Figure 1: Live First-Birth Rates by Age of Mothers
The 1960s were revolutionary times. As Bob Dylan - one of my favorite musicians and probably one of the most famous characters of that time - said, "there is nothing so stable as change". This was certainly true in the US at the time: The Civil Rights Movements, social unrest due to the Vietnam War, the invention of the microchip, antidiscrimination legislation, the women's movement. And the invention of Enovid, the first contraceptive pill. Yes, you read right. The contraceptive pill was a revolutionary element. And as such, it has also been studied by an economist (and by the way published in the Quarterly Journal of Economics, among the top 3 economics journals). Martha Bailey evaluated the effect the release of this little pill in 1960 had on female labor participation. Gary Becker had previously said that "the contraceptive revolution [...] has probably not been a major cause of the sharp drop in fertility". However, Bailey will show that even if fertility did not decrease because of the pill, it did delay it, allowing women to get more education and improve their labor outcomes.
Figure 1 shows trends in first-birth rates by age groups since 1940. A marked decline in childbearing among young women (focus on 20-24 years old) is seen since the pill was introduced. This lasted until 1976 when all unmarried minors were allowed obtain contraceptives under the law. Early access allowed women between 18 and 21 to get access to the pill and hence the largest decline is seen for those 18-19 years old. A first robustness check can be seen from those 15-17 years old. Since they are expected to be too young to benefit from the pill, we should and do observe no effect for them. This gives us confidence we are not just seeing a spurious result.
As the diffusion of the pill increases, the distribution of age at first-birth also changes. Figure 2 plots the fraction of women first giving birth by age groups and cohorts. Among women born before the 1940 who were too old to benefit from early access to the pill, around 62% report having children before age 22. For those born around 1955, this had dropped by 25%. Notice that both figures suggest that these effects were not due to preexisting trends. Also no changes are seen between 1955 and 1960, when all women would have already had access to the pill.
Figure 2: Distribution of Age at First-Birth, by Cohorts.
And where does the economics come in? Early access to the pill was reflected in female labor force participation. Before 1940 the increase in women's participation had been driven by married women over 30 years old, who returned after their children had grown. On the other hand, for those born in 1955 the "fertility dip" is not observed any more. Participation rates were 25% higher at age 25.
Figure 3: Labor Force Participation, by Age and Cohort.
But how can we disentangle the effect of the pill from all the other things going on the 1960s that I mentioned above? Here is were econometric tools come in. The expansion of the pill was different across states, which individually changed the legal rights of individuals ages 18 to 21. Indirectly, this effect empowered women to get early access to the pill, without parental consent.* This exogenous variation will allow Bailey to compare the effect of the pill on women's life cycle labor force participation. Just to fix ideas, the methodology is like taking two states that were previously equal. But one state decides to extend legal rights to younger individuals and the other does not. Consequently, only one state allows young women to get access to the pill. Then, the difference in the labor force participation of the women between the two states will be coming from the pill. More than two states and more controls are used to obtain the results, but the intuition of the technique is in the previous simple example.
A first thing to check is whether early access to the pill had an effect on fertility. Table 1 shows the baseline estimate (column 2) is that it reduced the probability of giving birth by age 22 by 14%. Interestingly, early access to abortion does not seem to drive the results (column 3). As expected, it did not reduce the number of children before 19, since women did not have legal access to the pill without parent consent before that age. Finally, as other people had reported, the pill did not reduce the number of children women had, suggesting it just delayed it.
Table 1: The effect of early legal access to the pill on fertility.
What effect did this have on labor outcomes? Bailey shows that early access to the pill increased labor force participation of women ages 26-30 by 7%, and also increased those of ages 31-35. They also seem to work more hours, hence getting closer to male labor outcome averages. For women under 25 years old, results suggest that the pill increased their enrollment in school. Changing career trajectories - resulting from delay in childbearing - was the primary mechanism this little pill increased female labor-force participation.
* Bailey goes into some detail to justify that this extension of rights was not related to states characteristics that could be directly related to the variables of interest. Most of the changes are suggested to have to do with discrepancy under federal law of being old enough to be drafted to the Vietnam war by age 18, but not being able to vote. At the state-level, legislation was extending rights to 18 year old men and women.
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.
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