Per-capita GDP Research Articles

The role of global collaboration in environmental technology development, natural resources, and marine energy generation technologies toward carbon neutrality in knowledge-based economies.

This study aims to explore the influence of renewable energy consumption (REC), global collaboration in environmental technology development (GCETD), gross domestic product per capita (GDPPC), marine energy generation technologies (MGT), trade openness (TDOT), natural resources (NRs), and carbon dioxide emissions (CO2e) in 34 selected knowledge-based economies from 1990 to 2020. The results show that MGT and REC, an environmentally friendly source of energy, are positively connected with zero carbon emissions, reflecting the ability to serve as an alternative energy option for a sustainable environment. In addition, the study reveals that NRS, such as the accessibility of hydrocarbon resources, can have a positive effect on CO2e, implying that the unsustainable use of NRs may lead to the expansion of CO2e. Additionally, the study pinpoints that GDPPC and TDOT, as a gauge of economic expansion, play an essential part in a carbon-neutral future, suggesting that greater amounts of commercial success could result in greater ecological sustainability. The results also show that GCETD is linked to lower CO2e. This means that working together on an international level helps to improve environmental technologies and slow down the effects of global warming. It is suggested that governments should focus on and encourages GCETD, the use of REC, and TDOT to speed up the path toward zero emissions. Also, decision-makers should think about backing research and development investments in MGT as a potential way to reach zero CO2e in a knowledge-based economies.

The interaction effects between exposure to ambient PM2.5 and economic development on the settlement intention for floating population in China.

Limited studies examined the interaction effects between exposure to ambient PM2.5 and economic development on the settlement intention of floating population. We used binary logistic model to examine the association of PM2.5, per capita GDP (PGDP), PM2.5 [Formula: see text] PGDP on the settlement intention. Additive interaction term of PM2.5 and PGDP level was used to investigate their interactive effects. Overall, each one grade increment in annual average PM2.5 was associated with decreased probability of settlement intention (OR = 0.847, 95%CI: 0.811-0.885). The interaction effect between PM2.5 and PGDP on settlement intention was significant (OR = 1.168, 95%CI: 1.142-1.194). The stratified analysis showed PM2.5 exhibits lower settlement intention in the aged 55years or above, engaged in low-skilled works, and living in the western China. This study indicates that PM2.5-exposed will decrease the settlement intention of floating population. High economic development level can weaken the relationship between PM2.5 and settlement intention. Policymakers should balance the socio-economic development and environmental health and focus on vulnerable population.

Air cargo transport demand forecasting using ConvLSTM2D, an artificial neural network architecture approach

The prediction of air traffic demand (passengers and cargo) in a regional/national air transport system is essential. Knowing the behavior of future demand helps, on the one hand, the design and execution of air transport public policies, which, for example, help to focus, guide and prioritize investment (public and private) for the expansion / modernization of airport infrastructures (or development of new airports), act on tariff policies, implement changes in regulatory policy, etc.; on the other hand, it helps airport managers to plan the airport. Therefore, in this paper, a short-term forecast (5 years) of the demand for air cargo transport was carried out, applied to a specific case study (Colombia), taking into account the most severe pandemic period (the year 2020). To perform the forecast, an approach based on Machine Learning/Deep Learning (ML/DL) method comprising a hybrid of convolutional and recurrent memory neural networks (that allow space-temporal non-linear analysis, such as multi-variable spaces and temporal multi-steps), is presented. The analysis developed here establishes the optimal length of the prediction period; on the other hand, the proposed methodology allows the identification of the most relevant socioeconomic features in the prediction of air cargo demand (domestic and international), i.e., interpreting the ML/DL results obtained through the variational analysis of different combinations of features. The results show that international air cargo demand is strongly dependent on Gross Domestic Product (GDP) and PCG (Per Capita GDP), while domestic air cargo demand is significantly dependent on PCG. Finally, the results show, for the case study country, very rapid recovery of air cargo demand at pre-pandemic rates (behavior already found in other recent studies and research).

Economic Growth and the Insurance Sector in South Africa: Toda-Yamamoto

Abstract. Introduction. The connection between the real economy and the financial sector continues to be a subject of debate amongst scholars. There is a plethora of studies dedicated to unravelling the relationship between economic activity and financial progress. However, a large share of those studies has concentrated on banking and the capital market industries; the studies that have undertaken to decipher the connection between the insurance industry and prosperity in the economy are insufficient. Purpose. This study attempts to explore the link between South Africa's insurance industry and economic growth. The study made use of secondary time-series quarterly data and employed the Toda-Yamamoto (non-Granger causality) test. Results. Firstly, the findings show a uni-directional causal relationship running from long-term insurance to GDP percapita. Implying that the South African long-term insurance sector is supply-leading. Secondly, the results revealed that GDP percapita and the short-term insurance sector are interdependent (bi-directional relationship). Lastly, the findings showed a unidirectional causal relationship between GDP per-capita and total insurance assets. Conclusions. The results imply that the South African total insurance sector is supply-leading.

Examining artificial intelligence and energy efficiency in the MENA region: The dual approach of DEA and SFA

The potential growth, privileged geographic location, emerging economies, and large market led to much potential research in the Middle East and North Africa (MENA) region. However, the role of Artificial intelligence (AI), along with other variables for energy efficiency (EE), to deliver economic and environmental benefits are yet underexplored in this region and brings a research lacuna. Thus, this study aims to present scientific evidence for the EE through the interaction effects of AI and energy use (ENEU) along with other variables in the MENA region from 2000–2021 using the dual approaches of data envelope analysis (DEA) and stochastic frontier analysis (SFA). The results confirm that countries such as Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Malta, Oman, Saudi Arabia, Tunisia, UEA, and the West Bank are the most energy efficient, deriving an efficiency value of one from their optimal solution of DEA. The panel’s SFA analysis shows that AI, patents, renewable energy, and gross domestic product per capita (GDPPC) are meaningful and directly contribute to the study countries’ EE gains. The interaction term of AI and ENEU is significant, which implies the relevance of AI and ENEU in achieving EE. Furthermore, EE is negatively related to carbon intensity between 17.4% and 64.5% or worsens EE by 49.3% depending on the country, allowing the region to meet its climate targets and achieve the environmental benefits of deploying AI. In addition, it means that the MENA region can integrate AI and ENEU to curb the increasing decentralization of the national grid, which leads to energy efficiency gains in this region. This calls for integrating Al and energy in an effective way to enhance the human consumption of energy, taking the needs of the environment into consideration in order to attain efficiency and intelligent energy use. The study attempts to shed light on energy use while integrating AI to achieve economic and environmental efficiencies in the MENA region.

The Clinical Impact and Cost-Effectiveness of Clinic-Based Cognitive Behavioral Therapy for People With HIV, Depression, and Virologic Failure in South Africa.

Depression affects 25%-30% of people with HIV (PWH) in the Republic of South Africa (RSA) and is associated with both antiretroviral therapy (ART) nonadherence and increased mortality. We evaluated the cost-effectiveness of task-shifted, cognitive behavioral therapy (CBT) for PWH with diagnosed depression and virologic failure from a randomized trial in RSA. RSA. Using the Cost-Effectiveness of Preventing AIDS Complications model, we simulated both trial strategies: enhanced treatment as usual (ETAU) and ETAU plus CBT for ART adherence and depression (CBT-AD; 8 sessions plus 2 follow-ups). In the trial, viral suppression at 1 year was 20% with ETAU and 32% with CBT-AD. Model inputs included mean initial age (39 years) and CD4 count (214/μL), ART costs ($7.5-22/mo), and CBT costs ($29/session). We projected 5- and 10-year viral suppression, quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratios (ICERs: $/QALY [discounted 3%/yr]; cost-effectiveness threshold: ≤$2545/QALY [0.5× per capita GDP]). In sensitivity analyses, we determined how input parameter variation affected cost-effectiveness. Model-projected 5- and 10-year viral suppression were 18.9% and 8.7% with ETAU and 21.2% and 9.7% with CBT-AD, respectively. Compared with ETAU, CBT-AD would increase discounted life expectancy from 4.12 to 4.68 QALYs and costs from $6210/person to $6670/person (incremental cost-effectiveness ratio: $840/QALY). CBT-AD would remain cost-effective unless CBT-AD cost >$70/session and simultaneously improved 1-year viral suppression by ≤4% compared with ETAU. CBT for PWH with depression and virologic failure in RSA could improve life expectancy and be cost-effective. Such targeted mental health interventions should be integrated into HIV care.

Asymmetric Association among Technological Spillover, Absorptive Capacity and Economic Development: A NARDL Approach

The spillover of external research and development (R&D) capital stock and the level of domestic human capital, as a source of absorptive capacity, plays a vital role in stimulating economic growth and development. The objective of this study is to examine the asymmetric associations among human capital, R&D stock spillover, capital import intensity, and output growth in Pakistan over the period 1982 to 2020. For this purpose, we develop and estimate a nonlinear autoregressive distributed lag (NAR DL) model. The results of the bounds test confirm the presence of a long run relationship, while the error correction model confirms the convergence from short run to long run equilibrium among the variables. The impact of machinery and technological good imports is positive on per capita GDP in the long run. In contrast, the impact of external R&D capital stock is symmetric in the short but becomes negative in the long run. The relationship between human capital and per capita GDP appears to be asymmetric because of low spending in the education sector. Based on these findings, we suggest that Pakistan should enhance its share of imports of high tech products and improve its absorptive capacity for the effective use of external R&D resources.

Foreign Assistance, Institutional Quality and Economic Growth in Selected Sub-Saharan African Countries: 1990 – 2021

This study adopts the Augmented Mean Group (AMG) estimator to analyse the impact of institutional quality and foreign assistance on economic growth in Sub-Saharan African countries, using time series data sourced from the World Bank Development Indicators (WDI) and World Bank Governance Indicators (WGI) for the period 1990 to 2021. The study went a step further to determine whether there is a significant interactive development effect of foreign assistance and institutional quality in sub-Saharan African countries. The results show that foreign assistance has a positive conditional marginal effect on per-capita GDP, institutional quality has a positive conditional marginal impact on per-capita GDP and their interaction is also positive. The results are all statistically significant at 10% level of significance. Thus, foreign assistance and good quality institutions improves economic growth. This does not imply that; sub-Saharan African region should heavily rely on foreign assistance, without putting in place specific developmental efforts. Thus, it is opined that policymakers should regulate the allocation of foreign assistance and improve the quality of institutions. In doing so, the production capacity of the region would increase and the region would attract huge amount of foreign direct investment (FDI). Consequently, there would be a distinction between the amount of foreign assistance needed to lessen poverty and the levels of foreign assistance required to achieve economic growth. Keywords: Foreign Assistance; economic growth; AMG Estimator, CCEMG Estimator, Institutional Quality and Sub-Saharan Africa DOI: 10.7176/JESD/14-8-07 Publication date: April 30 th 2023

The Effects of Digitalization and GDP Per-Capita Growth on Tax Revenue in ASEAN Countries

ASEAN’s digital economy is soaring up every year and bring potential additional tax revenue. In many countries, tax revenues dominate total state revenue but six out of ten ASEAN member countries couldn’t optimize its tax revenue hence causing a social inequality and poverty This study aims to analyze the effects of digitalization indicated in internet penetration level and Gross Domestic Product per capita growth on tax revenue in ASEAN countries. Samples consists of 6 countries in ASEAN: Indonesia, Malaysia, Singapore, Cambodia, Phillipines, and Thailand; with dataset of 15 years observation from 2005 to 2019. By using data panel multiple regression analysis in Fixed Effect Model, the results indicate that: (1)digitalization has significant and positive influence on tax revenue, (2) GDP per capita growth has significant and negative influence on tax revenue, and (3) simultaneously digitalization and GDP per capita growth have significant influence on tax revenue.

Corruption suppression and domestic investment of emerging economies: BRICS and CIVETS groups – panel ARDL approach

PurposeThis paper aims to examine the effects of corruption control on domestic investment in the BRICS and CIVETS of emerging economies. This paper’s primary goal is to investigate how corruption has impacted domestic private investment in BRICS and CIVETS, empirically evaluate that impact and offer appropriate policy recommendations.Design/methodology/approachThis paper uses secondary panel data from the World Bank spanning the period 2000–2020. The data covered the BRICS and CIVETS countries between 2000 and 2020. This study used gross domestic product (GDP) per capita growth, broad money as a percentage of GDP, real interest rate and corruption index as independent variables and domestic investment as a percentage of GDP as a dependent variable.FindingsThe significant results are presented using the panel, autoregressive distributed lag pooled mean group estimator. Growth in the per-capita GDP, money supply and the suppression of corruption all have long-term, positive and significant benefits on domestic investment. Comparatively, the real interest rate has a significant negative influence on investment, indicating that it may be necessary and beneficial to adopt anti-corruption measures to promote domestic investment. However, the country-specific analysis reveals that the long-term effects of corruption on investment tend to vary across countries, indicating that each country needs to research the issue of corruption independently. Finally, ensuring optimal levels of money supply and interest rates leaded by further control of corruption is necessary for strengthening the investment environment.Originality/valueThis study suggests several practical implications. For example, legislators and policymakers should pay more attention to anti-corruption policies. Central banks should put more effort into controlling the interest rate.

Workers' remittances and economic growth: new evidence from an ARDL bounds cointegration approach for Sri Lanka

PurposeThe objective of this study is to examine the long-run relationship between workers' remittances and economic growth in Sri Lanka using time series data spanning 1975–2021.Design/methodology/approachThis study employed both exploratory data analysis (EDA) and inferential data analysis (IDA) tools. EDA includes the scatter plots, confidence ellipse with Kernel fit, whereas IDA covers unit root test, the autoregressive distributed lag (ARDL) bounds technique, the Granger's causality test, and impulse response function (IRF) analysis.FindingsEDA confirms that workers' remittances have a positive relationship with per-capita gross domestic product (GDP). All variables used in this study are I(1). This study is exhibited that workers' remittances have a positive long-run relationship with per-capita GDP. The estimated coefficient of the error correction term shows that the dependent variable moves towards the long-run equilibrium path. Workers' remittances have a short-run and long-run causal relationship with per-capita GDP. The IRF analysis indicates that a one standard deviation shock to workers' remittances has initially an immediate significant positive impact on economic growth.Practical implicationsThis study provides insights into workers' remittances in economic growth in Sri Lanka. Further, the findings of this study also provide evidence that workers' remittances increase economic growth.Originality/valueUsing ARDL bounds test, Granger's Causality test and IRF analysis for examining the relationship between workers' remittances and economic growth are the originality of this study.

Artificial Light-at-Night Exposure and Overweight and Obesity across GDP Levels among Chinese Children and Adolescents

Evidence in adults suggests that exposure to artificial light-at-night (ALAN) leads to obesity. However, little is known about whether this effect exists in children and adolescents. We aimed to investigate whether ALAN exposure was associated with overweight and obesity in school-aged children and adolescents and whether this association varied with socioeconomic status. Data on the height and weight of 129,500 children and adolescents aged 10-18 years from 72 cities were extracted from the 2014 Chinese National Survey on Students' Constitution and Health (CNSSCH). The ALAN area percentage and average ALAN intensity were calculated using the Visible/Infrared Imager/Radiometer Suite. The subjects were separated into three categories based on the cities' gross domestic product per capita (GDPPC). A mixed-effect logistic regression model and generalized additive model (GAM) were utilized to evaluate the association between ALAN exposure and overweight and obesity in children and adolescents stratified by municipal GDPPC. Both ALAN area (OR = 1.194, 95% CI: 1.175-1.212) and ALAN intensity (OR = 1.019, 95% CI: 1.017-1.020) were positively associated with overweight and obesity in children and adolescents, and the associations remained robust after adjusting for covariates. ORs for overweight and obesity and ALAN area decreased as GDPPC level increased (first tertile: OR = 1.457, 95% CI: 1.335-1.590; second tertile: OR = 1.350, 95% CI: 1.245-1.464; third tertile: OR = 1.100, 95% CI: 1.081-1.119). Similar results were observed for ALAN intensity. In the GAM models, thresholds existed in almost all these spline trends, indicating that ALAN might have a nonlinear association with overweight and obesity. ALAN contributed to the development of overweight and obesity in children and adolescents and this effect differed with GDPPC. Future longitudinal studies should confirm the causal relationship between ALAN and obesity. Moreover, reducing unnecessary exposure to artificial light at night may have beneficial implications for controlling childhood and adolescent obesity, particularly in low-income areas.

Analysis of County-Scale Eco-Efficiency and Spatiotemporal Characteristics in China

Eco-efficiency is a key indicator to measure the level of regional sustainable development. The county is the basic spatial unit of socio-economic activities and sustainable policy implementation in China. Hence, this paper conducts eco-efficiency analysis at the county scale in order to provide reference for the central and local governments to formulate differentiated eco-efficiency enhancement policies, further promote Chinese ecological sustainable development, and advance the construction of ecological civilization with high quality. Based on the Super-Slacks-Based Measure (SBM) model and the Malmquist index, the paper constructed an eco-efficiency measurement method and analyzed the variation characteristics, influencing factors, spatial pattern of eco-efficiency in Chinese counties from 2000 to 2020. The results showed that: (1) The eco-efficiency of the county unit was generally low in China and was roughly distributed in a pyramid. The county-level eco-efficiency had a spatial distribution pattern of being high in the west and low in the east, and high in the south and low in the north, with significant non-equilibrium. There was a positive correlation between eco-efficiency of neighboring counties in China. (2) The per-capita GDP has a significant positive correlation with eco-efficiency, while energy consumption, arable land area and eco-efficiency have a negative correlation. The redundancy rate of input indicators was high in Chinese counties. (3) During the study period, the eco-efficiency of most counties displayed a fluctuating growth trend. The growth was mainly driven by technological progress.

Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model

Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.

Influencing factors and scenario forecasting of carbon emissions in Liaoning Province, China

Liaoning is a province with large energy consumption and carbon emissions. Management of carbon emissions in Liaoning Province is crucial to realizing China's carbon peaking and carbon neutrality goals. To clarify the driving factors and trends of carbon emissions in Liaoning Province, we analyzed the impacts of six factors on carbon emissions in Liaoning Province through STIRPAT model based on carbon emission data from 1999 to 2019. The impact factors included population, urbanization rate, per-capita GDP, secondary industry ratio, energy consumption per unit GDP, and coal consumption ratio. Nine forecasting scenarios with three economic and population growth models and three emission reduction models were set up, and their carbon emission trends under the above nine forecasting scenarios were predicted. The results showed that the main driving factor of carbon emissions in Liaoning Province was per-capita GDP, and that the main inhibitor was energy consumption per unit GDP. The carbon peak year in Liaoning Province would fluctuate between 2020 and 2055 under the nine forecasting scenarios, with peak values ranging from 544 to 1088 million tons CO2. The medium economic development growth and high carbon emission reduction scenario would be the optimal carbon emission scenario in Liaoning Province. Under this forecasting scenario, Liaoning Province could achieve carbon peak (611 million tons CO2) by 2030 without affec-ting economic development through optimizing energy structure and controlling the intensity of energy consumption. Our results would be helpful for seeking the best path for carbon emission reduction in Liaoning Province and providing a reference for its realization of carbon peaking and carbon neutrality goals.

Is the European Social Progress Index robust? Implications for the design of European Union regional Cohesion Policy

ABSTRACT The European Social Progress Index (EU-SPI) is a composite index launched by the European Commission in 2016 to assess social progress. It is constructed using non-economic indicators, and is intended to serve as a tool for European regional policies. This paper shows that the 2020 release of the index is robust to multiple alternative designs, and thus suitable for policymaking. The EU-SPI and gross domestic product per capita (GDPpc) are positively correlated, although they are in no way substitutes. These findings suggest that the EU-SPI could complement the GDPpc as an instrument to determine eligibility and achieve a more citizen-oriented allocation of European Cohesion Policy funds.

Spatio-Temporal Evolution and Influencing Factors of Ecological Well-Being Performance from the Perspective of Strong Sustainability: A Case Study of the Three Gorges Reservoir Area, China.

The concept of strong sustainability suggests that natural capital is irreplaceable and emphasizes that human natural consumption cannot exceed the carrying capacity of the ecological environment. In the realistic context of tightening resource constraints and ecological degradation, how to explore the optimal economic and well-being output under certain ecological constraints has become an important topic in sustainability research. Ecological well-being performance (EWP) can comprehensively reflect the efficiency of natural resources and ecological inputs into the well-being level and has become an important tool for sustainable development research. Based on strong sustainability, this paper constructs an EWP evaluation index system. It decomposes EWP into two stages: ecological economy and economic well-being, which opens the "black box" of the ecological well-being conversion process. PCA-DEA, the network super-efficiency Epsilon-based measure (Network SEBM) model, and Geodetector are used to dynamically measure the EWP in the Three Gorges Reservoir Area (TGRA) from 2010 to 2020 and analyze its spatial and temporal characteristics and influencing factors. The results show that (1) temporally, the EWP in the TGRA shows an increasing trend from 2010 to 2020, but the overall level is low. Spatially, it shows a high pattern in the east and low in the west, and spatial differences are gradually decreasing; (2) ecological economic efficiency is significantly lower than economic well-being efficiency, and ecological economic efficiency is the main reason limiting the improvement in EWP in the TGRA. The ecological well-being situation of the TGRA is not optimistic; (3) there is an overall problem of excessive ecological input and insufficient per-capita GDP and well-being output in the TGRA, and decisions should be made according to local conditions; (4) the level of economic development has an EWP level that plays a dominant role and also has a greater relationship with the policy system, socioeconomic conditions, and natural environment.

Determination Analysis of Indonesian Palm Oil Commodities in the Covid-19 Pandemic Era on Indonesian Per Capita Gdp and Export Tax Revenues

Background: The fundamental problem faced in the process of economictransformation in Indonesia is that the GDP growth of the agricultural andplantation sectors is still relatively low and the contribution of tax revenuefrom exports has not been optimal, especially in the midst of the Covid-19pandemic era.Aim: The data is compiled in the form of panel data consisting of 10provinces producing the largest production of palm oil in Indonesia and witha time series of research period 2012-2021. The research model wasformulated as a linear model and analyzed recursively using linearregression using the ordinary least squares method (OLS).Method: This study uses the explanatory method to explain the causalrelationship in Indonesia's GDP growth model per capita and Indonesia'sexport tax revenue through hypothesis testing.Findings: The research findings are that production, selling prices,exchange rates, and exports have a simultaneous effect on Indonesia's GDPper capita and export tax revenues. In part, Indonesia's GDP per capita ismore determined by production, selling prices, and exchange rates.Meanwhile, some export tax revenues are more influenced by production andselling prices

Determination Analysis of Indonesian Palm Oil Commodities in the Covid-19 Pandemic Era on Indonesian Per Capita Gdp and Export Tax Revenues

Background: The fundamental problem faced in the process of economictransformation in Indonesia is that the GDP growth of the agricultural andplantation sectors is still relatively low and the contribution of tax revenuefrom exports has not been optimal, especially in the midst of the Covid-19pandemic era.Aim: The data is compiled in the form of panel data consisting of 10provinces producing the largest production of palm oil in Indonesia and witha time series of research period 2012-2021. The research model wasformulated as a linear model and analyzed recursively using linearregression using the ordinary least squares method (OLS).Method: This study uses the explanatory method to explain the causalrelationship in Indonesia's GDP growth model per capita and Indonesia'sexport tax revenue through hypothesis testing.Findings: The research findings are that production, selling prices,exchange rates, and exports have a simultaneous effect on Indonesia's GDPper capita and export tax revenues. In part, Indonesia's GDP per capita ismore determined by production, selling prices, and exchange rates.Meanwhile, some export tax revenues are more influenced by production andselling prices

Globalization and Economic Stability: An Insight from the Rocket and Feather Hypothesis in Pakistan

The purpose of this study was to analyze the irregular pattern of changing inflation as a result of the pass-through of the exchange rate and fluctuations in oil prices in the current globalization scenario. We used annual data sets for crude oil prices, real effective exchange rates, and inflation in Pakistan from 1972 to 2021 for the analysis. The control variables used in the current study were imports (IMP), gross domestic product per capita (GDP), exports (EXP), globalization (GLOB), and interest rates (CRATE). Our findings from a non-linear autoregressive distributed lag (NARDL) analysis showed that inflation had an asymmetric rocket and feather pattern regardless of how globalization was defined or measured. On the other hand, GDP, EXP, and GLOB negatively impacted inflation, and CRATE and IMP had positive effects on inflation. Our study suggested that alternative policies, such as fixing the exchange rate, might decrease uncertainty and stabilize the Pakistani economy in the future. Moreover, increasing the use of sustainable energy would reduce the dependence of the economy on oil prices, which would lower its impact on the economy.