Gini Coefficient Research Articles

ОПРЕДЕЛЕНИЕ РАВНОМЕРНОСТИ РАСПРЕДЕЛЕНИЯ ВРП И ДОХОДОВ БЮДЖЕТОВ РЕГИОНОВ

The article considers topical issues of regional development in the Republic of Kazakhstan in the context of income inequality and economic growth. Regional inequality in Kazakhstan is a topical issue that affects various aspects of the population's life and the country's development. The purpose of this article is to analyze the uniformity of the distribution of economic indicators by regions of the Republic of Kazakhstan. For this purpose, the Lorenz curve and the Gini coefficient were used, which made it possible to analyze the uniformity of the distribution of two important economic indicators by regions: gross regional product (GRP) and regional budget revenues. The results of the analysis showed the uneven distribution of GRP depending on the population of the region. As a result of the analysis, the authors determined that the distribution of income between regional budgets is more uniform than the distribution of GRP. Thus, the formation of the size of regional budgets per capita has almost absolute equality. Keywords: regional budget, gross regional product (GRP), inequality, Lorenz curve, Gini coefficient.

Исследование распределения контроля и метрик разнообразия в децентрализованных автономных организациях

This study empirically investigates the distribution of control and diversity in decentralized autonomous organizations (DAOs) on the Ethereum blockchain. It uses a number of measures-the Nakamoto coefficient, Gini coefficient, Her-findahl-Hirschman index, Theil index, Shannon entropy, and Simpson diversity index-to assess decentralization and fairness in the distribution of control tokens. The analysis reveals a complex landscape in which some DAOs exhibit decentralization with diversity in the distribution of control, while others exhibit significant concentration of power and inequality in the distribution of power. A key observation is the coexistence of a broad distribution of control among EOA addresses with significant inequality in the ownership of control tokens. This indicates that a broad distribution of control does not always correlate with a fair distribution of management tokens. The study also highlights the possibil-ity of delegation mechanisms that may lead to a more equitable distribution of power compared to direct representa-tion. This study provides insight into the governance dynamics of DAOs by demonstrating the complex multi-level bal-ance between decentralization and centralization in a decentralized technology infrastructure.

Evaluation of the Smart Logistics Based on the SLDI Model: Evidence from China

Smart logistics (SL) reflects the digital transformation of the logistics industry, which is key for economic development. Most evaluations are based on the application of technology in SL, and few studies have evaluated SL from a comprehensive perspective. The paper builds the SL development index (SLDI) model from five dimensions based on the driving force, pressure, state, impact, and response (DPSIR) model and identifies the indicator weight by the entropy weight technique. The paper employs the ETDK method, a combined quantitative approach that incorporates entropy weight (E), the technique for order preference by similarity to an ideal solution (TOPSIS) (T), the Dagum Gini coefficient (D), and Kernel density estimation (K), to calculate the closeness degree, analyze spatial-temporal differentiation, and explain the distribution characteristics using data from China spanning 2013 to 2021. The findings show that (1) The SL evaluation is multidimensional and cannot be evaluated only based on technical indicators. A comprehensive evaluation indicator system is necessary. (2) A combined quantitative approach can measure SL development from multiple perspectives and get a clearer picture of the characteristics and regional differences of SL. (3) Influenced by economic development, infrastructure, regional clusters, location, talent, etc., the overall SL development is improving yearly, but SL development in different regions is unbalanced and has different distribution characteristics. The SLDI model developed in this paper will provide a more scientific and reasonable tool for comprehensively evaluating SL. The findings are helpful in proposing suggestions and optimization approaches for subsequent research on SL evaluation and development.

Analysis of the non-equilibrium and evolutionary driving forces of carbon emissions in China's construction industry

Clarifying the historical characteristics and influencing factors of carbon emissions in the construction industry is essential for formulating targeted emission reduction policies. The study utilized Dagum Gini coefficient and geographical detector to measure and analyze the regional disequilibrium characteristics and key driving factors of carbon emissions from the construction industry in China. The results indicate that regional disparities are the primary drivers of spatial imbalances in carbon emissions from China's construction industry. Indirect and interactive carbon emission driving forces were the main drivers of the spatial imbalance of carbon emissions. The output value of the regional economy and industrial structure exerted a significance influence. Notably construction output value, industrial structure and energy efficiency emerged as the main interaction factors. From these findings, four main conclusions are drawn: Controlling the carbon emission of building materials significantly alleviates the spatial imbalance of carbon emission in China's construction industry. The principle of regional coordinated carbon emission reduction in the construction industry should be followed. The regional economic level and industrial structure differences significantly affect the non-equilibrium of construction industry's carbon emissions. Accelerate the development of energy infrastructure can help to improve the construction industry's carbon emission balance. For the first time, Dagum Gini coefficient was combined with geographical detector analysis to study the disequilibrium of carbon emissions in the construction industry. This approach identified new multi-factor interaction driving forces, contributing to a deeper understanding of carbon emissions in the construction sector.

Rethinking Income Inequality: A Search for the Optimal Distribution

The United Nations’ ambitious 2030 Agenda, adopted in September 2015, outlines 17 Sustainable Development Goals (SDGs) with the overarching aim of eradicating poverty and hunger. This agenda represents an unprecedented opportunity to address global challenges. However, one crucial aspect, achieving an appropriate level of income disparity (income inequality), remains a significant hurdle for both academics and policymakers. This research gap necessitates a deeper exploration of the theoretical underpinnings of an optimal income distribution for a given population size. This study delves into this under-researched area by analysing the World Bank’s data on population size and the Gini coefficient (a metric for income inequality) for 103 countries (most recent year data available, up to 2023). The analysis employs regression techniques to unveil the relationship between the Gini coefficient and the natural logarithm of population size. The findings suggest a non-linear association, best characterized by a second-degree polynomial function. This implies that the relationship between population size and optimal income distribution is not a simple linear one. Furthermore, the estimated results indicate that the majority of countries in the sample exhibit Gini coefficients that deviate significantly from their theoretically optimal levels. This finding presents valuable insights for policymakers as they design and implement public policies aimed at achieving a more equitable income distribution. The subsequent section delves into a detailed case study of India, analysing its Gini coefficient and the extent of its deviation from the estimated optimal level.

Fighting Inequality Through a Progressive Tax System for a Just Future in Tanzania

The problem of inequality is one of the economic problems that developing countries still face including Tanzania. This research therefore focuses on presenting the status and trends of inequality and the progressive tax system to pay for a just future in Tanzania. Research was anchored on the Ability-to-Pay Principle. The study was typically a desk research whereby it dwelled on analyzing and synthesizing large amounts of complex secondary data/information and literature concerning the issue of inequality in Tanzania. The target population for this study consists of secondary data sources, which included Peer-reviewed journal articles on taxation and inequality Government reports and taxation data from the Tanzania Revenue Authority (TRA). A purposive sampling methodology was employed to choose relevant studies and reports. This approach ascertained that only sources directly related to the research goals were included. The sample consisted of 20 sources, selected based on criteria such as: Year of publication. Content analysis was adopted in analyzing data to discover themes, patterns and trends related to progressive tax systems, income inequality reduction, and wealth distribution. Key variables such as tax policies, inequality measures (Gini coefficient), and wealth distribution patterns were extracted and analyzed to determine the effectiveness of progressive taxation in Tanzania. Findings revealed that while progressive taxes can be a remedy, the presence of large informal sectors, as in Tanzania, hinder their reach. Thus, while Tanzania’s progressive tax structure holds potential, more reforms are in need to boost implementation alongside widening the tax base. Further, the study concluded that there is little wealth-related taxation in Tanzania. Also, assets such as land ownership or financial assets are hardly/not taxed. The study recommended that given the situation, there is a great need to improve the tax collection with a focus on the existing tax base in Tanzania.

The impact of local government expenditures on local gross regional domestic product per capita in Vietnam

The allocation of funds in the local budget is a matter of concern for the governments and economic scholars. The study examines the influence of local budget expenditures on the GRDP per capita of 63 provinces and municipalities in Vietnam from 2018 to 2022. Regression analysis of panel data reveals that capital expenditure has a positive correlation with local GRDP per capita, whereas current expenditure has a negative correlation with GRDP per capita. Furthermore, the analysis indicates that the percentage of individuals aged 15 and above who are employed and the percentage of urban citizens have an equivalent influence as the GRDP per capita. Conversely, the average age and local Gini coefficient have contrasting effects on GRDP per capita. The author suggests several policy alternatives to assist localities in boosting their GRDP per capita based on the findings of the study model.

Analyzing the impacts on passenger yield of incumbent companies after the entry of a new company into the aviation market: The case of Brazil

The public air transport market is generally oligopolistic, meaning that the market strategies of some companies can influence the decisions of rivals and impact consumer prices. This sector has been marked by a large flow of airline entries and exits. Considering this scenario, the objective of this work is to evaluate how the revenue from airline tickets of incumbent companies in the national aviation market in Brazil was impacted after the entry of a new company. A fixed-effects Difference-in-Differences model (DID) using a feasible generalized least squares (FGLS) estimator with panel data is used to assess this impact. To assess the impact of the entrant on yield dispersion, we apply the regression equation to P90 and P10 of the distribution. We also applied the equation, considering the Gini coefficient as the dependent variable. The results showed that the average income of incumbent companies reduced by an estimated magnitude of 5.9% in the period after the new company entered the market. Our findings also indicate an increase in distribution dispersion in the period following market entry, with a more pronounced reduction of 19% in the P10 and 1.5% in the P90. We conclude that the entry of the new company was readily assimilated by the incumbents, so that the competitive effect can explain the identified reduction in average income. The entry of a new competitor was especially beneficial to consumers in the lower tail of the distribution.

Equity in the geographical distribution of general practitioners in Iran.

The equitable distribution of workforce is imperative for achieving public health goals. This study was conducted to evaluate the equity of the distribution of general practitioners (GPs) in Iran. In this descriptive and cross-sectional study, data from the Information System of Benefits for Iranians and the Iranian Statistics Center were utilized. The study population encompasses all GPs operating in Iran in 2022. To assess equity in the geographical distribution of physicians, the Gini coefficient and Lorenz curve were calculated using Excel software. Additionally, ArcGIS software was used to create a distribution map. The population of Iran and active GPs in 2022 were 85,874,000 and 67,852 respectively, resulting in an average of 7.9 active GPs per 10,000 people. The provinces of Yazd, Tehran, Mazandaran, and Fars exhibited the highest concentration of active GPs per capita, collectively representing more than 28% of the population and 37% of the active GPs. Fifty percent of the active GPs were women. The Gini coefficients for the provincial and intercity distributions of GPs were 0.08 and 0.2, respectively. The distribution of GPs among provinces and cities was deemed fair and relatively fair, respectively. Iran has a lower ratio of GPs per capita than the European nations. Policymakers should pay more attention to the implementation of appropriate policies with the aim of increasing equity in the geographical distribution of GPs in the country.

Public Healthcare Spending, Business Dynamism, and Wage Inequality: Evidence from Developed and Developing Economies

AbstractThis paper examines the effects of public healthcare spending on wage inequality and social welfare in an economy with rural and urban regions. The objectives are twofold. First, we introduce a simple theoretical framework that captures the link between public healthcare spending and firms' output choice and subsequent wage provisions. We demonstrate that in the short run, a rise in public healthcare services can increase urban output and lower urban unemployment and raise both skilled and unskilled wage rates. In the long run, firm entry produces a business‐dynamism effect that enhances the rate of return on capital, reduces the unskilled wage rate and intensifies wage inequality. Second, we empirically test our theoretical predictions using data from 112 economies in the period 2006–2018. We find evidence supporting our theoretical findings concerning the effects of public healthcare on wage outcomes, especially in the long run. Results are highly consistent across the two groups: advanced economies (mainly OECD/European economies), and emerging and developing economies based on alternative measures for wage inequality, including the GINI coefficient and top income share. The paper provides useful information for the public healthcare policy.

Carbon emission measurement and regional decomposition analysis of China’s beef cattle farming industry

IntroductionWarming caused by greenhouse gas (GHG) emissions has become a global environmental issue of widespread concern, and China, as a responsible power, has the pressing task of reducing carbon emissions. China is one of the world’s major beef producers and consumers, and at the same time, beef cattle, as a large livestock, is the largest source of GHG emissions in the livestock industry.MethodsThis study considered the panel data of 31 provinces in China from 2008 to 2022. The kernel density estimation and Dagum Gini coefficient were used to analyze the spatiotemporal dynamic evolution patterns and influencing factors of carbon emissions from China’s beef cattle farming industry.Results(1) The carbon emission trajectory of beef cattle production follows a distinctive “ascend-descend-ascend” three-phase pattern. By 2022, the sector’s cumulative carbon emissions had burgeoned by 37.62% relative to 2008, reflecting an average annual escalation of 2.31%. Despite the overall upward trend in carbon emissions, significant regional differences were observed. The Central Plains region has witnessed a consistent decline, in stark contrast to the Southwest and Northeast regions, which have emerged as hotspots for heightened carbon emissions and intensified emission densities within China’s beef cattle production landscape, underscoring the intensifying significance of carbon mitigation measures. (2) The kernel density curve shows an overall rightward shift with a specific gradient effect on carbon emissions. In addition, the range of the right drag of the curve in 2022 was significantly reduced, which laterally reflects the narrowing of the difference between the provinces with the highest and lowest carbon emissions from beef cattle farming. The principal source of variance in the overall carbon emissions from beef cattle production is the disparities between regions, which accounts for an average annual contribution rate of 52.52%. Conversely, the within-region contribution rates have remained relatively stable, while those for the intensity of transvariation have witnessed a substantial rise, with annual averages of 18.31 and 28.96%, respectively. (3) Regarding the factors influencing carbon emissions reduction, environmental regulations and production efficiency significantly drive carbon emissions reduction in beef cattle farming.DiscussionRelevant government departments should actively guide farmers toward green production, establish perfect policies and regulations for low-carbon beef cattle farming, and promote low-carbon beef cattle farming models based on local conditions.

An automatically recursive feature elimination method based on threshold decision in random forest classification

ABSTRACT The rich feature information contained in the diverse remote sensing data has also exhibited growing potential in the field of image classification. However, the processing of multi-feature data still grapples with the challenges posed by the “curse of dimensionality” and the high computational costs. This paper proposes a remote sensing feature parameter selection method. This method is based on the Gini index of a Random Forest (RF) classifier and employs a 10% threshold decision to identify the optimal combination of remote sensing feature parameters. First, spectral features, texture features, temperature features, elevation features, and principal component features are selected to create a stack of remote sensing images. Second, multiple sets of decision trees are established to cross-validate the contributions of various feature parameters. The feature rankings are determined based on the normalized mean of feature importance. Subsequently, a threshold isset to filter out remote sensing feature parameters that meet the criteria. Finally, an iterative parameter optimization process is carried out to obtain the optimal combination of remote sensing feature parameters. The Landsat 8 image covering Hangzhou Bay in eastern China and the Sentinel-2 remote sensing image of Yancheng Nature Reserve in Jiangsu, China were selected for experiments. The results show that the feature parameters of the remote sensing image screened by the method in this paper are representative, and the proposed method demonstrates strong adaptability and generalization performance in complex environments. The study has important guiding significance for regional spatial planning and sustainable development.

Assessing the Reliability and Calibration of the Gini Coefficient

This research paper delves into the Gini Coefficient, a widely used statistical measure of income inequality. We assess the accuracy and reliability of this tool by examining its strengths, such as its simplicity and applicability across countries, alongside its limitations, including sensitivity to data variations and contextual differences. The paper explores how the Gini Coefficient is utilized in economics and policy analysis, providing a balanced view of its advantages and drawbacks, ultimately evaluating its effectiveness in capturing the complexities of income distribution.

Longitudinal analysis of social and community factors effective in increasing the number of liver donors in the United States.

In this research, it was aimed to evaluate of social and community factors effective in increasing the number of liver donors. Descriptive and relational scanning models were used to conduct the research. Data on liver donors was gathered from the USA Health Resources & Services Administration's Organ Procurement and Transplantation Network between 1988 and 2023. The United States (USA) World Bank Country Reports provided the mortality rates. The data was analyzed using Spearman rho correlation, year-controlled partial correlation, and Generalized Linear Model-Logit analysis. Deceased donor numbers were significantly and negatively correlated with government health expenditure (r = -0.816; P < .01), current health expenditure (r = -0.768; P < .01), female education attainment (r = -0.804; P < .01) and Gini index (r = 0.434; P < .05). Living donor numbers were significantly and negatively correlated with government health expenditure (r = -0.731; P < .01), current health expenditure (r = -0.781; P < .01), male percentage (r = -0.786; P < .01), female education attainment (r = -0.640; P < .05), employment (r = 0.751; P < .01), GDP (r = -0.792; P < .01) and Gini index (r = -0.486; P < .01). Living donor numbers were significantly and positively correlated with age dependency (r = 0.815; P < .01). Generalized Linear Model-Logit (GLM-L) results showed that effect of female education attainment had significant contribution on deceased liver donor (B = -3290.605; P < .01). Effects of significantly correlated community factors on living liver donor numbers were found to be statistically insignificant (P > .05). Research findings reveal that among community factors, especially women's participation in education has a statistically significant effect on liver donors. These results show that the health expenditures made over the years do not provide any added value for liver donors, and role of women on liver donor is significantly dominant.

Interwoven relationships between inequality, poverty, thuggery, and terrorism

This article aims to justify the relationship between inequality, poverty, thuggery, and terrorism, mainly in third-world countries. The primary issue is the incessant growth of inequality that leads to chronic poverty, which gives way to political thuggery and, consequently, terrorism in weak democracies or developing countries. In this particular context, the social disorganization theory was selected to elucidate the correlation between inequality, poverty, criminality, and terrorism in the top ten poorest countries in the world. The relationship between the stated societal negative factors could be mutualistic or commensalism, or both depending on the scenario and political cum economic idiosyncrasies of the affected society or community. In order to provide additional support for the arguments, data from various sources such as the Gini coefficient, per capita national income, crime, conflict, and terrorist indexes were utilized. The article justified that the higher the level of inequality, the higher the level of poverty, which have a mutually beneficial relationship with the pockets of criminality and terrorism in a given society.

A regression method for estimating Gini index by decile

Based on the three-parametric Lorenz curve proposed by Kakwani (1980), this paper builds a multiple linear regression model to estimate the parameters by the weighted least square method named the regression method. Using the Lorenz curve, the Gini index and its variance are then calculated. Compared to the error minimization technique, the regression method has a better performance in estimating the Gini index using Kakwani (1980)’s Lorenz curve and a dataset of sixteen economies from the United Nation University-World Income Inequality Database (UNU-WIID). The results also suggest that the regression method has an advantage when estimating the Gini index and fitting the income shares by decile for the medium and higher inequality economies. We find that the three-parametric Lorenz curve has a better performance than the double-parametric Lorenz curve, and the double-parametric Lorenz curve is superior to the single-parametric Lorenz curve, judged by the RMSE of the actual Gini index and the estimated ones.

Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.

Cardiac arrest (CA) is one of the leading causes of death among patients in the intensive care unit (ICU). Although many CA prediction models with high sensitivity have been developed to anticipate CA, their practical application has been challenging due to a lack of generalization and validation. Additionally, the heterogeneity among patients in different ICU subtypes has not been adequately addressed. This study aims to propose a clinically interpretable ensemble approach for the timely and accurate prediction of CA within 24 hours, regardless of patient heterogeneity, including variations across different populations and ICU subtypes. Additionally, we conducted patient-independent evaluations to emphasize the model's generalization performance and analyzed interpretable results that can be readily adopted by clinicians in real-time. Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and the eICU-Collaborative Research Database (eICU-CRD). To address the problem of underperformance, we constructed our framework using feature sets based on vital signs, multiresolution statistical analysis, and the Gini index, with a 12-hour window to capture the unique characteristics of CA. We extracted 3 types of features from each database to compare the performance of CA prediction between high-risk patient groups from MIMIC-IV and patients without CA from eICU-CRD. After feature extraction, we developed a tabular network (TabNet) model using feature screening with cost-sensitive learning. To assess real-time CA prediction performance, we used 10-fold leave-one-patient-out cross-validation and a cross-data set method. We evaluated MIMIC-IV and eICU-CRD across different cohort populations and subtypes of ICU within each database. Finally, external validation using the eICU-CRD and MIMIC-IV databases was conducted to assess the model's generalization ability. The decision mask of the proposed method was used to capture the interpretability of the model. The proposed method outperformed conventional approaches across different cohort populations in both MIMIC-IV and eICU-CRD. Additionally, it achieved higher accuracy than baseline models for various ICU subtypes within both databases. The interpretable prediction results can enhance clinicians' understanding of CA prediction by serving as a statistical comparison between non-CA and CA groups. Next, we tested the eICU-CRD and MIMIC-IV data sets using models trained on MIMIC-IV and eICU-CRD, respectively, to evaluate generalization ability. The results demonstrated superior performance compared with baseline models. Our novel framework for learning unique features provides stable predictive power across different ICU environments. Most of the interpretable global information reveals statistical differences between CA and non-CA groups, demonstrating its utility as an indicator for clinical decisions. Consequently, the proposed CA prediction system is a clinically validated algorithm that enables clinicians to intervene early based on CA prediction information and can be applied to clinical trials in digital health.

Mining industry development, enforcement intensity of security policy and intra-city development disparity

China, the world's largest developing country, is rich in mineral resources, albeit unevenly distributed geographically. Some cities with a surplus of mineral resources may form path dependence on these natural resource industries, leading to imbalanced urban development and widening development disparities within cities. However, in the event of safety incidents in the mining industry, local governments are compelled to regulate the excessive development of the mining sector and enhance the enforcement intensity of safety production policies. This study, based on the measurement of the Gini coefficient using nighttime light data for 281 prefecture-level cities in China, manually compiled the locations and death tolls of mining industry production safety incidents from 2001 to 2020, established an empirical analysis framework based on the instrumental variable two-stage least squares method. The research findings indicate: (1) Since the beginning of the 21st century, China's mining industry has experienced significant growth propelled by market-oriented reforms. However, it has also played a notable role in widening development disparities within cities. Robust results were obtained through instrumental variable estimation using the standard deviation of urban ground slope as a geographical indicator. (2) The occurrence of safety incidents in the mining industry will immediately escalate the enforcement intensity of regional safety policies, creating a significant inhibitory impact on alleviating the widening development disparities within cities caused by the expansion of the mining industry. In the case of larger safety incidents with a death toll of 3 or more, this impact will become even more pronounced. (3) Heterogeneity analysis reveals that the outlined mechanism is prominently present in the middle area, where the mining industry is relatively developed. In contrast, there is no significant correlation between the occurrence of safety accidents and the escalation of safety policy implementation intensity in the economically developed eastern region and the western region with lower levels of infrastructure development. However, in cities with a higher proportion of the mining industry, the aforementioned relationships remain stable. (4) Once a mining accident involving fatalities occurs in a prefecture-level city, local governments generally strengthen the enforcement of safety policies, comprehensively eliminate hidden dangers in production safety. This shift leads to a reversal in the impact of the mining industry on the development disparity within the city, changing from an expansion to a reduction effect. Moreover, this reduction effect persists for five years. In the case of a serious safety production incident resulting in 10 or more deaths, the impact of increased implementation of safety policies also extends for a prolonged period of five years. This article makes a clear marginal contribution to enrich the study on the effects of China's mining industry safety policies.

Unraveling the drivers of inequality in primary health-care resource distribution: Evidence from Guangzhou, China

BackgroundPrimary health-care (PHC) resource plays a critical role in addressing challenges related to healthcare accessibility and costs, as well as implementing a graded diagnosis and treatment system. This study aims to analyze the extent and components of inequality in PHC resource allocation in Guangzhou. MethodsBy utilizing data from the Annual Report on Healthcare Institutions for community and township health centers in Guangzhou from 2012 to 2020, this paper analyses the distribution of human, material and financial resources. It examines inequities in health resource allocation using the Gini coefficient. Additionally, it investigates the internal structure of overall inequality through the two-stage nested Theil decomposition method and explores the influencing mechanisms of inequality using the concentration index decomposition method. ResultsThe findings indicate that between 2012 and 2020, except for beds in 2018 and 2019, the Gini coefficients for resource allocation relative to population size remained below 0.3. Moreover, the Gini coefficient for geographical area ranged from 0.1228 to 0.3481. The two-stage nested Theil decomposition results reveal that within-district disparity contributes the most to the overall inequality, exceeding 46 %. The Concentration indexes show negative values, and the decomposition analysis highlights the significant contribution of the number of individuals served (over 72 %) to the inequality in health resource allocation. ConclusionAt the administrative district level, the allocation of PHC resources in Guangzhou demonstrates overall equity, with within-district inequality identified as the primary contributor to the overall inequality. The distribution of PHC resources in Guangzhou follows a pro-poor pattern. The key factors influencing equity enhancement in PHC resource allocation in Guangzhou include the number of individuals served, the presence of township health centers among institution types, the number of hospital admissions per capita, and the proportion of children aged 0–3.

An Application of the Dagum Type III Model to Measure Household Net Wealth Inequality in Indonesia

Abstract This paper applies the Dagum Type III model to measure household net wealth inequality in Indonesia utilising data from the Indonesian Family Life Survey (IFLS) 1993–2014. The results are the distribution of household net wealth in Indonesia is right-skewed, with long-and sparse-hand tails that reflect a large proportion of households that have very low net wealth and a small proportion of households that have very high net wealth. Further, the inequality of household net wealth in Indonesia declined, as shown by the decrease in the Gini coefficient.