Do Trade and Poverty Cause Each Other? Evidence from BRICS

Any two variables that are observable have one or the other form of relationship. This is particularly a statistical relationship. But for a statistical relationship to be cause and effect, a theoretical relationship is important. The theoretical relationship can be quantified to search for the evidence of causality. The possible outcomes can be no causality, unidirectional causality, or bidirectional causality. The present study aims at searching for evidence from BRICS countries regarding trade causing poverty or vice versa. Applied econometrics approach is used in the study. Panel econometric techniques have been employed to identify presence/absence of causality between the variables. Apart from this, the study also uses equality of means to identify whether trade and poverty proxies are symmetrical or asymmetrical. The study finds no causal relationship between trade and poverty for BRICS countries except that poverty headcount at $3.2 per day causes trade balance. With respect to the impact on the GINI index, lowest 10 percent income share and poverty headcount ratios are integral to reduce the inequality in the BRICS countries.

Income inequality, regional characteristics and household’s conspicuous consumption: An empirical study in developing market

This study aims to analyze the effect of income inequality and regional characteristics such as ethnicity and religion on conspicuous consumption for visible and invisible good types of households in the Indonesian regions by dividing regions into regions with low and high-income inequality levels based on the value median Gini index in Indonesia. The data set deployed in this study were pooled data collected from households provided by the Indonesian Central Bureau of Statistics 2017 and 2018. Employing the OLS method, we find that 1) income inequality has a negative effect on visible goods, and positive effect on invisible goods, 2) ethnicity and religion give an effect on visible and invisible goods. The government should pay attention to the phenomena of conspicuous consumption because numerous problems will likely arise if this conspicuous consumption is ignored. High conspicuous consumption would tend to lead to a materialistic lifestyle causing a higher inequality. In addition, the crime rate could equally increase given the high risk of conspicuous consumption in attracting others’ attention to individuals’ wealth.

DETERMINAN INDEKS KEBAHAGIAAN DI INDONESIA

ABSTRAK Tolok ukur untuk melihat kemakmuran sebuah Negara dapat dilihat dari GDP yang ada di negara tersebut. Ukuran kesejahteraan tidak hanya diukur berdasarkan substansi akan tetapi diukur berdasarkan keadaan subjektif atau kebahagiaan. Tujuan dari penelitian ini ialah mengetahui faktor yang mempengaruhi kebahagiaan di Indonesia periode 2014 dan 2017. Analisis yang digunakan yaitu model regresi data panel. Penelitian ini menunjukkan bahwa variabel pendidikan, kesehatan, indeks gini serta zis berpengaruh secara terhadap kebahagiaan di Indonesia. Sedangkan variabel PDRB per kapita, kemiskinan, dan Indeks Demokrasi. Kata Kunci: Indeks Kebahagiaan, IPM, Kemiskinan, Indeks Gini, Zakat, Indeks Demokrasi ABSTRACTThe benchmark for seeing the prosperity of a country can be seen from the GDP in that country. The measure of well-being is not only measured based on substance but is measured based on subjective states or happiness. The purpose of this study is to determine the factors that influence happiness in Indonesia for the period 2014 and 2017. The analysis used is a panel data regression model. This study shows that the variables of education, health, Gini index and zis have a significant effect on happiness in Indonesia. While the variables are per capita GRDP, poverty, and the Democracy Index.Keywords: Happiness Index, HDI, Poverty, Gini Index, Zakat, Democracy Index

Income inequality in China 1952-2017: persistence and main determinants

Research background: China's economic growth, however remarkable, is due to the Harrod-Domar nature of economic growth and, therefore, limited. The main limitation lies in the extension of the neoclassical growth model and the government need to decrease regional disparities using new migration, urbanization and social policy. Purpose of the article: It is the rising regional disparity in the total factor productivity to cause the income inequality increase (measured by GINI index) in China from 1952?2017. Our paper brings new insight into the main inequality determinants and causes in China, using a fractional integration modeling framework. Methods: Using fractional integration, we find total factor productivity (TFP), real gross domestic product per capita and growth and expenditures for the social safety net and employment effort to have a statistically significant impact on GINI. Income inequality in China is of a persistent nature with the effects of the shocks affecting the GINI index enduring over time. Findings & value added: The results of this study highlight the importance for model/policy changes by the policy makers and practitioners in China to deal with the inequality issue. This involves improving the growth model through innovation and technological advancement, relaxing TFP dependence on the physical inputs (labor and capital) to reduce income inequality.

The Impact of China’s Grassland Ecological Compensation Policy on the Income Gap between Herder Households? A Case Study from a Typical Pilot Area

China’s policy of subsidies and rewards for grassland ecological protection (PSRGEP) aims to maintain the ecological function of grasslands and increase the income of herder households. Since 2011, the Chinese government has invested more than 150 billion yuan in this policy, making it currently the largest grassland ecological compensation project in China. Based on a survey of 203 herder households in Xin Barag Left Banner, Inner Mongolia Autonomous Region, this study used the Lorenz curve and Gini index to describe the imbalance in the distribution of compensation funds. Then, the integrated livelihood capital scores before compensation were used as a baseline. The changes in ranking and standard deviation of the scores after receiving compensation funds were analysed to draw a conclusion about the impact on the income gap between herder households. Finally, we described the absolute income gap through a group comparison. The results show that the distribution of compensation funds is unbalanced (Gini index is 0.46). According to the order of compensation funds from high to low, the top 20% of sample herder households received 49% of the total funds. Given the unbalanced distribution, households with better family economic conditions received more compensation funds. After receiving the compensation funds, the change in the ranking of the household’s livelihood capital integrated score was small, but the standard deviation increased from 0.1697 to 0.1734, and the Gini index of the households’ capital integrated scores decreased from 0.35 to 0.34 (the coefficient of variation decreased from 0.66 to 0.63). The group with the highest integrated livelihood capital score received 3.6 times the compensation funds of the group with the lowest score. As a result, under the promotion of PSRGEP, the local absolute income gap has widened, but the relative income gap has reduced. This study evaluated the current distribution of compensation funds for PSRGEP, which could provide a scientific basis for managers to optimize the fund distribution in the future.

Single Imputation Methods and Confidence Intervals for the Gini Index

The problem of missing data is a common feature in any study, and a single imputation method is often applied to deal with this problem. The first contribution of this paper is to analyse the empirical performance of some traditional single imputation methods when they are applied to the estimation of the Gini index, a popular measure of inequality used in many studies. Various methods for constructing confidence intervals for the Gini index are also empirically evaluated. We consider several empirical measures to analyse the performance of estimators and confidence intervals, allowing us to quantify the magnitude of the non-response bias problem. We find extremely large biases under certain non-response mechanisms, and this problem gets noticeably worse as the proportion of missing data increases. For a large correlation coefficient between the target and auxiliary variables, the regression imputation method may notably mitigate this bias problem, yielding appropriate mean square errors. We also find that confidence intervals have poor coverage rates when the probability of data being missing is not uniform, and that the regression imputation method substantially improves the handling of this problem as the correlation coefficient increases.

Compound Fault Diagnosis of Rolling Bearing Based on ACMD, Gini Index Fusion and AO-LSTM

Due to the symmetry of the rolling bearing structure and the rotating operation mode, it will cause the coupling modulation phenomenon when it is damaged in multiple places at the same time, which makes it difficult to accurately identify all kinds of faults. For such problems, a compound fault diagnosis method based on adaptive chirp mode decomposition (ACMD), Gini index fusion and long short-term memory (LSTM) neural network optimized by Aquila Optimizer (AO) is proposed. Firstly, a series of IMF components are obtained by decomposing the vibration signal by means of ACMD, and the required components are selected by using the correlation coefficient method. Then, the Gini index of the square envelope (GISE) and the Gini index of the square envelope spectrum (GISES) of each component are calculated, respectively, and they are fused to construct a highly dimensional feature matrix. Then, with the aim of solving the problem of difficult selection of LSTM hyperparameters, the AO-LSTM model is constructed. Finally, the feature matrix is divided into a training set and a test set. The training set is input into the model for training, and then the training network is used to predict the test set, and outputs diagnostic results. The simulation and experimental results show that the proposed method can achieve higher accuracy and stronger robustness, compared with the existing intelligent diagnosis methods for bearing compound faults.

A survey of biomedical journals to detect editorial bias and nepotistic behavior

Alongside the growing concerns regarding predatory journal growth, other questionable editorial practices have gained visibility recently. Among them, we explored the usefulness of the Percentage of Papers by the Most Prolific author (PPMP) and the Gini index (level of inequality in the distribution of authorship among authors) as tools to identify journals that may show favoritism in accepting articles by specific authors. We examined whether the PPMP, complemented by the Gini index, could be useful for identifying cases of potential editorial bias, using all articles in a sample of 5,468 biomedical journals indexed in the National Library of Medicine. For articles published between 2015 and 2019, the median PPMP was 2.9%, and 5% of journal exhibited a PPMP of 10.6% or more. Among the journals with the highest PPMP or Gini index values, where a few authors were responsible for a disproportionate number of publications, a random sample was manually examined, revealing that the most prolific author was part of the editorial board in 60 cases (61%). The papers by the most prolific authors were more likely to be accepted for publication within 3 weeks of their submission. Results of analysis on a subset of articles, excluding nonresearch articles, were consistent with those of the principal analysis. In most journals, publications are distributed across a large number of authors. Our results reveal a subset of journals where a few authors, often members of the editorial board, were responsible for a disproportionate number of publications. To enhance trust in their practices, journals need to be transparent about their editorial and peer review practices.

A simple method for estimating the Lorenz curve

Given many popular functional forms for the Lorenz curve do not have a closed-form expression for the Gini index and no study has utilized the observed Gini index to estimate parameter(s) associated with the corresponding parametric functional form, a simple method for estimating the Lorenz curve is introduced. It utilizes three indicators, namely, the Gini index and the income shares of the bottom and the top in order to calculate the values of parameters associated with the specified functional form which has a closed-form expression for the Gini index. No error minimization technique is required in order to estimate the Lorenz curve. The data on the Gini index and the income shares of four countries that have a different level of income inequality, economic, sociological, and regional backgrounds from the United Nations University-World Income Inequality Database are used to illustrate how the simple method works. The overall results indicate that the estimated Lorenz curves fit the actual observations practically well. This simple method could be useful in the situation where the availability of data on income distribution is low. However, if more data on income distribution are available, this study shows that the specified functional form could be used to directly estimate the Lorenz curve. Moreover, the estimated values of the Gini index calculated based on the specified functional form are virtually identical to their actual observations.