Inequality poverty and growth where do we stand in this relationship

Discrimination, Inequality, and Poverty—A Human Rights Perspective | Human Rights Watch

inequality poverty and growth where do we stand in this relationship

Using non-parametric methods, we show that the growth rate is an inverted the relationship between the level of inequality and growth are so different from of poor countries and a positive relationship in the sample of rich countries. . stands in sharp contrast with the results obtained when estimating the same effect in. Economic growth has sharply reduced the incidence of poverty in “developed” It is well known that the relationship between inequality and growth is See OECD (), Divided We Stand: Why Inequality Keeps Rising. Goals related to poverty, education, child mortality, and access to clean water A functionalist might focus on why we have global inequality and what social . to as Dutch disease, the relationship between an increase in the development of .. of industrialization and can improve their global economic standing through.

As it turns out, the Gini coefficient is the ratio of the area between the Lorenz curve and the degree line of equality. It is also acknowledged that the relation between individual income and health status is concave, such that each additional dollar of income raises individual health by a decreasing amount.

The concave relation between income and health has important implications for the aggregate-level relation between income distribution and average health achievement, as noted by Rodgers 8.

As illustrated in figure 2in a hypothetical society consisting of just two individuals, that is, a rich one with income x4 and a poor one with income x1transferring a given amount of money amount x4 — amount x3 from the rich to the poor will result in an improvement in the average health from y1 to y2because the improvement in the health of the poor person more than offsets the loss in health of the rich person.

Consequently, researchers have posited that an aggregate relation between the average health status of a society and the level of income inequality in a society could be observed if the individual-level relation between income and health within society is concave. That is, the aggregate relation between income inequality and health may be observed simply because of the underlying functional form of the individual income-health relation and assuming an x amount of transfer of money from the rich to the poor.

Indeed, such a transfer also implies a reduction in the income inequality level in that particularly society and, as such, the society with the narrower distribution of income will have better average health status, all other things being equal 9. It is worth emphasizing that, if the relation between income and health at the individual level is linear not concavea transfer of income from the rich to the poor will reduce the level of income inequality but will not lead to improvements in the average health status of that society.

Indeed, there is nothing artifactual about improving the health of the poor and, hence, average population health through income and wealth redistribution. Moreover, the success of much philanthropy e. In addition to the concavity effect just described, researchers have posited an additional contextual effect of income inequality on health 6. This is the hypothesis that the distribution of income in society, over and above individual incomes as well as societal average income, matters for population health such that individuals regardless of their individual incomes tend to have worse health in societies that are more unequal.

The limitation of earlier studies 7 that utilized aggregate data to show a relation between income inequality and poor health status is that they were incapable of distinguishing between these two effects.

inequality poverty and growth where do we stand in this relationship

Using typical regression notations, we can specify the individual-level relation between income and health as follows: Making the usual independent and identical distribution assumption that the residual individual-level differences follow a normal distribution with a mean of zero, have a constant variance, and are independent of one another, we can summarize the residual differences through a variance parameter.

Meanwhile, the aggregate societal level relation between income inequality and health can be expressed in the following way: Following the above independent and identical distribution assumptions, one can summarize these societal differences in a variance parameter. An important aspect of the specification in equation 3 is that variation in health status is seen to be coming from two sources, that is, individual eij and society ujand the variation attributable to the level of individuals and to the level of societies is appropriately partitioned.

Thus, underlying the combined model presented in equation 3 are two models: Accordingly, explanatory variables of interest are also correctly specified according to their distinctive levels e.

Typical single-level regression models are inadequate since they anticipate and model only a single source of variation e. We summarize the published multilevel studies of income inequality and health in tables 1 and 2. It must be noted that use of multilevel data has not always involved adopting an explicit multilevel analytical model of the form specified in equation 3.

Indeed, as we show later, the majority of empirical work does not apply multilevel models to analyzing multilevel data. For comparability, the studies have been grouped according to those conducted within the United States table 1 and those outside the United States table 2.

Our intent here is not to provide a detailed assessment of each study. Rather, we draw attention to six sets of patterns that emerge from the empirical findings. First, in a comparison of tables 1 and 2it is evident that the bulk of studies that suggest an association between income inequality and poor health have been conducted so far within the United States 16 — However, even within the United States, several studies have not corroborated this association 26 — Second, studies conducted outside the United States have generally failed to find an association between income inequality and health 31 — Interestingly, almost all the non-US countries listed in table 2 are considerably more egalitarian in their distribution of incomes compared with the United States, and they have stronger safety-net provisions.

The Luxembourg Income Study provides a rigorous cross-national comparison of income distributions, using a summary measure called the decile ratio, which represents the ratio of the disposable income of the person at the 90th percentile of the distribution within each country to the income of the person at the 10th percentile The higher the decile ratio, the greater the social distance between the top and bottom in society and the more unequal is the societal distribution of income.

According to the Luxembourg Income Study, the decile ratios of the countries listed in table 2 were 2. The decile ratios in the United States were 5. The absence of an association between income distribution and health in the countries listed on table 2 may therefore reflect a threshold effect of inequality on poor health. When we turn to countries that are relatively more unequal than the United States e. Third, the geographic scale at which income inequality is assessed seems to matter. An examination of the US evidence overwhelmingly implicates the level of states 16192022 — The evidence at lower levels of aggregation, such as metropolitan areas 16counties 26and census tracts 20is decidedly mixed.

inequality poverty and growth where do we stand in this relationship

The more consistent association between state-level income inequality and health in the United States provides some clue about the pathways and mechanisms by which income distribution affects population health, an aspect that we shall return to later in this review. The state-level associations seem to suggest the importance of political mechanisms, such as the relation of economic disparities within each state to patterns of spending by state legislatures on social goods such as health care, education, and welfare.

In other words, economic polarization leads to political polarization, as reflected by state variations in the generosity of benefits to the poor 38 If income inequality matters to health because of differences in political behavior i.

As shown in table 2studies outside the United States have been primarily confined to smaller geographic scales e. Fourth, the US studies in table 1 show that the null studies were often based on smaller sample sizes and may have lacked statistical power to detect the effects of income inequality on health. For example, the only null study of state-level income inequality and mortality by Daly et al.

Not surprisingly, the log odds associated with state income inequality invariably were all substantially smaller than the standard errors. Moreover, the fact that the magnitude of the income inequality effect and in some cases the sign of the mortality-income inequality relation changes between the two time periods necessitates a cautious interpretation of these results.

By contrast, studies that found an association between state-level income inequality and mortality have tended to involve larger numbers. For example, Kennedy et al. Other null US studies carried out at levels of aggregation below the level of the state were similarly based on small sample sizes.

For example, in the study by Fiscella and Franks 26based on 14, adults in the National Health and Nutrition Examination Survey, the 95 percent confidence intervals around the mortality hazard ratio for county-level income inequality were quite wide point estimate: While these studies may have lacked statistical power, we also hasten to add that the lack of an association between income inequality and health at levels below the US states may be attributable to a true absence of an association a finding corroborated in studies that were adequately powered, for instance, at the metropolitan area level Fifth, with regard to the published multilevel studies in the United States, the state-level income inequality has been linked to a broad variety of health outcomes, ranging from mortality 22 and self-rated health 19212425 to depressive symptoms 21hypertension, smoking, body mass index, and sedentary behavior 18 table 1.

Therefore, the population health impacts of income inequality are potentially widespread, much like the impacts of income poverty on health outcomes. Sixth, a final observation to make about the published multilevel studies concerns differences in methods of statistical analysis.

Besides other general limitations 44the key issue lies in the treatment of the clustering and heterogeneity in the outcome. By the Bourguignon-Morrisson lines for poverty and extreme poverty showed only a small decline from their levels of Berry-Serieux93 estimate poverty incidence inand for and for international dollars of per year Table 112 and report that extreme poverty continued to decline rapidly during the s, after which the pace slackened markedly in the s.

Agrandir Original png, 25k 28During the s, the share of people in this category fell sharply in East Asia, mainly reflecting the growth of China and also in South Asia, while remaining about constant in Africa Table 2. In the s, such extreme poverty was again roughly halved in East Asia, though the rate of decline slowed sharply in South Asia due to increasing inequality in India, and incidence rose markedly in Sub-Saharan Africa.

These two decades saw only a modest reduction of extreme poverty in the world outside China and none at all in the world outside China and India. The experience of the s and the s is dramatically different with respect to poverty.

Inequality, Poverty, and Employment: What we Know

For the world as a whole and for the world minus China, the percentage point decline was considerably greater in the s Table 2. In summary, East Asia and South Asia, the two regions with the largest poor populations, reduced poverty rapidly during these two decades while the third one sub-Saharan Africa was going in the opposite direction. From the fact that developing country growth has been faster than that of the leading countries, it is clear that by some criteria inter-country inequality has diminished.

The main cause of pessimism about what is happening at the bottom is that many of the countries with low average incomes are now found in sub-Saharan Africa. Failure of the statistics to capture income from asset appreciation, which at times can constitute a quite substantial share of total capital income, especially that concentrated at the top of the distribution, could also constitute a downward biasing element in the estimated trend of concentration at the top.

Personal Correlates of Pre-Fisc Inequality: First, human capital, as approximated by level of education and degree of work experience, explains much of the earnings differentials across people that show up in household surveys. Average income of university graduates can be as much as times that of illiterates.

The traditionally high estimates of the payoff to education in developing countries may be seen as the other side of the coin from its high correlation with income. Recent studies Rosenzveig, ; Pritchett, point to probably serious upward biases in most estimates of the true causal impact of education on income, as opposed to that of other personal characteristics linked to the level of education e.

Second, differences in physical and financial capital are the other main direct determinant of inequality, in particular inequality at the top of the income hierarchy. This percent of income excluding that from asset appreciation is quite concentrated at the top, but its precise distribution is not yet known. That such characteristics should be related to income usually suggests either discrimination or some other form of market imperfection.

Since there are many intercorrelations among these variables and between each of them and level of education or human capital, average income or gross differences between categories of people defined by differences in these variables typically overstate their net causal contribution to inequality. Whereas the gross differentials can be 2: No one doubts its role, together with that of hard work, in determining who gets high incomes from sports, the arts and a few other activities where performance is relatively easy to measure.

But attempts to ascertain whether it plays a significant role in the creation of population-wide income differentials have thus far come up with little. Taken literately, most suggest a very small role for native ability in explaining income differentials within a population Boissiere et al, This may reflect the difficulty of measuring native skills in an adequate way; further, the fact that success in different areas may rely on different skills suggests than any native ability test needs to be geared differently for different people.

Structural and Policy Determinants 34Many aspects of the setting within which the personal differences among people play themselves out also have an impact on the level of inequality. We here consider four—the extent and pattern of technological change, population level and growth, market imperfections and the degree of openness of an economy. Technology Choice and Biased Technological Change 35Technology choice and biased technological change i.

There is no doubt of its relevance but the magnitude of its impact is hard to measure precisely and, since technological advance is essential to growth, there is a possible trade-off between growth and labour demand. How often policy and structure can be combined to produce this sort of result is the key empirical question. At the pessimistic end of the spectrum of possibilities, it may be that larger-scale capital intensive firms will continue to raise their share of output but not of employment and will squeeze the micro, small and medium enterprise MSME sector with the result that the generation of decent employment remains weak, wages low and inequality high.

Such empirical evidence is consistent with the theoretical prediction that the shift towards more capital intensive technologies would raise capital incomes and hold wages down.

But the magnitude of the effect, what variables that magnitude depends on, and the extent of any trade-off between achieving a high rate of economic growth and a low aggregate level of labour displacement remain open to discussion.

Reasonable guesses are that technological change is the most important single factor in raising inequality or keeping it high and that it takes quite good policy to avoid a significant trade-off between such displacement and growth. A Dense or Rising Population 37A dense or rising populationhas long been seen as a source of high or rising inequality. By increasing the supply of labour relative to that of other factors of production, it exerts downward pressure on the wage rate.

This may or may not lower the wage share of GDP ceteris paribus, a lower wage pushes this share down but more workers push it up but it does widen the gap between the income of the average capitalist and that of the average worker.

In the Ricardian growth model, where labour was seen as homogeneous, proletariat population growth was essentially a regulator which guaranteed that the wage rate stayed close to the level of subsistence, through a mechanism whereby a rising wage led to higher child survival rates and greater completed family size, but the resulting population growth then pushed the wage rate back down Malthus, A second link between demographic patterns and inequality has its roots in the fact that family size has often been larger in the working class than among the middle classes; this increases the size of the lower income groups while also leading to a more rapid divvying up of the limited capital they have than occurs among the rich.

inequality poverty and growth where do we stand in this relationship

By the time human capital becomes important as a source of income, their large families also push down the average amount they can invest in this way. Analyses are complicated by the fact of two-wa First, the Malthusian prediction of a devastatingly negative impact of population growth on GDP per capita turned out not to hold for the now industrial countries, 15 which in effect outran this threat to reach a threshold level of income, education and other determinants at which family size began to decline, eventually falling below replacement levels.

Similarly, many developing countries are in the process of outrunning the threat, leaving only Sub-Saharan Africa at 2. An ultimate escape from the Malthusian peril does not, of course, mean that many countries have not paid nor are still paying a heavy price for their fast population growth.

How economic inequality harms societies - Richard Wilkinson

It seems likely that such growth has with some frequency both slowed growth of income per capita and raised inequality, in which case its impact on poverty could have been or could be quite negative. How negative remains a matter of debate. In others, whose resources to population ratio is better, the impact may come from the fact that very fast growth over a shorter period makes it difficult for the economic system to absorb that labour increase rapidly and productively, leading among other things to serious employment problems.

That demographic pressure is not generally one of the dominant sources of inequality is, however, suggested by the fact that inequality has typically been lower in the densely populated countries of Asia than, for example, in less densely populated Latin America.

Market Imperfections 39Market imperfections are likely to affect inequality when the most important of them are orchestrated by well-off beneficiaries to in effect transfer income to them at the expense of the poor; this is most typically true of the capital market but occurs in many product markets as well.

In the labour market a degree of market power is exercised by unions and a degree of state intervention comes through labour legislation. The capital market has much greater potential to affect raise inequality, by favouring better off people vs.

The same is true of product markets, since extensive monopoly power raises the profits of the rich including favoured workers at the expense both of consumers and of labour as a whole, in the latter case through its downward pressure on the demand for that factor.

Many other imperfections also have clear impacts on inequality, usually tending to raise it. For most of the East Asian tigers the first burst of exports involved labour intensive goods and at least for Taiwan it appears that rising trade improved income distribution Fei et al, For many other developing countries whose comparative advantage lies in the export of primary goods minerals or their products, agricultural goods, etc.

Economic inequality

At one end of the spectrum are those minerals produced using very capital intensive technologies and generating high capital incomes for a few people. At the other end are agricultural products like coffee and tea which, when produced on small farms, generate high levels of employment and also provide capital and land-based income to relative low income families Bourguinon and Morrisson, ; since dispersed ownership of the land typically goes with substantially greater use of labour, these two determinants of distribution both contribute in a positive direction.

All of these impacts are, however, complicated by the role of government, which often owns and operates mineral industries and in other cases collects large tax revenues which provide a basis for spending whose allocation forms part of the final impact.

When the inflow brings a technology shift towards the capital intensive, as FDI often does, the impact is more likely to be negative; when it flows into labour intensive industries in order to take advantage of lower wages, the impact is likely to be positive.

The strongest views and disagreements occurred during the neoli Over time country-level evidence tends to support the theoretical prediction that increased trade will lower inequality in some countries, especially those exporting labour intensive goods, and raise it in others, especially those exporting primary products based on assets whose ownership is concentrated or those where freer trade leads to rapid labour-displacing technological change.

Taiwan exemplifies the former, and a number of Latin American countries the latter.

inequality poverty and growth where do we stand in this relationship

The higher inequality in mineral dependent exporting nations also fits this broad prediction Berry, But detectable effects are modest in relation to the inequality gaps across countries.

It seems likely that if major effects exist they occur through the dynamic processes of investment and technological change or perhaps through the process of growth acceleration, to which openness can contribute. The rapid turning outward in India and China and the enormous increase in inequality in the latter are consistent with an important role for those dynamic mechanisms. Confirmation of their greater role than that of differences in factor proportions comes from Meschi and Vivarelli, whose state of the art analysis focuses on over time changes in inequality within countries rather than cross-country differences.


They find that while changing total trade flows in relation to GDP are not significantly related to inequality, trade of middle income developing countries with the industrial countries does push that inequality up, while trade among developing countries pushes it down. This suggests that the former trade facilitates the diffusion of new technologies, in particular in the middle-income developing countries where the capacity to absorb such technologies is greater, with associated labour-displacing and inequality-increasing effects.

Trade among the developing countries is presumably weighted towards goods that are not capital or skill-intensive. Government enters the picture in various ways, most obviously by taxing the population and engaging in spending but also through a wide range of other policies, including those on international trade and capital flows.

Building a Theory of Inequality and its Evolution 45The main factors of production are land, physical capital, human capital i. As development proceeds, land and other natural resources become relatively less important and physical and later human capital relatively more so.

The distribution of physical capital is normally quite concentrated, that of land is in some countries but not in others, and that of what we may call basic labour the skills that are mainly manual and quickly acquired and hence not too different among people relatively equal. Human capital—the skills and capabilities that result from education and training may or may not be unequally distributed, depending on the country.

Overall income distribution is more equal when the functional distribution favours those factors that are least unequally distributed, especially basic labour. When the technologies favour capital, such that the physical and human capital shares of national income are high, then the pre-fisc distribution of income tends to be unequal.

Three mechanisms tend to bias technological change in favour of the better off owners of capital. One is the importance of borrowing technologies from the industrial countries; such technologies are usually capital intensive in relation to the factor endowments of developing countries and thus depress the demand for less—skilled labour and increase the demand for higher skills and the higher incomes that go with them.

A second is the fact that the resources to generate new technologies or forms of production are greater in larger rather than smaller firms, and the former tend to use and prefer capital intensive technologies.

Finally, public efforts to develop technologies for smaller scale enterprises are often far below their optimal level. Recently agricultural research has in many countries been increasingly privatized with an associated focus on use by large, capital-intensive farms.

  • What can monetary policy do about inequality?
  • Discrimination, Inequality, and Poverty—A Human Rights Perspective
  • There was a problem providing the content you requested

All of these mechanisms may be expected to raise inequality in a country, but it remains hard to ascertain how powerful their effects are. The vicious circle usually begins with a politically and economically self-reinforcing concentration of all major forms of capital. Usually it also involves market imperfections, some of them policy-induced or uncorrected by policy, that leave the return to a given factor higher for the better off and hence better connected.

Examples include the capacity of some large capitalists to achieve monopoly profits, while small informal enterprises may realize only very low returns on their capital because of the competition they face. Political power upholds monopoly profits by blocking anti-trust efforts. That political process channels public funds for education mainly to the better off, and co-habits with a financial system that also favours the better off, larger firms. The total impact of differences in asset concentration, the character of technological change and the pattern of market imperfections may be approximated in terms of the Gini coefficient as the difference between about 0.

Early critics of this view included Ade In the early days of development thinking it was often held that the goals of growth and equality were at odds; 21 allowing and fostering the wealth of a few was seen as a necessary incentive for entrepreneurship and the high savings rate needed to finance investment.

Since capitalists were the savers, innovators and inventors, 22 a premature pursuit of equity would come at the expense of growth. This view may have held considerable validity at some points during the economic history of the now industrial countries. Suggesting that the issue should be framed in a more compl Whether or not these claims stand the test of time, it is clearly true that fast growth can occur with either low or high levels of inequality, and that it can occur as inequality increases as recently in India, and more dramatically, in China or as it decreases Taiwan, earlier and some countries of Latin America more recently.

In short, theory suggests that some policy packages may achieve equality in ways that are promotive of growth and others in ways that deter growth; in the latter case it may be that neither objective is attained. Historically most gains have taken place through the former route, but by the 20th century social policy was becoming increasingly important in the now industrial countries and by the late 20th and the 21st it has taken on considerable significance in the developing countries, especially the higher income among them.

As noted, theory suggests that some policy packages may achieve equality in ways that promote growth and others in ways that deter growth. Such a system involves many components, one of the most important being the sharing of rents from natural resource exploitation broadly within the population. But in most countries none of them are pursued very effectively, with the result that the primary distribution of income remains quite unequal unless and until the tax and transfer system comes into play as a partial palliative, as has recently occurred in a number of mostly middle-income developing countries.

Where policy is good, it tends to be good on most of these fronts, a fact that suggests either that the technical and political prerequisites including the nature of the society and the social contract are similar for all, or that progress on some of them facilitates progress on others. Which have the greatest potential depends on the economic considerations involved. In most cases a good employment strategy brings faster as well as more equitable growth, along with the direct psychological benefit people get from having a decent job.

Labour outcomes wages, levels of employment, unemployment and underemployment, working conditions, etc. Facilitating a healthy demand for labour is almost always the most important of these three determinants of labour outcomes as well as being the biggest challenge to a better income distribution and the reduction of poverty; policy too often meets that challenge poorly. Though it is arguable that the demand for labour is the key determinant of labour outcomes it has probably received less theoretical and general attention than have supply and labour market functioning.

The main interacting factors lying behind it appear to be five: