Per-capita GDP Research Articles

The driving factors and spatial evolution of industrial water pollution governance efficiency in the Yangtze River Delta urban agglomeration in China.

In order to explore the effective path to improve the efficiency of industrial water pollution governance efficiency (WGE), this study takes 27 prefecture-level cities in the Yangtze River Delta urban agglomeration in China as the research object, measures WGE through the improved SBM model, and tests the impact of WGE drivers using the spatial Durbin model (SDM). The study found that environmental pollution governance investment (EPGI) is positively correlated with WGE, and industrial agglomeration status (IAS) has inhibitory effects on the improvement of WGE. By testing IAS2, it was determined that the impact of IAS on WGE has a "U"-shaped relationship. The direct impact of EPGI on WGE is 0.5016, and the indirect impact on WGE is 0.6428; the direct impact of IAS on WGE is -0.3036, and the indirect impact on WGE is -0.5158. Among the other tested impact drivers, per capita GDP (PCG), industrial structure (IS), and level of technological innovation (TIL) are positively correlated with the dependent variable WGE, while energy consumption intensity (ECI), environmental regulation intensity (ERI), and degree of openness to foreign investment (FIR) are negatively correlated with the dependent variable WGE. In addition to the impact of the aforementioned main drivers, IAS and EPGI, these six drivers also largely influence and determine the final impact on WGE.

Low vs. high income: The impact of high-rise buildings on prevalence of obesity

The relationship between health, proxied by the prevalence of obesity, and the level of income is well documented. Based on the CDC report at a US statewide level between 2011 and 2020, the current study extends this discussion by exploring the impact of urban environments, proxied by the number of skyscrapers, on this relationship. In this context, the question that still remains open is what is the extent of urban development that can reduce the dimensions of the obesity pandemic to a minimum and whether this effect is different across poor versus rich populations. Consequently, the contribution of this study is the use of the quadratic model, which permit non-monotonic relationships between obesity prevalence and the number of skyscrapers. We also examine the incremental change in the number of skyscrapers. The global aspect of our study may be described as follows. For countries with per-capita GDP higher than $75,000 (lower than $25,000) – urban development of skyscrapers is expected to be beneficial on obesity prevalence up to 142 (126) skyscrapers. Compared to poor countries, the incremental impact of high-rise construction on obesity prevalence at the downward domain of the U-shaped curve is expected to be much more beneficial among rich countries.

Statistical Analysis of Population and Economic Growth in Ghana.

Purpose: The relationship between population and economic growth has been a subject of extensive research and debate for decades. Population of a country is driven by factors such as fertility rate, mortality rate and migration. Rapid population growth can strain resources, infrastructure, and social services, potentially hampering economic development. However, it can also create a demographic dividend when the working age population surpasses dependent age group, leading to increased productivity and economic growth.
 Methodology: The researchers performed statistical analysis on population and economic growth using Time Series. The study revealed that Ghana’s age profile is much concentrated at the youthful age, which is also the most fertile part of the age distribution.
 Findings: The Percentage Change of Population (PCPOP), Per Capita Gross Domestic Product (PCGDP) and Exchange Rate (EXCR) are statistically significant. Also Population and the economic growth in Ghana are inversely proportional to each other.
 Unique contributor to theory, policy and practice: Furthermore, the future values of the population of the country is relatively high for the next ten years and there is a negative correlation between the population and economic growth hence the future population is likely to face poverty. Stakeholders and policy makers are to ensure that future policies are directed towards Entrepreneurship and Skills Training to enable income generation in the Country.

Analyzing the Impact of Rainfall Patterns on Agriculture, Economy and Tourism in India: A Statistical Approach

The current study examines rainfall trends in India, encompassing its effect on various economic aspects and forecasting for 2023-2030. The Mann-Kendall test and Sen’s slope estimator are utilized to analyze annual and seasonal rainfall patterns. Results reveal a pronounced winter decline (2001-2022) alongside significant pre- monsoon, monsoon, and post-monsoon increases. Annual rainfall consistently de- creases, contrasting with rising pre-monsoon, monsoon, and post-monsoon trends. Annual rainfall exhibits the steepest decline (-1.0891 mm/year), while the monsoon season displays the highest increase (3.2538 mm/year). Further, the present study explores relationships between rainfall and economic growth, tourism, and agriculture. A statistically insignificant yet positive correlation is found between annual rainfall and per-capita GDP, indicating other economic drivers. Tourism shows a weak, statistically insignificant link with annual rainfall. In contrast, a robust statistically significant correlation emerges between annual rainfall and food- grain production, highlighting its role in agriculture. Finally, the present research forecasts annual rainfall (2023-2030) using the ARIMA (1,0,0) model, predicting a continued decline. This has profound implications for water resources, agriculture, and the economy, necessitating proactive measures such as water conservation, drought-resistant farming, and alternative energy investments.

The Determinants of Health Care Expenditure in Egypt

Abstract Health care sector is one of the most important sectors that affect the economies of all countries. On the one hand, it affects the productivity of labor force, on the other hand, it affects the spending of the households and the allocation of income among the various aspects of spending. In addition, the case of Egypt is characterized by some peculiarity in terms of The large population and the existence of a development gap between the cities of the north and the cities of the south, and the volume of out of pocket expenditure on health care which exceeds its counterpart in most countries of the world. In addition, health care service providers in Egypt are represented in the public sector, the private sector and the cooperative or community sector, where the three sectors provide health care services with different quality and prices. Therefore, this study aimed to identify the determinants of health care expenditure in Egypt through an econometric model. The results showed that spending on health care in Egypt was affected by percapita GDP and population over 65 years old while the econometric model shows no significance for variables such as hospital beds and number of medical doctors.

Unintentional emissions of polychlorinated naphthalenes in China: Sources, composition, and historical trends

Polychlorinated naphthalenes (PCNs) are detrimental to human health and the environment. With the commercial production of PCNs banned, unintentional releases have emerged as a significant environmental source. However, relevant information is still scarce. In this study, provincial emissions for eight PCNs homologues from 37 sources in the Chinese mainland during the period of 1960-2019 were estimated based on a source-specific and time-varying emission factor database. The results showed that the total PCNs emissions in 2019 reached 757.0 kg with Hebei ranked at the top among all the provinces and iron & steel industry as the biggest source. Low-chlorinated PCNs comprised 90% of emissions by mass, while highly chlorinated PCNs dominated in terms of toxicity, highlighting divergent priorities for mitigating emissions and safeguarding human health. The emissions showed an overall upward trend from 1960 to 2019 driven by emission increase from iron & steel industry in terms of source, and from North China and East China in terms of geographic area. Per-capita emissions followed an inverted U-shaped environmental Kuznets curve while emission intensities decreased with increasing per-capita Gross Domestic Product (GDP) following a nearly linear pattern when log-transformed.

Quantifying the heterogeneous impacts of the urban built environment on traffic carbon emissions: New insights from machine learning techniques

The configuration of the urban built environment is critical for promoting sustainability and achieving carbon neutrality. However, existing studies mostly use linear and spatial econometric models to investigate the relationship between urban built environments and traffic carbon dioxide (CO2) emissions, in-depth studies exploring the heterogeneous impacts of related features on traffic CO2 emission by interpretive machine learning models are scarce. Hence, we extract four dimensionless features to depict the size, compactness, irregularity, and isolation of built-up areas, and road network-related features (i.e., average cluster coefficient, road topological density, and road geometric density), respectively. Subsequently, we develop an interpretive machine learning framework based on the extracted features related to the urban built-up areas and road networks. The interpretive results of the proposed framework uncover that urban morphological features, especially population density (POP), GDP per capita (GDPpc), and urban physical compactness (UPC), have a heterogeneous impact on the per capita traffic emission (PCCE) across different cities. GDPpc is more like a linear relationship with PCCE, and UPC has a significant influence on PCCE when its value is between 62% and 78%. Our results also reveal the nonlinear relationships and interactive effects between these features, providing the implications of urban morphological planning and carbon emission reduction.

Striving for carbon neutrality and economic prosperity in the top ten emitting countries: Testing N shape Kuznets curve hypothesis

Environmental deterioration brought on by the top ten greenhouse gas emitters is a critical worldwide issue with far-reaching ramifications for global economic sustainability. The main purpose of this research is to explore the link between economic growth, energy consumption, trade openness, and environmental factors with a focus on achieving carbon neutrality without compromising economic growth in the top ten emitting countries by employing a dynamic STIRPAT modeling technique. The data used in this research was taken from various sources, such as the International Energy Agency and the World Bank. The research found a robust association between energy consumption and economic growth, with a correlation coefficient of 0.87. The results revealed that carbon emissions (CO2) are positively related to per capita gross domestic product (PGDP), cubic GDP (GDP3), international tourism arrivals (Tour) and energy consumption (EC), while negatively related to trade openness (TO) and squared GDP (GDP2) in the top ten emitting countries, confirming the N shaped Kuznets curve hypothesis. Trade liberalization also impacted emissions with a coefficient of 0.65 (p < 0.01). The findings suggest that in order to simultaneously achieve carbon neutrality and continue economic development, it is imperative for the major polluting countries to embrace renewable energy sources and enact environmentally beneficial trade and economic measures.

Economic Growth and Childhood Malnutrition in Low- and Middle-Income Countries

Economic growth may reduce childhood malnutrition through improvements of several contributing factors, but the empirical evidence is mixed. Identifying the most important factors that contribute to child malnutrition and their associations with economic growth can inform decision-making about targeted investments to improve children's health. To assess the associations between economic growth and malnutrition, contributing factors and malnutrition, and economic growth and contributing factors of malnutrition in low- and middle-income countries (LMICs). This cross-sectional study used data from 239 Demographic and Health Surveys from January 1, 1990, to December 31, 2021. Observations included 1 138 568 children aged 0 to 35 months with valid anthropometric measures and information on contributing factors of malnutrition from 58 LMICs. Data were analyzed from May 20, 2022, to February 16, 2023. National per-capita gross domestic product (GDP) was used as a proxy for economic growth. Six measures of childhood malnutrition were constructed: stunting (height-for-age z score <-2), underweight (weight-for-age z score <-2), wasting (weight-for-height z score <-2), overweight (weight-for-height z score >2), obesity (weight-for-height z score >3), and dietary diversity failure (consumption of less than 5 of 8 different food groups in the past 24 hours). Eighteen contributing factors of malnutrition were constructed, of which 10 were underlying determinants (eg, access to improved sanitation) and 8 were immediate determinants (eg, breastfeeding initiation). A total of 1 138 568 children (mean [SD] age, 17.14 [10.26] months; 579 589 [50.9%] boys and 558 979 [49.1%] girls) were included in the analysis. Of these, 27.3% (95% CI, 27.2%-27.4%) had stunting; 25.7% (95% CI, 25.6%-25.8%), underweight; 11.2% (95% CI, 11.1%-11.2%), wasting; 3.8% (95% CI, 3.7%-3.8%), overweight; 1.1% (95% CI, 1.1%-1.1%), obesity; and 79.8% (95% CI, 79.7%-79.9%), dietary diversity failure. Per-capita GDP was weakly associated with childhood malnutrition. The odds ratios associated with a 5% increase in per-capita GDP were 0.99 (95% CI, 0.99-1.00) for stunting, 1.01 (95% CI, 1.00-1.01) for wasting, 1.00 (95% CI, 1.00-1.00) for underweight, 0.98 (95% CI, 0.98-0.98) for overweight, 0.98 (95% CI, 0.97-0.98) for obesity, and 1.03 (95% CI, 1.01-1.04) for dietary diversity failure. Although strong associations were found between many contributing factors and most outcomes for malnutrition, associations identified between per-capita GDP and these contributing factors themselves were ambiguous. In this multicountry cross-sectional study, economic growth was weakly associated with childhood malnutrition and several contributing factors. To reduce child malnutrition, economic growth may need to be accompanied by more targeted investments to improve contributing factors that are strongly associated with child malnutrition, such as maternal health and education.

Caselaw and England's economic performance during the Industrial Revolution: Data and evidence

We generate and analyze data pertinent to examining whether developments in caselaw were consequential for England's economic performance during the Industrial Revolution. Applying topic modeling to a corpus of 67,455 reports on English court cases, we construct annual time series of caselaw developments between 1765 and 1865. We then add a real per-capita GDP series to our caselaw series and estimate a structural VAR featuring a linear time trend. Our evidence shows that caselaw developments were an important determinant of economic fluctuations. Caselaw shocks jointly account for more of the variability in per-capita GDP around its long-term trend than do shocks directly to per-capita GDP. The response of per-capita GDP to caselaw innovations critically depends on the legal domain. Developments in caselaw on intellectual property, organizations, debt and finance, and inheritance boosted economic performance while developments in property and ecclesiastical caselaw had negative effects on per-capita GDP. Our analysis uncovers a 'bleak-law era' when the legal system misallocated attention between output-promoting and output-hindering areas of law.

Coexistence of sustainability and growth in different economies

Research backgroundThe Sustainable Development Goals (SDGs) and their practical application to national economies have raised different lines of investigation among which this research focuses on interactions between macroeconomic indicators, namely Gross Domestic Product per Capita (GDPpC), Stock Exchange Indexes and CO2 emissions as indexes representative of SDGs achievement. Purpose of the articleThis research aims to analyze the current compatibility of the different SDG, more specifically those related to economic growth and climate sustainability. MethodsTwo methods have been followed: a descriptive methodology based on the Pearson Correlation Coefficient to assess potential behavioral parallelisms between the indexes studied. Inference, to define functional relations and potential causality traits between the macroeconomic indicators. Findings and value addedThe main added value of this research is to question the true simultaneous applicability of the different SDGs.

Climate Risk and Vulnerability Assessment of Georgian Hydrology under Future Climate Change Scenarios

The Climate Risk and Vulnerability Assessment (CRVA) is a systematic process used to identify gaps in regional climate adaptation strategies. The CRVA method assesses regional vulnerability, adaptation capacity, exposure, and sensitivity to climate change to support improved adaptation policies. This CRVA study assesses Georgia’s climate exposure, geographic sensitivity, and socio-economic sensitivity by focusing on the impacts of climate change on regional hydrology. The projected change in climate extreme indices, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), is assessed against the 1961–1990 baseline under future Representative Concentration Pathway (RCP) scenarios. These indices encompass various climate factors such as the maximum daily temperature, warmth duration, total precipitation, heavy and extreme precipitation, maximum 5-day precipitation, and consecutive drought duration. This evaluation helps us understand the potential climate exposure impacts on Georgia. The climate-induced geographic sensitivity is examined based on water stress, drought risk, and changes in soil productivity using the Normalized Difference Vegetation Index (NDVI). The climate-induced socio-economic sensitivity is determined using the Gross Domestic Product per capita (GDP), Human Development Index, Education Index, and population density. The highest vulnerability to climate change was found in the Kakheti and Kvemo Kartli regions, with the vulnerability index values ranging from 6 to 15, followed by Mtskheta-Mtianeti, Samtskhe–Javakheti, and Shida Kartli with vulnerability index values ranging from 2 to 8. The location of these regions upstream of the Alazani-Iori, Khrami-Debeda, and Mktvari river basins indicates that the country’s water resources are vulnerable to climate change impacts in the future under the RCP 4.5 and 8.5 scenarios.

Innovation-driven economic growth under global turbulence: how countries strengthen innovation systems to deal with threats

ABSTRACT The role of innovation as a major force of economic growth is not to doubt. Despite this, geopolitical challenges in recent years pose difficulties for countries, especially for those who struggle with lower GDP per-capita, to enhance innovative performance and grow. Building upon the national innovation system concept and using the methodological framework of country-level composite innovation indexes, this paper aims to explore features of innovation systems inherent in lagging economies and their relevance for economic growth. The analysis, based on a panel dataset comprising the Global Innovation Index elementary indicators on 76 countries during 2015–2021, demonstrates the lower initial state, but faster development of innovation system components in lagging economies. Crucial to their growth is the external validation of national innovation efforts, proxied by international trade and foreign investment indicators. In addition, a sustainable economic growth requires balanced and synergetic development of all components and functions. Therefore, bottom-up approaches are needed to sustain further growth of lagging economies, starting with the provision of effective resource allocation, knowledge creation and its diffusion.

Environmental Performance and a Nation’s Growth: Does the Economic Status and Style of Governance of a Country Matter?

The literature abounds with studies on the impact of the growth of nations on the environment. However, studies on the financial materiality of environmental concerns are found less often. This study aims to determine the impact of environmental concerns on a nation’s GDP per capita (GDPC). In addition, the influence of developed nations and democracy is also explored. The data for 106 countries and ten years (2011–2020) are procured from World Bank’s official website. The countries with incomplete data for a balanced panel are not included. Panel data econometrics (quantile regression) is applied to analyze the data. Environmental concerns are measured with the help of environmental efficiency (EE) using data envelopment analysis (DEA). It is found that environmental efficiency (EE) negatively impacts the GDPC for low levels of GDPC. However, no association of EE with GDPC is witnessed in the case of high GDPC levels. In addition, developed nations positively moderate the EE’s impact on the GDPC when the GDPC levels are high. Moreover, democratic nations negatively moderate the EE’s impact on the GDPC when low GDPC levels exist. The main implication of the current study is that developed high GDPC countries could bear a significant chunk of the cost of EE. This way, the adverse impact of an increase in EE on the GDPC (by low GDPC counties) could be dodged, and by the efforts of developed high GDPC countries, EE could be increased significantly without adversely impacting their GDPC.

Agriculture related methane emissions embodied in China's interprovincial trade

Given that agriculture is the predominant source of anthropogenic methane, evaluating the spatio-temporal trends of agriculture related CH4 emissions embodied in trade and the related drivers is an important prerequisite for climate goals. This study first adopted the IPCC tier 2 method to account agriculture related methane emissions, by taking China as a case. The embodied CH4 emissions in the interprovincial trade were then explored by employing multiregional input-output analysis. Socioeconomic factors impacting embodied emissions were uncovered. The results show that the direct CH4 emissions from agriculture in China decreased from 15.50 Tg in 2010 to 12.49 Tg in 2020, mainly contributed by the decrease from livestock production. The direct emissions were dominated by Hunan, Sichuan and Jiangxi broken down by provinces, and rice cultivation and enteric fermentation by process. Total amount of embodied CH4 emissions increased from 11.36 Tg in 2012 to 12.10 Tg in 2015, and decreased to 11.48 Tg in 2017. Shandong had the largest embodied CH4 net inflow, while Heilongjiang had the largest embodied CH4 net outflow. The significant increases in embodied CH4 net outflow of Western China transferred from North were found. GDP per-capita and food production per unit GDP had the most positive and negative contributions to the embodied emissions, respectively. Reliability of the results was further demonstrated by using sensitivity analysis and comparing with similar studies. Finally, implications were raised, including reducing the direct emission intensities, bringing embodied CH4 emission into decision making and setting the interprovincial cooperation mechanisms.

Do Backbone High-Speed Rails Widen the North-South Gap in China?

The economic disparity between southern and northern China has widened in the past decade. This article explores the roles of the north-south stretched backbone high-speed rails (HSRs) in the widened north-south economic gap in China. By constructing panel data of 283 cities between 2005 and 2016 and estimating the difference in GDP and per-capita GDP of northern and southern cities before and after the first north-south stretched HSR, we show that the north-south economic gap widened by about 8% as the opening of the Beijing-Shanghai HSR, the first north-south stretched HSR. Further channel analysis reveals that the north-south gaps in population, fixed asset investment, public expenditure, and the relative size of secondary industry to tertiary industry also widened. These results suggest that fast transportation improvement caused by long-distance backbone HSRs can contribute to accelerating the large-scale regional disparity.

Analysis of transportation CO2 emission efficiency in the Yangtze River Economic Belt in China

ABSTRACT The transportation industry’s CO2 emissions and its main drivers are important for achieving sustainable development. With data from the Yangtze River Economic Belt (YEB) between 2004 and 2020, the efficiency of CO2 emissions is investigated by the super efficiency epsilon-based measurement method (super-EBM) and the Global Malmquist Luenberger Productivity Index (GML). The influencing factors on the different efficiency levels are explored by the panel quantile regression. This study shows that the overall CO2 emissions efficiency is not high, and the efficiency in different regions is largely consistent with the regional economy level. COVID-19 may reduce urban transport carbon emissions in the Yangtze River Economic Belt in 2020, as reflected by the reductions in average emissions and emissions efficiency. Findings also suggest that the influence of the number of private vehicles per 10,000 people on efficiency is positive at low and middle quantiles, and negative at high quantiles. Besides, the per-capita GDP, energy intensity, and transportation service structure negatively influence the efficiency at all quantiles, whereas urbanization and transportation intensity are positively related to the emissions efficiency.

Determinants of Pharmaceutical Expenditure in OECD Countries: Evidence from Panel Data

Purpose –The objective of this study is to identify the main factors affecting pharmaceutical expenditures in the Organization for Economic Cooperation and Development (OECD) countries.Design/methodology/approach –In this study, it was investigated whether the university graduate rate(UNI), the unemployment rate(UNEMP), gross domestic productper capita (GDP) in Purchasing Power Parity (PPP $),and the number of physicians per 1,000 people (PHY) affected per capita pharmaceutical expenditures (PHAR)(PPP $) by using panel data analysis. The population of the study was 33 OECD countries whose data were available. The data consisted of the time period of 2013-2016. Results –GDP and UNIstatistically significantly increased PHAR (p&lt;0.05). The UNEMPand PHYalso increased PHAR, but the effects of these variables were not found to be statistically significant (p&gt;0.05). The variables included in the model explained 22% of the variance in PHAR.Conclusion –PHAR are affected by economic factors and sociodemographic factors as well as health systems' structure. Therefore, governments wishing to control pharmaceutical expenditures should also focus on these variables.

Local Cost-Effectiveness of Nasopharyngeal Carcinoma Screening Strategies in Southern China: Secondary Analysis of the PRO-NPC-001 Cluster-Randomized Trial

Population-based screening for endemic nasopharyngeal carcinoma (NPC) detects most cases at an early stage. In a cluster-randomized trial conducted in Guangdong, a combination of Epstein-Barr Virus (EBV) anti-VCA/EBNA1 IgA serology and endoscopy reduced NPC mortality. We conducted a secondary analysis of this trial in conjunction with local incidence and cost data, hypothesizing that screening would be cost-effective in this region. We estimated population-level NPC mortality reduction, resource utilization, and cost-effectiveness of 12 unique screening strategies in six populations in Guangdong/Guangxi using a previously-validated time-inhomogeneous decision-analytic cohort model. These 12 strategies evaluated combinations of serology, nasopharyngeal swab PCR (NP PCR), endoscopy, and head/neck MRI. Incidence data, screening costs, and healthcare costs were obtained from local cancer registries, laboratories conducting ELISA/PCR, and the Guangdong provincial healthcare system. We evaluated variable screening ages, sexes, intervals, and durations to identify optimal screening approaches from the perspective of the healthcare system in southern China. An incremental cost-effectiveness ratio (ICER) willingness-to-pay threshold of 1.50 times the per-capita GDP was considered cost-effective in southern China. For the base strategy screening 50-year-old men and women using only serology and endoscopy, the average cost per screened subject for a single round of screening over a five-year cycle was ¥175.69. The addition of MRI improved sensitivity (76% vs. 62%) and approximately doubled screening costs. Triage with NP PCR was cost-neutral when used in conjunction with MRI and reduced endoscopy/MRI utilization by 37% with a 3-4% reduction in screening sensitivity. Among 50-year-old men and women, screening was cost-effective in all populations provided that medium-risk subjects were not referred for endoscopy/MRI (ICER/GDP 0.62-0.83). The use of NP PCR without MRI (ICER/GDP 0.83) was dominated by the base strategy (ICER/GDP 0.62) due to higher costs and NPC mortality. After a single five-year screening cycle, screening reduced population NPC mortality by 14% with serology + endoscopy and 21% with serology + endoscopy + MRI. Introduction of MRI with or without NP PCR could be cost-effective in all populations. For MRI-based strategies, the most efficient use of resources was deferral of endoscopy unless MRI was abnormal (ICER/GDP 0.67). Overall, the best-performing strategies balanced NPC mortality, screening costs, and MRI utilization. EBV serology-based screening for endemic NPC is likely to be cost-effective among adult men and women in Guangdong and Guangxi. Referring medium-risk subjects for endoscopy/MRI should be avoided, and NP PCR should be used to triage individuals for MRI rather than endoscopy. These data may aid the design of population-based screening programs in this region.

Economic Growth Model of Ecuador with Upper Middle Income to Explain and Predict Productivity during the Period 2000-2022

Economic growth is one of the most widely used measures of a country's macroeconomic performance, although it is not without its critics. There are strong arguments that per capita gross domestic product growth can reflect an important part of the success or failure of policies implemented in an economy. The objective, to analyse Ecuador's economic growth model with high average income to explain and predict productivity over the period 2000-2022. The materials and methods, this line of research addresses a hypothetical-deductive and descriptive level of inquiry, by developing aspects from the global perspective concerning economic theory and implicit models of classical growth; such as the model that describes the Cobb-Douglas production function, to explain and predict the temporal behaviour of Ecuador's economic development. The above, in order to establish a correlational analysis of the contributions of the macroeconomic determinants of productivity. The response variable to be measured has been the gross domestic product per capita (GDPpc ) with respect to the independent variables: Domestic Savings (S), Gross Fixed Capital Formation (FBKF), Government Final Consumption Expenditure (G), Exports (E) and as a control variable represented by Labor Force (L). A systematic documentary search was carried out through databases: Scielo, Scopus, Google Scholar and Science Direct, to support the present research with contributions with articles of temporal relevance within the last 5 years. In addition, it was based on annual secondary data extractions from official sources such as the Central Bank of Ecuador (BCE) and the World Bank (WB), according to the temporal coverage from 2000 to 2022. Results, 23 temporal observations were analysed to build an econometric model under the adaptation of a Cobb Douglas production function to explain and predict the behavior of elasticity in Ecuador's economic growth, which establishes that 99.81% of the total variability present in the data is explained by the model that includes variations in savings, gross fixed capital formation, labour force, government final consumption expenditure and exports in the analysed period. Conclusions, the imports variable is eliminated as it is not statistically significant, the resulting model complies with the basic assumptions and the corresponding economic validation in terms of the signs of the estimates for the determinants of economic growth in Ecuador.