Working Paper: NBER ID: w22938
Authors: Moira Daly; Dmytro Hryshko; Iourii Manovskii
Abstract: The stochastic process for earnings is the key element of incomplete markets models in modern quantitative macroeconomics. We show that a simple modification of the canonical process used in the literature leads to a dramatic improvement in the measurement of earnings dynamics in administrative and survey data alike. Empirically, earnings at the start or end of earnings spells are lower and more volatile than the observations in the interior of earnings histories, reflecting the effects of working less than the full year as well as deviations of wages due to e.g. tenure effects. Ignoring these properties of earnings, as is standard in the literature, leads to a substantial mismeasurement of the variances of permanent and transitory shocks and induces the large and widely documented divergence in the estimates of these variances based on fitting the earnings moments in levels or growth rates. Accounting for these effects enables more accurate analysis using quantitative models with permanent and transitory earnings risk, and improves empirical estimates of consumption insurance against permanent earnings shocks.
Keywords: Earnings Dynamics; Stochastic Process; Measurement Error; Consumption Insurance
JEL Codes: D31; D91; E21
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| stochastic process of earnings (C41) | understanding consumption and saving behaviors (E21) |
| traditional methods of estimating earnings processes (C51) | discrepancies in estimated variances of permanent and transitory shocks (E39) |
| irregularities in earnings observations (C20) | upward bias in estimates of permanent shocks (C51) |
| irregularities in earnings observations (C20) | upward bias in estimates of transitory shocks (C51) |
| correcting for biases (C83) | accurate economic modeling and policy design (E17) |