Value Of Gross Domestic Product Research Articles

Regional Differentiation of GDP at the NUTS-3 Level in Selected European Countries after their Accession to the European Union

The issue of regional development is gaining importance due to the disproportions in its socio-economic aspects. The study aims to identify changes in economic development of selected countries which joined the European Union (EU) in 2004. The study examines small NUTS-3 (Nomenclature of territorial units for statistics) regions, which are territories determined for statistical purpose, that are less often analysed in the literature. Moreover, it focuses on spatial aspects, also considering rarely examined urban-rural typology of regions. The value and dynamics of gross domestic product (GDP) changes were presented using the Eurostat data for 2004–2019 on GDP per capita ratio (PPS) and GDP per capita (in % in relation to the EU-28 average). The analysis uses basic statistical and convergence measures; regional disparities were presented on graphs and maps. It was found that the examined EU countries are internally different in terms of economic development. The growth of GDP per capita was most dynamic in the Baltic States, Slovakia and Poland. The dynamics of GDP per capita in relation to the EU average was higher in regions — regardless of the type — where the value of GDP per capita was lower at the time of accession to the EU. In rural regions, the dynamics of development changes was smaller in relation to other types of regions. Convergence (both beta and sigma) is occurring at a very low level. Further research may focus on the reasons for enclosed disparities and factors of the ongoing changes.

The application of PSO-BP combined model and GA-BP combined model in Chinese and V4’s economic growth model

Abstract This paper adopts different optimization algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization Algorithm (PSO-Algorithm) to train Back-Propagation (BP) neural networks, fits the Chinese, the Czech, Slovak, Hungarian, and Polish gross domestic product (GDP) growth model (from 1995 to 2020) and makes short-term simulation predictions. We use the PSO-Algorithm and GA with strong global search ability to optimize the weights and thresholds of the network, combine them with the BP neural network, and apply the resulting Particle Swarm Optimization Back-Propagation (PSO-BP) combined model or Genetic-Algorithm Back-Propagation (GA-BP) combined model to allow the network to achieve fast convergence. Besides, we also compare the above two hybrid models with standard multivariate regression model and BP neural network with different initialization methods like normal uniform and Xavier for fitting and short-term simulation predictions. Finally, we obtain the excellent results that all the above models have achieved a good fitting effect and PSO-BP combined model on the whole has a smaller error than others in predicting GDP values. Through the technology of PSO-BP and GA-BP, we have a clearer understanding of the five countries gross domestic product growth trends, which is conducive to the government to make reasonable decisions on the economic development.

The Relationsip between Population Ages 30-34 and Inflation of The Growth Domestic Bruto Inflator

This study aims to determine the relationship between the population aged 30-34 years and inflation as measured by the Gross Domestic Product (GDP) deflator. The method used in this study is the Granger Causality Test. The impact of this research can be seen from this study's results, which is men aged 30-34 years are very influential in the economy. Moreover, most workers in this world are men. The population aged 30-34 greatly influences economic growth and inflation. The male population aged 30-34 has more influence on economic growth and inflation than the female population aged 30-34. In the opposite direction, an increase in unemployment causes GDP to grow more slowly or even fall. Labor is one of the factors driving GDP growth. Increased population growth also has a positive effect on government spending. However, the inflation rate will also have a negative impact on government spending. A high inflation rate can worsen the value of a country's real GDP. If GDP increases, then a country's economic growth is improving. And if the average rate of economic growth in a country from year to year is higher, the income per capita of the community will also increase.

An Evaluation of the Efficiency of Tertiary Education in the Explanation of the Performance of GDP per Capita Applying Data Envelopment Analysis (DEA)

The scientific literature and decision makers debate and explore education’s influence on regional development. However, differences among EU regions remain to be explained. The present article proposes to measure these disparites in terms of the gross domestic product (GDP) per capita and understand how tertiary education can explain that performance. A data envelopment analysis (DEA) optimization technique was applied along with spatial econometric models for all EU NUTS 2 regions (as defined by Eurostat for regional policies application), using the year 2020 as reference. The case of Portugal as a particular set of (seven) NUTS 2 regions included in the EU is detailed and analyzed in-depth. The two-stage least squares regression seems to explain well the differences in GDP per capita with the independent (and instrumental) variables which include the percentage of tertiary education and the spatial lags of this variable. The DEA optimization can support and help to explain most of the spatial regression results. The study identifies the NUTS 2 regions with the best favorable relationships among GDP per capita and percentages of tertiary education, predominantly located in the central and northern European countries and some in Ireland. The south EU regions, as expected, were identified as the regions with the poorest performances for GDP per capita and percentage of tertiary education, as well as some regions in eastern Europe. The positive and significant impact of the percentage of people with tertiary education on the values of GDP per capita given by the spatial econometric model suggests that special priority must be given to education and science in public policies agenda and government budget.

Overweight increases COVID-19 morbidity and mortality

Currently, the COVID-19 pandemic is characterized by a significant decrease in mortality, however, the scale of infection of the population with SARS-CoV-2 is many times higher than the results of previous waves, so scientific research related to understanding the fundamental principles of the processes of origin and development of COVID-19 is extremely relevant today and will remain relevant for a long time to come. The scientific community is actively discussing the paradoxical effect of an avalanche-like morbidity and mortality of the population in most countries that are at a high level of socio-economic development, one of the reasons for which may be the prevalence of overweight people. The authors carried out a correlation analysis of the relationships between the prevalence of overweight (OW) in men and women in 173 countries of the world, incidence and mortality, the value of gross domestic product (GDP) per capita of the state, and the human development index (HDI). A review of the state of the problem and our statistical calculations clearly indicate that the prevalence of overweight is accompanied by an increase in the incidence and mortality of the population from COVID-19. The level of incidence and mortality of the population is associated with an exponential relationship with the prevalence of overweight both among the male and female population of the state. The prevalence of overweight has a slightly stronger effect on the increase in morbidity compared to the increase in mortality. The degree of association between incidence/mortality and the prevalence of overweight among men/women in 2021 is lower than in 2020, but the difference is not significant. This suggests that large-scale vaccination of the population in countries with high GDP per capita and high HDI only marginally reduced the impact of OW prevalence on COVID-19 incidence and mortality. The prevalence of overweight in the population is directly proportional to GDP per capita and, to a greater extent, to the HDI. This, in the opinion of the authors, is one of the main reasons for the significantly higher incidence and mortality in countries with a high level of socioeconomic development/

Potensi dan Peluang UMKM Terhadap Upaya Pemulihan Ekonomi di Kota Bogor Pada Era Pandemi Covid-19

In the current state of the COVID-19 pandemic, many sectors of life have stopped, including the MSME sector. This study aims to determine the potential and opportunities for Micro, Small and Medium Enterprises (MSMEs) in Bogor City in an effort to increase economic growth during the COVID-19 pandemic. The research method used is by collecting data primarily with qualitative methods whose research data is collected by distributing questionnaires to MSME actors scattered in the city of Bogor. From the research that has been done, it is found that the potential and opportunities for SMEs are very significant in an effort to increase employment, increase the value of gross domestic product, and advance the economy.
 Keywords: Covid-19; MSME Potential and Opportunities; and Economic Growth.

Econometrics Analysis on Significant of Transportation Sector to the Nation Gross Domestic Product (GDP)

This research work centered on econometrics analysis on significance of transportation sector to the Nation Gross Domestic Product (GDP) in Nigeria economic. The aim of this research work is to test for significance of transportation sector to the Nation Gross Domestic Product. The literature review explains extensively on the important of using econometrics to carry out this research and why is desirable to fit the model. The methodology employs the use of multiple regression analysis, test of parameter and coefficient of determination. The road, sea, rail and air transportation are useful in predicting the value of Gross Domestic Product. The value of R-square show that combination of RT, ST, RailT and AT explain variation in GDP which is significantly implies that the four variables are important in GDP and the Sea transportation contribute majorly to the nation Gross Domestic Product. Testing the significant of the parameter, it is observed that there is presence of multicollinearity, heteroscedascity and autocorrelation and necessary correction are made on them.

Pemetaan Industri Kreatif Dalam Meningktakan Pertumbuhan Ekonomi Pada Kawasan Urban di Kota Palembang

This study aims to analyze the mapping of potential, creative economy potential, review of the contribution of the creative industry, namely according to the value of Gross Domestic Product (GDP), based on Employment, and according to Company activities and increase economic growth in urban areas in Palembang. Based on the value of Gross Domestic Product (GDP), according to Employment, as well as based on Company activities and increasing economic growth of urban areas in Palembang. The research method used in this study means: Desk research sourced from the agency based on the value of Gross Domestic Product (GDP), based on employment, and based on activity. The company also increases the economic growth of urban areas in or related departments, namely the industrial department of Palembang City and literature on the creative industry using studies that are in accordance with the value of Gross Domestic Product (GDP), based on employment, as well as according to the Company's activities and increasing economic growth in urban areas. And a short survey to the creative industry was selected to receive primary data as an evidentiary activity supported by a structured survey tool. The mapping method used is the social mapping method.

Forecasting the development of the domestic economy by the system of artificial intelligence, assessment of the risk and stability of the financial sector

The article considers theoretical approaches to forecasting the development of the domestic economy and the stability of the fi nancial sector. The work uses an artificial intelligence system developed by the authors. The paper puts forward and proves the hypothesis that the use of the risk assessment of the financial sector calculated using the VaR model in the neural network AI system can be used to obtain the forecast value of the country’s GDP in order to provide support for managerial decision-making under conditions of uncertainty. Of great importance is the study of the theoretical foundations for increasing the stability of the Russian economy in modern conditions. The use of neural network models in GDP forecasting is an important tool for assessing and ensuring the sustainability of the Russian economy. The novelty of the study lies in the fact that an approach is proposed to fill the gap that concerns the problem of the lack of a reliable approach in assessing the risk of financial sector stability in order to ensure the sustainable development of the domestic economy. Get a correct forecast of the gross domestic product (GDP) in the face of market uncertainty. According to the VaR model, with a 99 % probability, the absolute value of the fi nancial risk of a reduction in the profits of the banking system of the Russian Federation may amount to 108.5 billion rubles in 2022, or 4.5226 %, and the amount of profi t may reach 2.3 trillion rubles. rub. The resulting neuroforecast of the value of GDP for 2022 is 79 023.4 billion rubles. 33.3 % higher than the 59 262.95 billion rubles, declared by experts, the total forecast value of GDP for 1Q. (28410) and 2 sq. (30853). To compare the accuracy of the forecast, it remains to wait until the actual values of GDP become known. The practical significance of the study lies in the fact that in the course of the study the prerequisites for solving an important national economic problem were formed — forecasting the value of GDP and ensuring the sustainable development of the country’s economy.

Pengaruh Utang Luar Negeri dan Ekspor Terhadap Pertumbuhan Ekonomi (PDB) Indonesia Periode Tahun 2006-2020

This review is expected to be able to carry out a test with the ultimate goal of deciding on the variables that affect Indonesia's GDP. This type of review uses a quantitative methodology. The factors in this review are the value of Indonesia's external debt and the value of Indonesia's GDP as the dependent factor and the value of Indonesia's GDP as the dependent factor. This information is taken as selected information (time series) from BI and the Central Statistics Agency for the period 2006-2020. This information checking technique uses multiple linear regression examination. The impact of this review proves that the relationship between Indonesia's external debt and the value of Indonesian exports has a very large impact together. The fractional test (t test) proves that the value of Indonesia's external debt basically has an impact on the value of Indonesia's economic growth. The export value factor does not affect the value of gross domestic product in Indonesia.

United States Tax Rates and Economic Growth

American politicians aim to create economic activity that will expand the economy and provide opportunities for citizens. Today (in 2022), President Joseph Biden presents an ambitious tax plan to grow the economy and provide for more equal opportunities. With Biden’s aim for a tax increase, this research examines the impacts of tax and other economic variables on economic wellbeing. In turn, this research provides a timely update on contributing factors to economic growth. Previous academic research shows the impacts of tax rates and common economic variables related to U.S. economic growth. We gather data from 1960 to 2020 to explore U.S. real gross domestic product (GDP) per capita. Through a series of multiple regression models, we find that increases in the highest statutory corporate and personal income tax rates reduce real GDP per capita. Growth in net exports of goods and services, M2 money supply, multifactor productivity and cost, collectively increase real GDP per capita, while, the personal savings rate, and the market value of gross federal debt decrease real GDP per capita. We recommend that if Congress elects to raise tax rates, it should start with the personal income tax rate.

Land Use Optimization for Coastal Urban Agglomerations Based on Economic and Ecological Gravitational Linkages and Accessibility

Urban agglomerations (UA) are attracting increasing research attention as a global emergent phenomenon, whereby regional collaborative linkages between cities attracts and agglomerates development. However, these studies also acknowledge that ecological values may be negatively impacted by re-development, ecological fragmentation, and proximity or downstream impacts. Sustainable development, therefore, requires balancing forces from economic attraction and ecological repulsion. Forces similar to economic ones may also operate in attracting ecological enhancement towards higher-valued ecological regions; however, research regarding the role of the self-collaborative gravity-like forces shaping UA is limited in land use optimization. To assist planners, this study developed a new multi-objective land use optimization of UA that explored the intensity of economic ties and ecological gradients using the multi-objective NSGA-II algorithm. In this model, economic linkage intensity (ELI) and accessibility were used to calculate a modified GDP (gross domestic product), while the NDVI (normalized difference vegetation index) was used for the modified ESV (ecosystem services value). Spatial allocation with implicit economic accessibility relationships was enhanced through a two-step mutation operator, including a “gravity flip” spatial orientation factor. Compared to the standard NSGA-II algorithm, models of future land use of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in 2030 have shown that the modified GDP value in our model increased by 7.41%, while the conversion rate of high-density vegetation reduced by 7.92%. The results highlighted the importance of linkage and accessibility factors in enhancing the clustering of cities. In tandem, the modified ESV also enhances ecosystem services contributions of higher value vegetated land through decentralized built-up developments. The proposed model provides managers with a comprehensive and efficient land use solution model that accounts for intrinsic linkage factors shaping the development of compact urban agglomerations.

Social Progress beyond GDP: A Principal Component Analysis (PCA) of GDP and Twelve Alternative Indicators

What social progress is and how to measure it are seemingly plain but essentially intri-cate questions that have not been clarified to date, which has led to various social problems and development failures. Designed after the Great Depression in the 1930s, Gross Domestic Product (GDP) has been, on the one hand, regarded as the greatest invention of the 20th century and is widely accepted as the primary indicator for social progress, but on the other hand, it has been criticized as knowing the price of everything but the value of nothing. The Beyond GDP Movement that has been active since the 2010s has inspired global interest in designing indicators to replace or supplement GDP, but none of them stands out as GDP’s successor. We take 12 influential indicators that consider beyond GDP and carry out a Principal Component Analysis (PCA) to investigate their correlations. The results indicate that GDP per capita (GDPP) can explain 65.61% of the information in the first principal component (PC) and account for 51.10% of the information related to the total 13 indicators, indicating its major role in social progress. Most indicators have strong correlations with GDPP, not beyond, and only the Ecological Footprint per capita (EFP) and Happy Planet Index (HPI) that have negative and weak correlations with GDPP, respectively, can provide new perspectives and values beyond GDP. Social progress is based upon various public values, and the indicators are the measurements of these values. Although GDP and economic values play major roles during social development, other indicators and their potential public values cannot be ignored. Prioritizing these public values and monitoring their indicators are essential to achieving sustainable and comprehensive social progress.

Assessment of the predictive potential of international Sovietology

Aim. The work aimed to review and analyze the opinions of international Sovietologists regarding the ways of the Soviet economy development of the late socialist period, as well as to identify problems of the prognostic potential of international Sovietology.Tasks. The work was performed to study scenarios for the development of the pre-perestroika Soviet economic model and problems in their prediction by international analysts.Methods. The object of study was the international historiography of the USSR economic history in the pre-perestroika period. The sources of information were official statistics and the works of international analysts. The authors of this article used general scientific research methods, as well as methods of historical and economic analysis.Results. An analysis of international studies shows that the hypothetical scenarios for the development of the Soviet Union put forward by Sovietologists before perestroika were based on prognosis of previous years, conflicting statistical data and scientific methods tested under the conditions of the capitalist economic model. Meanwhile, the Soviet planned socialist model represented a different type of economy, which had not previously been tried to be implemented on a national scale (the situation remains the same in the period considered). According to the position of international researchers, despite the low growth values of gross domestic product (GDP) per capita and the main leader of the economy, the industrial sector (taking into account the fall in oil revenues and stiffening of sanctions against the USSR in the early 1980s), the economy remained stable. Due to large margins of safety in the form of developed industry, agriculture, infrastructure, high scientific potential, and a huge stock of natural resources, the further existence of the USSR was not in doubt.Conclusion. Many Sovietologists have written about the need for fundamental changes in the Soviet planned economic system. At the same time, they took into account the significant difficulties and risks associated with possible reforms, believing that the consequences of the radical introduction of the market into the planned economy could be significant disturbances. This is one of the reasons why, in the pre-perestroika period, few Western analysts admitted the possibility of large-scale reforms in the near future.

The effect of COVID-19 pandemic on economic growth and public debt: an analysis of India and the global economy

PurposeThe study attempts to examine the effect of the COVID-19 pandemic on the economic growth and public debt of the Indian economy. The authors also attempt to make quarterly projections of economic growth and external debt (ED) for the next five years. The objective is to understand how much time the economy takes to recover and at what pace. Consequently, this study elucidates the composition of debt after the crisis in the next five years.Design/methodology/approachTo predict India's gross domestic product (GDP) and ED for the next five years, the authors used an auto-regressive integrated moving average (ARIMA) model. This model was built under a Box–Jenkins methodology (Box and Jenkins, 1976) and was subjected to an augmented Dickey–Fuller (ADF) test to check the stationarity of the data. The methodology includes three main steps to estimate and forecast the model: identification, estimation, and diagnostic and forecasting.FindingsThe study finds that the outbreak of the COVID-19 pandemic has significant implications for economic growth and public debt. The economy faced contraction in the first quarter of the year 2020 due to the suspension of economic activities and still struggling with the negative values of GDP. The forecasting results reveal that ED will continue to grow to meet the increasing health expenditure needs, and GDP will also bounce back slowly after the end of the year 2021. It has been noticed that the recurrent crisis derails the developing economies from the path of sustainable development to a prolonged economic slump with mounting public debt.Originality/valueThe study examines the impact of the COVID-19 pandemic on economic growth and public debt with particular reference to India. To the best of the authors’ knowledge, this is the first time the quarterly projections for GDP and ED have been made after the COVID-19 crisis.

AI-system and multifactor correlation-regression model for forecasting RF GDP in the Colab and XL

The article discusses the theoretical foundations for forecasting the gross domestic product (GDP) and the sustainability of the development of the Russian economy. The forecast values of GDP were calculated by two methods: based on the use of the AI-system and a multivariate correlationregression model for the purpose of subsequent comparison. The novelty of the study lies in the fact that a comparative assessment of the accuracy of the generated forecasted values of the RF GDP by different methods was carried out: using the AI system and a multivariate correlation-regression model in XLtables using the Microsoft Correlation and Regression packages. Some of the calculations were carried out in the Colab cloud environment using the Python programming language. The initial data for the AI-system and the correlation-regression model are statistical data — chain indices of the development of the main industries taken for the period 2000–2020. Studies have shown that the average forecast error calculated using a linear six-factor regression model between factorial features and the effective one was 0.153553 %, while the average forecast error calculated using the AI system was 0.010082 %. The forecasting accuracy using artificial intelligence algorithms is an order of magnitude higher than the traditional one — using a multivariate regression model. Based on the initial data, a dataset was collected, on which, using the Deductor analytical platform, an AI system was formed in order to obtain the forecast value of the RF GDP for the next calendar year. Using the capabilities of the Colab cloud service, in the Python language, the pairwise correlation coefficients between the factors were calculated, the results of which are presented in a heat matrix. Formed a correlation-regression model in XL-tables using the packages "Correlation" and "Regression" Microsoft. Also, the quality of the regression model, reflecting the dependence of the effective indicator "GDP" on the main factorial indicators, was assessed using the Student’s t-test, F-statistics and others.

The Mechanism of Influencing Green Technology Innovation Behavior: Evidence from Chinese Construction Enterprises

The Green Technology Innovation Behavior (GTIB) of construction enterprises is crucial for promoting green development in the construction industry. In order to clarify the mechanism of action affecting the GTIB of construction enterprises, this paper considers the context of green development in the construction industry based on the vector autoregressive model and constructs a theoretical model of GTIB in construction enterprises. Time series data collected by the Chinese government (2000–2018) were used to analyze the mechanism of action of the factors influencing the GTIB of construction enterprises by EViews 10.0. The results of the paper showed the following: (1) direct government investment has the greatest impact on the GTIB of construction enterprises and has made a positive contribution; (2) the added value of Gross Domestic Product (GDP) of the construction industry has a relatively small impact on the GTIB of construction enterprises; (3) the role of environmental regulation on the GTIB of construction enterprises is non-linear. This paper further broadens the research to the factors influencing the GTIB of construction enterprises. Meanwhile, this paper provides a reference basis for local governments to formulate policies related to the GTIB of construction enterprises.

Analysis on the Economic Recovery in the Post-COVID-19 Era: Evidence From China.

As a major public health emergency, the COVID-19 pandemic has had a huge impact on economies all over the world. The experience of post-COVID-19 economic recovery is of great significance for achieving sustainable and high-quality economic development. Taking the economic development of China as an example, based on the theory of resilient economy and related measurement methods, this article selects five major indicators that are generally recognized as closely connected with economic resilience to construct a system of economic resilience indicators. In addition, the autoregressive integrated moving average (ARIMA) model is used to predict gross domestic product (GDP) under the scenario of no epidemic. The actual value of China's GDP is compared with the predicted value in the absence of the epidemic, verifying that strong economic resilience plays an important role in the country's economic response to major shocks. Based on the results, policy recommendations are made for countries to strengthen their economic resilience in the postepidemic era.

Effectiveness of Public Spending before and during COVID-19 Pandemic

The pandemic of COVID-19 influences all sectors of the economy. It caused decreasing in produced Gross domestic product (GDP) and higher unemployment. As it is known, to overcome this negative tendency, it is possible to put in practice monetary and fiscal instruments. During the pandemic, the government tried to slow down negative economic results through public spending. With them, the government looks to be increased aggregate demand in the economy and as a result-GDP raises and unemployment reduces. The research is based on created original model for testing the impact of total public spending, capital, salary, social insurance and care, for maintenance by a consolidated fiscal program on the value of GDP. The changes of GDP measure the effectiveness of public spending. The period of research is before and during the COVID-19 crisis (2019-2020) in the case of Bulgaria. Before the pandemic the analysis shows coefficient of determination for capital spending is more significant compare with all other types of public expenditure and these cost predetermine economic growth. During the pandemic of COVID-19 public spending has used as the main instrument to overcome the negative results for the economy. For this period it found an extremely strong impact of labor costs and social care expenditure on aggregate demand. They bring more positive results to be solved health issues, but not for faster recovery of the economy.

Forecasting analysis of selected health- and economy-related indicators in South Eastern European and Balkan countries

Background/Aim. Health indicators provide comparable in-formation relevant to defining health goals. The aim of this study was to perform a forecasting analysis of the selected indicators, which could help anticipate the future necessities in the health economy and prevent the problems that would predictively grow in the future. Methods. Health indicators are collected from publicly available databases of the World Health Organization and Eurostat. We used the quantitative forecasting technique, commonly used for historical data, to predict several years in the future concerning selected health- and economy-related indicators. Results. Total health expenditures as a percentage of gross domestic product (GDP) by 2025 will increase in most of the 17 surveyed countries. The percentage of direct household budget payments (out-of-pocket) for health services will decrease in half of the countries, while values of GDP expressed in US$ will increase significantly com-pared to the last surveyed year (2017), except in Greece. The infant mortality indicator shows that the numbers will decrease in each surveyed country, while the urban population percent-age will rise almost in each country except Estonia. Life expectancy will increase in each surveyed country. Conclusion. Health- and economy-related indicators must be monitored over time, as they provide significant information concerning the relevant issues in the health care system. Moreover, they may indicate changes that should be made in order to accomplish progress in each individual country.