Pub Date : 2024-07-24DOI: 10.3390/economies12080194
Mohammed Bouasabah
In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow’s value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance.
{"title":"A Performance Analysis of Stochastic Processes and Machine Learning Algorithms in Stock Market Prediction","authors":"Mohammed Bouasabah","doi":"10.3390/economies12080194","DOIUrl":"https://doi.org/10.3390/economies12080194","url":null,"abstract":"In this study, we compare the performance of stochastic processes, namely, the Vasicek, Cox–Ingersoll–Ross (CIR), and geometric Brownian motion (GBM) models, with that of machine learning algorithms, such as Random Forest, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN), for predicting the trends of stock indices XLF (financial sector), XLK (technology sector), and XLV (healthcare sector). The results showed that stochastic processes achieved remarkable prediction performance, especially the CIR model. Additionally, this study demonstrated that the metrics of machine learning algorithms are relatively lower. However, it is important to note that stochastic processes use the actual current index value to predict tomorrow’s value, which may overestimate their performance. In contrast, machine learning algorithms offer a more flexible approach and are not as dependent on the current index value. Therefore, optimizing the hyperparameters of machine learning algorithms is crucial for further improving their performance.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"49 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although the coronavirus pandemic has now faded into the background, the global crisis caused by COVID-19 has had the most devastating impacts worldwide. Given the potential relapse of such unexpected and uncertain events, it is vital to specify the patterns thereof and develop proactive measures for the countries to acquire an advanced readiness to deal with the related incidents. The most infected countries faced an increase in business bankruptcies, unemployment and inflation rates, low production volumes, and a decline in Gross Domestic Product (GDP). To withstand such socioeconomic consequences, the countries had to employ a number of measures, with innovation development acceleration being one. This paper aims to assess the dependency of an increase in GDP and a decrease in inflation and unemployment rates on the country-level growth of innovation development according to such Global Innovation Index (GII) pillars as institutions, human capital and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs. The conducted research analysis covered the period from 2019 to 2022 based on the data for the GII pillar development level and economic performance indicators for 20 countries from five socioeconomic models. Descriptive and comparative statistics as well as correlation and regression analysis were used to prove the innovation development to be a key driver in increasing GDP and reducing inflation. To increase the GDP value, special attention should be paid to such GII pillars as institutions and human capital and research, while infrastructure and human capital and research are the pillars to reduce the inflation rates.
{"title":"Specific Effect of Innovation Factors on Socioeconomic Development of Countries in View of the Global Crisis","authors":"Sergey Mikhailovich Vasin, Daria Mikhailovna Timokhina","doi":"10.3390/economies12080190","DOIUrl":"https://doi.org/10.3390/economies12080190","url":null,"abstract":"Although the coronavirus pandemic has now faded into the background, the global crisis caused by COVID-19 has had the most devastating impacts worldwide. Given the potential relapse of such unexpected and uncertain events, it is vital to specify the patterns thereof and develop proactive measures for the countries to acquire an advanced readiness to deal with the related incidents. The most infected countries faced an increase in business bankruptcies, unemployment and inflation rates, low production volumes, and a decline in Gross Domestic Product (GDP). To withstand such socioeconomic consequences, the countries had to employ a number of measures, with innovation development acceleration being one. This paper aims to assess the dependency of an increase in GDP and a decrease in inflation and unemployment rates on the country-level growth of innovation development according to such Global Innovation Index (GII) pillars as institutions, human capital and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs. The conducted research analysis covered the period from 2019 to 2022 based on the data for the GII pillar development level and economic performance indicators for 20 countries from five socioeconomic models. Descriptive and comparative statistics as well as correlation and regression analysis were used to prove the innovation development to be a key driver in increasing GDP and reducing inflation. To increase the GDP value, special attention should be paid to such GII pillars as institutions and human capital and research, while infrastructure and human capital and research are the pillars to reduce the inflation rates.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"736 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-20DOI: 10.3390/economies12070189
Simon Kušar
The aim of this paper is to provide a systematic insight into the socio-economic aspects of the last two economic crises in Slovenia: the Economic crisis between 2009 and 2013, and the COVID-19 crisis in 2020. A three-stage territorial model was developed as a theoretical tool for this study. The data for the analyses came from various statistical sources and from the available literature. The socio-economic aspects of both economic crises were analysed in 11 categories and at three territorial levels: macro (national), meso (regional) and micro (locational). Both economic crises differ fundamentally in many aspects. Compared to the Economic crisis, the COVID-19 crisis was much shorter and less severe, and had relatively little impact on the socio-economic structure of Slovenia and its regions. Both economic crises had some common features: reduction of interregional disparities and different development paths of regions during the crisis, as well as strong economic growth in the first year of recovery. The proposed model can be extended by additional territorial levels and by adding additional social and political-geographical aspects.
{"title":"Selected Socio-Economic Aspects of the Last Two Economic Crises in Slovenia Assessed through a Three-Stage Territorial Model","authors":"Simon Kušar","doi":"10.3390/economies12070189","DOIUrl":"https://doi.org/10.3390/economies12070189","url":null,"abstract":"The aim of this paper is to provide a systematic insight into the socio-economic aspects of the last two economic crises in Slovenia: the Economic crisis between 2009 and 2013, and the COVID-19 crisis in 2020. A three-stage territorial model was developed as a theoretical tool for this study. The data for the analyses came from various statistical sources and from the available literature. The socio-economic aspects of both economic crises were analysed in 11 categories and at three territorial levels: macro (national), meso (regional) and micro (locational). Both economic crises differ fundamentally in many aspects. Compared to the Economic crisis, the COVID-19 crisis was much shorter and less severe, and had relatively little impact on the socio-economic structure of Slovenia and its regions. Both economic crises had some common features: reduction of interregional disparities and different development paths of regions during the crisis, as well as strong economic growth in the first year of recovery. The proposed model can be extended by additional territorial levels and by adding additional social and political-geographical aspects.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"63 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-17DOI: 10.3390/economies12070186
Tetsuya Nakamura, Steven Lloyd, Satoru Masuda
The year 2023 marks the 12th anniversary of the Great East Japan Earthquake (GEJE). Immediately after the disaster, the number of evacuees reached approximately 470,000, but by November 2022, the number had decreased to approximately 31,000. The reconstruction of housing, disposal of debris, public infrastructure development, and overall restoration and reconstruction has progressed steadily. However, a re-examination of the status of industrial restoration and reconstruction reveals that restoration and reconstruction have not progressed in some areas. This research statistically analyzes how the Japanese public perceives the issues around the recovery process and what memories and records they would like to learn from regarding the GEJE. The purpose of this study is to ask about reconstruction issues and lessons learned from the GEJE by conducting a web-based survey with 2000 respondents in Japan. The method of estimation is the use of ordinal logistic regression analysis to statistically estimate whether there are differences in recovery issues and lessons learned depending on individual attributes. The results suggest that those who are interested in, remember, and express anxiety about the recovery issues and lessons learned tend to be men, do not have children, are highly educated, and have a higher income. In sum, many of Japan’s citizens are highly interested in the reconstruction of agriculture, forestry, fisheries, housing, urban development, living environment, industry, and livelihood in the affected areas. In the future, they will play a central role in modernizing, scaling up, and integrating the agriculture, forestry, and fisheries industries, as well as in rebuilding towns and livelihoods. In the affected areas, it will be necessary to draw on the lessons learned from the GEJE and create reconstruction plans for the future, and then, policymakers will need to formulate reconstruction policies that reflect the concerns of the Japanese people.
{"title":"Resident Evaluation of Reconstruction Challenges and Lessons Learned from the Great East Japan Earthquake: Recommendations for Reconstruction and Industrial Policies 12 Years after the Disaster","authors":"Tetsuya Nakamura, Steven Lloyd, Satoru Masuda","doi":"10.3390/economies12070186","DOIUrl":"https://doi.org/10.3390/economies12070186","url":null,"abstract":"The year 2023 marks the 12th anniversary of the Great East Japan Earthquake (GEJE). Immediately after the disaster, the number of evacuees reached approximately 470,000, but by November 2022, the number had decreased to approximately 31,000. The reconstruction of housing, disposal of debris, public infrastructure development, and overall restoration and reconstruction has progressed steadily. However, a re-examination of the status of industrial restoration and reconstruction reveals that restoration and reconstruction have not progressed in some areas. This research statistically analyzes how the Japanese public perceives the issues around the recovery process and what memories and records they would like to learn from regarding the GEJE. The purpose of this study is to ask about reconstruction issues and lessons learned from the GEJE by conducting a web-based survey with 2000 respondents in Japan. The method of estimation is the use of ordinal logistic regression analysis to statistically estimate whether there are differences in recovery issues and lessons learned depending on individual attributes. The results suggest that those who are interested in, remember, and express anxiety about the recovery issues and lessons learned tend to be men, do not have children, are highly educated, and have a higher income. In sum, many of Japan’s citizens are highly interested in the reconstruction of agriculture, forestry, fisheries, housing, urban development, living environment, industry, and livelihood in the affected areas. In the future, they will play a central role in modernizing, scaling up, and integrating the agriculture, forestry, and fisheries industries, as well as in rebuilding towns and livelihoods. In the affected areas, it will be necessary to draw on the lessons learned from the GEJE and create reconstruction plans for the future, and then, policymakers will need to formulate reconstruction policies that reflect the concerns of the Japanese people.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"1 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-17DOI: 10.3390/economies12070185
Anastassiya Tkacheva, Saniya Saginova, Madina Karimbergenova, Timur Taipov, Gulnar Saparova
This article discusses the issues of cluster policy formation in the Republic of Kazakhstan on the basis of studying the experience of leading countries. The research aim is to find new effective tools and institutions for the development of the cluster structuring of the agro-industrial complex economy of Kazakhstan. Cluster policy in the field of supporting regional clusters starts with the identification of already existing clusters in the region, because by viewing the regional economy through the prism of various local industries and innovative enterprises, regional authorities can identify measures of effective impact and support for their clusters. This research examines the possibilities of using clusters and cluster policy as one of the most important components of the policy for the development and support of small and medium-sized enterprises in Kazakhstan’s agro-industrial complex. The research methodology includes qualitative and quantitative data analysis methods, comparative analysis, and mathematical processing (the Pearson correlation coefficient), as well as the modeling of possible development scenarios. The obtained results show that there are significant opportunities for a wider involvement of small and medium-sized enterprises in the formation of cluster structures of the agro-industrial sector through joint efforts by the government and regional centers in the conditions of innovative development of the country’s economy.
{"title":"Problems and Prospects for the Development of Cluster Structuring in the Economy of Kazakhstan’s Agricultural Sector: Theory and Practice","authors":"Anastassiya Tkacheva, Saniya Saginova, Madina Karimbergenova, Timur Taipov, Gulnar Saparova","doi":"10.3390/economies12070185","DOIUrl":"https://doi.org/10.3390/economies12070185","url":null,"abstract":"This article discusses the issues of cluster policy formation in the Republic of Kazakhstan on the basis of studying the experience of leading countries. The research aim is to find new effective tools and institutions for the development of the cluster structuring of the agro-industrial complex economy of Kazakhstan. Cluster policy in the field of supporting regional clusters starts with the identification of already existing clusters in the region, because by viewing the regional economy through the prism of various local industries and innovative enterprises, regional authorities can identify measures of effective impact and support for their clusters. This research examines the possibilities of using clusters and cluster policy as one of the most important components of the policy for the development and support of small and medium-sized enterprises in Kazakhstan’s agro-industrial complex. The research methodology includes qualitative and quantitative data analysis methods, comparative analysis, and mathematical processing (the Pearson correlation coefficient), as well as the modeling of possible development scenarios. The obtained results show that there are significant opportunities for a wider involvement of small and medium-sized enterprises in the formation of cluster structures of the agro-industrial sector through joint efforts by the government and regional centers in the conditions of innovative development of the country’s economy.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"33 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141721750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-11DOI: 10.3390/economies12070184
Jorge Luis Tonetto, Josep Miquel Pique, Adelar Fochezatto, Carina Rapetti
The impact of COVID-19 on the economy was devastating. Small businesses typically have few resources to fight against such adversity. Many businesses remained closed for some time during the pandemic period, resulting in significant consequences for people in terms of jobs, income and life. The objective of this research is to identify the factors that contributed to increasing company failures during the pandemic. Furthermore, this study aims to verify whether the size of the companies, the sectors of economic activity in which they operate and their geographic location influence enterprise failure. This article analyzes the survival of 8931 small businesses from 2017 to 2023, in Rio Grande do Sul, Brazil. The study applied a survival analysis using the Kaplan–Meier procedure, complemented with the Cox procedure, to determine the effects of the size of companies, sector activity and location on the survival time. The results indicate that survival is much higher in small companies with large revenues that are located in the Campaign and West Frontier regions, as well as in the Northeast, North, Production, South, Taquari, and Rio Pardo Valleys regions, whereas the survival rates were extremely lower in the commercial sector and in financial intermediation activities. In the second analysis restricted to the commerce sector, the data highlighted the retail activities, accommodation and food activities sectors as the most affected in terms of overall survival. The results indicated that the survival of small business remained relatively strong during the COVID-19 pandemic, signaling the pertinent support from the government. The smallest business with revenues under USD 15,576 (BRL 81,000) per year were the most affected, with only 39% survival after 7 years. Some activities and some regions suffered more than others, emphasizing the need for special attention from authorities in future catastrophes.
{"title":"Survival Analysis of Small Business during COVID-19 Pandemic, a Brazilian Case Study","authors":"Jorge Luis Tonetto, Josep Miquel Pique, Adelar Fochezatto, Carina Rapetti","doi":"10.3390/economies12070184","DOIUrl":"https://doi.org/10.3390/economies12070184","url":null,"abstract":"The impact of COVID-19 on the economy was devastating. Small businesses typically have few resources to fight against such adversity. Many businesses remained closed for some time during the pandemic period, resulting in significant consequences for people in terms of jobs, income and life. The objective of this research is to identify the factors that contributed to increasing company failures during the pandemic. Furthermore, this study aims to verify whether the size of the companies, the sectors of economic activity in which they operate and their geographic location influence enterprise failure. This article analyzes the survival of 8931 small businesses from 2017 to 2023, in Rio Grande do Sul, Brazil. The study applied a survival analysis using the Kaplan–Meier procedure, complemented with the Cox procedure, to determine the effects of the size of companies, sector activity and location on the survival time. The results indicate that survival is much higher in small companies with large revenues that are located in the Campaign and West Frontier regions, as well as in the Northeast, North, Production, South, Taquari, and Rio Pardo Valleys regions, whereas the survival rates were extremely lower in the commercial sector and in financial intermediation activities. In the second analysis restricted to the commerce sector, the data highlighted the retail activities, accommodation and food activities sectors as the most affected in terms of overall survival. The results indicated that the survival of small business remained relatively strong during the COVID-19 pandemic, signaling the pertinent support from the government. The smallest business with revenues under USD 15,576 (BRL 81,000) per year were the most affected, with only 39% survival after 7 years. Some activities and some regions suffered more than others, emphasizing the need for special attention from authorities in future catastrophes.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"10 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-11DOI: 10.3390/economies12070183
Samar Abou Ltaif, Simona Mihai Yiannaki
Amid Lebanon’s multifaceted economic crisis, this paper explores the intricate dynamics between political patronage networks and financial stability. Grounded in the theoretical frameworks of New Institutional Economics (NIE) and Project Management (PM), the study delves into how entrenched political elites and patronage networks have shaped Lebanon’s financial system, exacerbating vulnerabilities and perpetuating the ongoing crisis. Utilizing qualitative methods including in-depth interviews, document analysis, and case studies, the research illuminates the pivotal role of political actors and their vested interests in economic policies and financial institutions. The findings reveal systemic governance failures, crony capitalism, and institutional decay as underlying causes of Lebanon’s economic stress. In response, the paper proposes a comprehensive framework for governance reform that integrates insights from NIE and PM, emphasizing structured planning, accountability mechanisms, and institutional strengthening. The purpose of this study is not only to contribute to a nuanced understanding of Lebanon’s challenges but also to offer actionable insights for policymakers, academics, and stakeholders to address the root causes of the crisis and pave the way for sustainable economic recovery and revitalization.
{"title":"Exploring the Impact of Political Patronage Networks on Financial Stability: Lebanon’s 2019 Economic Crisis","authors":"Samar Abou Ltaif, Simona Mihai Yiannaki","doi":"10.3390/economies12070183","DOIUrl":"https://doi.org/10.3390/economies12070183","url":null,"abstract":"Amid Lebanon’s multifaceted economic crisis, this paper explores the intricate dynamics between political patronage networks and financial stability. Grounded in the theoretical frameworks of New Institutional Economics (NIE) and Project Management (PM), the study delves into how entrenched political elites and patronage networks have shaped Lebanon’s financial system, exacerbating vulnerabilities and perpetuating the ongoing crisis. Utilizing qualitative methods including in-depth interviews, document analysis, and case studies, the research illuminates the pivotal role of political actors and their vested interests in economic policies and financial institutions. The findings reveal systemic governance failures, crony capitalism, and institutional decay as underlying causes of Lebanon’s economic stress. In response, the paper proposes a comprehensive framework for governance reform that integrates insights from NIE and PM, emphasizing structured planning, accountability mechanisms, and institutional strengthening. The purpose of this study is not only to contribute to a nuanced understanding of Lebanon’s challenges but also to offer actionable insights for policymakers, academics, and stakeholders to address the root causes of the crisis and pave the way for sustainable economic recovery and revitalization.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"39 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-09DOI: 10.3390/economies12070179
Leon Esquierro, Sergio Da Silva
This study extends the concept of granularity from firms to cities, examining how large cities influence national economic dynamics beyond their relative size. By applying Zipf’s law, which describes the power law distribution of city sizes, we investigate the interplay between granularity and business cycles. Our aim is to test the granular hypothesis that large cities have a significant impact on the business cycle beyond their relative size. We analyze data from American and Brazilian cities between 2003 and 2019 assessing the granular residuals and their explanatory power. Our findings reveal that in the United States, the granular city size is three metropolitan areas or five counties when redefined. In Brazil, it equates to three municipalities. These results emphasize the substantial role large cities play in national economic fluctuations, suggesting that policy interventions that target infrastructure, education, and innovation in major urban centers could have widespread economic benefits. This paper’s contribution to the literature is to highlight a spatial component of granularity not considered so far.
{"title":"Granular Cities","authors":"Leon Esquierro, Sergio Da Silva","doi":"10.3390/economies12070179","DOIUrl":"https://doi.org/10.3390/economies12070179","url":null,"abstract":"This study extends the concept of granularity from firms to cities, examining how large cities influence national economic dynamics beyond their relative size. By applying Zipf’s law, which describes the power law distribution of city sizes, we investigate the interplay between granularity and business cycles. Our aim is to test the granular hypothesis that large cities have a significant impact on the business cycle beyond their relative size. We analyze data from American and Brazilian cities between 2003 and 2019 assessing the granular residuals and their explanatory power. Our findings reveal that in the United States, the granular city size is three metropolitan areas or five counties when redefined. In Brazil, it equates to three municipalities. These results emphasize the substantial role large cities play in national economic fluctuations, suggesting that policy interventions that target infrastructure, education, and innovation in major urban centers could have widespread economic benefits. This paper’s contribution to the literature is to highlight a spatial component of granularity not considered so far.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"37 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is reasonable to state that gastronomic tourism is an efficient tool that has the potential to refresh Thailand’s macroeconomic viability. With the aim of becoming a hub of tourism in Southeast Asia, Thailand’s tourism industry must urgently address and sustainably integrate gastronomic activities to navigate the troubled situation caused by its decline after the COVID-19 pandemic. This has led the authors to conduct a deep study on a regional input–output (I-O) table analysis for Thailand’s tourism system, specifically focusing on gastronomic activities and tourism industries. The tourism I-O data used in this study come from the official source provided by the Thailand Ministry of Tourism and Sport. Empirically, the results of the dynamic regional I-O model predict that Bangkok and its surrounding areas are the heart of gastronomic tourism development, driving income into Thailand’s economy. The eastern region stands as the second-largest area of gastronomy tourism, generating a positive impact on Thailand’s economy. On the other hand, the Northeast of Thailand receives less income from gastronomy tourism despite being the largest area in the country. Ultimately, there should be a greater emphasis on gastronomy tourism policies in order to fully maximize their potential for tourism development, stimulating every part of Thailand during the economic depression caused by COVID-19. Moreover, gastronomy tourism has the potential to play an important role in driving economic growth through the combination of cuisine and tourism development.
{"title":"The Impact of Gastronomic Tourism on the Regional Economy of Thailand: Examined by the Dynamic I-O Model after the Decline of COVID-19","authors":"Banjaponn Thongkaw, Nattapan Kongbuamai, Warattaya Chinnakum, Chukiat Chaiboonsri","doi":"10.3390/economies12070180","DOIUrl":"https://doi.org/10.3390/economies12070180","url":null,"abstract":"It is reasonable to state that gastronomic tourism is an efficient tool that has the potential to refresh Thailand’s macroeconomic viability. With the aim of becoming a hub of tourism in Southeast Asia, Thailand’s tourism industry must urgently address and sustainably integrate gastronomic activities to navigate the troubled situation caused by its decline after the COVID-19 pandemic. This has led the authors to conduct a deep study on a regional input–output (I-O) table analysis for Thailand’s tourism system, specifically focusing on gastronomic activities and tourism industries. The tourism I-O data used in this study come from the official source provided by the Thailand Ministry of Tourism and Sport. Empirically, the results of the dynamic regional I-O model predict that Bangkok and its surrounding areas are the heart of gastronomic tourism development, driving income into Thailand’s economy. The eastern region stands as the second-largest area of gastronomy tourism, generating a positive impact on Thailand’s economy. On the other hand, the Northeast of Thailand receives less income from gastronomy tourism despite being the largest area in the country. Ultimately, there should be a greater emphasis on gastronomy tourism policies in order to fully maximize their potential for tourism development, stimulating every part of Thailand during the economic depression caused by COVID-19. Moreover, gastronomy tourism has the potential to play an important role in driving economic growth through the combination of cuisine and tourism development.","PeriodicalId":52214,"journal":{"name":"Economies","volume":"37 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-09DOI: 10.3390/economies12070181
Lina Maddah, Hassan Sherry, Hussein Zeaiter
According to the IMF, the current public debt makes up nearly 40 percent of the global debt, marking the highest share since the mid-1960s. Despite the vast research on alarming levels of sovereign default, the literature remains inconclusive. This paper investigates macroeconomic, financial, and political determinants of IMF credit use in the post-2010 era. The main contribution of our study lies in its temporal analysis as we investigate how the robustness of different factors has evolved. By utilizing an extensive dataset on 216 countries over the period of 2010–2021 and employing a variant of the Extreme Bounds Analysis (EBA) method, our study reveals that fluctuations in the IMF credit to external debt ratio can be attributed to changes in the total reserves to external debt ratio, where this relationship is statistically significant and reliable. However, high political risks seem to no longer affect the IMF’s decision, post 2010. Furthermore, our findings demonstrate that excluding countries with low debt arrears strengthens the results’ robustness. These findings contribute to a better understanding of the complexities surrounding IMF credit use in the contemporary global economic scene and offer new standpoints on the Fund’s lending choices.
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