5 Things About Peak Desirability

A couple weeks ago after my College Educated White Women post, the AVI sent along an Atlantic article about how everyone on dating apps is trying to date almost exactly 25% out of their league. 

The bigger more attention grabbing headline from this study though, was the finding that women’s desirability peaked at age 18, whereas men’s peaked at age 50. They included this chart:

Since I always get hung up on how these things are calculated and what they’re really telling us, I decided to take a look at the paper and the supplementary materials. Here’s what I found:

  1. Desire = PageRank When looking at a study like this, one of the first things I always want to know is how they defined their terms. Here, the authors decided that using a model where desirability = the number of messages received would be too simplistic, so they decided to use the PageRank equation. Yes, from Google. This equation is useful because it doesn’t just measure overall number of messages received, but how desirable the people who got in touch with you were. So ten messages from desirable people were worth more than 100 from less desirable people…sort of like one link from a famous blogger is worth more than ten links from lesser known bloggers. This choice made a lot of sense as “desire” is not just about how many people want something, but also how hard it is to get. However, choosing this definition does have some interesting consequences, which I’ll get to in a minute.
  2. The pool was not randomly selected, and the most desirable people were the outliers When the AVI initially sent me this article, one of his first comments was that generalizing from a sample of dating website users was probably not a great idea. After looking at the sample, he was completely right. Not only are these dating website users, but they were exclusively dating website users in large cities. There were other interesting differences….like check out the demographics table:  As a reminder, only about a third of US adults have a college degree. Those numbers for NYC are really unusual. You’ll also note that the average age of a user tended to be just over 30. So where did our highly desirable 18 year old women and 50 year old men fall? On the long tails:  Yes, I drew pink and blue arrows to show where the most desirable men and women fell. Sorry about that. Anyway, as you can see, those who showed up as the most desirable were not the best represented. This makes a certain amount of sense….18 year olds don’t join dating sites as often because they are frequently still in high school and have lots of access to people their own age. 50 year old tend to be married, partnered, or otherwise not looking. This is important because it introduces the idea that those not in the peak age range for use (23-33 from what I can tell) may have some survivor bias going on. In other words, if they log on and are successful, they stay on the site. If they aren’t, they leave. From what I can tell in my friend group, a 30 year old will stick it out on dating sites until they find someone, because that’s simply what everyone does. Other age groups may have different strategies. Since all the data came from one month (January 2014) it would not capture people who came and went quickly.
  3. Desirable men and women probably don’t have the same experience One of the more interesting discussions in the “network analysis” section of the paper, was when the authors mentioned that they had to include two different measures of interest in order to cover both genders. Because men send 80% of the first messages, they realized that assessing “interest” only by first messages would basically mean they only knew who men were interested in. Given this, they decided to also include replies as markers of interest. Thus, while the same equation was applied to both genders, one suspects this plays out differently. Desirable women are likely those who get many messages from men, and desirable men are likely those who get a lot of replies from women. For example, the study authors note that the most popular person they found in their data was a 30 year old woman in NYC who received over 1500 messages (!) in the one month they studied. They don’t list how the most popular male did, but one has to imagine it’s an order of magnitude less than that woman. It’s simply much harder to compose messages than it is to receive them, and with reply rates hovering at 15-20% one imagines that even extremely popular men may only be hearing back from around 100 women a month. In other words, the experiences of the genders are hard to compare, even when you use the same grading criteria.
  4. Decreasing your messages out would increase your page rank Okay, back to the PageRank system. Ever since Google first released their PageRank algorithm, people have been trying to optimize their sites for it. While Google has definitely tweaked their algorithm since releasing it, this study used the original version, which used the number of links your site makes as a divisor. In other words, the less you link to other sites, the higher your own rank. An example: suppose an 18 year old woman and a 30 year old woman get 100 messages from the exact same group of men. The 18 year old kinda freaks out and only replies to 1 or 2. The 30 year old woman seriously wants to find someone and replies to 20. Per PageRank, the 18 year old is rated more highly than the 30 year old. Now take a 30 year old man and a 50 year old man. The 30 year old man is all in on his dating app game, and messages 100 women, receiving 20 replies. The 50 year old man isn’t quite as sure and carefully selects 10 messages to women he thinks he has a chance with, getting 3 replies. If those replies came from “higher ranking” women than the 20 the other guy got, the 50 year old is now more “highly desirable”. In other words, users who are highly engaged with the dating site and taking chances will not do as well ranking-wise. Being choosy about who you reply to/message helps.
  5. Some of this may be up front decision making rather than personal One of the weirder downsides to online dating is the ability to set hard stops on certain characteristics of others. While in pre-computer days you would generally find out someone’s attractiveness first, now you can ask the site only to show you matches that are taller than 6’/older than 25/younger than 40, and the algorithm will do exactly what you say. This almost certainly impacts messaging behavior, and it turns out men and women approach ages limits really differently. OKCupid pulled their data on this, and here’s what they found: So our median male keeps 18 year old women in his age range for 5 years of his life (18-23), while our median female will only date 18 year old men for 2 years (18-20). It appears once women get out of college and hop on a dating site they pretty much immediately want to drop college aged men. On the other end, 48 year old men have a preferred age range nearly double the size of the age range 48 year old women set. Men raise their floor as they age, just not nearly as quickly as women do. Both genders appear to raise their ceiling at similar rates, though women always keep theirs a little higher. Thus, younger women will always be receiving messages from a much larger pool of men than older women, particularly since participation in dating sites drops off precipitously with age. A 30 year old woman (the average age) has men 26-46 letting her through their filter, whereas a 30 year old man has women 26-35 letting him through theirs.

Well there you have it, my deep dive in to desirability and PageRank as applied to dating! For any of you single folks out there, it’s a good time to remind you that just like Google results, online dating can actually be hacked to optimize your results, and that the whole thing is not a terribly rational market. Good luck out there!

 

Data Driven Apps for Everyday Problems

Please note: I am in no way affiliated with any of these apps. I just got them recently and have been using them, and thought they were fairly interesting for people who like to think about data or in a systematic fashion. In other words, I get no money for this.

One of the lovelier parts of living in 2018 is the ability to Google solutions to any problem. Recently I’ve been doing a lot of poking around for apps that might keep me organized in a few different areas, and these are some good ones I’ve found. To the shock of no one, all of them are pretty checklist or data driven. Since I suspect many readers of this blog probably think similarly to me, I thought I’d throw the suggestions out there just in case they would be helpful to others.

Cleaning:

While I have no idea how to pronounce the title of the Tody app, I love how it works. Basically, you select a list of rooms you have in your house, then the app recommends various cleaning activities that should be done for each. You add the ones that make sense to your list, set what type of cleaner you are (relaxed, standard or proactive) and it generates a list for you of what you should be doing.

The fun part is that each task has a status, so it’s sorted by importance. Tasks you should be doing often (like a quick tidy up of the living room) get overdue faster, so they go to the top of the list. Tasks that are due less often (like cleaning out the closets) get overdue more slowly. Basically “overdue” works by percentage, not just the number of days. I knocked off all the basic tasks yesterday, so now my list looked like this:

Note: I downloaded the app 7 days ago, and set a bunch of things up as “due” which is why they are all identical.

Anyway, I like this because if I have a few minutes to clean, this helps me spread out my efforts. I generally fall in to a bit of a pattern with cleaning, hitting some things frequently and other things only when I think “oh gosh, that looks really gross”. Once you check something off it goes to the back of the line, but then reminds you again once it comes due. You can do this for both cleaning tasks and various “replace” type tasks. As you can see, it suggested I set a reminder for changing my toothbrush (I go to the dentist on Monday) and I have it set up for my water filter as well.

My favorite part though is that you can create a family account. Both my husband and I downloaded it, and we now share the list. We haven’t used it, but you can also assign tasks to people, and compare effort. I’d imagine this would be good for assigning chores to kids.

There’s a couple other fun features: you can see which room collectively needs the most attention, you can sort by room, you can add your own tasks with frequency as you think of them, and you can add outdoor or car related things as well. Pretty fun, and good for those of us who have no memory/sense of when housework should be done and instead are just sort of careening. Also, you can run reports and checking a box is just satisfying. Good stuff.

Price-wise, it’s a one time cost of $6.99 for each person who downloads it.

Workouts:

I’m still playing with this one, but Freeletics seems pretty fun. It’s a workout app (mostly high intensity interval workouts) that uses an AI coach to give you a personalized workout plan. Every week you record how you did on the previous workouts and how much time you have next week, and it generates you a new plan. While this seems kind of basic, it’s actually nice not to have to put any thought in to it.

I think using your own basic data (I have limited free time this week, last week’s workouts were too easy) to drive what should be assigned is nice and a good use of feedback loops. It ensures you’re taking a break when needed, and pushing when you can.

The subscription price is a little high: $35 for 3 months. However, paying that price gives you access to three different apps: the running one, the bodyweight workouts one, and a gym program. I look forward to playing with this more often.

Eating:

I don’t really like apps that give you a meal plan, but I do get a kick out of checklists. Dr Michael Greger’s Daily Dozen Checklist  is based on the book “How Not to Die” by Michael Greger, and lets you check off each serving of the recommended foods you’ve had that day.

The only issues with this one is that Dr Greger is vegan based, so it’s probably not fun for anyone who’s not in to that.

Overall: I think the number one thing I’m getting out of these apps is a lot of data about what a creature of habit I am. I clean the same few things over and over, am at a loss for how to adjust when my workouts don’t go according to plan, and focus on the same few healthy foods. Having somewhere to track what I’m focusing on and (by default) point out what I may be missing is helpful. If anyone has any other apps they like, I’d be interested in hearing them!

 

College Educated White Women

As a college educated white woman, I was rather interested to see that my people have been making headlines lately. It all started with a Steve Bannon interview in Vanity Fair where he said “The Republican college-educated woman is done,” he said. “They’re gone. They were going anyway at some point in time. Trump triggers them. This is now the Trump movement.”

This quote launched quite a few op-eds and poll questions, which culminated in this chart from the Washington Post (source here):

Now I was pretty interested in this chart, since Andrew Gelman has taught me to be deeply suspicious of any reportedly wild swings in voting preference. However, looking a little closer I noted that he had the expanding gender gap as one of his 19 takeaways from the 2016 election, so I decided to take a closer look. What does it mean if a party loses a key demographic like that?

Well, it doesn’t entirely cause the drop you might think. I took a look at Gallup and Pew, and noted that Pew doesn’t show a dramatic drop in preference through 2017: 

Note: preference is not the same as people saying “I’m a Republican” or “I’m a Democrat” (both of which are down), but what people end up actually voting.

Anyway, I was curious why a massive drop-off in support from white college educated women wouldn’t translate in to a big jump in that graph, when it occurred to me that the underlying numbers are probably pretty different. Sure enough, per the Census Bureau, there are about twice as many Americans without a college degree (about 68%) vs with (about 32%). I can’t find exact breakdowns that take more than 2 factors in to account, but the same report shows that men and women have Bachelor’s degrees at about equal rates, and white people as a group have Bachelor’s degree at around a rate of 36%. The Washington Post puts the number of white people with college degrees at around 50 million, with the number without around 90 million. In other words, college educated white women are likely around 10% of the voter population, whereas non-college educated white men are likely around 20% of the total. A drop in one is made up for by the rise in the other.

So what does this mean for elections? Well, it’s complicated. Unlike gender, educational attainment varies wildly by state. According to Wiki, my state (Massachusetts) has the highest percentage of people with Bachelor’s Degrees in the country (about 40%), almost double the rate of West Virginia at about 20%. Going just off the Wiki list, the top 14 states with the highest levels of Bachelor’s degrees actually all voted for Hillary in 2016. Assuming those degrees are somewhat evenly distributed by gender, this may mean the loss has already been felt and that any further change will just be driving the states further blue. Since the electoral college system is in play, this could make the impact on national elections even smaller.

To complicate things in the other direction though, people with a Bachelor’s degree are much more likely to vote than those without one. For some reason the Census Bureau page that used to report this is down, but this site makes it look like the gap is pretty decent (80% for those with a Master’s or higher, vs 50% for a high school diploma). Again though, if the votes are unevenly distributed that could lead to less of an impact than you’d think.

I’ll be interested to see what the mid-term elections bring for updates to these trends.

What I’m Reading: August 2018

Well it’s summer and we’ve had one heck of a heat wave up here, with humidity that I heard dubbed “sweatpants heat”. It’s gross. I’m particularly miserable about the whole thing because I’ve caught a terrible cold, which does not play well with the heat/humidity. I was thinking about the unique pain that summer colds bring, and decided to Google around to see if there was any research on this. Turns out there is! It appears that summer colds are actually caused by different viruses than winter colds, so the feeling that they’re a little different is a real one.

In other important news, this website went through and used the water displacement method to figure out the air to chip ratio for most common brands of chips. The results are oddly fascinating:

The Assistant Village Idiot did an interesting post about media bias, in the form of compiling Time and Newsweek covers of Presidents from Ford on. A quick look gives you a feel of how each President was portrayed. I previously might have argued that magazine covers didn’t effect me that much, but ever since I found out that I thought Billy Graham died years before he did (probably) because of a TV Guide cover I’ve been less confident about this. Donna B raises a good question in the comments about the covers given to those who ran for the presidency but lost during those years.

Some surprisingly bad news: a huge randomized control trial of state sponsored pre-K shows the positive effect is only present at the end of the pre-K year, and turns negative by 3rd grade with kids who went to free pre-K performing worse in math and science than their peers who stayed home. This is surprising as I thought the worst case scenario would be “no effect”. The study authors have two theories for the results. First, it’s possible some kids just do better at home. Second and not unrelated, they note that more of the kids in the pre-K program ended up in special needs classes (remember, they were randomly assigned to pre-K vs home) and they wonder if some slower to develop kids got inappropriately tagged as special needs. It appears this early designation then followed them and they may have eventually held back beyond what was needed. Still a good data point in the “not everything that sounds like a good idea is one” train of thought.

I’ve been thinking a lot about viruses and other various pathogens recently, and the theories that they impact chronic disease more than we realize at the moment. For example, a recent Harvard study suggests that the herpes virus may help precipitate Alzheimer’s Disease. Celiac disease may be triggered by a candida infection. Some strains of intestinal bacteria may make it really hard for people to lose weight. The mechanisms for this are interesting and varied. The Alzheimer’s theory hinges on the idea that amyloid-B (the thing that ends up clogging up the neurons in Alzheimers patients) is produced to try to fight off HHV-6 and HSV-1, but something gets out of control and it takes over. Candida and gluten apparently bind to the same site on the walls of the intestines, so the theory is the body starts trying to fight off candida but produces antibodies to everything binding at that site. Gut bacteria that thrives on sugar or other “bad” treats might release toxins when you stop eating it that make you feel miserable until you resume previous eating patterns.  One statement I read somewhere while reading this stuff stuck with me “maybe our pathogens didn’t go away when we invented antibiotics and vaccines, maybe they just got less deadly and more subtle”. I’m completely fascinated by these theories, though we’ll see if they pan out.

 

Refugees and Resettlement

It’s always somewhat gratifying when I hear someone in my personal life change the way they speak about an issue because of my blogging. It’s even more gratifying when I get the sense they’ve actually internalized some of the ideas and aren’t just being careful because I’m around. This happened last weekend when my brother casually mentioned that he’d heard that the US actually resettled about a third of the world’s refugees. He mentioned that he wasn’t sure how that was possible since he knew the number of refugees the US took in was dropping, but he wondered if there was some meaning to “resettled” he was missing. As a thank you to him for being so conscientious about his adjectives, I figured I’d look in to the stats and definitions for him.

First, I have to admit it took me a few minutes to find anything on this, mostly because I thought he said the data came from “UNH”, which I took to mean the University of New Hampshire. Turns out he actually said the UNHCR, or the UN High Commissioner for Refugees. Oops.

When I finally found the right page, I was impressed to see that they actually have really wonderful resources defining all of what their terms mean. For refugees, the three solutions they work towards are voluntary repatriation (returning to their home country when it is more stable), resettlement (moving permanently to another country) or integration (becoming part of your host country).

It’s those last two that seemed to be causing the confusion (at least for me), but it made sense when I read it. A host country is the country the refugees initially go to when they flee their own country. Unsurprisingly, these are most often countries closer in proximity to them that will allow them to stay there. From their fact sheets, the top host countries are Turkey, Pakistan, Lebanon, Iran and Uganda: The refugees that stay in those countries aren’t considered “resettled”, because it’s considered temporary. The UNHCR works with those refugees to identify those who are most vulnerable (you cannot apply), and then submits their application. They don’t get to pick the country they go to. Unlike the host country, the countries that accept refugees through this program agree to give them permanent legal status in their country.

So does the US really accept a third of all resettled refugees cases? Yes, last year that was true. In other years it’s been even higher. I can’t embed it here, but this page has a really nifty graph of the total applications/departures each year, and you can filter it by resettlement country. In 2017 there were about 75,000 UNHCR applications for resettlement, the US took 26,000 of those.  In 2016 there were 163,000 applications, and the US took 108,000. Now I should mention that by “took” I mean took the application. Countries still do their own screening process before people are actually resettled.

Question 1, answered! Oh, did I mention there was a second question?

As we were talking about the plausibility of this stat, we raised the issue that we were constantly hearing about how many refugees countries like Germany were taking in. We were trying to figure out what category those people fell in to, and how they fit in to this picture.

As far as I can tell, the migrants making the headlines in Europe are either asylum seekers or economic migrants. Asylum seekers differ from resettled refugees in that resettled refugees are sent to a country under an agreement/discussion with the UNHCR and after a screening, and asylum seekers just show up and are screened after the fact. Since only 1% of the world’s refugees are ever resettled, the vast majority of refugee discussions are talking about asylum seekers. I won’t pontificate much more on the differences as that gets in to all sorts of legal issues, but I can say I had fun playing around with the graph generator on the UNHCR website. Here’s the number of resettled refugees France, Germany and the US have taken since 2003, and what countries they came from:

 

5 More Things About Fertility Rates

Normally when I write a blog post, it’s because some topic was rattling around in my head too much and I want to get it out of there. This works most of the time, and after hitting publish I tend to stop thinking as often about whatever it is I wrote about. Sometimes however, this works in reverse and my initial post sparks me and various readers/others in my life to keep talking about the topic. My last post on fertility rates was of the latter group, and I’ve spent the past week discussing it with people both online and in real life. The roundup below is 5 of the most interesting things that came out of those discussions:

  1. Male fertility is dropping I mentioned last week that while fertility rates are always counted in children/woman, we shouldn’t forget the role of men in the whole thing. To help prove that point, commenter Christopher B pointed me to an interesting article I hadn’t seen about dropping sperm counts in Western men. According to the meta-analysis cited, sperm counts have dropped 50-60% since about 1973. There wasn’t a particular reason cited, but the Assistant Village Idiot mentioned sleep deprivation, and the authors didn’t rule out chemical exposures or increasing obesity. I also found a paper that found that “After adjusting for female age, conception during a 12-month period was 30% less likely for men over age 40 years as compared with men younger than age 30 years”. This is almost certainly playing a role in dropping fertility rates, particularly if you approach it from the “why don’t people have 3 or more children as often anymore?” angle. If you struggle to have a first child, you may pay for infertility treatments, but very few people go through the time and expense of them for a third child. The biggest impact however, may be on my next topic…..
  2. Reducing unplanned pregnancies reduces fertility rates The sentiments “lower teen pregnancy rate” and it’s close cousin “reduce unintended pregnancies” are pretty non-controversial as far as public health goals go. While the methods proposed to meet these goals can be quite controversial (abortion, free birth control, abstinence only education, etc), most people actually agree on the end game. Thus when we look at the fertility rate and why it’s dropping, we have to consider that 45% of pregnancies in America are still considered “unintended”, with about 40% of those ending in an abortion. This got me wondering a few things. First, I wonder if the dropping sperm counts have actually impacted how frequently unplanned pregnancies occur. Teen pregnancy rates have been trending downward for quite some time, and one wonders if that’s been helped by things like dropping sperm counts. It’s probably not the whole reason, but it certainly seems unlikely to hurt.
  3. Our messages around teen and unplanned pregnancies may bleed over in to our thinking about planned pregnancies. One of the posts that kicked off all my thoughts on fertility rates was this one by the Assistant Village Idiot. I don’t know that I agreed with the example he gave, but the core thought of his post seems true: it is really really hard to discourage teens from having babies without saying things about how challenging kids are or how important it is that you have your ducks in a row before you have them. I mean, imagine that you find out that a 15 year old you know and care about is having unprotected sex with a partner. What do you say to them? Your first thoughts are almost certainly about how many opportunities they’ll be giving up and how much work kids are. This is the dominate message most kids receive until at least 18, longer if they’re college bound, and almost always including some time to figure yourself out. Even groups that don’t necessarily support the “figure yourself out” phase tend to have their own pressures. For example, in my Baptist high school, you definitely needed to find someone to marry first (that you wouldn’t divorce), and you needed to have enough money to make sure you never had to rely on welfare. The point here is not that any of this advice is wrong, but rather that it’s the dominant message for the first 10-15 years most people are biologically capable of having children, and people likely take them to heart for much longer than that.
  4. Kin influence One of the more interesting theories I read while reading up on fertility rates was the theory of “kin influence”. As I mentioned, it’s been noted that increased education drops fertility rates quite quickly. One proposed mechanism for this is that it’s not necessarily what education adds, but what it subtracts: 24-7 time around your family. The idea is that biologically, your family has a high motivation to encourage you to have kids, because this helps your families DNA continue. Educators and friends may care for you, but they don’t not have the same interest in encouraging you to have kids. Interestingly, even in the developed world, people who live closer/are closer emotionally to their family tend to have more children. Some of this is likely also related to resources…most people take advantage of grandma/grandpa babysitters before they look at other options. The paper didn’t mention it, but I have to wonder how this theory overlaps with the issues in #3. Parents tend to be some of the strongest voices telling teens not to get pregnant, which suggests that development doesn’t just shift the attitudes of those who might be having children, but the generation above them as well. When fertility rates fall rapidly in a country like Iran, is that all men and women of childbearing age deciding to have fewer children, or are their own parents there encouraging them to take advantage of more educational opportunities first?
  5. Child mortality rates To end on a sad note, it’s terrible to realize that some of the very high fertility rates in the developing world may actually be driven by child mortality. While it’s hard to prove causality, it appears that everywhere child mortality drops, fertility rates drop with it. From Our World in Data:  This is a good reminder that countries with total fertility rates of 6 children/woman or more almost never result in families of 6 adult children, and that our drops in fertility rate aren’t always as dramatic as they sound. For example, in the year 1800 in the US, the fertility rate was nearly 7 children/woman, while today it is just under 2. However, if you factor child mortality in, the drop is much less dramatic: I don’t know exactly what to make of this, but I can speculate that if you have good confidence your children will live, you may plan more for each of their births. It also just reminds me how grateful I am to live in this time period.

Overall this has been an interesting discussion and I appreciate everyone’s comments!

5 Things About Fertility Rates

Birth order is a hot topic in my family. I’m the oldest of four, and for as long as I can remember I’ve been grousing that being the oldest child is a bad deal. Your parents try out all their bright shiny untested parenting theories on you, relaxing the rules for all the subsequent kids, you’re held responsible for everything, and generally it’s just not faaaaaaaaaaaaaaaaair. Of course all this extra pressure does have some upsides later in life, like an increased likelihood of being a CEO or President. Anyway, given how often I’ve brought this up over the years, my parents (a youngest-of-3 and middle-of-5, respectively) were quick to point me to this article about the disappearance of the middle child in the US. After reading this article and the AVIs post about birthrates earlier this week, I went on a bit of a Google-bender on the whole topic. I figured I’d do a roundup of the most interesting numbers I found.

A quick note before I get started: for ease-of-counting purposes, fertility rates and family sizes are normally measured by “number of kids per woman”. This makes the data less messy, since you don’t have to worry about controlling for people who have children with multiple partners. However, it does often make discussions of fertility rates sound as though women are having kids in a vacuum and that men have nothing to do with it. This is simply not true. Social and economic pressures that encourage women to have fewer kids are almost certainly impacting men as well, and the compounding effect can decrease birthrates quite quickly.  So basically while I’ll be making a lot of references to women below, that’s just a data thing, not a “this is how it actually works” thing. Also, I’m going to mostly stick to numbers here as opposed to speculate on causality, because that’s just how I roll.

Alright, with that out of the way, let’s get started!

  1. Birthrates are declining worldwide. It’s not surprising that most discussions of birthrates and family size in the US immediately start with a discussion of the factors in the US that could have led to falling birthrates. However, it’s important to realize that declining fertility rates is a global phenomena. Our World in Data shows that in 1950, the total fertility rate (TFR) for women everywhere was 5 children. In 2015, it was at 2.49. In that same time period, the US went from about 3 children per woman to 1.84.  This is notable because sometimes the explanations that are offered for declining birthrates in the US (like expensive daycare or lack of parental leave policies) don’t hold when you compare them to other countries. Sweden and Denmark are both known for having robust childcare/time off policies for parents, yet their fertility rates are identical to or lower than ours. Whatever it is that pushes birth rates lower, it seems to have a pretty cross cultural impact.
  2. Birthrates can fall fast. Like, really really fast. Growing up in the US, I always thought of birthrates as something that sort of slowly trended downward as countries grew more developed. What I didn’t realize is that it doesn’t always happen this way. Our World in Data has an interesting chart that shows how long it took for various countries to go from a birthrate of 6 or more children to 3 or fewer:  What’s stunning about this is that some of these numbers are half a generation. For birthrates to fall that quickly in Iran for example, it doesn’t just mean women were having fewer children than their mothers, it means they started having fewer children than their older sisters. In case you’re curious if these trends were just a product of instability in those countries during those times: today the birthrate in Bangladesh is 2.17, South Korea is 1.26, China is 1.60, Iran is 1.97 (per Wiki/CIA Factbook). It seems like all the downward trends shown here kept up or accelerated. China obviously made this a formal policy, but it does not appear the other countries did. I found this interesting because we often hear about subtle factors/cultural messages that impact birthrates, but there’s nothing subtle about these drop offs.
  3. A reduction in those having large families impacts the average as much (or more) than the number of women going childless. One of the first things that comes up when you talk about dropping fertility rates is the number of women who remain childless. While childless women certainly cause a drop in fertility rates, it’s important to note that they are also lowered by the number of women who don’t have large numbers of kids. I don’t have the numbers, but I would guess that the countries in point #2 ended up with lower fertility rates not because of a surge in childless women, but by a major decrease in women having 6 or more children. If we look at the change in family size in the US since 1976, the most notable drop is women having 4+ kids. From Pew Research:My first takeaway from this is that the appeal of having 3 children is timeless. My second takeaway is that it appears a large number of people aren’t crazy about having a large family. This matches my experience, because while you often hear people ask those without children or with one child “why don’t you have more kids?” you don’t often hear people ask those with 2 children the same thing. My friends with 3 children inform me that they actually start getting”you’re not having more are you?” type comments and I’d imagine those with 4 or more get the same thing routinely. Now I grew up going to Baptist school and my siblings were all home schooled at some point, so I am well aware that there are still groups that support/encourage big families. However, even among those who like “big families”, I think the perception of what “big” is has shrunk. I have friends who talked incessantly about wanting big families, married early and were stay at home moms, and none of them have more than 5 children. Most of us don’t have to go more than a generation or two back in our family trees to find a family of 5 kids or more. It seems like even those who want a big family think of it in terms of “more children than others” as opposed to an absolute number. Yes, the Duggars exist, but they are so rare they got a TV show out of the whole thing.
  4. International adoption likely doesn’t get factored in. As mentioned above, I probably know an above average number of people with 4+ children. Many of these families have a mix of biological and adopted children, frequently foreign adoptions. According the the CDC though, it doesn’t appear those adopted children are not counted in birthrate data, as they calculate that off of birth certificates issued for live births taking place in the US during a given year. Now of course this isn’t a huge impact on overall numbers: there are currently only about 5,000 international adoptions/year in the US, down from a high of 15,000 or so, vs 4,000,000 overall births. However, it is interesting to note that “number of kids” does not always equal birthrate. Since the US is the biggest adopter of foreign children in the world, it is a thing to keep in mind here.
  5. The demographics of who doesn’t have kids are changing When you mention “women without children” the vision that immediately springs to mind is a well educated white woman who put her career first. Interestingly enough, this stereotype is increasingly untrue, and is changing in many countries. According to Pew Research, childlessness among women with post-graduate degrees has dropped quite a bit in the last 20 years, and the number of women in that group with 3+ kids has gone up:According to the Economist, in Finland women with a basic education are less likely to have children than their more educated peers, and other countries are trending the same way. The US is nowhere near flipping, but it is an interesting trend to keep an eye on. Historically, education has always been associated with dropping fertility rates, so this would be huge if it switched.

Overall, I thought the data out there on the topic was pretty interesting. The worldwide trends make it interesting to try to come up with a hypothesis that fits all scenarios. For example, we know that effective birth control must impact the number of children people have, but Britain and the US both had birthrates under 3 decades before oral contraceptives came in to play. Economic resources must play a part, and yet it’s the richest countries that have the lowest birthrates. Wealth is sometimes linked to higher numbers of children (particularly among men), but sometimes it’s not. Education always lowers fertility rates, except that’s started to reverse. Things to puzzle over.