Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/1973
Title: Understanding Covid-19 Mobility Through Human Capital: a Unified Causal Framework
Authors: Bilgel, Fırat
Karahasan, Burhan Can
Keywords: C36
Directed acyclic graphs
Machine learning
Causal structure discovery
J68
Instrumental variables
C14
Sample selection
Do-calculus
Workplace mobility
J62
Publisher: Springer
Source: Bilgel, F., & Karahasan, B. C. (2023). Understanding Covid-19 Mobility Through Human Capital: A Unified Causal Framework. Computational Economics, 1-41.
Abstract: This paper seeks to identify the causal impact of educational human capital on social distancing behavior at workplace in Turkey using district-level data for the period of April 2020 - February 2021. We adopt a unified causal framework, predicated on domain knowledge, theory-justified constraints anda data-driven causal structure discovery using causal graphs. We answer our causal query by employing machine learning prediction algorithms; instrumental variables in the presence of latent confounding and Heckman's model in the presence of selection bias. Results show that educated regions are able to distance-work and educational human capital is a key factor in reducing workplace mobility, possibly through its impact on employment. This pattern leads to higher workplace mobility for less educated regions and translates into higher Covid-19 infection rates. The future of the pandemic lies in less educated segments of developing countries and calls for public health action to decrease its unequal and pervasive impact.
URI: https://hdl.handle.net/20.500.11779/1973
https://doi.org/10.1007/s10614-023-10359-6
ISSN: 0927-7099
1572-9974
Appears in Collections:Ekonomi Bölümü Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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