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Population Statistics and Qualitative Measures

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The HS Dent Methodology,which is based on predictable consumer spending patterns and the age of a population, is very effective in forecasting trends in developed,consumer-centric economies like the US and Japan. The main indicator of this methodology is the Spending Wave. With the wealth of demographic data now provided by the United Nations, we have the data necessary to forecast the economy of virtually every country in the world.

There is,however, an important caveat. In order for this analysis to work, a country must have already developed a sizable middle class. It is the middle class,the “Average Joes and Janes”, that move the economy with their spending. They also tend to be educated and are highly productive workers. The US, Japan, and Europe are all areas that have achieved affluence and quality education for the masses. Even the poor in these areas have electricity, quality plumbing, and color TVs. Though it is common to hear complaints about the educational systems, nearly all citizens are literate and have basic math skills. This is not the case in many underdeveloped countries in which the bulk of the population lacks the money to consume, the skills to produce, and the infrastructures and systems to leverage both.

The government must pursue the right policies to foster growth and allow economic choice as well. Though highly-regulated or command economies may be capable of meeting some of these needs (Cuba is reputed to have an excellent health system, for example), they generally failt o create mass affluence. A short list of the qualities that are necessary for our analysis to be effective are:

  1. A stable government – If the population has little confidence in the ability of the government to enforce the rule of law, maintain order, and properly conduct the business of the public good (infrastructure, education, evolution of laws, etc.), then there cannot be a focus on innovation and productivity, only conservation of whatever resources a family or community has.
  2. A stable financial system – If the currency of a nation is in free-fall, there will be tremendous pressure to hold no currency whatsoever, instead turning it into hard assets and keeping it there, distorting otherwise normal economic functions. The emergence of efficient financial exchanges and capital markets are a major plus.
  3. Reasonably free markets – When innovators, entrepreneurs, and consumers are directed in their production and/or spending, it distorts what would otherwise be the normal pattern of supply and demand. This does not mean that a society has to have totally free or open markets, only that there must be a level of freedom that allows for choice on the part of the producer and consumer.
  4. Significant modernization – A society must have progressed past the agrarian, rural stage, with urbanization well underway, broad access to basic infrastructures and the broad distribution of the wealth of the nation at least starting to occur.
  5. Economic autonomy – Our approach assumes that the domestic population is the main consumer of what that population produces. If the country is too small, like Singapore, which is a city-state, then the analysis must include the regional trading partners. This is the same reason that the argument of “Globalization will save us all” does not work. It assumes the free movement of all goods and services, without accounting for tariffs or feasibility (would you actually fly to Cuba to find a doctor?). Most domestic consumption of goods and services should happen within the country for our analysis to work.

With this in mind, we have listed here a number of key development measures that should be reviewed along with a country’s Spending Wave in order to get a more complete picture of their economic development, or lack thereof. Remember, in this kind of analysis, measurement is relative. For example, India scores much lower than the US across the board, but much higher than North Korea or many of the other underdeveloped countries in Africa and Asia. Here are some of the measures that we find valuable:

  • Published by the Heritage Foundation and the Wall Street Journal, the Index of Economic Freedom measures 161 countries against a list of 50 independent variables divided into 10 broad factors of economic freedom. Low scores are more desirable. The higher the score on a factor, the greater the level of government interference in the economy and the less economic freedom a country enjoys.
  • The Transparency International Corruption Perceptions Index ranks countries in terms of the degree to which corruption is perceived to exist among public officials and politicians. It is a composite index, a poll of polls, drawing on corruption-related data from expert and business surveys carried out by a variety of independent and reputable institutions. The CPI reflects views from around the world, including those of experts who are living in the countries evaluated. Transparency International commissions the CPI from Johann Graf Lambsdorff, a university professor based in Passau, Germany.
  • The Human Development Index is a standard means of measuring well-being, comparing measures of life expectancy, literacy, education, and standards of living for countries worldwide. It is used to distinguish whether the country is a developed, developing, or under developed country, and also to measure the impact of economic policies on quality of life. The index was developed in 1990 by Pakistani economist Mahbub ul Haq, and has been used since 1993 by the United Nations Development Programme in its annual Human Development Report.
  • The Gini Index measures the degree of inequality in the distribution of family income in a country. The index is calculated from the Lorenz curve, in which cumulative family income is plotted against the number of families arranged from the poorest to the richest. The more nearly equal a country’s income distribution, the lower its Gini index, e.g., a Scandinavian country with an index of 25. The more unequal a country’s income distribution, higher its Gini index, e.g., a Sub-Saharan country with an index of 50. If income were distributed with perfect equality, the index would be zero; if income were distributed with perfect inequality, the index would be 100.
  • Phone and Internet penetration, urbanization, and per capita GDP are simple ways to gauge a country’s economic development. There is no absolute measure of these statistics that defines a “developed country,” though higher for each of these is generally considered to be better. These statistics are intended for comparison purposes; a country’s development using these numbers can be measured relative to the United States, Japan, or the Western European countries. High levels of internet and phone usage, urbanization, and per capita GDP can be viewed both as a result of current development and a pre-condition for future development into the modern, services and information based economy.

Qualitative Measures

Country Economic Freedom Corruption Perception Human Development GINI
Albania 2.75 2.60 no data 28.2
Algeria 3.46 3.10 0.784 35.3
Angola 3.84 2.20 0.728 no data
Argentina 3.30 2.90 0.439 52.2
Armenia 2.26 2.90 0.863 41.3
Australia 1.84 8.70 0.768 35.2
Austria 1.95 8.60 0.957 31.0
Azerbaijan 3.51 2.40 0.944 36.5
Bahrain 2.23 5.70 0.736 no data
Bangladesh 3.88 2.00 0.859 31.8
Belarus 4.11 2.10 0.530 30.4
Belgium 2.11 7.30 0.794 25.0
Belize 2.78 3.50 0.945 no data
Benin 3.40 2.50 0.751 no data
Bhutan no data 6.00 0.428 no data
Bolivia 2.96 2.70 0.538 60.6
Bosniaand Herzegovina 3.01 2.90 0.692 26.2
Botswana 2.29 5.60 0.800 63.0
Brazil 3.08 3.30 0.570 59.7
Brunei no data no data 0.792 no data
Bulgaria 2.88 4.00 0.871 31.9
BurkinaFaso 3.28 3.20 0.816 48.2
Burundi 3.69 2.40 no data 33.3
Cambodia 2.98 2.10 no data 40.0
Cameroon 3.46 2.30 0.583 44.6
Canada 1.85 8.50 0.506 33.1
CapeVerde 2.69 no data 0.950 no data
CentralAfrican Republic 3.41 2.40 0.722 61.3
Chad 3.29 2.00 no data no data
Chile 1.88 7.30 no data 57.1
China 3.34 3.30 0.859 44.0
Colombia 3.16 3.90 0.768 53.8
Comoros no data no data 0.790 no data
Congo,Democratic Republic of the no data 2.00 0.556 no data
Congo,Republic of the 3.90 2.20 no data no data
CostaRica 2.69 4.10 0.520 46.5
Côted’Ivoire 3.14 2.10 0.841 45.2
Croatia 2.78 3.40 0.421 29.0
Cyprus 1.90 5.60 0.846 no data
CzechRepublic 2.10 4.80 0.885 25.4
Denmark 1.78 9.50 0.943 23.2
Djibouti 3.20 no data 0.494 no data
DominicanRepublic 3.39 2.80 0.751 47.4
Ecuador 3.30 2.30 0.765 42.0
Egypt 3.59 3.30 0.702 34.4
ElSalvador 2.35 4.00 0.729 52.5
EquatorialGuinea 3.74 2.10 0.653 no data
Eritrea no data 2.90 0.454 no data
Estonia 1.75 6.70 0.858 37.2
Ethiopia 3.70 2.40 no data 30.0
Finland 1.85 9.60 0.947 26.9
France 2.51 7.40 0.942 32.7
Gabon 3.28 3.00 0.633 no data
Gambia 3.51 2.50 0.479 no data
Georgia 2.98 2.80 0.743 38.0
Germany 1.96 8.00 0.932 28.3
Ghana 3.29 3.30 0.532 30.0
Greece 2.80 4.40 0.921 35.1
Guatemala 3.01 2.60 0.673 48.3
Guinea 3.55 1.90 0.445 40.3
Guyana 3.11 2.50 0.725 no data
Haiti 4.03 1.80 0.482 no data
Honduras 3.28 2.50 0.683 55.0
HongKong 1.28 8.30 0.927 43.4
Hungary 2.44 5.20 0.869 24.4
Iceland 1.74 9.60 0.960 no data
India 3.49 3.30 0.611 32.5
Indonesia 3.71 2.40 0.711 34.3
Iran 4.51 2.70 0.746 43.0
Ireland 1.58 7.40 0.956 35.9
Israel 2.36 5.90 0.927 34.0
Italy 2.50 4.90 0.940 36.0
Jamaica 2.76 3.70 0.724 37.9
Japan 2.26 7.60 0.949 37.9
Jordan 2.80 5.30 0.760 36.4
Kazakhstan 3.35 2.60 0.774 31.5
Kenya 3.20 2.20 0.491 44.5
Kuwait 2.74 4.80 0.871 no data
Kyrgyzstan 2.99 2.20 0.705 29.0
Laos 4.08 2.60 0.553 37.0
Latvia 2.43 4.70 0.845 32.0
Lebanon 3.00 3.60 0.774 no data
Lesotho 3.24 3.20 0.494 63.2
Libya 4.16 2.70 0.798 no data
Lithuania 2.14 4.80 0.857 31.9
Luxembourg 1.60 8.60 0.945 no data
Macedonia 2.80 2.70 0.796 28.2
Madagascar 2.75 3.10 0.509 47.5
Malawi 3.63 2.70 0.400 50.3
Malaysia 2.98 5.00 0.805 49.2
Mali 3.14 2.80 no data 50.5
Malta 2.16 6.40 0.875 no data
Mauritania 3.08 3.10 0.486 39.0
Mauritius 3.03 5.10 0.800 37.0
Mexico 2.83 3.30 0.821 54.6
Moldova 3.10 3.20 0.694 36.2
Mongolia 2.83 2.80 0.691 44.0
Morocco 3.21 3.20 0.640 40.0
Mozambique 3.35 2.80 no data 39.6
Myanmar 4.46 1.90 0.581 no data
Namibia 3.11 4.10 0.626 70.7
Nepal 3.53 2.50 0.527 37.7
Netherlands 1.90 8.70 0.947 30.9
NewZealand 1.84 9.60 0.936 36.2
Nicaragua 3.05 2.60 0.698 55.1
Niger 3.38 2.30 no data 50.5
Nigeria 4.00 2.20 0.448 50.6
Norway 2.29 8.80 0.965 25.8
Oman 3.01 5.40 0.810 no data
Pakistan 3.33 2.20 0.539 41.0
Panama 2.70 3.10 0.809 56.4
Paraguay 3.31 2.60 0.757 56.8
Peru 2.86 3.30 0.767 49.8
Philippines 3.23 2.50 0.763 46.6
Poland 2.49 3.70 0.862 34.1
Portugal 2.29 6.60 0.904 38.5
Qatar 3.04 6.00 0.844 no data
Romania 3.19 3.10 0.805 28.8
Russia 3.50 2.50 0.797 40.0
Rwanda 3.53 2.50 0.450 28.9
SaudiArabia 2.84 3.30 0.777 no data
Senegal 3.10 3.30 0.460 41.3
Serbia no data 3.00 no data no data
SierraLeone 3.76 2.20 no data 62.9
Singapore 1.56 9.40 0.916 42.5
Slovakia 2.35 4.70 0.856 25.8
Slovenia 2.41 6.40 0.910 28.4
SouthAfrica 2.74 4.60 0.653 59.3
SouthKorea 2.63 5.10 0.912 35.8
Spain 2.33 6.80 0.938 32.5
SriLanka 3.19 3.10 0.755 34.4
Sudan no data 2.00 no data no data
Suriname 3.60 3.00 0.759 no data
Sweden 1.96 9.20 0.951 25.0
Switzerland 1.89 9.10 0.947 33.1
Syria 3.93 2.90 0.716 no data
Tajikistan 3.76 2.20 0.652 34.7
Tanzania 3.20 2.90 0.430 38.2
Thailand 2.99 3.60 0.784 51.1
Trinidadand Tobago 2.50 3.20 0.809 no data
Tunisia 3.24 4.60 0.760 40.0
Turkey 3.11 3.80 0.757 42.0
Turkmenistan 4.04 2.20 0.724 40.8
Uganda 2.95 2.70 0.502 43.0
Ukraine 3.24 2.80 0.774 29.0
United Arab Emirates 2.93 6.20 0.839 no data
United Kingdom 1.74 8.60 0.940 36.8
United States 1.84 7.30 0.948 45.0
Uruguay 2.69 6.40 0.851 44.6
Uzbekistan 3.91 2.10 0.696 26.8
Venezuela 4.16 2.30 0.784 49.1
Vietnam 3.89 2.60 0.709 36.1
Yemen 3.84 2.60 0.492 33.4
Zambia 3.34 2.60 0.407 52.6
Zimbabwe 4.23 2.40 0.491 56.8

Population Statistics

Country 2005Population (000’s) Projected Population Growth(2005-2035) GDP Per Capita ($PPP) Percent Urban Phones Per 1,000 Pop. Internet Per 1,000 Pop.
Afghanistan 29,865 140.3% 800 24% 22.75 0.87
Albania 3,129 12.5% 5,300 45% 154.26 24.10
Algeria 32,853 40.7% 7,200 60% 215.43 26.11
Angola 15,940 109.4% 3,800 37% 53.99 11.10
Argentina 38,745 25.8% 13,700 91% 578.88 133.43
Armenia 3,015 -8.1% 4,800 64% 259.67 49.57
Australia 20,155 29.1% 31,600 93% 1,358.51 646.41
Austria 8,191 1.3% 32,500 66% 1,437.98 477.17
Azerbaijan 8,410 16.1% 5,400 50% 333.06 49.12
Bahrain 729 45.8% 23,100 90% 1,175.32 213.35
Bangladesh 141,823 52.8% 2,100 25% 37.03 2.15
Belarus 9,754 -18.1% 7,100 72% 578.19 162.86
Belgium 10,418 1.3% 31,100 97% 1,332.71 403.03
Belize 268 50.4% 6,800 49% 465.41 123.85
Benin 8,439 106.3% 1,100 46% 38.22 12.23
Bhutan 2,164 71.3% 1,400 9% 52.90 22.32
Bolivia 9,180 48.4% 2,900 64% 269.31 38.85
Bosniaand Herzegovina 3,906 -9.7% 5,200 45% 507.38 57.55
Botswana 1,765 -7.5% 10,700 53% 395.83 33.92
Brazil 186,404 29.7% 8,300 84% 587.17 119.62
Brunei 373 59.8% 23,600 78% 660.32 153.14
Bulgaria 7,727 -23.2% 9,600 70% 966.36 283.47
BurkinaFaso 13,229 121.8% 1,200 19% 37.39 4.15
Burundi 7,549 140.5% 700 11% 12.49 3.43
Cambodia 14,068 60.5% 2,500 20% 39.52 2.97
Cameroon 16,321 46.8% 2,300 53% 102.67 10.41
Canada 32,267 24.5% 33,900 81% 1,052.67 625.50
CapeVerde 507 70.6% 6,200 58% 281.14 50.49
CentralAfrican Republic 4,038 45.4% 1,100 44% 17.56 2.26
Chad 9,749 131.1% 1,400 26% 14.39 6.35
Chile 16,293 23.8% 11,900 88% 799.12 266.69
China 1,315,843 9.7% 6,800 41% 499.37 72.52
Colombia 45,600 36.3% 7,900 77% 426.78 79.83
Comoros 799 84.2% 600 36% 26.48 13.61
Congo,Democratic Republic of the 57,547 130.1% 700 33% 36.95 0.95
Congo,Republic of the 3,999 144.4% 1,300 54% 102.36 9.27
CostaRica 4,327 38.8% 11,400 62% 532.86 235.13
Côted’Ivoire 18,153 57.8% 1,600 46% 98.30 16.79
Croatia 4,550 -11.0% 12,400 60% 1,064.66 293.31
Cyprus 835 29.7% 21,600 69% 1,282.07 360.81
CzechRepublic 10,219 -9.4% 20,000 75% 1,392.05 469.85
Denmark 5,431 6.7% 34,800 86% 1,598.76 696.18
Djibouti 793 62.8% 1,000 85% 43.37 11.55
DominicanRepublic 8,896 35.0% 7,500 60% 395.79 91.24
Ecuador 13,229 36.9% 4,300 63% 472.12 47.90
Egypt 74,034 52.2% 3,900 42% 235.50 53.69
ElSalvador 6,880 44.4% 4,700 60% 402.28 86.87
EquatorialGuinea 504 81.2% 50,200 50% 106.21 10.16
Eritrea 4,404 102.8% 1,000 21% 14.01 11.82
Estonia 1,327 -9.9% 17,500 70% 1,260.02 496.66
Ethiopia 77,430 80.1% 900 16% 7.77 1.62
Finland 5,248 3.5% 31,000 61% 1,407.00 628.52
France 60,494 5.5% 29,600 77% 1,298.80 414.04
Gabon 1,383 44.8% 7,000 85% 387.59 29.36
Gambia 1,517 72.7% 1,900 26% 99.01 33.16
Georgia 4,475 -20.1% 3,400 51% 337.28 38.87
Germany 82,689 -2.2% 30,100 88% 1,525.35 500.06
Ghana 22,114 58.8% 2,500 46% 92.70 16.99
Greece 11,119 -0.6% 22,300 61% 1,465.31 176.81
Guatemala 12,601 75.7% 4,700 47% 349.77 61.49
Guinea 9,404 92.5% 2,000 36% 15.29 5.00
Guinea-Bissau 1,586 139.2% 800 36% 7.92 16.89
Guyana 752 -14.9% 4,500 38% 328.74 193.27
Haiti 8,529 38.8% 1,700 39% 64.23 59.47
Honduras 7,205 59.0% 2,900 46% 153.03 31.54
HongKong 7,041 25.3% 34,000 100% 1,733.12 505.58
Hungary 10,098 -11.0% 16,300 66% 1,217.41 267.14
Iceland 296 20.3% 35,700 93% 1,649.63 772.38
India 1,103,369 35.4% 3,400 29% 84.52 32.42
Indonesia 222,779 24.3% 3,600 48% 183.79 66.68
Iran 69,515 37.0% 8,400 68% 270.33 82.08
Ireland 4,147 30.3% 41,100 60% 1,425.45 265.40
Israel 6,726 41.9% 25,000 92% 1,498.68 470.75
Italy 58,092 -6.2% 28,700 68% 1,540.77 501.45
Jamaica 2,651 4.7% 4,500 52% 1,020.95 403.46
Japan 128,085 -6.2% 31,600 66% 1,176.09 587.02
Jordan 5,701 60.5% 4,700 79% 419.35 113.76
Kazakhstan 14,825 -3.8% 8,300 56% 350.30 26.64
Kenya 34,254 93.8% 1,100 42% 85.02 44.82
Kuwait 2,687 70.2% 20,300 96% 1,015.22 243.95
Kyrgyzstan 5,264 24.5% 2,000 34% 106.07 51.64
Laos 5,925 69.3% 2,000 22% 48.21 3.61
Latvia 2,309 -17.6% 13,700 66% 937.25 350.23
Lebanon 3,576 26.6% 6,000 88% 428.78 169.48
Lesotho 1,795 -8.7% 2,500 18% 109.14 23.92
Libya 5,851 48.5% 11,800 87% 155.81 35.71
Lithuania 3,429 -15.0% 13,700 67% 1,234.60 281.76
Luxembourg 465 37.6% 65,900 92% 1,998.12 597.42
Macedonia 2,032 -1.6% 7,800 60% 642.02 78.31
Madagascar 18,606 89.1% 900 27% 19.48 4.97
Malawi 12,882 83.4% 600 17% 24.99 3.66
Malaysia 25,345 42.2% 12,000 65% 765.56 396.80
Mali 13,520 129.0% 1,200 34% 36.19 3.81
Malta 400 7.8% 19,700 92% 1,248.78 750.07
Mauritania 3,068 95.4% 2,200 64% 134.51 4.70
Mauritius 1,244 17.3% 12,800 44% 699.87 145.84
Mexico 107,029 27.2% 10,000 76% 553.89 137.55
Moldova 4,205 -11.3% 1,900 46% 391.28 96.26
Mongolia 2,648 31.4% 1,900 57% 184.36 79.53
Morocco 31,478 38.3% 4,100 59% 356.95 117.36
Mozambique 19,791 59.8% 1,300 38% 26.94 7.10
Myanmar 50,520 22.5% 1,700 31% 10.34 1.27
Namibia 2,031 35.3% 7,000 33% 206.06 37.33
Nepal 27,132 62.6% 1,400 16% 21.79 6.58
Netherlands 16,297 6.5% 30,300 67% 1,393.09 614.19
NewZealand 4,026 16.8% 25,300 86% 1,188.75 787.98
Nicaragua 5,486 54.8% 2,900 58% 177.28 23.25
Niger 13,958 152.7% 1,000 23% 12.77 1.78
Nigeria 131,530 66.1% 1,400 48% 79.05 13.75
Norway 4,620 14.2% 42,800 80% 1,529.67 390.32
Oman 2,568 68.0% 13,500 79% 412.56 96.69
Pakistan 157,935 66.3% 2,400 35% 62.64 13.15
Panama 3,233 44.8% 7,400 58% 387.97 94.48
Paraguay 6,158 69.0% 4,600 58% 344.22 24.93
Peru 27,969 41.1% 6,000 75% 222.85 116.83
Philippines 83,055 42.3% 4,700 63% 445.66 53.91
Poland 38,529 -8.5% 13,100 62% 777.40 235.71
Portugal 10,493 4.2% 19,000 56% 1,384.29 281.00
Qatar 813 49.2% 28,300 92% 876.79 212.37
Romania 21,710 -13.9% 8,100 55% 673.49 207.52
Russia 143,200 -15.0% 11,000 73% 508.25 111.23
Rwanda 9,035 70.1% 1,500 22% 18.20 4.28
SaudiArabia 24,574 74.4% 13,100 88% 537.41 66.22
Senegal 11,657 71.2% 1,800 51% 72.38 42.33
Serbia 10,503 -5.0% 4,400 52% 910.18 147.30
SierraLeone 5,525 93.2% 800 40% 27.17 1.87
Singapore 4,327 23.0% 28,600 100% 1,350.05 571.13
Slovakia 5,402 -6.2% 16,300 58% 1,026.59 422.86
Slovenia 1,965 -8.7% 21,500 51% 1,278.48 475.71
SouthAfrica 47,434 2.1% 12,200 58% 473.09 78.36
SouthKorea 47,814 1.3% 22,600 81% 1,302.86 656.79
Spain 43,063 1.7% 25,600 77% 1,320.55 335.74
SriLanka 20,743 14.9% 4,300 21% 164.91 14.42
Sudan 36,235 59.8% 2,100 41% 58.48 32.09
Suriname 447 5.8% 6,600 77% 658.80 67.20
Sweden 9,043 8.9% 29,800 83% 1,742.55 756.23
Switzerland 7,253 2.0% 32,200 68% 1,559.63 473.64
Syria 19,042 66.6% 3,900 50% 269.35 43.05
Tajikistan 6,507 47.8% 1,200 24% 46.04 0.78
Tanzania 38,329 54.8% 700 38% 32.23 8.85
Thailand 64,234 16.2% 8,600 32% 536.57 109.46
Trinidadand Tobago 1,306 0.6% 16,800 76% 744.73 122.95
Tunisia 10,102 25.1% 8,200 64% 479.89 84.07
Turkey 73,191 31.9% 8,400 67% 750.52 142.48
Turkmenistan 4,831 33.3% 7,900 46% 82.01 7.55
Uganda 28,815 194.5% 1,800 12% 44.45 7.19
Ukraine 46,480 -29.4% 7,000 67% 545.34 79.03
UnitedArab Emirates 4,496 72.0% 45,200 85% 1,127.51 320.56
UnitedKingdom 59,668 9.7% 30,100 89% 1,583.51 628.06
UnitedStates 298,214 24.3% 41,600 81% 1,222.70 629.99
Uruguay 3,463 14.6% 9,900 93% 465.19 197.70
Uzbekistan 26,594 37.2% 1,900 36% 78.90 33.58
Venezuela 26,747 44.8% 6,400 88% 450.39 88.52
Vietnam 84,238 32.1% 2,800 27% 130.64 71.44
Yemen 20,973 119.2% 900 26% 91.99 8.85
Zambia 11,669 62.7% 900 37% 33.74 20.12
Zimbabwe 13,011 15.1% 2,100 36% 55.23 63.39
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