Higher education systems and regional economic development in Europe: A combined approach using econometric and machine learning methods

Bertoletti, Alice; Berbegal‐Mirabent, Jasmina; Agasisti, Tommaso

doi:10.1016/j.seps.2022.101231

Cited by 22 publications

(18 citation statements)

References 93 publications

(140 reference statements)

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“…ML tools can expand the capabilities of traditional models e.g., capture nonlinear eects which are not detected by traditional econometric models. This has been demonstrated by detecting important factors and nonlinear relationships between regional GDP per capita and Higher Education Systems indicators that have provided useful insights and suggestions for policymakers (Bertoletti et al 2022) or to incorporate spatial, contemporaneous, and historical dependencies e.g., lead-lag non-linear relationships among past urban changes in each region and its neighbors (Kim et al 2022). As indicated above, the discussion in literature of comparing traditional models (mainly statistical) with ML models is active.…”

Section: Machine Learning and Resiliencementioning

confidence: 99%

“…A recent study of resilience focused on earthquakes using historical data from previous seismic events and long-term historical behavior of regions (Fantechi, Modica 2022) is another example of combining traditional econometric with ML techniques (Bertoletti et al 2022), which can apply ML to land-use change modeling (Kim et al 2022). ML is also expected to play a major role in building better and modern Community Resilience Assessment tools by incorporating the use of big data, machine learning, and articial intelligence to take care of spatio-temporal dynamism (Abdul-Rahman et al 2021).…”

Section: Machine Learning and Resiliencementioning

confidence: 99%

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Prediction models and testing of resilience in regions

Manousiadis

Gaki

2023

REGION

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A significant amount of research has been conducted regarding the resilience of the regions and the factors that contribute to allow them to face challenges, crises, or disasters. The rise of promising sectors like Machine learning (ML) and Artificial Intelligence (AI) can enhance this research using computing power in regional economic, social, and environmental data analysis to find patterns and create prediction models. Through Machine Learning, the following research introduces the use of models that can predict the performance of a region in disasters. A case study of the performance of USA Counties during the Covid19 first wave period of the pandemic and the related restrictions that were applied by the authorities was used in order to reveal the obvious or hidden parameters and factors that affected their resilience, in particular their economic response, and other interesting patterns between all the involved attributes. This paper aims to contribute to a methodology and to offer useful guidelines in how regional factors can be translated and processed by data and ML/AI tools and techniques. The proposed models were evaluated on their ability to predict the economic performance of each county and in particular the difference of its unemployment rate between March and June of 2020. The former is based on several economic, social, and environmental data -up to that point in time- using classifiers like neural networks and decision trees. A comparison of the different models' execution was performed, and the best models were further analyzed and presented. Further execution results that identified patterns and connections between regional data and attributes are also presented. The main results of this research are i) a methodological framework of how regional status can be translated into digital models and ii) related examples of predictive models in a real case. An effort was also made to decode the results in terms of regional science to produce useful and meaningful conclusions, thus a decision tree is also presented to demonstrate how these models can be interpreted. Finally, the connection between this work and the strong current trend of regional and urban digitalization towards sustainability is established.

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Section: Machine Learning and Resiliencementioning

confidence: 99%

Section: Machine Learning and Resiliencementioning

confidence: 99%

Prediction models and testing of resilience in regions

Manousiadis

Gaki

2023

REGION

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“…A number of scholars have now confirmed the significant contribution of universities to the regional economy, such as Jan Youtie et al (2008) [ 11 ], who studied the functional transformation of Georgia Tech (from its traditional role of education and research to that of a knowledge center promoting innovation) to promote technological innovation and economic development in its region, and the study concluded that universities play a greater role in technology-based economic development. Alice Bertoletti et al (2022) [ 12 ], Ioannis Dokas et al (2022) [ 13 ] and others combined traditional econometric methods with random forests to analyze data and investigated the influence degree of the characteristics of higher education system on regional economic development, noting in particular that the most important factors for regional economic development are scale of higher education, internationalization of students and research productivity. Some detailed case study of the University of Waterloo in Canada to demonstrate the university's contribution to the growth and innovation of the local and regional economy (Allison Bramwell.et al, 2008; Juying Zeng et al, 2023) [ 14 , 15 ].…”

Section: Analysis Of the Coupling Mechanism Between The Transformatio...mentioning

confidence: 99%

Technological achievements in regional economic development: An econometrics analysis based on DEA

Chen

2023

Heliyon

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“…ECONOMIC SCIENCES (08.00.05 (5.2.3, 5.2.6, 5.4.3), 08.00.13 (5.2.2, 5.2.4, 5.2.5) Большинство исследований на сегодняшний день сфокусированы относительно влияния системы высшего образования, как важной составляющий НИОКР, на экономическое развитие регионов. В частности, ряд исследователей [1,2] выявили, что наиболее важными факторами регионального экономического развития являются размер университетов, степень интернационализации студентов, а также исследовательская продуктивность университетов. Более того, на сегодняшний день университеты являются основным источником инноваций.…”

Section: Abstract: Gross Regional Product Economic Development Of Ter...unclassified

ASSESSMENT OF THE IMPACT OF R&D ON THE ECONOMIC DEVELOPMENT OF RUSSIAN REGIONS

Makarova¹,

Koroleva²,

Popova³

et al. 2022

ФИ (FR)

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На сегодняшний день в современной экономической науке активно обсуждаются вопросы влияния различных факторов на экономическое развитие регионов. Исследователи фокусируются на таких аспектах, как цифровизация, инновационный потенциал, социальные и демографические факторы, и прогнозируют их влияние на экономический рост регионов России. В рамках данного исследования авторы сфокусировались на восполнении пробела в исследованиях в части анализа влияния научно-технического развития на экономический рост регионов России. В рамках исследования были использованы общенаучные методы исследования (анализ, синтез, наблюдение), а также корреляционно-регрессионный анализ. Информационной базой исследования являлись данные, полученные из статистических сборников государственных и региональных органов. В целом нами были получены неоднозначные результаты относительно влияния деятельности научно-исследовательских институтов на экономическое развитие регионов. С одной стороны, корреляционно-регрессионный анализ выявил положительную зависимость ВРП регионов от количества поступлений патентных заявок и выдачи патентов, удельного веса принципиально новых технологий, доли исследователей до 39 лет в общей численности исследователей. С другой стороны, такие показатели деятельности научно-исследовательских институтов, как количество разработанных передовых производственных технологий, количество докторантов и кандидатов наук, негативно влияют на уровень ВРП регионов. Теоретическая значимость исследования заключается в выявлении факторов НИОКР, которые являются значимыми в экономическом развитии регионов и дополнении предыдущих исследований, сфокусированных на анализе влияния схожих факторов на территориальное развитие. Результаты имеют также практическую значимость в области планирования научно-исследовательской активности и оценки ее влияния на экономическое развитие регионов. Полученные результаты могут быть применены при стратегическом планировании развития деятельности научно-исследовательских институтов, а также при управлении государственными программами НИОКР.

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Higher education systems and regional economic development in Europe: A combined approach using econometric and machine learning methods

Cited by 22 publications

References 93 publications

Prediction models and testing of resilience in regions

Prediction models and testing of resilience in regions

Technological achievements in regional economic development: An econometrics analysis based on DEA

ASSESSMENT OF THE IMPACT OF R&D ON THE ECONOMIC DEVELOPMENT OF RUSSIAN REGIONS

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