Lorenz curve. Fund Ratios

Assess the degree of wage differentiation among workers in each sector of the Russian economy, as well as the impact of the crisis on the redistribution of income within the industry.

Materials used

Rosstat data

Brief explanations

Equal distribution of income among all residents of the country is the basis of social stability.

The Gini coefficient is a statistical indicator of the degree of stratification of society along a certain basis. This indicator is often used to determine the uneven distribution of income among the world's population.

Using the methodology for calculating the Gini coefficient (it is presented in detail in the text of the study), we examined not the entire Russian economy, but its individual sectors.

Calculation of the Gini coefficient

A few words about how this indicator is calculated.

The values ​​that the coefficient can take range from 0 to 1. Zero means complete equality of income among all residents (in this case, workers in a particular industry), one means complete inequality (an unrealistic situation when all wages in an industry are concentrated in the hands of one person ).

If the coefficient is presented as a percentage, then it is called the Gini index.

Let's illustrate with an example.

Let's assume that all residents of the country receive the same salary, in this case the graph will look like this:

10% of the population will receive 10% of total income, 20% of residents, respectively, 20% of total income, etc. This is a completely equal distribution of income.

In the opposite case, if we assume that one person receives a salary and everyone else works for free, the Gini coefficient will be equal to one, and the income concentration graph will look like this:

In reality, the income distribution usually looks like this:

The purple curve here is a graph of the shares of income of each group of residents (in our case, workers) in total income. For example, according to this graph, the lowest 10% of employees receive only 0.8% of total industry income, 90% of employees receive 60% of total income, which means that 40% of the income is in the hands of the top 10% of employees.

The figure formed by the intersection of the red straight line and the purple curve is the inequality of income distribution. The value of the Gini coefficient is the ratio of the area of ​​this figure to the area of ​​the entire triangle.

An example of calculating the Gini coefficient for one of the economic sectors

Let’s use Rosstat data “Distribution of the number of employees by size wages» by type of economic activity and let’s try, based on these data, to construct a Lorenz curve and calculate the value of the Gini coefficient.

Table 1 (part 1). Distribution of the number of employees by wages and types of economic activity, in 2015 *

Table 1 (part 2). Distribution of the number of employees by wages and types of economic activity, in 2015 *

*Data is published once every 2 years, in April.

Table 1 (part 3). Distribution of the number of employees by wages and types of economic activity, in 2015 *

*Data is published once every 2 years, in April.

To construct the Lorenz curve and calculate the Gini coefficient, data is needed on the share of income of each population group (in this case, industry workers) in total income. This data is in Table 1 are missing. In order to obtain such data, we will use a mathematical technique: we will multiply the average income for each interval (we define it as the middle of the interval) by the corresponding specific weights (shares) of the population, thereby obtaining the so-called percentage numbers of group incomes. Then, by calculating the shares of groups in total income and summing them up, we obtain a cumulative series of incomes, expressed as a percentage.

As an example, let’s carry out calculations for one of the industries, for example, agriculture, hunting and forestry.

Table 2. Estimated data for calculating the Gini coefficient for the industry "Agriculture, hunting and forestry"

• Income
• Middle of the interval – average level wages in each group of workers.
• Proportion of employees receiving the appropriate level of wages– Rosstat data (see Table 1).
• Cumulative number of employees– accumulated frequencies. In order to calculate the value of the i-series, it is necessary to sum up the shares of workers (column 3 of Table 2) from 1 to i inclusive.
• Group income percentages– calculated data used to determine the share of income of a particular group of workers in total income. They are calculated by multiplying the middle of the interval by the specific gravity (column 2 times column 3).
• Share in total income– the share of income of a particular group of employees in total income. The ratio of group income (column 5) to the sum of all income (sum of income in column 5).
• Cumulative income series- sum specific gravity income to the relevant group.

Let's build a diagram where the cumulative series of the number of employees will be plotted along the X-axis, and the cumulative series of income will be plotted along the Y-axis.

The area of ​​the figure under the purple line can be calculated by summing up the areas of the trapezoids that make up the figure. Their total area is 3313.

The area of ​​the figure with an absolutely uniform distribution of income is 5000 (the triangle under the straight line on Diagram 2).

Thus, the area of ​​the figure reflecting the inequality of income distribution is 5000-3313=1687.

Therefore, the Gini coefficient for the industry Agriculture, hunting and forestry equal to 1687/5000=0.337

Gini coefficient for other sectors of the economy

Using the same model, we will calculate the values ​​of the Gini coefficient for all 17 sectors of the economy that Rosstat takes into account.

Table 3. Gini coefficient for economic sectors in 2015

By ranking the data and presenting it in chart form, we can see that currently the greatest income equality is observed among employees in the mining sector, and the greatest inequality is in the fishing and fish farming sector.

To illustrate how different an inequality coefficient of 0.486 is from a coefficient of 0.314, here is a simple example. In fisheries and aquaculture, the top 12.4% of employees receive 40% of total income. But in the most “fair” sector from this point of view – the mining sector – a little more than 40% of the total income is already received by 22.1% of employees (see. Table 4).

Table 4

The impact of the crisis on the differentiation of wages in economic sectors

By calculating the Gini coefficient for sectors of the economy in 2013 and comparing these values ​​with the indicators for 2015, we will see how the crisis affected the differentiation of wages in a particular area.

Let's see if somewhere in the industry income has begun to be distributed more “fairly” among employees.

– rating of industries by growth of the Gini coefficient. The chart shows that over the past 2 years, inequality in the distribution of wages has increased significantly in the areas of fishing, fish farming (+15.3%), hotel and restaurant business (+4.82%) and construction (+3.66%).

The distribution of wages became more “fair” in healthcare and the provision of social services (-3.47%), in the sphere of wholesale and retail motor vehicles(-2.27%), in the area scientific research and development (-2.16%).

In the fisheries and aquaculture sector in 2013, 8.2% of the highest paid employees had 23.56% of total income. In 2015, 22.08% of total income belonged to 3.9% of the highest paid employees. That is, in 2013, the top 1% of employees accounted for 2.87% of total industry income, and in 2015, each percent of these employees already accounted for 5.66% of total industry income.

Table 5

conclusions

1. The greatest income inequality among workers in sectors of the Russian economy is observed in the sphere fisheries and fish farming. The Gini coefficient for this industry is 0,486 .
2. In the field fishing and fish farming 12.4% the highest paid employees receive 40% total income.
3. Among the top three in terms of greatest income differentiation are: activities for organizing recreation, entertainment, culture and sports(Gini coefficient 0,417 ) And activities to provide utilities (0,412 ).
4. The most “fair” distribution of income is in the sphere mining. There the income differentiation coefficient is equal to 0,314 , and a little more 40% total income already received 22,1% employees.
5. Over the past two years (from 2013 to 2015), the degree of income stratification has changed in many areas of the economy.
6. Inequality in wage distribution (as measured by the Gini coefficient) has increased significantly in the areas fishing, fish farming (+15,3% ), hotel and restaurant business (+4,82% ) And construction (+3,66% ).
7. The distribution of wages has become more “fair” in healthcare and social services (-3,47% ), in the field wholesale and retail trade in motor vehicles (-2,27% ), in the field research and development (-2,16% ).
8. The differentiation of employees by wages in such areas as manufacturing industries, mining, provision of utilities, education, activities for organizing recreation, entertainment, etc..

Gini coefficient- a statistical indicator of the degree of stratification of society in a given country or region in relation to any characteristic being studied.

The Gini coefficient varies from 0 to 1. The closer its value is to zero, the more evenly distributed the indicator is.

Most often in modern economic calculations, the level of annual income is taken as the characteristic being studied. The Gini coefficient can be defined as a macroeconomic indicator that characterizes the differentiation of monetary incomes of the population in the form of the degree of deviation of the actual distribution of income from their absolutely equal distribution among the inhabitants of the country.

Sometimes a percentage representation of this coefficient is used, called Gini index .

Sometimes the Gini coefficient (like the Lorenz curve) is also used to identify the level of inequality in accumulated wealth, but in this case the non-negativity of the household’s net assets becomes a necessary condition.

The Gini index is also used in machine learning to predict continuous quantities. Its meaning is that the error should be as uniform as possible.

Background

Benefits of the Gini Coefficient

• Allows you to compare the distribution of a characteristic in populations with different number units (for example, regions with different populations).

• Complements data on GDP and per capita income. Serves as a kind of correction for these indicators.

• Can be used to compare the distribution of a trait (income) between different populations (for example, different countries). At the same time, there is no dependence on the scale of the economy of the countries being compared.

• Can be used to compare the distribution of a trait (income) across different population groups (for example, the Gini coefficient for the rural population and the Gini coefficient for the urban population).

• Allows you to track the dynamics of uneven distribution of a characteristic (income) in the aggregate at different stages.

• Anonymity is one of the main advantages of the Gini coefficient. There is no need to know who has what income personally.

Disadvantages of the Gini coefficient

• Quite often, the Gini coefficient is given without describing the grouping of the population, that is, there is often no information about exactly which quantiles the population is divided into. Thus, the more groups the same population is divided into (more quantiles), the higher the Gini coefficient value for it.

• The Gini coefficient does not take into account the source of income, that is, for a certain geographical unit (country, region, etc.) the Gini coefficient can be quite low, but at the same time some part of the population provides their income through backbreaking labor, and the other through property account. For example, in Sweden the Gini coefficient is quite low, but only 5% of households own 77% of the shares of the total number of shares owned by all households. This provides these 5% with the income that the rest of the population receives through labor.

• The method of the Lorenz curve and the Gini coefficient in studying the uneven distribution of income among the population deals only with cash income, while some workers are paid wages in the form of food, etc.; The practice of issuing wages to employees in the form of options to purchase shares of the employer company is also becoming widespread (the last consideration is unimportant, the option itself is not income, it is only an opportunity to receive income by selling, for example, shares, and when the shares are sold and the seller receives money, this income is already taken into account when calculating the Gini coefficient).

• Differences in methods for collecting statistical data to calculate the Gini coefficient lead to difficulties (or even impossibility) in comparing the obtained coefficients.

Example of calculating the Gini coefficient

According to Rosstat, the Gini coefficient in Russia in 2012 was 0.420, in 2011 - 0.417, in 2010 - 0.421. According to the CIA Fact Book, the Gini index (Gini coefficient in brackets) in Russia in 2012 was 42.0% (0.420), in 2011 - 41.7% (0.417), in 2009 - 42.2% (0.422), in 2001 - 39.9% (0.399), in 1997 - 37.5% (0.375), in 1991 - 26% (0.260).

According to China's own statistics, the Gini coefficient in this country in 2012 was 0.474; over the past 10 years, the coefficient reached a local maximum in 2008, when it was 0.491. In 2000, the same figure in China was 0.41, in 1990 - 0.33, in 1980 - 0.31. Prof. Hu Angang in 2004, when China's Gini coefficient was estimated World Bank was 0.437, in an interview he noted: “If we also take into account unofficial income, tax evasion, corruption, then the Gini coefficient will be 0.51 and higher. Judging by official nominal incomes, the gap is already quite large. During the reforms, that is, in one generation, China went from a coefficient of 0.2 to 0.5. The transition from a just to a distinctly unjust society is very noticeable. Moreover, on initial stage reforms, the number of poor people decreased on a large scale, and since the second half of the 1990s these proportions have changed very little.”

Credit Suisse's Global Wealth Report puts Russia's 2012 Gini index at 84% (0.84; wealth, not income), which the bank says is maximum value among all major countries in the world. Russian experts consider such an assessment to be biased, inflated and unreliable. According to economists and analysts surveyed by Expert magazine, Credit Suisse’s findings do not correspond to reality, and “in terms of wealth inequality, Russia is approximately comparable to countries such as the United States, Japan, India and China.” The cost of housing alone in Russia is several times higher than the figure indicated in the Global Wealth Report as the value of all property of Russian residents. From which it follows that in reality the richest percent of the Russian population owns not 71% of all wealth, as stated in the Global Wealth Report, but less than 7%.

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Notes

Excerpt describing the Gini Coefficient

On the night of October 6th to 7th, the movement of the French speakers began: kitchens and booths were broken down, carts were packed, and troops and convoys were moving.
At seven o'clock in the morning, a convoy of Frenchmen, in marching uniform, in shakos, with guns, knapsacks and huge bags, stood in front of the booths, and animated French conversation, sprinkled with curses, rolled along the entire line.
In the booth, everyone was ready, dressed, belted, shod, and just waiting for the order to go out. The sick soldier Sokolov, pale, thin, with blue circles around his eyes, alone, without shoes or clothes, sat in his place and, with eyes rolling out of his thinness, looked questioningly at his comrades who were not paying attention to him and moaned quietly and evenly. Apparently, it was not so much suffering - he was sick with bloody diarrhea - but fear and grief of being alone that made him moan.
Pierre, shod in shoes sewn for him by Karataev from tsibik, which the Frenchman had brought for hemming of his soles, belted with a rope, approached the patient and squatted down in front of him.
- Well, Sokolov, they’re not completely leaving! They have a hospital here. Maybe you’ll be even better than ours,” said Pierre.
- Oh my God! O my death! Oh my God! – the soldier groaned louder.
“Yes, I’ll ask them again now,” said Pierre and, getting up, went to the door of the booth. While Pierre was approaching the door, the corporal who had treated Pierre to a pipe yesterday approached with two soldiers from outside. Both the corporal and the soldiers were in marching uniform, in knapsacks and shakos with buttoned scales that changed their familiar faces.
The corporal walked to the door in order to, by order of his superiors, close it. Before release, it was necessary to count the prisoners.
“Caporal, que fera t on du malade?.. [Corporal, what should we do with the patient?..] - Pierre began; but at that moment, as he said this, he doubted whether it was the corporal he knew or another, unknown person: the corporal was so unlike himself at that moment. In addition, at the moment Pierre was saying this, the crash of drums was suddenly heard from both sides. The corporal frowned at Pierre's words and, uttering a meaningless curse, slammed the door. It became semi-dark in the booth; Drums crackled sharply on both sides, drowning out the patient’s groans.
“Here it is!.. It’s here again!” - Pierre said to himself, and an involuntary chill ran down his spine. In the changed face of the corporal, in the sound of his voice, in the exciting and muffled crackling of the drums, Pierre recognized that mysterious, indifferent force that forced people against their will to kill their own kind, that force whose effect he saw during the execution. It was useless to be afraid, to try to avoid this force, to make requests or admonitions to people who served as its instruments. Pierre knew this now. We had to wait and be patient. Pierre did not approach the patient again and did not look back at him. He stood silently, frowning, at the door of the booth.
When the doors of the booth opened and the prisoners, like a herd of sheep, crushing each other, crowded into the exit, Pierre made his way ahead of them and approached the very captain who, according to the corporal, was ready to do everything for Pierre. The captain was also in field uniform, and from his cold face there was also “it,” which Pierre recognized in the words of the corporal and in the crash of the drums.
“Filez, filez, [Come in, come in.],” the captain said, frowning sternly and looking at the prisoners crowding past him. Pierre knew that his attempt would be in vain, but he approached him.
– Eh bien, qu"est ce qu"il y a? [Well, what else?] - the officer said, looking around coldly, as if not recognizing him. Pierre said about the patient.
– Il pourra marcher, que diable! - said the captain. – Filez, filez, [He’ll go, damn it! Come in, come in,” he continued to say, without looking at Pierre.
“Mais non, il est a l"agonie... [No, he’s dying...] - Pierre began.
– Voulez vous bien?! [Go to...] - the captain shouted, frowning angrily.
Drum yes yes dam, dam, dam, the drums crackled. And Pierre realized that the mysterious force had already completely taken possession of these people and that now it was useless to say anything else.
The captured officers were separated from the soldiers and ordered to go ahead. There were about thirty officers, including Pierre, and about three hundred soldiers.
The captured officers, released from other booths, were all strangers, were much better dressed than Pierre, and looked at him, in his shoes, with distrust and aloofness. Not far from Pierre walked, apparently enjoying the general respect of his fellow prisoners, a fat major in a Kazan robe, belted with a towel, with a plump, yellow, angry face. He held one hand with a pouch behind his bosom, the other leaned on his chibouk. The major, puffing and puffing, grumbled and was angry at everyone because it seemed to him that he was being pushed and that everyone was in a hurry when there was nowhere to hurry, everyone was surprised at something when there was nothing surprising in anything. Another, a small, thin officer, spoke to everyone, making assumptions about where they were being led now and how far they would have time to travel that day. An official, in felt boots and a commissariat uniform, ran in with different sides and looked out for the burned-out Moscow, loudly communicating his observations about what had burned and what this or that visible part of Moscow was like. The third officer, of Polish origin by accent, argued with the commissariat official, proving to him that he was mistaken in defining the districts of Moscow.
-What are you arguing about? - the major said angrily. - Whether it’s Nikola, or Vlas, it’s all the same; you see, everything burned down, well, that’s the end... Why are you pushing, isn’t there enough road,” he turned angrily to the one walking behind who was not pushing him at all.
- Oh, oh, oh, what have you done! - However, the voices of prisoners were heard, now from one side or the other, looking around the fire. - And Zamoskvorechye, and Zubovo, and in the Kremlin, look, half of them are gone... Yes, I told you that all of Zamoskvorechye, that’s how it is.
- Well, you know what burned, well, what’s there to talk about! - said the major.
Passing through Khamovniki (one of the few unburned quarters of Moscow) past the church, the entire crowd of prisoners suddenly huddled to one side, and exclamations of horror and disgust were heard.
- Look, you scoundrels! That's unchrist! Yes, he’s dead, he’s dead... They smeared him with something.
Pierre also moved towards the church, where there was something that caused exclamations, and vaguely saw something leaning against the fence of the church. From the words of his comrades, who saw better than him, he learned that it was something like the corpse of a man, stood upright by the fence and smeared with soot on his face...
– Marchez, sacre nom... Filez... trente mille diables... [Go! go! Damn it! Devils!] - curses from the guards were heard, and the French soldiers, with new anger, dispersed the crowd of prisoners who were looking at the dead man with cutlasses.

Along the lanes of Khamovniki, the prisoners walked alone with their convoy and carts and wagons that belonged to the guards and were driving behind them; but, going out to the supply stores, they found themselves in the middle of a huge, closely moving artillery convoy, mixed with private carts.
At the bridge itself, everyone stopped, waiting for those traveling in front to advance. From the bridge, the prisoners saw endless rows of other moving convoys behind and ahead. To the right, where the Kaluga road curved past Neskuchny, disappearing into the distance, stretched endless rows of troops and convoys. These were the troops of the Beauharnais corps who came out first; back, along the embankment and across the Stone Bridge, Ney's troops and convoys stretched.
Davout's troops, to which the prisoners belonged, marched through the Crimean Ford and had already partly entered Kaluzhskaya Street. But the convoys were so stretched out that the last convoys of Beauharnais had not yet left Moscow for Kaluzhskaya Street, and the head of Ney’s troops was already leaving Bolshaya Ordynka.
Having passed the Crimean Ford, the prisoners moved a few steps at a time and stopped, and moved again, and on all sides the crews and people became more and more embarrassed. After walking for more than an hour the few hundred steps that separate the bridge from Kaluzhskaya Street, and reaching the square where Zamoskvoretsky Streets meet Kaluzhskaya, the prisoners, squeezed into a heap, stopped and stood at this intersection for several hours. From all sides one could hear the incessant rumble of wheels, the trampling of feet, and incessant angry screams and curses, like the sound of the sea. Pierre stood pressed against the wall of the burnt house, listening to this sound, which in his imagination merged with the sounds of a drum.

Gini coefficient(Gini coefficient) is a quantitative indicator showing the degree of inequality of different options for the distribution of income, developed by the Italian economist, statistician and demographer Corrado Gini (1884-1965).

The Gini coefficient is a coefficient characterizing the differentiation of monetary incomes of the population in the form of the degree of deviation of the actual distribution of income from their absolutely equal distribution among all residents of the country. Most often in modern economic calculations, the level of annual income is used as the characteristic being studied. Sometimes a percentage representation of this coefficient is used, called the Gini index.

The Gini coefficient is calculated using the so-called. If all citizens have the same income, then the Gini coefficient is equal to zero, but if we assume the hypothesis that all income is concentrated in one person, the coefficient will be equal to one. Thus, the Gini coefficient in a given country is between zero and one.

Advantages of the Gini coefficient:

• allows you to compare the distribution of a characteristic in populations with different numbers of units (for example, regions with different populations);
• complements data on average per capita income. Serves as a kind of correction for these indicators;
• can be used to compare the distribution of a trait (income) between different populations (for example, different countries). At the same time, there is no dependence on the scale of the economy of the countries being compared;
• can be used to compare the distribution of an attribute (income) across different population groups (for example, the Gini coefficient for the rural population and the Gini coefficient for the urban population);
• allows you to track the dynamics of uneven distribution of a characteristic (income) in the aggregate at different stages;
• anonymity is one of the main advantages of the Gini coefficient (identification of the subjects of assessment is not required).

Disadvantages of the Gini coefficient:

• quite often the Gini coefficient is given without describing the grouping of the population, that is, there is often no information about exactly which quantiles the population is divided into. Thus, the more groups the same population is divided into (more quantiles), the higher the value of the Gini coefficient for it;
• The Gini coefficient does not take into account the source of income, that is, for a certain geographical unit (country, region, etc.) the Gini coefficient can be quite low, but at the same time, some part of the population provides their income through backbreaking labor, and the other through property account. For example, in Sweden the Gini coefficient is quite low, but only 5% of households own 77% of the shares of the total number of shares owned by all households. This provides these 5% of the income that the rest of the population receives through labor;
• the method of the Lorenz curve and the Gini coefficient in the field of studying the uneven distribution of income among the population deals only with monetary income, while some workers may receive remuneration for their work in the form of food and other material goods; The practice of issuing wages to employees in the form of purchasing shares of the employing company is also widespread (the last consideration is unimportant, the option itself is not income, it is only an opportunity to receive income by selling, for example, shares, and when the shares are sold and the seller receives money , this income is already taken into account when calculating the Gini coefficient);
• Differences in methods for collecting statistical data to calculate the Gini coefficient lead to difficulties (or even impossibility) in comparing the obtained coefficients.

Gini coefficient

Gini coefficient- a statistical indicator of the degree of stratification of society in a given country or region in relation to any characteristic being studied.

Most often in modern economic calculations, the level of annual income is taken as the characteristic being studied. The Gini coefficient can be defined as a macroeconomic indicator that characterizes the differentiation of monetary incomes of the population in the form of the degree of deviation of the actual distribution of income from their absolutely equal distribution among the inhabitants of the country.

Sometimes a percentage representation of this coefficient is used, called Gini index.

Sometimes the Gini coefficient (like the Lorenz curve) is also used to identify the level of inequality in accumulated wealth, but in this case the non-negativity of the household’s net assets becomes a necessary condition.

Background

This statistical model was proposed and developed by the Italian statistician and demographer Corrado Gini (1884-1965) and published in 1912 in his work “Variability and Variability of a Character” (“Variability and Inconstancy”).

Calculation

The coefficient can be calculated as the ratio of the area of ​​the figure formed by the Lorenz curve and the equality curve to the area of ​​the triangle formed by the equality and inequality curves. In other words, you should find the area of ​​the first figure and divide it by the area of ​​the second. In case of complete equality, the coefficient will be equal to 0; in case of complete inequality it will be equal to 1.

Sometimes the Gini index is used - a percentage representation of the Gini coefficient.

or according to the Gini formula:

where is the Gini coefficient, is the cumulative share of the population (the population is pre-ranked by increasing income), is the share of income that the total receives, is the number of households, is the share of household income in total income, is the arithmetic mean of the shares of household income.

Benefits of the Gini Coefficient

• Allows you to compare the distribution of a characteristic in populations with different numbers of units (for example, regions with different populations).

• Complements data on GDP and per capita income. Serves as a kind of correction for these indicators.

• Can be used to compare the distribution of a trait (income) between different populations (for example, different countries). At the same time, there is no dependence on the scale of the economy of the countries being compared.

• Can be used to compare the distribution of a trait (income) across different population groups (for example, the Gini coefficient for the rural population and the Gini coefficient for the urban population).

• Allows you to track the dynamics of uneven distribution of a characteristic (income) in the aggregate at different stages.

• Anonymity is one of the main advantages of the Gini coefficient. There is no need to know who has what income personally.

Disadvantages of the Gini coefficient

• Quite often, the Gini coefficient is given without describing the grouping of the population, that is, there is often no information about exactly which quantiles the population is divided into. Thus, the more groups the same population is divided into (more quantiles), the higher the Gini coefficient value for it.

• The Gini coefficient does not take into account the source of income, that is, for a certain location (country, region, etc.) the Gini coefficient can be quite low, but at the same time some part of the population provides their income through backbreaking labor, and the other through property. For example, in Sweden the Gini coefficient is quite low, but only 5% of households own 77% of the shares of the total number of shares owned by all households. This provides these 5% with the income that the rest of the population receives through labor.

• The method of the Lorenz curve and the Gini coefficient in studying the uneven distribution of income among the population deals only with cash income, while some workers are paid wages in the form of food, etc.; The practice of issuing wages to employees in the form of options to purchase shares of the employer company is also becoming widespread (the last consideration is unimportant, the option itself is not income, it is only an opportunity to receive income by selling, for example, shares, and when the shares are sold and the seller receives money, this income is already taken into account when calculating the Gini coefficient).

• Differences in methods for collecting statistical data to calculate the Gini coefficient lead to difficulties (or even impossibility) in comparing the obtained coefficients.

Example of calculating the Gini coefficient

The preliminary coefficient in 2010 was 42% (0.420) The Gini coefficient in Russia in 2009 was 42.2% (0.422), in 2001 39.9% (0.399) In 2012, according to the Global Wealth Report, Russia is ahead of all major countries and has coefficient 0.84

see also

Notes

Wikimedia Foundation. 2010.

See what the “Gini Coefficient” is in other dictionaries:

- (Gini coefficient) Statistical indicator of inequality. For example, if yi is the income of the i th person, the Gini coefficient is equal to half the expected absolute difference between the incomes of two randomly selected people, i and j, divided by the average income. On the… … Economic dictionary

- (Gini coefficient) See: Lorenz curve. Business. Dictionary. M.: INFRA M, Ves Mir Publishing House. Graham Betts, Barry Brindley, S. Williams and others. General editor: Ph.D. Osadchaya I.M.. 1998 ... Dictionary of business terms

A coefficient characterizing the differentiation of monetary incomes of the population in the form of the degree of deviation of the actual distribution of income from their absolutely equal distribution among all residents of the country. See t.zh. INCOME CONCENTRATION INDEX… Encyclopedic Dictionary of Economics and Law

GINI COEFFICIENT- an indicator characterizing the degree of deviation of the actual distribution of income from absolute equality or absolute inequality. If all citizens have the same income, then K.D. is equal to zero, but if we assume the hypothesis that all income... ... Large economic dictionary

Gini coefficient- income concentration index, showing the nature of the distribution of the entire amount of income of the population between its individual groups... Sociology: dictionary

Gini coefficient- indicator of population income concentration; The higher the inequality in a society, the closer it is to 1... Economics: glossary

Gini coefficient- a macroeconomic indicator characterizing the differentiation of monetary incomes of the population in the form of the degree of deviation of the actual distribution of income from their absolutely equal distribution among the inhabitants of the country... Dictionary of economic terms

Index of concentration of incomes, Income concentration index, Gini coefficient A macroeconomic indicator characterizing the differentiation of monetary incomes of the population in the form of the degree of deviation of the actual distribution of income from the absolute... ... Dictionary of business terms, I. G. Tsarev. The work models the distribution of income between economic entities in a closed economic system. The equilibrium function of income distribution in society is calculated, its... eBook

Gini coefficient (Gini index) - a statistical indicator indicating the degree of stratification of the society of a given country or region in relation to any characteristic being studied (for example, according to the level of annual income - the most common use, especially in modern economic calculations). The Gini coefficient can be used to reveal the level of inequality in accumulated wealth.

This statistical model was proposed and developed by the Italian statistician and demographer Corrado Gini (1884–1965) and published in 1912 in his famous work Variation and Variation of Character (Variability and Inconstancy). Thus, this is a macroeconomic indicator that characterizes the differentiation of monetary incomes of the population in the form of the degree of deviation of the actual distribution of income from their absolutely equal distribution among the inhabitants of the country.

Gini coefficientdetermines the degree of distribution deviationincome by population groups from uniform. The closer it is to zero, especially equal distribution of income; the closer the Gini coefficient is to one, the more income is concentrated by the richest group of citizens. For example, the Gini coefficient in the USA is 0.408, in the UK - 0.361, in Sweden - 0.250, in Japan - 0.249, in Zimbabwe - 0.568, in Mexico - 0.537, in Chile - 0.565. European bloc countries such as the Czech Republic, Sweden, Norway, Denmark, Slovenia have a lower Gini coefficient, ranging from 0.2 to 0.3.

According to some estimates, one sixth of the Russian population concentrated in their hands 57% of all monetary income and 92% of property income. The model of social stratification that has emerged in Russia today characterizes a highly differentiated society.

There is a concept decile coefficient of income differentiation, which shows how many times the minimum income of the richest 10% of the population exceeds the maximum income of the poorest 10% of the population. In 1991, the decile coefficient was 4.5 times; in 1992 - already 8.0 times; in 1994, its record value was observed for the entire period of reforms - 15 times, in last years- on average 14 times. The Gini coefficient in Russia in 1991 was 26%, in 1992 - 28.9%, in 1994 - 40.9%, in 1998 - 37.9%, in recent years its value has averaged 39% (2008 data).

World practice confirms that the danger of social conflicts is minimized if the gap between the incomes of rich and poor does not exceed 10 times.

The upper layer of Russian society is heterogeneous, it includes members of the government involved in economics; ministers and their deputies; heads of the largest state and semi-state companies; heads of new commercial structures; economic consultants public organizations; leading scientists and economists; persons collaborating with or belonging to the criminal world, highly qualified specialists. Among rich people, more than half are first-level managers. In the pre-reform period, high official position ensured the possibility of control over property and the right to privileges, and today - the appropriation of property and income.

Elite from the French elite - “the best, selected part.” In the theory of elites, economic, political and spiritual elites are distinguished. The economic elite refers to people who receive high and ultra-high incomes and control the main financial and economic structures of the country, regardless of their form of ownership. Almost all theories of elites are associated with the system of power relations in society and note inequality between the elite and all other members of society. In other words, the elite are the leading representatives of society, determining the priorities for the development of society and influencing the bulk of the population. The economic elite includes individuals who occupy leading positions in economic, political and social structures, who have and are aware of common interests and interact with each other. P . According to most experts, the economic elite of Russian society should include the gas, oil and aerospace groups. The coal, gold, and banking groups are called proto-elites, noting their powerful potential in the absence of constant intra-group interaction and contacts.

The overwhelming majority of the country's citizens perceive the emergence and constant increase in the number of dollar billionaires against the background of poverty of a significant part of the population as a blatant anomaly. With a GDP per capita of $17 thousand, approximately 13 percent of people in Russia live below the poverty line, which, according to experts, is almost nonsense. Especially when you consider that the share of the shadow economy in our country remains quite high - 25-30 percent. This money is not taken into account in GDP, which means that its real level is higher than the official one. At the same time, most of the income from the shadow sector goes to people who are not poor, and, therefore, the real stratification of society is higher.

One way to eliminate income inequality involves government support for health, welfare, and education systems. In this case, people with lower incomes can obtain satisfactory physical condition, confidence in the future and education. This approach provides the necessary living conditions for everyone. Another way to combat inequality involves changes in the tax system and, in particular, the progressive tax system. income tax. In this regard, there is a significant difference in the applicable rules of different countries, the range of interest rates in different countries is different. In the USA, income tax rates are set by the government in the range of 10% - 35%, in Japan - 5% - 50%, in Canada - 15% - 29%. Only in Russia interest rate income tax is the same for everyone - 13%, which leads to the fact that there are no demarcating boundaries in the income of different segments of the population, and the rich become richer, and the poor become even poorer.

The number of Russian millionaires whose wealth exceeds $100 million will grow by 76 percent by 2017. This forecast was published in a report by the consulting company Knight Frank and Citi Private Bank.

Now in our country there are 2.1 thousand such centa-millionaires. And all over the world - 63 thousand people. Their total wealth is estimated at $39.9 trillion.