Gini Coefficient - Limitations of Gini Coefficient

Limitations of Gini Coefficient

Gini coefficient is inherently limited because of its relative nature. Its proper use and interpretation of income Gini coefficient is controversial. As example, Mellor explains, income Gini index of developing countries can rise, that is the income distribution get more unequal at the same time that the number of people in absolute poverty are reduced substantially. Kwok claims income inequality implied by Gini coefficients over time is misleading because Gini ignores structural changes in a society, changes such as growing population (baby boom, elderly population households, increased divorces, extended family households splitting into nuclear families), population changes from emigration, immigration and income mobility. Gini coefficient is simple, and this simplicity encourages misunderstandings. The simplicity can confuse comparison of two different populations; for example, while both Bangladesh with per capita income of $1,693 and Netherlands with per capita income of $42,183 had an income Gini index of 0.31 in 2010, it does not mean the quality of life, economic opportunities and income equality are same for Bangladesh and Netherlands. Countries may have identical Gini coefficients, but differ greatly in wealth. Basic necessities may be equal and available to all in a developed economy population, while in an undeveloped economy with same Gini coefficient, even basic necessities are unequally available.

Different income inequality, yet same Gini

Even when the total income of a population is the same, in certain situations two countries with very different income distributions can have the same Gini index (e.g. cases when income Lorenz Curves cross). Table A in this section illustrates one such situation. Both countries have a Gini index of 0.2, but the income distributions are very different. As another example, a less equal society EE where bottom 50% of individuals had no income and the other 50% shared all the income equally has a Gini coefficient of 0.5; a more equal society FF where bottom 75% of people equally shared 25% of income while the top 25% equally shared 75% also has a Gini index of 0.5. Economies with similar incomes and Gini coefficients can still have very different income distributions. Bellù and Liberati claim ranking income inequality between two different populations with same or different Gini indices is sometimes not possible, or misleading.

Extreme wealth inequality, yet low income Gini coefficient

Gini index loses information about absolute national and personal incomes. Populations can have very low income inequality Gini indices yet simultaneously very high wealth Gini index. By measuring inequality in income, the Gini ignores the differential efficiency of use of household income. By ignoring wealth (except as it contributes to income) the Gini can create the appearance of inequality when the people compared are at different stages in their life. Wealthy countries such as Sweden can show a low Gini coefficient for disposal income of 0.31 thereby appearing equal, yet have very high Gini coefficient for wealth of 0.79 to 0.86 thereby suggesting an extremely unequal wealth distribution in its society. These factors are not assessed in income-based Gini.

Small sample bias - sparsely populated regions more likely to have low Gini coefficient

Gini index has a downward-bias for small populations. Counties or states or countries with small populations and less diverse economies will tend to report small Gini coefficients. For economically diverse large population groups, a much higher coefficient is expected than for each of its regions. Taking world economy as one, and income distribution for all human beings, for example, different scholars estimate global Gini index to range between 0.61 and 0.68.

Same population with same income distribution, analyzed differently, yields different Gini coefficients

As with other inequality coefficients, the Gini coefficient is influenced by the granularity of the measurements. For example, five 20% quantiles (low granularity) will usually yield a lower Gini coefficient than twenty 5% quantiles (high granularity) for the same distribution. Philippe Monfort has shown inconsistent or unspecified granularity limits usefulness of Gini coefficient measurements.

The Gini coefficient measure gives different results when applied to individuals instead of households, for the same economy and same income distributions. If household data is used, the measured value of income Gini depends on how the household is defined. When different populations are not measured with consistent definitions, comparison is not meaningful.

Deininger and Squire show that income Gini coefficient based on individual income, rather than household income, are different. For United States, for example, they find that individual income-based Gini index was 0.35, while for France they report individual income-based Gini index to be 0.43. According to their individual focussed method, in the 108 countries they studied, South Africa had the world's highest Gini index at 0.62, Malaysia had Asia's highest Gini index at 0.5, Brazil the highest at 0.57 in Latin America and Caribbean region, and Turkey the highest at 0.5 in OECD countries.

Gini coefficient is unable to discern the effects of structural changes in populations

Expanding on the importance of life-span measures, the Gini coefficient as a point-estimate of equality at a certain time, ignores life-span changes in income. Typically, increases in the proportion of young or old members of a society will drive apparent changes in equality, simply because people generally have lower incomes and wealth when they are young than when they are old. Because of this, factors such as age distribution within a population and mobility within income classes can create the appearance of inequality when none exist taking into account demographic effects. Thus a given economy may have a higher Gini coefficient at any one point in time compared to another, while the Gini coefficient calculated over individuals' lifetime income is actually lower than the apparently more equal (at a given point in time) economy's. Essentially, what matters is not just inequality in any particular year, but the composition of the distribution over time.

Kwok claims income Gini index for Hong Kong has been high (0.434 in 2010), in part because of structural changes in its population. Over recent decades, Hong Kong has witnessed increasing numbers of small households, elderly households and elderly living alone. The combined income is now split into more households. Many old people are living separately from their children in Hong Kong. These social changes have caused substantial changes in household income distribution. Income Gini coefficient, claims Kwok, does not discern these structural changes in its society. Household money income distribution for the United States, summarized in Table C of this section, confirms that this issue is not limited to just Hong Kong. According to the US Census Bureau, between 1979 and 2010, the population of United States experienced structural changes in overall households, the income for all income brackets increased in inflation-adjusted terms, household income distributions shifted into higher income brackets over time, while the income Gini coefficient increased.

Another limitation of Gini coefficient is that it is not a proper measure of egalitarianism, as it is only measures income dispersion. For example, if two equally egalitarian countries pursue different immigration policies, the country accepting a higher proportion of low-income or impoverished migrants will report a higher Gini coefficient and therefore may appear to exhibit more income inequality.

Gini coefficient falls yet the poor gets poorer, Gini coefficient rises yet everyone getting richer

Arnold describes one limitation of Gini coefficient to be income distribution situations where it misleads. The income of poorest fifth of households can be lower when Gini coefficient is lower, than when the poorest income bracket is earning a larger percentage of all income. Table D illustrates this case, where the lowest income bracket has an average household market income of $500 per year at Gini index of 0.51, and zero income at Gini index of 0.48. This is counter-intuitive and Gini coefficient cannot tell what is happening to each income bracket or the absolute income, cautions Arnold.

Feldstein similarly explains one limitation of Gini coefficient as its focus on relative income distribution, rather than real levels of poverty and prosperity in society. He claims Gini coefficient analysis is limited because in many situations it intuitively implies inequality that violate the so-called Pareto improvement principle.

The Pareto improvement principle, named after the Italian economist Vilfredo Pareto, states that a social, economic or income change is good if it makes one or more people better off without making anyone else worse off. Gini coefficient can rise if some or all income brackets experience a rising income. Feldstein’s explanation is summarized in Table D. The table shows that in a growing economy, consistent with Pareto improvement principle, where income of every segment of the population has increased, from one year to next, the income inequality Gini coefficient can rise too. In contrast, in another economy, if everyone gets poorer and is worse off, income inequality is less and Gini coefficient lower.

Inability to value benefits and income from informal economy affects Gini coefficient accuracy

Some countries distribute benefits that are difficult to value. Countries that provide subsidized housing, medical care, education or other such services are difficult to value objectively, as it depends on quality and extent of the benefit. In absence of free markets, valuing these income transfers as household income is subjective. The theoretical model of Gini coefficient is limited to accepting correct or incorrect subjective assumptions.

In subsistence-driven and informal economies, people may have significant income in other forms than money, for example through subsistence farming or bartering. These income tend to accrue to the segment of population that is below-poverty line or very poor, in emerging and transitional economy countries such as those in sub-Saharan Africa, Latin America, Asia and Eastern Europe. Informal economy accounts for over half of global employment and as much as 90 per cent of employment in some of the poorer sub-Saharan countries with high official Gini inequality coefficients. Schneider et al., in their 2010 study of 162 countries, report about 31.2%, or about $20 trillion, of world's GDP is informal. In developing countries, the informal economy predominates for all income brackets except for the richer, urban upper income bracket populations. Even in developed economies, between 8% (United States) to 27% (Italy) of each nation's GDP is informal, and resulting informal income predominates as a livelihood activity for those in the lowest income brackets. The value and distribution of the incomes from informal or underground economy is difficult to quantify, making true income Gini coefficients estimates difficult. Different assumptions and quantifications of these incomes will yield different Gini coefficients.

Gini has some mathematical limitations as well. It is not additive and different sets of people cannot be averaged to obtain the Gini coefficient of all the people in the sets.