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The purpose of this paper is to propose the application of Gini, Theil and concentration indices for measuring inequality in water usage.

Gini coefficients and Theil indices have been used to estimate the overall inequality in domestic water use in a sample of 30 countries around the world. Along with Theil’s L (unweighted) index, liters per capita per day and gross national income weighted Theil index have also been estimated. Theil indices have been further disintegrated into within- and between-group inequalities. Concentration curve is also constructed to study the inequality in water use in accordance to the countries’ economic standing.

Domestic water use is high among the well-off countries considered in the study. Also, the Theil indices indicate that between group inequality contributes more to the overall inequality. It is observed that Theil indices, which consider only per capita water usage and can be decomposed, give a better insight into the existing inequality.

Different approaches were used to quantify inequality. The choice of index depends on the context of the study. The proposed approaches can contribute to planning of sustainable water management and development policies.

There is a dearth of metrics for quantifying inequality in water access or use. The study presents the application of indices, widely used in quantifying inequality in access to other resources such as income and energy, in assessing water inequality.

Based on government data from 1993 to 2008, this chapter aims to compute and analyze the trends of inequity in interprovincial and regional per-student spending in China's compulsory education, and to ascertain the potential impact of changes in education financing policies. Appropriate inequity measures (Gini and Theil index and Gini decomposition, among others) are employed to provide a systematic picture of the trends. Main findings include: (1) all inequity measures show large and overall increased disparities among provinces and among regions, between 1993 and 2008. (2) However, a slight drop of spending inequity is observed at the primary education level around 2002 and a larger reduction in 2005 and on. There are more turning points in the trend of lower-secondary per-student spending among provinces. These patterns are consistent across different inequity measures and spending indicators (per-student total spending, per-student recurrent spending, and per-student nonpersonnel spending). (3) The trend toward more balanced resource allocation around 2002 and 2005 could be the impact from the Reform of Tax and Administrative Charges and the New Mechanism for Financing Rural Compulsory Education. An increased share of budgetary expenditure in determining total spending suggests that equalizing financing policies have the potential to induce a significant reduction in spending inequity. These findings may help policy makers to better understand and alter the extent of spending inequity in compulsory education. This is an original empirical study that systematically derives the spending inequity trends over a long period in China's compulsory education.

The relation between economic growth and income distribution has been the subject of a growing body of empirical studies ever since the initial discussion by Kuznets, who argued that the income distribution in the early stages of growth in developing areas would most likely move in the direction of greater inequality. While there is substantial support for this hypothesis of relative inequality increase, recent cross‐country evidence does not corroborate a stronger hypothesis of a decline in the absolute income level of the poorer groups. Nevertheless, cross‐sectional analyses may be fraught with misleading generalisations, and the validity of the hypothesis will have to be assessed by the secular experience of individual countries. Within this focus, one country which does not appear to support the Kuznets thesis is Puerto Rico, where recent studies point to a movement toward greater income equality over the 1949–69 period. This paper, which covers the same two‐decade interval, attempts to throw further light upon the overall question by employing, in addition to the well‐known Lorenz/Gini measure, Theil's entropy index. The latter coefficient permits decomposition of given sets by quantifying between‐set and within‐set inequalities.

The last two decades have been characterised by a rise in income and wage inequality in a wide range of countries, including European transition countries. The rise in globalisation is one major factor explaining this increasing wage inequality. International trade and FDI have increased significantly since the beginning of transition and the purpose of this paper is to focus on whether FDI plays an important role in explaining the pattern of wage inequality in selected transition countries.

A cross-country empirical investigation has been conducted using two alternative measures of wage inequality: the Gini coefficient and the Theil index. Several model specifications and estimation strategies have been employed to obtain consistent estimates and to check for the robustness of the results.

The results indicate that a rising share of inward FDI in gross domestic product (GDP) increased wage inequality in transition economies, though its overall effect was relatively small. Considering the long run, there is no clear evidence of a concave relationship between FDI and wage inequality, which may be a consequence of the relatively low levels of FDI in many transition countries.

Inwards FDI has made a small contribution to increasing wage inequality in European transition economies. However, its overall beneficial effects on labour markets in these countries suggest that rather than restricting FDI governments should target increasing the supply of skilled labour.

This new empirical evidence supports the hypothesis that an increased inward FDI stock as a share of GDP increases wage inequality in transition economies, however, this relationship is a complex one. Differences in average wages, wage differentials, employment shares of skilled workers and relative size of the foreign-owned sector are all likely to be important for the behaviour of wage inequality.

This study is aimed at better understanding the evolution of inequality in carbon emission in intraincome and interincome groups in the world, and then to uncover the driving factors that affect inequality in carbon emission.

The approach is developed by combining the Theil index and the decomposition technique. Specifically, the Theil index is used to measure the inequality in carbon emissions from the perspective of global and each income group level. The extended logarithmic mean Divisia index was developed to explore the driving factors.

This study finds that the inequality in carbon emissions of intraincome group is getting better, whereas the inequality in carbon emission of interincome group is getting worse. And the difference in global carbon emissions between income groups is the main source of global carbon emission inequality, which is greater than that within each income group. In addition, the high-income group has transferred their carbon emissions to upper-middle income group by importing high-carbon-intensive products to meet the domestic demand, while lower-middle-income group do not fully participate in the international trade.

To alleviate the global carbon inequality, more attention should be paid to the inequality in carbon emission of interincome group, especially the trade between high-income group and upper-middle income group. From the perspective of driving factors, the impact of import and export trade dependence on the per capita carbon emissions of different income groups can almost offset each other, so the trade surplus effect should be the focus of each group.

In order to consider the impact of international trade, this study conducts a comprehensive analysis of global carbon emissions inequality from the perspective of income levels and introduces the import and export dependence effect and the trade surplus effect into the analysis framework of global carbon emission inequality drivers, which has not been any research carried out so far. The results of this paper not only provide policy recommendations for mitigating global carbon emissions but also provide a new research perspective for subsequent inequality research.

Existing research shows that popular income inequality measures fail to reflect their respondents’ perceptions of income inequality. However, most of the current literature focuses on what is negative, telling us what individuals do not perceive. This paper presents an alternative methodology to help uncover actual perceptions of inequality, how people perceive inequality instead of how they don’t. Multidimensional scaling, a statistical tool for visualizing dissimilarity data as a low-dimensional map, is used on results of a simple grouping task with a given distribution set. The outcome is a perception map that presents respondents’ answers spatially, which enables additional insight into respondents’ thinking. The map created by the respondents’ replies, presented in this paper, indicates that their decisions are driven by two factors: what the biggest gap in incomes of a given distribution is and whether some groups have equal incomes. The result additionally validates multidimensional scaling as a tool for measuring income inequality perception and opens new ways of improving inequality perception questionnaires.

In this paper the authors examine evidence on exchange rate predictability through commodity prices for a set of countries categorized as commodity import- and export-dependent developed and emerging countries.

The authors perform in-sample and out-of-sample forecasting analysis. The commodity prices are modeled to predict the exchange rate and to analyze whether this commodity price model can perform better than the random walk model (RWM) or not. These two models are compared and evaluated in terms of exchange rate forecasting abilities based on mean squared forecast error and Theil inequality coefficient.

The authors find that primary commodity prices better predict exchange rates in almost two-thirds of export-dependent developed countries. In contrast, the RWM shows superior performance in the majority of export-dependent emerging, import-dependent emerging and developed countries.

Previous studies examined the exchange rate of commodity export-dependent developed countries mainly. This study examines both developed and emerging countries and finds for which one the changes in prices of export commodities (in case of commodity export-dependent country) or prices of major importing commodities (in case of import-dependent countries) can significantly predict the exchange rate.

Given the regional heterogeneity of economic development, electricity consumption in various regions exhibits a discrepant growth pattern. The purpose of this study is to construct a grey system forecasting model with intelligent parameters for predicting provincial electricity consumption in China.

First, parameter optimization and structural expansion are simultaneously integrated into a unified grey system prediction framework, enhancing its adaptive capabilities. Second, by setting the minimum simulation percentage error as the optimization goal, the authors apply the particle swarm optimization (PSO) algorithm to search for the optimal grey generation order and background value coefficient. Third, to assess the performance across diverse power consumption systems, the authors use two electricity consumption cases and select eight other benchmark models to analyze the simulation and prediction errors. Further, the authors conduct simulations and trend predictions using data from all 31 provinces in China, analyzing and predicting the development trends in electricity consumption for each province from 2021 to 2026.

The study identifies significant heterogeneity in the development trends of electricity consumption systems among diverse provinces in China. The grey prediction model, optimized with multiple intelligent parameters, demonstrates superior adaptability and dynamic adjustment capabilities compared to traditional fixed-parameter models. Outperforming benchmark models across various evaluation indicators such as root mean square error (RMSE), average percentage error and Theil’s index, the new model establishes its robustness in predicting electricity system behavior.

Acknowledging the limitations of traditional grey prediction models in capturing diverse growth patterns under fixed-generation orders, single structures and unadjustable background values, this study proposes a fractional grey intelligent prediction model with multiple parameter optimization. By incorporating multiple parameter optimizations and structure expansion, it substantiates the model’s superiority in forecasting provincial electricity consumption.

The purpose of this paper is to measure the unidimensional and multidimensional inequality in the case of Pakistan and compare their results at the provincial as well as regional (urban and rural areas) level. The authors collected data from Pakistan Social and Living Standard Measurement and Household Integrated Economic Survey for fiscal years of 1998–1999 and 2013–2014.

The authors used Gini coefficient for unidimensional inequality and multidimensional indexing approach of Araar (2009) for multidimensional inequality.

The findings predicted that unidimensional inequality is relatively high in the urban area due to uneven dissemination of income, but multidimensional inequality is quite high in rural areas because of higher disparities among all dimensions. At the provincial level, Punjab has relatively high-income inequality followed by Sindh, KPK and Baluchistan.

This study is a pioneering effort to compare two time periods to explore unidimensional and multidimensional inequality in all provinces of Pakistan and their representative rural-urban regions by applying Araar and Duclos’s (2009) approach. Further, this study opens some new insights for policy makers.

This study examines the effects of the accruals vs. cash flow bases on firm’s MB ratio as a proxy for shareholder value. The methodology utilizes the benefits of the ‘partial adjustment model’ where it addresses the extent to which the shareholder value adjusts to a target level. The final results indicate that (a) the accrual basis helps adjust the shareholder value to a target level more than the cash flow basis, (b) the shareholder value is associated with profitability‐related ratios and dividend‐related ratios, (c) in both bases, the shareholders value is positively associated with earnings per share and price‐to‐earnings ratio, (d) the significant effects of firm‐specific controls indicate that the shareholder value is affected by the accounting base in certain industries, certain size, and affected by the time as well. The results of the sensitivity analysis show that the accruals‐based estimates and cash flow estimates are robust and reliable.