Investigating the relationship of government revenue and expenditure on economic growth using a generalized method of moments: Does state-level panel ensure sustainable growth?

The current research project investigates the correlation between economic growth, government spending, and public revenue in seventeen Indian states spanning the years 1990 to 2020. An analysis of the relationship between key fiscal policy variables and economic growth was conducted utilising a panel data approach, the Generalised Method of Moments (GMM), and fully modified Ordinary Least Squares (FMOLS & DOLS) estimation. In our investigation, we assessed the impacts of non-tax revenue, development plan expenditure, tax revenue, and development non-plan expenditure on (i) the net state domestic product (NSDP) and (ii) the NSDP per capita. The findings indicate that the selected fiscal variables are significantly related. The results indicate that expeditious expansion of the fiscal sector is obligatory to stimulate economic growth in India and advance the actual development of the economies of these states.

The effects of selected neglected tropical diseases on economic performance at the macrolevel in Africa.

BACKGROUND: Neglected tropical diseases (NTDs) such as leprosy, lymphatic filariasis (LF), schistosomiasis and onchocerciasis are endemic in several African countries. These diseases can lead to severe pain and permanent disability, which can negatively affect the economic productivity of the affected person(s), and hence resulting into low economic performance at the macrolevel. Nonetheless, empirical evidence of the effects of these NTDs on economic performance at the macrolevel is sparse. This study therefore investigates the effects of the above-mentioned NTDs on economic performance at the macrolevel in Africa. METHODS: The study employs a panel design with data comprising 24 to 45 African countries depending on the NTD in question, over the period, 2002 to 2019. Gross domestic product (GDP) is used as the proxy for economic performance (Dependent variable) and the prevalence of the above-mentioned NTDs are used as the main independent variables. The random effects (RE), fixed effects (FE) and the instrumental variable fixed effects (IVFE) panel data regressions are used as estimation techniques. RESULTS: We find that, an increase in the prevalence of the selected NTDs is associated with a fall in economic performance in the selected African countries, irrespective of the estimation technique used. Specifically, using the IVFE regression estimates, we find that a percentage increase in the prevalence of leprosy, LF, schistosomiasis and onchocerciasis is associated with a reduction in economic performance by 0.43%, 0.24%, 0.28% and 0.36% respectively, at either 1% or 5% level of significance. CONCLUSION: The findings highlight the need to increase attention and bolster integrated efforts or measures towards tackling these diseases in order to curb their deleterious effects on economic performance. Such measures can include effective mass drug administration (MDA), enhancing access to basic drinking water and sanitation among others.

The economic consequence of large-scale epidemic outbreak: The path and loss evaluation of COVID-19 in China based on input-output analysis.

The COVID-19 pandemic has significantly impacted China's economic and social development. Understanding the direct and indirect effects of the epidemic on the economy is vital for formulating scientifically grounded epidemic management policies. This study assesses the economic losses and influence paths of a large-scale epidemic in China. We proposed three COVID-19 scenarios - serious, normal, and mild - to evaluate the direct economic impact on China's GDP from a demand perspective. An input-output model was used to estimate the indirect impact. Our findings show that China's GDP could lose 94,206, 75,365, and 56,524 hundred million yuan under serious, normal, and mild scenarios, respectively, with corresponding GDP decline rates of 9.27%, 7.42%, and 5.56%. Under the normal scenario, indirect economic loss and total loss are projected at 75,364 and 489,386 hundred million yuan, respectively. Additionally, the pandemic led to a reduction in carbon emissions: direct emissions decreased by 1,218.69 million tons, indirect emissions by 9,594.32 million tons, and total emissions by 10,813.01 million tons across various industries. This study provides a comprehensive analysis of the economic and environmental impacts of the pandemic.

Malaria Prevalence and Macroeconomic Output in Ghana, 1990 to 2019.

In Ghana, malaria remains the number 1 reason for outpatient department visits, making it a major public health problem. Thus, there could be significant lost productivity days as a result of malaria morbidity and mortality, which could negatively affect economic output at the macrolevel. Nonetheless, there is a dearth of empirical evidence of the effect of malaria on macroeconomic output in Ghana. This study therefore aims to provide the foremost empirical evidence regarding the effect of malaria prevalence on macroeconomic output in Ghana using a time series design with data spanning the period 1990 to 2019. Gross Domestic Product (GDP), serving as a proxy for macroeconomic output, is the dependent variable, while the prevalence of malaria (overall, among only males and among only females) serves as the main independent variable. The Ordinary Least Square (OLS) regression is used as the baseline estimation technique and the Instrumental Variable Two-Stage Least Square (IV2SLS) regression is employed as the robustness check estimator due to its ability to deal with endogeneity. The IV2SLS regression results show that a percentage increase in the overall prevalence of malaria is associated with a 1.16% decrease in macroeconomic output at 1% significance level. We also find that the effect of malaria in males on macroeconomic output is slightly higher relative to females. The findings from the OLS regression are not qualitatively different from the IV2SLS regression estimates. There is therefore the need to strengthen efforts such as quality case management, larval source management, mass distribution of long-lasting insecticide-treated bed nets, social behavior change, surveillance (both epidemiological and entomological), intermittent preventive treatment of malaria in pregnancy, research among others, which are important toward eliminating malaria.

Estimation of the lost productivity to the GDP and the national cost of correcting visual impairment from refractive error in Kenya.

BACKGROUND: In developing countries such as Kenya, minimal attention has been directed towards population based studies on uncorrected refractive error (URE). However, the absence of population based studies, warrants utilization of other avenues to showcase to the stakeholders in eye health the worth of addressing URE. Hence this study estimated the lost productivity to the Gross Domestic Product (GDP) as a result of URE and the national cost required to address visual impairment from URE in Kenya. METHODS: The lost productivity to the GDP for the population aged 16-60 years was calculated. Thereafter the productivity loss of the caregivers of severe visual impaired individuals was computed as a product of the average annual productivity for each caregiver and a 5% productivity loss due to visual impairment. The productivity benefit of correcting refractive error was estimated based on the minimum wage for individuals aged between 16-60 years with URE. Estimation of the national cost of addressing URE was based on spectacle provision cost, cost of training functional clinical refractionists and the cost of establishing vision centres. A cost benefit analysis was undertaken based on the national cost estimates and a factor of 3.5 times. RESULTS: The estimated lost productivity to the GDP due to URE in in Kenya is approximately US$ 671,455,575 -US$ 1,044,486,450 annually for population aged between 16-60 years. The productivity loss of caregivers for the severe visually impaired is approximately US$ 13,882,899 annually. Approximately US$ 246,750,000 is required to provide corrective devices, US$ 413,280- US$ 108,262,300 to train clinical refractionists and US$ 39,800,000 to establish vision centres. The productivity benefit of correcting visual impairment is approximately US$ 41,126,400 annually. Finally, a cost benefit analysis showed a return of US$ 378,918,050 for human resources, US$ 863,625,000 for corrective devices and US$ 139,300,000 for establishment of vision centres. CONCLUSION: The magnitude of productivity loss due to URE in Kenya is significant warranting prioritization of refractive error services by the government and all stakeholders since any investment directed towards addressing URE has the potential to contribute a positive return.

Managing government debt.

To construct a stochastic version of [R. J. Barro, J. Polit. Econ. 87, 940-971 (1979)] normative model of tax rates and debt/GDP dynamics, we add risks and markets for trading them along lines suggested by [K. J. Arrow, Rev. Econ. Stud. 31, 91-96 (1964)] and [R. J. Shiller, Creating Institutions for Managing Society's Largest Economic Risks (OUP, Oxford, 1994)]. These modifications preserve Barro's prescriptions that a government should keep its debt-gross domestic product (GDP) ratio and tax rate constant over time and also prescribe that the government insure its primary surplus risk by selling or buying the same number of shares of a Shiller macro security each period.

Regional economic forecast using Elman neural networks with wavelet function.

Recently, the economy in Guangdong province has ranked first in the country, maintaining a good growth momentum. The prediction of Gross Domestic Product (GDP) for Guangdong province is an important issue. Through predicting the GDP, it is possible to analyze whether the economy in Guangdong province can maintain high-quality growth. Hence, to accurately forecast the economy in Guangdong, this paper proposed an Elman neural network combining with wavelet function. The wavelet function not only stimulates the forecast ability of Elman neural network, but also improves the convergence speed of Elman neural network. Experimental results indicate that our model has good forecast ability of regional economy, and the forecast accuracy reach 0.971. In terms of forecast precision and errors, our model defeats the competitors. Moreover, our model gains advanced forecast results to both individual economic indicator and multiple economic indicators. This means that our model is independently of specific scenarios in regional economic forecast. We also find that the investment in education has a major positive impact on regional economic development in Guangdong province, and the both surges positive correlation. Experimental results also show that our model does not exhibit exponential training time with the augmenting of data volume. Consequently, we propose that our model is suitable for the prediction of large-scale datasets. Additionally, we demonstrate that using wavelet function gains more profits than using complex network architectures in forecast accuracy and training cost. Moreover, using wavelet function can simplify the designs of complexity network architectures, reducing the training parameter of neural networks.

Towards a greener future: examining carbon emission dynamics in Asia amid gross domestic product, energy consumption, and trade openness.

The purpose of this study is to examine the impact of gross domestic product, energy consumption, and trade openness on carbon emission in Asia. Among the 48 countries in Asia, 42 were included in the analysis, spanning a period of 20 years. Given that Asia is the predominant contributor, accounting for 53% of global emissions as of 2019, a comprehensive examination at both continental and individual country levels becomes imperative. Such an approach aligns with local, regional, and global development agendas, contributing directly and indirectly to climate change mitigation. The analytical techniques employed in this study encompassed panel regression and multiple linear regression, illuminating the specific contributions of each country to the study variables and their impact on carbon emissions. The findings suggest that gross domestic product (13 out of 42 countries), energy consumption (21 out of 42 countries), and trade openness (eight out of 42 countries) have a highly significant impact (p < 0.01) on carbon emissions in Asia. Energy consumption plays a vital role in increasing carbon emissions in Asia, driven by rising populations, urbanisation, and oil and gas production. Policymakers can take several actions such as adopting a carbon pricing system, using sustainable transportation, renewable energy development, and international cooperation within Asia to reach the goal of being carbon neutral by 2050.

Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.

BACKGROUND: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country. METHODOLOGY: Stochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model. RESULTS: The findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023. CONCLUSION: The selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics.

Successive national agricultural policies/programmes, growth of gross domestic product (GDP) and expansion of agribusinesses in Nigeria.

In Sub Saharan Africa, agriculture's contribution to employment and Gross Domestic Product (GDP) is estimated to be higher than other sectors. Policies designed and implemented for the agricultural sector could be an influencing factor to the variations in the contributions of agriculture to the annual national GDP. These policies are believed to have shaped and (some) still shaping the landscape of agriculture and national economy. The study analysed agriculture's GDP contribution during the implementation of various national agricultural policies, and the potential of the policies to foster agrobusiness development in Nigeria between 2000 and 2021. The study adopted mixed-method approach. Primary data were collected through a structured questionnaire administered on 29 purposively sampled state Agricultural Development Programme (ADP) directors across Nigeria. The questionnaire was face-validated by three experts. Reliability test was carryout using Cronbach Alpha approach, which yielded an index of 0.89. Copies of the questionnaire were administered on the respondents through direct contact. Secondary data were collected from the Nigeria's Federal Ministry of Agriculture and Rural Development, National Bureau of Statistics, and World Bank. Data was analysed with mean, standard deviation, percentages and ANOVA. Findings of the study revealed that the performance of implemented agricultural policies had influence on agricultural sector's percentage contribution to national GDP, and changes in agriculture's GDP contribution had significant impact on national GDP growth. The duration of active life of the policies did not influence their performance, like the Root and Tuber Expansion Programme which lasted longer yet performed less than the National Special Programme on Food Security in terms of improvement in agriculture's GDP contributions. All the policies implemented had several limitations in their ability to foster agribusinesses in Nigeria. The study recommends that future policies should focus on providing sustainable frameworks for developing the business in agriculture through value chain optimisation and the use of the teeming, young, and affordable labour force like China and India did to become global food producers.

The spatiotemporal trend of human brucellosis in China and driving factors using interpretability analysis.

Human brucellosis has reemerged in China, with a distinct change in its geographical distribution. The incidence of human brucellosis has significantly risen in inland regions of China. To gain insights into epidemic characteristics and identify factors influencing the geographic spread of human brucellosis, our study utilized the Extreme Gradient Boosting (XGBoost) algorithm and interpretable machine learning techniques. The results showed a consistent upward trend in the incidence of human brucellosis, with a significant increase of 8.20% from 2004 to 2021 (95% CI: 1.70, 15.10). The northern region continued to face a serious human situation, with a gradual upward trend. Meanwhile, the western and southern regions have experienced a gradual spread of human brucellosis, encompassing all regions of China over the past decade. Further analysis using Shapley Additive Explanations (SHAP) demonstrated that higher Gross Domestic Product (GDP) per capita and increased funding for education have the potential to reduce the spread. Conversely, the expansion of human brucellosis showed a positive correlation with bed availability per 1000 individuals, humidity, railway mileage, and GDP. These findings strongly suggest that socioeconomic factors play a more significant role in the spread of human brucellosis than other factors.

Dynamic nonlinear CO2 emission effects of urbanization routes in the eight most populous countries.

A dynamic STIRPAT model used in the current study is based on panel data from the eight most populous countries from 1975 to 2020, revealing the nonlinear effects of urbanization routes (percentage of total urbanization, percentage of small cities and percentage of large cities) on carbon dioxide (CO2) emissions. Using "Dynamic Display Unrelated Regression (DSUR)" and "Fully Modified Ordinary Least Squares (FMOLS)" regressions, the outcomes reflect that percentage of total urbanization and percentage of small cities have an incremental influence on carbon dioxide emissions. However, square percentage of small cities and square percentage of total urbanization have significant adverse effects on carbon dioxide (CO2) emissions. The positive relationship between the percentage of small cities, percentage of total urbanization and CO2 emissions and the negative relationship between the square percentage of small cities, square percentage of total urbanization and CO2 emissions legitimize the inverted U-shaped EKC hypothesis. The impact of the percentage of large cities on carbon dioxide emissions is significantly negative, while the impact of the square percentage of large cities on carbon dioxide emissions is significantly positive, validating a U-shaped EKC hypothesis. The incremental effect of percentage of small cities and percentage of total urbanization on long-term environmental degradation can provide support for ecological modernization theory. Energy intensity, Gross Domestic Product (GDP), industrial growth and transport infrastructure stimulate long-term CO2 emissions. Country-level findings from the AMG estimator support a U-shaped link between the percentage of small cities and CO2 emissions for each country in the entire panel except the United States. In addition, the Dumitrescu and Hulin causality tests yield a two-way causality between emission of carbon dioxide and squared percentage of total urbanization, between the percentage of the large cities and emission of carbon dioxide, and between energy intensity and emission of carbon dioxide. This study proposes renewable energy options and green city-friendly technologies to improve the environmental quality of urban areas.

A global analysis of how human infrastructure squeezes sandy coasts.

Coastal ecosystems provide vital services, but human disturbance causes massive losses. Remaining ecosystems are squeezed between rising seas and human infrastructure development. While shoreline retreat is intensively studied, coastal congestion through infrastructure remains unquantified. Here we analyse 235,469 transects worldwide to show that infrastructure occurs at a median distance of 392 meter from sandy shorelines. Moreover, we find that 33% of sandy shores harbour less than 100 m of infrastructure-free space, and that 23-30% of this space may be lost by 2100 due to rising sea levels. Further analyses show that population density and gross domestic product explain 35-39% of observed squeeze variation, emphasizing the intensifying pressure imposed as countries develop and populations grow. Encouragingly, we find that nature reserves relieve squeezing by 4-7 times. Yet, at present only 16% of world's sandy shores have a protected status. We therefore advocate the incorporation of nature protection into spatial planning policies.

Dynamic spillover effects of renewable energy efficiency in the European countries.

Renewable energy has gained significant attention due to the growing concern for environmental sustainability and the high reliance on energy imports in European countries. In this study, we use a two- stage approach to assess renewable energy efficiency (REEF) of European countries. Initially, we employ the data envelopment analysis (DEA) method to quantify the efficiency of renewable energy. Subsequently, we investigate the factors influencing REEF between 2005 and 2020. Our findings reveal a generally high level of REEF across European countries, but some countries have become worse in this regard (e.g., France, Ukraine, Russia, Belgium, Germany, Norway, and Serbia). In order to find the causes of these changes, we considered the explanatory variables of gross domestic product (GDP), energy price, renewable energy consumption, information and communications technology (ICT), and industrial value added in a spatial system generalized method of moments (spatial SYS-GMM) model. The findings provide confirmation of the spatial spillover effects of REEF within European countries. The strongest positive effect is related to energy prices. In simpler terms, as energy prices rise, the efficiency of renewable energy has increased in European countries. Additionally, ICT and renewable energy consumption have positive impacts, too. But GDP and industrial value added, have decreasing effects. Based on these findings, we put forth several policy suggestions aimed at enhancing the efficiency of renewable energy in European countries.

The economic burden associated with unmet surgical needs in Liberia: a retrospective macroeconomic analysis based on a nationwide enumeration of surgical procedures.

OBJECTIVES: The economic consequences of untreated surgical disease are potentially large. The aim of this study was to estimate the economic burden associated with unmet surgical needs in Liberia. DESIGN: A nationwide enumeration of surgical procedures and providers was conducted in Liberia in 2018. We estimated the number of disability-adjusted life years (DALYs) saved by operative activities and converted these into economic losses averted using gross national income per capita and value of a statistical life (VSL) approaches. The total, the met and the unmet needs for surgery were determined, and economic losses caused by unmet surgical needs were estimated. Finally, we valued the economic losses avoided by various surgical provider groups. RESULTS: A total of 55 890 DALYs were averted by surgical activities in 2018; these activities prevented an economic loss of between US$35 and US$141 million. About half of these values were generated by the non-specialist physician workforce. Furthermore, a non-specialist physician working a full-time position for 1 year prevented an economic loss of US$717 069 using the VSL approach, while a specialist resident and a certified specialist saved US$726 606 and US$698 877, respectively. The burden of unmet surgical need was associated with productivity losses of between US$388 million and US$1.6 billion; these losses equate to 11% and 46% of the annual gross domestic product for Liberia. CONCLUSION: The economic burden of untreated surgical disease is large in Liberia. There is a need to strengthen the surgical system to reduce ongoing economic losses; a framework where specialist and non-specialist physicians collaborate may result in better economic return than a narrower focus on training specialists alone.

Prospects of natural gas consumption in Pakistan: based on the LMDI-STIRPAT PLSR framework.

The natural gas (NG) forms the sizeable portion of the primary energy consumption in Pakistan. However, its depleting domestic reserves and increasing demand is challenging to balance the supply-demand in the country. This paper investigates the relationship between NG consumption and driving factors using LMDI-STIRPAT PLSR framework. It is learned that fossil energy structure and per capita gross domestic product (GDP) are most influencing factors on NG consumption, followed by non-clean energy structure, energy intensity, and population. The factors were further modelled to forecast the future values of NG consumption for various scenarios. It is found that NG consumption would be 42.107 MTOE under the high development scenario which would be twice the baseline scenario. It is projected that indigenous NG production will fall from 4 to 2 billion cubic feet/day and demand will increase by 1.5 billion cubic feet/day. Therefore, an optimized strategy is required for a long-term solution to cater this increasing supply-demand.

Corporate social responsibility-an antidote for sustainable business performance: interconnecting role of digital technologies, employee eco-behavior, and tax avoidance.

Small- and medium-sized enterprises (SMEs) have consistently contributed significantly to the economy's gross domestic product (GDP). Organizations are motivated to achieve sustainable performance by mitigating the adverse impacts of company operations by improving productivity and optimizing resource utilization. In order to achieve this objective, the businesses are making all their efforts and developing the systems to ensure sustainable performance. Based on the prevailing research gaps, the current study examines the role of a portfolio of responsible digital technologies (DT), which provides a competitive advantage and helps achieve sustainable firm performance (SFP). Using a simple random sampling technique, data from 294 textile manufacturing SMEs is collected and analyzed using the structural equation model (SEM) in AMOS v.24. The results indicated that digital technologies, tax avoidance, green employee behavior, and corporate social responsibility facilitated improving the SFP of SMEs. Furthermore, it is worth noting that the link connecting TA and SFP is unaffected by CSR activities aimed at the environment. This finding, however, should not lessen the importance of TA, which deserves significant attention and thought from management.

Unequal climate impacts on global values of natural capital.

Ecosystems generate a wide range of benefits for humans, including some market goods as well as other benefits that are not directly reflected in market activity1. Climate change will alter the distribution of ecosystems around the world and change the flow of these benefits2,3. However, the specific implications of ecosystem changes for human welfare remain unclear, as they depend on the nature of these changes, the value of the affected benefits and the extent to which communities rely on natural systems for their well-being4. Here we estimate country-level changes in economic production and the value of non-market ecosystem benefits resulting from climate-change-induced shifts in terrestrial vegetation cover, as projected by dynamic global vegetation models (DGVMs) driven by general circulation climate models. Our results show that the annual population-weighted mean global flow of non-market ecosystem benefits valued in the wealth accounts of the World Bank will be reduced by 9.2% in 2100 under the Shared Socioeconomic Pathway SSP2-6.0 with respect to the baseline no climate change scenario and that the global population-weighted average change in gross domestic product (GDP) by 2100 is -1.3% of the baseline GDP. Because lower-income countries are more reliant on natural capital, these GDP effects are regressive. Approximately 90% of these damages are borne by the poorest 50% of countries and regions, whereas the wealthiest 10% experience only 2% of these losses.