Using the GB2 Income Distribution

Chotikapanich, Duangkamon; Griffiths, William E.; Hajargasht, Gholamreza; Karunarathne, W.V.A.D.; Rao, D. S. Prasada

doi:10.3390/econometrics6020021

Cited by 24 publications

(21 citation statements)

References 29 publications

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“…These functions along with the kth moment and the Gini index are presented in online supplementary materials (Table S1). Following Arnold and Sarabia (2018) and Chotikapanich et al (2018), the Lorenz curve of the GB2 distribution is given by,…”

Section: Parametric Modelsmentioning

confidence: 99%

“…Following Arnold and Sarabia (2018) and Chotikapanich et al. (2018), the Lorenz curve of the GB2 distribution is given by,

L_{G B 2} false(u ; a, p, q false) = B (B^{‐ 1} (u ; p, q) ; p + \frac{1}{a}, q ‐ \frac{1}{a}), 0 \leq u \leq 1,

where q >1/ a and B −1 ( x ; p , q ) is the inverse of the incomplete beta function ratio.…”

Section: Estimating Income Inequality From Grouped Datamentioning

confidence: 99%

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Inequality Measurement with Grouped Data: Parametric and Non-Parametric Methods

Jordá

Sarabia²,

Jäntti

2021

Journal of the Royal Statistical Society Series A: Statistics in Society

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The analysis of income distribution has a venerable history in economics and statistics. Income inequality would be relatively simple to estimate if individual records on personal or household income data were available. Unfortunately, much of the existing scholarship on economic inequality has been plagued by a lack of individual data. Nevertheless, the periodic release of certain summary statistics on the distribution of income has become relatively common. The World Bank's PovcalNet, the World Income Inequality Database (WIID) and the World Wealth and Income Database (WID) are the largest cross-country databases that provide grouped income/consumption data, typically including information on income and population shares. This type of grouped data depicts sparse points of the Lorenz curve, which makes defining a method to link those points an essential requisite for estimating inequality measures.

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Section: Parametric Modelsmentioning

confidence: 99%

“…Following Arnold and Sarabia (2018) and Chotikapanich et al. (2018), the Lorenz curve of the GB2 distribution is given by,

L_{G B 2} false(u ; a, p, q false) = B (B^{‐ 1} (u ; p, q) ; p + \frac{1}{a}, q ‐ \frac{1}{a}), 0 \leq u \leq 1,

where q >1/ a and B −1 ( x ; p , q ) is the inverse of the incomplete beta function ratio.…”

Section: Estimating Income Inequality From Grouped Datamentioning

confidence: 99%

Inequality Measurement with Grouped Data: Parametric and Non-Parametric Methods

Jordá

Sarabia²,

Jäntti

2021

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…We arbitrarily choose these bounds around those that are commonly found for income distributions (see, e.g. Chotikapanich et al ., 2018, for the case of the generalized beta of the second kind and the help file of the van Kerm, 2017, package). From each GB2 distribution, we then extract randomly incomes of 10,000 individuals and use these for the calculation of the inequality indices.…”

Section: Empirical Considerationsmentioning

confidence: 99%

Extreme Values, Means, and Inequality Measurement

Bossert

D’Ambrosio

Kamaga

2020

Review of Income and Wealth

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We examine some ordinal measures of inequality that are familiar from the literature. These measures have a quite simple structure in that their values are determined by combinations of specific summary statistics such as the extreme values and the arithmetic mean of a distribution. In spite of their common appearance, there seem to be no axiomatizations available so far, and this paper is intended to fill that gap. In particular, we consider the absolute and relative variants of the range, the max‐mean and the mean‐min orderings, and quantile‐based measures. In addition, we provide some empirical observations that are intended to illustrate that, although these orderings are straightforward to define, some of them display a surprisingly high correlation with alternative (more complex) measures.

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“…Therefore, we assumed that each region has their own income distribution function, and the parameters of the distribution function were obtained using the maximum likelihood estimation method. The income distribution function uses the generalized beta of the second kind (GB2), which was explained in [24,25]. We randomly sampled from the distribution function using the parameters obtained for each region to determine the income of a household, and then sampled the electricity consumption at that income from the model.…”

Section: Model Evaluationmentioning

confidence: 99%

Electricity Consumption Forecast Model Using Household Income: Case Study in Tanzania

Shibano

Mogi

2020

Energies

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When considering the electrification of a particular region in developing country, the electricity consumption in that region must be estimated. In sub-Saharan Africa, which is one of the areas with the lowest electrification rates in the world, the villages of minority groups are scattered over a vast area of land, so electrification using distributed generators is being actively studied. Specifically, constructing a microgrid or introducing a solar system to each household is being considered. In this case, the electricity consumption of each area needs to be estimated, then a system with enough capacity could be introduced. In this study, we propose a household income electricity consumption model to estimate the electricity consumption of a specific area. We first estimate the electricity consumption of each household based on income and the electricity consumption of a specific area can be derived by adding up them in that area. Through a case study in Tanzania, electricity consumption derived using this model was compared with electricity consumption published by TANESCO, and the validity of the model was verified. We forecasted the electricity consumption in each region using the household income electricity consumption model, and the average forecast accuracy was 74%. The accuracy was 87% when the electricity consumption in Tanzania mainland was forecasted by adding the predicted values.

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Using the GB2 Income Distribution

Cited by 24 publications

References 29 publications

Inequality Measurement with Grouped Data: Parametric and Non-Parametric Methods

Inequality Measurement with Grouped Data: Parametric and Non-Parametric Methods

Extreme Values, Means, and Inequality Measurement

Electricity Consumption Forecast Model Using Household Income: Case Study in Tanzania

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