Measuring income inequality based on unequally distributed income

Park, Joong-Yang; Youngsoon, Kim; Ju, Ae-Jin

doi:10.1007/s11403-020-00295-1

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…This model of income distribution is at the core of the Lewis theory of modern-sector growth based on the reinvestment of rising capitalist profits (Pélabon et al, 2020). Unfortunately, the relevance of the functional theory is greatly diminished by its failure to take into account the important role and influence of nonmarket forces such as power in determining these factor prices (Park et al, 2021).…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…In general, new approaches are emerging to make the results of income inequality analysis more meaningful. Park et al (2021) for example, have proposed a new framework for measuring income inequality based on the unequally distributed (UD) incomes that are obtained by removing the equally distributed parts from incomes. They then derive the normalized norm indexes from the cumulative distribution function and the un-scaled Lorenz curve of the UD incomes.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…They then derive the normalized norm indexes from the cumulative distribution function and the un-scaled Lorenz curve of the UD incomes. Using the example of income distributions and the Luxembourg Income Study datasets, Park et al (2021) show that, the normalized norm indexes evaluated income inequality appropriately and solved the negative income problem. Elsewhere in the Mediterranean countries, Benedetti et al (2020) provide point and variance estimates of two widely used income-poverty indicators, belonging to the class of the Foster-Greer-Thorbecke (FGT), and two widely used incomeinequality indicators.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

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Income Inequality in Mountain Areas: The Case of Agroforestry Farming Systems in Uluguru Mountains, Tanzania

Kadigi¹

2021

OJF

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Land degradation due to use of unsustainable agricultural practices has affected many communities in rural mountain areas rendering them to be more vulnerable to income poverty and inequality. In this case, agroforestry systems promise to offer great solutions as they can be developed in unfavourable conditions where other production systems would either rapidly degrade the land or otherwise would not be possible. However, little is known whether agroforestry can address issues of income inequality in mountain areas. Hence, we conducted a study to investigate the nature and determinants of income inequality in Uluguru Mountains, Tanzania. Specifically, we used the cross-sectional research design and we calculated the income percentile shares, Gini coefficients and the coefficient of variation (CV), to pinpoint the nature of income inequality in the study area. The determinants of income inequality were analysed using the step by step multiple linear model. The results of analysis suggested prevalence of income inequality. Crop production was the main source of income in the agroforestry systems of the study area. Earnings from crops and timber were decreasing income-inequality amongst smallholder farmers. Our disaggregated analysis showed that offfarm income was also decreasing income-inequality for farmers with farmlands located close to homestead, for female-headed households, for farmers who did not access extension services, and those who were members of community-based financial institutions. Estimated incomes increased with household assets, size of farmland, and age of household head. However, the same decreased with household size. We found gender disparity to be one of the key issues that need attention in formulating future policies to reduce inequality. We recommend promotion of livelihood diversification as well as the designing and implementation of tailor-made training and farm financing How to cite this paper: Kadigi, R.M.J.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

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Income Inequality in Mountain Areas: The Case of Agroforestry Farming Systems in Uluguru Mountains, Tanzania

Kadigi¹

2021

OJF

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“…These findings provide preliminary evidence regarding possible contributory factors to guide further research. Park, Kim, and Ju (2020) propose a new framework for measuring income inequality. The framework is based on the unequally distributed (UD) incomes that are obtained by removing the equally distributed parts from incomes.…”

Section: Literature Reviewmentioning

confidence: 99%

A panel data examination of income inequality in 24 African countries

Yadav¹,

Antil²,

Gupta³

et al. 2020

Journal of Public Affairs

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Income inequality measures the highlighted gaps between the household disposable income between individuals, groups, etc. Rising concern for financial access to all has greatly influenced this macroeconomic indicator. Africa has majority of countries with worse equality conditions. Though African countries are recipients of external aids from different entities, a high level of corruption directs the benefits to private wealth. The gain of economic growth is disproportionately shared and is skewed. The purpose of the study is to determine the factors responsible for income inequality. We have used unbalanced panel data of 24 African countries for the period 1990–2015 (5‐year averages) of several macroeconomic indicators such as GINI index, literacy, control of corruption, gross domestic product (GDP) growth, household consumption, inflation, etc. We estimated four regression models (OLS fixed effects and random effects) with an increasing number of independent variables. Further, we chose the best model according to Hausman test and conducted a restricted F‐test to check for non‐spurious variables. We concluded one of the models as statistically significant and robust to explain Income Inequality in Africa. Also, we tested for multicollinearity in variables via the correlation matrix and variance inflation index (VIF) and eliminated the population variable, which was significant. We concluded that six factors are non‐spurious and explain the income inequality variable in a statistically significant manner. Implications of this report can be used for making policies and schemes with respect to the underline variables so that the nations will be able to reduce the inequality gap.

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Measuring Income and Wealth Inequality: A Note on the Gini Coefficient for Samples with Negative Values

Lee,

Suh

2024

Soc Indic Res

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Measuring income inequality based on unequally distributed income

Cited by 6 publications

References 15 publications

Income Inequality in Mountain Areas: The Case of Agroforestry Farming Systems in Uluguru Mountains, Tanzania

Income Inequality in Mountain Areas: The Case of Agroforestry Farming Systems in Uluguru Mountains, Tanzania

A panel data examination of income inequality in 24 African countries

Measuring Income and Wealth Inequality: A Note on the Gini Coefficient for Samples with Negative Values

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