MRAs, MGTOW, and the Case Against Marriage
There was a time when I was once loosely on the side of MRAs.
To the best of my recollection, I never called myself one (quite the contrary, even). But I thought they often raised good points. Perhaps more compelling still, they often did so against feminism.
Feminism has fallen by the wayside in the past five years or so. But back in the late 2000s, even ordinary people conflated criticism of feminism with hatred of women. MRAs really paved the way for being able to treat feminism as an ideology. It’s hard to overstate how valuable that kind of work is.
But alongside the MRA movement was a parallel movement — one not grounded in notions of “rights,” but in risk. These were the “Men Going Their Own Way” — MGTOW for short. And in the past decade or so, these groups seemed to have loosely merged.
The defining feature of the MGTOW movement is their opposition to marriage. They say that marriage is a “bad deal” for men, that between the legal and cultural incentives, there is no good reason for men to get married.
It is even common to hear that “men who get married are idiots.”
The justification for the arguments against men getting married are broadly statistical. There are two in particular: first, the argument that 50% of marriages end in divorce; second, that 70% of divorces are initiated by women. Given what often happens to men in divorce court (which can get especially ugly when kids are involved), it is then taken that given these odds — for it is assumed in a sort of axiomatic manner that statistics give you odds — marriage doesn’t make sense.
The use and abuse of statistics is something I’ve been writing about for a few years now. One of the earliest posts I ever wrote on this blog (in fact, the first post transferred from my old blog-page to this one, in 2016) was a criticism of statistics used to validate an Australian gun-control law. In that case, my own thoughts were built off of the analysis of a YouTuber, one with far more mathematical and statistical training than I have. But given the fairly unsophisticated manner in which statistics are regularly tortured to advance particular ideas, one need not even have an advanced degree and an understanding of logistic regression — or even what a standard deviation is — in order to see most of the problems. They tend to be logical, and not technical.
In that particular case, the metric for “mass-shootings” had been changed arbitrarily to suit the narrative of the advocacy position, in an entirely ad hoc fashion.
Another prominent example: one of the most blown-up and utterly destroyed statistics is the supposed “gender-wage gap,” where — on the basis of broad statistics — it was argued that women earn seventy-six cents for every dollar that a man earns, for the same work (my italics).
Thomas Sowell eviscerated those arguments all the way back in 1981, and in more recent years, we’ve seen Jordan Peterson reiterate the same points (including in his famous interview with Cathy Newman). The problem with the gender wage gap — in broad terms — is not that the data is inaccurate. It is that the proper interpretation of data requires circumspection and an appreciation for context. The feminist analysis is low-resolution. It takes the numbers at face value, smacks the gavel, and digs no further into questions of causality.
They never discovered the complex manner in which motherhood, choice of work, and a host of other factors all work together to explain away the gap entirely, leaving almost no room for “sexism” as a cause for pay disparity at all.
Vox Day performed a similar statistical correction in his book The Irrational Atheist (2014). In addressing Sam Harris’ “Red State-Blue State argument,” Vox points out that Harris’ data might be correct, but analyzing the data at the wrong level leads not only to incorrect conclusions, but in fact backwards conclusions.
Harris had looked at the distribution of crime across the country, and observed that in conservative states, crime was actually worse than in more liberal states… therefore, it must be silly to think of religion as any kind of source of morality, at least as measured by crime.
But when one breaks down the same data by county, rather than by state, the trend reverses: crime is highest in the highly liberal cities, which often tilt the statistics in otherwise conservative states.
(Vox does not assert that religion does make people more moral and law-abiding — only that Sam Harris’ statistical argument is embarrassingly bad.)
This same problem with statistics applies to those employed by the MRAs and MGTOWs… who should really know better, given how early they were to the problem with feminist gender pay-gap analysis.
The 50% Divorce Rate
The myth of the 50% divorce rate in America emerged in the 1980s, which happened to be about the peak of divorce rates in America. Though it has declined since then, even at the time it was nonsense. Statistics-based criticisms of this number go back at least to 1993, where it was calculated that in the worst year (1979), the divorce rate was 22.8% — not good, but hardly a coin-flip.
The 50% myth emerged — as one might have guessed when statistics are involved — with a projection from the 1970s based on a trend of increases in divorce. The projection never came true (never actually came close to being true). But at that point the number had stuck in people’s heads, and somehow remains with us to this day.
(As a rule of thumb, any statement preceded by “if current trends continue…” can be ignored, and in fact bet against. Current trends never continue.)
If you look at the CDC and other sources (one finds many law firms supplying this information…), you will still see numbers in the 40%-50% range. Forbes, for example, says “it’s true” that half of all marriages end in divorce… but that’s only first marriages — it’s much worse for second and third marriages.
Yet when you follow their source (a 2012 article from Psychology Today), the article says “statistics have shown that in the U.S., 50% of first marriages […]end in divorce”… and yet it shows no statistics.
There actually is no source.
It is simply asserted.
This is data-laundering. A number gets picked up and published in one place, then is cited somewhere else using the credibility of the original source, when in fact the emperor never at any point had even a loincloth.
But we need not throw out the baby with the bath water. Indeed, we don’t need to look at any data at all to grasp the intuitive sensibility of the idea that second and third marriages have higher divorce rates than first marriages.
Why is this? Because the sort of person who isn’t able to get along with one person has a higher likelihood of not being able to get along with another person.
This means that no matter what the overall divorce rate is, the divorce rate for first-time marriages will be lower.
Where this is pertinent is when jaded MRAs or MGTOWs assert the 50% divorce rate as if that tells you something about your odds of success in marriage.
There is a kind of implicit allegation of hubris against anyone who would imagine that they might “beat the odds,” but there is almost no domain of skill in which the average cannot be surpassed by anyone through effort. We have known the kinds of dynamics that lead to divorce with degrees of accuracy unprecedented in the human sciences since John Gottman presented his research on the “four horsemen“: criticism, contempt, defensiveness, and stonewalling.
If you choose to marry someone in your generation and class, who shares your religious and political views, and with whom you can work together to avoid these poor relationship habits, you will probably enjoy a 90-95% chance of staying together for life.
I am making up that 90-95%, but it is certainly closer to accurate than the unrealized projection of 50% that has taken hold in the Red Pill internet world.
The 70% Female Divorce-Initiation Rate (Conclusion)
I don’t feel the need to spend any serious time on this statistic, since frankly the data is actually not relevant to what is implied in the claim.
Given how statistics have been used thus far, I see no reason to take the data at face value.
But let’s do it anyways.
Let us suppose that women are the ones who serve the divorce papers in 70% of cases.
Here’s a trick question: does that mean that the woman caused the divorce?
The answer is: not enough information.
If a man were to cheat on his wife, and his wife found out, and then she filed for divorce, most people would say that the man caused the divorce.
But hold on — you say: why did the man cheat on his wife? Sure, there are opportunistic men who cheat just because they think they can get away with it. But in most cases, the man who cheats is often feeling neglected, is not feeling respected, seen or loved by his wife. Does this possibility excuse or justify the cheating? Of course not, but understanding the causal relationship between patterns of neglect and infidelity is empowering. It allows the man, in turn, to ask “why is my wife ignoring and neglecting me?” without criticism, and it elevates the individual who understands and implements these dynamics in his relationship above the randomness of statistical distributions.
Because that’s the point of bringing up these statistics, isn’t it? To impart a sense of dread in the would-be newlywed, or to justify a sour-grapes withdrawal from the institution: a kind of Quixotic “Going Galt” against statistical ghosts, parroted by vainglorious internet accounts with Roman statue profiles. Statistics imply powerlessness. You could wind up anywhere along some bell-curve or other. To put on a ring is to roll the dice. You just don’t know — can’t know — who you’re getting.
The reality is that who initiates a divorce is actually irrelevant. At the point of divorce, there is (almost) always a long train of misunderstandings, offenses, and mistakes within the relationship itself. Those faults follow a chain of causality that is — and always was — within your sphere of influence. If the perverse incentives of the state and the legal favoritism towards women have any impact at all, it is at most a straw-that-broke-the-camel’s-back level of effect.
I am speaking, of course, in broad generalizations because we are addressing broad claims. There are men (mostly those who accidentally hitch up with sociopathic or bi-polar women) who genuinely get unlucky, and for whom no reasonable precaution could have been taken. They are truly unfortunate, and I don’t mean to “victim-blame.”
Imagining that you are a victim — or a potential victim — automatically closes the mental doors on any possible answers or solutions (because to have had an alternative path is, in some sense, to repudiate the identity of “victim” almost by definition).
And appealing to statistics has this same effect. To be a “victim” is, as some say, to “become a statistic.” But at the end of the day, this identification is a choice.
For anyone wading through the morass of internet Victim Advocacy Groups (or VAGs, as I like to call them), it is worth remembering that your life is not set in stone by statistics. Whether it’s the incredibly low resolution wage gap figure, the dire consequences of an “if current trends continue” model for divorce, outright fraudulence in the data, or any other dubious stat that leaves you feeling demoralized, they all fall to the same fallacy.
Simply rejecting the frame that you will be a victim to your circumstances is enough to break their hold on you — in marriage, or in life generally.