A few weeks ago I wrote about the life cycle of earnings, where Guvenen, Karahan, Ozkan and Song had used over 200 million tax data observations from the Social Security Administration (between 1978 and 2010) to see how income moved with age. With that amazing data they showed that mean income would peak around the age of 50, though for the median person income would peak earlier. Given their findings I decided to look (though with worse data) at how the life cycle of earnings has changed over time.
Using Census data from the US (available through IPUMS for any other data addicts reading this), I looked at average income by age for each decade. The caveat from using this information is that if there are some cohort effects (meaning earnings are changing differently for young than for older people within one decade) I will not capture this directly, possibly leading to some confusion in the analysis. Nevertheless, the patterns are quite striking.
Figure 1 shows that average labor income used to peak a lot earlier than it does nowadays. Back in the 1960s, it used to peak around the age of 35. Income was expected to start going down after 35. However, decade by decade, this peaking point has been increasing. By the 1990 the peak seemed closer to 45, and nowadays the peak can be as high as 50. Given the results found in Guvenen's research it might be that nowadays the median worker's labor experience is much more different from the mean worker than it used to be. But why?
Figure 1: Average Income by Age, over time.
Figure 2: Relative variance of Income by Age, over time.
Source: IPUMS Census USA. Family labor income by age of head, excluding people in school or with no income.
One possibility is the increase in the share of people going to school and looking for skill demanding jobs. Back in the 1960s, the share of young people who were high school graduates was around 53%. Another 13% had graduated from college as well, hence leaving a 34% of high school dropouts. Nowadays, there are only 10% high school dropouts, while the share of college graduates has increased to around 34%. I believe this might be pushing the peaking point. For example, an engineer or lawyer probably needs to go through some lower paying job training (or internship) and needs to try many different offices until it finds the one that suits him best. Hence, they start with a quite low pay but see a high increase over time. On the other hand, a construction worker's income will probably not change much over his life. Most companies will probably pay similarly, and his wage will not change as much over his life as it will for the lawyer/engineer.
This is consistent with what is found for the variance of income. Figure 2 shows variance relative to the variance at age 26, so that we can see how it moves over life. Another interesting pattern emerges here. It used to be that income differences were quite constant until the age of 40. However, since the 2000s differences seem to have started showing up earlier. A constant increase since the age of 25 is found nowadays.
Source: IPUMS Census USA. Variance of log family labor income by age of head, excluding people in school or with no income, relative to variance at age 26.
Once again, I believe this probably might have to do with education. Back in the 1960s, more than 80% of the population would start looking for jobs around the age of 18, leaving many years until the age of 25 - where my plots start - for them to find the appropriate job.* Moreover, these types of jobs probably did not experience much wage differentials between employers. On the other hand, nowadays, 34% of young people graduate to college (and even attempt go and fail to graduate), leaving them with less years to find a job. Moreover, once again their skills probably need more time to find the appropriate employer ("matching" issues in the economics jargon).** Hence, incomes are a lot more varied nowadays since earlier stages of life. And my income peak is getting farther and farther away...
* Plots start at age 25, so as to avoid having selection issues with people who go to college and start showing up in the sample after they graduate (say at age 22). For example, if plots started at age 18, the data until age 22 would include only people who did not go to college. Starting age 22 the pool of people would change a lot, as college graduates come in. The mean income might change significantly, but this would not be due to the life cycle of earnings of the workers, just because the pool of people in the data changed. Hence, this problem is reduced by starting the analysis at age 25.
** An interesting way to evaluate this would be to look at the same data but focusing only on college graduates. Maybe another week.
Follow me on twitter at @diedaruich
News and posts for an active mind.