Meaningful Gender Ratio

One thing a man might be interested to know about various countries in the world is: how easy would it be to find a girlfriend? Obviously, there are many factors influencing that, but one of them is the gender ratio: the higher the percentage of women in the population, the less competition there is among the men.

Now, the official gender ratios are readily available, but they aren't very useful. That is because they take into account all males and all females, including the children and the elderly.

I began to think: without making things too complicated, how could one compute a gender ratio that would be more useful for men looking to find a sex partner? [By the way, you can just scroll down if you only want to see the results and are not interested in the theoretical background.]

I quickly found somewhat better gender statistics: numbers of males and females for pretty much every country in the world by 5-year age groups (0–4, 5–9, 10–14 and so on). That's good enough.

Regardless of the age of consent in my location, I decided to play it safe and draw the line at 18 years. But where would the upper limit be? Well, being very generous, I could say that I've met some women above 44 who could still be considered attractive, at least with their clothes on, but really very few. So let's say that women aged 45 and above are out. (I'll talk about the men in a moment.)

But we can do better than just disregard the too old and the too young. For example, suppose that country A has 2 million women aged 20–30 and 1 million women aged 30–40, whereas country B has 1 million women aged 20–30 and 2 million women aged 30–40. If all other things are equal, it is clear that country A is to be preferred – even though the overall number of women is the same, country A has a higher percentage of younger (that is, more attractive) women.

So we ought to give weights to age groups. How to calculate those?

We should consider the following facts:

1) younger women are more attractive than older women;

2) older persons are more likely to be hooked up with a spouse and children, and thus less likely to be in the competition for sex partners;

3) younger men's sex drive is stronger than elder men's.

Those facts suggest that younger age groups ought to be given larger weights.

Of course, there are some other factors, like:

1) women's sex drive increases with age (although they may be just compensating for decreasing attractiveness);

2) older men tend to be richer and thus more attractive to women;

3) reaching a certain age, married men tend to start looking for new, younger partners.

But I think the second group of factors is significantly less important than the first group, and I was only trying to calculate a somewhat more informative gender ratio, not to write a scientific paper on getting laid. So I disregarded the second group of factors and decided to just give preference to younger age groups.

In order to determine the most reasonable weights, I considered that nearly all women I've seen begin to lose their youthful look somewhere between 25 and 30. I further figured, after some reflection, that I would consider a 20–25-year-old woman 5/3 = 1.667 times as attractive as a 25–30-year-old one. (Meaning, I would just as well have 3 women between 20 and 24, as 5 women between 25 and 29.) Extrapolating that, I decided to give each female age group above 20 a weight 1.667 times larger than the next age group.

There was no separate statistics for the age group 18–19, but I figured they would make up 2/5 of the age group 15–19. Since I consider women aged 18–19 as desirable as women aged 20–24, I set the weight for the age group 15–19 equal to the weight of the age group 20–24 times 0.4.

Now, what to do with the men? Considering the above theoretical considerations, we should weight younger male age groups higher than the older ones. Also, considering that men are usually interested in somewhat younger women and women are interested in somewhat older men, I ended up giving male age groups weights that are symmetrical to those of female ones. That is, the male age group 25–29 gets the same weight as the female age group 20–24; the male age group 30–34 gets the same weight as the female age group 25–29, and so on.

                   female            male

–14               0.000            0.000  

15–19           3.086            0.000              

20–24           7.716            3.086

25–29           4.630            7.716

30–34           2.778            4.630

35–39           1.667            2.778

40–44           1.000            1.667

45–49           0.000            1.000

50–               0.000            0.000

One obvious flaw of this system is that is disregards men in their late teens and early 20's, but that's difficult to account for without making the system unreasonably complicated, so let's just hope they are not competing for the same women we are. :-)

Anyway, I tried a few non-symmetrical weight systems, but none was clearly more logical than this one, and this is by far the simplest. So I ended up sticking to the system of symmetrical weights. It's not perfect but I think it's good enough to give one an idea about the competition for sex partners in one or another country. Should anybody have an idea how to improve upon those weights, please let me know.

And here are the results: the number of women in fuckable age for one man in fucking age

Please note that while the official gender ratios indicate the number of men divided by the number of women (larger numbers mean less women), this is the opposite: larger numbers mean more women per man.

    1) Northern Mariana Islands 1.62

    2) Djibouti 1.51

    3) Mayotte 1.49

    4) Chad 1.45

    5) Zimbabwe 1.45

    6) Mozambique 1.43

    7) Nepal 1.36

    8) Senegal 1.33

    9) Mauritania 1.31

  10) Guatemala 1.30

  11) Bangladesh 1.30

  12) Macau 1.28

  13) Mali 1.28

  14) Comoros 1.26

  15) Uganda 1.26

  16) Sierra Leone 1.25

  17) Ethiopia 1.25

  18) El Salvador 1.25

  19) Nicaragua 1.24

  20) Lesotho 1.23

  21) Congo (Kinshasa) 1.23

  22) The Gambia 1.23

  23) Virgin Islands, U.S. 1.23

  24) Albania 1.22

  25) Burundi 1.22

  26) Gabon 1.22

  27) Congo (Brazzaville) 1.21

  28) Cape Verde 1.21

  29) Tanzania 1.21

  30) Afghanistan 1.20

  31) Madagascar 1.20

  32) Laos 1.20

  33) Cook Islands 1.20

  34) Malawi 1.20

  35) Kiribati 1.20

  36) Sudan (incl. South Sudan) 1.19

  37) Cambodia 1.19

  38) Gaza Strip 1.19

  39) Sao Tome and Principe 1.19

  40) Togo 1.19

  41) Central African Republic 1.19

  42) Guinea-Bissau 1.19

  43) Haiti 1.18

  44) Jamaica 1.18

  45) Zambia 1.18

  46) Eritrea 1.18

  47) Burkina Faso 1.17

  48) Niger 1.17

  49) Western Sahara 1.17

  50) Angola 1.17

  51) Micronesia, Federated States of 1.16

  52) Guinea 1.16

  53) Somalia 1.16

  54) Peru 1.16

  55) Antigua and Barbuda 1.16

  56) Sint Maarten 1.16

  57) Benin 1.16

  58) Paraguay 1.15

  59) Cote d'Ivoire 1.15

  60) Equatorial Guinea 1.15

  61) Tonga 1.15

  62) Cameroon 1.15

  63) Bolivia 1.15

  64) Nigeria 1.15

  65) Ghana 1.15

  66) Swaziland 1.14

  67) Rwanda 1.14

  68) Liberia 1.14

  69) Honduras 1.13

  70) Belize 1.13

  71) Tajikistan 1.13

  72) Timor-Leste 1.13

  73) Turkmenistan 1.13

  74) Botswana 1.13

  75) Yemen 1.13

  76) Ecuador 1.12

  77) Mexico 1.12

  78) Kyrgyzstan 1.12

  79) Kenya 1.12

  80) West Bank 1.12

  81) Uzbekistan 1.12

  82) Morocco 1.11

  83) Namibia 1.11

  84) Samoa 1.11

  85) Venezuela 1.11

  86) American Samoa 1.11

  87) Syria 1.11

  88) Tuvalu 1.10

  89) Solomon Islands 1.10

  90) Pakistan 1.10

  91) Anguilla 1.10

  92) Jordan 1.10

  93) Saint Lucia 1.10

  94) Grenada 1.09

  95) Singapore 1.08

  96) Iraq 1.08

  97) Armenia 1.08

  98) Papua New Guinea 1.08

  99) Azerbaijan 1.08

100) Colombia 1.08

101) Philippines 1.08

102) Kazakhstan 1.07

103) Tunisia 1.07

104) Guyana 1.07

105) Mongolia 1.07

106) Lebanon 1.06

107) Vanuatu 1.06

108) Uruguay 1.06

109) Brunei 1.06

110) Virgin Islands, British 1.06

111) Burma 1.06

112) Guam 1.05

113) Georgia 1.05

114) Dominican Republic 1.05

115) Wallis and Futuna 1.05

116) Dominica 1.04

117) Puerto Rico 1.04

118) Algeria 1.04

119) Curacao 1.04

120) Iran 1.03

121) Marshall Islands 1.03

122) Malaysia 1.03

123) Argentina 1.03

124) South Africa 1.03

125) Chile 1.03

126) Panama 1.03

127) Costa Rica 1.02

128) Jersey 1.02

129) Aruba 1.02

130) Vietnam 1.02

131) French Polynesia 1.02

132) New Caledonia 1.02

133) Korea, North 1.02

134) Bahamas, The 1.02

135) Greenland 1.02

136) Nauru 1.01

137) Estonia 1.01

138) Bhutan 1.01

139) Libya 1.01

140) Egypt 1.01

141) Brazil 1.01

142) Gibraltar 1.01

143) Saint Vincent and the Grenadines 1.01

144) Sri Lanka 1.01

145) New Zealand 1.01

146) Turkey 1.00

147) Fiji 1.00

148) Cayman Islands 1.00

149) Iceland 1.00

150) Israel 1.00

151) Moldova 1.00

152) Denmark 1.00

153) Indonesia 0.99

154) Saint Kitts and Nevis 0.99

155) Sweden 0.99

156) Mauritius 0.99

157) Kosovo 0.99

158) India 0.99

159) Norway 0.99

160) Isle of Man 0.98

161) United States 0.98

162) Montserrat 0.98

163) Bermuda 0.97

164) San Marino 0.97

165) Suriname 0.97

166) Barbados 0.97

167) China 0.97

168) Russia 0.97

169) Lithuania 0.96

170) Netherlands 0.96

171) Latvia 0.96

172) Thailand 0.96

173) Luxembourg 0.96

174) Ukraine 0.96

175) Cuba 0.96

176) Monaco 0.95

177) Belarus 0.95

178) United Kingdom 0.95

179) Macedonia 0.95

180) Switzerland 0.95

181) Canada 0.95

182) France 0.95

183) Croatia 0.95

184) Hong Kong 0.94

185) Guernsey 0.94

186) Australia 0.94

187) Finland 0.94

188) Austria 0.94

189) Belgium 0.94

190) Japan 0.94

191) Faroe Islands 0.94

192) Bosnia and Herzegovina 0.93

193) Malta 0.93

194) Serbia 0.93

195) Saint Martin 0.93

196) Turks and Caicos Islands 0.92

197) Hungary 0.92

198) Liechtenstein 0.92

199) Slovakia 0.92

200) Germany 0.92

201) Poland 0.92

202) Ireland 0.92

203) Romania 0.91

204) Saint Helena 0.91

205) Taiwan 0.91

206) Italy 0.91

207) Bulgaria 0.91

208) Trinidad and Tobago 0.90

209) Czech Republic 0.89

210) Greece 0.88

211) Saint Pierre and Miquelon 0.88

212) Slovenia 0.88

213) Seychelles 0.87

214) Portugal 0.86

215) Korea, South 0.86

216) Cyprus 0.84

217) Oman 0.82

218) Spain 0.81

219) Saudi Arabia 0.81

220) Montenegro 0.80

221) Andorra 0.80

222) Palau 0.72

223) Maldives 0.68

224) Saint Barthelemy 0.65

225) Kuwait 0.57

226) Bahrain 0.54

227) United Arab Emirates 0.35

228) Qatar 0.22

The data reflects the stand of sometime in 2012.

The results are rather discouraging. Women seem to be readily available in black Africa, while being rather scarce in the developed countries. Most obviously, though, men looking for women would do wisely to stay away from the rich Arab countries in the Persian Gulf region.

More specifically, the situation in various regions is like this:

Among the white countries, the clear leader on fuckable women's surplus is Albania (1.22), followed by Jersey (1.02), Greenland (1.02) and Estonia (1.01). Almost all white countries have a slight deficit of fuckable women, the worst being Andorra (0.80), Montenegro (0.80) and Spain (0.81).

In yellow Asia, the leaders are Macau (1.26), Laos (1.20) and Cambodia (1.19). Most countries are close to 1. Even the worst three – South Korea (0.86), Taiwan (0.91) and Japan (0.94) – are comparable to the white countries' average.

In Latin America, the ratios are somewhat better, the leaders being Guatemala (1.30), El Salvador (1.25) and Nicaragua (1.24). Suriname is the only country below 1 (0.97), with Brazil, Costa Rica, Panama, Chile and Argentina very close to 1.

Sub-Saharan Africa is, as mentioned, man's paradise in this particular respect (if hardly in anything else). Djibouti is above 1.50, that is 3 women per 2 men, then Mayotte, Chad, Zimbabwe and Mozambique above 4 women per 3 men, and 7 more countries above 5 women per 4 men. There are only four countries below 1.1: the Seychelles (0.87), Saint Helena (0.91), Mauritius (0.99) and South Africa (1.03).

The heterogenous Indian-Semitic area between the white Europe, black Africa and yellow Asia offers a very varied picture with Nepal (1.36), Bangladesh (1.30) and Afghanistan (1.20) near the top of the rankings and Qatar, UAE, Bahrain and Kuwait at absolute bottom.

The leader of the Caribbean region are the U.S. Virgin Islands (1.23) with several more countries with a ratio near 1.2. Near the absolute bottom is the obscure French colony of Saint Barthelemy (0.65), way below the second-last in this region, Trinidad and Tobago (0.91) who are near equal with Turks and Caicos (0.92) and Saint Martin (0.93).

Oceania has the overall winner – Northern Mariana Islands with a whopping 1.62 women per man. The runner-up Cook Islands are way behind but still commendable 1.20, followed closely by Kiribati and Micronesia. The only country below 1 is Palau (0.72), with Fiji, Nauru, New Caledonia and French Polynesia only slightly above 1. Nothing like the bleak picture in the white countries, here either.

While collecting the data for the above rankings, I couldn't help paying attention to the population pyramids. Many black countries' age structures show a perfect pyramid like this:

European countries' population pyramids look... well, sick:

Somewhat peculiar were the population pyramids of a few countries like Pakistan:

They are perfect pyramids on top, and then get narrower near the bottom. It would appear that they have exprienced a remarkable living standard increase in the last couple decades, so that people are no longer so eager to make children.