scholarly journals Stress-matrix-based formation transformation control under mixed control variables

2020 ◽  
Vol 2 (2) ◽  
pp. e36
Author(s):  
Yunlong Pan ◽  
Qingkai Yang ◽  
Bo Zhou ◽  
Hao Fang
Keyword(s):  
Control Variables ◽  
Mixed Control ◽  
Stress Matrix

Pengaruh Konvergensi International Financial Reporting Standards (Ifrs) Terhadap Penghindaran Pajak(Tax Avoidance)

2018 ◽  
Vol 26 (2) ◽  
pp. 158-169
Author(s):  
Umi Wahidah ◽  
Sri Ayem
Keyword(s):  
Financial Reporting ◽  
Tax Evasion ◽  
Stock Exchange ◽  
Control Variable ◽  
Tax Avoidance ◽  
Sampling Technique ◽  
International Financial Reporting Standards ◽  
Reporting Standards ◽  
Control Variables ◽  
Financial Reporting Standards

This research aimed to examine the effect of the convergence of International Financial Reporting Standards (IFRS) on tax avoidance on companies listed in Indonesia Stock Exchange. Tax avoidance that used in this research was Cash Efective Tax Rate (CETR). This research is also use the control variable to get other different influence that different such as CSR, size, and earning management (EM. This research used populations sector of transport service companies that listed in Indonesia Stock Exchange. The data of this research taken from secondary data that was from the Indonesia Stock Exchange in the form of Indonesian Capital Market Directory (ICMD) and the annual report of the company 2011-2015. The method of collecting sample was purposive sampling technique, the population that to be sampling in this research was populations that has the criteria of a particular sample. Companies that has the criteria of the research sample as many as 78 companies. The method of analysis used in this research is multiple regression analysis. Based on regression testing shows that the convergence of International Financial Reporting Standards (IFRS) has a positiveand significant impact on tax evasion. This shows that IFRS convergence actually improves tax evasion practices. The control variables of firm size and earnings management also significantly influence the application of IFRS in improving tax avoidance practices, while CSR control variables have no role in convergence IFRS in improving tax evasion practice.

An Analysis of the Link between Water Infrastructure Financing and Water Provision South Africa

2018 ◽  
Vol 10 (3(J)) ◽  
pp. 74-83
Author(s):  
Lerato C. Bapela ◽  
Collins C. Ngwakwe ◽  
Mokoko P. Sebola
Keyword(s):  
South Africa ◽  
Quantitative Research ◽  
Secondary Data ◽  
P Value ◽  
Unique Contribution ◽  
Water Infrastructure ◽  
Control Variables ◽  
Policy Makers ◽  
Infrastructure Finance ◽  
Water Provision

This paper evaluated the relationship between water infrastructure financing and water provision in South Africa. The research followed a quantitative research design; secondary data for water infrastructure financing and water provision in South Africa was obtained from the Trans - Caledon Tunneling Agency (TCTA) and the World Bank for the period 1994 - 2014 . The regression results indicated two separate findings which offers unique contribution to the current literature; results from water asset finance as a single independent variable on water provision showed a significant relationship. However, an addition of two control variables , corruption and violence, neutralised the effectiveness of water asset finance on water provision to the extent that water asset finance became less significant with a P value of 0.05. The paper makes a nuance contribution from the findings, which specifically is that finance alone may not deliver target water provision if corruption and violence is left unbridled. The paper thus recommends the need for public policy makers to control the rate of corruption and violence to enable effective application of water infrastructure finance in water provision. The paper also recommends the need for further research on other government departments to integrate corruption and violence as control variables. 

scholarly journals The link between ethnic diversity and scientific impact: the mediating effect of novelty and audience diversity

2021 ◽  
Author(s):  
Jielan Ding ◽  
Zhesi Shen ◽  
Per Ahlgren ◽  
Tobias Jeppsson ◽  
David Minguillo ◽  
...  
Keyword(s):  
Ethnic Diversity ◽  
Scientific Collaboration ◽  
Information Science ◽  
Mediating Effect ◽  
Path Modeling ◽  
Control Variables ◽  
Scientific Impact ◽  
Subject Categories ◽  
Sound Management ◽  
The Relationship

AbstractUnderstanding the nature and value of scientific collaboration is essential for sound management and proactive research policies. One component of collaboration is the composition and diversity of contributing authors. This study explores how ethnic diversity in scientific collaboration affects scientific impact, by presenting a conceptual model to connect ethnic diversity, based on author names, with scientific impact, assuming novelty and audience diversity as mediators. The model also controls for affiliated country diversity and affiliated country size. Using path modeling, we apply the model to the Web of Science subject categories Nanoscience & Nanotechnology, Ecology and Information Science & Library. For all three subject categories, and regardless of if control variables are considered or not, we find a weak positive relationship between ethnic diversity and scientific impact. The relationship is weaker, however, when control variables are included. For all three fields, the mediated effect through audience diversity is substantially stronger than the mediated effect through novelty in the relationship, and the former effect is much stronger than the direct effect between the ethnic diversity and scientific impact. Our findings further suggest that ethnic diversity is more associated with short-term scientific impact compared to long-term scientific impact.

scholarly journals Using Quantile Regression to Analyze the Relationship between Socioeconomic Indicators and Carbon Dioxide Emissions in G20 Countries

2021 ◽  
Vol 13 (13) ◽  
pp. 7011
Author(s):  
Abdulaziz A. Alotaibi ◽  
Naif Alajlan
Keyword(s):  
Carbon Dioxide ◽  
Quantile Regression ◽  
Fuel Consumption ◽  
Co2 Emissions ◽  
Fossil Fuel ◽  
Trade Openness ◽  
Policy Coordination ◽  
Socioeconomic Indicators ◽  
Control Variables ◽  
Fossil Fuel Consumption

Numerous studies addressed the impacts of social development and economic growth on the environment. This paper presents a study about the inclusive impact of social and economic factors on the environment by analyzing the association between carbon dioxide (CO2) emissions and two socioeconomic indicators, namely, Human Development Index (HDI) and Legatum Prosperity Index (LPI), under the Environmental Kuznets Curve (EKC) framework. To this end, we developed a two-stage methodology. At first, a multivariate model was constructed that accurately explains CO2 emissions by selecting the appropriate set of control variables based on model quality statistics. The control variables include GDP per capita, urbanization, fossil fuel consumption, and trade openness. Then, quantile regression was used to empirically analyze the inclusive relationship between CO2 emissions and the socioeconomic indicators, which revealed many interesting results. First, decreasing CO2 emissions was coupled with inclusive socioeconomic development. Both LPI and HDI had a negative marginal relationship with CO2 emissions at quantiles from 0.2 to 1. Second, the EKC hypothesis was valid for G20 countries during the study period with an inflection point around quantile 0.15. Third, the fossil fuel consumption had a significant positive relation with CO2 emissions, whereas urbanization and trade openness had a negative relation during the study period. Finally, this study empirically indicates that effective policies and policy coordination on broad social, living, and economic dimensions can lead to reductions in CO2 emissions while preserving inclusive growth.

A mixed control chart for monitoring failure times under accelerated hybrid censoring

2020 ◽  
Vol 48 (1) ◽  
pp. 138-153
Author(s):  
Muhammad Aslam ◽  
Muhammad Ali Raza ◽  
Rehan Ahmad Khan Sherwani ◽  
Muhammad Farooq ◽  
Jun Yong Jeong ◽  
...  
Keyword(s):  
Control Chart ◽  
Hybrid Censoring ◽  
Failure Times ◽  
Mixed Control

scholarly journals Spatial Externalities of Income Inequality on Security in Latin America

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 245
Author(s):  
Pablo Ponce ◽  
José Álvarez-García ◽  
Mary Cumbicus ◽  
María de la Cruz del Río-Rama
Keyword(s):  
Income Inequality ◽  
Latin American ◽  
Spatial Dependence ◽  
Generalized Least Squares ◽  
Homicide Rate ◽  
World Health ◽  
Control Variables ◽  
Spatial Error ◽  
Middle Income ◽  
Middle Income Countries

The aim of this research is to analyse the effect of income inequality on the homicide rate. The study is carried out in 18 Latin American countries for the period 2005–2018. The methodology used is the Generalized Least Squares (GLS) model and the data were obtained from World Development Indicators, the World Health Organization and the Inter-American Development Bank. Thus, the dependent variable is the homicide rate and the independent variable is income inequality. In addition, some control variables are included, such as: poverty, urban population rate, unemployment, schooling rate, spending on security and GDP per capita, which improve the consistency of the model. The results obtained through GLS model determine that inequality has a negative and significant effect on the homicide rate for high-income countries (HIC) and lower-middle-income countries (LMIC), whereas it is positive and significant for upper-middle-income countries (UMIC). On the other hand, the control variables show different results by group of countries. In the case of unemployment, it is not significant in any group of countries. Negative spatial dependence was found regarding spatial models such as: the spatial lag (SAR) and spatial error (SEM) method. In the spatial Durbin model (SDM), positive spatial dependence between the variables was corroborated. However, spatial auto-regressive moving average (SARMA) identified no spatial dependence. Under these results it is proposed: to improve productivity, education and improve the efficiency of security-oriented resources.

scholarly journals Prediction of all-cause mortality from 24 month trajectories in patient-reported psychological, clinical and quality of life outcomes in uveal melanoma patients

2021 ◽  
Author(s):  
Stephen L. Brown ◽  
Peter L. Fisher ◽  
Laura Hope-Stone ◽  
Heinrich Heimann ◽  
Rumana Hussain ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Life Expectancy ◽  
Uveal Melanoma ◽  
Cox Regression ◽  
Multivariate Analyses ◽  
Tumor Diameter ◽  
Control Variables ◽  
Patient Reported ◽  
All Cause Mortality

AbstractA number of patient-reported outcomes (PROs) predict increased mortality after primary cancer treatment. Studies, though, are sometimes affected by methodological limitations. They often use control variables that poorly predict life expectancy, examine only one or two PROs thus not controlling potential confounding by unmeasured PROs, and observe PROs at only a single point in time. To predict all-cause mortality, this study used control variables affording good estimates of life expectancy, conducted multivariate analyses of multiple PROs to identify independent predictors, and monitored PROs two years after diagnosis. We recruited a consecutive sample of 824 patients with uveal melanoma between April 2008 and December 2014. PROs were variables shown to predict mortality in previous studies; anxiety, depression, visual and ocular symptoms, visual function impairment, worry about cancer recurrence, and physical, emotional, social and functional quality of life (QoL), measured 6, 12 and 24 months after diagnosis. We conducted Cox regression analyses with a census date of December 2018. Covariates were age, gender, marital and employment status, self-reported co-morbidities, tumor diameter and thickness, treatment modality and chromosome 3 mutation status, the latter a genetic mutation strongly associated with mortality. Single predictor analyses (with covariates), showed 6-month depression and poorer functional QoL predicting mortality, as did 6–12 month increases in anxiety and 6–12 month decreases in physical and functional QoL. Multivariate analyses using all PROs showed independent prediction by 6-month depression and decreasing QoL over 6–12 months and 12–24 months. Elevated depression scores six months post-diagnosis constituted an increased mortality risk. Early intervention for depressive symptoms may reduce mortality.

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