Subjective Earnings Risk
What this paper finds — and why it matters
The paper introduces a survey instrument — fielded in the Copenhagen Life Panel in January 2021 to about 10,900 employed Danes aged 20-65 — that measures how much earnings risk workers subjectively perceive over the year ahead, conditioning explicitly on whether they expect to stay in their job, quit, or be laid off. Linking each survey response to third-party-reported Danish administrative records provides multiple credibility checks: survey-reported past earnings, job-transition probabilities, and time out of work line up closely with their registry counterparts. The central finding is that subjective earnings risk is many times smaller — the authors report administratively-estimated risk being between two and six times higher — than the risk conventionally inferred from the cross-sectional dispersion of realized earnings growth. The authors attribute this gap to heterogeneity: even within narrow age-and-earnings cells, workers differ systematically in expected earnings growth, so pooling them misassigns predictable differences in means to luck (a mixture-distribution / Jensen’s-inequality argument), and the gap is largest where expected-growth heterogeneity is largest, such as among young workers. Possible job transitions are shown to be central to the level and the higher-order shape (skewness, kurtosis) of subjective risk. When a standard life-cycle search-and-matching model (Menzio, Telyukova, and Visschers, 2016) is calibrated to the administrative data in the usual way, its model-implied beliefs imply far higher individual earnings risk than workers report, whether or not they switch jobs — which the authors read as highlighting the value of survey-based measures for disciplining such models.
Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.
Q1. What gap does the paper document between subjective and administratively-estimated earnings risk?
Directly measured subjective earnings risk is many times lower than earnings risk inferred from administrative data — administratively-estimated risk is between two and six times higher than its survey-based counterpart across age-and-earnings cells. Risk is measured as the interdecile range (p90 minus p10) of the distribution of one-year-ahead earnings growth. Partitioning the Danish population into 300 cells by three age groups (20-34, 35-49, 50-65) and earnings percentiles following Guvenen et al. (2021), the average of individuals’ subjective interdecile ranges within a cell is much smaller than the interdecile range of realized earnings growth computed from the administrative data in that same cell.
Q2. Why do the two measures diverge?
The divergence arises because expected earnings growth is heterogeneous even within narrow demographic and earnings cells, so the cross-sectional dispersion of realized earnings misassigns ex-ante differences in means to luck. The pooled distribution of earnings growth is a mixture of individuals’ subjective distributions; by a variance-of-a-mixture decomposition (and Jensen’s inequality), the variance of the pooled distribution is weakly larger than the average of the individual subjective variances, with the excess reflecting differences in subjective means. Consistent with this channel, the gap between subjective and administrative risk is particularly high for groups with highly heterogeneous expected growth rates, such as younger workers, and the authors report the gap narrows as the stratification is refined — results are practically identical with an even finer 1,800-cell grid or when an individual past-growth-rate covariate is added.
Q3. How credible are the subjective survey measures?
A link to third-party-reported Danish administrative records provides multiple credibility checks, and on each the survey aligns closely with the registry. Survey-reported last-year earnings match their administrative counterpart; the average reported probability of staying with the same employer tracks the administrative share of stable job matches by age; average expected time out of work following a separation matches registry durations; and life-cycle patterns of all four moments (mean, interdecile range, skewness, kurtosis) of pooled expected earnings growth mirror those of realized administrative earnings growth. The authors note COVID-19 hit the Danish economy only lightly in 2020 (the lowest employment level was only about 40,000 below the roughly 2.77 million pre-pandemic baseline, two-thirds recovered by year-end), limiting concerns about pandemic distortion.
Q4. What did the survey reveal about expected job transitions and earnings by branch?
On average respondents assign an 82% probability to staying with their current employer, 12% to quitting, and 6% to being laid off, and they expect markedly different earnings outcomes across these branches. The average respondent expects a 3% earnings increase if staying, an 11% decrease upon reemployment after a layoff, and a 7% increase after a quit. Among those reporting a positive layoff probability, 73% expect earnings to fall if laid off; among those reporting a positive quit probability, 81% expect earnings to rise if they quit. Expected time out of work averages about 4.4-4.6 months after a layoff and about 2.7 months after a quit; the authors note that expecting positive time out of work after a quit contrasts with the standard registry-based assumption that quits correspond to direct job-to-job transfers.
Q5. What role do job transitions play in the structure of subjective risk?
Possible job transitions are shown to be central determinants of the level and the higher-order moments of subjective earnings risk. Fixing risk to the “stay” branch sharply reduces perceived uncertainty at all ages — most dramatically for the young — and largely removes both the negative skewness and the substantial excess kurtosis (about 10-20 on the holistic measure) present in the holistic distribution. The authors read this as indicating that job transitions are, in expectation, responsible for the downside and extreme-change risk that workers perceive.
Q6. Does a standard calibrated search model reproduce these subjective beliefs?
A life-cycle directed-search model (Menzio, Telyukova, and Visschers, 2016), calibrated in the standard manner to Danish administrative transition and wage data, produces far higher estimates of individual earnings risk than workers subjectively report, even conditioning on job transitions. The model matches average branch probabilities and reemployment durations well (model stay/EU/EE probabilities of 84%/6%/10% against survey 82%/6%/12%, and 4.2 vs 4.4 months out of work), but its conditional earnings-growth distributions are too dispersed and too homogeneous: on the stay branch it generates a double-peaked distribution absent from the survey, and on the quit and layoff branches the interdecile ranges are much higher and less heterogeneous than reported. The authors trace this to features common to search models — workers “starting from the bottom” of the job ladder after unemployment and match quality being initially unknown — and argue these features, which are not unique to this model, are why such models overstate risk relative to elicited beliefs.
Q7. What does the paper conclude about how earnings risk should be measured and used?
The authors conclude that survey-based measures of subjective earnings risk carry information that administrative-data inference and standard calibrated models miss, and that they are valuable for modeling labor-market transitions and other choices affected by earnings risk, such as savings and portfolio decisions. As suggestive evidence linking beliefs to behavior, they regress expected time out of work after a quit on liquid assets relative to disposable income and find that workers with less liquid wealth expect to spend less time out of work after quitting, as if pressured back to work more quickly. The paper frames its contribution as reviving and extending Dominitz and Manski’s (1997) thesis that administratively-estimated earnings risk may differ significantly from its subjective, survey-estimated counterpart.
Key concepts
Subjective earnings risk : earnings risk as perceived and reported by workers themselves about their own one-year-ahead earnings, elicited as full probability distributions conditional on possible job transitions, rather than inferred from the dispersion of realized earnings across workers.
Holistic (expected) earnings growth : an individual’s overall subjective distribution over next year’s earnings growth, formed by weighting the stay, quit, and layoff branch distributions by the subjective probabilities of each transition and the associated time out of work.
Administratively-estimated earnings risk : risk inferred from the cross-sectional distribution of realized earnings growth within demographic/earnings cells (as in Guvenen et al., 2021), which relies on the assumption that workers within a cell draw from the same underlying distribution.
Interdecile range (p90 minus p10) : the quantile-based measure of dispersion the paper uses to summarize “risk” in earnings growth, chosen for robustness relative to the variance.
Balls-in-bins elicitation : a graphical survey method (Delavande and Rohwedder, 2008) in which respondents allocate 20 balls — each interpreted as 5% probability — across earnings bins to report a subjective probability distribution.
Mixture-distribution (heterogeneity-as-risk) channel : the result that a population distribution pooling heterogeneous individual means has variance weakly larger than the average individual variance, so pooling predictable differences in means inflates measured “risk.”
Key concepts
Subjective earnings risk : earnings risk as perceived and reported by workers themselves about their own one-year-ahead earnings, elicited as full probability distributions conditional on possible job transitions, rather than inferred from the dispersion of realized earnings across workers.
Holistic (expected) earnings growth : an individual’s overall subjective distribution over next year’s earnings growth, formed by weighting the stay, quit, and layoff branch distributions by the subjective probabilities of each transition and the associated time out of work.
Administratively-estimated earnings risk : risk inferred from the cross-sectional distribution of realized earnings growth within demographic/earnings cells (as in Guvenen et al., 2021), which relies on the assumption that workers within a cell draw from the same underlying distribution.
Interdecile range (p90 minus p10) : the quantile-based measure of dispersion the paper uses to summarize “risk” in earnings growth, chosen for robustness relative to the variance.
Balls-in-bins elicitation : a graphical survey method (Delavande and Rohwedder, 2008) in which respondents allocate 20 balls — each interpreted as 5% probability — across earnings bins to report a subjective probability distribution.
Mixture-distribution (heterogeneity-as-risk) channel : the result that a population distribution pooling heterogeneous individual means has variance weakly larger than the average individual variance, so pooling predictable differences in means inflates measured “risk.”