Pub Date : 2024-07-24DOI: 10.3390/economies12080191
Olajide O. Oyadeyi
This paper’s objective is to examine the asymmetric cointegration and asymmetric effects of financial development and monetary policy on monetary transmission mechanisms in the Nigerian context using annual data spanning the period from 1986 to 2023. This study pushes the frontiers of knowledge by providing information on the nonlinear impacts of monetary policy and financial sector innovations on monetary transmission mechanisms in Nigeria to help policymakers tailor their strategies to local conditions, enhancing the effectiveness of monetary interventions in the economy. To achieve this, this paper adopted nonlinear ARDL models to understand how changes in the direction of monetary policy and developments in the financial system induce changes in the transmission of monetary policy. The findings document the existence of asymmetries in both the short and long run, revealing that the impacts of financial development and monetary policy on the different monetary policy channels are not uniform. These asymmetries indicate that the responses of various economic variables to monetary policy actions differ depending on the level of financial development. These findings underscore the complexity of the monetary transmission mechanism and the necessity for a nuanced understanding of how financial development and monetary policy interact in different contexts. Consequently, this finding is symptomatic of some characteristics of those financial markets on their way toward advanced developments. As the financial system matures, monetary policy may have a greater impact on the cost of short-term funding for banks without having any discernible effect on the rates at which businesses and households access funding. Therefore, this paper recommends focusing on the policies that will foster the financial system across the banking sector, capital market, bond market, and overall financial sector to improve the efficiency of the monetary transmission process.
{"title":"Financial Development, Monetary Policy, and the Monetary Transmission Mechanism—An Asymmetric ARDL Analysis","authors":"Olajide O. Oyadeyi","doi":"10.3390/economies12080191","DOIUrl":"https://doi.org/10.3390/economies12080191","url":null,"abstract":"This paper’s objective is to examine the asymmetric cointegration and asymmetric effects of financial development and monetary policy on monetary transmission mechanisms in the Nigerian context using annual data spanning the period from 1986 to 2023. This study pushes the frontiers of knowledge by providing information on the nonlinear impacts of monetary policy and financial sector innovations on monetary transmission mechanisms in Nigeria to help policymakers tailor their strategies to local conditions, enhancing the effectiveness of monetary interventions in the economy. To achieve this, this paper adopted nonlinear ARDL models to understand how changes in the direction of monetary policy and developments in the financial system induce changes in the transmission of monetary policy. The findings document the existence of asymmetries in both the short and long run, revealing that the impacts of financial development and monetary policy on the different monetary policy channels are not uniform. These asymmetries indicate that the responses of various economic variables to monetary policy actions differ depending on the level of financial development. These findings underscore the complexity of the monetary transmission mechanism and the necessity for a nuanced understanding of how financial development and monetary policy interact in different contexts. Consequently, this finding is symptomatic of some characteristics of those financial markets on their way toward advanced developments. As the financial system matures, monetary policy may have a greater impact on the cost of short-term funding for banks without having any discernible effect on the rates at which businesses and households access funding. Therefore, this paper recommends focusing on the policies that will foster the financial system across the banking sector, capital market, bond market, and overall financial sector to improve the efficiency of the monetary transmission process.","PeriodicalId":52214,"journal":{"name":"Economies","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141777223","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 : 2024-07-24DOI: 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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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 : 2024-07-18DOI: 10.3390/economies12070187
Josephine Wuri
Currently, international trade has evolved into international production fragmentation captured in GVCs. Countries must enhance intermediate exports in comparative advantage sectors to increase their trade in value-added (TVA) in global production chains. However, traditional measurements of revealed comparative advantage (RCA) based on gross exports need to be updated due to overvaluation, double counting, and implicit distortions in international trade. This study uses a new comparative advantage measure, “new revealed symmetric comparative advantage” (NRSCA). Using a dynamic General Method of Moment (GMM) approach, we investigate the role of comparative advantage in driving TVA regarding backward and forward linkages and examine the impact of the COVID-19 pandemic. We use data from the current Asian Development Bank multi-regional input–output database for 2010–2020. Our findings reveal that comparative advantage significantly impacted international TVA, along with the support of quality institutional services in each country. Implementing a new comparative advantage measure, NRSCA, provided accurate estimation results to overcome the overvaluation problem. Moreover, the COVID-19 pandemic disrupted value-added trade.
{"title":"The Role of Comparative Advantage in Enhancing Trade in Value-Added Using a Dynamic GMM Model","authors":"Josephine Wuri","doi":"10.3390/economies12070187","DOIUrl":"https://doi.org/10.3390/economies12070187","url":null,"abstract":"Currently, international trade has evolved into international production fragmentation captured in GVCs. Countries must enhance intermediate exports in comparative advantage sectors to increase their trade in value-added (TVA) in global production chains. However, traditional measurements of revealed comparative advantage (RCA) based on gross exports need to be updated due to overvaluation, double counting, and implicit distortions in international trade. This study uses a new comparative advantage measure, “new revealed symmetric comparative advantage” (NRSCA). Using a dynamic General Method of Moment (GMM) approach, we investigate the role of comparative advantage in driving TVA regarding backward and forward linkages and examine the impact of the COVID-19 pandemic. We use data from the current Asian Development Bank multi-regional input–output database for 2010–2020. Our findings reveal that comparative advantage significantly impacted international TVA, along with the support of quality institutional services in each country. Implementing a new comparative advantage measure, NRSCA, provided accurate estimation results to overcome the overvaluation problem. Moreover, the COVID-19 pandemic disrupted value-added trade.","PeriodicalId":52214,"journal":{"name":"Economies","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141827811","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 : 2024-07-18DOI: 10.3390/economies12070188
Kristina Alekseyevna Zakharova, Danil Anatolyevich Muravyev, E. Karagulian
This article describes a new approach to determining the optimal amount of state financial support provided to business entities. It is shown that there are three available methods to support economic agents. The most cost-effective option is subsidizing business entities to expand their current assets. It has been revealed that there are not just optimal amounts of government financial support but also optimal not-to-exceed amounts that make it possible to identify the boundaries of the so-called highly productive state of the economy. In this case, when the economy is highly productive, the prices of goods (services) fall, workers spend their savings, and the volume of production increases. This ultimately leads to an increase in the well-being of the population. The differential equations are the basis for the model, which is similar to the model of a simple two-sector single-product economy. The Monte Carlo method is used to determine the optimal not-to-exceed amount for government financial support. The identification of such intervals allows us to determine the amount of state financial support that will lead to a highly productive state and will not contribute to an unreasonable expansion of the budget expenditure. This study’s results can be utilized by government authorities for the development of a comprehensive system of state financial support for entrepreneurship. Business entities can use the results of this research concerning the calculation of the optimal not-to-exceed amount of financial support.
{"title":"Modeling of Complex State Financial Support for Small and Medium-Sized Enterprises","authors":"Kristina Alekseyevna Zakharova, Danil Anatolyevich Muravyev, E. Karagulian","doi":"10.3390/economies12070188","DOIUrl":"https://doi.org/10.3390/economies12070188","url":null,"abstract":"This article describes a new approach to determining the optimal amount of state financial support provided to business entities. It is shown that there are three available methods to support economic agents. The most cost-effective option is subsidizing business entities to expand their current assets. It has been revealed that there are not just optimal amounts of government financial support but also optimal not-to-exceed amounts that make it possible to identify the boundaries of the so-called highly productive state of the economy. In this case, when the economy is highly productive, the prices of goods (services) fall, workers spend their savings, and the volume of production increases. This ultimately leads to an increase in the well-being of the population. The differential equations are the basis for the model, which is similar to the model of a simple two-sector single-product economy. The Monte Carlo method is used to determine the optimal not-to-exceed amount for government financial support. The identification of such intervals allows us to determine the amount of state financial support that will lead to a highly productive state and will not contribute to an unreasonable expansion of the budget expenditure. This study’s results can be utilized by government authorities for the development of a comprehensive system of state financial support for entrepreneurship. Business entities can use the results of this research concerning the calculation of the optimal not-to-exceed amount of financial support.","PeriodicalId":52214,"journal":{"name":"Economies","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825943","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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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.
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