Pengaruh Produk Domestik Regional Bruto (PDRB) terhadap Tingkat Kemiskinan Kota Medan

This study has the benefit of analyzing the effect of regional gross domestic product on poverty in the city of Medan in 2010-2020. The research method used is a quantitative method with reference to a descriptive approach. The data used is time series data on economic growth and poverty at the Central Statistics Agency (BPS) of Medan City in 2010-2020. Data collection techniques used are journals, book documentation, and previous reports. The technique of analyzing the data uses simple linear regression analysis which is carried out to determine whether the model used is free from deviations from the classical assumption test. The equations obtained from the simple linear regression analysis test Y = 24576.325 – 0.365X and have the understanding that the GRDP variable (X) has a significant effect on Poverty (Y). Obtained a value of R2 (R square) of 0.556 with the understanding that the independent variable, namely GRDP, affects the variable of the poverty level in Medan City by 55.6%. Meanwhile, the remaining 44.4% are influenced by different independent variables and are not included in this study. For this reason, it can be concluded that when GRDP increases, it will have an impact on decreasing the value of Poverty in Medan City, and vice versa. Keywords: Gross Regional Domestic Product; Poverty; Medan city

On Exports and Economic Growth: Revisiting Export-Led Growth Hypothesis Including North-South Divide

Objective: The purpose of the present study is to revisit the export-led growth hypothesis in the wake of globalization. This will help in trade policy decisions and make it possible to standpoint whether the export promotion is a good idea to accelerate economic growth. Design: The ELG hypothesis is examined for 107 countries through panel data analysis using cointegration and panel regression tests from 1990 to 2018. The study finds strong support for the long-run relationship between exports and gross domestic product and the export-led growth hypothesis in a two-variable regression framework. Findings: It is evident from the long-run coefficient of dynamic ordinary least squared that a 1.0 percent increase in real exports increases the real gross domestic product by 0.53 percent. The long-run coefficient of real exports for the Global South (0.55) is found higher than that of the Global North (0.51), which indicates that in the wave of globalization, the evidence of export-led growth hypothesis is stronger for comparatively poor Global South than the richer Global North. Practical Implications: The results indicate implications for export promotion policy in the Global South countries to accelerate economic growth and increase real gross domestic product. Originality: The study is the first to explore the ELG hypothesis using a big pool of 107 countries, including the global north-south divide.

Determining the Impact of Economic Factors to the Gross Domestic Product in Bangladesh

Gross Domestic Product (GDP) is believed to be an indicator of a country’s economic condition. Bangladesh’s GDP increased at a pace of 8.15% in fiscal 2018-19 as per the base year 2005-06. By the year 2019, Bangladesh has become the seventh fastest-growing economy in the world. This paper used multiple regression analysis model for the macroeconomic factors. The aim of this study is to measure the effects of macroeconomic factors considering GDP as the dependent variables and inflation rate, import, and export are considered as independent. This paper represents that import and export are positively associated factors with GDP whereas inflation rate is a negatively associated factor. This study concluded with revealing the importance of conducting further study by considering more economic variables to measure the economic growth as a whole.

Back-Propagation Neural Network and ARIMA Algorithm for GDP Trend Analysis

GDP (gross domestic product) is a key indicator for assessing a country’s or region’s macroeconomic situation, as well as a foundation for the government to develop economic development strategies and macroeconomic policies. Currently, the majority of methods for forecasting GDP are linear methods, which only take into account the linear factors that affect GDP. GDP (gross domestic product) is widely regarded as the most accurate indicator of a country’s economic health. GDP not only reflects a country’s economic development over time but can also reflect its national strength and wealth. As a result, the GDP trend forecast partially reflects China’s transformation and future development. The time series ARIMA (Autoregressive Integrated Moving Average) model and the BPNN (BP neural network) model are combined in this article to create the ARIMA-BPNN fusion prediction model. The predicted values of the two models were then weighted averaged to obtain the predicted values of the linear part of the improved fusion model. To get the predicted values of the improved fusion model, we weighted average the residual parts of the two models, predict the nonlinear residual with BPNN, and add the predicted values of the two parts. It is applied to the actual GDP forecast in H province from 2019 to 2022, and the actual forecast verifies the effectiveness of the fusion forecast model in the actual forecast.

The effect of asset quality, profit and loss sharing on Sharia Banking Liquidity in Indonesia

This study aims to determine the effect of asset quality variables (Non-Performing Financing), Profit and Loss Sharing (profit-loss sharing investment and profit-sharing investment account), capital adequacy ratio, bank size, return on assets, and gross domestic product on Islamic banking liquidity in Indonesia. The analysis was conducted using a sample of 7 Islamic commercial banks from the period March 2015 to December 2019. This study uses 2 multiple regression models of panel data with the results showing that Non-Performing Financing, profit-loss sharing investment, bank size, gross domestic product affect the liquidity of Islamic banks. , then for-profit sharing investment account, capital adequacy ratio, return on assets, does not affect the liquidity of Islamic banks.

Time Series Analysis of Global Energy Indices: Logarithmic and Normalized Techniques for Developmental Studies

Diverse opinions exist in the time series analysis of energy and related indices, difference in methodology, sample size, and time variation. This paper will make a conscious effort to converge the divergent outlooks. To accomplish this essential task, five energy indices consisting of energy consumption (EC), gross domestic product (GDP), carbon dioxide emission (CDE), the human development index (HDI), and oil price (OP) were selected. Two analytical methods were adopted, namely logarithmic and normalized techniques, which are designed to complement each other in drawing unfalsified statistical inference concerning the causality between the energy indices. The methods were subjected to four statistical tests and analyses: the augmented Dickey-Fuller, cointegration, pairwise Granger causality, and vector error correction model (VECM). Irrespective of prevailing challenges, both logarithmic and normalized techniques unanimously filtered out causalities. This consisted of neural flow between oil price and energy consumption, gross domestic product and carbon dioxide emission, and energy consumption and the human development index, unidirectional flow between energy consumption and the human development index, oil price and energy consumption, gross domestic product and carbon dioxide emission, and the human development index and oil price, whereas a normalized technique established bidirectional flow between gross domestic product and the human development index, and the human development index and oil price. Pertinently, the research suggests appropriate policies that will generate sustainable development in all the causal directions. Assiduously, the overwhelming agreement between both techniques at the 0.05 level is recommended for further validation with more modern econometric tests.

An Exploratory Study on Personal Finance in the Context of COVID-19

The world is currently experiencing a dramatic crisis that has not yet reached bottom. In Mexico, in the second quarter of 2020, there was a drop in the gross domestic product of 18.9% compared to the same quarter of 2019. In this context, the objective is to identify types of personal expenses in households located in Culiacán, Sinaloa, Mexico as of July 15, 2020. The main results were that most of the respondents' budgets spend according to their income, have had no problem paying their bank loans on time, would consider a fund for future contingencies, have not purchased health insurance, have not bought a computer or cell phone, among other issues analyzed. The main findings are oriented to the fact that the studied population has not acquired additional medical insurance despite the pandemic. It is also concluded that the population under study has become aware of having savings for contingency funds and that digital life still shows resistance in making personal financial decisions.