Subjective earnings risk

Title: Subjective earnings risk (Andrew Caplin, Victoria Gregory, Eungik Lee, Søren Leth-Petersen, Johan Sæverud)

Abstract: We introduce a survey instrument to measure earnings risk allowing for the possibility of quitting or being fired from the current job. We find these transitions to be the key drivers of subjective risk. A link with administrative data provides multiple credibility checks for correspondingly aggregated data. Yet it reveals subjective earning risk to be many times smaller than traditional estimates imply even when conditioning richly on demographics and job history. A life-cycle search model calibrated to match data on job transitions and earnings can replicate the distribution of subjective beliefs reported in the survey. Job-match quality, which directly impacts subjective risk but is impossible to identify in administrative data, contributes significantly to earnings risk. This highlights the importance of administratively-linked subjective risk measures.