On variance estimation for a Gini coefficient estimator obtained from complex survey data

Hoque, Ahmed Anisul; Clarke, Judith A.

doi:10.1080/23737484.2015.1044050

Cited by 6 publications

(4 citation statements)

References 57 publications

(104 reference statements)

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“…The most widely used measure of income inequality is the Gini coefficient (Hoque and Clarke 2015;Peñaloza 2016). Although these coefficients are typically calculated for individual or household incomes, we use them to examine the spatial patterns of inequality over time in the metropolitan area.…”

Section: Measures Of Inequalitymentioning

confidence: 99%

Reordering Occupation, Race, and Place in Metropolitan New York

Zapatka

Mollenkopf

Romalewski

2021

The Urban Book Series

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The New York metropolitan area is one of the oldest, largest, and perhaps most complex urban region in the United States (U.S.). Its 23.7 million residents live across four states, produce a GDP of more than $1.7 trillion, are governed by a fragmented political system, and experience persistently high degrees of geographic and racial/ethnic inequality and segregation. This chapter investigates the evolving spatial organization of occupation and race across the metropolitan area. While white professionals have traditionally lived in an outer ring of suburbs and blue-collar immigrant and minority groups have lived closer to the city center, our research shows that the forces of gentrification and minority and immigrant suburbanization have been turning the metropolitan area inside out. Specifically, young, usually white, professionals are increasingly located in and around the central city whereas many working-class minorities have shifted away from it. At the heart of this spatial reordering lie the diminishing plurality of native-born whites within the region and the increasing share of immigrant minority groups, especially for foreign-born Hispanics and Asians. This trend has lessened the share of white males in better occupations even as the region’s occupational structure slowly but inexorably tilts toward managerial and professional occupations. Technology is transforming white-collar work as blue-collar work continues to disappear. Dramatic shifts are thus afoot, yet inequality and segregation remain high. We argue that these changes in the spatial organization of the metropolitan area challenge us to see these inequalities from a new vantage point. As elites are now more likely to live among less advantaged groups, this may provide the social basis for new thinking.

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Section: Measures Of Inequalitymentioning

confidence: 99%

Reordering Occupation, Race, and Place in Metropolitan New York

Zapatka

Mollenkopf

Romalewski

2021

The Urban Book Series

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“…However, Hoque and Clarke (2015) showed that the estimators in (6) and (7) are numerically the same, i.e., V 2 n,1 = V 2 n,2 . We therefore chose V 2 n,1 as a consistent estimator of ξ 2 and drop the second subscript, without loss of generality (i.e., we use V 2 n as the estimator of ξ 2 ).…”

Section: Estimation Of ξmentioning

confidence: 99%

Gini Index Estimation within Pre-Specified Error Bound: Application to Indian Household Survey Data

2020

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The Gini index, a widely used economic inequality measure, is computed using data whose designs involve clustering and stratification, generally known as complex household surveys. Under complex household survey, we develop two novel procedures for estimating Gini index with a pre-specified error bound and confidence level. The two proposed approaches are based on the concept of sequential analysis which is known to be economical in the sense of obtaining an optimal cluster size which reduces project cost (that is total sampling cost) thereby achieving the pre-specified error bound and the confidence level under reasonable assumptions. Some large sample properties of the proposed procedures are examined without assuming any specific distribution. Empirical illustrations of both procedures are provided using the consumption expenditure data obtained by National Sample Survey (NSS) Organization in India.

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“…Wireless Communications and Mobile Computing Equation (3) offers a direct calculation method for Gini coefficient valued between 0 and 1, and the smaller, the more fair and the bigger the more unfair. Since this formula just involves the arithmetic operation of income data, this estimation method can be used unconditionally in theory without errors [22,23]. Based on the Lorenz curve and the calculation method, a simple formula was put forward by Jianhua [24] as follows:…”

Section: The Modelmentioning

confidence: 99%

Gleer: A Novel Gini‐Based Energy Balancing Scheme for Mobile Botnet Retopology

Wang

Zhang

et al. 2018

Wireless Communications and Mobile Computing

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Mobile botnet has recently evolved due to the rapid growth of smartphone technologies. Unlike legacy botnets, mobile devices are characterized by limited power capacity, calculation capabilities, and wide communication methods. As such, the logical topology structure and communication mode have to be redesigned for mobile botnets to narrow energy gap and lower the reduction speed of nodes. In this paper, we try to design a novel Gini-based energy balancing scheme (Gleer) for the atomic network, which is a fundamental component of the heterogeneous multilayer mobile botnet. Firstly, for each operation cycle, we utilize the dynamic energy threshold to categorize atomic network into two groups. Then, the Gini coefficient is introduced to estimate botnet energy gap and to regulate the probability for each node to be picked as a region C&C server. Experimental results indicate that our proposed method can effectively prolong the botnet lifetime and prevent the reduction of network size. Meanwhile, the stealthiness of botnet with Gleer scheme is analyzed from users’ perspective, and results show that the proposed scheme works well in the reduction of user’ detection awareness.

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On variance estimation for a Gini coefficient estimator obtained from complex survey data

Cited by 6 publications

References 57 publications

Reordering Occupation, Race, and Place in Metropolitan New York

Reordering Occupation, Race, and Place in Metropolitan New York

Gini Index Estimation within Pre-Specified Error Bound: Application to Indian Household Survey Data

Gleer: A Novel Gini‐Based Energy Balancing Scheme for Mobile Botnet Retopology

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