scholarly journals Equity analysis of Chinese physician allocation based on Gini coefficient and Theil index

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huimin Yu ◽  
Shuangyan Yu ◽  
Da He ◽  
Yuanan Lu
Keyword(s):  
Gini Coefficient ◽  
Service Management ◽  
Service Area ◽  
Theil Index ◽  
National Bureau ◽  
Gini Coefficients ◽  
Equity Analysis ◽  
The Lorenz Curve ◽  
Chinese Physician ◽  
Distribution Of Physicians

Abstract Background Unequal allocation of medical physician resource represents one of major problems in the current medical service management in China and many other countries. This study is designed to analyze the current distribution of physicians in 31 provincial administrative regions in China, to estimate the fairness of the distribution of physicians and provide a theoretical basis for the improvement of the allocation of physicians. Methods This study took physicians from 31 provincial administrative regions in China as the study objects, and the data were obtained from the China Health Statistics Yearbook 2019 and the official website of the National Bureau of Statistics of China. Calculation of the Gini coefficient (G) and the Theil index (T) were carried out by drawing the Lorenz curve. The fairness of present physician location in 31 provincial administrative regions in China was analyzed from the perspective of distribution by both population and service area. Results The Gini coefficients of medical physicians in China are 0.003 and 0.88 by population and by service area, respectively. This shows that the distribution of medical physicians is fair basing on population, and there is little difference in the number of physicians per 1000 population in different regions. However, the physician distribution basing on service area is highly unfair and shows a large gap in the number of physicians per square kilometer between different regions. In general, Beijing, Zhejiang, Shanghai, Jiangsu, Shandong, and Tianjin are higher than the overall level of 31 provincial administrative regions. In addition, the number of medical physicians in Zhejiang, Shandong, Beijing and Jiangsu is over-provisioned. Conclusion Bridging the number of medical physicians in different regions is a key step to improve the equity of physicians’ resource allocation. Thus, findings from this study emphasize the need to take more measures to reduce physician quality differences between regions, balance and coordinate medical resources. This will increase the access of all citizens to quality medical services.

scholarly journals The Gini Coefficient as a useful measure of malaria inequality among populations

2020 ◽  
Author(s):  
Jonathan Abeles ◽  
David Conway
Keyword(s):  
Global Health ◽  
Confidence Intervals ◽  
Gini Coefficient ◽  
Malaria Incidence ◽  
Potential Range ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
World Regions ◽  
Village Communities ◽  
High Level

BACKGROUND: Understanding inequality in infectious disease burden requires clear and unbiased indicators. The Gini coefficient, conventionally used as a macroeconomic descriptor of inequality, is potentially useful to quantify epidemiological heterogeneity. With a potential range from 0 (all populations equal) to 1 (populations having maximal differences), this coefficient is used here to show the extent and persistence of inequality of malaria infection burden at a wide variety of population levels. METHODS: We first applied the Gini coefficient to quantify variation among WHO world regions for malaria and other major global health problems. Malaria heterogeneity was then measured among countries within the geographical sub-region where burden is greatest, among the major administrative divisions in several of these countries, and among selected local communities. Data were analysed from previous research studies, national surveys, and global reports, and Gini coefficients were calculated together with confidence intervals using bootstrap resampling methods. RESULTS: Malaria showed a very high level of inequality among the world regions (Gini coefficient, G = 0.77, 95% CI 0.66-0.81), more extreme than for any of the other major global health challenges compared at this level. Within the most highly endemic geographical sub-region, there was substantial inequality in estimated malaria incidence among countries of West Africa, which did not decrease between 2010 (G = 0.28, 95% CI 0.19-0.36) and 2018 (G = 0.31, 0.22-0.39). There was a high level of sub-national variation in prevalence among states within Nigeria (G = 0.30, 95% CI 0.26-0.35), but more moderate variation within Ghana (G = 0.18, 95% CI 0.12-0.25) and Sierra Leone (G = 0.17, 95% CI 0.12-0.22). There was also significant inequality in prevalence among local village communities, generally more marked during dry seasons when there was lower mean prevalence. The Gini coefficient correlated strongly with the Coefficient of Variation which has no finite range. CONCLUSIONS: The Gini coefficient is a useful descriptor of epidemiological inequality at all population levels, with confidence intervals and interpretable bounds. Wider use of the coefficient would give broader understanding of malaria heterogeneity revealed by multiple types of studies, surveys and reports, providing more accessible insight from available data.

scholarly journals An International Comparison of Equity in Accessibility to Jobs: London, São Paulo, and the Randstad

10.32866/7412 ◽  
2019 ◽  
Author(s):  
John P. Pritchard ◽  
Diego Tomasiello ◽  
Mariana Giannotti ◽  
Karst Geurs
Keyword(s):  
The Netherlands ◽  
International Comparison ◽  
Gini Coefficient ◽  
Sao Paulo ◽  
Network Data ◽  
São Paulo ◽  
Equity Analysis ◽  
Potential Population ◽  
The Impact ◽  
Accessibility Measure

We analyze the impact of different accessibility measures on the interpretation of associated equity analysis using the Gini coefficient and the (pseudo) Palma ratio, and the impact of the method of assigning zonal accessibility on Gini estimation results using four different alternatives. Two types of potential accessibility measures (zonal and person-based) and two ratios of potential jobs to potential population (intra-modal and multi-modal) are estimated for car and transit in the Netherlands' Randstad region, Greater London, and São Paulo relying on network data, schedule-based data, and speed profiles. Gini results are heavily influenced by the accessibility indicator and the method of assignment. The Palma ratio is also influenced by the choice of accessibility indicator, with the person-based potential accessibility measure tending to show greater inequity.

scholarly journals Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity

2018 ◽  
Author(s):  
Sandro Gsteiger ◽  
Nicola Low ◽  
Pam Sonnenberg ◽  
Catherine H Mercer ◽  
Christian L Althaus
Keyword(s):  
Sexual Activity ◽  
Gini Coefficient ◽  
Treatment Strategies ◽  
Mycoplasma Genitalium ◽  
Sti Prevention ◽  
Sexually Transmitted ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
Hpv Types ◽  
Different Levels

AbstractObjectivesGini coefficients have been used to describe the distribution of Chlamydia trachomatis (CT) infections among individuals with different levels of sexual activity. The objectives of this study were to investigate Gini coefficients for different sexually transmitted infections (STIs), and to determine how STI control interventions might affect the Gini coefficient over time.MethodsWe used population-based data for sexually experienced women from two British National Surveys of Sexual Attitudes and Lifestyles (Natsal-2: 1999-2001; Natsal-3: 2010-2012) to calculate Gini coefficients for CT, Mycoplasma genitalium (MG), and human papillomavirus (HPV) types 6, 11, 16 and 18. We applied bootstrap methods to assess uncertainty and to compare Gini coefficients for different STIs. We then used a mathematical model of STI transmission to study how control interventions affect Gini coefficients.ResultsGini coefficients for CT and MG were 0.33 (95% confidence interval (CI): 0.18-0.49) and 0.16 (95% CI: 0.02-0.36), respectively. The relatively small coefficient for MG suggests a longer infectious duration compared with CT. The coefficients for HPV types 6, 11, 16 and 18 ranged from 0.15-0.38. During the decade between Natsal-2 and Natsal-3, the Gini coefficient for CT did not change. The transmission model shows that higher STI treatment rates are expected to reduce prevalence and increase the Gini coefficient of STIs. In contrast, increased condom use reduces STI prevalence but does not affect the Gini coefficient.ConclusionsGini coefficients for STIs can help us to understand the distribution of STIs in the population, according to level of sexual activity, and could be used to inform STI prevention and treatment strategies.Key messagesThe Gini coefficient can be used to describe the distribution of STIs in a population, according to different levels of sexual activity.Gini coefficients for Chlamydia trachomatis (CT) and human papillomavirus (HPV) type 18 appear to be higher than for Mycoplasma genitalium and HPV 6, 11 and 16.Mathematical modelling suggests that CT screening interventions should reduce prevalence and increase the Gini coefficient, whilst condom use reduces prevalence without affecting the Gini coefficient.Changes in Gini coefficients over time could be used to assess the impact of STI prevention and treatment strategies.

INCOME DISTRIBUTION OF RED CHILLI FARMER, AT MEREMBU VILLAGE, LABUAPI DISTRICT, WEST LOMBOK REGENCY 2021

2021 ◽  
Vol 1 (2) ◽  
pp. 27-33
Author(s):  
Ida Bagus Eka Artika
Keyword(s):  
Income Distribution ◽  
World Bank ◽  
Gini Coefficient ◽  
Lorenz Curve ◽  
Research Area ◽  
Distribution Analysis ◽  
Agricultural Commodity ◽  
The World Bank ◽  
The World ◽  
The Lorenz Curve

Red chili is an agricultural commodity planted by farmers on the sidelines of rice planting as the main agricultural commodity, especially in Merembu Village, Labuapi District, West Lombok Regency. This study aims to determine the level of income distribution of chili farmers in Merembu Village, Labuapi District, West Lombok Regency. The sample respondent as many as 35 red chili farmers in the research area. The analysis technique uses income distribution analysis with three approaches, namely Gini Coefficient approach, Lorenz Curve and World Bank Criteria Approach. The results of the Gini Ratio calculation for the 35 respondents studied, obtained a Gini Index or Gini Coefficient of 0.143, this shows the income inequality of red chili farmers in Merembu Village, Labuapi District in the mild or low category. This statement is reinforced by the Lorenz Curve approach and the World Bank Criteria Approach, namely the results of calculations based on the World Bank approach obtained that 40% of the population with low incomes receive an income of 29.6% or greater than 17% (Low Category) of the total existing income. This indicates that the category of the level of income distribution of chili farmers in Merembu Village, Labuapi District is relatively low

scholarly journals Assessment of the income situation of households in the Czech Republic

2011 ◽  
Vol 57 (No. 7) ◽  
pp. 322-330
Author(s):  
J. Turčínková ◽  
J. Stávková
Keyword(s):  
Czech Republic ◽  
Gini Coefficient ◽  
Household Income ◽  
Lorenz Curve ◽  
Explanatory Power ◽  
Primary Source ◽  
The Czech Republic ◽  
Poverty Threshold ◽  
Average Household Income ◽  
The Lorenz Curve

The paper deals with the assessment of income situation of households in the Czech Republic. The primary source for the analysis were the data of the survey EU-SILC European Union – Statistics on Income and Living Conditions. The basic variable for the analysis is the level of the household income in 2005–2008. In addition to the decile classification, characteristics such as the average income per one household member, poverty threshold, poverty depth coefficient, Lorenz curve and Gini coefficient. were calculated in order to evaluate the income situation. The results show an increase of the average household income. The Lorenz curve followed by the Gini coefficient demonstrate the uniformity of distribution of income values. The results show a decreasing income differentiation. The poverty threshold was defined on the level of 60% of the median value and with this given threshold, the households were assessed, whether they belong to the ones at the risk of poverty. The results reveal a decreasing number of households at the risk of poverty. The poverty depth coefficient has a stronger explanatory power and shows how far below the poverty threshold the households are, or what is an income deficit of these households. Each category of households at the risk of poverty varies with the depth of poverty. The analysis also provides the results of how the households' income situation or poverty is perceived by the households themselves.

scholarly journals Examining manufacturer concentration metrics in consumer packaged goods

2020 ◽  
pp. 147078532090397
Author(s):  
Arry Tanusondjaja ◽  
Steven Dunn ◽  
Christopher Miari
Keyword(s):  
Market Share ◽  
Gini Coefficient ◽  
Concentration Ratio ◽  
Market Competition ◽  
Market Concentration ◽  
Gini Coefficients ◽  
Packaged Goods ◽  
Consumer Packaged Goods ◽  
Positive Correlations ◽  
Multiple Metrics

The research compares three different market concentration metrics (Concentration Ratio, Herfindahl–Hirschman Index, and Gini Coefficient) over the share of revenue (market share) and their application in consumer packaged goods markets. The metrics are further extended into measuring the share of the ownership of brands and stock-keeping units, to provide further insights into the nature of market competition. These metrics are reported across 16 categories between 2010 and 2014 from the United Kingdom. The Concentration Ratio results show an average market share of 88% going to the top 10 manufacturers, despite accounting for 19% of all manufacturers on average. Similarly, Gini Coefficients show large disparities in revenue shares across manufacturers (0.85), while the Herfindahl–Hirschman Index classifies most markets as being moderately concentrated. The research highlights the advantage of observing multiple metrics in measuring market concentration, as a single metric is unlikely to convey the nature of market competition. The results show Concentration Ratio for the top 4 or top 10 to be good proxies for Herfindahl–Hirschman Index, while the top 10% or top 20% market concentration can be used as proxies for Gini Coefficients due to their strong positive correlations. Rather than applying onerous Herfindahl–Hirschman Index and Gini Coefficient calculations and requiring the details for all competing entities as required, the result enables researchers and industry practitioners to diagnose the state of the competition by simply calculating the aggregate market share of the top N and the top N% manufacturers.

Frequency distribution of shoot weight of plants in populations ofTrifolium repenspersisting by clonal growth in grazed pastures

1990 ◽  
Vol 115 (1) ◽  
pp. 41-47 ◽  
Author(s):  
M. J. M. Hay ◽  
V. J. Thomas ◽  
J. L. Brock
Keyword(s):  
Gini Coefficient ◽  
White Clover ◽  
Clonal Growth ◽  
Dry Weight ◽  
Plant Size ◽  
Size Hierarchy ◽  
Gini Coefficients ◽  
Shoot Dry Weight ◽  
The Gini Coefficient ◽  
Variable Nature

SUMMARYOver two years (1984/85 and 1986/87), monthly sampling of shoots of white clover plants compared the populations of white clover in mixed swards at Palmerston North, New Zealand, under set stocking, rotational grazing and a combination of both systems, at a common stocking rate of 22·5 ewe equivalents/ha.The frequency distributions of shoot (or stolon) dry weight per plant in each population over the study period was described by a log-normal model, which indicated that the populations consisted of many small individuals and few large individuals. Such inequality of shoot dry weight within populations is commonly termed size hierarchy; a statistic giving a measure of such size hierarchy is the Gini coefficient. The populations under different managements had similar Gini coefficients which differed little among seasons or between years. Lack of significant correlation between the Gini coefficient and mean shoot dry weight per plant of each population indicated that, in these white clover populations, size hierarchy was independent of mean plant size.These results were considered in relation to the clonal growth of white clover in grazed swards and it is suggested that the variable nature of death of older basal stolons makes an important contribution to the variability in size of individual plants and hence to size hierarchy. As size hierarchy, as assessed by Gini coefficients, was relatively stable in these populations over 3½ years, it appears that clonal growth of white clover incorporates sufficient variability within the growth and death processes at the individual plant level to maintain the size hierarchy, irrespective of variations in mean plant size of populations.

scholarly journals Commentary: Measuring the shape of degree distributions

2013 ◽  
Vol 1 (2) ◽  
pp. 213-225 ◽  
Author(s):  
JENNIFER M. BADHAM
Keyword(s):  
Gini Coefficient ◽  
Degree Distribution ◽  
Fundamental Property ◽  
Standard Measure ◽  
Gini Coefficients ◽  
The Gini Coefficient ◽  
Different Types ◽  
Network Properties ◽  
The Relationship

AbstractDegree distribution is a fundamental property of networks. While mean degree provides a standard measure of scale, there are several commonly used shape measures. Widespread use of a single shape measure would enable comparisons between networks and facilitate investigations about the relationship between degree distribution properties and other network features. This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient. It has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations. While this paper focuses on degree, the distribution of other node-based network properties could also be described with Gini coefficients.

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