Tatsuru Kikuchi
arXiv 6 Jan 2026 · Econometrics
arXiv:2601.04246 · PDF · DOI · OpenAlex · Extracted main text
This paper develops a unified framework for analyzing technology adoption in financial networks that incorporates spatial spillovers, network externalities, and their interaction. The framework characterizes adoption dynamics through a master equation whose solution admits a Feynman-Kac representation as expected cumulative adoption pressure along stochastic paths through spatial-network space. From this representation, I derive the Adoption Amplification Factor -- a structural measure of technology leadership that captures the ratio of total system-wide adoption to initial adoption following a localized shock. A Levy jump-diffusion extension with state-dependent jump intensity captures critical mass dynamics: below threshold, adoption evolves through gradual diffusion; above threshold, cascade dynamics accelerate adoption through discrete jumps. Applying the framework to SWIFT gpi adoption among 17 Global Systemically Important Banks, I find strong support for the two-regime characterization. Network-central banks adopt significantly earlier ($ρ= -0.69$, $p = 0.002$), and pre-threshold adopters have significantly higher amplification factors than post-threshold adopters (11.81 versus 7.83, $p = 0.010$). Founding members, representing 29 percent of banks, account for 39 percent of total system amplification -- sufficient to trigger cascade dynamics. Controlling for firm size and network position, CEO age delays adoption by 11-15 days per year.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Guimaraes, Bernardo, Caio Machado, and Ana E. Pereira (2020) Dynamic Coordination with Timing Frictions: Theory and Applications | 1.000 | 8 | 3 | 100% |
| 2 | Katz, Michael L., and Carl Shapiro (1985) Network Externalities, Competition, and Compatibility | 1.000 | 6 | 3 | 100% |
| 3 | Frankel, David, and Ady Pauzner (2000) Resolving Indeterminacy in Dynamic Settings: The Role of Shocks | 0.843 | 3 | 3 | 100% |
| 4 | Arthur, W. Brian (1989) Competing Technologies, Increasing Returns, and Lock-In by Historical Events | 0.811 | 4 | 2 | 100% |
| 5 | Crouzet, Nicolas, Apoorv Gupta, and Filippo Mezzanotti (2023) Shocks and Technology Adoption: Evidence from Electronic Payment Systems | 0.644 | 2 | 2 | 100% |
| 6 | Acemoglu, Daron, Asuman Ozdaglar, and Alireza Tahbaz-Salehi (2015) Systemic Risk and Stability in Financial Networks | 0.405 | 1 | 1 | 100% |
| 7 | Aiyagari, S. R (1994) Uninsured idiosyncratic risk and aggregate saving | 0.405 | 1 | 1 | 100% |
| 8 | Allen, Franklin, and Douglas Gale (2000) Financial Contagion | 0.405 | 1 | 1 | 100% |
| 9 | Buchak, Greg, Gregor Matvos, Tomasz Piskorski, and Amit Seru (2018) Fintech, Regulatory Arbitrage, and the Rise of Shadow Banks | 0.405 | 1 | 1 | 100% |
| 10 | Burdzy, Krzysztof, David M. Frankel, and Ady Pauzner (2001) Fast Equilibrium Selection by Rational Players Living in a Changing World | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 24 scored citations.