A Lorenz curve is one of the most sensible ways an economist would measure inequality in a society. The yellow line in the graph represents a situation of perfect equality, where each quintile (or one fifth) of the population earns exactly one quintile of the income. That situation is certainly not argued by most economists to be a desirable state of affairs, since perfect income equality is not the policy prerogative of economists or governments. But it is a good measure of how well a society is developing, since development should give rise to, among other things, rising income and equity. By development I simply mean the transition of economy from an agrarian society to a modern, industrialized or consumer society. Any society that is in transition from an agrarian society to some modern form of production is developing.
The Lorenz curve above shows the income equality in both China and Brazil. We notice that the equality in Brazil is in fact much lower than China even. And China is known to exacerbate their system of inequality, since its rural working classes are not given any of the societal benefits and opportunities that its urban classes are. Rural workers are given fewer legal rights, while the urban class has more rights and fewer restrictions anywhere else in the country. The income inequality in China, overall, is rising. In 1985 the GINI coefficient, the representational number that comes from this model, was 0.31. With “0” being the line of perfect income equality and “1” being the line of perfect income inequality. In 2004, however, that number had moved closer to the line of inequality at 4.6.
In the previous entry, Middle Classistics, I mentioned the state of rising inequality in Brazil. Other things being equal, that is, if we assume no disasters (whether natural, financial, or political), economic growth theory stipulates that economies ought to see rising income equality after an initial period of inequality. It is not clear how long the rising inequality period is supposed to take, however, this stages-model was developed by Simon Kuznets who won the Nobel Prize in economics in 1971 “for his empirically founded interpretation of economic growth which has led to new and deepened insight into the economic and social structure and process of development”. It has been argued in recent years, however, that the Kuznets Curve — the U-Curve — is wrong in its assumptions since it was based on static data. The data Kuznets used was based on static data from the current situations in developed and developing countries. At the time, developing countries were showing great differences in income equality. Kuznets argued that since developed countries were showing higher income equalities, this meant that developing countries, once they develop, will also show higher income equalities.
Economists, if they are going to reason this way, are now pressured to build time-series data models, which will argue more persuasively that what was experienced in one country will also happen in another. Inductive validity and causality should be more persuasively argued for. But the Kuznets model, it turns out, is largely falsified. The U-Curve might happen in some cases, but it is not a necessary stage in development, and therefore income inequalities cannot be justified on the basis that incomes will simply equal out in the future.
It is my view that states rarely develop in ways that do not significantly limit or violate the rights of their population. For example, all sorts of landgrab reforms in Brazil have taken place over the years. The development strategies are often aimed at enriching a certain class, the upper class, with a trickle-down theory about how that income will reach the rest of society.
We can demonstrate the lack of persuasion the Kuznets model should have with a time-series model of Brazil and China. Despite China’s effort to develop in recent years, income inequality has risen at a dramatic pace. As I mentioned earlier, the GINI coefficient in 1985 was 0.31. In 2004 it is 0.46. If we look closer at the rural and urban distinction, we see this transformation had even greater effects on the rural population, who have been systematically denied participation in China’s development scheme. In 1985, the rural GINI was 0.27, which reflects inequality among rural workers themselves. In 2001 that number was 0.343. The average coefficient over the years 0.31, the same level of inequality that existed in the entirety of China in 1985. The average coefficient among the urban population has been 0.21, which means urban equality has been higher than rural equality over the years.
The international GINI warning level is 0.4, and this has provoked a discussion about whether we are really considering two distinct countries–the rich and the poor. However, the situation in Brazil, whose GINI in 2004 was 0.59, is much more unequal than China. In Brazil the poorest quintile of the population earn 3% of the income, while the richest quintile earn 61% of the income. In China the inequalities are increasing, yet the lowest quintile earns 4% of the income and the highest quintile earns 52%.
We can see also from the 2004 World Bank data that China would appear to have a much more prosperous middle class than Brazil. China’s middle quintile earns 14% of the income, while in Brazil they earn 11% of the income. This is reflected in the Lorenz curve shown above. The entire population in both countries is unimodally skewed toward the richest quintile. But Brazil is far worse.
The GINI time-series model for Brazil, however, seems to fit the overall Kuznets picture of an inverted-U dynamic. In fact, it appears to be a perfect example of the U-curve hypothesis. This, I argue later, is not always the case, although Brazil for now appears to be a good example of it. It is important to say, however, that the U-curve model itself would be insignificant if the inequalities did not give rise to anything else, that is, if they were not justified by anything else. Kuznets’ intention was to show that the increase in inequality was justified due to an increase in overall economic activity as measured by GDP. And the real GDP growth estimates show that Brazil has increased from $15b in 1960 to $1068b in 2006, using current US currency value as the base-year value.
But — and this is a big but — it’s important to mention, just before we start thinking the Kuznets model has circumnavigated Brazil, and we reify this information as socioeconomic laws of nature, it needs to be said that the GINI Coefficients in my graph are misleading. If you look at the Y-axis, you’ll see I had to squish the numbers so that the only variation we see is between 0.57 and 0.61. This is a very small margin, but I needed to do that so it would appear to be a noticeable difference. If we look at overall GINI coefficients for the time period, in full scale, the picture we get is much different:
Brazil’s GINI coefficients have hardly moved from around the 0.6 area. We can rightly ask whether this is a good example of the inverted-U hypothesis, although many think that it is. To use Brazil as an example of the empirical justification of the model seems absurd. China, on the other hand, has grown faster than Brazil since 1960, and we see that its GINI index over time has evidently not become an inverted-U shape. In 1960, GDP in China was $61b in current US dollars. By 2006 that value had risen to $2,668b, or $2.6 trillion. By measuring gross domestic product, China has grown faster than Brazil by more than 70%. Yet take a look at the GINI values over time:
The GINI coefficients on a time-series scale do not match the Kuznets prediction that they will eventually drop to levels equal to or below the GINI coefficients at the time inequalities began rising. One may say that the isolated urban population would be expected to display lower levels of inequality, since they are the most rapidly developing areas of China. Yet even in the urban areas inequality measurements are increasing at the same pace each year. The rural population suffers the most, due to all sorts of social and legal issues, but also due to the rising inequality amongst themselves. The GINI index of rural populations in China is increasing at an increasing rate, which is not desirable.
In fact, overall, the World Bank measurements are more optimistic than other measurements. The 2004 World Bank GINI puts the number at 0.41, whereas other data have found this number to be closer to 0.46. We might expect this to be the case since the World Bank analysis is tightly connected to the Washington Consensus, which argues that rising inequalities are justified if shock therapeutic developmental policies are in place.
One could say that what I have said about the Kuznets model is not yet proven correct, since China still has the opportunity to decrease inequality rates and increase GDP rates, thus proving the model correct. However, there is no reason to accept this idea if there is a persuasive case to be made that the methodological approach of the model is not cogent.
The argument that inequality must increase before it decreases, the conclusion of Kuznets’ work, is based on cross-sectional data. The other way to gather and present data in economics is through time-series analyses. The U-shape in the curve comes not from progression in the development of individual countries, but rather from historical differences between countries. In his data set, many of the middle income countries were Latin American countries. The individual countries Kuznets used as the basis for argument were developed countries, such as the United States, where income inequalities had historically been lower. In Latin America, with its history of colonialism and presence of neo-colonial and white minority who own the majority of the means of production, inequalities were historically very high. When we factor in this time-series variable, the shape of the U-curve tends to disappear. And in some instances, such as the time-series GINI index of Canada 1970-1999, we see what would appear to be an inverted U-curve. That is, a situation in which rising equality eventually gave way to inequality conditions that existed before the increase in equality.
The status of the Kuznets curve was once reified as a socioeconomic law. Economists and social scientists have recently demonstrated how Kuznets’s arguments, originally advanced under more limited conditions, became transformed into overarching theoretical, empirical, and political constructions. The time-series analyses suggests that even empirically grounded and testable social science models are contingent on the broader social and political contexts in which they are produced and negotiated.