Occupations, fertility, men, and politics

Having a second look at occupations and fertility

In response to my prior post on the fertility rates of American women by occupation, some asked for the results for men, and others pointed out that there seems to be a correlation with the political leaning of the occupations (left-wing = lower fertility).

Tackling first the matter of political leaning. There are some websites and academics sources that have published occupation level results for political leanings, mainly based on political donations. The most extensive I could find was Verdant Labs results from 2016 with results for 549 occupations. This is actually more than those in my ACS list (311), and Claude was able to match up 515 of them to the ACS occupation codes (OCCP). Here’s the most extremely skewed political occupations:

AI wrote 2008-2014 on the image, but actually it was just guessing. The website doesn’t appear to be updated since 2016, so the data are from around that time. This is somewhat before the Republican change into the populist party, so it is possible that some occupations have moved substantially since then. However, for our fertility estimates, the time is sensible because the fertility data is from 2013-2022. To note about these data based on donations is that they reflect people who actually donate money to politicians, which are those who are more invested in politics, and richer. This demographic skews Democrat in modern USA. There may also be issues with shy Republicans who don’t donate publicly (out of fear of reprisals), or just don’t donate at all (surveys show Democrat voters are more interested in politics in general). At least, there seems to be a dearth of activist class jobs with ~100% Republican donations emerging. Every single occupation in the top Democrat list is more skewed towards Democrats than the most Republican occupation is towards Republicans (10% Democrat for Miners vs. 1-9% Republican in all top 25 for Democrats). This is also why many occupations aren’t seen at all despite being large. There are 600k American women working with hand packing things, but they don’t donate a lot of money to politicians, so their leaning cannot be estimated this way.

One of the lowest female fertility occupations was in fact librarian (various subtypes), which we see is in the top list for most left-wing occupations. Using the matching overlap of occupations we get this simple result:

The correlation is a bit better than the other ones we tried, r = -0.23.

Regarding the modeling, there are some thorny questions about measurement/sampling error and representativeness:

• The fertility rates are estimates, with some error bars. If these were measured better, the model itself would fit better. Thus, our results are underestimates compared to the hypothetical situation where we sampled an infinite number of women in each occupation.

• The simplest way to counteract this bias is to use precision weights, that is, 1/standard error. However, because precision is directly a function of the sample size and the sample size is a function of the size of the occupations, weighing by precision also weighs results towards the larger occupations. This thus changes what is being modeled from being “the universe of all jobs” to “the universe of jobs relative to their size”.

• Alternatively, one can use the cool SIMEX method, which should produce about the same results.

• By joining more variables to the dataset, we are losing some occupations that couldn’t be mapped confidently. This thus represents another subset of the number of occupations which itself can affect the results aside from the introduction of another predictor.

With these caveats in mind, here are some model results:

I’ve tried every modeling approach I could think of, which are:

• OLS, no weights. This is the cookie cutter and identical to the method used before.

• WLS, weighted. This weighs each occupation by the precision with which we have measured its fertility (the outcome variable), and thus its size in the workforce.

• FGLS, also weighted, but less so. This is a poor man’s alternative to the multi-level model. The difference being that we add a constant to the denominator to prevent large occupations from having almost all the weight. This is akin to random effects meta-analysis.

• HLM, also weighted but using a multi-level model that uses the fact that the occupations are part of 25 clusters to stabilize the results (23 with data post merge).

• SIMEX, which uses simulated extra errors to the variables to try to back-estimate the effects of the variables at SE=0. The clever thing about this is that it can use cell-level error estimates in any variable, and thus we can also account for the estimation error in the political leaning variable (100 total donors for occupation X and 90% gave to Democrats vs. 10000 total donors for occupation Y and 90% gave to Democrats, these estimates are not equally precise, the former may be quite wrong but the latter is not likely to be).

Looking across the models, we see at least some consistency. All models find that female% of occupations predict higher fertility with about the same effect size around 0.25. The relatively unweighted models find large negative effects of Democrat% support, but these are smaller or not above chance levels in the weighted models. I think this is because the high Democrat% occupations are numerically small and thus imprecise and thus have small importance when using weights. Artistic jobs predict lower fertility with fairly stable slopes around -0.30, but varying p values. These models have only 143 cases because many occupations could not be matched with the OCCP codes or the ones with political donation data, and with weights, the effective sample size is much smaller and thus the results will become unstable.

Regarding the male results, it is tricky. There is a variable for cohabiting opposite sex partner, which is then the father presumably. Any non-cohabiting father is not counted this way. There is another method which is the count of resident children below the age of 1, but this would presumably give about the same result. With this in mind, here are the results across sex:

The correlation is not very strong 0.30. However, this is downwards biased by both the estimation errors for women and the even larger ones for men. The doubly adjusted correlation is 0.45, about 50% larger, based on reliabilities of 0.78 for women and 0.67 for men. Interestingly, the cross-wave stability for both sexes was 0.74 corrected for reliability, so the temporal changes seem to be happening at the same speed in both sexes (between the 2013-2017 and 2018-2022 datasets). Here’s the top and bottom male occupations:

Funny that even men in nurse-like roles are high fertility. And for good measure, here are the same models fit on the male data:

Note that Democrat% predicts negatively also for men regardless of the model, while female% does not reliably predict, though it is p<5% in 2 models. Conventional and artistic also show up as negative predictors, the same as for women. These models with politics added did not have sufficient precision to estimate the nonlinear effects of complexity, or at least, it was not detectable with this reduced sample and extra covariates.

Overall, the differences in fertility across occupations are generally explainable in sensible terms for both men and women, and largely due to the same factors. Of course, we cannot be confident about causality with these cross-sectional results, but the results do point in some directions of interest.