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In this chapter we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).

Purpose – This paper considers methods for decomposing indexes that incorporate economic disadvantage into a measure of segregation. According to such indexes, segregation in high-economic-status occupations is worse than similar segregation in low-economic-status occupations. The paper presents three decompositions of these indexes.

Methodology/Approach – The paper first characterizes a class of segregation indexes that include economic disadvantage in the index. It then develops mathematical methods for decomposing a change in such an index. The change is decomposed into two or more components: components that indicate either the effect of changes in economic disadvantage or the effect of changes in a standard measure of segregation – a measure that essentially ignores economic disadvantage. The paper then implements the decompositions using data on U.S. occupational segregation by gender between 1970 and 2000.

Findings – The primary finding is that a segregation index that incorporate economic disadvantage can be decomposed in interesting ways. A secondary finding is that such indexes indicate reduced segregation between 1970 and 2000. The dominant forces associated with the reduction were (a) the convergence of occupational gender ratios and (b) the movement of women out of less advantaged occupations and into the comparatively well-compensated professional and managerial occupations.

Research limitations/Implications – The 1970–2000 results are mainly illustrative. They are based on three broad occupational categories for which there were compatible earnings data, and the analysis could quite feasibly be done with more detailed occupational categories.

Non-fossil fuels are receiving increasing attention within the context of addressing global climate challenges. Based on a review of non-fossil fuel consumption in major countries worldwide from 1985 to 2015, the purpose of this paper is to analyze trends for global non-fossil fuel consumption, share of fuel consumption and inequality.

The similarities were obtained between the logarithmic mean divisia index and the mean-rate-of-change index decomposition analysis methods, and a method was proposed for complete decomposition of the incremental Gini coefficient.

Empirical analysis showed that: global non-fossil fuel consumption accounts for a small share of the total energy consumption, but presents an increasing trend; the level of global non-fossil fuel consumption inequality is high but has gradually declined, which is mainly attributed to the concentration effect; inequality in global non-fossil fuel consumption is mainly due to the difference between nuclear power and hydropower consumption, but the contributions of nuclear power and hydropower to per capita non-fossil fuel consumption are declining; and population has the greatest influence on global non-fossil fuel consumption during the sampling period.

The main contribution of this study is its analysis of global non-fossil fuel consumption trends, disparities and driving factors. In addition, a general formula for complete index decomposition is proposed and the incremental Gini coefficient is wholly decomposed.

This paper aims to measure the change and the sources of change in total factor productivity (TFP) of the Indian non-life insurance sector over the period 2005–2016.

This study employs the bootstrapped Malmquist index (MI) to assess the changes in the TFP and adopts a decomposition approach proposed by Balk and Zofío (2018). Moreover, it utilises truncated regression to identify the determinants of the TFP. In addition, it employs Wilcoxon-W test and t-test to scrutinise the difference between the state-owned and the private insurers in terms of variations in TFP and its various components.

The results divulge a miniature improvement in TFP of the insurance sector, which is primarily attributable to the improvement in scale efficiency (economies of scale). The results also reveal that there are no significant TFP differences across the ownership. However, private insurers have better scale efficiency and lower input-mix efficiency than state-owned insurers. In addition, the results unveil that size, diversification and reinsurance have a negative impact on the TFP, while age has a positive impact on it.

The results may help the policymakers to frame new consolidation policies. Moreover, the findings may guide the decision-makers of the Indian non-life insurance companies to abate inefficiency and improve TFP.

This study estimates bias-corrected changes in TFP and efficiency in the non-life insurance sector. Moreover, it adopts an elaborated decomposition of the MI to identify the true sources of change in the TFP.

Enhancing household consumption and reducing inequality are among the fundamental goals of many developing countries. The purpose of this study therefore is to disaggregate household consumption expenditure into food and non-food and, thus, decompose inequality into within- and between-groups.

The study adopts generalised entropy (GE) measures. Second, the study uses regression-based inequality decomposition to ascertain the determinants of inequality in food and non-food expenditure using household demographic and socioeconomic characteristics as covariates.

The results show that non-food expenditure is the major source of inequality in household consumption expenditure in both urban and rural areas with inequality coefficients of above 0.6 compared to about 0.4 for food expenditure. The decompositions also show that within-group inequalities for non-food and food expenditure are, respectively, 0.97 and 0.365 using the Theil index, while between-group inequalities for non-food and food are, respectively, 0.016 and 0.035. Furthermore, the regression-based inequality decompositions show that variables such as living in rural areas, household size, household dwelling and household dwelling characteristics account for the significant proportion of inequality in food and non-food expenditure.

The policy implication of the findings, among others, is that policies should focus on addressing inequality within rural and urban areas, especially with respect to non-food expenditure than in inequality existing between urban and rural areas. These non-food expenditures include expenditure in education, health, energy, accommodation, water and sanitation.

This paper aims to investigate the connectedness of Islamic Stock Markets in five regional financial systems, namely, the United States, the United Kingdom, Europe (EU), GCC (Gulf Cooperation Council) and APAC (Asia-Pacific Countries), and across different asset classes (i.e. bonds, gold and crude oil).

This methodology is inspired by Diebold and Yilmaz (2012) and Barunlik and Krehlik (2017) for performing dynamic variance decomposition network and for studying time–frequency dynamics of connectedness at different frequencies.

Results show that the nature of connectedness over the past decade is time–frequency dynamics. The decomposition of the total volatility spillovers is mostly dominated by the long-run component. Furthermore, dominant regions are the largest contributors of spillover index, with the lowest contribution in the system coming from the GCC market. Results also reveal a slightly higher volatility spillover index of Islamic than conventional equity indexes. Finally, the system that encompasses commodities and Islamic finance instruments, generates the much lower volatility spillover.

The findings have significant implications for portfolio managers who are interested in being able to predict asset returns, as well as for policymakers who are concerned with market stability.

This paper aims to study the co-integration among the stock markets of BRIC nations of Brazil, Russia, Indian and China to analyze if the series move apart or they move together in the long term. and to examine the implied volatility transmission between the Indian implied volatility index and three international indices and vice-versa by using synchronized daily data by using techniques such as generalized impulse response functions and variance decompositions. More specifically, the authors investigate how shock to one volatility index affects another volatility index and what is the magnitude and sign of affect and how long does the effect persist?

Unit root tests are conducted to determine the order of integration for each index. The cointegration analysis is used to evaluate the co-movement of a long-term equilibrium relationship among the four stock market indices. Variance decomposition test helps to explain that how much movement in the dependent variable is explained due to its own shock vis-a-vis to the shock of other variables under the study. Impulse response function is used to find out the impact of the standard deviation of shock given to one variable on the impact on the other variable.

There exists one long-run cointegrating relationship between the four stock markets under study. The coefficient of VECM is −0.00031 which is negative and highly significant at 1 per cent. This confirms the existence of a stable long-run causal relationship between the variables. Variance decomposition shows that indices of Brazil, China and Russia can explain on average 4, 0.5 and 5 per cent, respectively, of the forecast error variance of Indian index. On the other hand, Indian market can explain on an average 6.7, 5 and 3 per cent of the forecast error of Brazilian, Chinese and Russian markets, respectively.

The research paper is an original work of the author.

The purpose of this paper is to examine the interrelation between two important dimensions of gender segregation: education and occupation. It further investigates the gender wage gap.

The author uses a three-way additive decomposition of the mutual information index – an index based on the concept of entropy. A non-parametric wage decomposition method that uses matching comparisons is used for measuring the wage gap.

The results show that the extent of gender segregation in India is higher in urban areas than that in rural areas. Most of the observed segregation in rural labour markets originates from educational outcomes, whereas in urban markets it is due to occupational profile of individuals. The findings of the wage decomposition analysis suggest that education in rural areas also explains a sizeable part of the gender wage differential. Nevertheless, a large share of the wage gap remains unexplained in both rural and urban areas.

While much research has looked at occupational segregation, less attention has been paid to educational segregation. The paper uses a unique approach to understand the joint effect of occupation and education in explaining gender segregation.

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.

The purpose of this paper is to use an index decomposition analysis to investigate the driving forces of China’s CO₂ emissions related to fixed asset investments from 2003 to 2015.

This paper uses an index decomposition analysis to investigate the driving forces of China’s CO₂ emissions related to fixed asset investments from 2003 to 2015. To make policy recommendations, this paper identifies three effects. An approach to calculating energy-relevant CO₂ emissions is also presented.

The results suggest that the amount of CO₂ emissions related to fixed asset investments increased during the entire period. The social and economic effect played a major role in promoting carbon emissions, followed by the fixed asset effect. Therefore, the activity factor was the dominant positive factor, followed by the construction factor. The negative element was the energy effect, in which the energy intensity factor played an important role in reducing emissions, followed by the structural factor. Moreover, the carbon intensity factor might be a potential inhibitory force in reducing carbon emissions.

A steady financial policy, relaxed family planning, sustainable urbanization, strategy of innovation-driven development, reform of scientific and technological structures, development of science and technology and exploration of new energy sources are proposed to mitigate carbon emissions from fixed asset investments. The conclusion also provides a reference for developing countries in similar situations.

This paper uses an index decomposition analysis to investigate the driving forces of China’s CO₂ emissions related to fixed asset investments from 2003 to 2015. To make policy recommendations, this paper identifies three effects. An approach to calculating energy-relevant CO₂ emissions is also presented.