A week ago I posted on the gender gap in politics; today Statistical Modeling, Causal Inference, and Social Science critiques a similar argument:
Via Craig Newmark, I saw a column by John Lott summarizing his 1999 paper with Lawrence Kenny, "Did women's suffrage change the size and scope of government?" Lott and Kenny conclude Yes, by comparing the spending and revenue patterns of state governments before and after women were allowed to vote. I haven't looked at the analysis carefully and would need a little more convincing that it's not just a story of coinciding time trends (they have a little bit of leverage because women were given the vote sooner in some states than others), but the story is plausible, at least from the perspective of voting patterns nowadays.
On the other hand . . .
poll data appear to show that the gender gap in voting between men and women is relatively recent--if anything, women used to vote more Republican than men did--so it's not clear if the effect Lott seems to be finding is occurring from women actually voting for conservative candidates or from some indirect effect of legislators trying to adapt to what they perceive as the preferences of women.
History is good for you. Really. It increases the sample space from which you can select data. You want to test your hypotheses on different populations, but let's include the dimension of time as well as space.
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Single women, especially younger ones, tend to vote the left side of the political spectrum while married women, especially those with children, tend to vote the right side. Sources? .... I dunno; perhaps Republican Party polling research. If true, then maybe before the mid 1960s when everyone married in their early twenties, women as a group voted right wing.
Perhaps in those days, the married women voted for whom their husbands told them to like a good woman should!
/sarcasm
There are two types of social science that historical data usually destroy:
1) Autistic, armchair social science. Look at the discussion on declining fertility rates in developed countries. The trend has been downward in England since 1820, and in France since the late 18th C. But how many accounts do you hear about the opportunity costs of higher education and a career for women? Someone did a pretty good job of hiding all of those go-getter career women in 1820s England.
2) What that Statistical Modeling blog refers to as "coinciding time trends." Some aspects of cultural change resemble epidemics, and it is rare even with infectious disease that we can identify who Typhoid Mary was; even less so in cultural change.
Lieberson's *A Matter of Taste* provides lots of good data on fashion in names and other things, which show that the popular cause is never right because the fashion trend began long before -- but that was when the trend was still at low levels, and so not very noticeable. It's only once the epidemic reaches its fast-growth stage that people notice and impute some cause that occurred at about or slightly before that time.
Actually, the arguments about education and careers for declining fertility are like that too. With so many stupid reasons given for it, it's no wonder I'm the only one who can see what caused it.