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<p>The Sahm Rule has historically provided reliable early warnings for U.S. recessions. However, rigid threshold-based indicators can be misleading because business cycles evolve at different speeds and intensities. In this paper, we introduce a broader framework—anchored by a “universal cycle clock”—that systematically aligns and compares unemployment cycles across history. Using Dynamic Time Warping (DTW), we map past labor market patterns onto a template cycle with a common timeline and show that triggers like Sahm’s Rule typically mark the onset of a large unemployment cycle—identified as the first 12.5% of the universal cycle clock. This framework situates statistical rules like Sahm’s within a unified cycle-based perspective, helping to distinguish temporary deviations from true cycle onsets. We also place these findings in the context of dynamical systems research, which suggests that slowly growing instabilities can create self-reinforcing cycles. Our assessment of current labor market data highlights factual deviations from historical patterns—such as persistently elevated unemployment durations even at low headline unemployment rates—suggesting that deeper structural imbalances may be emerging. While linking these signals to a new cycle is necessarily speculative, we highlight AI adoption as a potential disruptor supported by early data and sectoral anecdotes. These insights can help policymakers, investors, and business leaders interpret current labor market signals and prepare for potential shifts.</p>

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Where are we in the cycle?

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • Raffaele M. Ghigliazza

摘要

The Sahm Rule has historically provided reliable early warnings for U.S. recessions. However, rigid threshold-based indicators can be misleading because business cycles evolve at different speeds and intensities. In this paper, we introduce a broader framework—anchored by a “universal cycle clock”—that systematically aligns and compares unemployment cycles across history. Using Dynamic Time Warping (DTW), we map past labor market patterns onto a template cycle with a common timeline and show that triggers like Sahm’s Rule typically mark the onset of a large unemployment cycle—identified as the first 12.5% of the universal cycle clock. This framework situates statistical rules like Sahm’s within a unified cycle-based perspective, helping to distinguish temporary deviations from true cycle onsets. We also place these findings in the context of dynamical systems research, which suggests that slowly growing instabilities can create self-reinforcing cycles. Our assessment of current labor market data highlights factual deviations from historical patterns—such as persistently elevated unemployment durations even at low headline unemployment rates—suggesting that deeper structural imbalances may be emerging. While linking these signals to a new cycle is necessarily speculative, we highlight AI adoption as a potential disruptor supported by early data and sectoral anecdotes. These insights can help policymakers, investors, and business leaders interpret current labor market signals and prepare for potential shifts.