Federal Reserve Bank of San Francisco Working Paper 2019-17
Federal Reserve Bank of San Francisco Working Paper 2019-17
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旧金山联储工作文件 2019-17
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
Zheng Liu
中科院分区：
文献类型：
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作者：
Sylvain Leduc;Zheng Liu
我们认为，自动化的威胁削弱了工人在工资谈判中的议价能力，抑制了工资调整，放大了失业率的波动。我们基于一个带有劳动力市场搜索摩擦的定量商业周期模型提出了这一论点，该模型被推广到包含自动化决策，并估计适合美国时间序列。我们发现，自动化渠道在解释Covid-19大流行前十年工资增长缓慢和失业率持续下降方面具有重要的定量意义。更广泛地说，我们表明自动化机制有助于解释失业率和空缺相对于实际工资的大幅波动，这是一个通过标准模型观察到的令人困惑的现象。我们提出了一些微观层面的经验证据来支持我们模型的机制。
We argue that the threat of automation weakens workers’ bargaining power in wage negotiations, dampening wage adjustments and amplifying unemployment fluctuations. We make this argument based on a quantitative business cycle model with labor market search frictions, generalized to incorporate automation decisions and estimated to fit U.S. time series. We find that the automation channel is quantitatively important for explaining the sluggish wage growth and persistent declines in unemployment in the decade prior to the Covid-19 pandemic. More broadly, we show that the automation mechanism helps account for the large fluctuations in unemployment and vacancies relative to that in real wages, a puzzling observation through the lens of standard models. We present some micro-level empirical evidence supporting our model’s mechanism.