Technology Adoption in a Hierarchical Network
Xintong Han () and
No 18-05, Working Papers from NET Institute
This paper studies the effect of network structure on technology adoption, in the setting of the Python programming language. A major release of Python, Python 3, provides more advanced but backward-incompatible features to Python 2. We model the dynamics of Python 3 adoption made by package developers. Python packages form a hierarchical network through dependency requirements. The adoption decision involves not only updating one's own source code, but also dealing with dependency packages lacking Python 3 support. We build a dynamic model of technology adoption where each package makes an irreversible decision to provide support for Python 3. The optimal timing of adoption requires a prediction of all future states, for the package itself as well as each of its dependencies. With a complete dataset of package characteristics for all historical releases, we are able to draw the complete hierarchical structure of the network, and simplify the estimation by grouping packages into different layers based on the dependency relationship. We study how individual adoption decisions can propagate along the links in such a hierarchical network. We also test the effectiveness of various counterfactual policies that can promote a faster adoption process.
Keywords: Technology Adoption; Network Structure; Hierarchical Network; Input-Output Network; Dynamics; Network Effects; Python (search for similar items in EconPapers)
JEL-codes: C10 C61 L10 L86 O32 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:net:wpaper:1805
Access Statistics for this paper
More papers in Working Papers from NET Institute
Bibliographic data for series maintained by Nicholas Economides ().