Caplan’s Selfish Reasons to Have More Kids

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Bryan Caplan has a simple recommendation. Have more kids. If you have one, have another. If you have two, consider three or four. As Caplan spells out in his book, Selfish Reasons to Have More Kids, children have higher private benefits than most people think. Research shows that parents can take it easy, as there is not much they can change about their children. He also argues that there are social benefits to a higher population, with more people leading to more ideas, which are the foundation of modern economic growth.

Despite being someone who is about to face the number of children question, I am not sure that I am the target audience for Caplan’s book. I don’t mean that Caplan wouldn’t recommend to me that I have more children. Rather, as someone who has thought a lot about evolution and economics and having read many of the giants on whose shoulders Caplan stands (particularly Judith Rich Harris and Julian Simon), I didn’t learn a lot from the book. As Caplan ran through the examples of twin studies showing all the different facets of a child’s personality or life outcomes that a parent has no influence over, I found myself wanting more meat and analysis. I felt similarly about his arguments for a larger population.

Having said that, and recognising that I am not the target audience, most readers would probably learn a lot. Caplan provides a fun, easy to read book that gives a great, swift overview of his case. This is the book I’ll be giving to parents, grandparents and friends who have heard me go on about twin studies and genetics. I particularly like it that Caplan gives some practicality to the swathes of findings about trait heritability.

I felt that the largest shortcoming of the book was that it does not address the third factor affecting outcomes for the child – non-shared environment. While heritability explains some of the variation in a child’s traits and outcomes, and nurture generally explains close to nothing, Caplan does not explore the research into non-shared environment. Instead, he puts the variation down to free will:

So far, researchers have failed to explain why identical twins – not to mention ordinary siblings – are so different. Discrediting popular explanations is easy, but finding credible alternatives is not. Personally, I doubt that scientists will ever account for my sons’ differences, because I think their primary source is free will. Despite genes, despite family, despite everything, human beings always have choices – and when we can make different choices, we often do.

Caplan states that several of his friends call his belief in free will his “most absurd belief”. While I don’t know all of Caplan’s beliefs, for the moment I will agree with his friends. In Judith Rich Harris’s The Nurture Assumption, she explored what this non-shared environment might be. In her case, she argued for the effect of peers. What bothered me most with Caplan’s take on free will was not that he did not agree with Harris’s suggestion, but rather, his “it’s all too hard” approach. Unlike Caplan, I expect that over the next few years we will add even further to the explanations for how non-shared environment influences children.

When Caplan came to addressing potential reasons why family size has decreased over the last 60 years, I wanted to hear his arguments in more depth. Take Caplan’s take on Gary Becker’s argument that as women now earn more, they have to give up more income to have kids:

This explanation sounds good, but it’s not as smart as it seems. Women lose more income when they take time off, but they also have a lot more income to lose. They could have worked less, earned more, and had more kids. Since men’s wages rose, too, staying home with the kids is actually more affordable for married moms than ever. If that’s too retro, women could have responded to rising wages by working more, having more kids, and using their extra riches to hire extra help.

It sounds neat, but Caplan assumes that the income effect, which would tend to increase the number of children, dominates the substitution effect, which would tend to decrease the number. It is perfectly plausible for the substitution effect to dominate and women to decide to have fewer children, but Caplan does not address this. He might be right, but as there is no depth to his discussion, it is hard to judge the strength of his argument.

Caplan does point out that in the United States, fertility bottomed out in the 1970s. This occurred despite further increases in income and Caplan uses this as evidence against any income based hypothesis. But the people having children in the 1970s are different to the people having children now. For those women who chose to have no children in the 1970s and possibly responded most strongly to the income effect, they did not contribute to the gene pool and any heritable predisposition has disappeared with them. It is the children of larger families that are having children today. Second, the net fertility rate in the United States is substantially affected by recent immigrants.

Caplan’s preferred view on the decline in fertility is that we have gained a small amount of foresight, allowing us to see the negative effects of early childhood, but not gained enough foresight to note the benefits of children when they are older. There might be some truth to this, but I expect that the other factors that Caplan dismisses are also relevant.

One point where I disagree with Caplan is around his statement that men and women see eye to eye on the number of children they wish to have. Caplan considers that this puts to bed any arguments around women having increased bargaining power. While Caplan’s statistic is true in the most basic sense, the number of children that a man or woman want are a function of a number of things. The main one of these is who the other parent will be. If a woman is paired with the man of her dreams she is likely to want more children than if she is married to a guy who showed promise but has gone nowhere. While Caplan notes that condoms have been widely available since the end of World War II, the pill gave women extra power to decide who exactly the parent is. There is some interesting scope for sexual conflict here.

When it comes to policy prescriptions arising from his position, Caplan explicitly opposes natalist policies to increase birth rates. Caplan states:

After natalists finish lamenting low birthrates, they usually get on a soapbox and demand that the government “do something about it.” There are two big reasons why I refuse to join their chorus. First, while I agree that more kids make the world a better place, I oppose social engineering – especially for such a personal decision. When people are deciding how many children to have, government ought to mind its own business.

Instead, Caplan suggests that grandparents replicate the natalist incentives privately. Given this, it is interesting that Caplan drifts into supporting natalist tax credits in his recent Cato Unbound essay (as I have commented on here). I prefer his arguments for the use of private incentives from his book than his more recent encouragement of government action.

*This is a cross-post from my blog Evolving Economics.

4 Comments

  1. “Free-will” is a poor explanation, but I believe there is unexplained variance for purely physical traits like height as well. What if it’s not that or peer effects but noise?

  2. TGGP, I wasn’t aware of that literature on noise, so thanks for the link. If all the remaining variation is noise, my expectation will be wrong (as I wasn’t including random variation in my definition of non-shared environment). However, its not time to give up looking yet!

  3. Just goes to show, it pays to regularly read GNXP!

  4. By any non-supernatural definition, “free will” refers to behaviors that emanate directly from an individuals mind rather than external factors.

    Variation in the mind is a product solely of genes and environments, so we would expect free will to be influenced by both. To separate them from free will as Caplan does is a logical error.

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