scholarly journals Pengurangan Subsidi Pupuk dan Kenaikan Tingkat Suku Bunga terhadap Kesejahteraan Petani Lada Indonesia

2017 ◽  
Author(s):  
La Diadhan Hukama
Keyword(s):  
Interest Rates ◽  
Structural Equation ◽  
Estimation Method ◽  
Coefficient Of Determination ◽  
Estimation Model ◽  
Explanatory Variables ◽  
Price Subsidy ◽  
Endogenous Variables ◽  
P Fertilizer ◽  
The Impact

Fertilizer Price Subsidy Reduction and Increase in Interest Rates Pepper Farmers Welfare in Indonesia, This aims of this study are to explain the influence of price subsidy reduction of fertilizer and to measure the impact of increasing interest rates for farmers welfare in Indonesia by using the model of simultaneous equations system estimation model with the condition of each equation is identified excessive, then the parameter estimation method was used Two Stage Least Squares (2 SLS). Based on the analysis results show the model prediction coefficient of determination (R2) ranged from 52% to 98%. This shows the diversity of each endogenous variable can be explained quite well by the explanatory variables in each structural equation. Explanatory variables in each equation are jointly significant enough to explain the diversity of endogenous variables, which is shown from the statistical value of F ranges between 2.678 to 172.427.Artikel ini merupakan post print yang sudah terbit di Jurnal Dikta Ekonomi Volume 7, Nomor 3, Desember 2010 / Muharram 1431 H

scholarly journals The Impact of Motivation for Attendance on Destination Loyalty via the Mediating Effect of Tourist Satisfaction

2017 ◽  
Vol 8 (4) ◽  
pp. 34
Author(s):  
Ra’ed Masa’deh ◽  
Mohammed Abdullah Nasseef ◽  
Ala Alkoudary ◽  
Hanaa Mansour ◽  
Mervat Aldarabah
Keyword(s):  
Structural Equation ◽  
Equation Model ◽  
Mediating Effect ◽  
Coefficient Of Determination ◽  
Destination Loyalty ◽  
Tourist Satisfaction ◽  
Proposed Model ◽  
Endogenous Variables ◽  
The Impact ◽  
Research Analysis

The aim of this research is to explore the associations among motivation for attendance to Aqaba city, destination satisfaction, and destination loyalty. The research surveyed samples of 200 and used Structural Equation Model for research analysis and testing. The results show that motivation for attendance to Aqaba city positively affects tourists’ destination loyalty. The motivation for attendance positively affects destination satisfaction; and tourists’ destination satisfaction affects tourists’ destination loyalty. Furthermore, the coefficient of determination (R²) for the research endogenous variables for tourists’ destination satisfaction, and tourists’ destination loyalty were 0.46, and 0.66 respectively, which indicates that the model does moderately account for the variation of the proposed model; however, opens the gate for further research.

scholarly journals Determinants of access to the SARS-CoV-2 vaccine: a preliminary approach

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Brigitte Renata Bezerra de Oliveira ◽  
Ana Iza Gomes da Penha Sobral ◽  
Marcelo Luiz Monteiro Marinho ◽  
Marcos Felipe Falcão Sobral ◽  
André de Souza Melo ◽  
...  
Keyword(s):  
Human Development ◽  
Structural Equation ◽  
The United States ◽  
Equation Modeling ◽  
Income Poverty ◽  
Socioeconomic Indicators ◽  
Extreme Poverty ◽  
Explanatory Variables ◽  
Endogenous Variables ◽  
The Impact

Abstract Background The determinants of access to immunizers are still poorly understood, leading to questions about which criteria were considered in this distribution. Given the above, the present study aimed to analyze the determinants of access to the SARS-CoV-2 vaccine by different countries. Methods The study covered 189 countries using data from different public databases, and collected until February 19, 2021. We used eight explanatory variables: gross domestic product (GDP), extreme poverty, human development index (HDI), life expectancy, median age, coronavirus disease 2019 (COVID-19) cases, COVID-19 tests, and COVID-19 deaths. The endogenous variables were total vaccine doses, vaccine doses per thousand, and days of vaccination. The structural equation modeling (SEM) technique was applied to establish the causal relationship between the country's COVID-19 impact, socioeconomic variables, and vaccine access. To support SEM, we used confirmatory factor analysis, t-test, and Pearson's correlation. Results We collected the sample on February 19, and to date, 80 countries (42.1%) had already received a batch of immunizers against COVID-19. The countries with first access to the vaccine (e.g., number of days elapsed since they took the first dose) were the United Kingdom (68), China (68), Russia (66), and Israel (62). The countries receiving the highest doses were the United States, China, India, and Israel. The countries with extreme poverty had lower access to vaccines and the richer countries gained priority access. Countries most affected by COVID (deaths and cases) also received immunizers earlier and in greater volumes. Unfortunately, similar to other vaccines, indicators, such as income, poverty, and human development, influence vaccines' access. Thus affecting the population of vulnerable and less protected countries. Therefore, global initiatives for the equitable distribution of COVID need to be discussed and encouraged. Conclusions Determinants of vaccine distribution consider the impact of the disease in the country and are also affected by favorable socioeconomic indicators. The COVID-19 vaccines need to be accessible to all affected countries, regardless of their social hands.

scholarly journals The Influence of COVID-19-Induced Daily Activities on Health Parameters—A Case Study in Malaysia

2021 ◽  
Vol 13 (13) ◽  
pp. 7465
Author(s):  
Mujahid Ali ◽  
Afonso R. G. de Azevedo ◽  
Markssuel T. Marvila ◽  
Muhammad Imran Khan ◽  
Abdul Muhaimin Memon ◽  
...  
Keyword(s):  
Structural Equation ◽  
Linear Regression Analysis ◽  
Daily Activities ◽  
Social Health ◽  
Coefficient Of Determination ◽  
Equation Modeling ◽  
Risk Of Death ◽  
Unit Increase ◽  
The Impact ◽  
Health Parameters

Since December 2019, the COVID-19 epidemic has been spreading all over the world. This epidemic has brought a risk of death in the daily activity (physical and social) participation that influences travellers’ physical, social, and mental health. To analyze the impact of the COVID-19-induced daily activities on health parameters of higher education institutes, 150 students of the Universiti Teknologi PETRONAS, Perak, Malaysia, were surveyed through an online web survey using random sampling techniques. The data were analyzed through RStudio and SPSS using multilevel linear regression analysis and Hierarchical Structural Equation Modeling. The estimated results indicate that restricting individuals from doing out-of-home activities negatively influences physical and social health. A unit increase in the in-home maintenance activities during the COVID-19 pandemic introduced a daily increase of 0.5% in physical health. Moreover, a unit increase in the in-home activities at leisure time represents a 1% positive improvement in social health. Thus, physical activity has proven to be beneficial in improving physical and social health with severe COVID-19. In contrast, the coefficient of determination (R2) for all endogenous variables ranges from 0.148 to 0.227, which is incredibly acceptable in psychological research. For a healthier society with a better quality of life, this study adopted multidisciplinary approaches that are needed to be designed.

scholarly journals Rack Temperature Prediction Model Using Machine Learning after Stopping Computer Room Air Conditioner in Server Room

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4300
Author(s):  
Kosuke Sasakura ◽  
Takeshi Aoki ◽  
Masayoshi Komatsu ◽  
Takeshi Watanabe
Keyword(s):  
Machine Learning ◽  
High Heat ◽  
Training Data ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Air Conditioner ◽  
Tree Model ◽  
Explanatory Variables ◽  
Temperature Environment ◽  
The Impact

Data centers (DCs) are becoming increasingly important in recent years, and highly efficient and reliable operation and management of DCs is now required. The generated heat density of the rack and information and communication technology (ICT) equipment is predicted to get higher in the future, so it is crucial to maintain the appropriate temperature environment in the server room where high heat is generated in order to ensure continuous service. It is especially important to predict changes of rack intake temperature in the server room when the computer room air conditioner (CRAC) is shut down, which can cause a rapid rise in temperature. However, it is quite difficult to predict the rack temperature accurately, which in turn makes it difficult to determine the impact on service in advance. In this research, we propose a model that predicts the rack intake temperature after the CRAC is shut down. Specifically, we use machine learning to construct a gradient boosting decision tree model with data from the CRAC, ICT equipment, and rack intake temperature. Experimental results demonstrate that the proposed method has a very high prediction accuracy: the coefficient of determination was 0.90 and the root mean square error (RMSE) was 0.54. Our model makes it possible to evaluate the impact on service and determine if action to maintain the temperature environment is required. We also clarify the effect of explanatory variables and training data of the machine learning on the model accuracy.

scholarly journals Modeling the Commuting Travel Activities within Historic Districts in Chinese Cities

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Mao Ye ◽  
Miao Yu ◽  
Zhibin Li ◽  
Fengjun Yin ◽  
Qizhou Hu
Keyword(s):  
Structural Equation ◽  
Household Survey ◽  
Primary Objective ◽  
Equation Modeling ◽  
Historic District ◽  
Contributing Factors ◽  
Historic Districts ◽  
Explanatory Variables ◽  
Home Based ◽  
Endogenous Variables

The primary objective of this study is to analyze the characteristics of commuting activities within the historical districts in cities of China. The impacts of various explanatory variables on commuters’ travels are evaluated using the structural equation modeling (SEM) approach. The household survey was conducted in the historical districts in Yangzhou, China. Based on the data, various individual and household attributes were considered exogenous variables, while the subsistence activity characteristics, travel times, numbers of three typical home-based trip chains, trip chains, and travel mode were considered as the endogenous variables. Commuters in our study were classified into two main groups according to their working location, which were the commuters in the historic district and those out of the district. The modeling results show that several individual and household attributes of commuters in historic district have significant impacts on the characteristics of travel activities. Additionally, the characteristics of travel activities within the two groups are quite different, and the contributing factors related to commuting travels are different as well.

scholarly journals The Impact of Monetary Policy on the Investment Complex of Russia

2021 ◽  
Vol 21 (3) ◽  
pp. 5-29
Author(s):  
A. O. Baranov ◽  
E. V. Ageeva
Keyword(s):  
Monetary Policy ◽  
Mechanical Engineering ◽  
Policy Instruments ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Economic Activities ◽  
Explanatory Variables ◽  
Fixed Assets ◽  
Investment In Fixed Assets ◽  
The Impact

An active discussion continues in Russia regarding the monetary instruments used and the degree of their impact on economic dynamics. The article examines the impact of the monetary policy implemented by the Central Bank of the Russian Federation on the investment complex in the period from 2000 to 2021. For this period, an analysis of the dependence characterizing the investment complex of Russia on the monetary policy is carried out. In the paper, the investment complex is considered with a breakdown into mechanical engineering, residential and non-residential construction, investments in fixed assets. At the same time, mechanical engineering is studied in detail with a breakdown into subsections of Russian National Classifier of Economic Activities and, additionally, for five enlarged industries. Most of the constructed regression equations demonstrate a statistically significant effect of indicators that are influenced by monetary policy instruments on the dynamics of the main indicators of the investment complex. For example, the interest rate affects the overwhelming number of variables under consideration, and the money supply affects each type of investment in fixed assets, in contrast to other independent variables. For the predominant part of the equations, the value of the coefficient of determination is more than 80%, which indicates a good quality of the model and reliably selected explanatory variables. Based on the results of the calculations, it was concluded that monetary policy has an impact on the investment complex of Russia. This determines its importance for the formation of promising dynamics of the Russian economy.

scholarly journals The Impact of Economic Value Added & Return on Investment on the Changes in Stock Market’s Value (Analytical Study: ASE: 2006-2015)

2017 ◽  
Vol 12 (10) ◽  
pp. 132 ◽  
Author(s):  
Mohammad Abdel Mohsen Al-Afeef
Keyword(s):  
Interest Rates ◽  
Return On Investment ◽  
Analytical Study ◽  
Economic Value ◽  
Value Added ◽  
Coefficient Of Determination ◽  
Systemic Risks ◽  
Macroeconomic Indicators ◽  
The Impact ◽  
Better Than

This study aims to determine the effect of EVA and ROI and which is better able to explain the change in Stock market’s value in the companies listed in (ASE) (2006-2015), the researcher addresses a random sample consisting of (46) Company, and uses regression model, which connects the dependent and independent variables.The results of the study shows that the return on investment (ROI) is better than (EVA) to interpret the changes in Stock market’s value, where the coefficient of determination (R2) for the ROI is (22.5%), while the R2 for EVA Only 1.3%.This study also recommends the need to look for additional factors that would explain the changes in stock market's value such as: the degree of leverage, systemic risks, and macroeconomic indicators such as: (interest rates and inflation).

scholarly journals Trade Openness and Green Total Factor Productivity in China: The Role of ICT-Based Digital Trade

2021 ◽  
Vol 9 ◽  
Author(s):  
Huan Zhang
Keyword(s):  
Total Factor Productivity ◽  
Scale Effect ◽  
Trade Openness ◽  
Estimation Method ◽  
Policy Implications ◽  
Structure Effect ◽  
Factor Productivity ◽  
Estimation Model ◽  
Promoting Effect ◽  
The Impact

The vigorous development of modern information and communication technology (ICT) has driven the digital trade featured by the ICT technique and industry as the carrier. This study empirically tests the impact of ICT-based digital trade openness on green total factor productivity (GTFP) by selecting ICT as the representative digital trade data of 30 provinces in China over the timespan 2002–2018. We employ the slack-based model and global Malmquist–Luenberger (SBM-GML) estimation method to calculate the provincial GTFP and explore the heterogeneous impact of digital trade openness on GTFP through the scale effect, technology effect, and structure effect. In terms of empirical results, the panel fixed model and panel quantile estimation model both suggest the same findings. With the continuous expansion of the scale of digital trade, its scale effect has a significant inhibitory effect on GTFP, whereas the structure effect combined with human capital and the technology effect correlated with technological research and development (R&D) have a significant promoting effect on GTFP. The panel quantile regression model reveals that the interaction intensity increases gradually from a low quantile to high quantile. Further robustness tests also verify the consistency and stability of the results. Finally, the study puts forward corresponding practical suggestions for the construction of a high-quality open pattern of digital trade and the coordinated development of GTFP. The specific policy implications include the following: (1) Emphasize on the penetration and connection effect of the new generation of ICT, and strengthen the construction of enterprise informatization. (2) Expand digital trade openness and broaden the field of industrial cooperation. (3) Optimize the industrial structure of digital trade, and accelerate the development of core industries of digital trade. (4) Gradually promote the transformation of digital trade from relying on quantity and scale to product quality.

scholarly journals Differences of Cyberloafing Behavior Outcomes on Men and Women Employees

2021 ◽  
Vol 10 (2) ◽  
pp. 121-132
Author(s):  
Rauly Sijabat
Keyword(s):  
Organizational Behavior ◽  
Job Stress ◽  
Structural Equation ◽  
Job Characteristics ◽  
Self Control ◽  
Work Behavior ◽  
Equation Modeling ◽  
Women Employees ◽  
Endogenous Variables ◽  
The Impact

The activities of reading news, chatting, viewing YouTube and Facebook or even status updates, Instagram, buying and selling online, to playing online games that are not related to work by using internet facilities are cyberloafing behaviors that are often carried out by employees. The limitations of previous research examining the impact of cyberloafing on work behavior empirically are still very limited. In addition, previous research that explains the occurrence of cyberloafing behavior also shows results that have not been established. Encouraged by these findings, this study aimed to examine the factors that explain cyberloafing behavior and its impact on employees' organizational behavior. To meet these objectives, an empirical model was developed with job characteristics and self-control variables as exogenous variables, job stress and cyberloafing as mediating variables and laziness as endogenous variables. Testing the influence between these variables was carried out with an analytical approach to Structural Equation Modeling (SEM) which used empirical data obtained through questionnaires as an interview guide to employee respondents in various fields of work. The results of data analysis showed that job characteristics, self control, and job stress were statistically proven to have an effect on cyberloafing behavior. Cyberloafing testing on negative organizational behavior, namely laziness also shows a real influence. In addition, the results of this study also show that there are differences in prevalence caused by cyberloafing behavior between male and female employees.

scholarly journals Analisis Penerapan Kebijakan Moneter Suku Bunga Jangka Pendek pada Variabel-variabel Endogen MakroEkonomi Indonesia

2014 ◽  
Vol 5 (2) ◽  
pp. 638
Author(s):  
Sari Damayanti
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Interest Rate ◽  
Interest Rates ◽  
Careful Examination ◽  
Vector Autoregressive ◽  
Real Gdp ◽  
Endogenous Variables ◽  
Potential Risks ◽  
The Impact

This study analyzed the impact of the implementation of monetary policy through short-term interest rates setting on the variation that occurs in the endogenous variables of Indonesian macro economy in the period of 2000-2009 by implementing the Structural Vector Autoregressive approach (SVAR) which is the development of Vector Autoregressive (VAR) modelling with Eviews program. By careful examination of the results, this study indicates that the value of interest rate changes is significantly associated with shocks that are associated with monetary policy. The monetary sector is heavily influenced by real GDP shock, liquidity, and inflation shock. However, the monetary sector is only slightly affected by the decomposition of the variance of the exchange rate, which is very sensitive to the inflation shock. The study also indicates that the endogenous variables in the value of changes in interest rates and real exchange rate of rupiah will be close to convergence in the long term. The endogenous variables are more susceptible to changes in variables derived from domestic, such as the level of demand for domestic currency liquidity, compared to variables derived from international capital exposure. Thus, the value of the variable interest rate changes can be used to reduce the potential risks derived from domestic money demand shock.

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