Reconceptualizing Church and State: A Theoretical and Empirical Analysis of the Impact of Separation of Religion and State on Democracy

2011 ◽  
Vol 4 (2) ◽  
pp. 229-263 ◽  
Author(s):  
Robert Brathwaite ◽  
Andrew Bramsen
Keyword(s):  
Principal Component Analysis ◽  
Empirical Analysis ◽  
Church And State ◽  
Principal Component ◽  
Set Covering ◽  
Data Set ◽  
Religion And State ◽  
The Impact ◽  
The Relationship ◽  
State Separation

AbstractThis article argues that the relationship between democracy and the separation of religion and state needs to be reexamined. We argue that previous studies have misconceptualized the impact that a lack of church-state separation can have on democracy, or have taken a narrow focus by concentrating on specific cases. We use principal component analysis and a large-ndata set covering 125 countries to show that the separation of religion and state should be conceptualized multi-dimensionally and that it should be considered a component of democracy. Our findings show that as separation of religion and state increases, the level of democracy also increases.

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

Income and healthcare financing system in the United States: an asymmetric analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehdi Barati ◽  
Hadiseh Fariditavana
Keyword(s):  
The United States ◽  
Set Covering ◽  
Healthcare Financing ◽  
Financing System ◽  
Data Set ◽  
Content Type ◽  
Funding Sources ◽  
Distributed Lag ◽  
The Impact ◽  
The Relationship

PurposeThe purpose of this study is to first assess how the US healthcare financing system is influenced by income variation. Then, it examines whether or not the impact of income variation is asymmetric.Design/methodology/approachFor the analyses of this paper, the autoregressive distributed lag (ARDL) model is implemented to a data set covering the period from 1960 to 2018.FindingsThe results provide evidence that major funding sources of aggregate healthcare expenditure (HCE) respond differently to changes in income. The results also imply that the effect of income is not always symmetric.Originality/valueMany studies have attempted to identify the relationship between income and HCE. A common feature of past studies is that they have only focused on aggregate HCE, while one might be interested in knowing how major funders of aggregate HCE would be affected by changes in income. Another common feature of past studies is that they have assumed that the relationship between income and HCE is symmetric.

Can BRICS and ASEAN-5 emerging economies benefit from bank diversification?

2019 ◽  
Vol 27 (1) ◽  
pp. 43-69 ◽  
Author(s):  
Syed Moudud-Ul-Huq
Keyword(s):  
Risk Taking ◽  
Generalized Method Of Moments ◽  
The Other ◽  
Set Covering ◽  
Diversification Strategy ◽  
Data Set ◽  
Content Type ◽  
Diversification Benefit ◽  
The Impact ◽  
The Relationship

Purpose This paper aims to empirically investigate the impact of bank diversification on performance and risk-taking behavior. The analysis uses an unbalanced panel data set covering the period between 2007 and 2015 for a total of 1,397 banks from ASEAN-5 and BRICS economies. Design/methodology/approach Dynamic panel generalized method of moments (GMM) has been used primarily to examine the relationship between bank diversification on performance and risk-taking and later, validate the core results by incorporating two-stage least squares (2SLS). Findings Similar to the results of previous studies based on the developed economy, this study also confirms the hypothesis of the portfolio diversification. The key robust result is that the benefits from revenue and assets diversification are heterogeneous and the BRICS banks achieve higher benefit from using both diversification strategies. On the other hand, ASEAN-5 banks fail to show the significant advantage from assets diversification. Among the diverse sources of income, interest is not a major determinant of efficiency and bank’s stability, while ASEAN-5 banks should foster commission and others income as mechanisms for diversification benefit in the region. Originality/value A few studies are available in the current literature which examines the impact of revenue and assets diversification on either bank performance or risk-taking in the developed economy’s context. However, very few studies are found that examine the relationship between bank diversification, performance and risk-taking together. Moreover, to the best of the author’s knowledge, there is a dearth of literature on this topic that built on the comparative analysis between two regions, i.e. ASEAN-5 and BRICS. As a result, the empirical results of this research provide useful information to the stakeholders so that they can enhance bank diversification strategy and implement them successfully by considering the other factors.

scholarly journals THE IMPACT OF REAL ESTATE INVESTMENTS ON THE PERFORMANCE OF THE ENTITIES LISTED AT THE BVB

2021 ◽  
Vol 30 (30 (1)) ◽  
pp. 177-186
Author(s):  
Silviu Cornel Virgil Chiriac
Keyword(s):  
Principal Component Analysis ◽  
Real Estate ◽  
Financial Performance ◽  
Descriptive Analysis ◽  
Composite Index ◽  
Principal Component ◽  
Component Analysis ◽  
Multidimensional Data ◽  
Data Set ◽  
The Impact

The current paper is part of a wider study which aims at identifying the determining factors of the performances of the entities in the real estate field and the setting up of a composite index of the companies’ performances based on a sample of 29 companies listed at the BVB Bucharest (Bucharest Stock Exchange) in the year 2019 using one of the multidimensional data analysis techniques, the principal component analysis. The descriptive analysis, the principal component analysis for setting up the composite index of the companies performances were applied within the study in order to highlight the most important companies from the point of view of the financial performance. The descriptive analysis of the data set highlights the overview within the companies selected for analysis. The study aims at building a synthetic indicator that will show the financial performance of the companies selected based on 9 financial indicators using the principal component analysis PCA. The 9 indicators considered for the analysis were selected based on specialised articles and they are: ROA – return on assets, which reflect the company’s capacity of using its assets productively, ROE – return on equity, which measures the efficiency of use of the stockholders’ capitals, rotation of total assets, general liquidity ratio, general solvency ratio, general dent-to-equity level, net profit margin, gross return of portfolio.

scholarly journals Impact of sample size on principal component analysis ordination of an environmental data set: effects on eigenstructure

2016 ◽  
Vol 35 (2) ◽  
pp. 173-190 ◽  
Author(s):  
S. Shahid Shaukat ◽  
Toqeer Ahmed Rao ◽  
Moazzam A. Khan
Keyword(s):  
Principal Component Analysis ◽  
Sample Size ◽  
Principal Component ◽  
Component Analysis ◽  
Small Sample ◽  
Environmental Data ◽  
Data Matrix ◽  
Data Sets ◽  
Data Set ◽  
The Impact

AbstractIn this study, we used bootstrap simulation of a real data set to investigate the impact of sample size (N = 20, 30, 40 and 50) on the eigenvalues and eigenvectors resulting from principal component analysis (PCA). For each sample size, 100 bootstrap samples were drawn from environmental data matrix pertaining to water quality variables (p = 22) of a small data set comprising of 55 samples (stations from where water samples were collected). Because in ecology and environmental sciences the data sets are invariably small owing to high cost of collection and analysis of samples, we restricted our study to relatively small sample sizes. We focused attention on comparison of first 6 eigenvectors and first 10 eigenvalues. Data sets were compared using agglomerative cluster analysis using Ward’s method that does not require any stringent distributional assumptions.

scholarly journals The Impact of Immigration on Firm-Level Offshoring

2019 ◽  
pp. 1-19
Author(s):  
William W. Olney ◽  
Dario Pozzoli
Keyword(s):  
Natural Experiment ◽  
Set Covering ◽  
Foreign Workers ◽  
Firm Level ◽  
Data Set ◽  
Extensive And Intensive Margins ◽  
Matched Data ◽  
The Impact ◽  
The Relationship ◽  
The Universe

This paper studies the relationship between immigration and offshoring by examining whether an influx of foreign workers reduces the need for firms to relocate jobs abroad. Using a Danish natural experiment and their employer-employee matched data set covering the universe of workers and firms (1995–2011), our findings show that an exogenous influx of immigrants into a municipality reduces firm-level offshoring at both the extensive and intensive margins. While the multilateral relationship is negative, a subsequent bilateral analysis shows that immigrants have connections in their country of origin that increase the likelihood that firms offshore to that particular foreign country.

scholarly journals Identification of Turbomachinery Noise Sources via Processing Beamforming Data Using Principal Component Analysis

2021 ◽  
Author(s):  
Bence Fenyvesi ◽  
Csaba Horváth
Keyword(s):  
Principal Component Analysis ◽  
Noise Source ◽  
Principal Component ◽  
Component Analysis ◽  
Design Guidelines ◽  
Noise Sources ◽  
Data Set ◽  
Wide Range ◽  
Open Rotor ◽  
The Impact

Complex turbomachinery systems produce a wide range of noise components. The goal is to identify noise source categories, determine their characteristic noise patterns and locations. Researchers can then use this information to quantify the impact of these noise sources, based on which new design guidelines can be proposed. Phased array microphone measurements processed with acoustic beamforming technology provide noise source maps for pre-determined frequency bands (i.e., bins) of the investigated spectrum. However, multiple noise generation mechanisms can be active in any given frequency bin. Therefore, the identification of individual noise sources is difficult and time consuming when using conventional methods, such as manual sorting. This study presents a method for combining beamforming with Principal Component Analysis (PCA) methods in order to identify and separate apart turbomachinery noise sources with strong harmonics. The method is presented through the investigation of Counter-Rotating Open Rotor (CROR) noise sources. It has been found that the proposed semi-automatic method was able to extract even weak noise source patterns that repeat throughout the data set of the beamforming maps. The analysis yields results that are easy to comprehend without special prior knowledge and is an effective tool for identifying and localizing noise sources for the acoustic investigation of various turbomachinery applications.

Capital Structure and Earnings per Share: An Empirical Analysis of Indian Corporate Sector

2015 ◽  
Vol 5 (3) ◽  
Author(s):  
Harvinder Singh Mand ◽  
Manjit Singh
Keyword(s):  
Capital Structure ◽  
Empirical Analysis ◽  
Corporate Sector ◽  
Independent Variables ◽  
Earnings Per Share ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Impact ◽  
The Relationship ◽  
The Empirical Analysis

This paper intends to measure the impact of capital structure on EPS (earnings per share) in Indian corporate sector. Fifteen control variables along with capital structure have been selected to know their impact on EPS. Panel data regression has been applied to establish the relationship among dependent and independent variables. It is found from the empirical analysis that the relation of capital structure with EPS has been statistically insignificant in Indian corporate sector among all specific industries except telecommunication industry. The results are consistent with Modigliani-Miller approach.

scholarly journals Quantify Coal Macrolithotypes of a Whole Coal Seam: A Method Combing Multiple Geophysical Logging and Principal Component Analysis

Energies ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 213
Author(s):  
Chao Cui ◽  
Suoliang Chang ◽  
Yanbin Yao ◽  
Lutong Cao
Keyword(s):  
Principal Component Analysis ◽  
Coal Seam ◽  
Coalbed Methane ◽  
Principal Component ◽  
Component Analysis ◽  
Gas Production ◽  
Index Model ◽  
Geophysical Logging ◽  
The Relationship ◽  
Observation Results

Coal macrolithotypes control the reservoir heterogeneity, which plays a significant role in the exploration and development of coalbed methane. Traditional methods for coal macrolithotype evaluation often rely on core observation, but these techniques are non-economical and insufficient. The geophysical logging data are easily available for coalbed methane exploration; thus, it is necessary to find a relationship between core observation results and wireline logging data, and then to provide a new method to quantify coal macrolithotypes of a whole coal seam. In this study, we propose a L-Index model by combing the multiple geophysical logging data with principal component analysis, and we use the L-Index model to quantitatively evaluate the vertical and regional distributions of the macrolithotypes of No. 3 coal seam in Zhengzhuang field, southern Qinshui basin. Moreover, we also proposed a S-Index model to quantitatively evaluate the general brightness of a whole coal seam: the increase of the S-Index from 1 to 3.7, indicates decreasing brightness, i.e., from bright coal to dull coal. Finally, we discussed the relationship between S-Index and the hydro-fracturing effect. It was found that the coal seam with low S-Index values can easily form long extending fractures during hydraulic fracturing. Therefore, the lower S-Index values indicate much more favorable gas production potential in the Zhengzhuang field. This study provides a new methodology to evaluate coal macrolithotypes by using geophysical logging data.

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