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Learning in Networks: An Experiment on Large Networks with Real-World Features

Learning in Networks: An Experiment on Large Networks with Real-World Features, Syngjoo Choi, Sanjeev Goyal, Frederic Moisan and Yu Yang Tony To, Management Science (2023).

Abstract: 

Subjects observe a private signal and make an initial guess; they then observe their neighbors’ guesses, update their own guess, and so forth. We study learning dynamics in three large-scale networks capturing features of real-world social networks: Erdös–Rényi, Stochastic Block (reflecting network homophily), and Royal Family (that accommodates both highly connected celebrities and local interactions). We find that the Royal Family network is more likely to sustain incorrect consensus and that the Stochastic Block network is more likely to persist with diverse beliefs. These patterns are consistent with the predictions of DeGroot updating. It lends support to the notion that the use of simple heuristics in information aggregation is prevalent in large and complex networks.

Publication Authors: 
Syngjoo Choi, Sanjeev Goyal, Frederic Moisan and Yu Yang Tony To
Year Publication: 
2023
Publication Type: 
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Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness

Rising Temperatures, Falling Ratings: The Effect of Climate Change on Sovereign Creditworthiness, Patrycja Klusak, Matthew Agarwala, Matt Burke, Moritz Kraemer and Kamiar Mohaddes, Management Science (2023).

Abstract: 

Enthusiasm for “greening the financial system” is welcome, but a fundamental challenge remains: financial decision makers lack the necessary information. It is not enough to know that climate change is bad. Markets need credible, digestible information on how climate change translates into material risks. To bridge the gap between climate science and real-world financial indicators, we simulate the effect of climate change on sovereign credit ratings for 109 countries, creating the world’s first climate-adjusted sovereign credit rating. Under various warming scenarios, we find evidence of climate-induced sovereign downgrades as early as 2030, increasing in intensity and across more countries over the century. We find strong evidence that stringent climate policy consistent with limiting warming to below 2 °C, honoring the Paris Climate Agreement and following representative concentration pathway (RCP) 2.6, could nearly eliminate the effect of climate change on ratings. In contrast, under higher emissions scenarios (i.e., RCP 8.5), 59 sovereigns experience climate-induced downgrades by 2030, with an average reduction of 0.68 notches, rising to 81 sovereigns facing an average downgrade of 2.18 notches by 2100. We calculate the effect of climate-induced sovereign downgrades on the cost of corporate and sovereign debt. Across the sample, climate change could increase the annual interest payments on sovereign debt by US$45–$67 billion under RCP 2.6, rising to US$135–$203 billion under RCP 8.5. The additional cost to corporations is US$10–$17 billion under RCP 2.6 and US$35–$61 billion under RCP 8.5.

Publication Authors: 
Klusak, P., Agarwala, M., Burke, M., Kraemer, M. and Mohaddes, K.
Year Publication: 
2023
Publication Type: 
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