I was reading about a post on reddit.com/r/theredpill that talked about a study that confirmed how men tend to get more action later-on than women, and how the numbers were justified. This discussion centers around the concept of the sexual market value (SMV for short) of an individual as they go throughout their life. Composed of looks, financials, and knowledge, the SMV for an individual depends on their natural biology, as well as active action to improve their own value. Better descriptions and articles of SMV can be shown here:
http://therationalmale.com/2012/06/04/final-exam-navigating-the-smp/
In general, the hypothesis follows that women tend to gain their highest value as they approach their late teens though their mid twenties. This type of graph was based from well thought out personal experiences of many males, so it could be attacked for its data integrity because of the bias of the individual who created the graph. In my personal experience, I’m finding this SMV graph to be completely true in my day to day life (23 year old male, solid job, improving my value constantly)
But I wanted to know if there was a way to put some science behind the numbers. Which is what the Kinsey Institue has provided (Source at bottom of article). Their study covers a vast amount of information related to the sexual tendencies of individuals, but the two graphs that I focused on are below (snapshots, unmodified, bad quality, but you’ll get the idea with the graphs):
I thought that because my title at work is a Business Analyst, I could take that data and put it in a form that the average indidivual would be able to understand in a snapshot, with some justifications. This is not a graph displaying the sexual market value (SMV) of an individual. What it is displaying is direct results of the the average SMV of the sample size, indicative of the US population. (Note: My apologies for the soft language, can’t have that all over my computer while at work)
These numbers were broken down and weighted based upon the average number of encounters per category (Monthly = 1, 2-3 times/week = 10, etc.) so that we could multiple the percentage by this average to get a weighted value. Those weighted values were then averaged to produce the average number of encounters/100 individuals/month, which was then divided by 100 to create an individual output of # of Encounters/individual. (for some reason my image quality is horrendous, so I’ll look at putting the excel file on a shared drive for people to access)
That finalized information is displayed below:
(Image = Crap, Analysis = Gold)
Those numbers were then put into a display, broken down into the three categories (Single, Partnered, Married). The results speak for themselves, but of course being an Analyst, I put my own words beneath each of them.
Singles Information
Analysis: This is probably the most stark comparison and example of the hypergamy and SMV models combined. It’s very evident from the layout of how women tend to peak with regards to fulfilling their needs before the age of 30, and then experience an immediate drop in number of encounters. The drop-off could be due to many factors (decrease in SMV, decrease in desire for more encounters), but it shows the objective decrease in encounters. For males, you can see how an increase in SMV would result in this data output, with the number of encounters being much higher and sustained between the 30-60 year old period compared to their female counterparts. The differentials also show that the males in the 30-60 year old brackets are securing the women in the 18-30 year old bracket (possibly the 70+ bracket as well).
Partnered Information
Analysis: Partnered individuals are showing a hybrid between the Singe and Married graphical outputs. In comparison to the Single data, Partnered averages track each other more closely. Causation could be due to multiple figures, mainly because an individual is a partner with their male counterpart. In comparison to the Married data, we are still seeing the same trend that the Single’s shows, where women peak earlier than the men, but men show their own peak in the following years.
Married Information
Analysis: This is what I would think is expected for individuals who are married. The numbers should track each other (in an ideal situation, the curves would match exactly). Confirms the thought that as a marriage continues, the amount of sexual encounters, on the average, decreases.
I could do some t-testing, ANOVA, p-value, etc. if need to make some of my statements based in scientific conclusions, but I didn’t feel like it at the time I wrote this. That would add more weight to the analysis that has been completed by my end, but it seemed a little too obvious to get into that much detail. But if this becomes an issue I will gladly create that analysis (and bore to death half the crowd reading this).
So now we actually have some SMV data correlations that is backed by a solid study done by a 3rd party to reduce the chance of bias. Kinda cool when “what you think is probably true” can be confirmed through some analysis of well collected data.
I would appreciate it if someone would confirm my analysis so we can get the data displays verified. Somewhat of a “this guy is full of shit” analysis. I can send the excel analysis if anyone is interested, or can post it if want to look at the logic behind it.
Source of data: http://www.kinseyinstitute.org/resources/FAQ.html#frequency