These redistribution effects were greatly helped by attempts at financial libera- lization in many countries around the world, especially so before the international financial crisis of 2007/2008. Of particular importance for our purposes was the fi- nancial liberalization framework in the US, especially the repeal of the 1933 Glass- Steagall Act in 1999. Both the redistribution and the financial liberalization policies led to a period of financial engineering in the US, namely securitisation in the form of what we now know as interlinked securities, based on subprime mortgages. The sale of the interlinked securities to international investors made the US housing bub- ble a global problem and provided the transmission mechanism for the contagion to the rest of the world. The spread of the interlinked securities worldwide produced the international financial crisis of August 2007 and the subsequent “great recession”. In all these main causes of the financial and real crises there were also contributory ones, namely the international imbalances, the monetary policy pursued at the time and the role of credit rating agencies (for further details see, Arestis and Karakitsos 2013; Arestis forthcoming). The link between financialisation - the increase in size and importance of unregulated financial sector, which creates a process whereby fi- nancial markets dominate over the rest of the markets in the economy - and rising inequality should be emphasised. For it is true that “led by a dismantling of the con- trols over financial flows, the finance sector has been the main component of a deci- sive shift in the share of gross domestic product (GDP) towards capital and away from labour” (New Economics Foundation (NEF) 2014, p. 4). It is interesting to compare the period prior to the “great recession” with that of after the “great depres- sion” in the case of the US. In the latter case, regulation of the financial sector was very successful in terms of producing almost four decades of financial stability and rapid growth. Following the Glass-Steagall Act of 1933, banks focussed on lending, thereby providing credit as needed for the rapid expansion of the enterprise sector. Markets acted the way markets are supposed to function, by reducing the scope for risk-taking. But beginning with financial liberalisation in the mid-1970s and culmi- nating to the repeal of the Glass-Steagall Act of 1933 in 1999 the deregulation that emerged led to instability (see, also, Stiglitz 2013, Chapter 6).
The chronology of the data per government periods allows the reconciliation of the analyses of the fluctuations in the short and long term trends in intervals of administrative term. The limitations of this kind of analysis are factors outside the control of the federal government, such as international crises, and actions of other factors such as families, companies NGOs, etc, and the remaining levels of government. Nevertheless, it can be argued that the role of the federal government as facilitator, coordinator and motivator of the other agents is part of its responsibility. Another consideration refers to the lagging of the effects of social andeconomicpolicies. As the centre of our analysis here are the so called incomepolicies, which by virtue of their operational speed are more immune to these types of problems.
The studies that search to verify that reciprocal effect as a single direction effect are just making an abstraction with respect to the empirical nature of the problem. Starting from the fact that indeed there exist a simultaneity between growth andincomeinequality, and taking for reference the most recent contributions in light of the theory of economic growth, it can grouped the growth models in at least four types: the) the models of human capital; b) the trade models that appear as the trade opening and its influence on the productive structure, have been affecting the dynamics of the labor market and, therefore in the relative wages of the qualified labor vis a vis the non-qualified labor; c) the models of political economy that emphasize the existence of social and political conflicts in the decisions of implementations of government policies that can influence direct and indirectly the distribution of income; d) the restriction models (especially) of credit that, based on the hypotheses of market imperfections, incomplete markets, and in the theories of adverse selection and moral hazard, accentuate how the restricted conditions of capital and wealth create adverse effects on the relationship between inequalityand growth.
The model can be related to different studies, providing some contribution to each of them. In this context, Guerrieri & Lorenzoni (2011) analyse the effects of a credit constraint period and increased spreads on the dynamics of aggregate variables such as household consumption, interest rate and product in a model of heterogeneous agents. The authors demonstrate that an increase in the spread decreases the interest rate and the product, a result also found in the present study. However, we have focused on the redistributive impacts of interest rate variations. In this line of research, the study of Buera et al. (2012), in which the authors find that microfinance policiesand credit programs targeted toward small businesses have strong impacts on income distribution, increasing welfare, particularly for less skilled and poorer agents, is relevant.
In addition to the initial conditions mentioned above, that are largely determined by inheritance and societal traditions and norms, there are more individually-nested, or random factors, which also play important roles. These are (a) the distribution of skills, intelligence, and even look not directly inherited and (b) what could be called luck, or the role that randomness plays in determining incomes in non-traditional and market- oriented economies. The chance that someone will end up with the skills or acumen of Tiger Woods, Bill Gates, or Warren Buffett cannot be determined by the initial conditions or by government policies. In a market economy, individuals with exceptional skills in various areas (entertainment, sport, economic or financial activities, and so on) are more likely to end up with exceptional incomes. In many cases luck (or a randomness factor) will also play a role. Some of these individuals may end up in the annual Forbes or similar lists of the world richest individuals and will have an impact on Gini coefficients or on other measures of inequality.
Overcoming poverty involves a series of measures that implicate government policy as a whole. The persistence in Brazil of strong inequalities, not only in terms of income, but also access to public goods and services, means dealing with different types of vulnerability through various modes of intervention, combining more immediate measures for alleviating poverty, focused on the more vulnerable groups, with measures that alter the distribution of goods and services in a more equitable direction, aiming to guarantee universal social rights (Cohn 1995). Likewise, it is useful to differentiate the strategies according to their structural, distributive or compensatory nature. Strategies of a structural type include those that raise the level of human skills or the value of human productivity by intervening in the factors responsible for poverty linked to the social andeconomic structure, such as education and loan programs for workers. Distributive measures, for their part, alter the distribution of goods and services – for example, the reduction in the market value of certain products, increasing the buying power of consumers. Finally, compensatory interventions aim to attenuate the negative effects of a particular socioeconomic context (unemployment benefit, distribution of food, etc.) (Barros & Camargo 1993).
Previous comparative studies on developed countries, predominantly on countries of the Organization for Economic Cooperation and Development (OECD), have shown that the State reduces inequality. These studies found that public work contributes to reduced inequality (BLAU and KAHN, 1996; GUSTAFSSON and JOHANSSON, 1999; MILANOVIC, 1994), that strong unions and centralized bargaining of wages typical of public workers are determinants of lower levels of incomeinequality (CHECCHI and GARCÍA-PEÑALOSA, 2010; GOTTSCHALK and SMEEDING, 1997; GUSTAFSSON and JOHANSSON, 1999) and that corporatist welfare state policies are more capable of reducing inequality than targeted policies because of the "paradox of redistribution", that is, (contributory) universalism legitimizes more spending than targeting and it is the level of expenditures that matters most to inequality (GOUDSWAARD and CAMINADA, 2010; KORPI and PALME, 1998; MAHLER and JESUIT, 2006; SMEEDING, 2005). Other studies have identified that taxation, particularly direct taxation, tends to be progressive and the higher the taxation, the lower the level of inequality (ATKINSON, 2003; GOTTSCHALK and SMEEDING, 1997; GOUDSWAARD and CAMINADA, 2010).
evidence allows us to assume that social, economic, cultural, environmental and health-system-related determinants are part of a web of possible causes of dental caries. It follows then that understand- ing these determinants deserves more attention by researchers, if they search really to understand the distribution of caries in the population. Although it may be dificult to separate these determinants, they are probably not on the same level. The social struc- ture may comprise aspects related to public policies of social protection andeconomic adjustment that emerge as incomeinequalityand unequal access to community-based oral health programs and clinical services. These may, in turn, affect the population’s social context, such as its neighborhood, local orga- nizations and family, work or school environment, which have implications related to individual mate- rial resources, literacy and behaviors. Moreover, all of these are related to luoride exposure, oral hy- giene and sugar consumption, which will affect the occurrence of caries and its rate of progression.
In describing the incomeinequality of societies, a range of indices is used by economists and social scientists. The most popular are the Gini coefficient, the members of the Generalised Entropy (GE) class of indices (such as the Theil and Atkinson coefficients and the Mean Logarithmic Deviation), and the percentile ratios P90/P10 and P75/P25. The values of the coefficients tell us about the overall inequality at a certain point in time, or – by displaying a time series of coefficients – about trends in overall inequality. One of the central issues in studying incomeinequality concerns the underlying factors and processes. Most studies focus on mechanisms of individual income attainment and as- sume that differences in individual income can be aggregated to macro-level incomeinequality (e.g. Verhoeven, 2007). This is problematic because mechanisms at the individual level can be counteracted by mechanisms at the macro-level, like governmental policiesand market reforms. In understanding changes in income distribution, it is important to study the factors and processes that influence in- come inequality directly. The decomposition of incomeinequality may shed light on these factors and processes. The decomposition of overall incomeinequality by population subgroups and by income sources was introduced in the early 1980s in publications by Bourguignon (1979) and by Shorrocks (1980, 1982, 1984). They showed that a number of inequality measures could be additively decomposed, but not all of them. Since then, a large number of socio-economic studies have shown standard de- compositions of incomeinequality.
The unemployment rate also showed a significant positive correlation in comparison to the poverty index. In the end, the higher the unemployment rate, the higher the proportion of poor people. Since this variable is affected by business cycles and macroeconomic policies, the government should be concerned about implementing measures that can stabilize the economy. Although incomeinequality has been reduced in recent years, it still contributes strongly to the poverty growth. This result confirms findings from national and international papers. Besides, the impact of this variable on poverty is much higher than the per capita GDP. Therefore, policies aimed at the reduction of inequalities are more efficient to fight poverty than those solely focused on economic growth.
In the last section, I presented some evidence of the positive expectations of Brazilians towards their lives in the future. In a sample of 132 countries in 2006, Brazil is where citizens are most optimistic about their happiness in 5 years time. The world’s greatest prospective happiness! Now, why expect so much if our economic scenario does not rival other emerging countries’? At the pace of the national accounts statistics, and GDP in particular, we would not be a real BRIC (Brasil, Russia, India and China) or a building brick of future global wealth. Intrisic optimism helps to explain why the Brazilian expectation and reality are out of beat with each other. Inebriated by this optimism, Brazilian’s glass is always half full. Nonetheless, even by calculating the difference between future expectations and the current reality and by cleansing the psychological biases off subjective questions, Brazil’s ranking is still remarkable because it has nearly equaled the Chinese rates of expected happiness increases. If we are not growing as much as the Chinese, however, why do we experience such a similar feeling of prosperity about our future?
In addition to theoretical problems, Forbes (2000) considers that most of the empirical works presented in the area are subject to methodological problems. First, they cannot be con- sidered robust, considering that after the sensibility tests, the inequality coefficient becomes non-significant, particularly when regional dummies are included. Second, the problem of ine- quality measurement and the omission of variables can bias the estimation. Third, and according to Forbes the main problem, is that studies do not properly explain how changes in the level of a country’s inequality are related to that country’s growth. The technique employed by these studies shows the pattern of growth of the economies for a long period of time, usually 25 years. Thus, it can be said that only countries with lower inequality indexes have a growth pattern above those featuring very high initial levels. Thanks to the panel technique this problem is di- minished because there are more observations per country.
1970s the growth decade 6 . In Brazilian history statistically documented (since 1960), there has never been a similar inequality reduction to that observed since 2001: we grew a third of the 1970s growth, but we reduced poverty more in this current decade. The cumulative decrease in inequality is comparable in magnitude to the famous increase of inequality in the 1960s, which placed Brazil in the international scenario as the land of inertial inequality. According to World Bank data, 2005 data already put Brazil as the 10 th country in inequality rates in the world – beforehand we were 3 rd . That is, the bad news is that we are still very unequal; the good news is that there is a lot of inequality to be reduced and, consequently, a lot of income growth to be generated at the bottom of the income pyramid. It is as if Brazil had discovered – in this century alone – these reserves of pro poor growth. For instance, India is an evenly poor country with an inequality index that is half of ours and has as basic alternative to fight poverty just the income growth of society. Similarly, Belgium, an evenly rich country, does not have in substantive terms, an additional alternative to improve the population’s welfare but growth. In the so-called Brazilian Belindia, besides growth – which is a limitless source of welfare improvement – we can also reduce inequality as a way to relief poverty and welfare. Obviously, equity has a lower limit, it is finite, as for instance the oil reserves, but we are very distant from its exhaustion. No other country in the world may reduce poverty through redistribution of income in such a high scale as Brazil
One of the mechanisms linking labour markets to inequality is the“skill-biased technolog- ical change” (SBTC), i.e., the increase in the relative demand for highly skilled workers vis-` a-vis the unskilled caused by the technological revolution of circa 1980. Empirically, it is difficult to assess and “is usually subsumed in the unexplained part of modeling”, although Katz and Autor (1999) view it as the most important driver of inequality based on their literature review. 6 Using a panel of 51 countries from 1981 to 2003, Jaumotte et al. (2013) finds that the share of ICT in capital stock was the main determinant of in- equality. Kanbur and Stiglitz (2015) argue that these competitive marginal productivity theories of factor returns assume a constant share of capital, which is not consistent with the reality of many industrialized economies, and that new models need to incorporate rent-generating mechanisms and a greater focus on the “rules of the game” (Stiglitz et al. 2015). Brown and Cambell (2002) show that ICT explains inequality in less devel- oped countries, whereas in advanced ones globalization is more important. 7 Card and
sectors (labeled A and B, respectively), which dif- fered by the level and structure of income. He hy- pothesized that economic development occurred with the non-agricultural sector expanding and agricultural sector narrowing. On the basis of ab- stract data, Kuznets then traced the change in in- equality in population incomes when the agricul- tural sector (A) share of total output changed from 0.8 to 0.2. To assess the impact of various struc- tural parameters on the shape of the curve charac- terizing the dynamics of inequality, Kuznets con- sidered several models with different values of key parameters. As a result, he was able to demon- strate that changes in its parameters affected the shape of the income distribution curve (in particu- lar, its maximum point) but not its general charac- ter (or the inverted U curve).
When alpha is very close to one (but not one), the Gini is very close to one as well, because a very small percentage of the population happens to get job o¤ers. All the remaining workers have no o¤ers and a wage equal to zero. Having theta close to one, though, does not imply a Gini coe¢cient tending towards one. The reason is that, even when theta is equal to one, those workers who were not employed last period are allowed (with probability 1 ) to get job o¤ers and, possibly, to accept them.