A machine learning approach to rural entrepreneurship

Celbiş, Mehmet Güney

doi:10.1111/pirs.12595

Cited by 15 publications

(11 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…On the other hand, while we observed that initial health status is also important in the private sector, there have been distinct attributes related to age, industry, and mobility/stringency effects – particularly in the retail and recreation category – that had even stronger effects on employment outcomes. Such stringency policies may lead to long‐term impacts for the individuals in the self‐employed group as trust in governments and institutions are crucial factors that support entrepreneurial behavior (Celbiş, 2021b ). Education alongside health are found to be important determinants for persons born before 1955.…”

Section: Concluding Remarks and Policy Implicationsmentioning

confidence: 99%

Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups

Celbiş

Wong

Kourtit

et al. 2023

Regional Science Policy & Practice

Self Cite

View full text Add to dashboard Cite

We identify vulnerable groups through the examination of their employment status in the face of the initial coronavirus disease 2019 (COVID‐19) shock through the application of tree‐based ensemble machine learning algorithms on a sample of individuals over 50 years old. The present study elaborates on the findings through various interpretable machine learning techniques, namely Shapley values, individual conditional expectations, partial dependences, and variable importance scores. The structure of the data obtained from the Survey of Health, Aging and Retirement in Europe (SHARE) dataset enables us to specifically observe the before versus the after effects of the pandemic shock on individual job status in spatial labor markets. We identify small but distinct subgroups that may require particular policy interventions. We find that the persons in these groups are prone to pandemic‐related job loss owing to different sets of individual‐level factors such as employment type and sector, age, education, and prepandemic health status in addition to location‐specific factors such as drops in mobility and stringency policies affecting particular regions or countries.

show abstract

Section: Concluding Remarks and Policy Implicationsmentioning

confidence: 99%

Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups

Celbiş

Wong

Kourtit

et al. 2023

Regional Science Policy & Practice

Self Cite

View full text Add to dashboard Cite

show abstract

“…Business startups in rural areas depend on various factors, including the economic context (e.g., agglomeration forces and banking environment), demographics (e.g., age composition, education, and gender), local environment (e.g., natural amenities, such as green spaces, water areas, and topographic conditions), and public policies (e.g., labor regulation; Celbiş, 2021; Markeson & Deller, 2012; Naldi et al, 2021).…”

Section: Contingent Effect Of Broadband On Business Startupsmentioning

confidence: 99%

“…Alongside local economic conditions, the literature has concentrated on the role of demographics, particularly education and age composition, as major determinants of new firm formation in rural areas (Artz et al, 2021; Celbiş, 2021).…”

Section: Contingent Effect Of Broadband On Business Startupsmentioning

confidence: 99%

The contingent nature of broadband as an engine for business startups in rural areas

Duvivier

Bussière

2022

Journal of Regional Science

View full text Add to dashboard Cite

The introduction of the Internet in the 2000s was believed to be an effective solution for helping rural economies but previous studies found mixed results. Following the prevailing COVID‐19 pandemic, the idea has surfaced that broadband accessibility would result in an increasing number of people and firms relocating to smaller towns and rural areas. In this study, we provide new evidence on the impact of ultrafast broadband on rural economies by considering industrial and spatial heterogeneity. To achieve this, we evaluated the impact of the French Very High‐Speed Broadband Plan on business startups in rural municipalities between 2013 and 2018 using difference‐in‐differences with prematching. The evidence reveals that positive broadband effects are limited to municipalities with good initial conditions in terms of local economic climate, natural amenities, and demographics. From a policy perspective, our results suggest that broadband is not a panacea, and focusing on broadband access only is likely to have little impact in structurally weak rural areas.

show abstract

“…Past research has shown that entrepreneurship and human capital are key determinants of innovative activity and crucial factors of development in lagging areas [57][58][59][60]. Together with human capital, measured by the level of education (Degree), we include variables that measure the importance of entrepreneurs (Self) and of private industry (Private) within the model.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper seeks to study work-related and geographical conditions under which innovativeness is stimulated through the analysis of individual and regional data dating from just prior to the smartphone age. As a result, by using the ISSP 2005 Work Orientations Survey, we are able to examine the role of work flexibility, among other work-related conditions, in a relatively more traditional context that mostly excludes modern, smartphone-driven, remote-working practices. Our study confirms that individual freedom in the work place, flexible work hours, job security, living in suburban areas, low stress, private business activity, and the ability to take free time off work are important drivers of innovation. In particular, through a spatial econometric model, we identified an optimum level for weekly work time of about 36 h, which is supported by our findings from tree-based ensemble models. The originality of the present study is particularly due to its examination of innovative output rather than general productivity through the integration of person-level data on individual work conditions, in addition to its novel methodological approach which combines machine learning and spatial econometric findings.

show abstract

A machine learning approach to rural entrepreneurship

Cited by 15 publications

References 73 publications

Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups

Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups

The contingent nature of broadband as an engine for business startups in rural areas

Innovativeness, Work Flexibility, and Place Characteristics: A Spatial Econometric and Machine Learning Approach

Contact Info

Product

Resources

About