Review of Economic Dynamics · 2026 · Scott French、Tess M. Stafford
中文摘要
我们使用一个独特的数据集和新颖的实证策略来验证包含经验回报的劳动供给模型的预测,量化假设工资形成外生的标准估计中跨期替代弹性 (IES) 的偏误,并获得 IES 的无偏估计。我们的方法利用了如下洞见:随着经验回报相对于工资的重要性下降,标准估计中的偏误会缩小。我们的识别策略不依赖于人力资本积累过程的结构,但需要在职业生涯末期的多个时期观察工人。利用佛罗里达州渔民每日劳动供给的数据,我们得到一个较大的 IES 估计值 (2.5)。与理论一致,我们发现"天真"估计的向下偏误幅度接近 2 倍,且该偏误对处于职业生涯早期的渔民更大。(JEL E24, J22, J24, J31)
Abstract
We use a unique dataset and novel empirical strategy to validate the predictions of labor supply models featuring returns to experience, quantify the bias in standard estimates of the intertemporal elasticity of substitution (IES) that assume exogenous wage formation, and obtain an unbiased estimate of the IES. Our approach uses the insight that the bias in standard estimates shrinks as returns to experience decline in importance relative to the wage. Our identification strategy does not rely on the structure of the human capital accumulation process but does require observing workers over multiple periods at the very end of their careers. Using data on the daily labor supply of Florida fishermen, we obtain an estimate of the IES that is large (2.5). Consistent with the theory, we find that a “naive” estimate is biased downward by nearly a factor of 2 and that the bias is larger for fishermen early in their career. (JEL E24, J22, J24, J31)