INCOME DISTRIBUTION IN THE PROCESS OF STATE COFFEE PRODUCTION: THE APPLICATION OF THE CURVE OF CONCENTRATION (LORENZ CURVE) AND THE GINI INDEX

1991 ◽  
pp. 305-314 ◽  
Author(s):  
B. G. Mariam
Keyword(s):  
Income Distribution ◽  
Lorenz Curve ◽  
Gini Index ◽  
Coffee Production

scholarly journals A simple method for estimating the Lorenz curve

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Thitithep Sitthiyot ◽  
Kanyarat Holasut
Keyword(s):  
Income Inequality ◽  
Income Distribution ◽  
Closed Form ◽  
Lorenz Curve ◽  
Gini Index ◽  
Functional Form ◽  
Closed Form Expression ◽  
Form Expression ◽  
Simple Method ◽  
The Lorenz Curve

AbstractGiven many popular functional forms for the Lorenz curve do not have a closed-form expression for the Gini index and no study has utilized the observed Gini index to estimate parameter(s) associated with the corresponding parametric functional form, a simple method for estimating the Lorenz curve is introduced. It utilizes three indicators, namely, the Gini index and the income shares of the bottom and the top in order to calculate the values of parameters associated with the specified functional form which has a closed-form expression for the Gini index. No error minimization technique is required in order to estimate the Lorenz curve. The data on the Gini index and the income shares of four countries that have a different level of income inequality, economic, sociological, and regional backgrounds from the United Nations University-World Income Inequality Database are used to illustrate how the simple method works. The overall results indicate that the estimated Lorenz curves fit the actual observations practically well. This simple method could be useful in the situation where the availability of data on income distribution is low. However, if more data on income distribution are available, this study shows that the specified functional form could be used to directly estimate the Lorenz curve. Moreover, the estimated values of the Gini index calculated based on the specified functional form are virtually identical to their actual observations.

Comparisons Between Poorest and Richest to Measure Inequality

2018 ◽  
Vol 49 (2) ◽  
pp. 526-561 ◽  
Author(s):  
Youri Davydov ◽  
Francesca Greselin
Keyword(s):  
Income Distribution ◽  
Household Income ◽  
Lorenz Curve ◽  
Gini Index ◽  
Specific Information ◽  
Grouped Data ◽  
Inequality Measure ◽  
Policy Makers ◽  
The One ◽  
Measures Of Inequality

The observed increase in economic inequality, where the major concern is relative to the huge growth of the highest incomes, motivates to revisit classical measures of inequality and to offer new ways to synthesize the variability of the entire income distribution. The idea is to provide policy makers a way to contrast the economic position of the group of the poorer [Formula: see text] percent of the population and to compare their mean income to the one owned by the [Formula: see text] percent of the richest. The new measure is still a Lorenz-based one, but the significant focus is based here in equally sized and opposite parts of the population whose difference is so remarkable nowadays. We then highlight the specific information given by the new inequality measure and curve, by comparing it to the widely employed Lorenz curve and Gini index and the more recent Zenga approach, and provide an application to Italian data on household income, wealth, and consumption along the years 1980–2012. The effects of estimating inequality indices and curves from grouped data are also discussed.

Lorenz Curve and Gini Index

1990 ◽  
Vol 40 (1-4) ◽  
pp. 307-324
Author(s):  
T. S. K. Moothathu
Keyword(s):  
Lorenz Curve ◽  
Gini Index

The Contest for Olympic Success as a Public Good

2013 ◽  
Author(s):  
Loek Groot
Keyword(s):  
Lorenz Curve ◽  
Gini Index ◽  
World Population ◽  
Level Playing Field ◽  
Actual Distribution ◽  
Playing Field ◽  
Inequality Measures ◽  
The Rich ◽  
The Lorenz Curve ◽  
Rich Countries

In this study it is demonstrated that standard income inequality measures, such as the Lorenz curve and the Gini index, can successfully be applied to the distribution of Olympic success. Olympic success is distributed very unevenly, with the rich countries capturing a disproportionately higher share compared to their world population share, which suggests that the Olympic Games do not provide a level playing field. The actual distribution of Olympic success is compared with alternative hypothetical distributions, among which are chosen the distribution according to population shares, the welfare optimal distribution under the assumption of zero government expenditures, and the non-cooperating Nash-Cournot distribution. By way of conclusion, a device is proposed to make the distribution of Olympic success more equitable.

scholarly journals PERTUMBUHAN EKONOMI, KETIMPANGAN PEDAPATAN DAN DESENTRALISASI DI INDONESIA

KINERJA ◽  
2016 ◽  
Vol 20 (1) ◽  
pp. 53
Author(s):  
Lestari Agusalim
Keyword(s):  
Economic Growth ◽  
Income Inequality ◽  
Linear Regression ◽  
Income Distribution ◽  
Gross Domestic Product ◽  
Gini Index ◽  
National Income ◽  
Secondary Data ◽  
Independent Variables ◽  
Regression Approach

AbstrakPenelitian ini bertujuan untuk mengkaji pengaruh desentralisasi dalam mendistribusikan pendapatan nasional untuk mengurangi ketimpangan pendapatan di Indonesia. Data yang digunakan adalah data sekunder, yaitu PDB sebagai representasi pendapatan nasional dan data indeks gini sebagai representasi tingkat ketimpangan pendapatan dengan rentang waktu 1978-2015. Metode analisis menggunakan regresi linear dengan pendekatan OLS dimana Indeks gini digunakan sebagai variabel dependen, dan PDB sebagai variabel independen. Selain itu, terdapat variabel independen lainnya, yaitu variabel dummy desentralisasi yang berguna untuk mengetahui pengaruh desentralisasi terhadap ketimpangan pendapatan. Hasil analisis menunjukkan bahwa dari aspek ekonomi, desentralisasi belum mampu mendistribusikan pertumbuhan ekonomi untuk memperkecil ketimpangan pendapatan masyarakat.Kata Kunci: Pertumbuhan Ekonomi, Ketimpangan Pendapatan, DesentralisasiAbstractThis research aims to analyze the effect of decentralization on national income distribution and the reduce of income Inequality in Indonesia. This research used secondary data with gross domestic product (GDP) representing national income and gini index data representing income inequality from 1978 to 2015. An OLS Linear Regression approach was employed where the gini index was the dependent variable, and the independent variables were GDP and the Dummy for decentralization implementation. The result revealed that decentralization had not been able to distribute economic growth to minimize income Inequality.Keywords: Economic Growth, Income Inequality, Decentralization

scholarly journals Economic Development

2019 ◽  
Author(s):  
Dhina Vadyza
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Income Distribution ◽  
Urban Areas ◽  
Gini Index ◽  
Production Factor ◽  
Real Economy ◽  
Long Run ◽  
Per Capita ◽  
Capita Output

Economic growth is a process of increasing per capita output that occurs continuously in the long run. Economic growth is one indicator of the success of development. Increasingly increasing economic growth usually increases people's welfare. While economic development is an effort to increase per capita income by processing potential economic forces into the real economy through investment, increasing knowledge, increasing skills, using technology, adding management skills and organizing.Economic growth is also related to the increase in "per capita output". The theory must include theories about GDP growth and theories about population growth. Then the third aspect is economic growth in a long-term perspective, that is, if for a long period of time the per capita output shows an increasing tendency.The distribution of income distribution in Indonesia is increasingly uneven. This can be seen from the increasing Indonesian Gini Index. As is known, the Gini index measures the income distribution of a country. The size of the Gini index Between 0 (zero) to 1 (one), the Gini index Equal to 0 (zero) indicates the index that the income distribution is perfectly equal, while the Gini index is 1 (one ) shows that the income distribution is totally uneven. Based on the data, the Indonesian Gini index continues to increase from year to year.The state of income distribution in Indonesia since 1970 can be said not to improve, this is caused by many factors, including the First production factor market (input market) which is the increase in labor supply which results in excess labor, low labor wages and limited employment opportunities in urban areas resulting in unemployment and urban slums.Second, land ownership. Land distribution is the main determinant of the extent of poverty and income distribution.

The Interpolation of the Lorenz Curve and Gini Index from Grouped Data

Econometrica ◽  
1976 ◽  
Vol 44 (3) ◽  
pp. 479 ◽  
Author(s):  
Joseph L. Gastwirth ◽  
Marcia Glauberman
Keyword(s):  
Lorenz Curve ◽  
Gini Index ◽  
Grouped Data ◽  
The Lorenz Curve

What Happened to Polarization and Inequality Between 2008 and 2018 in Spain?

2020 ◽  
Vol 66 (3) ◽  
pp. 209-222
Author(s):  
Rosa María García-Fernández ◽  
Candela Ruiz Tobar
Keyword(s):  
Economic Growth ◽  
Middle Class ◽  
Income Distribution ◽  
Gini Index ◽  
Upward Trend ◽  
Middle Income

The main objective of this paper is to empirically examine the dynamics of income distribution in Spain between 2008 and 2018 using SILC data. We consider the concepts of polarization and inequality as different aspects of the income distribution. To compute both aspects, we use the measures of Palacios-González and García-Fernández (2012) and the Gini index respectively. Findings indicate that polarization and inequality fluctuate around an upward trend during these years in Spain. Furthermore, the lower and middle income households have been more adversely affected than those with higher incomes, and the middle class has not benefited from the economic growth in Spain.

scholarly journals Measuring income inequalities beyond the Gini coefficient

2019 ◽  
Vol 28 (2) ◽  
pp. 561-578 ◽  
Author(s):  
Mikuláš Luptáčik ◽  
Eduard Nežinský
Keyword(s):  
Income Distribution ◽  
Gini Coefficient ◽  
Gini Index ◽  
European Countries ◽  
Lower Income ◽  
Poverty Index ◽  
New Approach ◽  
Income Groups ◽  
Head Count ◽  
Weights Restrictions

AbstractGrowing interest in the analysis of interrelationships between income distribution and economic growth has recently stimulated new theoretical and empirical research. Measures such as the head-count ratio for the poverty index or the widely used Gini coefficient are aggregated indicators describing the general extent of inequality without deeper insights into income distribution among households. To derive an indicator accounting for income distribution among income groups, we propose a new approach based on an output oriented DEA model where the input value is unitized to 1 for each country and weights restrictions imposed so as to favour a higher income share in the lower quantiles. We demonstrate the merits of this approach on the quintile income breakdown data of 29 European countries. Prioritizing lower income groups’ welfare, countries such as Slovenia and Slovakia can be equally favoured by the new proposed indicator while being assessed differently by the Gini index. An intertemporal analysis reveals a slight deterioration of income distribution in the majority of 29 European countries over the period of 2007–2016 in a Rawlsian sense.

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