Niccolo Rescia*, Ivan Conjeaud**

*This paper examines how droughts and floods affect Kenya’s domestic debt market between 2000 and 2024. Combining monthly issuance data for Treasury bills and bonds with disaster and macro-fiscal records, we estimate the dynamic effects of climate shocks on borrowing costs, issuance, and transmission channels. Droughts significantly raise long-term borrowing costs; Treasury bond yields rise by about 200 basis points within seven to eight months, while floods cause short-lived spikes in short-term rates and liquidity tightening. Issuance volumes show no consistent adjustment, suggesting that prices rather than quantities absorb climate stress. Fiscal responses appear muted, indicating that financial rather than fiscal channels dominate. These findings imply that climate risks are already embedded in Kenya’s domestic financing conditions. As such shocks intensify, debt managers should integrate climate risk into issuance strategies, enhance transparency, and strengthen liquidity support mechanisms.

**We study a model of algorithmic collusion in continuous time in which two firms use Q-learning algorithms to set prices in a Bertrand duopoly. The firms update their prices at times dictated by a Poisson clock. We introduce a simple parameter controlling for the synchronicity of the firms' algorithms' updates, ranging from perfect synchronicity like in previous models of algorithmic collusion, to independence. Using extensive numerical experiments, we show that algorithmic collusion gradually disappears when the synchronicity between the algorithms' updates decreases, and completely vanishes when they update independently. Specifically, we show that (i) the payoffs collected in the long run by the algorithms are no different than the ones collected by algorithms playing randomly (ii) the punishment-reward strategies gradually appear when the level of synchronicity increases, and are absent when the algorithms update independently. We show the last point by recording a large number of the algorithms' reactions to unilateral price cuts and compare them with the reactions of strategy-agnostic algorithms. Performing a clustering task on these reactions reveals that for synchronous algorithms, the reactions to price cuts are very well distinguishable from that of random algorithms, while they are not distinguishable at all for algorithms that update independently. These findings have important implications for algorithmic collusion.