game (à la North), for others institutions can be resources. It is very important to understand the objective functions of these agents. Third, we need to understand how the operation of complementary relationships can be influenced by exogenous factors. At this point, in neoclassical economic theory one would try to define the objective function to maximise subject to a specific resource constraint. Sticking to this approach, one can say that the institutional entrepreneurs would set up institutions in order to take the maximum feasible advantage from the foundation of the institution itself. Using a more general language, it is important to understand which “ex-ante” purpose the institution pursues. Basically, that means that it is important to understand why a specific institution has been set up and its dynamics. However, it is important to emphasise that, unless we assume the existence of a “homo economicus”, gifted with unlimited capability to work out the outcomes of her/his actions and choices, the effects of a process of institutional building may well differ “expost” from what the institution was “ex-ante” designed for. This is a consequence of the complementary relationships which can be established among a bundle of institutions; the setting up of a specific institution affects the sub-system for which it has been conceived and, through the 46 For example, as to the effects of the laws concerning the firing restrictions, the standard approach focuses on the capabilities of firms to adjust the level of employment in order to reach the optimal equilibrium. This analysis rules out the effects, of this legislation on the propensity of firms to provide training for their employees, emphasised by Soskice and Hall (2001). This side of the analysis can be caught only if one takes into account the complementary relationship among institutions. In the standard microeconomic neoclassical approach, institutions are conceived as deviation with respect to an institution-free equilibrium. 47 See Aoki (2001) for a formal definition using a game theoretical approach. 48 Or the “institutional entrepreneurs”. 14 complementary relationships established with other institutions, it can affect other sub-systems. 49 In the process of institutional building, different institutional entrepreneurs, with possible contrasting objective functions, can be involved either in the setting up of the same institution or in the assembly of interacting institutions. This process follows a trial and error procedure pursued by agents with limited rationality, in a highly uncertain environment. As a result, institutions do not necessarily establish optimal relationships and, actually, can hinder each other. Even though they are neglected in the literature, in this respect we can refer to the establishment of “negative complementary relationship”. However, the alleged negativity/positivity of a set of institutional complementary relationships depends on the objective functions of both individual agents and the whole social system. For example, let us consider a certain framework of institutional complementarity promoting the compression of the relative wage structure. Can we deem this effect satisfactory, neutral or negative? We cannot reach a definite answer. 4.2. Complementarity and models of capitalism A model of capitalism is a cobweb of complementary institutions which affect the performance of a socio-economic system, based on capitalistic relationships among agents. This definition is consistent with Amable's approach who maintains that “a variety of institutional complementarities is possible, generating a diversity of models of capitalism (Amable, 2003, pp. 102). Amable points out five different types of institutional domains: (a) product markets; (b) labour markets; (c) financial system; (d) social protection system; (e) educational system. As institutions affect the distribution of income among wages, profits and rents through the structuring of both the labour markets and the markets for goods, services and productive factors, the model of capitalism defines the distribution of income. Starting from this point of departure, we can state that different models of capitalism should exhibit different level of inequality in income distribution. From this, following the Bourdieu's framework of analysis, inequality in access to education, health services arise. It is important to stress that the definition of a model of capitalism is based on the operation of a set of institutions and does not imply an interpretation about the functional relationships among economic variables which define an economic model. Of course, this does not mean that the model of capitalism is neutral as to the relationships among economic variables, but simply that the model of capitalism is defined regardless of any economic model. We could mention institutional change as a consequence of change in the environment, in the agents affected, in the operation of the institution and/or in their objective functions. Contrary to Amable's statement that “institutional forms x and y are compatible if their coexistence does not set in motion a process of institutional change, in the sense that some political forces would like to keep x and change y”, when there are negative complementary relationships we might observe the start of an institutional change only if one of the institutional entrepreneurs prevails on the others or if the institutional entrepreneurs reach an agreement for a change. As institutions are linked together by complementary relationships, then the effects of institutional building can be properly appraised only taking into account the cobweb of complementary relationships which can be potentially activated with the introduction of a new institution. An institution is not efficient only “per se” but also in accordance with the relationships established with the operation of other institutions. 50 In any case, important questions remain open: can we analyse the effect of structural adjustments prescriptions enforced by IMF through the perspective of institutional complementarity? can we interpret the failures and, especially, the negative side 49 The example can be made of labour laws regulating hiring and firing and propensity of firms to provide training for their employees. 50 In paragraph 5. we will try to develop some ideas concerning the EU evolution. 15 effects of these policy through the lens of institutional complementarity? 4.3. The empirical analysis 4.3.1. The research targets After discussing the pivotal role played by the institutional complementarities in the definition of the models of capitalism, we have to test the empirical relevance of this analysis. The analysis proceeds along two different stages. First of all, we have to verify that by using a set of indicators of the operation of institutions and their complex interactions, we can classify different countries into distinct groups, each one corresponding to a model of capitalism. Secondly, assuming that the previous stage of empirical analysis is successful, we have to see if the classification introduced discriminates significantly the countries as far as income inequality is concerned. Basically the research questions we are going to address through empirical analysis are the following: (i) Is the approach followed by MCT, which defines models of capitalism on the basis of institutional complementarities, empirically robust? (ii) Does income inequality depend on the model of capitalism or is it an inherent characteristic of capitalism itself with more or less similar thresholds everywhere? Of course, the question does not refer to the existence itself of income inequality in capitalistic societies, which one should take for granted, but to the degree of inequality in income distribution. Empirical analysis is based on two distinct steps. The first step consists in an application of cluster analysis in order to identify empirically the models of capitalism. Once we have defined the various models of capitalism, we proceed to the second step in which we estimate the relationship between inequality and model of capitalism. 4.3.2 Cluster analysis and models of capitalism First of all, we have refined Amable's (2003) classification of the five sub-systems 51 . He points out the five domains mentioned earlier and, given the relevance of technological change in labour market dynamics, we have decided to add a set of indicators concerning the degree of technological development of a country and the role played by both the private and the public sector in activities of R&D. Following (Everitt, Dunn, 2001, pp. 125) cluster analysis provides a “parsimonious way of describing the patterns of similarities and differences in the data”. Basically, this means that through cluster analysis one can group different statistical units and classify them. However, as Everitt and Dunn state, we have to remember that “any classification is simply a division of the objects or individuals of interest into groups based on a set of rules – it is neither true or false (unlike, say, a theory) and should be judged largely on its usefulness” (ibidem, pp. 126). Each cluster groups a set of countries which, on the basis of a series of statistical indicators, outlined in the Appendix, exhibits a high degree of homogeneity among them and a corresponding high degree of heterogeneity with the countries grouped in other clusters. More details on cluster analysis can be found in Appendix. 51 He uses the term domains. 16 4.3.2 1. The data All the data used for the cluster analysis come from the OECD.StatExtract, the database set up by OECD and the World Development Indicators (WDI-WB), collected by the World Bank 52 . For each institutional domain (Labour market, Product market, etc.) we try to recover data for each year in the time span between 1995 and 2010 for the maximum number of countries. The final set of indicators used in the analysis is the result of the process that try to minimise the loss of information on countries and, at the same time, to retain the maximum possible number of years. However, it was impossible to obtain complete time series for all the years between 1995-2010 for all indicators and all the countries. So we decided to implement the analysis at two specific point in time: at the beginning and at the end of the period (1995 and 2010). The definition and the source of indicators we used in the analysis is reported in Table A.1 in appendix For the analysis of income inequality, we have used an estimate of Gini index (gini_net) of inequality in equivalized (square root scale) household disposable (post-tax, post-transfer) income, using Luxembourg Income Study (LIS) data as the standard. This datum comes from Standardized World Income Inequality Database (SWIID) provided by the university of Iowa. Following Liskanen (2007), the LIS disposable income concept is given by gross income (GIW) minus mandatory contributions and income taxes. In terms of income variables, it can be expressed by means of the following equation: LIS_DPI = GIW – CONTRIB – INCTAX 53 As far as the selection of countries is concerned, the complexity and the richness of data required for the empirical research has forced us to focus the analysis on a selection of 24 OECD countries, which refer to: (a) 19 European countries - Austria, Belgium, France, Germany, Italy, Netherlands, Denmark, Finland, Norway, Sweden, Czech Republic, Poland, Portugal, Slovak Republic, Spain, Hungary, Ireland, Switzerland and United Kingdom; (b) 2 Asian countries - Japan and Korea; (c) 2 American countries - Canada and the United States; (d) 1 country from Oceania - Australia. The composition of the sample of countries is strongly Eurocentric, despite it includes some important extra-European economies. One of the most remarkable limits of this analysis is the exclusion of both developing countries and, especially, the so-called Emerging Powers (Brazil, Russia, India, China and South Africa). As a partial justification for this limitation, of which we are aware, we can say that data availability for these countries can be rather problematic. 4.3.2.2. The cluster analysis In order to identify whether different models of capitalism exist, we apply the cluster analysis to the institutional indicators of the 24 countries for which we have data at 1995 and 2010. We select the maximum set of indicators common to both periods. Before applying the cluster analysis, we proceed to standardize the raw data in order to eliminate the influence of the different measurement units. In Tables 1 and 2 the cluster composition in 1995 and 2010 is presented. The choice of the number of cluster in each year are based on the dendrograms shown in Figures 1 and 2, and the measures reported in Tables A.2 and A.3 in Appendix. 52 See the Appendix for the list of statistical variables used and the source. 53 Roughly speaking Gross Income can be defined as the flows of earnings of productive factors plus the pensions plus transfer income. For a precise definition see Niskanen (2007). 17 Table 1. - Cluster composition – 1995 1 2 3 4 5 Czech Republic, Poland, Portugal, Slovak Republic, Spain, Hungary, Ireland Denmark, Finland, Norway, Sweden Austria, Belgium, France, Germany, Italy, Netherlands Japan, Switzerland, Korea Australia, Canada, United Kingdom, United States Data source: our elaboration on OECD data 18 Figure 1. - The dendrogram based on 1995 indicators Table 2. - Cluster composition - 2010 1 2 3 4 5 6 Czech Republic, Poland, Slovak Republic, Hungary France, Ireland, Netherlands, Portugal, Spain Denmark, Finland, Norway, Sweden Austria, Belgium, Germany, Italy Japan, Korea Australia, Canada, United Kingdom, United States, Switzerland Data source: our elaboration on OECD data 19 Figure 2. - The dendrogram based on 2010 indicators In the following we try to characterize the different groups found by the cluster analysis for each time period and compare the differences in the grouping and the characteristics across time. We start from the results of the cluster analysis for the year 1995, so we can compare our results to the grouping in Amable (2003) for the late ‘90. Using data at 1995, two clusters are clearly identified as the ones that correspond to the two opposite models of capitalism identified by Amable (2000, 2003): the market-based capitalism model (or Anglo-Saxon economies) that corresponds to the cluster composed by USA, Canada, Australia and UK, and the Social-democratic model, which corresponds to the cluster composed by the Scandinavian countries (Finland, Norway, Denmark, and Sweden). In addition to those clusters, a cluster with Japan and Korea, associated by Amable to the Asian model, emerged in our analysis. However, in our analysis this cluster includes Switzerland, which in Amable’s analysis can be found in a cluster with other continental European countries. The main differences between our cluster analysis and Amable’s one can be identified in the composition of both cluster 3 and cluster 1. Cluster 3 is associated to the model of Continental European Capitalism. Although the composition of the cluster in our and Amable’s analysis is similar, there are a few exceptions. For example, in our analysis Norway is classified in the cluster that identify clearly the social-democratic model while Amable classifies this country together with France, Germany, Austria, Belgium and Netherlands (plus Switzerland and Ireland). Moreover, we have found that Italy is in a cluster with Germany and France, while Amable groups Italy with Spain, Portugal and Greece in a cluster collecting all the most Mediterranean countries. Finally, in our analysis one can observe the rising of a cluster (cluster 1) including Spain, Portugal and Ireland as well as four European countries from central and eastern Europe (former socialist-block countries) that were never considered in previous empirical analyses. As already stressed by Amable (2003), even though the identification of clusters allows to group countries into homogeneous subsets, that does not mean that one cannot observe a certain degree of heterogeneity among countries of the same cluster, as far as certain variables are concerned. Therefore, each cluster identifies a specific Model of Capitalism, which can be considered as a sort 20 of ideal-type, however countries belonging to the same cluster can significantly differ as to certain variables. In describing the clusters we look at the cluster’s average for each indicator (Table A.4.) and consider a measure of the level of heterogeneity between clusters and homogeneity across countries within clusters. This is based on the share of the between-cluster variability (deviance) of each indicator on total variability: it measures how much of the total variability depends on differences among clusters, and so it indicates the characteristics that mostly discriminate the clusters (Table A.5.). Cluster 6 is characterized by a deregulated product market, since we can observe low values of the indicators of Product Market Regulation (PMR ) – State control, Barriers to entrepreneurship, and Barriers to trade and investments. As to labour market, in this cluster countries present low values of the Employment Protection indicators, little coordination or centralization for wage bargaining, and low value of trade union density. Interestingly, these countries have a sophisticated financial system, with high stock market capitalization on GDP, highly dispersed banking systems, significant presence of foreign banks and a relevant domestic credit on GDP. In this cluster social protection is poor and the welfare system is less developed; social public expenditure on GDP is quite small in comparison to the other clusters. The same can be said for unemployment benefit and the incidence of public expenditure on passive labour market policies. This group of countries exhibits high level of gross domestic expenditure in R&D on GDP and a high number of researchers among employees. Finally, one can observe what happens to the indicators related to the working of the fiscal system. In this cluster both the level of taxation on GDP and the value of the tax wedge are lower than in other clusters (with the exception of cluster 4). This framework is confirmed by the structure of revenues by the different sources of taxation: taxes on income, profit etc. accounts for the 46% of total taxation, while the amount of social security contribution on total taxation accounts only for the 14%. At the other end of the range of clusters cluster 2, which includes the Scandinavian countries and Denmark. This cluster fits in with the social-democratic model of capitalism, characterized by a highly developed welfare system with the highest social public expenditures and high values of other welfare indicators (public expenditures on unemployment, public expenditure on passive and active labour market policies). Labour market is characterized by the highest participation in trade union and a high value of the indicator of coordination and centralization of wage bargaining, as well as a stringent employment regulation, even if not as strict as cluster 3. This cluster also presents high values of the level of taxation The financial system is highly concentrated with a low presence of foreign bank. Besides, the system of Research and Development is quite competitive because this cluster presents one of highest value of Gross domestic expenditure in R&D on GDP and the highest number of researchers on total employment. Cluster 5, composed by Japan, Korea, and Switzerland, is close to the cluster 6 in many domains and it is the most distant from the social-democratic model. It is characterized by low level of social protection, low public social expenditures and a low level of taxation on GDP and on labour. The labour market is more regulated than the market-based model with a certain level of employment protection, especially for temporary employment, and a high level of coordination and centralization of wage bargaining but a very low participation in trade unions. It differentiates most from the market-based model regarding the product market regulation, with high levels of State control and different type of barriers. The financial system is well developed, with high value of all indicators concerning the stock market and limited banking concentration, but it differentiates for the major role of the bank system (the highest indicator of domestic credit provided by banks). It is also characterized by a R&D system with high investment, provided especially by the private sector, and a high number of researchers on total employment. The other two clusters are somewhere in between the two extremes and are similar in some respect. Cluster 3 groups the countries that in the Amable’s analysis were associated to the continental European capitalisms, with some exception (Norway, Italy and Ireland). This cluster is characterised 21 by a high level of employment protection (especially for temporary employment) coordination and centralization of wage bargaining and trade union density. In addition to that, one can observe a high level of social protection, especially of employment-based benefits. The product market is quite regulated, with higher level of state control and barriers to entrepreneurship compared to the others clusters. The level of taxation is very high, especially on the labour factor. Hence, in many aspects this cluster is close to the cluster of the Scandinavian countries. The difference lies in the domain of education and R&D. In fact, both the level of public expenditures on education (% of GDP) and the Gross domestic expenditure in R&D on GDP are the lowest among all clusters. The same can be said as far as the number of researchers on total employees is concerned. Cluster 1 is a novelty with respect to the analysis of Amable, due to the presence of some countries that were not previously available. We recognise a group of eastern European countries, and other three countries (Ireland, Portugal and Spain) that were classified differently in previous analyses. This cluster is close to cluster 3 with regard to many aspects. It has a strict employment protection (less for temporary contracts) and a comparatively low level of coordination and centralization of wage bargaining. Participation in trade unions is not so high as in the former cluster. In addition to that, countries in this cluster have a limited welfare state with a lower level of social protection than the previous cluster but higher than the “market-based” cluster. Product markets are strictly regulated and the financial system is characterized by a high concentration of the banking system and a small and not too much efficient stock market. The level of taxation is comparable to cluster 3 but the structure by type of taxation evidences a higher incidence of “indirect” taxation rather “direct taxation”. Finally, one can observe both very low levels of investments in R&D and in the number of employees working in R&D activities. However, the description hides the within-cluster heterogeneity across the countries because we compare the clusters’ averages. Indeed, we find that while for some indicators the clusters are well differentiated and, to some extent, homogeneous inside, for others still remains high heterogeneity within clusters. This emerge from the data in Table A.5: differences between clusters are well accounted by a small number of indicators, for which the between-cluster deviance is greater than 65%. For many indicators the within-cluster deviance is greater than 50% of the total deviance, indicating that there is a substantial heterogeneity within clusters. The time lag between 1995 and 2010 is long enough to leave room for structural change and for radical institutional transformations to occur, which can shift one country from one model (cluster) to another one. Indeed, the situation at 2010 shows a greater diversification, above all for the European countries: now we can clearly distinguish a cluster of Eastern European countries (cluster 1) and a new cluster (cluster 2) is formed putting together some countries that were previously allocated to two different clusters. This may also be revealed by looking at the share of betweencluster deviance, that is higher than 65% for a greater number of indicators. The clusters differentiate mostly on institutional indicators related to the labour market, the fiscal and financial system and the system of drivers of technological innovation. Looking at Table A.6, which shows the cluster mean of each indicator, we notice there are large differences in trade union density, with the highest value for the cluster that includes the North European countries (cluster 3) and the cluster with Austria, Germany, Italy, and Belgium (cluster 4), and the lowest values for the Asian countries (Japan and Korea - cluster 5) and the Eastern European countries (cluster 1). We observe a quite similar pattern for the degree of coordination of bargaining, with the exception of the cluster of Japan and Korea, which is closer to the cluster of North European countries. There are also large differences in the incidence of labour taxation and the level of the statutory minimum wages. Tax wegde is wide especially for cluster 4 (Austria, Germany et al.), and the North European countries, as well as for Eastern European countries, while cluster 6 (Australia, Canada, UK, Switzerland, and USA) and cluster 5 has lower values of tax wedge than the former. Consistently with the previous pattern, these two clusters have the lowest values of minimum wage in comparison, for example, to cluster 1 or cluster 2. The small value of this indicator for cluster 3 and 4 is due to the fact that in the majority of those countries there is no minimum wage. 22 Taxation also varies greatly between clusters. Regarding the share of income, profits etc. taxes on total taxation, for two clusters (cluster 6 and 3) total taxation flows come largely from direct taxation while there is a better balance between different sources of State revenues for the other clusters. The lowest value is observed for cluster 1 (Japan and Korea). However, as far as total revenue on GPD is concerned the lowest share is observed for the cluster of Anglo-Saxon countries and the Asian countries, while all the European countries have larger levels of taxation, in particular cluster 3 and 4, which also have the highest values of average tax rate. Very different welfare systems characterized the clusters: especially low values of Gross Replacement Ratio (GRR) are observed for the cluster of Eastern European countries and the cluster 5 (Japan and Korea) while, as expected, the highest benefit for unemployment are in the Northern European countries but also in cluster 2 (composed by a variety of countries: Spain, France, Portugal, Ireland, and Netherlands). The same pattern is evidenced for Total Public Social Expenditures and Total Public Expenditures on passive labour market policies. There are quite large differences in the level of Stock Market Capitalization to GDP, with the highest value in cluster 6 – the Anglo-Saxon countries – and the lowest value in cluster 4 and in cluster 1 (Eastern European countries). Finally, other influential indicators are the two indicators related to the technological level of the economic system: the Northern European countries (cluster 3) and Japan and Korea (cluster 5) exhibit the highest levels of investment in both R&D and human capital, while the Eastern European countries have the lowest values for both these indicators. From this descriptive analysis, one can draw some conclusions. First of all, we find that the empirical support of the diversity of capitalism pointed out in Amable is largely confirmed. It is worth mentioning that it is not possible to identify a single European model of capitalism. The 19 European countries are split along five different clusters in both years. In addition to that, one can say that, with the exclusion of the United Kingdom, in 1995 the European countries were distributed among three different clusters, whereas fifteen years later the same countries were split into four different clusters. Apparently, this suggests that no process of convergence was at work in Europe in that period of time. As far as Europe is concerned, the interpretation of cluster analysis with 2010 data suggests the existence of the following clusters: (i) a group of formerly centrally planned economies; (ii) a group of Scandinavian countries and Denmark; (iii) two clusters of continental countries which, in these fifteen years of interest, passed through a stage of institutional restructuring, which, however, did not imply any dynamics of convergence. Second, an Anglo-Saxon model can be detected whose composition seems to be quite steady. Third, a stable cluster, which includes the two Asian countries, can be observed . 4.3.3 Models of capitalism and inequality. Some preliminary empirical evidence In order to understand whether each cluster differs from the others as far as income distribution is concerned, one can estimate the following econometric model:  ,       1    2    3    4    5   , The dependent variable (gini i,t ) is a Gini index of income distribution for each one of the 24 countries. Cluster i (where i= 1,2…5) is a dummy variable indicating the cluster of each country, based on cluster composition of 2010. Cluster 6 is the benchmark. This preliminary econometric analysis shows some interesting results. First of all, considering that the cluster 6 is the benchmark of this straightforward model, one can observe that all the parameters are negative and highly significant; from a statistical point of view this means that the six clusters are statistically different as far as inequality in income distribution is concerned. The negativity of the signs for all the coefficients indicates that the cluster 6 (Australia, Canada, United Kingdom, 29 “decent work agenda” ILO has implemented several programmes at the global, regional and national levels. 63 Particular attention has been paid to equal remuneration, the elimination of racial discrimination and better enforcement of legislation in general. Since this is well known from many years, together with the severe risks faced by many countries in the case of an overload of flexibility in labour markets compromising human capital creation, we could wonder why the EU suggests as a best practice the “flexicurity model” which has been tested only in a small, wealthy, non Euro zone and, in this respect, unrepresentative country like Denmark? And why “outsiders”, at least in countries like Italy, seem to accept a reform of the labour markets which tends to equalize their condition with that of “insiders” in a general frame of reduced job security and tenure cutback? As far as the Italian situation is concerned, we can maintain with ISFOL (2013) that the deregulation of labour markets policies, implemented from the beginning of the ‘90s, did not led to improve neither productivity, nor job creation, or wages. Liberalization and disproportionate use of short term contracts and other forms of temporary works aimed at implementing more flexibility in labour markets initially rather rigid. But, while reducing the legal protection of some worker categories, they were not able to enhance, on the supply, side neither sales volumes of the firms nor job creation, on the contrary they even contributed to raise the cost of labour per product unit. At the same time, they have increased inequality, reduced the demand for consumption and worsened the crisis from the demand side. A candidate explanation is that during the long phase of decline in economic conditions, preceding the global crisis, the misconduct of public and private sectors coupled with twisted mass communication, led many young outsiders to accept the view that they have been deprived by old insiders, understood as employees with tenure 64 , and impersonal mechanisms based on competition, meritocracy and flexibility are more fair. 5.3. Migration between the two shores of the Mediterranean Sea The economic prospects of the Euro-Mediterranean Countries (EMCs) are uncertain, not only due to the long-lasting effects of the global crisis, social turbulence, political unrest and war, but also because of the impact of long term structural changes in the international division of labour and the shortage of political and institutional tools pre-arranged by EU and the international community at large. The sequence of events taking place from 2011 in the Southern shore of the Mediterranean sea asks Europeans to reconsider the integration process, taking into account the new challenges and the deep interactions between the Northern and the Southern shores as well as to implement new priorities and regional economic unions. We may even say that, after all, at least some of the many determinants of the “Arab spring” were not unpredictable. For sure, what has been called the “fever under the skin” of the Arab world heads to a brand new stage of development. As it has been the case with the transformation of the Eastern-Central Europe Countries (ECECs) after the fall of the Berlin wall, the puzzle of the present crisis reflects many of the factors acting at a global scale: the impact of long term fragmentation in trade and production; the emergence of new economic powers; the changing composition of the population and of the working force. In fact, if we take into account the proper time lag, we may note clear similarities between the ongoing transition of the ECEC and the potential transformation of the Arab League Countries (ALCs). 63 Nondiscrimination has been included as a priority in the Decent Work Country Programmes of 36 countries. (ILO, 2011) 64 Possibly organized in unions. 30 For instance, the number of countries in each group is similar (27 countries and 22 respectively); the two groups of countries are similar in the size of projected population in 2030 (384.7 and 412.8 million), even if the latter exhibit a higher speed of growth and the median age is much lower. Of course strong differences are also present: while the EU first comers countries are all included in the group with a very high HDI, the ECEC are concentrated in the group with high HDI and the ALC are mostly distributed between the high HDI group and the low HDI group. But this can be also consistent with the assimilation of the present events to a new transition process. However, the key question is: how a new transition is likely to take place if EU, in competition/cooperation with the US, will not invest enough in it? Structural and behavioural changes are now taking an accelerated speed, following a long run trend pre-existent to the global crisis: scale effects in many spheres of economic life (e.g., availability of broad production factors, production and trade); diversification in the models of capitalism; variety in economic leadership (e.g., China, with its 38,800 workers in Libya evacuated in quick time, but also Brazil as observer state in the Arab League, are very influent in the Mediterranean area); supply of new financial sources (e.g., sovereign funds from Saudi Arabia and Libya). In labour markets deep changes are taking place on a multi layers basis: (i) in the composition of the labour force by age groups and generations; (ii) in the composition of the labour force by gender; (iii) in the composition of the labour force by educational qualifications; (iv) in the composition of the labour force by geographical origin; (v) the level of inequality is high and increasing. Higher education graduates do not seem to represent anymore the main tool for shifting the production frontier. They are becoming, together with upper higher education diploma holders, the majority of the working force in the more advanced countries, and in the developing countries as well. The economic, but also the social and political role of education is changing over time. This brings about several key research questions. Are labour market adjustments to be still conceived as the residual to the economic integration processes? Which role is played by mismatch, over-education, under-utilization of qualified manpower? Do we experience complementarities or substitution between the workforce of the two shores? To what extent labour markets governance can be one of the main determinants in regional integration processes? The capability of European regions and countries to face and solve in a cooperative way the problems deriving from their inner structural imbalances should be coupled with the capacity to solve internal problems with an outward oriented approach. With reference to labour markets and social convergence, three are the main implications. First, complementarities in demographic growth and labour force participation with the South Mediterranean regions and other regions of the global South should be explored, especially as far as future professional needs are concerned, in order to make the most of positive externalities. Second, educational and training initiatives at the various stages of the life cycle 65 should be started on both sides of the Mediterranean shores in order to improve design and regulation of international flows of migration. The participation of business organizations would be a key component of these initiatives aiming at bridging the gap between the two shores. Third, regional cooperation plans concerned with the implementation of a new geography of jobs could be tested. Regional networks, involving not only neighbouring partners, could be engaged both in an offshore and inland creation of new job opportunities in the relevant sectors. Of course many constraints limit the European capability to face this big issue. But why, instead of engaging thoroughly in it, the main matter of harsh political debate has become the Mare Nostrum operations in the Mediterranean sea? And why the local governors of the continental regions of Europe, while neglecting international rights and benefits incoming with the migrants human capital, argue about the suspension of the Schengen Treaty, aiming at excluding, rejecting and 65 Higher education can play a crucial role in these strategies both in the education of the forthcoming local teachers and in the development of a qualified manpower. 31 expelling migrant workers? Social tensions and hardening attitudes towards migrants lead to widespread discrimination and populist policies take advantage of this fostering greater xenophobia and discrimination directed towards migrants. Also in this case a candidate explanation can be perhaps found in the day by day game of symbolic dominance played by immigrants and lower-middle class autochthones in which the former frequently interiorize the interests of the dominant agents. 66 5.4. Does it exists a leading model for Emerging Countries? Accepting variety in the way capitalism institutionally organize its functioning in different regions of the world does not amount to admit that no common structure lies beneath all capitalist societies. Financial capital and also cultural capital and social capital tend to preserve common fundamentals in each model. Not even it can imply the acknowledgment that each model, once born, can be sustainable and able to persist for ever. We have seen that within each model an evolution can take place over time and this evolution can be influenced by the interactions between the different models of capitalism. However, the joint action of the synchronic and diachronic dimensions makes the analysis very complex for reaching clear cut results at this stage. Intuition can be of some help, but is also subject to multiple mistakes. Therefore, for the time being no easy answer can be reached. In any case, the competition between the different models can be expected quite strong due to the frictions caused by the combination of common fundamentals with diversity in interrelated subsystems. Moreover, the competition is fostered by the conviction that a zero sum game is being played. If we apply to the interaction between developed countries and emerging powers the metaphor of converging margins in the theory of plate tectonics (Antonelli, 2014), we can reach, at least in principle, similar results compared to those we can reach when applying it to the EU scaffold. Starting from highly differentiated economic conditions, the Emerging Powers are confronted with a triple corners’ choice. On the first corner, each one of them could be driven to imitate the model of capitalism prevailing in the developed countries with which they are connected in terms of trade and economic interdependences. On the second one, each one of them could be pushed to adapt to the model of capitalism which is perceived as nearer to their starting conditions, in order to simplify the traverse path and maintain more autonomy. On the third corner, each one of them should take carefully in consideration the reciprocal interdependencies with the other Emerging Powers, and, more generally, with the global South. Therefore, if, on the one hand, more degrees of freedom in adapting their economic policies to their strategies are available to Emerging Powers compared with EU, on the other, they can suffer more heavy constraints in terms of global interdependence feedbacks. Apart the always possible occurrence of diverging or transforms/conservative margins, which sounds as rather optimistic in the midst of a global crisis, we can easily predict that the case of converging margins will prevail. In this case, it is also easy to foresee that the most damaging effects will accrue, even for Emerging Powers, to the sections of their economic structure less regulated, or dealt with as residuals, in the adopted strategies. In particular no monetary zone can be designed without coherent strategies of convergence in labour markets. Moreover, positive-sum game outcomes, which are not predicted in plate tectonics, could be carried 66 In many countries migrant workers make up 8 to 20 per cent of the labour force, and in certain regions the figure is significantly higher. They face widespread pervasive discrimination in access to employment, and many encounter discrimination when employed. Migrant workers have been particularly affected by the economic crisis, with reduced employment or migration opportunities and increased xenophobia, a deterioration in working conditions and even violence. Unfair working conditions are faced by migrants in both developed and developing countries. (ILO, 2011, p. xii) 32 out in the political and institutional setting, but they require prudent and proactive foresight, long term planning and competent governance. 6. Conclusion From this articulated paper it is quite complicated to draw some synthetic conclusions, as it is full of possible paths of development for future research both from a theoretical and an empirical perspective. Therefore, one has to limit to draw only few conclusions in order to focus the attention on some highly relevant and selected problems, considering that this does not exhaust the possible issues which can be taken into account. In our opinion one can work out two distinct levels of discussion as a conclusive stage of this paper; a methodological level, based on the methodologies adopted and the theories introduced and furthered and an empirical level, based on the non-trivial results obtained in the quantitative analysis. From a methodological point of view this paper has emphasized how institutions affect the definition of the notion of model of capitalism. Of course, the analysis of the role of institutions on the functioning of markets for goods, capital and services and on the economic system as a whole is not new at all. Nowadays, whatever approach she/he decides to adopt, no economist would dream of denying the pivotal role played by institutions for the determination of the performances of markets. But in the analysis developed in this contribution institutions play a role which is quite at odds with other approaches for which institutions are a cornerstone. First of all, in our approach, institutions define the model of capitalism itself regardless of any interpretative economic model. This, of course, does not mean that micro and macro-economic models are irrelevant if one wants to understand and analyze the dynamics and the interactions of economic variables. But the complex and interrelated fabric of institutions which establish among themselves a complex cobweb of complementary relationships (positive or negative) provide the background on which the economic variables interact and affect each other. In this approach institutions are not only a constraint on the dynamics of economic variables or on their paths of quantitative adjustments. Institutions contribute to define the economic variables themselves in the sense that without institutions we would not be able to observe, for example, the interactions between demand and supply on a market for goods and services. Hence, in this sense, institutions can be considered as resources at disposal of economic actors and not simply as constraints to the behaviour of maximising individuals. In addition to that, institutions and the way these evolve explain the role of time and path dependence in the determination of economic variables; the level of a variable X at time t depends also on the level of a variable at time t-1 and affects what will occur to the variable at time t+1. History matters. This way to conceive institutions is consistent with Rehbein and Souza’s (2014, p. 20) approach, when, in their paper affirm: “Social structures, cultures and practices are subject to constant transformations and sometimes even revolutions. New institutions appear, old ones are done away with, new discourses emerge, economic crises erupt or oil is discovered. These transformations have an impact on the configurations of inequality. However, these do not appear out of the blue but literally are transformations of earlier configurations. Structures of inequality are relatively persistent. Aristocracy or working class, the value of a PhD or the reputation of doctors do not disappear overnight. The structures, on which they are based, change even slowlier, but they do change. Through social revolutions new cultural frameworks for inequality emerge. We refer to these frameworks as sociocultures. As sociocultures persist, so do forms of action or institutions that appear outdated. Monarchic rituals, bar associations, village structures or sociolects would be examples for this”. This approach is very close to our approach where the institutions and their relationships define the model of capitalism which, in turn, affect the level and the dynamics of income inequality. In this paper there is second methodological point which is worth mentioning and can be considered as another point of contact with Rehbein and Souza’s approach. As a matter of fact, despite we focus our attention on inequality in income distribution, we have claimed the relevance of 33 inequality as a multi-dimensional phenomenon. Summarizing briefly what we said in the initial part of this contribution, we can say that we do not conceive inequality as unequal distribution of material resources, but also, at least, as unequal access to educational opportunities and/or to health services. This is very strictly related to the idea of social “milieux” developed by Vester and, later, adopted by Rehbein and Souza as the educational opportunities and the access to health services can be considered as a constituent element of the social milieux. Turning to the results obtained in the empirical analysis of inequality, the analysis allows to draw some preliminary conclusions about the alleged process of convergence in progress in both the EU and at a world level, as a consequence of the process of globalisation. As to the first point the empirical evidence outlined in this work shows that there is not a European model of capitalism and apparently European countries are not converging. This is quite surprising because in the interval between 1995-2010 the introduction of Euro pushed towards the convergence of fiscal policies. Apparently, this did not promote a process of convergence, as the discussion about labour markets in the EU and middle class decline has demonstrated. The EU policies did not manage to merge the different economies into a single model of capitalism. As to the effects of the process of globalisation, the empirical analysis developed in this paper has said nothing. Therefore, it seems to be extremely important to extend the analysis by focusing on emerging countries. One of the big questions is to understand if these countries are converging towards an existing model or if they are creating a new one. 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(2006), Sono davvero importanti le istituzioni del mercato del lavoro per capire la disoccupazione?, Dipartimento di Scienze Economiche e Statistiche, Università di Trieste, Working Paper N. 103. 37 APPENDIX Cluster Analysis The Cluster Analysis (CA) is an exploratory data analysis tool for organizing observed data (e.g. people, things, events, brands, companies) into meaningful taxonomies, groups, or clusters, based on combinations of certain variables that describe units’ characteristics, which maximizes the similarity of cases within each cluster while maximizing the dissimilarity between groups that are initially unknown. In this sense, CA creates new groupings without any notion of what clusters may arise. CA provides no explanation as to why the clusters exist nor is any interpretation made. Each cluster thus describes, in terms of the data collected, the class to which its members belong. Items in each cluster are similar in some ways to each other and dissimilar to those in other clusters. There are two basic approaches for generating a hierarchical clustering: i) agglomerative (are by far the most common techniques): start with two points as individual clusters and, at each step, merge the closest pair of clusters. This require defining a notion of cluster similarity or distance; ii) divisive: start with one all-inclusive cluster and, at each step, split a cluster until only a singleton clusters of individual points remain; in this case we need to decide which cluster to split at each step and how to do the splitting. Many agglomerative hierarchical clustering techniques are variation on a single approach: starting with individual point as clusters, successively merge the two closest clusters until one cluster remains. The merge of two clusters at each step is based on a measure of similarity or distance between the two cluster to be merged. The definition of cluster similarity differentiates the various agglomerative hierarchical techniques (for more detail see, for example, Everitt et al. 2001). In this application we use the WARD’s Minimum Variance Method, that assumes that a cluster is represented by its centroid and measures the distance between two cluster in terms of the increase in the within-clusters Sum of Squares (SSE) that result merging the two clusters. This methods attempts to minimize the sum of the squared distances of points from their cluster centroids. If we indicate as  the vector of the observed variables, in the Ward’s minimum-variance method the distance between two clusters K and L is defined by:  ! " # $  # ! "  1 % 1 % ! where " # $  # ! "  is the square of the Euclidian distance between the centroids of the clusters K and L (the centroid is the mean vector in the group for all the variables), and % and % ! are the the number of observations in the clusters K and L. The distance between the two clusters is the ANOVA sum of squares (between-cluster sum of squares) between the two clusters added up over all the variables. At each generation, the withincluster sum of squares is minimized over all partitions obtainable by merging two clusters from the previous generation. The total sum of squares are easier to interpret when they are divided by the total sum of squares to give proportions of variance. At each step of the procedure, the square multiple correlation (R-square) is the proportion of total variance accounted for by the clusters. The semi-partial R-square represents the decrease in the proportion of variance accounted for by joining two clusters. When carrying out a hierarchical cluster analysis, the process can be represented on a diagram known as a dendrogram. This diagram illustrates which clusters have been joined at each stage of the analysis and the distance between clusters at the time of joining (this is usually the semi-partial R-square). If there is a large jump in the distance between clusters from one stage to another then this suggests that at one stage clusters that are relatively close together were joined whereas, at the 38 following stage, the clusters that were joined were relatively far apart. This implies that the optimum number of clusters may be the number present just before that large jump in distance. This is easier to understand by actually looking at the dendrogram, like the one reported in Figure 1 and 2. The choice of the number of cluster to consider can be done visually looking at the dendrogram. A more formal rule for the choice of the number of clusters is those based on the pseudo F statistic calculated at each step of the procedure. The pseudo F statistic is the ratio of between-cluster to within-cluster sum of squares, divided respectively by the numbers g–1 and N–g, where g is the number of clusters. A relatively large value (compared to the preceding and the following values in the series) indicates a stopping point. TABLES Table A.1: Definition of institutional indicators Indicator Description and source Public expenditures on Active measures on Labour Market Public expenditures as a percentage of GDP on Active Labour market policies (Active measures (20-70): Training - Employment incentives - Sheltered and supported employment and rehabilitation - Direct job creationStart-up incentives): OECD Dataset: Public expenditure and participant stocks on LMP; 1985-2011 Public expenditure on Passive Labour Market Policies ( % GDP) Public expenditures as a percentage of GDP on Passive Labour market policies (Out-of-work income maintenance and support - Early retirement): OECD Dataset: Public expenditure and participant stocks on LMP; 1985-2011 Public unemployment expenditure (% GDP) Public unemployment expenditure % GDP: OECD Dataset: Social Expenditure - Aggregated data; 1995-2013 Total public social expenditure (% GDP) Total public social expenditure % GDP: OECD Dataset: Social Expenditure - Aggregated data; 1995-2013 Public expenditure on education (% on government expenditure) Public expenditure on education % on government expenditures: UN; 1970-2012 (selected years and countries) Wage coordination Wage setting coordination indicator: WCoord of Visser (2009 ICTWSS data base). It is a five-point classification of wage-setting coordination scores, ranging from one (no coordination or fragmented bargaining) to five (economy-wide bargaining); 1985-2012. Trade union density Union density rate defined as the percentage of employees who are members of a trade union (OECD Employment Database); 1985-?? Minimum wage/Median earning The ratio of minimum-to-median earnings. Minimum wages are measured relative to the median value of basic earnings (excluding overtime and bonus payments) of full-time employees: OECD Employment Database, 2000-2011. Average Tax wadge (%) Average Tax wadge (%) for a single person at 100 % of average earnings, no child; OECD: Taxing wages (comparative tables); 20002012 45 Table A.6. Cluster averages for each indicator, 2010 CLUSTER 1 2 3 4 5 6 Public expenditure on Active Labour Market Policies (% on GDP) 0.40 0.75 0.91 0.70 0.32 0.20 Average income tax rate (%) 10.18 13.53 22.37 20.85 6.07 16.13 Bank concentration (%) 63.08 73.23 90.91 70.47 47.58 60.29 Domestic credit provided by banking sector (% of GDP) 65.45 204.32 136.19 135.07 215.54 192.14 PMR - Barrier to entrepreneurship 1.78 1.14 1.16 1.25 1.25 1.11 PMR - Barrier to trade and investments 0.79 0.17 0.37 0.54 0.96 0.23 Domestic credit to private sector (% of GDP) 54.67 186.77 133.22 111.61 136.60 165.72 Public expenditure on education (% on government expenditure) 10.36 10.71 13.98 10.86 12.59 13.35 Employment protection 2.56 2.74 2.33 2.94 2.07 1.68 Temporary employment protection 1.45 2.03 1.69 1.67 1.50 0.58 Percentage of foreign banks among total banks (%) 78.75 35.00 8.50 20.25 12.50 38.60 Percentage of GERD financed by private sector 40.26 51.34 59.70 55.57 77.71 61.99 Taxes on goods and services (% total tax) 37.94 31.76 30.10 27.24 26.30 24.82 Gross replacement ratio 8.72 35.41 33.80 24.10 10.41 19.76 Net personal marginal tax rate: Principal earner (%) 35.36 33.83 40.59 49.89 23.87 32.89 Marginal tax wedge: Principal earner (%) 48.30 46.20 48.82 60.96 31.85 38.08 Minimum wage/Median earning 0.43 0.51 0.00 0.13 0.39 0.37 Public expenditure on Passive Labour Market Policies (on GDP) 0.51 2.14 1.21 1.61 0.35 0.63 Total public social expenditure (% GDP) 21.16 26.33 27.87 28.30 15.76 20.17 Public unemployment expenditure (% GDP) 0.73 2.04 1.35 1.83 0.39 0.68 PMR - State Control 2.33 1.97 1.84 2.21 1.71 1.62 Share of temporary employment 12.90 18.20 11.90 11.22 18.39 8.50 Social security contributions (% total tax) 38.60 32.29 19.85 34.32 31.96 16.76 Gross domestic expenditure on R&D (% of GDP) 1.02 1.76 3.01 2.21 3.50 2.31 Total researchers per thousand total employment 5.52 7.87 12.79 7.43 10.63 8.07 Percentage of GERD financed by government 47.43 39.79 31.74 33.95 21.96 31.65 Stock market capitalization to GDP (%) 21.48 58.19 69.88 31.66 85.09 142.96 Stock market total value traded to GDP (%) 10.91 51.64 58.75 24.56 123.80 149.25 Stock market turnover ratio (%) 43.77 76.27 85.94 101.10 135.71 102.86 Total tax revenue on GDP 33.03 34.55 44.63 41.12 26.35 28.88 Trade union density 16.42 18.81 65.37 33.25 13.98 20.07 Taxes on income, profits and capital gains (% total tax) 19.73 28.26 44.86 31.05 29.21 46.25 Average Tax wedge (%) 40.21 38.11 40.16 50.12 25.19 28.39 Wage coordination 1.75 3.20 3.75 4.00 3.50 1.60 Countries Czech R. Slovakia Hungary Poland Netherl. Portugal Spain France Ireland Finland Sweden Norway Denmark Austria Germany Italy Belgium Japan Korea Canada UK Australia USA Switzerl. 46 Table A.7. Between and within-cluster deviance, 2010 Indicator Within cluster SS Between cluster SS Total SS Between SS % Trade union density 1242.68 7358.54 8601.22 85.55 Gross replacement ratio 638.43 2420.58 3059.01 79.13 Taxes on income, profits and capital gains (% total tax) 781.18 2281.47 3062.65 74.49 Stock market capitalization to GDP (%) 14765.9 43054.5 57820.4 74.46 Total tax revenue on GDP 323.1 872.89 1195.99 72.98 Average Tax wedge (%) 572.14 1414.3 1986.44 71.20 Wage coordination 10 22.63 32.63 69.35 Average income tax rate (%) 284.87 615.7 900.57 68.37 Total researchers per thousand total employment 60.76 125.56 186.32 67.39 Minimum wage/Median earning 0.4 0.81 1.21 66.94 Total public social expenditure (% GDP) 204.73 401.81 606.54 66.25 Public expenditure on Passive Labour Market Policies ( % on GDP) 5.15 10.1 15.25 66.23 Marginal tax wedge: Principal earner (%) 846.88 1655.09 2501.97 66.15 Gross domestic expenditure on R&D (% of GDP) 6.47 12.57 19.04 66.02 Stock market total value traded to GDP (%) 33794.62 61497.06 95291.68 64.54 Employment protection 2.61 4.7 7.31 64.30 Domestic credit to private sector (% of GDP) 26084.03 46136.21 72220.24 63.88 Percentage of GERD financed by private sector 1306.28 2276.71 3582.99 63.54 Percentage of foreign banks among total banks (%) 7280.2 12400.76 19680.96 63.01 Product market Protection - Barrier to entrepreneurship 0.79 1.31 2.1 62.38 Public expenditure on Active Labour Market Policies ( % on GDP) 1.24 1.62 2.86 56.64 Domestic credit provided by banking sector (% of GDP) 48006.35 61398.01 109404.4 56.12 Net personal marginal tax rate: Principal earner (%) 969.63 1205.71 2175.34 55.43 Bank concentration (%) 3040.21 3453.77 6493.98 53.18 Social security contributions (% total tax) 1437.73 1627.94 3065.67 53.10 Public expenditure on education (% on government expenditure) 46.01 49.58 95.59 51.87 Percentage of GERD financed by government 1118.33 1167.98 2286.31 51.09 Taxes on goods and services (% total tax) 446.59 459.38 905.97 50.71 Product market Protection - Barrier to trade and investments 1.77 1.67 3.44 48.55 Public unemployment expenditure (% GDP) 12.15 8.7 20.85 41.73 Stock market turnover ratio (value traded/capitalization) (%) 29259.99 14852.37 44112.36 33.67 Share of temporary employment 633.41 311.93 945.34 33.00 Temporary employment protection 12.12 5.9 18.02 32.74 Product market Protection - State Control 5.02 1.55 6.57 23.59 