The Estimation of the Lorenz Curve and Gini Index

Gastwirth, Joseph L.

doi:10.2307/1937992

Cited by 879 publications

(497 citation statements)

References 12 publications

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“…The Gini coefficient was employed as a measure of income inequality; it is the most commonly used measure of inequality and varies between 0, which reflects complete equality and 1, which indicates complete inequality [20,32,33]. Logged GDP per capita was used as a measure for the standard of living, whereas a social trust index was used as an indicator of social capital.…”

Section: Data Sources and Measuresmentioning

confidence: 99%

Social determinants of tuberculosis in Europe: a prospective ecological study

et al. 2012

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Tuberculosis (TB) is considered to be a disease of poverty, since its incidence is exacerbated by socioeconomic factors, inconsistent or partial treatment practices, and immigration from endemic countries.A prospective country level study, using a comprehensive dataset of TB incidence and prevalence taken from countries within the World Health Organization (WHO) European region, was conducted. We employed quintile regression to investigate the prospective association between baseline (measured in 2000) and a nation's wealth, level of egalitarianism, migration rate, health-related lifestyle and social capital with TB incidence and prevalence over a 10-yr period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009).We found that ,50% of TB variation is accounted for by a nation's wealth and level of egalitarianism. We observed a negative prospective association between logged gross domestic product and TB rates, and a positive prospective association between income inequality and TB.National income levels per capita and income inequality are important predictors for TB incidence and prevalence in the WHO European region. They account for 50% of country-level variation, indicating the importance of a combined absolute and relative socioeconomic disadvantage in the development of TB. These findings also provide a tool for forecasting potential fluctuations in the level of TB epidemics in the WHO European region, with respect to socioeconomic changes.

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Section: Data Sources and Measuresmentioning

confidence: 99%

Social determinants of tuberculosis in Europe: a prospective ecological study

et al. 2012

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“…"Source-sink" landscape theory (Chen et al, 2008) classifies the attribute class within a landscape as "source," "sink" or "flow," according to its contribution to the targeted ecological process. The construction of the LWLI falls within the frameworks of the Lorenz Curve and the GINI index (Chen et al, 2009;Gastwirth, 1972). One trial, in which the LWLI and "sourcesink" landscape theory were used to assess the effects of landscape type and configuration on nutrient losses in a watershed (Chen et al, 2009) proved to be both efficient and effective.…”

Section: Introductionmentioning

confidence: 99%

Application of a new integrated landscape index to predict potential urban heat islands

Chen

Zhao

Yao

et al. 2016

Ecological Indicators

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a b s t r a c tStudies on the interactions between urban landscape patterns and land surface temperatures are the key to urban heat island (UHI) mitigation. However, the landscape pattern indices used in previous studies were inconsistent in type and number. Furthermore, few studies combined the composition and configuration indices into one integrated indicator. The description of landscape pattern is thus a great yet rewarding challenge. In this study, we used the integrated location weighted landscape index (LWLI) proposed by Chen et al. (2009) and revised it to indicate potential UHIs. Fifty-six circular landscape samples along four transects were created and the LWLI was derived based on the theory of the GINI index. The LWLI considers the type, composition and configuration of different land covers inside a landscape, where configuration is treated as location weights. The potential UHI was represented by the land surface temperature (LST) of each circle landscape center. The correlation analysis results showed that the LWLI was significantly positively correlated with summer potential UHIs, with a Pearson R equaling 0.736, and barely correlated with winter potential UHI. Moreover, the LWLI was slightly more strongly correlated with potential UHI than composition alone. These correlations weakened as the landscapes grew larger. The linear regression results further revealed that the LWLI explained about 53% of the summer potential UHI, which was slightly better than composition alone (49%). This indicated that the LWLI was as effective in predicting the potential UHI as the combination of several pattern indicators, echoing Tobler's first law of geography, which states that "all things are related, but nearby things are more related than distant things." This study also leaves room for improvement of the index by integrating more environmental/ecological parameters as weights, and for further application of the index in other fields.

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“…It is also possible to describe the Lorenz curve as a formal functional relationship, requiring the selection of some underlying distribution function. Some common distributions include: equal, exponential, shifted exponential, general uniform, and Pareto (1897) distributions (Gastwirth, 1972). For example, Majumder and Chakravarty (1990) model the probability distribution of income, comparing the empirical performances of various distributions for United States income data, including the Pareto (1897), Lognormal, Gamma, Singh-Maddala (1976), Dagum (1977), and McDonald's (1984) Generalized Beta distribution.…”

Section: Empirical Models and Datamentioning

confidence: 99%

Simulating the spatial distribution of population and emissions to 2100

Asadoorian

2007

Environ Resource Econ

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The MIT Joint Program on the Science and Policy of Global Change is an organization for research, independent policy analysis, and public education in global environmental change. It seeks to provide leadership in understanding scientific, economic, and ecological aspects of this difficult issue, and combining them into policy assessments that serve the needs of ongoing national and international discussions. To this end, the Program brings together an interdisciplinary group from two established research centers at MIT: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers bridge many key areas of the needed intellectual work, and additional essential areas are covered by other MIT departments, by collaboration with the Ecosystems Center of the Marine Biology Laboratory (MBL) at Woods Hole, and by short-and long-term visitors to the Program. The Program involves sponsorship and active participation by industry, government, and non-profit organizations.To inform processes of policy development and implementation, climate change research needs to focus on improving the prediction of those variables that are most relevant to economic, social, and environmental effects. In turn, the greenhouse gas and atmospheric aerosol assumptions underlying climate analysis need to be related to the economic, technological, and political forces that drive emissions, and to the results of international agreements and mitigation. Further, assessments of possible societal and ecosystem impacts, and analysis of mitigation strategies, need to be based on realistic evaluation of the uncertainties of climate science.This report is one of a series intended to communicate research results and improve public understanding of climate issues, thereby contributing to informed debate about the climate issue, the uncertainties, and the economic and social implications of policy alternatives. Titles in the Report Series to date are listed on the inside back cover.

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The Estimation of the Lorenz Curve and Gini Index

Cited by 879 publications

References 12 publications

Social determinants of tuberculosis in Europe: a prospective ecological study

Social determinants of tuberculosis in Europe: a prospective ecological study

Application of a new integrated landscape index to predict potential urban heat islands

Simulating the spatial distribution of population and emissions to 2100

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