Showing posts with label by country. Show all posts
Showing posts with label by country. Show all posts

25 December 2012

Countries ranked by the women's beauty

 

  1) Colombia 7.45
  2) Portugal 7.30
  3) Serbia 7.22
  4) Russia 6.97
  5) Italy 6.90
  6) Croatia 6.82
  7) Venezuela 6.80
  8) Romania 6.70
  9) Dominican Republic 6.65
10) Moldova 6.65
11) Philippines 6.62
12) Macedonia 6.55
13) Thailand 6.47
14) Slovakia 6.45
15) Belarus 6.40
16) Greece 6.25
17) Czech Republic 6.25
18) Malaysia 6.25
19) Bulgaria 6.17
20) Argentina 6.12
21) Mexico 6.10
22) Ireland 6.10
23) Japan 6.10
24) Canada 6.10
25) New Zealand 6.07
26) Cuba 6.07
27) Ukraine 6.07
28) Poland 6.05
29) Brazil 6.02
30) Costa Rica 6.02
31) Taiwan 5.97
32) Qatar 5.92
33) China 5.85
34) Germany 5.80
35) UAE 5.70
36) Malta 5.65
37) Belgium 5.62
38) Latvia 5.57
39) Cape Verde 5.47
40) South Africa 5.47
41) Norway 5.45
42) Algeria 5.45
43) Singapore 5.45
44) Lithuania 5.42
45) France 5.35
46) Sweden 5.27
47) Hungary 5.27
48) Slovenia 5.22
49) Netherlands 5.20
50) Mongolia 5.17
51) Tunesia 5.15
52) Spain 5.12
53) Turkey 5.07
54) Switzerland 5.02
55) Hong Kong 5.00
56) Western Sahara 4.97
57) Dominica 4.97
58) USA 4.95
59) Montenegro 4.80
60) Austria 4.75
61) Jersey 4.62
62) Finland 4.52
63) Australia 4.50
64) Luxemburg 4.40
65) Chile 4.37
66) UK 4.32
67) Morocco 4.27
68) Vietnam 4.27
69) Gibraltar 4.27
70) Kenya 4.25
71) Honduras 4.10
72) Peru 4.10
73) Bolivia 3.90
74) Kyrgyzstan 3.82
75) Estonia 3.67
76) Namibia 3.62
77) Panama 3.57
78) Saint Kitts and Nevis 3.20
79) Ecuador 2.95
80) Vatican 2.40*

(40 women from each country; each woman rated 1 to 10; country's score computed as the average of those)

* Vatican's score is based on 5 photos only, because I couldn't find more.



Explanations

Background

It started as a part of another project but soon acquired a life of its own. I mean, I was really curious to find out which country in the world has the most beautiful women.


Method

How can one find out how beautiful is an average woman in one or another country? It would seem that, short of travelling to a country after country and checking out the women in person, the most accurate means is by finding a number of photos of women on the Internet and rating them.

As a matter of fact, there is an astounding number of online discussions where one guy would post a couple of photos of, say, Italian women, believing they will convince everyone that the Italian women are the most beautiful in the world, and then another guy would say "no, the Venezuelan women are the most beautiful in the world", and, to "prove" it, post a couple of photos of Venezuelan women. That is obviously not a way of getting anywhere. I tried to find something more objective.

So I defined a certain search algorithm and I defined requirements for picture quality, with the goal of finding photos of 40 women from each country without spending unreasonably much time.
Then I rated each woman with 1 to 10 points and calculated the average for each country.


Choice of countries

My research includes only a minority of the world's countries. That is because I only included countries that had at least some interest for me as travel destinations. I mean, I couldn't care less if Yemen, Guinea-Bissau or Suriname disappeared from the face of the Earth tomorrow, so I would hardly have any motivation to search for photos of women from there.


What is a country?

I count a territory as a country if it has its own Internet domain suffix. Thus, Jersey, French Polynesia and Western Sahara are countries; Martinique, Northern Cyprus and Kosovo aren't. That is absolutely not to be interpreted as an expression of any political sympathies.


What is a woman?

Obviously, I discarded children. Whenever the age was not indicated (and that was usually the case), I just looked at the girl and if I even suspected that she might be a child, I discarded the photo.

I also discarded women who were clearly too old to be considered sex objects.


Choice of photos

Admitting that a certain amount of subconscious whoreness bias was probably just humanly unavoidable, I consciously gave my best to discard photos only based on picture quality and the woman's apparent age, rather than keep pretty ones and discard ugly ones.

I gave my best reasonable effort to eliminate multiple photos of the same woman, but the Orientals were really tough to tell apart, so I might have made some mistakes there.

I discarded celebrities, because I might have been tempted to evaluate their reputation rather than beauty, and that would have distorted the results. (By the way, it was weird how Google searches for virtually any country's women kept turning out photos of Rihanna.)

In course of the selection process, I ended up setting stricter quality standards for photos of professional models, compared to regular people. There were two reasons to that. Firstly, the photos of professional models were so numerous. Secondly, in some countries (most strikingly, Thailand) the models you see on billboards and in magazines look very different from the actual women you see on the street. So I didn't want them to dominate too much.

The idea behind this survey was to give an idea as to in which country a traveller or expat could hope to find the most beautiful women. That's why, when a woman was from the country A and the photo was taken in the country B, I counted the photo for the country B, even if the woman was just travelling. By the way, I was amazed to notice how the Russian whores seem to have found their way to most countries of the world. Well, nothing doing. If Yekaterina lives in Malta, I count her as a woman in Malta.


Credibility

I gave my best reasonable effort to make sure that the photos were actually from those countries. Of course, I had to rely on the information provided on third party websites, so if they were lying, that would have distorted the survey results, but there was nothing I could do to avoid that. I believe it's reasonable to expect that most websites don't lie about the origin of their photos. (I found several that do, and blacklisted them, of course. Strangely enough, some websites that sell photos lie shamelessly about the model's nationality.)

Obviously the ratings given to individual women are totally subjective and reflect my personal taste only. I have seen many astonishing examples of different men's having extremely different preferences on women. There was no way to avoid this subjectivity, and I did this for myself anyway. Should that be not good enough for someone, they are welcome to conduct their own, more objective survey, if they so desire.


Comments on results

I rated the photos in random order, not country by country, so when I began to add up the points, I had little idea of what the outcome would be. Some results were quite surprising.

While wondering which country might turn out to be the overall winner, I spent no thought at all on Colombia, but to think of it, I've seen remarkably beautiful women from that country, so its victory is not all that surprising.
What doesn't make sense at all, though, is that Montenegro and Serbia should have so different results – or, for that matter, Dominica and St. Kitts & Nevis. Or Australia and New Zealand.

The very low rankings of Bolivia, Peru and Ecuador are not surprising, considering that they have large Amerind populations and I find the Amerind women the least attractive of all. What doesn't fit in at all, though, is Mexico scoring approximately as high as Cuba. The population of Mexico is mostly Mediterranean-Amerind mix, and the one of Cuba is mostly Mediterranean-Negro mix, and when I visited those two countries, I was amazed at the ugliness of Mexican women compared to Cuban ones – or, for that matter, to any other country where I've been. I mean, the German women just don't take care of themselves, but the Mexican women are really ugly.
It might also rise an eyebrow or two that Panama has scored so low, compared to Colombia and Costa Rica. That is apparently because among the photos of Panamanian women on the Internet, too many are pictures of Amerinds taken by the tourists, while the dominant group in Colombia and Costa Rica are photos of chicks in search for US husbands. Well, can't be helped. I could hardly have begun to discard photos based on race, could I?
(By the way, the tourists' obsession with minority groups is probably also the reason why Namibia's score turned out so much lower than South Africa's.)

I already mentioned Germany, the country full of ugly women, the country where people use in all seriousness the term Schönheitswahn (beauty-mania), considering foreign women's pursuit of beauty and foreign men's preference for beautiful women to be some sort of a collective mental disease. So it's implausible that Germany would score clearly higher than Austria and almost as high as the Czech Republic, whereas in the real world the contrast between the Austrian and Czech women's beauty and the German women's ugliness is striking. But again, nothing doing. I rated photos individually and it would have been grossly unfair to penalise photos of German women on the grounds that most German women are actually not that pretty.

Thailand and the Philippines scored almost equally in this survey. In the real life, though, I assure you that the Filipina women are very much more beautiful than the Thai women. There is absolutely no comparison. Neither are Thai women any prettier than Vietnamese women, it's rather the other way around. I also think that Mongolia should have scored higher – at any rate, in the real world I've found Nothern Oriental women clearly prettier on average than Southern Oriental ones. The reason to those illogicalities is obvious – some Oriental countries are overflowing the Internet with photos of hand-picked extraordinary beauties (often surgery-enhanced) who look nothing like the vast majority of the women in those countries. So a random selection from Google search results is not always a representative picture of a country's womanhood, especially so in East Asia.

And last but not least, I was shocked by the extremely low score of my native country, Estonia. I admit that I am thoroughly fed up with the Estonian women's ever-increasing arrogance, but as to their physical beauty, I never expected I would rate it so low compared to that of Russian, Latvian and Swedish, and even Finnish women. Now, as I mentioned, the German women look ugly because of their wide-spread disregard and even contempt of personal style, but in Finland there seems to be an outright feminist conspiracy with the aim of choosing one's clothes and accessories so as to make oneself look as ugly as possible. There is no denying that the Estonian women have gotten clearly uglier during the last decade or two, obviously because they imitate the horrible personal style choices that are fashionable in Finland. However, they are nowhere near as ugly as the Finns themselves. This survey, though, says the opposite.

All in all, those illogical results suggest that 40 women per country are too few. They also seem to suggest that an Internet search engine is not the best means of acquiring women's photos for such a purpose. Still, those results are better than nothing and they're the best there are so far.


Future

I am planning to increase my database to 100 women from each country, and sooner or later I'm likely to add a couple dozen more countries. I have ideas how to make sure the results won't be dominated either by professional models or by minority groups fancied by tourists.

However, I have many other things to do, so I will only be spending a little time on this project occasionally when I feel like it. Therefore, the new ratings won't be there any time soon.

I've been suggested that I sell my database of photos. That is absolutely out of the question. The reason is very simple: the photos are presumably copyrighted and it would be against the law for me to distribute them. I would have to contact each photo's copyright owner and ask him for a permission. That would obviously be a grossly unreasonable amount of effort.




26 August 2012

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.