Life events and risk types

joint analysis of estimated earnings risk and such life events allows us to link risk type differences to observable realizations of labor market risk. We document that the high earnings volatility group not only has lower income growth, it also has higher unemployment and health risks as we show next. These two life events are typically associated with large earnings shocks (e.g., Low and Pistaferri, 2010; Gu-venen, Karahan, Ozkan, and Song, 2015). The retirement records are particularly well suited for such an analysis as the social security system is also responsible for unemployment and long-term sickness insurance and therefore the data comprise high-quality and high-frequency information on these events. We assign employ-ment and health status following the coding in the data. For employemploy-ment, we only consider employment under social security legislation. For unemployment, we rely on a benefit-based definition.⁷ Sickness spells in the data are either long-term sick-ness periods (typically longer than six weeks) during which workers are unable to work or situations when workers have to be hospitalized.⁸

2.2.3.1 Unemployment

The incidence of unemployment is highly unequally distributed across workers. Just as earnings growth risk is concentrated, so is unemployment risk. Most people do not experience any unemployment during their entire working lives while others circle repeatedly in and out of low-paid employment. To construct a measure of concen-tration of unemployment risk, we follow workers from cohorts born between 1935 and 1953 over their entire working life and count for each worker all transitions between employment and unemployment between age 20 and 60.⁹ At age 60, we have a cross-section of workers with different stocks of accumulated employment to unemployment transitions. We compute the Lorenz curve of these stocks as our measure of the concentration of unemployment risk. Figure 2.5a shows the result-ing Lorenz curve together with the Lorenz curve of a simulation assumresult-ing uniform unemployment risk. In Figure 2.5b we add to the extensive margin of unemploy-ment risk also the intensive margin by considering the distribution of accumulated unemployment duration for the same cohorts.

We see that transitions into unemployment out of employment are highly con-centrated among a small group of workers. While roughly 50 percent of all workers do not transit from employment into unemployment during their entire working life, 10 percent of all workers account for roughly 50 percent of all transitions and about 20 percent of workers account for almost 80 percent of all transitions. In the

simula-7. Hartung, Jung, and Kuhn (2016) show based on data from unemployment records that there is an overlap of 80 percent of benefit recipients and registered unemployed in the 2000s before the overhaul of the unemployment insurance system in Germany by theHartzreforms.

8. Workers receive sick leave benefits (Lohnfortzahlung im Krankheitfall) during the first six weeks of a sickness spell.

9. We report figures for individual cohorts in Appendix 2.A.7.

Figure 2.5. Concentration of unemployment risk

0.2.4.6.81Share of unemployment spells

0 .2 .4 .6 .8 1

Worker share Data

Simulation equal risk

(a)Spells

0.2.4.6.81Share of unemployment duration

0 .2 .4 .6 .8 1

Worker share Data

Simulation equal risk

(b)Duration

Notes: Lorenz curve of accumulated unemployment spells (left panel) and unemployment duration (right panel) between ages 20 and 60 for cohorts of workers born between 1935 and 1953. The simulation assumes all workers have the same unemployment risk.

tion with uniform risk, we find only a fifth of workers without unemployment spells and that unemployment is much more equally spread with 50 percent of workers accounting for roughly 80 percent of unemployment transitions. Morchio (2020) reports a similar concentration of labor market transitions for the United States.

When we look at the distribution of unemployment duration in Figure 2.5b, we find a similar pattern of inequality in unemployment risk.

Figure 2.6. Lifetime unemployment by risk type

01234

20 30 40 50 60

Age Low risk

High risk

(a)Unemployment spells

01234

20 30 40 50 60

Age Low risk

High risk

(b)Unemployment duration

Notes: The left panel shows the life-cycle profile of the number of independent unemployment spells. The right panel shows the life-cycle profile of the total number of years spent as unemployed. Risk types are defined using the standard deviation of earnings growth rates between ages 25 and 55, where the bottom 80% of workers belong to the low-risk group and the top 20% to the high-risk group. Log growth rates for earnings are computed only for earnings observations that are below the social security contribution limit in both years and only for workers that work at least 90 days in both years. At least 20 valid growth rate observations are required for assigning a risk type. Data for cohorts born between 1935 and 1953 used.

Figure 2.6a relates observable unemployment risk to worker risk types from the previous subsection, where we categorize workers on the basis of their real-ized earnings growth rate volatility between age 25 and 55. For both risk types, we document the average accumulated number of unemployment spells by age. The resulting life-cycle patterns of accumulated unemployment spells are striking. We find a strong correlation between unemployment risk and risk types. Workers in the high-risk group experience on average around three unemployment spells during their working life, while the low-risk majority experiences only slightly more than one unemployment spell on average.

2.2.3.2 Health shocks

Health shocks are one of the major sources of earnings risk (Low and Pistaferri, 2010). To measure health risk, we proceed as in the case of unemployment risk by playing on the strengths of the social security data that provide direct information on transitions to long-term sickness periods capturing disability according to the social security system.1⁰ Health risk is defined as transiting to such a sickness period.

Figure 2.7.Concentration of sickness risk

0.2.4.6.81Share of sickness spells

0 .2 .4 .6 .8 1

Worker share Data

Simulation equal risk

(a)Spells

0.2.4.6.81Share of sickness duration

0 .2 .4 .6 .8 1

Worker share Data

Simulation equal risk

(b)Duration

Notes: Lorenz curve of accumulated sickness spells (left panel) and sickness duration (right panel) between ages 20 and 60 of cohorts of workers born between 1935 and 1953. Simulation assumes all workers have the same health risk.

Figure 2.7a shows the Lorenz curve of transitions to sickness periods together with a Lorenz curve for a simulation where all workers have the same probability of getting sick. Figure 2.7b adds to the extensive margin of health risk the inten-sive margin of health risk by considering the Lorenz curve of accumulated sickness duration for the same cohorts.

10. The German disability insurance system distinguishes between not being able to work in the old occupation and not being able to work at all. Disability in our data only refers to the latter definition. Occupational disability is not covered by the social security program.

Like in the case of unemployment risk, we find that health risk is highly concen-trated, although slightly less than unemployment risk. The 20 percent of workers with the most transitions account for 60 percent of all transitions from healthy to sick and around 40 percent of workers do not go through a single sickness period up to age 60 (Figure 2.7a). Looking at the sickness duration in Figure 2.7b, we see that durations are slightly more concentrated than incidences with 20 percent of workers accounting for 70 percent of the total duration in sickness. We conclude that severe health shocks are rare, severe, and unequally distributed across workers.

Finally, Figure 2.8a relates observable health risk to the worker risk types from the previous section. For both risk types, we document the average accumulated number of sickness spells by age. As for unemployment before, we find again a strong positive correlation between observable health risk and risk types. High earn-ings risk workers have on average more than 50% more sickness spells and they have them much earlier in their lives. At age 50 the average low-risk worker has had slightly more than one long-term sickness episode while the average high-risk worker has had twice as many. In terms of health risk, the divergence happens mostly between ages 40 and 50. High-risk workers accumulate as many long-term sickness spells at age 50 as their low-risk counterparts accumulate when they turn 60.

To summarize, we document large heterogeneity in earnings risk and that risk is highly concentrated. But we also provide evidence for transitions between risk types over the working life. Finally, we document a strong negative correlation between earnings risk and earnings growth and levels, a finding inconsistent with a

risk-Figure 2.8. Lifetime sickness by risk type

0123

20 30 40 50 60

Age Low risk

High risk

(a)Sickness spells

0.51

20 30 40 50 60

Age Low risk

High risk

(b)Sickness duration

Notes: The left panel shows the life-cycle profile of the number of independent sickness spells. The right panel shows the life-cycle profile of the total number of years spent sick. Risk types are defined using the standard deviation of earnings growth rates between ages 25 and 55, where the bottom 80% of workers belong to the low-risk group and the top 20% to the high-risk group. Log growth rates for earnings are computed only for earnings observations that are below the social security contribution limit in both years and only for workers that work at least 90 days in both years. At least 20 valid growth rate observations are required for assigning a risk type. Data for cohorts born between 1935 and 1953 used.

return trade-off for human capital. In the next step, we explore if and how this heterogeneity in earnings risk correlates with the savings behavior by looking at wealth accumulation and portfolio allocation.