Domestic Product Research Articles

Inefficiency of laying hens farms in Benin: an input directional distance function approach

BackgroundIn Benin, the productivity of poultry production systems is a major concern. This paper aims first to estimate the cost, technical and allocative inefficiencies of modern and traditional poultry production systems, and then to determine the factors that influence these types of inefficiencies.ResultsThe study reveals significant cost inefficiencies, with just 9% and 18% of traditional and modern systems, respectively, being cost-efficient, highlighting the necessity of distinguishing production systems due to different operational requirements, particularly for modern systems. Addressing these inefficiencies requires crucial measures such as providing training, accessible credit, and mortality rate reduction to boost local production, with tailored support for small-scale farmers.ConclusionsThe poultry sector’s intense competition and the decline in local production, particularly among small-scale farmers, are primarily linked to high domestic production costs and local farmers' poor performance. Our study unveils substantial cost inefficiencies in both traditional and modern poultry farming systems, emphasizing the imperative to differentiate interventions based on their distinct operational requirements.

Exploring the Combined Impact of Environmental Policy Stringency and Institutional Quality on Sustainable Energy: Evidence from Top 14 Green Economies

The growing concern for a green economy increases the demand for sustainable energy. Previous studies massively concentrate on solutions to achieve the target of sustainable energy. However, limited studies focus on the contribution of stringency to sustainable energy. This study explores the contribution of environmental policy stringency and institutional quality on sustainable energy for the top 14 green economies by utilizing data from 1990 to 2022. In empirical analysis, the study applies the PMG-ARDL and CS-ARDL models to obtain the short- and long-run estimates. The study's findings indicate that after controlling the impact of technological innovation, foreign direct investment, population, economic globalization, and gross domestic product per capita environmental policy stringency and institutional quality significantly promote sustainable energy in the long run. Based on the results, the study recommends strictness in implementing policies is more critical than just designing environmental policies.

The impact of gross domestic product, exchange rates and ACFTA implementation on Indonesia’s trade intensity index

The impact of gross domestic product, exchange rates and ACFTA implementation on Indonesia’s trade intensity index

Exports, FDI Inflows, Renewable Energy and Economic Growth: An Empirical Evidence from Singapore

The present study captures the impact of exports; FDI inflows and renewable energy on economic growth of Singapore economy by covering time period 1989-2022 in the context of Cobb-Douglas production function. This study applies ARDL approach for obtaining empirical results. The estimated results will further be tested by taking most of the diagnostic tests to make check whether the results are robust or not. The findings of this study reveal that exports; FDI inflows and renewable energy have a positive and statistically significant impact on the economic growth. However, short run impact of renewable energy is witnessed as insignificant while exports and FDI inflows contribute significantly in boosting economic growth. Moreover, labor and capital also play a positive role in enhancing economic growth. Based on these findings; this study suggests that exports; FDI inflows, and renewable energy may be given more importance by expanding their size if domestic production in Singapore is required to be increased.

GDP, GDI, and Trading Gains: An Alternative View

This note argues that, from an economic‐statistical point of view, trading gains, as difference between real gross domestic income and real gross domestic product, may be considered as a statistical artifact; that is, trading gains vanish if a particular deflator for the trade balance is chosen.

Identifying determinants of spatial agglomeration of healthcare resources by using spatial econometric methods: a longitudinal study in China.

Healthcare resources are necessary for individuals to maintain their health. The Chinese government has implemented policies to optimize the allocation of healthcare resources and achieve the goal of equality in healthcare for the Chinese people since the implementation of the new medical reform in 2009. Given that no study has investigated regional differences from the perspective of healthcare resource agglomeration, this study aimed to investigate China's healthcare agglomeration from 2009 to 2017 in China and identify its determinants to provide theoretical evidence for the government to develop and implement scientific and rational healthcare policies. The study was conducted using 2009-2017 data to analyze health-resource agglomeration on institutions, beds, and workforce in China. An agglomeration index was applied to evaluate the degree of regional differences in healthcare resource allocation, and spatial econometric models were constructed to identify determinants of the spatial agglomeration of healthcare resources. From 2009 to 2017, all the agglomeration indexes of healthcare exhibited a downward trend except for the number of institutions in China. Population density (PD), government health expenditures (GHE), urban resident's disposable income (URDI), geographical location (GL), and urbanization level (UL) all had positive significant effects on the agglomeration of beds, whereas both per capita health expenditures (PCHE), number of college students (NCS), and maternal mortality rate (MMR) had significant negative effects on the agglomeration of institutions, beds, and the workforce. In addition, population density (PD) and per capita gross domestic product (PCGDP) in one province had negative spatial spillover effects on the agglomeration of beds and the workforce in neighboring provinces. However, MMR had a positive spatial spillover effect on the agglomeration of beds and the workforce in those regions. The agglomeration of healthcare resources was observed to remain at an ideal level in China from 2009 to 2017. According to the significant determinants, some corresponding targeted measures for the Chinese government and other developing countries should be fully developed to balance regional disparities in the agglomeration of healthcare resources across administrative regions.

Effect of Inflation on Kenya’s Maize Production and Food Security

The aim of this project was to examine the major determinants of Kenya's maize production. These objectives were actualized by evaluating the role of government policies on Kenya’s maize production for food security between 1992 and 2022 and examining the relationship between inflation and maize production for food security in Kenya between 1992 and 2022. The food availability decline theory would be instrumental in guiding this study. Time-series secondary data from the World Bank, FAOSTAT, and World Trade Organization (WTO) for the relevant period 1990–2022 was used to do this. Using a correlation research design, the econometric analysis of the time-series data revealed that inflation, unemployment, and eventually gross domestic GDP determined maize production. In the near term, the findings demonstrated that the exchange rate, one period's lag in the exchange rate, and the amount and quantity of maize imports all influence maize production. The results imply that in order to lessen the over-reliance on maize production, macroeconomic conditions should be effectively managed to improve domestic maize production, deter a spike in imports of the grain, and enhance food security in the nation.

Effective measures and tools to support the export of domestic agri-food products in the context of international requirements

A comprehensive analysis of the regulatory legal acts of the Republic of Belarus regarding support for the export of agricultural and food products is presented, and issues of organizational and financial measures are considered in detail. Current tools for stimulating exports are proposed for implementation in domestic practice to support enterprises exporting agri-food products in the context of international requirements and taking into account current trends in the world market.

Analysis Of Government Expenditure On Gross Regional Domestic Product In Eastern Kalimantan

Government spending plays a crucial role in a region's economy, especially in Eastern Kalimantan, which has a wealth of natural resources becoming one of the main drivers of the Indonesian economy. The study aims to analyze the influence of government spending on Gross Regional Domestic Product (GRDP) in Eastern Kalimantan from 2010-2023. The dependent variable of the study is regional gross domestic product while the independent variable is health spending, education expenditure, public service spending and housing and public facilities spending. The research method used is a double linear regression analysis with time series data. The results of the study show that government spending on education, public services and housing, and public facilities has a significant influence on the increase in GRDP in Eastern Kalimantan. It highlights the importance of strategically allocating government spending to boost the regional economy.

Government Expenditure and Inflation in India: An Empirical Analysis

In any economy, inflation increases with an increase in economic growth and government size. However, when inflation increases beyond a certain undesirable level, it becomes a problem for the economy and hurts economic growth. The central bank uses various techniques to tackle inflation and reduce the possibility of harmful increase in it. Since India is a developing country, the government expenditure plays a crucial role and provides an important stimulus to economic growth. The main focus of this study is on the correlation between government spending and inflation in the country and its impact over the period 1983–2020. Along with that, the impact of gross domestic product and money supply has also been studied for the same time period. The results of the study suggest that in India, increase in an government expenditure causes an increase in inflation, and the other two variables also share a similar relationship with inflation.

Economic impacts of power-to-liquid fuels in aviation: A general equilibrium analysis of production and utilization in Germany

The aviation industry faces an urgent need to adopt sustainable aviation fuels for significant decarbonization. Power-to-Liquid (PtL) fuel is considered a potential game changer, but questions remain about the wider economic impacts of introducing PtL fuel in the aviation sector. This paper examines the economic impacts of introducing PtL fuel blending quotas along with a price policy consisting of a kerosene tax and PtL fuel subsidies for the case of Germany. Based on a detailed supply chain analysis, we apply a social accounting matrix and a computable general equilibrium model to take into account both, the production and utilization perspectives of PtL jet fuel. Our results show that the influence of low blending quotas is mainly limited to the aviation sector, with a 10 % blending quota increasing consumer prices by 7.9 % and reducing aviation industry output by 3.1 %. When quota levels increase, however, the effects go beyond the air transport system. On inter-sectoral level, we identify three main patterns: First, industries that substantially contribute to the PtL fuel supply chain, such as metal products and electrical equipment, see increasing levels in both, domestic production, and imports. Second, aviation upstream industries like transport infrastructure and aircraft production see reduced domestic production and imports. Third, aviation downstream industries, such as delivery services and travel agencies, see substitution effects, where imports partly replace domestic output. Macroeconomic indicators are affected negatively by the quota scenarios, but the relative impact is low as the maximum decrease in the gross domestic product (GDP) does not exceed 0.35 %. PtL fuel production subsidies can largely mitigate the decrease in aviation demand but come at the cost of a stronger reduction in the GDP and government income. Moreover, the sensitivity analysis emphasizes that various assumptions and parameters, such as the cost projections of PtL fuel, import options, and elasticities of demand, affect the intensity of economic consequences. Our analysis implies the trade-offs of policymaking between sectoral and macroeconomic interests in the context of sustainable fuels. The main contribution of this study is the investigation of the broader economic effects resulting from the adoption of PtL fuels in aviation. In particular, the production as well as the utilization perspective are considered simultaneously in this study.

OPTIMIZING LONG TEXT CLASSIFICATION PERFORMANCE THROUGH KEYWORD-BASED SENTENCE SELECTION: A CASE STUDY ON ONLINE NEWS CLASSIFICATION FOR INDONESIAN GDP GROWTH-RATE DETECTION

Efficiently managing lengthy textual data, particularly in online news, is crucial for enhancing the performance of long text classification. This study delves into innovative approaches to streamline the Gross Domestic Product (GDP) computation process by harnessing modern data analytics, Natural Language Processing (NLP), and online news sources. Leveraging online news data introduces real-time information, promising to improve the accuracy and timeliness of economic indicators like GDP. However, handling the complexity of extensive textual data poses a challenge, demanding advanced NLP techniques. This research shifts from traditional word-weight-based methods to keyword-based extractive summarization techniques to address this. These tailored approaches ensure that selected sentences align precisely with specific keywords relevant to the research case, such as GDP growth rate detection. The study emphasizes the necessity of adapting summarization methods to capture information in unique research contexts effectively. According to classification results, the implementation of sentence selection successfully demonstrated improved performance in terms of classification accuracy. Specifically, there was an average accuracy increase of 0.0226 for machine learning and 0.0164 for transfer learning models. Additionally, in terms of computational efficiency, sentence selection also accelerates processing time during hyperparameter tuning and fine-tuning, as observed using the same computational resources.

The distribution and abundance of woody invasive alien plants in small towns in Eastern Cape Province, South Africa

AbstractInvasive alien plants (IAPs) are rapidly increasing around the world, mainly due to land transformation, climate change, and urbanisation. Whilst urban areas are highly susceptible to invasion by IAPs, crucial information on the distribution and abundance of woody invaders in small towns is limited. Here, we determined the distribution and abundance of woody IAPs across different suburbs and land use types in 12 small towns in the Eastern Cape province of South Africa. We enumerated 38 427 individual woody IAPs, and their abundance and distribution varied across surveyed towns. Although species such as Melia azedarach, Pinus elliotti, Jacaranda mimosifolia, Eucalyptus camaldulensis, and Ligustrum lucidum were the most distributed, species density varied across towns. More than half of the woody IAPs were identified in affluent (56%) suburbs compared to the townships (23%), Central business district (CBD) (16%), and low-income housing neighbourhoods (5%). There were significant differences in woody IAPs distribution across different land use types, with more than half (54%) found in the residential areas compared to road verges (32%) and public urban green spaces (14%). Some socio-economic and environmental predictors such as town population, mean temperature, unemployment rate, and Gross domestic product (GDP) showed significant and positive correlations with woody IAPs species richness and density. Our findings suggest an uneven spatial distribution and diversity of woody IAPs across the different small towns, suburbs, and land use types in the region. It is recommended that small towns in South Africa need financial and human resources support to effectively manage IAPs.

APPLICATION OF ARIMA MODEL FOR FORECASTING NATIONAL ECONOMIC GROWTH: A FOCUS ON GROSS DOMESTIC PRODUCT DATA

This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) model to predict national economic growth, specifically focusing on Gross Domestic Product (GDP) data. GDP data were collected from 2012 to 2023, categorized into training data for the period 2012-2022 and testing data for the year 2023. Utilizing the training data, the research findings indicate that the ARIMA (0,1,0) (0,0,1) model emerges as the most effective in forecasting Indonesia's GDP on a quarterly basis, considering current prices. Subsequently, the model was tested on the 2023 dataset, and it demonstrated accurate predictions aligned with patterns and trends identified during the training phase. The outcomes of this research contribute significantly to the field of economic forecasting in Indonesia, particularly in understanding and predicting the quarterly developments of GDP. The proposed ARIMA model can serve as an effective tool for decision-makers and economic analysts to strategically plan for future economic dynamics on a quarterly basis.

The Study of Trade Deficits in Belize

The objective of this research is to examine the impact of trade deficits on Belize and the relationship between trade deficits and factors that influence economic growth. In this study, the trade deficit is the dependent variable. The economic factors, which are the independent variables, include Gross Domestic Product (GDP), Foreign Direct Investment (FDI), Inflation, Agriculture, and Tourism. This research is beneficial in understanding the impact of trade deficits in developing countries. It is particularly important because studies on trade deficits and developing countries are available, but studies specifically about Belize and trade deficits are scarce. This research fills a gap in the literature and explores an under-researched area. The findings indicate that (i) Trade Deficit impacts GDP and Inflation, and (ii) Trade Deficit is not correlated with FDI, Agriculture, and Tourism.

SPATIALLY INFORMED INSIGHTS: MODELING PERCENTAGE POVERTY IN EAST JAVA PROVINCE USING SEM WITH SPATIAL WEIGHT VARIATIONS

The East Java Province stands as one of Indonesia's regions grappling with a notably elevated poverty rate, accounting for 11.32% of the populace. A strategic approach employed to comprehend and redress this issue involves the application of spatial analysis, wherein spatial factors are intricately integrated into the modeling and cartographic representation of poverty data. The primary objective of this research is to discern the principal determinants influencing the incidence of poverty in East Java Province, employing data reflective of the population's poverty percentages within the province for the year 2021. The study incorporates six pivotal variables, namely: the population poverty rate, open unemployment rate, labor force participation rate, average years of schooling, adjusted per capita expenditure, and the gross regional domestic product (GRDP), predicated on adjusted expenditure. Diverse weighting schemes are applied based on both distance (1) and contiguity (2). The optimal predictive model utilized is the Spatial Error Model (SEM) incorporating a Distance Band Weighing (DBW) mechanism with a designated maximum distance ( ) of 75000 meters. Outcomes indicate that the variable wielding the most substantial influence on the poverty percentage in East Java Province is the average years of schooling. Specifically, an increase in the pursuit of formal education manifests as a negative correlate to the poverty percentage, implying an inverse relationship. Moreover, the SEM model adheres to the requisite assumptions, encompassing (1) the normality of residuals, (2) homogeneity of residuals, and (3) non-spatial autocorrelation of residuals. Comparative analyses reveal that the SEM model utilizing DBW yields diminished values for MAE, MSE, RMSE, AIC, and MAPE in comparison to its linear regression counterpart. Furthermore, the pseudo- values obtained from the SEM surpass those derived from the linear regression model. Rigorous likelihood ratio tests underscore substantial disparities between the SEM and linear regression models, with the former proving more efficient and markedly enhancing the model's explanatory prowess concerning variations in the dataset.

The U-shaped relationship between corruption and international tourism demand: A gravity model approach

The tourism industry significantly contributes to a country’s economic growth and creates a positive image of the destination. This study assesses the impact of corruption on international tourism demand. It employs two-dimensional analyses using the gravity model and a pooled ordinary least square estimator to provide a unique recognition of international tourism demand. The study utilized a conditional quantile regression technique to analyze a dataset of 200 destination countries from 1995 to 2022. The empirical results demonstrate a mixed effect of corruption on international tourism demand. The analysis reveals a non-linear relationship between corruption and international tourism demand, with the inverted-U relationship being statistically significant only at the 50th–75th quantiles and not holding at the upper and lower quantiles. The research confirms that income has a positive impact on tourism across quantiles, but the impact is disproportionate. The sub-period of 2006–2022 experienced a significant decline in gross domestic product (GDP) due to the global financial crisis and its aftershocks, which severely impacted the attractiveness of destination countries for tourists. These valuable insights can inform national tourism policies and businesses.

Application of Linear Regression in GDP Forecasting

Gross Domestic Product (GDP) is the core indicator of national economic accounts, which reflects the economic capacity of a region. It is necessary to have an accurate forecast of GDP. GDP prediction plays an important role in formulating economic policies. By predicting the growth trend of GDP, the government can make correct adjustments and regulate economic activities. This paper takes Guangdong Province and Shandong Province as examples, aiming to establish their GDP models respectively, and obtains their important economic factors on the basis of the models. Firstly, the correlation test and stepwise regression method were used to screen out the variables with significant impact on GDP, and then ridge regression was used to solve the multicollinearity to make the model more accurate. Finally, the similarities and differences of important economic factors in the two provinces were compared. The important economic factors of these two provinces can reflect the characteristics of the economic development of China's north and south, which is conducive to promoting the coordinated development of regional economy and win-win cooperation.

The chronic illness of poverty: the effects of microfinance on the severity of poverty and the cost of its eradication

PurposeThis paper investigates the impact of microfinance on poverty gap which is the shortfall in income or consumption expenditures below $1.90, $3.20 and $5.50 per day. The paper’s primary goal is to investigate how microloans have impacted the severity of poverty and influenced the cost of poverty eradication in Latin America, empirically evaluate these effects and offer appropriate policy recommendations.Design/methodology/approachThis paper used panel data for 13 Latin American countries from world bank spanning the period 2001–2019 and Fully Modified Ordinary Least Squares model for heterogeneous cointegrated panels. This study used Gross Loan Portfolio per active borrowers, gross domestic product per capita, Gini index, Inflation and Unemployment rate as independent variables and poverty gaps as dependent variables.FindingsPoverty gaps narrow as the loan per borrower increases, and the degree of effect differs with the poverty line, with the magnitude increasing as the poverty line falls, underscoring microloans as an effective tool in closing poverty gaps and lowering the cost of poverty eradication. Growth of GDP per capita is helpful reducing the poverty gap, especially for the less poor of the poor. Inflation and unemployment have no to little impact on the severe poverty gaps, but they start to matter when the poverty line is $5.5 per day. Finally, income distribution inequality widens the poverty gap regardless of the poverty line used.Originality/valueThis study suggests several implications. For example, Latin American nations need to embrace tangible policies that encourage economic growth while reducing inequalities in income distribution to effectively eradicate poverty. More supportive environment is necessary to increase the effectiveness of microfinance operations, particularly for the poorest populations. Microfinance institutions need to set less stringent conditions for loan accessibility and repayment schedules that are commensurate with different levels of poverty. Finally, strengthening microfinance as a strategic policy to gradually close poverty gaps and reduce the cost of poverty eradication.

Examining the Relationship between Economic Diversification and Sectoral Linkages: Insights from the Iraqi Economy

Economic diversification is considered one of the important factors in driving economic growth, sectoral growth, and creating economic linkages between the sectors of the national economy. In Iraq, the relative importance of the oil sector in the gross domestic product is higher compared to other sectors such as agriculture and industry, where economic diversification and sectoral linkages are absent, as Iraq is considered a rent-based, one-sided country that lacks diversification of the export and production structure. This research assesses the status of economic diversification in Iraq and the role of intersectoral linkages in facilitating or hindering this process. Overall, the research followed a structured, inductive approach, utilizing secondary data sources and a combination of descriptive and analytical techniques to address the research objectives. Results showed that the relative contribution of the agricultural sector to GDP in 2010 was 5%, and for the industrial sector it was 2.3%, while the direct forward linkages of the agricultural sector were 4%, for the industrial sector 14%, and the indirect linkages were 1.59% for the industrial sector and 1.74% for the transport and communications sector. The research proposes establishing a broad-based manufacturing industry in Iraq that is driven by intersectoral linkages and dependence on domestic economic resources.