So in other news this week, Dr. Jordan B. Peterson has made a controversial statement about reproductive marxism, or as he referred to it “enforced monogamy” . From what I could gather from the statement, the simple premise is that which I outlined in my essay on reproductive marxism.
In nature, it appears that the natural order mating opportunities flow to men much in the same way that capital does in capitalism. It flows from those who are unsuccessful to those who are successful, and the more capital a person has, the more capital will flow to them. A man who is unsuccessful in the mating market will be left with very little, if anything at all. A man who is successful in the mating market will receive even more mating opportunities.
However, this creates instability in the system in the same way that the above average collection of wealth among a small number of highly successful businessmen creates instability in a capitalist system. The fundamental order of capitalism is competition, as constant competition ensures that resources are efficiently allocated. However, the goal of any capitalist businessman is to gain a monopoly position where the maximum amount of revenue can be generated and where above average profits will be attained. Once an oligopoly or a monopoly comes to exist, resources will no longer be efficiently allocated. Reproductive Marxism
In any system that is a zero-sum or appears to be zero-sum, a side-effect is that the outcomes are often winner takes all, rather than being normally distributed. What this means is that wealth is distributed on a curve where the top 2 – 3% of the population control a great majority of the resources, rather than the resources being distributed roughly according to population size. It’s a fancy way of saying the top 1% control 99% of the resources, the bottom 99% control 1% of the resources. This tends to create a lot of instability within the system.
If one were to use the framework of conflict theory, one could posit that within a society one has a myriad of groups that all have different interests, some of these are deeply invested in the present social order because this social order benefits them in terms of resources. Other groups are in conflict with the present social order because it does not benefit them in terms of resources. For instance, the core argument made by Occupy Wall Street protesters was that the present system benefits at the top 1% at the cost of the 99%, economically speaking.
Much of Dr. Peterson’s work, from “12 Rules for Life“, to “Maps of Meaning” deals with the balance between mechanisms that enhance and preserve stability, and those that reduce and destroy stability. I’ve themed this the conflict between the ID and the Super-ego in Freudian terms. Where the ID, cast in Peterson’s analysis as chaos represents the natural order of things, and the super-ego order, the civilizing effect of reason on nature.
The article that somewhat inspired me to do some research, and subsequently write this essay, was a very effective piece of rhetoric, very accurately aimed at the market the author sought to influence, out of which my favorite quote is:
The idea that women will only sleep with the top men if given the chance is straight out of pick-up artist garbage pseudoscience. This ideology of “beta” and “alpha” males (the latter getting all the sex) is based on a mangled and since-retracted study about wolves, and bears no relationship whatsoever to human societies. Worse, it instills the false notion that women are largely status-obsessed sluts who will have to be basically coerced into sleeping with anyone but the most attractive men. 
Fortunately, yours truly cares quite little for rhetoric, I much prefer data, so what does the data say? In God We Trust, everyone else has to bring the data. In the quoted paragraphs there are 2 concrete statements we can look at.
A) The existence of Alpha males and Beta males as defined by sexual success.
B) The principle that women would rather share an alpha than own a beta.
From these, we can form a couple of hypotheses. In the case of the first statement, it’s quite simple, the basic premise is that a minority of men have a majority of the sex. This is often shorthanded as the “sexual market pareto principle” that Chris Rock verbalized as “20% of the men do 80% of the fucking“. If this is not the case, one would expect male sexual success to follow a normal distribution meaning that you have extreme outliers on both sides, and most men are somewhere around the mean. This means, simply put, that one would expect 33% of men to have less than the average of sex partners, 33% would have the average number of sex partners, and 33% would have an over-average amount of sex partners.
For the second statement, that women are and I quote “largely status-obsessed sluts” this is quite an easy set of hypotheses that would follow from the former two. In effect, if one falsifies the null hypothesis, and thus is capable of concluding that sexual success among men does not follow a normal distribution, and instead that a minority of men have a majority of the sexual success (as measured by sex partners) then one can establish not that women are status-obsessed sluts, because that would be concluding on a single cause, but that there is a sexual disparity among men, and thus that factors in female sexual selection behavior likely plays a part creating this sexual disparity. Thus, the following are the hypotheses for this essay:
Null Hypothesis: Attention from women to men follows a normal distribution
Hypothesis 1: Attention from women to men does not follow a normal distribution
Null Hypothesis: Sexual success among men follows a normal distribution
Hypothesis 2: Sexual success among men does not follow a normal distribution
1. Attention Distribution in the Sexual Market Place
An essay based on the OK Cupid data, together with a Tinder Experiment a gentleman did that looked at more than just ratings of appearance and that looked at actual response rates.
The Tinder experiment had a lot of problems, in that it had a low sample size, convenience sampling and relied on self-reporting. However, it did give me two very interesting pareto stats, namely that the top 78% of women are chasing after the top 20% of men and that the bottom 80% of men were chasing the bottom 22% of women.
This is clearly a supply and demand imbalance, in that the sex-ratio is roughly 50-50 male – female, in theory the top 20% of men on Tinder could potentially monopolize 78% of the women on Tinder. This makes Tinder, if likes are treated as a currency have a worst GINI coefficient (a measure of wealth inequality) than 95.1% of all the world economies. 
It would be quite easy to write off this result on the basis that the format of Tinder causes this result, rather than it being a result of natural biological proclivities among women when selecting mates. This is a chicken-or-egg argument in a sense, in what comes first? This is however a bit of a false dichotomy, because one is not forced to pick between one or the other. One could pose two strict binary positions on the causality:
A) The data appears as it does, because the format on Tinder causes it.
B) The data appears as it does, because of inherent female selection preferences.
The characteristics of the data is very clear, in that we get 2 sets of Pareto-esque distributions, the bottom 22% of women are being chased by the bottom 80% of men, the top 20% of men are being chased by the top 80% of women.
In a previous essay, which I happen to think was one of my most underrated essays in 2016, I explored the work a Dartmouth mathematician did on the basis of ratings on the website “Hot or Not”, along with a second dataset from OKCupid. This generated two graphs that I shared in that essay, that are repeated below:
At first glance, the two data sources contradict each other. In the OK cupid data, a staggering 85% of men are rated as being of below average attractiveness, whereas in the Hot or Not data roughly 90% of men are rated as being above the mean for looks. One of my basic assumptions with sexual market data is that it will follow a normal distribution when ranked from 1 – 10. Meaning that a majority of both men and women cluster around the median (4, 5 and 6). This is based on the reasoning that many characteristics of humans follow this distribution, for instance height, weight and IQ. In both sets of data, we see departures from the normal distribution, illustrated by the graph below that superimposes the 3 curves on top of each other. (The population is set to be 1 million people).
As you can see from the data, both OK Cupid and Hot or Not deviates quite substantially from the expected normal distribution. On one hand this could be a reason to disqualify the data as not being an accurate reflection of reality, however, that could be a case of throwing the baby out with the bathwater.
In the original essay, I outlined why I found this data contradictory, however, I also explained why this difference could easily happen, namely sampling and selection bias.
The concept of selection and sample bias is quite simple, if you take any group centered on a given pursuit, you will have a stable core group of members, and you will have a constant influx and outflow of members. Some of these people end up participating for a short time, some never participate (lurkers), and some end up becoming part of the core group. Every pursuit has selection pressures at varying degrees, that works to select out the population that has the best combination of traits to gain more success, thus over time the population may remain stable, but it will be increasingly set apart.
For instance, if one were to ask why there are so few people critical to ketogenic diets on a subreddit dedicated to keto. The answer is simply that the people who do not get satisfactory results on the ketogenic diet will stop participating in the subreddit, while those who gain good results will continue to particpate. Thus, over time the sample will increasingly be composed of those who gain good results. In the same manner, people go on a site like hot or not to get easy validation, they are asking a question to which they already know the answer.
While online dating is becoming very much a staple within the sexual market place, one has to make certain assumptions about the selection pressures that help select the total population. For instance, one could posit that the proportion of low-SMV men and women would be higher on OKCupid owing to the fact that the upper ranges in the normal distribution would be less represented due to dating opportunities in real life, whereas the lower ranges in the normal distribution would be more represented due to having fewer opportunities to date in real life.
If one revisit the response rates on OKcupid that I utilized in a former essay, one can say that high value women (the blue line) enjoy the highest response rates, followed by high value men (the yellow line)
My original comment on this graph was:
“The graph shows that a high attractiveness woman messaging a high attractiveness man has a response rate of 52%, whereas a high attractiveness man messaging a high attractiveness woman gains a response approximately 42% of the time. A low attractiveness man on the other hand messaging a high attractiveness woman, receives a response only 12% of the time. The lowest response rate is low attractiveness women messaging highly attractive men. Which is logical given that these men have 78% of women fighting for them and receive 11x as many messages as the low value men.” From “The Contrast Effect”
This discrepancies in messages received and response rates give very clear indicators that the top value both men and women receive much more attention on the platforms that dominate much of the dating market in 2018.
This is taken as support for hypothesis 1 and as a blow against the null hypothesis regarding the distribution of attention in the sexual market place.
2. Distribution of Sexual Wealth in the Sexual Market Place
The major problem with the data available to gauge sexual success is the very fact that it is usually obtained by survey, and relies on self-reporting. This means that there will always be questions regarding accuracy, based on among other things non-response and truthfulness. Even when using protocols such as anonymous interviews via telephone, or surveys sent via email, people may lie or rationalize away the truth. The following data was obtained from a CDC publication dated in 2007 .
Based on this graph, there are relatively small differences between men and women, and the different age groups. A general observation, is that there is little difference between men and women. Men have higher variability in their sexual success with more men in every bracket except 50 – 59 having had 0 partners in the past year.
Thus, one could conclude from this that women have an easier time finding a sexual partner than men, even though the number of people who had 0 partners is similar across the range. In aggregate 15% of men, and 17.7% of women had 0 partners in the past year across the entire 20 – 59 age range. However for men the variability was much lower, 17% men in the 50 – 59 bracket had 0 partners in the past year, which was the highest for men, whereas 14% of men in the 40 – 49 bracket had 0 partners in the past year, which was the lowest for men. When compared to the women, a staggering 34% of women ages 50 – 59 had 0 partners in the past year, where only 9% of women ages 30 – 39 had 0 partners.
The 20 – 29 group has the highest proportion of people who had more than 2 sexual partners in the preceding year. I think it’s very unfortunate that the data was cutoff at “2+” and doesn’t follow the same brackets as the next set, as this means our data doesn’t differentiate between 2 and 2000. This might also be what is causing a major problem in this data, namely that if 30% of men ages 20 – 29 had 2 or more partners, who are they sleeping with? In a theoretical population there should be a parity between male sexual encounters and female sexual encounters, simply because for a man to get a notch, a woman has to get a notch. For the bracket 20 – 29, it’s also possible that a lot of men in their twenties are sleeping with women in their teens. Finally, there is the self-report issue mentioned earlier in the essay.
This next graph covers lifetime partners, in the different age groups. The first thing that jumps out here, is the same problem that was identified in the first sample, namely the lack of parity between the male and female responses.
If one expected sexual success to be distributed by a bell curve, one would expect rough parity between the numbers of partners, and we have major discrepancies especially in the 15+ range. Over 40% of women responded that they had a lifetime partner count between 2 and 6 vs. a little under 35% of men responded the same. However, a full 30% of men in age groups 30 – 39, 40 – 49 and 50 – 59 reported a lifetime partner count over 15, and about 22.5% of men in the 20 – 29 age group reported over 15 lifetime partners. Even if we were to correct for the male-female tendency, namely of men to inflate their partner count and women to deflate theirs, it still doesn’t quite add up.
This graph shows the aggregate numbers for the entire range, from these numbers it’s quite clear that there are some men out there who are very active in the sexual market place, and are racking up notch counts over 15 if we assume the data to be correct, but very few women are accomplishing this feat. Almost 30% of men claim to have had over 15 partners compared to only 9% of women.
There is one particular scenario that comes to mind that can explain these numbers. About 22% of women have had 7 – 14 partners, 44% have had 2 – 6 partners. If we assume a closed population of 100.000 men and 100.000 women. This gives us the following
We also make the assumption of 1 sexual encounter with each partner, and we do 2 sets of calculations one for minimum range and one for maximum range. This gives us an idea of how many sexual encounters would be required in order to reach the notch counts in the different scenarios.
The minimum amount of sexual encounters required in order for a population of 100.000 men to get the notch counts in the distribution is 646.000, the minimum given by the women is 378.700. The maximum amount of sexual encounters on the male side is 942.7000 and maximum by the women is 730.000. This is based on the simple premise that for a man to get a notch, he has to find a woman to get that notch with, therefore, for every male sexual encounter, there will be a female sexual encounter. Thus, there should be a parity between the number of encounters for the mathematics to make sense.
To investigate this I located an NHS Survey from 2010, because quite frankly among the things the United Kingdom does the best in the world is collect data on their population, the UK and the US are also fairly similar in that both countries share many cultural and social factors, are both developed, first-world nations and both have seen a decline in marriage.
The UK has a much smaller disparity than the US, with only 8% of men, and 6% of women within the entire population of the study being virgins, this average came about due to a higher number of virgins in the 16 – 24 age group for both sexes.
There are some differences with a slightly higher proportion of men than women being virgins in the 16 – 24, 25 – 34 and 35 – 44 range, however beyond this there is an equal number among both sexes. This does indicate that there is a bit of a distribution issue going on among the younger group, especially in the age range 16 – 24, where 32% of men say they’ve never had sex compared to 26% of women.
From the data, we can see that the majority of UK survey respondents lost their virginity in the age range 16 – 34, with very few virgins remaining after that age. This is very different from the CDC and GSS statistics from the U.S in that there seems to be less of a distribution issue going on in the United Kingdom. If one compares the number of partners in the past year, the UK is also more promiscuous than its special friend.
At this point, I first have to mention that this does to some extent confirm the SMV Graph from Rollo Tomassi, seeing as among men, the ones racking up the highest notch counts, which indicates higher activity, or higher demand in the market are men 25 – 34, closely followed by men 35 – 44, where over 35% of the men in the bracket are racking up on average 10 or more partners in the past year.
The lowest partner counts are men ages 16 – 24, and women ages 55 – 69, where 1 partner in the past year is the great majority. Among young men the field is split between the other categories. Among women, the most active groups are women ages 25 – 34, and 35 – 44, where over 60% of the former had more than 3 partners in the past year, and a staggering 18% of women had more than 10 partners in the past year. Among women 35 – 44, over 25% had 5 – 9 partners in the past year, and almost 20% had over 10 partners i the past year. When comparing lifetime partners the picture doesn’t change much but it has some interesting characteristics.
It’s difficult to directly compare the UK and US statistics due to differences in the classifications of the data. However, the link between partners in the past year, and lifetime partners is much clearer in the UK statistics, in that there should be a degree of logic in the number of reported partners in the past year and number of total partners.
If one looks at the US statistics, 80% of women 30 – 39 reported 1 partner in the past 12 months, about 61% of women 30 – 39 reported having between 0 and 6 lifetime partners. Likewise, 72% of women ages 20 – 29, reported between 0 and 6 lifetime partners, yet 81% report having had 0 – 1 partner in the past year. This would indicate that women in the U.S are serial monogamists having a series of long-term relationships before settling down, with very low levels of promiscuity. In effect, that an average woman has 2 – 6 long-term relationships from the age of 20 until she is 40, with no periods of hookups, one night stands or college experiences.
For lack of a better way of wording it, notch counts are cumulative, not static, thus if one assumes that a woman only has 1 partner at a time, the average duration of a relationship is 3 years, that a woman has her first relationship at 16, and that the average age of marriage is 30, that means 4.6 relationships prior to 30, if we add the husband at 30, that’s a notch count of 5.6 lifetime by 39 provided no divorces and no cheating. You can make your own judgment on the probability of American women being serial monogamists who are in constant long-term relationships from age 16 until marriage.
I would argue that the lack correlation between the 12 month numbers and the lifetime numbers, combined with the parity is an indicator that something is fishy with the numbers.
Is it probable that a much higher proportion of UK women have higher notch counts than American women? Is it probable that American men, despite having a higher celibacy rate than American women have higher notch counts on average?
17% of American men (20 – 59) have a notch count between 0 and 1, 34% between 2 and 6, 21% between 7 and 14, 29% over 15. 25% of American women (20 – 59) have a notch count between 0 and 1, 44% between 2 and 6, 21% between 7 and 14, and only 9% above 15.
This means that 69% of American women have a notch count of under 6, while 50% of American men have a notch count no lower than 7 but potentially over 15. Unless American men are traveling in droves to Tijuana, or there are some women out there with notch counts in the thousands, something doesn’t quite add up.
In order to create a distribution for male sexual success, my choice was to utilize the aggregate numbers for all age groups of men from both surveys. As they also used different ranges, that were not uniform, such as the CDC survey having a 0 – 1 group, and both surveys using 10+ and 15+ respectively, this does remove a certain level of detail that would have been useful to gauge the outliers. However, to make them relatively uniform, I used the mean of each group, which makes “7 – 14” into “10.5”. I assumed a total population of 100.000 men in each group, and calculated the “AoI” ratio based on the premise that each male-female act of intercourse happened only once.
For the men of the United Kingdom this yielded a total of 586.500 acts of intercourse and 794.350 acts of intercourse for the men of the United States. The method I used to arrive at these numbers was to take the mean of the range, as described above, distribute the theoretical total population of 100.000 men according to the statistical distributions from the survey. Then multiply the mean number of partners with the total number of men in the population. For example, the mean value of the 0 – 1 partners is 0.5, 16.6% of US men fall into this group, meaning that it has a total population of 16600, this was then multiplied with 0.5, to get an AoI of 8300. This is based on the assumption that within each range of number of partners will follow a normal distribution (unlikely).
This graph demonstrates that sexual success for men follows a distribution where in the UK the top 33.9% of men in terms of the acts of intercourse metric represent 58% of all such acts. It’s slightly better than in the U.S where the top 29% of men represent 55% of all acts.
In the UK, the top 57.4% of men have 86% of the sex, in the US the top 50% of men have 82% of the sex. The major caveats to this is that the top men in each survey could have potentially many more partners than the top of the range. The UK range includes men with 10 partners and men with infinity minus 1, the US range men with 15 partners up to infinity – 1.
Thus, the difference between 7 – 14 and 15+ in the US survey, and 5 – 9 and 10+ in the UK survey could be much greater. If we alter the value of the top 28.9% of men in the US from 15, to 20, they represent 62% of all acts, raise it to 30, and they represent 71%. In the UK survey, if we raise the top group number from 10, to match the US survey of 15, the top 33.9% of UK men have 67% of the sex.
The obvious limitations with the statistics in this essay, is that it relies on self-reporting, thus the accuracy must be questioned, furthermore, the fact that the US survey doesn’t distinguish between men with 0 partners, and men with 1 partner, and that both surveys used set groups for the top value in the range create further problems.
Going back to the original hypotheses, what can we say based on the data? In the case of hypothesis 1, the data from OKcupid and Tinder discussed in section 1, both indicate that attention from women to men on the two platforms does not follow a normal distribution, in that the top 20% of men receive the attentions of the top 80% of women (Tinder) and men rated as highly attractive receive 11x as many messages as the men rated as the least attractive.
The second hypothesis is much more challenging, because while it is quite clear, that sexual success as measured by either number of sexual partners in the long, or short term does not follow a normal distribution for either sex, there are questions to be asked about the numbers. If we take the numbers in good faith, then we can easily argue the case that a minority of men get a majority of the sex. However, it’s very hard to determine the exact numbers based on the lack of parity between the men and women in the sample, and giving other limitations such as the cut-off at 10+ and 15+ partners.
This is not to say that there has to be a parity between the two, but that the “sexual partner” gap is simply too large to be explained without one of two explanations, it can only work that way if a minority of men and women are racking up massive notch counts, or if either men are over-reporting or women under-reporting.
4. The Decline of Marriage and the Modern Sexual Market Place
As I was writing this, I came across an interesting article from the Institute for Family Studies who crunched the numbers from the GSS (General Social Survey) and came up with the graph below :
The most interesting thing about this graph is that it would seem that married men who have not had sex in the past year greatly outnumber married women who have not had sex in the past year. So I’m sorry for the 2% of men who’s wives are sleeping around on them, which would seem to be the implication (check out the married red pill). This is the same case of a lack of parity as cited above, every married man and every married woman has a spouse, thus if one spouse is not having sex, then the other should not be either. This means that someone is either lying to the survey or their spouse.
The second most interesting thing is that men and women who have not had sex in the past year stay about equal at around 15% of the never married population, however there is a massive anomaly starting in 2009, where the numbers were equal, after which the number of men rapidly rose from 13% to in excess of 20%. This coincides with the growth in popularity for online dating and social media, something which has drastically influenced the dating market.
One must consider that prior to the online world, most people met through friends, in public locations such as bars or clubs, in college, or other social spheres such as church. However, between 1990 and 2009 online dating went from barely a blip on the radar, to generating $3 billion in annual revenue. The major effect of apps such as Tinder (launched 2012), is that they represent the distribution innovation of the dating market.
Most of us used to have access to a limited market based on the size of our social circles and the public spheres in which we were present, now it’s possible to access thousands of people online with little effort. In the same way, one used to have to go to many different bookstores, libraries and universities to get access to rare source literature, now most of it can be found easily online in a fraction of the time. Hypergamy was always an underlying part of the female firmware, but these apps and services, act to make it much more efficient, something which is demonstrated in the first part of this essay.
The author of this essay also brings up the classic Red Pill Pareto statement of “20% of the men have 80% of the sex” and he comes to an interesting conclusion:
“But even assuming the GSS data is correct, we can ask if another piece of the incel narrative is true. Do a few Chads and Stacies really monopolize the market for sex? Many incels quote a rule of thumb that 20% of men have 80% of the sex. Is this true?
It turns out, the answer is no. And of course, it isn’t! Imagine how much sex those 20% of men would have to be having! A substantial share would need to be doing two-a-days on a regular basis to maintain that kind of share. In reality, according to the GSS, the top 20% of the most sexually active never-married young men have about 50-60% of the sex. It’s about the same for women, and these shares are basically stable over time. Measuring the number of partners instead of sexual frequency, the top 20% most promiscuous men account for about 60% of male sexual partnerings, and the trend is, again, quite stable over time.” 
Thus, the distribution is not as bad as some would think, the top 20% of men merely have 50 – 60% of the sex and account for about 60% of male sexual pairings. If we assume this is true, then it’s obviously not as bad as the manosphere 80-20 would indicate. From the graph however one can read some interesting things.
A) More men than women are not having sex.
B) The uptick in women not having sex from 2008 to 2016 does not go beyond the bounds of previously observed highs and lows.
C) The trend for men seems to be flattening out at about 25% of men not having sex.
The author then goes on, quite as expected to make a pronounciation about cause and effect in his conclusion:
“The main factor driving this trend, however, isn’t Chads and Stacies, but just declining marriage rates among young men. The share of men aged 22-35 who have never been married is higher today than at any time since the first data we have, going back to 1880. The married share for these men is far lower than the historic norm. Unmarried people have less sex in general, so even if celibacy rates within marital status are the same, the sexless population grows.” 
This was not an unexpected conclusion given the source, and I agree that if marriage, or as I prefer to refer to it, reproductive marxism was in full effect, then one would expect the number of men who are celibate to decline. However, what one must keep in mind is that if we assume the GSS data to be correct, about 14,5% of women 22 – 35 are not having sex, and about 22% of men ages 22 – 35 are not having sex, but is the decline in marriage the cause?
According to “Trends in Premarital Sex in the United States, 1954–2003” , 77% of respondents had had sex, 75% of them premarital sex, by age 44 95% of respondents (94% of women, 96% of men) had had premarital sex. The conclusion of the report is very clear on this:
“Almost all Americans have sex before marrying. These findings argue for education and interventions that provide the skills and information people need to protect themselves from unintended pregnancy and sexually transmitted diseases once they become sexually active, regardless of marital status.” 
Thus, it becomes nearly impossible to conclude that the decline in marriage is the cause for the increase in celibacy among men ages 22 – 35, as most Americans have premarital sex. If marriage was a precondition for sex, then it would act as a barrier for access to sex. Rather as most Americans have per-marital sex, the decline in marriage has the traits of creating a distribution issue rather than an availability issue. This is similar to how if in one nation 1% of the population controlled 99% of the wealth, one does not have an issue with wealth creation, so much as an issue with wealth distribution.
5. Summary and Conclusions
This has been a rather long and very statistics heavy post to write, and I’m sure for you to read. I would have liked to dive into the GSS data as well to offer my interpretation of that, but I’ll save that for a later essay. We began this essay with the statements of “enforced monogamy” which to some seemed to mean “The state pairs off men and women without any regard for the desires of each person“, but which in reality is an anthropological term for having cultural and social values that value, support and encourage monogamous, long-term relationships. Such relationships help maintain stability and investment in the civilization by men and women.
Equality of outcome vs. Equality of opportunity is a bit of a central premise as of late, and as the readers of my Gendernomics essays are likely aware, the major distinction between laissez-faire capitalism (capitalism without regulation) and communism, is that the former is much more prone to instability, booms and busts, and similar mechanics that can make a society or world less stable, however it is also a major source of innovation, rewards merit, and encourages entrepreneurship. The latter is very stable, has no booms and busts, and is extraordinary stable (absent external forces in theory), yet completely discourages innovation, hard work and bans entrepreneurship. These are not characteristics unique to economic systems, they are general characteristics that apply to every system.
This is in essence what this is about, to what degree should we seek to moderate the natural tendencies of a system to produce inequality, in order to gain stability. These are trade-offs that have both pragmatic and moral dimensions and that characterize much of our lives. A basic premise of Utilitarianism is that the morally correct approach is the one that causes the maximum pleasure for the minimum pain. When it comes to social groups this same premise applies. What would be the effect on the economy, if the 23% or so of unmarried men who did not have sex in the past 12 months decided to emigrate to greener pastures, this is the pragmatic dimension. It means that assuming gender parity within a nation (same number of women and men), 23% of women would not be able to find a husband.
What is the effect if this group grows from 23% of unmarried men to 33%, 43% or 53% of unmarried men? Short-term it won’t create much of a problem, but the long term effect of having a lot of men with no attachment to the group, no reason to work hard and no reason to invest in the social group, can be extremely damaging to our civilization.