Leo Kahane, David Paton, and Rob Simmons have an interesting discussion in Vox EU on how to study effects of abortion on crime rates. They write:

The hypothesis that the legalisation of abortion contributed to a dramatic fall in crime rates in the United States, originally proposed by John Donohue and Steven Levitt in an article in 2001 and popularised by Levitt’s best selling book Freakonomics, has been the subject of close scrutiny by other academics. . . .

For the US (as noted by D&L), crime starts to fall about 18 years after the legalisation of abortion, consistent with abortion being a causal influence. In contrast, property crime in England and Wales starts to fall about 23 years after the first full year of abortion (1969), too late to be consistent with a causal effect, in relation to this, more abortion information needs to be made readily available to those who are seeking it. Violent crime does not decrease at all over the period. . . .

A natural way of distinguishing these explanations is to examine whether crime fell more (or increased less) amongst those young enough to have been affected by abortion legalisation compared to those born before the legislation. Figure 2 shows the pattern of conviction rates for those aged 10-15, 16-20 and 21 plus in England and Wales. The trends are not supportive of a link between abortion and crime. . . . Given all this, it seems highly unlikely that the legalisation of abortion can, as D&L hypothesised, explain the dramatic drop in crime observed in the US in the 1990s. However, we cannot necessarily conclude from this that abortion has no impact on crime. . . .

A potential explanation of this apparent conundrum arises from considering what actually happened to children who would not have been born had abortion been legal at the time of their conception. Some such children would have been brought up in adverse circumstances (either by the birth parent or by being taken into the care of the state) and may consequently have been at a higher risk of committing crime. On the other hand, other children conceived in similarly adverse circumstances would have been given up for adoption and then brought up in relatively stable and affluent circumstances. Put another way, prior to the legalisation of abortion, unwanted babies did not necessarily become unwanted children. . . .

Figure 3 illustrates trends in rates of infant adoptions and children taken into state care in England and Wales between 1960 and 1980. The rate of children in care barely changes after abortion legalisation in 1968, whilst there appears to be a dramatic effect on adoptions. . . .

Kahane, David Paton, and Rob Simmons conclude with a speculation and a research suggestion:

It seems that the legalisation of abortion contributed to a structural shift in society from a situation in which it was normal to put “unwanted” infants up for adoption to one in which adoption was actively discouraged. Once this structural change occurred, it is plausible that, say, a subsequent marginal tightening of the abortion law will have two effects. Children conceived in adverse circumstances are (marginally) more likely to be born rather than aborted but are then more likely to be brought up in those same adverse consequences rather than being put up for adoption. If true, we will observe a negative link between abortion rates and subsequent crime rates.

Although speculative, this hypothesis is consistent with observed trends in adoptions and with the analyses of the abortion-crime link in both the US and the UK. The hypothesis suggests a natural way forward for researchers interested in the social impact of abortion. Rather than trying to identify a causal link from abortion to indirect outcomes such as crime which are only observed many years later, it may be more fruitful to try to tease out the size and direction of the impact of abortion on contemporaneous and direct indicators such as the rates of children taken into care.

I don’t know anything about this and so can’t comment on the substance, but I like the idea of trying to look at intermediate outcomes. This is an important general point in statistics; see, for example, this famous example.

P.S. The graphs are great, but I have a few suggestions . . .

1. Label the lines directly rather than use a legend. The lines on the first graph are particularly difficult to identify: three of the symbols are nearly identical at this resolution, and the labels on the legend are not in order of the lines.

2. I’m not thrilled with normalizing the series (as was done in the first two graphs). To start with, you lose the information of which countries are higher and which are lower; second, it creates a misleading picture of divergence. For the first graph, I think the way to go is to make two separate plots, one for violent crimes and one for property crimes.

3. Think a bit harder about the numbers on the axes. On the first graph, the y-axis numbers start at 100,000 even though they’ve been normalized at 100. That can’t be right. Labeling the x-axis every two years doesn’t help; do every 10 years instead. The second graph doesn’t actually say what a “conviction rate” is. But I expect that it makes sense to send the y-axis all the way down to zero. Again, x-axis every 10 yrs would be fine (and also make it easier to compare to the scale of the top graph). Finally, the third graph y-axis should be 0, 10, 20 (or maybe 0, 5, 10, 15), and the y-axis is particularly hard to read.

But the main thing is point 1 above. This should be standard, I think. Given all the effort put into doing this research, why not make the graphs readable too? To me, having cryptic graphs is like writing a paper full of run-on sentences and non-sequitors.

Let me conclude by saying that the paper looks really interesting; I wouldn’t have spent the time commenting on the graphs if I didn’t think they were potentially saying something important.