Gary Becker, the Nobel laureate in economics, applied economic reasoning to social circumstances and particularly to families. He argued that children are a normal consumption good, and people consume more children with higher incomes. However, he also emphasized a quantity-quality trade-off. More children in a family means fewer resources and attention for each child. Higher-income couples may opt to invest in classes, training, and spend more time with a unitary child rather than increasing the number of children.
However, goods have multiple attributes and children do not merely provide a stream of consumption value while in the household. They offer access to future resources when they become employed themselves. Having more children or higher-quality children increases the economic benefits that older parents can enjoy, such as more help with household activities and the ability to travel with their adult children. Old-age benefits such as social security now serve the function of insulating people from their prior investments in future consumption.
We are going through some tough economic times right now: high rates of inflation (generally exceeding wage growth) with the strong possibility of a recession in the near future. In times like this, I think it is useful to also consider the historical perspective. The US economy has gone through challenging times in the past, but the long-run track record is impressive.
Here is one way to show the data. It comes from the Census Bureau, and shows the total money income of households in the US. The data is, of course, adjusted for inflation, and not just with the regular CPI-U: they use the superior CPI-U-RS, which attempts to maintain a consistent methodology for how prices are measured (BLS is constantly improving the CPI, but that sometimes makes historical comparisons challenging). I present the data both as a percent of the total number of households, and the absolute numbers.
I’ve shaded the chart to suggest that over $100,000 of annual income is high income, and under $35,000 is low income, with everything else considered “middle class.” By these definitions, the number of high-income households in the US increased dramatically from 6.6 million (10.9% of the total) in 1967 to 43.7 million (33.6% of the total) in 2020. The number of low-income households also rose, unfortunately, from 21.4 million in 1967 to 34 million in 2020, but the portion of the total fell (from 35.2% to 26.2%) since it increased slower than the overall growth of the number of households. Today, there are more high-income households (43.7 million) than low-income households (34 million) in the US.
But even if you don’t like those definitions, I’ve provided as much detail in the chart as Census makes available publicly. For example, let’s say you think $200,000 is what makes you high income. There were fewer than 1 million of these households in 1967 (1.3% of the total). Today, there are over 13 million of them (10.3% of the total). However we slice the data, there are a lot more high-income households in the US than in the past. (Remember remember, this is all adjusted for inflation.)
Many people found this data interesting when I posted it to Twitter, including the world’s richest person. But among the many objections raised is that this is driven by the rise of female employment and dual-income households. And indeed, that is a factor. But how much of a factor?
This weekend I’ll be at the Southern Economic Association Conference in Houston Texas. I’m organizing and chairing a session called Education Policy Impacts by Sex (you should come by and see me if you will be there too!).
Personally, I will be presenting on the impact of compulsory school attendance laws on attendance. Today I just want to share and discuss a single graph that’s not my presentation.
Prior to my research, there was already a canon of existing literature on compulsory attendance legislation (CSL) and I’ve previously written on this blog about it (attendance, CSL, and differences by sex). However, the literature had some limitations. Authors examined smaller samples, ignored gender, or ignored different effects by age.
I examine full-count IPUMS data from the 1850-1910 US censuses of whites in order to investigate the so-far-omitted margins mentioned above. Here are some conclusions:
Prior to CSL:
Males and females attended school at similar rates until the age of 14.
After 14, women stopped attending school as much as men.
By the age of 18, the attendance gender gap was 10 percentage points.
After CSL
Male and female attendance increased from the ages of 6 to 14
Women began attending school more than prior to CSL until about age 18.
After the age of 18, women experienced no greater attendance than previously.
But, both sexes attended school less than prior to CSL for ages 5 and younger.
Men began attending school less after the age of 17.
CSL increased lifetime attendance for both males and females
Overall, examining the impact of CSL across many ages allows us to see when and not just whether people attended more school. Previous authors would say something like “CSL increased total years of school by about 5% on average”. For men, almost all of those gains were between the ages of 6 & 16. But women experienced greater attendance from ages 6 to 18.
Additionally, examining the data by age reveals that there was some intertemporal substitution. Once it became legally mandatory for children to attend school between the ages of 6 & 14, parents began sending their younger children to school at lower rates. Indeed – why invest in education for two or three early years of life if you’ll just have to send your children to school for another eight years anyway. Older boys dropped out of school at higher rates after CSL too. Essentially, the above figure became compressed horizontally. People ‘put in their time’, but then reduced investments at non-mandatory ages.
This reveals a shortcoming of the current literature, which focuses mostly on 14 year olds. By focusing on a popular age of attendance that was also compulsory, previous authors have missed the compensating fall in attendance at other ages. Granted, the life-time effect is still positive – but it’s attenuated by a richer picture. The picture reveals that individuals were not attending school by accident. Students or their parents had in mind an amount of educational investment for which they were aiming. When children were forced to attend school at particular ages, the attendance for other ages declined.
Dear Mark: in your last letter you made one palpable hit, but only one: I admit that the atomic wars of the Twenty-first Century and the cataclysms of the Twenty-second Century destroyed so much of our cultural inheritance, including nearly all our Nineteenth and Twentieth Century history, that there is very little we can turn to of those times that is authentic. Apparently that is the only point we will be able to agree on.
I cannot possibly believe, for instance, as you do, that there ever did exist an Abraham Lincoln as so glowingly portrayed by our two or three surviving “history” digests; nor can I believe there ever was a World War II, at least such as they described. Wars, yes – there have always been wars, and a World War II may have occurred – but certainly not with such incredible concomitants.
In short, your history is much too fictional for me.
I’ve written previously about initial US state compulsory schooling laws in regard to literacy and in school attendance rates. I ended with a political economy hypothesis. Here’s the logic:
Legislators like lower costs, all else constant (more funding is available for other priorities).
Enforcing truancy and educating an illiterate populous is costly.
Therefore, state legislatures that passed compulsory attendance legislation will already have had relatively high rates of school attendance and literacy.
That’s it. Standard political economy incentives. But is it true? Well, we can’t tell what’s going on in politician heads today, much less 150 years ago. Though, we can observe evidence that might corroborate the story. In plain terms, consistent evidence for the hypothesis would be that school attendance and literacy rates were rising prior to compulsory schooling legislation. The figures below show attendance and literacy rates for children ages 10 to 18.
My last post of 2020 tried to end the year on an optimistic note: the rapid innovation of a new vaccine was truly a marvel. But I also warned you that I would have a post in the new year talking about the deaths of 2020 during the pandemic. And here it is.
Throughout 2020, I have tried to keep up with the most recent data, not only on officially coded COVID-19 deaths, but also on other measures. An important one is known as excess mortality, which is an attempt to measure the number of deaths in a year that are above the normal level. Defining “normal” is sometimes challenging, but looking at deaths for recent years, especially if nothing unusual was happening, is one way to define normal. The team at Our World in Data has a nice essay explaining the concept of excess mortality.
One thing to remember about death data is that it is often reported with a lag. The CDC does a good job of regularly posting death data as it is reported, but these numbers can be unfortunately deceptive. For example, while the CDC has some death data reported through 51 weeks of 2020, but they note that death data can be delayed for 1-8 weeks, and some states report slower than others (for reasons that are not totally clear to me, North Carolina seems to be way behind in reporting, with very little data reporting after August).
So there’s the caution. What can we do with this data? Since 2019 was a pretty “normal” year for deaths, we can compare the deaths in 2020 to the same weeks of data in 2019. In the chart at the right, I use the first 48 weeks of the year (through November), as this seems to be fairly complete data (but not 100% complete!). The red line in the chart shows excess deaths, the difference between 2019 and 2020 deaths. From this, we can see that there were over 357,000 excess deaths in 2020 in the first 11 months of the year, or about a 13.6% increase over the prior year.
Is 13.6% a large increase? In short, yes. It is very large. I’ll explain more below, but essentially this is the largest increase since the 1918 flu pandemic.
Economist Writing Every Day 