The Gini coefficient's main advantage is that it is a measure of inequality by means of a ratio analysis, rather than a variable unrepresentative of most of the population, such as per capita income or gross domestic product.

It can be used to compare income distributions across different population sectors as well as countries, for example the Gini coefficient for urban areas differs from that of rural areas in many countries (though the United States' urban and rural Gini coefficients are nearly identical).

It is sufficiently simple that it can be compared across countries and be easily interpreted. GDP statistics are often criticised as they do not represent changes for the whole population; the Gini coefficient demonstrates how income has changed for poor and rich. If the Gini coefficient is rising as well as GDP, poverty may not be improving for the majority of the population.

The Gini coefficient can be used to indicate how the distribution of income has changed within a country over a period of time, thus it is possible to see if inequality is increasing or decreasing.

The Gini coefficient satisfies four important principles:
- Anonymity: it does not matter who the high and low earners are.
- Scale independence: the Gini coefficient does not consider the size of the economy, the way it is measured, or whether it is a rich or poor country on average.
- Population independence: it does not matter how large the population of the country is.
- Transfer principle: if income (less than the difference), is transferred from a rich person to a poor person the resulting distribution is more equal.

[edit] Disadvantages as a measure of inequality

The Gini coefficient measured for a large economically diverse country will generally result in a much higher coefficient than each of its regions has individually. For this reason the scores calculated for individual countries within the EU are difficult to compare with the score of the entire US.

Comparing income distributions among countries may be difficult because benefits systems may differ. For example, some countries give benefits in the form of money while others give food stamps, which may not be counted as income in the Lorenz curve and therefore not taken into account in the Gini coefficient.

The measure will give different results when applied to individuals instead of households. When different populations are not measured with consistent definitions, comparison is not meaningful.

The Lorenz curve may understate the actual amount of inequality if richer households are able to use income more efficiently than lower income households. From another point of view, measured inequality may be the result of more or less efficient use of household incomes.

As for all statistics, there will be systematic and random errors in the data. The meaning of the Gini coefficient decreases as the data become less accurate. Also, countries may collect data differently, making it difficult to compare statistics between countries.

Economies with similar incomes and Gini coefficients can still have very different income distributions. This is because the Lorenz curves can have different shapes and yet still yield the same Gini coefficient. As an extreme example, an economy where half the households have no income, and the other half share income equally has a Gini coefficient of ; but an economy with complete income equality, except for one wealthy household that has half the total income, also has a Gini coefficient of .

The Gini coefficient is used to measure income more than net worth, which can be misleading. For example, is Sweden egalitarian? Sweden has a low Gini coefficient for income distribution but a high Gini coefficient for wealth (0.1% of Swedish shareholders control 25% of all shares).

Too often only the Gini coefficient is quoted without describing the proportions of the quantiles used for measurement. As with other inequality coefficients, the Gini coefficient is influenced by the granularity of the measurements. For example, five 20% quantiles (low granularity) will yield a lower Gini coefficient than twenty 5% quantiles (high granularity) taken from the same distribution.

As one result of this criticism, additionally to or in competition with the Gini coefficient entropy measures are frequently used (e.g. the Atkinson and Theil indices). These measures attempt to compare the distribution of resources by intelligent players in the market with a maximum entropy random distribution, which would occur if these players acted like non-intelligent particles in a closed system following the laws of statistical physics.

[edit] Optimal Gini-coefficient

In their study for the World Institute for Development Economics Research, Giovanni Andrea Cornia and Julius Court (2001) reach policy conclusions as to the optimal distribution of wealth. The authors recommend to pursue moderation also as to the distribution of wealth and particularly to avoid the extremes. Both very high egalitarianism and very high inequality cause slow growth. Extreme egalitarianism leads to incentive-traps, free-riding, high operation costs and corruption in the redistribution system, all reducing a country's growth potential. However, extreme inequality also diminishes growth potential by eroding social cohesion, and increasing social unrest and social conflict, causing uncertainty of property rights. Therefore, public policy should target an 'efficient inequality range'. The authors claim that such efficiency range lies between the values of the Gini coefficients of 0.25 (the inequality value of a typical Northern European country) and 0.40 (slightly lower than that of countries such as China and the USA). see Gini-Growth curve here The precise shape of the inequality-growth relationship depicted in the Chart obviously varies across countries depending upon their resource endowment, history, remaining levels of absolute poverty and available stock of social programs, as well as on the distribution of physical and human capital. ^[2]

[edit] Notes

^ Note that the calculation of the index for the United States was changed in 1992, resulting in an upwards shift of about 0.02 in the coefficient. Comparisons before and after that period may be misleading. (Data from the US Census Bureau.)
^ See . The Path to sustainable Growth - Lessons from 20 Years Growth Differentials in Europe (pdf 4Mb)

[edit] References

Anand, Sudhir (1983). Inequality and Poverty in Malaysia. New York: Oxford University Press.
Brown, Malcolm (1994). 'Using Gini-Style Indices to Evaluate the Spatial Patterns of Health Practitioners: Theoretical Considerations and an Application Based on Alberta Data'. Social Science Medicine 38: 1243-1256.
Chakravarty, S. R. (1990). Ethical Social Index Numbers. New York: Springer-Verlag.
Dixon, PM, Weiner J., Mitchell-Olds T, Woodley R. (1987). 'Bootstrapping the Gini coefficient of inequality'. Ecology 68: 1548-1551.
Dorfman, Robert (1979). 'A Formula for the Gini Coefficient'. The Review of Economics and Statistics 61: 146-149.
Gastwirth, Joseph L. (1972). 'The Estimation of the Lorenz Curve and Gini Index'. The Review of Economics and Statistics 54: 306-316.
Gini, Corrado (1912). 'Variabilità e mutabilità' Reprinted in Memorie di metodologica statistica (Ed. Pizetti E, Salvemini, T). Rome: Libreria Eredi Virgilio Veschi (1955).
Gini, Corrado (1921). 'Measurement of Inequality and Incomes'. The Economic Journal 31: 124-126.
Mills, Jeffrey A.; Zandvakili, Sourushe (1997). 'Statistical Inference via Bootstrapping for Measures of Inequality'. Journal of Applied Econometrics 12: 133-150.
Morgan, James (1962). 'The Anatomy of Income Distribution'. The Review of Economics and Statistics 44: 270-283.
Xu, Kuan (January, 2004). 'How Has the Literature on Gini's Index Evolved in the Past 80 Years?'. Department of Economics, Dalhousie University. Retrieved on June 1, 2006.

[edit] See also

[edit] External links

UN Human Development Report 2004, p50-53: Gini Index calculated for all countries.
[1] Comparison of Urban and Rural Areas' Gini Coefficients Within States
[2] World Income Inequality Database
[3], Forbes Article, In praise of inequality
Gini index map from WorldPolicy.org

Software:
- Free Online Calculator computes the Gini Coefficient, plots the Lorenz curve, and computes many other measures of concentration for any dataset
- Free Calculator: Online and downloadable scripts (Python and Lua) for Atkinson, Gini, Hoover and Kullback-Leibler inequalities
- Users of the R data analysis software can install the 'ineq' package which allows for computation of a variety of inequality indices including Gini, Atkinson, Theil.

Retrieved from 'http://en.wikipedia.org/wiki/Gini_coefficient'

Categories: Welfare economics | Index numbers | Socioeconomics | Economic indicators

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Gini coefficient(2007-02-12 08:42)

Gini coefficient

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Graphical representation of the Gini coefficient

The Gini coefficient is a measure of inequality of a distribution. It is defined as a ratio with values between 0 and 1: the numerator is the area between the Lorenz curve of the distribution and the uniform (perfect) distribution line; the denominator is the area under the uniform distribution line. It was developed by the Italian statistician Corrado Gini and published in his 1912 paper 'Variabilità e mutabilità' ('Variability and Mutability'). The Gini index is the Gini coefficient expressed as a percentage, and is equal to the Gini coefficient multiplied by 100. (The Gini coefficient is equal to half of the relative mean difference.)

The Gini coefficient is often used to measure income inequality. Here, 0 corresponds to perfect income equality (i.e. everyone has the same income) and 1 corresponds to perfect income inequality (i.e. one person has all the income, while everyone else has zero income).

The Gini coefficient can also be used to measure wealth inequality. This use requires that no one has a negative net wealth. It is also commonly used for the measurement of discriminatory power of rating systems in the credit risk management.

[hide]

[edit] Calculation

The Gini coefficient is defined as a ratio of the areas on the Lorenz curve diagram. If the area between the line of perfect equality and Lorenz curve is A, and the area under the Lorenz curve is B, then the Gini coefficient is A/(A+B). Since A+B = 0.5, the Gini coefficient, G = A/(.5) = 2A = 1-2B. If the Lorenz curve is represented by the function Y = L(X), the value of B can be found with integration and:

$G = 1 - 2\,\int_0^1 L(X) dX$

In some cases, this equation can be applied to calculate the Gini coefficient without direct reference to the Lorenz curve. For example:

For a population with values y_i, i = 1 to n, that are indexed in non-decreasing order ( y_i ≤ y_i+1):

$G = \frac{1}{n}(n+1 - 2\,\frac{\Sigma_{i=1}^n \; (n+1-i)y_i}{\Sigma_{i=1}^n y_i})\,$

For a discrete probability function f(y), where y_i, i = 1 to n, are the points with nonzero probabilities and which are indexed in increasing order ( y_i < y_i+1):

$G = 1 - \frac{\Sigma_{i=1}^n \; f(y_i)(S_{i-1}+S_i)}{S_n}$

where:

$S_i = \Sigma_{j=1}^i \; f(y_j)\,y_j\,$ and $S_0 = 0\,$

For a cumulative distribution function F(y) that is piecewise differentiable, has a mean μ, and is zero for all negative values of y:

$G = 1 - \frac{1}{\mu}\int_0^\infty (1-F(y))^2dy$

Since the Gini coefficient is half the relative mean difference, it can also be calculated using formulas for the relative mean difference.

For a random sample S consisting of values y_i, i = 1 to n, that are indexed in non-decreasing order ( y_i ≤ y_i+1), the statistic:

$G(S) = \frac{1}{n-1}(n+1 - 2\,\frac{\Sigma_{i=1}^n \; (n+1-i)y_i}{\Sigma_{i=1}^n y_i})\,$

is a consistent estimator of the population Gini coefficient, but is not, in general, unbiased. Like the relative mean difference, there does not exist a sample statistic that is in general an unbiased estimator of the population Gini coefficient. Confidence intervals for the population Gini coefficient can be calculated using bootstrap techniques.

Sometimes the entire Lorenz curve is not known, and only values at certain intervals are given. In that case, the Gini coefficient can be approximated by using various techniques for interpolating the missing values of the Lorenz curve. If ( X _k , Y_k ) are the known points on the Lorenz curve, with the X _k indexed in increasing order ( X _{k - 1} < X_k ), so that:

X_k is the cumulated proportion of the population variable, for k = 0,...,n, with X₀ = 0, X_n = 1.
Y_k is the cumulated proportion of the income variable, for k = 0,...,n, with Y₀ = 0, Y_n = 1.

If the Lorenz curve is approximated on each interval as a line between consecutive points, then the area B can be approximated with trapezoids and:

$G_1 = 1 - \sum_{k=1}^{n} (X_{k} - X_{k-1}) (Y_{k} + Y_{k-1})$

is the resulting approximation for G. More accurate results can be obtained using other methods to approximate the area B, such as approximating the Lorenz curve with a quadratic function across pairs of intervals, or building an appropriately smooth approximation to the underlying distribution function that matches the known data. If the population mean and boundary values for each interval are also known, these can also often be used to improve the accuracy of the approximation.

[edit] Income Gini coefficients in the world

See complete listing in list of countries by income equality.

Gini coefficient, income distribution by country

While most developed European nations tend to have Gini coefficients between 0.24 and 0.36, the United States Gini coefficient is above 0.4, indicating that the United States has greater inequality. Using the Gini can help quantify differences in welfare and compensation policies and philosophies. However it should be borne in mind that the Gini coefficient can be misleading when used to make political comparisons between large and small countries (see criticisms section).

Gini coefficients, income distribution over time for selected countries

[edit] Correlation with per-capita GDP

Poor countries (those with low per-capita GDP) have Gini coefficients that fall over the whole range from low (0.25) to high (0.71), while rich countries have generally low Gini coefficient (under 0.40).

[edit] US income gini coefficients over time

Gini coefficients for the United States at various times, according to the US Census Bureau:

1970: 0.394
1980: 0.403
1990: 0.428
2000: 0.462
2005: 0.469^[1]

Some argue this rise corresponds to the lowering of the highest tax bracket, for example, from 70% in the 1960s to 35% by 2000. However, many other variables that could affect the Gini coefficient have changed during this period as well. For example, much technological progress has occurred, eliminating formerly middle-class factory jobs in favor of the service sector; additionally, the economy has shifted towards professions that require higher education.

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Theil index(2007-02-12 08:41)

$Theil index$

From Wikipedia, the free encyclopedia

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The Theil index, derived by econometrician Henri Theil, is a statistic used to measure economic inequality.

[hide]

[edit] Mathematics

The formula is

$T=\frac{1}{N}\sum_{i=1}^N \left( \frac{x_i}{\overline{x}} \cdot \ln{\frac{x_i}{\overline{x}}} \right)$

where $x i$ is the income of the $i$ th person, $\overline{x}= \frac{1}{N} \sum_{i=1}^N x_i$ is the mean income, and $N$ is the number of people. The first term inside the sum can be considered the individual's share of aggregate income, and the second term is that person's income relative to the mean. If everyone has the same (i.e., mean) income, then the index = 0. If one person has all the income, then the index = $ln N$ .

The Theil index is derived from Shannon's measure of information entropy. Letting $T$ be the Theil Index and $S$ be Shannon's information entropy measure,

$T = ln(N)$

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Argentine economic crisis(4)(2006-12-02 05:42)

Criticism of the IMF

The International Monetary Fund suffered no discounts in its part of the Argentine debt. Some payments were refinanced or postponed on agreement. However, the authorities of the IMF at times expressed harsh criticism of the discounts and actively lobbied for the private creditors.

In a speech before the United Nations General Assembly on 2004-09-21, President Kirchner said that 'An urgent, tough, and structural redesign of the International Monetary Fund is needed, to prevent crises and help in [providing] solutions'. Implicitly referencing the fact that the intent of the original Bretton Woods system was to encourage economic development, Kirchner warned that the IMF today must 'change that direction which took it from being a lender for development to a creditor demanding privileges'.

During the weekend of October 1–2, 2004, at the annual meeting of the International Monetary Fund/World Bank, leaders of the IMF, the European Union, the Group of Seven industrialised nations, and the Institute of International Finance (IIF), warned President Kirchner that Argentina had to come to an immediate debt-restructuring agreement with the speculative 'vulture funds', increase its primary budget surplus in order to pay more debt, and impose 'structural reforms' to prove to the world financial community that it deserves their loans and investments.

In 2005, as a large and consistently growing fiscal surplus made it possible, Argentina shifted to a policy of 'disindebtment' towards the IMF: paying the IMF in schedule, with no negotiation whenever possible, with the intention of gaining independence from it. On 2005-12-15, in a sudden move following Brazil, President Kirchner announced that Argentina would pay the whole debt to the IMF which had been previously financed in installments until 2008, for a total of 9,810 million USD, employing the Central Bank's foreign currency reserves.

In a report published in June 2006, a group of independent experts hired by the IMF to revise the work of its Independent Evaluation Office (IEO) stated that the assessment of the Argentine case suffered from informative manipulation and lack of collaboration on the part of the IMF; the IEO is claimed to have unduly softened its conclusions to avoid criticizing the IMF's board of directors.

[edit] Allegations of use of un-published Clearstream accounts

Further information: Clearstream scandal

As a clearing house, Clearstream has a 'dominant position' in Europe, according to the European Commission. Funds composing the private and public Argentine debt have transited through Clearstream, which is inevitable because of its quasi-monopoly situation. However, according to 'Revelation$' (2001), written by reporter Denis Robert and Ernest Backes, some Argentine funds have transited through an illegal system of non-published accounts used by Clearstream; the Citibank in particular, which held a large part of the private Argentine fund, had numerous unpublished bank accounts in Clearstream. This illegal system of non-published accounts makes of Clearstream, according to several judges as Eva Joly and Renaud van Ruymbeke, European members of parliament (MPs) such as Harlem Désir, Glyn Ford and Francis Wurtz, and Attac NGO, a major actor of the underground economy, through which global tax evasion and money laundering may be investigated. Henceforth, Clearstream is a major key in the understanding of the evaporation of the Argentine funds which led to the economic crisis.

[edit] Endnotes

↑ 'Argentina's banking scandal deepens', BBC, February 21, 2001.

[edit] Films

[edit] References

The Crisis that Was Not Prevented: Lessons for Argentina, the IMF, and Globalisation, Jan Joost Teunissen and Age Akkerman (eds.), Fondad, 2003, book, PDF)
Argentina Didn't Fall on Its Own (GlobalExchange.org)
Argentina's debt restructuring: A victory by default? (Economist.com)
The gap between rich and poor (news on the study on economic inequality by Equis, in Spanish)
Soy Revolution Kills Argentine Farming (deaths related to poverty and overexploitation of soy)
'Unlawful debt or financial crime against human development', ATTAC, June 30, 2001. (also available in French and Spanish)
Wide Angle, The Empty ATM
IMF Policy Paper: Report of the External Evaluation of the Independent Evaluation Office

[edit] See also

Piquetero

[edit] External links

Banco Central de la República Argentina (Argentina's Central Bank website, with various economic statistics available on the fly)
Eye of the Storm (14-minute film about the crisis and how the public responded)
Survival Stories (A first person perspective of day-to-day living through the crisis and after)
Video: 'Argentina's Economic Recovery: Four Years After the Meltdown' featuring CEPR co-director Mark Weisbrot and former IMF Research Director Michael Mussa, November 30, 2005

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Argentine economic crisis(3)(2006-12-02 05:41)

[edit] The recovery

Eduardo Duhalde finally managed to stabilise the situation to a certain extent, and called for elections. On 25 May 2003 President Néstor Kirchner took charge. Kirchner kept Duhalde's Minister of Economy, Roberto Lavagna, in his post. Lavagna, a respected economist with moderate-centre-wing views, showed a considerable aptitude at managing the crisis, with the help of heterodox measures.

Evolution of the Argentine GNP, 1999–2004

The economic outlook was completely different from that of the 1990s; the high exchange rate made Argentine exports cheap and competitive abroad, while discouraging imports. In addition, the high price of soy in the international market produced an injection of massive amounts of foreign currency (with China becoming a major buyer of Argentina's soy products).

The government encouraged import substitution and accessible credit for businesses, staged an aggressive plan to improve tax collection, and set aside large amounts of money for social welfare, while controlling expenditure in other fields.

As a result of the administration's productive model and controlling measures (selling reserve dollars in the public market), the peso slowly revalued, reaching a 3-to-1 rate to the dollar. Agricultural exports grew and tourism returned.

Foreign currency reserves of Argentina's Central Bank, in millions of USD

The huge trade surplus ultimately caused such an inflow of dollars that the government was forced to begin intervening in order to keep the peso from revaluing further, which would have ruined the tax collection scheme (largely based on imports taxes and royalties) and discourage further reindustrialisation. The Central Bank started buying dollars in the local market and stocking them as reserves. By December 2005, foreign currency reserves had reached $28,000 million (they were greatly reduced by the anticipated payment of the full debt to the IMF in January 2006). The downside of this reserve accumulation strategy is that the dollars have to be bought with freshly-emitted pesos, which may induce inflation. The Central Bank neutralises a part of this monetary emission by selling Treasury letters. In this way the exchange rate has been stabilised near a reference value of 3 pesos to the dollar.

The currency exchange issue is complicated by two mutually opposing factors: a sharp increase in imports since 2004 (which raises the demand of dollars), and the return of foreign investment (which brings fresh currency from abroad) after the successful restructuring of about three quarters of the external debt. The government has set up controls and restrictions aimed at keeping short-term speculative investment from destabilising the financial market.

Argentina's recovery suffered a minor drawback in 2004 when rising industrial demand caused a short-lived energy crisis. Scenarios of energy shortage are not discarded in the near future.

Argentina has managed to return to growth with surprising strength; GNP jumped 8.8% in 2003, 9.0% in 2004, and 9.1% in 2005 (with expectations of 8% for 2006). Consumer prices, however, have accompanied this surge, and average wages have barely caught up; though not comparable to the levels of former crises, the inflation rate was in 2005 12.5% (with expectations around 10% for 2006). It has prompted the government to 'cool down' some sectors of the economy, restrict benefits for exporters, and put pressure on companies in order to stabilize prices. While unemployment was considerably reduced in 2005, Argentina has so far failed to reach a more equitable distribution of income.

[edit] Effects on wealth distribution

According to Argentine agronomist Alberto Lapolla, who has written extensively on the transformation of Argentina from the 'granary of the world' to a 'soy republic', 450,000 Argentines died of hunger between 1990 and 2003. Citing the Institute of State and Participation Studies (IDEP), a thinktank, Lapolla adds that every day, 55 children, 35 adults and 15 elderly die in the country from illnesses related to hunger.

Although GDP has grown consistently and quickly since 2003, it was only in late 2004 that it reached the levels of 1998 (the last year before the recession). Other macroeconomic indicators have followed suit. A study by Equis, an independent counseling organisation, found out that two measures of economic inequality, the Gini coefficient and the wealth gap between the 10% poorest and the 10% richest among the population, grew continuously since 2001, and decreased for the first time in March 2005.

*Poverty in Argentina*
Date of measurement	Extreme poverty	Under poverty line
May 2001	11.6%	35.9%
Oct 2001	13.6%	38.3%
May 2002	24.8%	53.0%
Oct 2002	27.5%	57.5%
May 2003	26.3%	54.7%
2nd sem 2003	20.5%	47.8%
1st sem 2004	17.0%	44.3%
2nd sem 2004	15.0%	40.2%
1st sem 2005	13.6%	38.5%

The table on the left shows statistics of poverty in Argentina, in percent of the population. The first column shows the date of the measurement (note that the method and time changed in 2003; poverty is now measured each semester). Extreme poverty is here defined as not having enough money to eat properly. The poverty line is set higher: it is the minimum income needed for basic needs including food, clothing, shelter, and studies.

[edit] Debt restructuring

Main article: Argentine debt restructuring

When the default was declared in 2002, foreign investment fled the country, and capital flow towards Argentina ceased almost completely. The Argentine government met severe challenges trying to refinance the debt. The state had no spare money at the time, and the Central Bank's foreign currency reserves were almost depleted.

The Argentine government kept a firm stance, and finally got a deal by which 76% of the defaulted bonds were exchanged by others, of a much lower nominal value (25–35% of the original) and at longer terms.

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Argentine economic crisis(2)(2006-12-02 05:40)

The crisis

Argentina quickly lost the confidence of investors and the flight of money away from the country increased. In 2001, people fearing the worst began withdrawing large sums of money from their bank accounts, turning pesos into dollars and sending them abroad, causing a run on the banks. The government then enacted a set of measures (informally known as the corralito) that effectively froze all bank accounts for twelve months, allowing for only minor sums of cash to be withdrawn.

Because of this allowance limit and the serious problems it caused in certain cases, many Argentines became enraged and took to the streets of important cities, especially Buenos Aires. They engaged in a form of popular protest that became known as cacerolazo (banging pots and pans). These protests occurred especially during the period of 2001 to 2002. At first the cacerolazos were simply noisy demonstrations, but soon they included property destruction, often directed at banks, foreign privatized companies, and especially big American companies. Many businesses installed metal barriers because windows and glass facades were being broken, and even fires being ignited at their doors. Billboards of such companies as Coca Cola and others were brought down by the masses of demonstrators.

Confrontations between the police and citizens became a common sight, and fires were also set on Buenos Aires avenues. The violent protests of 20 and 21 December 2001 in Plaza de Mayo, where demonstrators clashed with the police, ended with several dead, and precipitated the fall of the government. Fernando de la Rúa fled the Casa Rosada in a helicopter on 21 December.

During the last week of 2001, the interim government led by Adolfo Rodríguez Saá, facing the impossibility of meeting debt payments, defaulted on the larger part of the public debt, totalling no less than 93,000 million dollars. Rodríguez Saá, utterly incapable to deal with the crisis and unsupported by his own party, resigned before the end of the year. The Peronist Eduardo Duhalde was appointed by Congress to take his place.

[edit] The end of convertibility

Monthtly inflation in Argentina, 2002 (the peak is 10.4%, in April).

After much deliberation, Duhalde abandoned in January 2002 the fixed 1-to-1 peso-dollar parity that had been in place for ten years. In a matter of days, the peso lost a large part of its value in the unregulated market. A provisional 'official' exchange rate was set at 1.4 pesos per dollar.

In addition to the corralito, the Ministry of Economy dictated the pesificación ('peso-ification'), by which all bank accounts denominated in dollars would be converted to pesos at official rate. This measure angered most savings holders and appeals were made by many citizens to declare it unconstitutional.

After a few months, the exchange rate was left to float more or less freely. The peso suffered a huge devaluation, which in turn prompted inflation (since Argentina depended heavily on imports, and had no means to replace them locally at the time).

The economic situation became steadily worse with regards to inflation and unemployment during 2002. By that time the original 1-to-1 rate had skyrocketed to nearly 4 pesos per dollar, while the accumulated inflation since the devaluation was about 80%. (It should be noted that these figures were considerably lower than those foretold by most orthodox economists at the time.) The quality of life of the average Argentinian was lowered proportionally; many businesses closed or went bankrupt, many imported products became virtually inaccessible, and salaries were left as they were before the crisis.

[edit] Immediate effects

Many private companies were affected by the crisis: Aerolíneas Argentinas, for example, was one of the most affected Argentine companies, having to stop all international flights for various days in 2002. The airline came close to bankruptcy, but it survived.

Agriculture was also affected: Argentine products were rejected in some international markets, in fear that they might come damaged because of the poor conditions in which they grew, and the USDA put restrictions on Argentine food and drugs arriving at the United States.

Producers of television channels were forced to produce more reality shows than any other type of shows, because these were generally cheap to produce as compared to other programmes. Virtually all education-related TV programmes were cancelled.

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Argentine economic crisis (1)(2006-12-02 05:39)

Argentine economic crisis (1999-2002)

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Argentine economic crisis (1999–2002)
Economy of Argentina Currency Currency Board Corralito Cacerolazo 2001 Riots Apagón Debt exchange
edit

The Argentine economic crisis was part of the situation that affected Argentina's economy during the late 1990s and early 2000s. Macroeconomically speaking, the critical period started with the decrease of real GDP in 1999 and ended in 2002 with the return to GDP growth, but the origins of the collapse of Argentina's economy, and their effects on the population, can be found in action before. As of 2005, the crisis is arguably over, though many challenges remain for the country.

[hide]

[edit] Origins

Argentina was subject to military dictatorship (alternating with weak, short-lived democratic governments) for many years. Huge debt was acquired for money that was later lost in different unfinished projects, the Falkland/Malvinas Islands War, and the state's takeover of private debts.

In 1983, democracy in the country was restored with the election of president Raúl Alfonsín. The new government's plans included stabilizing Argentina's economy, for which new loans were required. The state eventually became unable to pay the interest of this debt, the economy collapsed and inflation began increasing. In 1989, Argentina's inflation reached 200% per month, topping 3,000% annually. President Alfonsín resigned six months before ending his term, and Carlos Menem took office.

Menem, who had campaigned on a populist platform, immediately went back on his promises and began a plan, aligned on the neoliberal Washington consensus, of trade liberalisation, labor deregulation and privatisation of state companies which were the source of much spending (such as those providing the telephone, energy and water services).

[edit] The 1990s

The fight against inflation did not go well. In 1991, under the rule of Minister of Economy Domingo Cavallo, the Argentine peso's monetary value was fixed by law to the value of the United States dollar. The law (Ley de Convertibilidad) stated that any citizen could go to a bank and ask for any amount of cash in pesos to be converted to an equal amount of dollars; in order to secure this 'convertibility', the Central Bank was bound to keep its dollar reserves at the same level as the cash in circulation.

As a result of the convertibility law, inflation dropped sharply, price stability was assured, and the value of the currency was preserved. This raised the quality of life for many citizens, who could now afford to travel abroad, buy imported domestic appliances and electronic products or ask for credits in dollars at very low interest rates.

But Argentina had international debts to pay, and it needed to keep borrowing money. The fixed exchange rate made imports cheap, producing a constant flight of dollars away from the country and a progressive loss of Argentina's industrial infrastructure, which led to an increase in unemployment.

In the meantime, government spending continued to be high and corruption was rampant. Argentina's public debt grew enormously during the 1990s, and the country showed no true signs of being able to pay it. The IMF, however, kept lending money to Argentina and postponing its payment schedules. Massive tax evasion and money laundering explained a large part of the evaporation of funds toward offshore banks. A congressional committee started investigations in 2001 about accusations that Argentina's Central Bank governor, Pedro Pou, as well as part of the board of directors, had failed to investigate cases of alleged money laundering through Argentina's financial system ^[1]. Clearstream was also accused of being instrumental in this global financial process.

Other countries, such as Mexico and Brazil (both of which also happen to be important trade partners for Argentina) faced economic crises of their own, leading other countries to mistrust Latin American countries moneywise, and affecting the overall economy of the region. The influx of foreign currency provided by the privatisation of state companies had dried out, and after 1997 Argentine exports were harmed by the devaluation of the Brazilian real and a considerable international revaluation of the dollar, effectively revaluing the peso against its major trading partners, Brazil (30% of total trade flows) and the euro area (23% of total trade flows).

By 1999, newly elected President Fernando de la Rúa faced a country where unemployment had risen to a critical point, and the undesirable effects of the fixed exchange rate were showing forcefully. In 1999 Argentina's GDP dropped 4% and the country entered a recession (which was to last three years, ending in a collapse). Stability became stagnation (even deflation at times), and the economic measures taken did nothing to avert it; in fact, the government continued the contractive economic policies of its predecessor. The possible solution (abandonment of the exchange peg, with a voluntary devaluation of the peso) was considered a political suicide and a recipe for economic disaster. By the end of the century, complementary currencies such as the LECOP and the Patacón (bonds issued by the national and provincial states) emerged due to the shortage of cash.

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Phillips curve(2006-12-02 04:25)

Phillips curve

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Phillips curve

The Phillips curve is a historical inverse relation and tradeoff between the rate of unemployment and the rate of inflation in an economy. Stated simply, the lower the unemployment in an economy, the higher the rate of change in wages paid to labour in that economy.

The New Zealand-born economist A.W. Phillips, in his 1958 paper 'The relationship between unemployment and the rate of change of money wages in the UK 1861-1957' published in Economica, observed an inverse relationship between money wage changes and unemployment in the British economy over the period examined. Similar patterns were found in other countries and in 1960 Paul Samuelson and Robert Solow took Phillips' work and made explicit the link between inflation and unemployment—when inflation was high, unemployment was low, and vice-versa.

It is little known that the American economist Irving Fisher pointed to this kind of Phillips curve relationship back in the 1920s. On the other hand, Phillips' original curve described the behavior of money wages. So some believe that the PC should be called the 'Fisher curve.'

In the years following his 1958 paper, many economists in the advanced industrial countries believed that Phillips' results showed that there was a permanently stable relationship between inflation and unemployment. One implication of this for government policy was that governments could control unemployment and inflation within a Keynesian policy. They could tolerate a reasonably high rate of inflation as this would lead to lower unemployment – there would be a trade-off between inflation and unemployment. For example, monetary policy and/or fiscal policy (i.e., deficit spending) could be used to stimulate the economy, raising gross domestic product and lowering the unemployment rate. Moving along the Phillips curve, this would lead to a higher inflation rate, the cost of enjoying lower unemployment rates.

To a large extent, a leftward movement along the PC describes the path of the U.S. economy during the 1960s, though this move was not a matter of deciding to achieve low unemployment as much as an unplanned side-effect of the Vietnam war. In other countries, the economic boom was more the result of conscious policies.

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Economics and Identity(2006-11-29 07:38)

Economics and Identity

Nov 1st

George Akerlof

Economics

- about incentitives

- freak economics

price theory (supply & demand)

(conceptual tools)

Missing motivation in current economics

è New tool for economics

Standard method of economics

. Derivative of supply & demand

- Max profits (firms)

- Max utility (individuals)

è Missing motivation: Norms

Pareto:

People have opinions how they should, or should not, behave

People have views how others should, or should not, behave

Depends on their identity:

Who people think they are.

è Ideal

. Tend to be happy if they live up they should be

. Teacher

. Dignity at work (most care)

. Betty Friedan (norms of housewives in early 1900)

. Merry-Go-Round (activities differ from children in different age-gap)

Protocol for adding identity to economics

. How dose the decision maker conceive of herself?

. What is her ideal?

. How unhappy will she be, insofar as she fails to live up her ideals?

è How she will behave

. Gossip columns, people, children, gruesome dictators

4 function

1. Directly care about identity

. Self-destruction

. Example: Beauty practice

2. Identity externalities

. Culture & Honor ( Iraq War)

3. Manipulation of others

. Advertising

. Politics

4. Time inconsistency

Gender and the work place

. Traditional economics

- Division of labor according to comparative advantage

. Identity economics

- Gender task

Impoverished Minorities

. African-Americans

- Oppositional culture

- History of injustice

Economics of Education

- How much (demand)

- Returns

- Effectiveness of programs

Literature on education

- History of education

- Shopping Mall High

-Fail to impose identity

- Adolescent society

-Role of the leading crowd

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P-value Calculation(2006-11-15 08:39)

P-value Calculation
If the test statistic is z,
Research Rejection

Hypothesis Region P-value
Ha: μ > μo z > zα P(z > zcalculated)

Ha: μ < μo z < -zα
P(z < zcalculated)

Ha: μ =/ μo z < -zα/2 or z > zα/2 2P(z > zcalculated )

Ha: p > po z > zα P(z > zcalculated)

Ha: p < po z < -zα
P(z < zcalculated)

Ha: p =/ po z < -zα/2 or z > zα/2 2P(z > zcalculated )

Ha: θ > θo z > zα P(z > zcalculated)

Ha: θ < θo z < -zα
P(z < zcalculated)

Ha: θ =/ θo z < -zα/2 or z > zα/2 2P(z > zcalculated )

If the test statistic is t,
Research Rejection
Hypothesis Region P-value

Ha: μ > μo t > tα P(t > tcalculated)

Ha: μ < μo t < -tαP(t < tcalculated)

Ha: μ =/ μo t < -tα/2 or t > tα/2 2P(t > tcalculated )

Ha: θ > θo t > tα P(t > tcalculated)

Ha: θ < θo t < -tα
P(t < tcalculated)

Ha: θ =/ θo t < -tα/2 or t > tα/2 2P(t > tcalculated )

If the test statistic is F,
Research Rejection
Hypothesis Region P-value

Ha: σ1
2 =/ σ2
2 F > Fα/2, num, dem 2P(F > Fcalculated)
The test statistic is F = . Let num = degrees of freedom for
smax
2 and den = degrees of freedom for smin
2.

Ha: σ1
2 < σ2
2 F > Fα, num, dem P(F > Fcalculated)
The test statistic is F = . Let num = n2 -1 and den = n1 - 1.

Ha: σ1
2 > σ2
2 F > Fα, num, dem P(F > Fcalculated)
The test statistic is F = . Let num = n1 -1 and den = n2 - 1.
The test statistic, F, is often used for hypotheses other than those
listed above. The computation of the p-value depends upon the type of
rejection region the test uses.
If the test statistic is χ2
r and r = degrees of freedom,
Research Rejection
Hypothesis Region P-value

Ha: σ2 =/ σo
2 χr
2 < χ2
r, 1-α/2 2P(χr
2 < χ2
calculated) if χ2
calculated < r
or χr
2 > χ2
r, α/2 or 2P(χr
2 < χ2
calculated) if χ2
calculated > r

Ha: σ2 > σo
2 χr
2 > χ2
r, α P(χr
2 < χ2
calculated)

Ha: σ2 < σo
2 χr
2 < χ2
r,1-α P(χr
2 < χ2
calculated)
The test statistic, χ2
r, is often used for hypotheses other than those
listed above. The computation of the p-value depends upon the type of
rejection region the test uses.

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[edit] Advantages as a measure of inequality

[edit] Disadvantages as a measure of inequality

[edit] Optimal Gini-coefficient

[edit] Notes

[edit] References

[edit] See also

[edit] External links

Gini coefficient

From Wikipedia, the free encyclopedia

Contents

[edit] Calculation

[edit] Income Gini coefficients in the world

[edit] Correlation with per-capita GDP

[edit] US income gini coefficients over time

$Theil index$

From Wikipedia, the free encyclopedia

Contents

[edit] Mathematics

[edit] Allegations of use of un-published Clearstream accounts

[edit] Endnotes

[edit] Films

[edit] References

[edit] See also

[edit] External links

[edit] The recovery

[edit] Effects on wealth distribution

[edit] Debt restructuring

[edit] The end of convertibility

[edit] Immediate effects

Argentine economic crisis (1999-2002)

From Wikipedia, the free encyclopedia

Contents

[edit] Origins

[edit] The 1990s

Phillips curve

From Wikipedia, the free encyclopedia