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In this paper, we aim to remark on the nine crucial economic factors that affect GDP (Gross Domestic Product) per capita as assess the importance, effect, or value of economic development for a sample of 31 years of economic data from Bangladesh for the period of 1992 to 2022. The research question is "What are the effects of these variables on GDP per capita?" To inquire into that business in detail we use multiple regression analysis in this paper as GDP (Gross Domestic Product) per capita is the regressand or dependent variable and the rest of the factors or variables are the regressors or independent variables. In this regard, we use nine independent variables in the regression analysis: Gross Domestic Product (GDP) in current billions of USD, GDP growth rate (latest) per annum, Inflation (on consumer price index), Gross Savings (%GDP), Literacy rate, Unemployment rate, Investment rate, Population growth rate, Poverty Rate. After the data analysis we found that among the nine independent variables, five have a significant impact on GDP per capita. For instance, Inflation (% of GDP), GDP (current USD), GDP growth rate, Gross Savings rate, and Poverty rate play a vital role in determining GDP per capita. Since we use time series data, to identify an accurate estimation as being present we test the stationarity of data, to conduct the time series has no unit root problem we use first difference and log difference such that the data and the estimation will become acceptable. An assortment of statistical tests, such as Normality Test, Multicollinearity, Heteroskedasticity, Autocorrelation, Goodness-of-Fit (GoF) Test, ANOVA (Analysis of Variance) Test, and Multiple Regression Analysis, were performed using GRETL (Gnu Regression, Econometrics and Time-series Library). We use CUSUM (cumulative sum) test and CUSUM (cumulative sum) square test to find out the stability of the coefficient or parameters. The research question is "What are the effects of these variables on GDP per capita?" [ABSTRACT FROM AUTHOR]

This paper asks the following question: can the macroeconomic fluctuations of Gross Domestic Product (GDP) explain the electoral success of political parties? Notwithstanding other important determinants such as the charisma of the leader of a popular political party or ideology, the economy may very well explain electoral successes. In this paper, I use a very simple variable to account for the economy: the rate of growth of GDP. It is claimed that years of relative higher growth are associated with the incumbent government retaining its power, while years of zero or negative growth rates are related with the fall of an incumbent government. The period between 1974-2023 shows evidence of Greek economic growth and the impact on elections and therefore the hypothesis cannot be rejected that the economy plays a central role in explaining electoral successes, and especially failures, of incumbent governments. [ABSTRACT FROM AUTHOR]

Agriculture was and remains the support of human existence, representing the backbone of the Romanian rural economy. The gross domestic product represents a macroeconomic indicator of results, being the consequence of the efficient use of resources. In this context, the authors analyze the place that agriculture holds in achieving the gross domestic product in Romania compared to the European Union, as well as its dynamics. In this sense, the share of resources in the formation of the gross domestic product in the period 2000-2022 is presented. The main research methods used for this work are: analysis, synthesis, comparison and mathematical analysis. From the analysis carried out, conclusive ideas emerge, and the article becomes even more suggestive, in that it is accompanied by tables synthesized by the authors and relevant graphics that highlight and support the assessments that the authors make in the content of this article. At the end of the paper, the authors emphasize the fact that the GDP is the most complex indicator that reflects the transformations that the Romanian economy has gone through in recent years. From the point of view of the use of resources, it can be seen that the share of the agricultural sector has registered a downward evolution, which can be largely explained by the increase in the importance of industry and the service sector in the overall national economy. [ABSTRACT FROM AUTHOR]

Gross Domestic Product (GDP) known as the pulse of economy for any country depends on multiple factors like export–import, inflation rate and unemployment rate etc. Statistical assessment of GDP demands fresh concepts to explain GDP through its covariates in order to improve and strengthen the estimation process. In the present paper, a linear regression model is proposed for modelling GDP of India. Descriptive analysis for the considered data on GDP and its covariates obtained from the World Bank archives is presented through Heatmap. Identification and relevance of the possible set of covariates is established by using step-wise regression (SR). The use of SR reflects its credibility vis-à-vis Ordinary Least Squares (OLS) regression by registering a decline in Deviance Information Criterion (DIC) from − 18.30 to − 13.63, from a full model to only a few significant factor models. We propose an alternative statistical algorithm implemented under Bayesian paradigm through Integrated Nested Laplace Approximation which bridges the gap of accuracy in estimates as opposed to the frequentist OLS regression for explaining GDP of the country India. Model selection is decided from a battery of normal priors through DIC. Comparison of Bayesian and frequentist modelling results is done using several criteria such as Mean Absolute Deviation, Root Mean Square Error, and Mean Absolute Percent Error. India is one of the emerging economies, the economy of India is the 5th largest in terms of nominal gross domestic product in the world, and the 3rd largest in the terms of purchasing power parity as recorded by World Bank (2022). GDP of India for 2022 is recorded as $3.38 trillion World Bank (2022) which is in close agreement to GDP of $3.361 trillion predicted by the model proposed in the present paper. [ABSTRACT FROM AUTHOR]

Over the last decade, the dependence on fossil fuels and their prices has been increasingly discussed. This paper aimed to assess the development of fossil fuel prices and their relationship to GDP in the Czech Republic. To achieve this objective, a VAR model and Granger causality test were used. All fossil fuel prices peaked in 2022. The causal relationship with GDP in the Czech Republic was confirmed for natural gas and coal prices. On the other hand, the causal relationship between oil and GDP in the Czech Republic was not confirmed during the period under study. Weak correlations were found with the Covid-19 pandemic, inflation, and unemployment in the EU. The only exception is the correlation between coal and inflation in the EU, which was moderately strong. A limitation of the paper is the length of the period studied, with data collection taking place only once a month. [ABSTRACT FROM AUTHOR]

This paper measures the resilience of Eurozone economies following the economic shock of the Covid-19 pandemic that hit the global economy. Q2 2022 to Q4 2050 real GDP forecasts of 17 countries of the Eurozone are generated with wavelet analysis using historical real GDP quarterly data excluding the pandemic (Q4 1994 up to Q3 2019) and including the pandemic (Q4 1994 up to the Q1 2022). The means of the Q2 2022 up to Q4 2050 forecasts of the quarterly growth rates (annualized) are computed with the two sets of historical data including and excluding the pandemic. The difference in mean forecasts measures the resilience of economies during the pandemic, the more positive the difference, the stronger the resilience. Based on this indicator of resilience, among high GDP countries, Italy is the most resilient economy towards the Covid-19 pandemic (ranked No 1 among 17 countries), Germany is the least (No 16). Among medium GDP countries, Finland is the most resilient economy towards the Covid-19 pandemic (ranked No 5), Greece is the least (No 14). Among low GDP countries, Malta is the most resilient economy towards the Covid-19 pandemic (ranked No 6), Latvia is the least (No 17). [ABSTRACT FROM AUTHOR]

The quality of nighttime light (NTL) data is an important factor affecting the estimation of gross domestic product (GDP), but most studies do not use the latest NPP/VIIRS V2 annual composite product, and there is a lack of China's GDP estimation products in recent years. To address this problem, this paper studies the NPP/VIIRS remote sensing estimation method for the GDP in mainland China from 2013 to 2023. First, the remote sensing data are preprocessed, and the noise masking method is used to remove outliers. The total amount of NTL, average NTL value, and comprehensive NTL index data are extracted. Combined with the GDP data from the Statistical Yearbook, a fitting model of the GDP and NTL index is constructed. The differences between different GDP estimation models are compared and analyzed, and the optimal model is selected as the estimation model. In addition, through the optimal fitting model, GDP spatial estimation products from 2013 to 2023 are produced. Moreover, the spatiotemporal variation characteristics of the GDP in mainland China are analyzed, with a focus on the spatiotemporal variation of GDP decline regions and the changes in the GDP rankings of provinces and cities. The main conclusions include the following: (1) In the time regression analysis, the linear model MNL has a strong correlation with the GDP, with an R2 of 0.972. This model is selected as the optimal fitting model to calculate the spatial data of the GDP. (2) The spatial distribution of the GDP in mainland China is high in the east and low in the west, and it shows a characteristic of extending from the provincial capital to the surrounding cities. The connectivity between adjacent high-GDP areas continues to increase. (3) From 2013 to 2023, the GDP in most parts of China showed an upward trend, with 98.56% of pixels growing and only 0.99% of pixels declining. The declining pixels are mainly distributed in heavy industrial cities supported by fossil fuel resources, such as Ordos, Daqing, Aksu, etc. (4) Compared with statistical data, the overall difference of the GDP estimated by NTL data is not large, and the relative error is between 0.04% and 1.95%. From the perspective of the GDP ranking of each province, the ranking of most provinces is not much different, fluctuating between ±2. A small number of provinces have large ranking differences due to reasons such as dominant industries and power supply. By spatializing the GDP data of mainland China in the past 11 years, the spatiotemporal changes of the GDP within mainland China were analyzed. The research results can provide support for government economic decisions such as urban development. [ABSTRACT FROM AUTHOR]

The Central Bank of Somalia faces challenges in implementing effective monetary policies due to limited authority over the nation's monetary system and the incomplete development of the banking industry. This constraint may impede the bank's ability to regulate inflation effectively. Nevertheless, this paper investigates the asymmetric impacts of inflation on public debt sustainability in Somalia from 1977 to 2021. This research employs a Nonlinear Autoregression Distributed Lag (NARDL) model. The research explores how inflation impacts changes in public debt and uncovers potential nonlinearities or threshold effects within this dynamic association. Findings reveal asymmetric effects of inflation on public debt, indicating that inflation's increase positively influences public debt while its decrease adversely impacts it. Additionally, foreign direct investment exhibits a negative long-term correlation with inflation, and GDP shows a positive yet statistically insignificant connection with inflation in the long run. The study underscores the significance of prudent debt management practices and advocates caution when accumulating public debt, especially during periods of heightened inflation. Moreover, this study highlights the implications for governments in Somalia and similar economies facing analogous challenges. It emphasizes the importance of accounting for inflation's impact on public debt dynamics and advocates strategies to control inflation to mitigate the risks associated with rising public debt. The study supports the presence of asymmetrical effects between these variables, enriching theoretical frameworks. Despite, acknowledging limitations, such as temporal constraints and model assumptions, the study recommends future research employ more sophisticated methodologies, extend sequential scope, integrate qualitative approaches, explore policy interventions, and enhance data collection mechanisms for a more comprehensive analysis. Moreover, based on the study outcomes, policy recommendations proposed to enhance fiscal stability. These include implementing measures to curb inflation rates, prioritizing prudent debt management, strengthening fiscal responsibility, enhancing monetary policy's role in inflation management, diversifying investments, improving data monitoring, and focusing on long-term economic planning. [ABSTRACT FROM AUTHOR]

This paper aims to present an input-output model of the forest-based bioeconomy, enabling insight into its contribution to total gross value added (GDP), employment, and CO2 emissions in Estonia, Latvia, Lithuania, and Finland. To quantify this contribution, the paper utilizes input-output tables of the four countries. The forest-based bioeconomy includes forestry, the processing of forestry products (forward linkages), and the direct and indirect use of inputs (backward linkages) from forestry and processing industries. Our analysis reveals that while forestry itself is a small industry, contributing minimally to GDP (ranging from 0.40% in Lithuania to 1.90% in Finland), and the forest-based bioeconomy is considerably larger. For instance, its contribution to GDP ranges from 3.59% in Lithuania to 7.22% in Estonia. Moreover, the forestry-based bioeconomy exhibits negative net CO2 emissions, primarily due to CO2 sequestration by forest lands and harvested wood products. Despite the substantial negative CO2 emissions associated with forestry activities, without sequestration, the forestry-based bioeconomy's share in total emissions ranges from 5.14% in Lithuania to 14.88% in Finland. [ABSTRACT FROM AUTHOR]

Copyright of Mineral Resources Management / Gospodarka Surowcami Mineralnymi is the property of Mineral & Energy Economy Research Institute of the Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we address the effects of two fiscal policy delays on the dynamical analysis of macroeconomics. First, a time gap between the accrual of taxes and their payment is considered. Second, the time spent between the purchasing decisions and the actual expenditure is also taken into consideration. Since both these delays are significant in controlling macroeconomic conditions, this paper incorporates aforementioned delays into the IS–LM model. At first, a mathematical model is developed using delayed differential equations. Then a unique steady state solution is obtained. Around the equilibrium point, linear stability analysis is done. Also, the occurance of Hopf bifurcation is observed when delay crosses a critical point and switches in stability are also detected. Properties of Hopf bifurcation using center manifold theorem are discussed. Lastly, numerical simulations are run to verify our analysis. In this work, we considered a case study to perform simulation wherein GDP of India for last ten years is recorded for estimating some parameters. In different investment scenarios, numerical simulations corroborate the analytical findings of the model. Furthermore, rigorous analysis shows that adding the right mix of delays can help in maintaining/ regaining the stability after periods of instability, or even gaining stability in the long run. [ABSTRACT FROM AUTHOR]

The travel and tourism industry plays a crucial role in economies around the world. The impact of the COVID-19 pandemic on the tourism industry has been very pronounced. This paper aims to study the relationship between the country's T&T industry Share of Employment (TTEMPL) and the country's T&T industry Share of Gross Domestic Product (TTGDP). This study is specific because we do not focus on the development of indicators over time; instead, we propose the models for 117 countries using the quantile regression (QR) while comparing models in the context of COVID-19 (between 2019 and 2021). The results of the QR determined that individual percentiles of the TTGDP are more affected by the TTEMPL than other percentiles of the TTGDP, which is then reflected in the changes in regression coefficients. In addition, we compare analyzed indicators among countries according to region and income group. The study reveals that the tourism downturn caused by COVID-19 has adverse effects on the TTEMPL and the TTGDP. In addition, the results show that the impact of COVID-19 on the tourism industry appears to be varied among countries, regions, and income groups. [ABSTRACT FROM AUTHOR]

Green taxes are taxes collected to protect the environment by controlling the negative effects of certain activities and products on the environment. They are also an instrument of environmental policy and can therefore contribute to several sustainable development goals. According to the studies carried out, the green economy aims to ensure sustainable development. The main objective of this paper is to identify the existing relationships between green taxes and sustainable economic development through a dynamic panel analysis. A dynamic panel analysis was therefore carried out on the existing links between environmental taxes and charges at the European level and the indicators of the circular economy. The results of the two dynamic regressions for the two dependent variables, namely total green taxes and energy taxes, show a positive and significant correlation with the variation of GDP and with primary energy consumption, confirming the hypothesis that environmental taxes and energy taxes are closely linked to these two important indicators of sustainable development. Thus, as GDP changes, the taxes on energy production and the energy products used in both transport and stationary applications increase. As a result of the analysis, we can note that the increase in primary energy consumption and the consumption of raw materials leads to an increase in environmental and energy taxes. Energy taxes are a possible solution to reduce CO2 emissions in third world countries and may even stimulate climate action. In contrast, we found no significant correlation between green taxes and the following variables: Human Development Index, net greenhouse gas emissions, private investment and gross value added related to circular economy sectors, the consumption of raw materials, waste generated, waste treatment, the supply, transformation, and consumption of renewable energy, public expenditure on environmental protection, and climate-related economic losses. [ABSTRACT FROM AUTHOR]

Copyright of Social Inequalities & Economic Growth / Nierownosci Spoleczne a Wzrost Gospodarczy is the property of University of Rzeszow and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents the first reconstruction of the value-added in Italy during the Liberal Age at the provincial level for four benchmark years: 1871, 1881, 1901 and 1911. Figures are provided for agriculture, industry, and services, for all 69 Italian provinces. The exercise is performed using an implementation of the Geary-Stark framework. Additionally, this paper also addresses the accounting problem in the textile workforce known as the «textile bias». The results provide an extensive look into the economy of each province. They show that the Industrial Triangle constituted by the provinces of Genoa, Milan and Turin was the cornerstone of the entire economy, fostering growth in the surrounding provinces. However, the reconstruction shows a large heterogeneity amongst provinces even within each macro-area. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of alternative energy use, urbanization, GDP, agriculture, ICT development, and FDI on carbon dioxide (CO2) emissions in the 14 leading CO2-emitting countries in Asia. This research comprises various econometric techniques, including MMQR, FMOLS, DOLS, and Driscoll–Kraay, to extend the data analysis from 1996 to 2020. The findings provide significant support for an inverted U-shaped link between economic expansion and environmental deterioration, known as the environmental Kuznets curve. Moreover, this paper verifies that the GDP square, renewable energy use, and agriculture are shown to help to decrease pollution, as indicated by the research findings. On the contrary, urbanization and the GDP are demonstrated to be variables that contribute to carbon emissions. Furthermore, the panel quantile regression models validate that the impacts of each explanatory variable on CO2 emissions vary across various quantiles. Finally, this analysis provides valuable suggestions to scholars, environmentalists, politicians, and authorities for identifying and mitigating the main cause of emissions. [ABSTRACT FROM AUTHOR]

Against the backdrop of the current political developments in Central and Eastern European (CEE) countries, such as Ukraine, Poland, and Romania, the question arises as to the role played by economic transformation and the resulting innovation linkages in these countries over the last 20 years. The main purpose of this paper is to explore the impact of economic and institutional dimensions on the development of CEE countries, explicitly distinguishing between European Union (EU) members and non‐members, and thus reflecting the differences in institutions and path dependency. Furthermore, the paper contrasts the performance of CEE countries with that of Western European countries. To achieve these objectives, the impact of factors such as innovation, institutions, and political practices on the economic development of 37 European countries from 2000 until 2020 is followed using fixed effects regression and Ordinary least squares (OLS) regression. The results of the analysis show the importance of institutional factors such as low levels of corruption, political freedoms, and intellectual property. The effect of institutional variables was particularly pronounced in the case of non‐EU countries, which indicates the particular importance of the development of stable institutions for achieving higher levels of economic development for this category of countries. [ABSTRACT FROM AUTHOR]

There is a lack of understanding of the fisheries production impact on the national economy, as the contribution of the industry to the Turkish economy has not been adequately reflected in the official Gross Domestic Product (GDP) statistics and economic research. Therefore, this paper aims to reveal the industry's contribution to Türkiye's economic growth. We analysed the effect of fisheries production on economic growth by employing the Auto Regressive Distributed Lag (ARDL) model from 1990-2019. The results showed a positive relationship between fisheries production and economic growth in the long-term. We hope this paper will support the policy-makers and development agencies in their efforts to reshape the industry in Türkiye towards an increased role in economic development in a sustainable manner. [ABSTRACT FROM AUTHOR]

This paper investigates whether global uncertainty predicts economic growth rates using a global sample of 136 countries. We use the panel regression model and find strong evidence that global uncertainty negatively predicts the economic growth rate. Further, the negative impact of global uncertainty on economic growth rates is amplified during pandemic periods versus non-pandemic periods. Our main findings hold after a range of robustness tests. [ABSTRACT FROM AUTHOR]

Copyright of Transformations in Business & Economics is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines the causal relationship between human capital and economic factors in BRICS countries using a panel vector autoregressive model and data from 1997 to 2020. The economic factors considered include foreign direct investment (FDI), imports, exports, and gross domestic product (GDP). The study conducts a comparative analysis of Brazil, India, China, Russia, and South Africa by adopting a vector autoregressive (VAR) model. The findings indicate a bidirectional causality between human capital and FDI in China, while a unidirectional causality from FDI to human capital is observed in Brazil. Moreover, a unidirectional causality exists from human capital to GDP in Brazil, Russia, India, and South Africa. Additionally, a unidirectional causality is found from human capital to imports and exports in South Africa. Overall, the results suggest the pivotal role of human capital in achieving sustainable economic development in BRICS countries. Policymakers should ensure sustained investment in human capital, focusing on economic growth, FDI, and international trade. [ABSTRACT FROM AUTHOR]

Understanding the long‐term effects of population and GDP changes requires a multisectoral and regional understanding of the coupled human‐Earth system, as the long‐term evolution of this coupled system is influenced by human decisions and the Earth system. This study investigates the impact of compounding economic and population growth uncertainties on long‐term multisectoral outcomes. We use the Global Change Analysis Model (GCAM) to explore the influence of compounding and feedback between future GDP and population growth on four key sectors: final energy consumption, water withdrawal, staple food prices, and CO2 emissions. The results show that uncertainties in GDP and population compound, resulting in a magnification of tail risks for outcomes across sectors and regions. Compounding uncertainties significantly impact metrics such as CO2 emissions and final energy consumption, particularly at the upper tail at both global and regional levels. However, the impact of staple food prices and water withdrawal depends on regional factors. Additionally, an alternative low‐carbon transition scenario could compound uncertainties and increase tail risk, particularly in staple food prices, highlighting the influence of emergent constraints on land availability and food‐energy competition for land use. The findings underscore the importance of considering and adequately accounting for compounding uncertainties in key drivers of multisectoral systems to enhance our comprehensive understanding of the complex nature of multisectoral systems. The paper provides valuable insights into the potential implications of compounding uncertainties. Plain Language Summary: Understanding how future population and economic growth changes could impact our energy, water, food, and environment requires looking across multiple sectors and regions. This is because these systems are all connected. For example, increasing population drives higher food demand but requires more energy and water to grow food. At the same time, reducing greenhouse gas emissions from energy through increased biofuel consumption can compete with food production for land resources. This study explored how uncertainties in future population and GDP growth could compound and interact to affect future energy use, water withdrawal, staple food prices, and carbon dioxide emissions. Using a model that connects all these systems, the study found that combined uncertainties in population and GDP lead to more significant risks of extreme outcomes than individually looking at those uncertainties. There are substantial regional differences in future outcomes, like higher uncertainty in food prices for India and West Africa compared to other regions. We highlight the need to consider multisector and multisystem interactions between uncertainties across different regions. Accounting for these compounding uncertainties provides valuable insights into potential risks, like higher extreme projections for food prices or water use. These insights are essential for long‐term planning in our connected human‐Earth system. Key Points: Long‐term implications of population and GDP changes on the coupled human‐Earth system focusing on multisectoral and regional understandingUncertainties in GDP and population growth can compound, resulting in more extreme outcomes for multisectoral systemsCompounding uncertainties differ by region and metrics; staple food prices and water withdrawal show significant regional variability [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate whether a country's innovative capacity increases when the business sector invests more in research and development. The empirical investigation covers Serbia and its three neighboring countries in the European Union - Hungary, Romania, and Bulgaria - for the period from 2011 to 2021. The results indicate that an increase in investment by the business sector at the current level of GDP influences the increase in innovation in the case of Serbia, Hungary, and Bulgaria. [ABSTRACT FROM AUTHOR]

This paper analyzes the persistence of the sovereign debt / GDP and sovereign debt / GDP per capita series for the five largest European economies and the US using fractional integration. We use historical data starting in 1800 for the US and the UK, in 1861 for Italy and in 1880 for Germany, France and Spain. The results indicate high degrees of persistence of debt-to-GDP and debt per capita ratios with clear evidence of no mean reversion in both debt indicators. We also find evidence of correlation with the primary deficit, supporting the idea that one of the causes of this debt persistence is precisely the primary deficit persistence. Empirical results support the need for a better coordination between fiscal and monetary policies to enhance debt control in the countries under examination. [ABSTRACT FROM AUTHOR]

Since the 1960s and the reduction in the share of agricultural sector in GDP in different countries, based on extensive forward and backward linkages of the agricultural sector, the concept of agribusiness has been introduced to explain the valuable contribution of agriculture to the national economy. This paper estimates the share of agribusiness in gross domestic product using input-output tables for 1986, 1991, 2001, and 2016. The results showed that the contribution of agribusinesses to GDP was about 2.5 times that of agricultural production (the average share of agribusinesses in 1986-2016 was about 23%, while the corresponding figure for agricultural value added was 9.25%). In a similar trend to developing and developed countries, the share of agribusinesses in GDP had decreased from 27.2 to 17% in 1986-2016. However, the examination of the components of agribusinesses in Iran compared to other countries shows significant differences, which can be attributed to Iran's arid and semi-arid climate, low rate of capital formation, low productivity of production factors, as well as lack of participation in regional and global chains due to long-term sanctions imposed on the economy. [ABSTRACT FROM AUTHOR]

The construction industry has a major impact on the growth of any country due to its contributions to economic activity and development.Up until thirty-three years ago, Albania was perceived as a nation with a closed society that had undergone rapid development. By contemporary standards, the settlement was tiny. She observed the change from a stable economy to one that is reliant on markets. Particularly in recent years, the area primarily the city of Tirana recorded a significant building boom. This research endeavor aimed to examine the correlation between the building industry and the overall economy. Regression analysis and the correlation between the variables are the fundamental techniques that will guide the creation of this kind of evaluation study. Starting with the quarter of 2008 and ending with the first quarter of 2023, the data is quarterly.In Albania, buildings make up a fairly large portion of the country's total built environment—roughly 15.20%. Before 1990, they constructed 165 new subway buildings a year on average. Currently, the city's market economy is made up of approximately 4.28% of the construction industry. He lists 1,143 building companies around the nation. The data was obtained from the Institute of Statistics (INSTAT). The growth of the construction industry raised demand during economic booms, spurred by both public and private sources of funding. During these times, public spending on the building industry is higher and depends on outside funding sources (Grebler and Burns, 1982). GDP is assumed to have a greater influence on the building industry in developing nations; yet, the influence of GDP may decline or even turn negative when a country's degree of development rises (Crosthwaite, 2000). One of the largest sectors of the global economy and a significant contributor to GDPs is construction. The aim of this paper is to analyze whether the value of construction permits affects economic growth in the Albanian case, for the time period of 2008-2023, based on quarterly data. The database is collected from the National Statistics Office (INSTAT). The results of the study have revealed that cities may plan their spaces compactly to maximize public transportation use and maintain environmental and economic sustainability. They should create and update their regulatory plans, known as the land use plan in compliance with the national objectives and local vision in order to save the environment or other public interests. [ABSTRACT FROM AUTHOR]

This study explores the dynamic relationships among key macroeconomic variables influencing inflation in India from 1981 to 2021. Utilizing a comprehensive empirical approach, the research employs the Autoregressive Distributive Lag (ARDL) model to analyse both short-term and long-term effects of variables such as money supply (M3), GDP growth rate, international oil prices, exchange rates, current account balance, and lending interest rates on inflation. The methodology includes stationarity tests to ensure the robustness of the time series data, followed by cointegration tests to confirm the long-run relationships among the variables. Additionally, the Quantile ARDL model examines these variables' impact across different inflation distribution quantiles, providing a more nuanced understanding of their effects. The findings highlight significant positive and negative relationships between these variables and inflation, depending on their lag periods. Notably, the results reveal that money supply and international oil prices play critical roles in shaping inflationary trends, with both direct and lagged effects. The study also identifies that GDP growth generally negatively influences inflation, suggesting that higher economic growth may not necessarily lead to inflationary pressures in the Indian context. The mixed effects of exchange rates on inflation further underscore the complexity of managing inflation in a volatile global environment. This research provides valuable insights for policymakers, emphasizing the need for a balanced approach that includes flexible monetary policy, effective exchange rate management, and strategies to reduce dependency on imported goods to mitigate external shocks. These findings offer a comprehensive understanding of the determinants of inflation in India, contributing to formulating evidence-based policies for economic stability and growth. [ABSTRACT FROM AUTHOR]