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INTRODUCTION: We previously reported global regional differences in smoking cessation outcomes, with smokers of US origin having lower quit rates than smokers from some other countries. This post-hoc analysis examined global regional differences in individual-level and country-level epidemiological, economic and tobacco regulatory factors that may affect cessation outcomes. METHODS: EAGLES (Evaluating Adverse Events in a Global Smoking Cessation Study) was a randomised controlled trial that evaluated first-line cessation medications and placebo in 8144 smokers with and without psychiatric disorders from 16 countries across seven regions. Generalised linear and stepwise logistic regression models that considered pharmacotherapy treatment, psychiatric diagnoses, traditional individual-level predictors (eg, demographic and smoking characteristics) and country-specific smoking prevalence rates, gross domestic product (GDP) per capita, relative cigarette cost and WHO-derived MPOWER scores were used to predict 7-day point prevalence abstinence at the end of treatment. RESULTS: In addition to several traditional predictors, three of four country-level variables predicted short-term abstinence: GDP (0.54 (95% CI 0.47, 0.63)), cigarette relative income price (0.62 (95% CI 0.53, 0.72)) and MPOWER score (1.03 (95% CI 1.01, 1.06)). Quit rates varied across regions (22.0% in Australasia to 55.9% in Mexico). With northern North America (USA and Canada) as the referent, the likelihood of achieving short-term abstinence was significantly higher in Western Europe (OR 1.4 (95% CI 1.14, 1.61)), but significantly lower in Eastern Europe (0.39 (95% CI 0.22, 0.69)) and South America (0.17 (95% CI 0.08, 0.35)). CONCLUSIONS: Increased tobacco regulation was associated with enhanced quitting among participants in the EAGLES trial. Paradoxically, lower GDP, and more affordable cigarette pricing relative to a countrys GDP, were also associated with higher odds of quitting. Geographical region was also a significant independent predictor. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov, NCT01456936.

Objective: To describe the evolution of physical activity (PA) research in Africa, examine income and gender inequalities, and discuss future possibilities. Methods: A secondary analyses of the Global Observatory for Physical Activity data on PA research in Africa (1950–2019). Results: We identified 514 PA articles from 47 African countries in the past 70 years. Majority (83.1%) of the articles were published between 2012 and 2019. Fifteen countries had no publications. Six countries (South Africa [n = 156], Nigeria [n = 85], Ethiopia [n = 44], Ghana [n = 41], Kenya [n = 39], and Cameroon [n = 20]) accounted for about 75% of the publications. Most articles were observational (92.4%), single-country studies (78.4%), with male first (58.4%) and last authors (68%), and were classified as surveillance studies (45.1%). Few studies addressed interventions (5.8%) and policy (3.5%) or used device-based PA measurement (14.0%). The number of articles per country was positively related to human population level (r =.552, P =.000) and gross domestic product % spent on research and development (r =.301, P =.040). The publication rate per 100,000 people was positively related with the human development index (r =.349, P =.016) and negatively with the gender inequality index (r = −.360, P =.019). Conclusions: Our results provide an overview and status of PA research in Africa, highlighting country differences and gender inequalities in authorship. The findings may be used to benchmark the evolution of research in the region and to inform areas for improvement. There is an urgent need for more PA interventions and policy studies in Africa. [ABSTRACT FROM AUTHOR]

The paper aims to assess the macroeconomic impacts of government investment in Research and Development (R&D) and more generic fiscal policies by quantifying the Gross domestic product (GDP) and business R&D investment multipliers. Following the recent literature on fiscal policy, we combine the Local Projection approach with fiscal shocks estimated using Structural Vector Autoregressive modeling by focusing on a panel of 15 Organization for Economic Co-operation and Development (OECD) countries for the 1981–2017 period. Our findings support the idea that expansionary fiscal policies can positively and persistently affect the GDP level and crowd in business R&D investment. Additionally, our results show that public investment in R&D generates the largest multiplicative effect both on GDP and business R&D than the one associated with more generic public expenditures, even when fiscal expectations are considered. [ABSTRACT FROM AUTHOR]

Disequilibrating macro shocks affect different firms' prospects differently, increasing idiosyncratic variation in forward-looking stock returns before affecting economic growth. Consistent with most such shocks from 1947 to 2020 enhancing productivity, increased idiosyncratic stock return variation forecasts next-quarter real GDP growth, industrial production growth, and consumption growth both in-sample and out-of-sample. These effects persist after controlling for other leading economic indicators. [ABSTRACT FROM AUTHOR]

The development index (DI) of a region is defined by various elements such as geography, population density, gross domestic product (GDP), GDP per capita, natural resources, markets, economic development, and other relevant factors. The traditional approach to classification usually depends on the country’s total land area measured in square kilometers. Nevertheless, the transition points between different levels of development size (small, medium, and giant) are highly subjective, resulting in frequent inconsistencies and conflicts. Fuzzy logic, a superset of conventional (Boolean) logic, extends the framework to include partial truth values, ranging from “fully true” to “totally false”. This development is especially essential since human reasoning, particularly commonsense reasoning, is approximate rather than precise. This study offers a model that uses fuzzy logic and the Mamdani fuzzy inference system (MFIS) to evaluate nine main cities in Pakistan based on their populations (POPs), gross domestic products (GDPs), and literacy rates (LR). The model includes variables for the antecedents (inputs) and the consequences (outputs). The input variables are the population (POP), GDP, and LR, while the output variable is the DI. The MFIS is used with Python programming tools, including the scikit-fuzzy library for inference and aggregation and matplotlib for graphics. [ABSTRACT FROM AUTHOR]

Climate change and economic development impact the population expansion and water shortage in the middle reaches of the Yangtze River Basin (MYRB), leading directly to drought aggravation-expansion and impacting agricultural production in the MYRB. Therefore, this study quantitatively evaluated agricultural drought vulnerability in the MYRB based on the variable fuzzy evaluation model (VFEM). The main conclusions are as follows: (1) The arable land in the MYRB gradually decreased between 2005 and 2020, whereas the forest cover decreased and then increased; (2) precipitation and evapotranspiration were the key factors affecting the agricultural drought vulnerability (e.g., weights of 0.24 and 0.21), whereas arable land density and gross domestic product (GDP) per capita played less significant roles; and (3) the agricultural drought vulnerability in the MYRB during 2005–2020 was mainly at level 3 and below, with higher drought vulnerability in the western and northern regions, which had a higher drought risk potential. [ABSTRACT FROM AUTHOR]

The staggered enactment of special economic zones (SEZ) laws in the last three decades provides us with a unique opportunity to identify the causal impact of place-based policies on structural change. Using difference-in-differences and event studies, we provide new evidence that SEZs have a causal impact on structural change and industrialization. We find that adopting an SEZ policy increased the GDP share of industry and decreased the shares of the agriculture and service sectors. We disentangle these effects by examining the heterogeneities by income groups and geographic regions. We find that SEZ policies have been positive and significant for industrial growth in Asia and Latin America, while having no significant effect for African countries. Overall, this study represents one of the first efforts to identify the causal impact of SEZs using a large panel dataset of more than 126 countries for 1995–2018. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate the effects of renewable energy consumption along with some other regressors namely income by gross domestic product (GDP) per capita, foreign remittances, inflation rate, growth in population on human development in eight Asian countries (Pakistan, Malaysia, Sri Lanka, India, China, Indonesia, Bangladesh, and Japan) from 1995 to 2018. Based on the nature of panel balanced data, we employed the fully modified ordinary least squares and fixed-effects estimators for the empirical investigation. The empirical estimates exhibit that renewable energy usage, foreign remittances and real per capita income have substantial positive impacts on human development. In contrast, inflation rate and growth in population have negative link with human development in the sample countries. Empirical estimates reveal that renewable energy usage positively contribute enhancing human development. These findings expose that the availability and accessibility of renewable energy will not only improve human development rather will also help in accomplishing sustainable development goals. Empirical findings suggest that the management authorities need to allocate more budget in investment in renewable energy schemes and also create environment which facilitate by providing incentives to the private sector also for renewable energy production in the Asian countries. [ABSTRACT FROM AUTHOR]

Purpose: This 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/approach: This 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. Findings: Poverty 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/value: This 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. [ABSTRACT FROM AUTHOR]

The poultry population is an integral part of Ethiopia's Gross Domestic Product (GDP) but, due to various infectious diseases such as infectious bursal disease (IBD), the expected economic impact in the country remains limited. The status of the disease in Ethiopia is obscured; thus, a systematic review and meta-analysis were employed to estimate the overall pooled prevalence of IBD in Ethiopia. Meta-analysis was conducted to determine the effects of each identified risk factor, while meta-regression and sub-group analysis were employed to assess the relationship between study-level covariates and effect size. The pooled prevalence of IBD in Ethiopia was 69.4% (95% CI 30.7–96.2), while the pooled logit prevalence was 0.94 (95% CI: 0.68–1.20) with significant inter-study variance (Q test = 948.28, df = 43 , P < 0.001; τ2 = 0.71, I2 = 95.47%). A small-study effect was detected in the regression-based Egger test (Prob > |z| < 0.0001). Significant variation was observed among different groups such as sex, age, breed, and type of farm of the chickens. The effect size for the study period from 2018 to 2021 was significantly lower by −0.204 compared to the study period from 2009 to 2015 (P < 0.0001. In conclusion, the IBD pooled prevalence estimate is high, even though the number of studies in the country is insufficient. The high prevalence of the disease requires prompt attention from all stakeholders in the sector to bring it under control through comprehensive disease prevention and control intervention strategies. [ABSTRACT FROM AUTHOR]

This is the first empirical study that analyzes the nature of business cycles in India using quarterly GDP growth data from 1998Q1 to 2022Q4 and the Markov Switching Auto-regressive model. In order to capture the impact of exclusion and inclusion of post pandemic period data, it has developed two alternative models. Based on these models, the study provides the forecast of quarterly GDP growth and cumulative real output losses due to the pandemic. It also provides alternative scenarios and trajectories of the Indian economy towards the US$ 5 trillion milestone. It is our hope that these results will be useful to policymakers and other stakeholders to understand the business cycle issue in India and take appropriate strategies to overcome the problems associated with these cycles. [ABSTRACT FROM AUTHOR]

This article evaluates the gross domestic product (GDP) growth of 2,089 counties in 31 provinces in China from three aspects over the period of 2015–2019: all of 2,089 counties at the national level, all counties within each province, and the county economy of 31 provinces. It has three key findings on the low-growth performance of the county economy: (1) There were 27 national-level low-growth counties, 24 of which were in three northeastern provinces (i.e. Liaoning, Jilin, and Heilongjiang) that are major grain producing provinces in China. (2) There were five provincial-level low-growth counties in four provinces. (3) Three provinces (i.e. Liaoning, Jilin, and Tianjin) had low performances in the growth of the county economy. This article sheds light on the regional patterns of the low-growth county economy in China. Policy recommendations for improving the development of the county economy in China are also discussed. [ABSTRACT FROM AUTHOR]

In the recent years many countries were hit by a series of macroeconomic shocks, most notably as a consequence of the COVID-19 pandemic and Russia's invasion in Ukraine, raising inflation rates to multi-decade highs and suspending well-documented macroeconomic relationships. To capture these tail events, we propose a mixed-frequency Bayesian vector autoregressive (BVAR) model with Student t-distributed innovations or with stochastic volatility. Whereas inflation, industrial production, as well as oil and gas prices are available at monthly frequencies, real gross domestic product (GDP) is observed at a quarterly frequency. Thus, we apply a mixed-frequency setup using the forward-filtering–backward-sampling algorithm to generate monthly real GDP growth rates. We forecast inflation in those euro area countries that extensively import energy from Russia and therefore have been heavily exposed to the recent oil and gas price shocks. To measure the forecast performance of the mixed-frequency BVAR model, we compare our inflation forecasts with those generated by a battery of competing inflation forecasting models. The proposed BVAR models dominate the competition for all countries in terms of the log predictive density score. [ABSTRACT FROM AUTHOR]

Climate change poses an existential threat to small island developing states, particularly atoll nations like the Republic of the Marshall Islands (RMI). This study examines the economic and financial challenges of implementing climate change adaptation measures in these vulnerable countries. Through a comprehensive literature review and a case study of the RMI, the costs and feasibility of various adaptation strategies, including coastal protection, land raising, and migration, were analyzed. Findings reveal that the financial requirements for effective adaptation far exceed the economic capacities of the RMI. Even basic protective measures require investments multiple times the country's gross domestic product. The benefit-cost ratios of adaptation projects fall below 1 when evaluated solely on disaster risk reduction, highlighting the need for a paradigm shift in assessing adaptation investments in highly vulnerable nations. This study underscores the critical importance of international climate finance and support. While the RMI has received significant climate development finance, the scale of required adaptation measures far surpasses this level of assistance. The key challenges included not only debt sustainability concerns but also traditional land tenure systems, ecological impacts, and the limitations of conventional cost-benefit analyses in the context of national survival. This study offers valuable insights for policymakers, international organizations, and researchers working on climate resilience in vulnerable coastal nations. [ABSTRACT FROM AUTHOR]

We conduct empirical analyses on Argentina from 1998 to 2023 to provide new evidence that GDP growth rate ultimately depends on the growth rate of autonomous demand, in line with the Sraffian Supermultiplier (SSM). Firstly, we estimate a Vector Equilibrium Correction Model (VECM) to establish the weak exogeneity of autonomous demand components, with output adjusting to long‐run deviations between these autonomous components and GDP. Secondly, we test a mechanism adjusting productive capacity to demand based on the flexible accelerator principle. Employing a VAR analysis, we present evidence that investment share is Granger‐caused by GDP, supporting the SSM framework. [ABSTRACT FROM AUTHOR]

National Statistical Institutes (NSIs) in advanced countries have generally adopted chain-linking in their national accounts. The United States uses a chained Fisher, an example of a superlative index number, in its national accounts. However the Fisher is only one of an infinite number of superlative index numbers. So an important issue is how sensitive are the estimates of output growth to the choice of index number. This issue is analysed by examining data from the BEA/BLS industry-level integrated production account, 1987–2020. Estimates of superlative and other index numbers are presented for this dataset. The sensitivity of real GDP growth to the value of the crucial parameter in a superlative index number is tested. The extent to which the desirable characteristics of value consistency and aggregation consistency are satisfied for different superlative index numbers is also analysed. The desirability of chain-linking does not follow automatically just from the use of superlative indices. So I also compare chained and unchained versions of these same index numbers. Finally, Europe uses a different approach to output measurement to the US, chained Laspeyres versus chained Fisher. I look at how different US estimates would be if they employed European methodology. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this paper is to contribute to empirical research by identifying the key macroeconomic drivers of equity market development in Sub-Saharan Africa (SSA) and to ascertain if banking sector development complements equity market development in the SSA region. Design/methodology/approach: The study employed the dynamic panel data approach using the pool mean group (PMG). The sample covered is twenty-seven (27) SSA countries between the period 2000 to 2020. Findings: The result suggests that banking sector development, economic growth, migrant remittance and trade openness are the key drivers of equity market development in the SSA region. The study also revealed that banking sector development complements equity market development in the SSA region. Originality/value: The use of robust measure in measuring equity market development (i.e. ratio of portfolio equity to gross domestic product) in ascertaining the macroeconomic drivers of equity market development. Likewise, exploring whether banking sector development complements equity market development in the SSA region makes the paper more unique, especially using the ratio of bank credit to bank deposit as a measure banking sector development. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2024-0005 [ABSTRACT FROM AUTHOR]

By forecasting the total export-import volume of China's economic trade, valuable guidance can be provided for the formulation of relevant policies, thus holding significant practical importance. This paper first analyzed the current situation of the import and export of China's economic trade. Subsequently, key indicators such as gross domestic product (GDP) and producer price index (PPI) were selected. After eliminating irrelevant indicators through correlation coefficient calculation, seven remaining indicators were employed for research. Building upon the machine learning algorithm-support vector machine (SVM), an improved sparrow search algorithm (ISSA) was developed to optimize SVM parameters, forming the ISSA-SVM prediction method. Experimental validation was conducted using data from 2003 to 2022. The results revealed that the average time consumed by ISSA-SVM for forecasting was 1.742401 s, displaying a minimal difference compared to the SVM method. In terms of total volume prediction, the ISSA-SVM approach achieved a mean absolute percentage error of 0.02% and a root-mean-square error of 1068.25, surpassing logistic regression, back-propagation neural network (BPNN), and other methods. These outcomes verify the reliability of the ISSA-SVM method in total volume prediction, showcasing its practical applicability. [ABSTRACT FROM AUTHOR]

The objective of this research is to analyze the factors influencing port efficiency from 2000 to 2023 using data envelopment analysis methodology and a dynamic ordinary least squares econometric model. In the first phase, the evaluation of port efficiency revealed an average score of 52%; with 10 ports showing high efficiency and two ports with efficiency below 1%. The results indicated the need for strategies to improve port efficiency in the region as enhancing infrastructure, streamlining processes, and improving logistics. In the second phase, Gross domestic product (GDP) per capita, the economically active population, and connectivity were used as independent variables to examine their impact on efficiency. The dynamic ordinary least squares model revealed that GDP per capita is the most influential factor in port efficiency, followed by maritime connectivity. These findings suggest that economic capacity and logistic networks are fundamental for improving port efficiency. On the other hand, the economically active population had a lesser impact, highlighting the importance of having a highly skilled workforce. The research concludes that it is crucial for port authorities to invest in infrastructure and human capital development to achieve more efficient and competitive ports. These efforts should focus on improving connectivity and promoting economic growth, which is essential for the sustainable development of the port sector. [ABSTRACT FROM AUTHOR]

We present a comprehensive gridded GDP per capita dataset downscaled to the admin 2 level (43,501 units) covering 1990–2022. It updates existing outdated datasets, which use reported subnational data only up to 2010. Our dataset, which is based on reported subnational GDP per capita data from 89 countries and 2,708 administrative units, employs various novel methods for extrapolation and downscaling. Downscaling with machine learning algorithms showed high performance (R2 = 0.79 for cross-validation, R2 = 0.80 for the test dataset) and accuracy against reported datasets (Pearson R = 0.88). The dataset includes reported and downscaled annual data (1990–2022) for three administrative levels: 0 (national; reported data for 237 administrative units), 1 (provincial; reported data for 2,708 administrative units for 89 countries), and 2 (municipality; downscaled data for 43,501 administrative units). The dataset has a higher spatial resolution and wider temporal range than the existing data do and will thus contribute to global or regional spatial analyses such as socioenvironmental modelling and economic resilience evaluation. The data are available at https://doi.org/10.5281/zenodo.10976733. [ABSTRACT FROM AUTHOR]

AbstractIn time series analysis, the decision of modeling the trend term as a stochastic or a deterministic component is a primordial step. For decades, authors have been focused on developing statistical tests to guide the analysts in such a decision. Surprisingly, less attention has been given to evaluate the robustness of the methods. This paper compares the robustness of four of the prominent tests. For this, various scenarios were used considering (i) the actual distribution of the noise term, (ii) the sample size, and (iii) the actual time series structure among auto-regressive moving average, linear structural space state, and the exponential generalize auto-regressive conditional heteroskedasticity models. An example of application on the Brazilian Gross Domestic Product is used to discuss the behavior of the methods vis-à-vis to their inferred robustness. [ABSTRACT FROM AUTHOR]

This research freshly investigates the effect of business competition on national welfare in Indonesia. This research uses business competition index data obtained from the Indonesia Competition Commission (KPPU) and welfare indicators data collected from the Indonesian Bureau of Central Statistics (BPS) at the economic sectoral and provincial levels for the period 2018–2020. The relationship between business competition and welfare is estimated using pooled regression model combining year, economic sector, and province. This research also uses business competition index applying both same weights for all dimensions of competition and the weights derived from principal component analysis. This research finds that business competition has a positive effect on welfare as represented by gross regional domestic product, regional productivity, productivity growth, wages, and human development index. Furthermore, the regions with high scores on the competition index mostly come from Java Island. Therefore, Indonesian government must encourage the mainstreaming of business competition in all provinces in Indonesia. [ABSTRACT FROM AUTHOR]