はてなキーワード: mexicoとは
Florida 1190963 8.69
New York 780287 5.7 ZEV
Pennsylvania 525152 3.83
Michigan 492813 3.59
Illinois 465434 3.39
%%%%%%%%%%%% 50percentile %%%%%%%%%%%%
New Jersey 457744 3.34 ZEV
Georgia 424366 3.09
North Carolina 385174 2.81
Oklahoma 383299 2.80
%%%%%%%%%%%% 60percentile %%%%%%%%%%%%
Arizona 328542 2.4
Virginia 301163 2.19 ZEV
Massachusetts 278785 2.03 ZEV
Washington 268391 1.96
%%%%%%%%%%%% 70percentile %%%%%%%%%%%%
Missouri 260096 1.90
Maryland 245179 1.79 ZEV
Tennessee 232717 1.69
Indiana 219028 1.6
Louisiana 197733 1.44
Minnesota 195376 1.42 ZEV
%%%%%%%%%%%% 80percentile %%%%%%%%%%%%
Colorado 194186 1.41 ZEV
South Carolina 193354 1.41
Wisconsin 191759 1.40
Alabama 177960 1.29
Oregon 150582 1.09 ZEV
Utah 136892 0.99
Connecticut 135724 0.99 ZEV
Kentucky 121943 0.89
Iowa 109514 0.8
Mississippi 97867 0.71
Arkansas 96324 0.70
Kansas 95520 0.69
New Hampshire 93825 0.67
Nebraska 70186 0.51
New Mexico 69855 0.51
Idaho 68446 0.49
West Virginia 67939 0.49
Hawaii 66324 0.48
Montana 56868 0.41
Maine 52646 0.38 ZEV
Delaware 43763 0.31
Rhode Island 42285 0.3 ZEV
Vermont 41068 0.3 ZEV
North Dakota 28489 0.2
Alaska 26683 0.19
South Dakota 26267 0.19
Wyoming 13574 0.09
そうなのかー
単位人件費あたりのGDPが金銭的な投資効率であったのに対し、単位労働時間あたりのGDPはいわば働き方改革で言われるような労働者の幸福度に関係する指標と言える。
つまり労働時間1時間あたりにどれだけのGDPを生み出すのかということである。これが高ければ高いほど、労働時間を減らしても国民は裕福に暮らせるというわけだ。
単位を見ると、GDP per capita = $/1人年, work hours = 時間/1人年, GDP per capita / work hours = $/時間
gdp per capita | work hours | Country Name | gdp per capita / work hours |
---|---|---|---|
142214 | 1473.26 | Luxembourg | 96.5302 |
114899 | 1424.58 | Norway | 80.6543 |
126905 | 1657.47 | Ireland | 76.5657 |
83598.5 | 1528.66 | Switzerland | 54.6874 |
74005.5 | 1371.61 | Denmark | 53.9553 |
69577.4 | 1427.02 | Netherlands | 48.7573 |
69081.3 | 1449.22 | Iceland | 47.6679 |
63149.6 | 1340.86 | Germany | 47.0963 |
67935.8 | 1443.72 | Austria | 47.0561 |
64578.4 | 1440.46 | Sweden | 44.8318 |
65027.3 | 1525.82 | Belgium | 42.6181 |
76398.6 | 1810.94 | United States | 42.1872 |
59026.7 | 1498.07 | Finland | 39.4019 |
55492.6 | 1511.4 | France | 36.716 |
62625.4 | 1707.33 | Australia | 36.6804 |
54602.5 | 1531.71 | United Kingdom | 35.648 |
58399.5 | 1686 | Canada | 34.6379 |
50031.7 | 1619.01 | Slovenia | 30.9026 |
51865 | 1694.45 | Italy | 30.6087 |
48396.7 | 1624.16 | Lithuania | 29.7981 |
51966.9 | 1748 | New Zealand | 29.7293 |
55927.9 | 1881.93 | Malta | 29.7184 |
49945.5 | 1754.05 | Czechia | 28.4744 |
45572.7 | 1607 | Japan | 28.359 |
45825.2 | 1643.55 | Spain | 27.8819 |
49930.9 | 1837.1 | Cyprus | 27.1792 |
46697.4 | 1770.41 | Estonia | 26.3766 |
50069.8 | 1901 | Korea, Rep. | 26.3387 |
49509.1 | 1891.9 | Israel | 26.169 |
39956.2 | 1553.24 | Latvia | 25.7245 |
41451.6 | 1635.1 | Portugal | 25.3512 |
41906.7 | 1699.6 | Hungary | 24.6568 |
43268.5 | 1814.79 | Poland | 23.8421 |
41887.9 | 1808.23 | Romania | 23.1651 |
37459.5 | 1622.07 | Slovak Republic | 23.0937 |
40379.6 | 1810.5 | Croatia | 22.303 |
37273.7 | 1732.09 | Turkiye | 21.5195 |
33582.3 | 1618.73 | Bulgaria | 20.746 |
36834.9 | 1886.29 | Greece | 19.5276 |
36484.7 | 1874 | Russian Federation | 19.4689 |
30208.8 | 1962.8 | Chile | 15.3907 |
24922.7 | 2149 | Costa Rica | 11.5973 |
21512.3 | 2226.3 | Mexico | 9.66279 |
20287.4 | 2405.39 | Colombia | 8.43416 |
gdp per capita | salary | Country Name | gdp per capita / salary |
---|---|---|---|
126905 | 52242.6 | Ireland | 2.42915 |
114899 | 53755.9 | Norway | 2.13742 |
142214 | 78310.1 | Luxembourg | 1.81603 |
49945.5 | 33475.5 | Czechia | 1.492 |
41906.7 | 28474.6 | Hungary | 1.47172 |
37459.5 | 26262.8 | Slovak Republic | 1.42633 |
36834.9 | 25979 | Greece | 1.41787 |
46697.4 | 34704.6 | Estonia | 1.34557 |
41451.6 | 31921.7 | Portugal | 1.29854 |
21512.3 | 16685.4 | Mexico | 1.28929 |
64578.4 | 50406.8 | Sweden | 1.28114 |
37273.7 | 31761 | Turkiye | 1.17357 |
43268.5 | 36896.6 | Poland | 1.1727 |
39956.2 | 34136.2 | Latvia | 1.17049 |
51865 | 44893.3 | Italy | 1.15529 |
74005.5 | 64126.7 | Denmark | 1.15405 |
83598.5 | 72993 | Switzerland | 1.14529 |
59026.7 | 51835.9 | Finland | 1.13872 |
49509.1 | 44155.9 | Israel | 1.12123 |
48396.7 | 43874.6 | Lithuania | 1.10307 |
69577.4 | 63225 | Netherlands | 1.10047 |
45572.7 | 41509.2 | Japan | 1.09789 |
63149.6 | 58940.3 | Germany | 1.07142 |
45825.2 | 42859.3 | Spain | 1.0692 |
67935.8 | 63801.6 | Austria | 1.0648 |
50031.7 | 47203.6 | Slovenia | 1.05991 |
62625.4 | 59407.9 | Australia | 1.05416 |
55492.6 | 52763.6 | France | 1.05172 |
51966.9 | 50722.5 | New Zealand | 1.02453 |
50069.8 | 48921.9 | Korea, Rep. | 1.02346 |
54602.5 | 53985.1 | United Kingdom | 1.01144 |
65027.3 | 64847.6 | Belgium | 1.00277 |
58399.5 | 59050.4 | Canada | 0.988978 |
76398.6 | 77463.5 | United States | 0.986253 |
30208.8 | 33042.3 | Chile | 0.914246 |
20287.4 | 22248.5 | Colombia | 0.911855 |
69081.3 | 79473.4 | Iceland | 0.869238 |
24922.7 | 31117.8 | Costa Rica | 0.800912 |
ジェンダー・ギャップ・ランキングの数値の中でも、GGIスコア(ジェンダー指数)は、0.001刻みで比べると、
お互いにほとんど同じ値をとる国が多い。ほんの小さな差では、扱いにくい面がある。
まず、153か国で、単純に順位と出生率の相関係数を計算すると、0.43になった。
単純に見ると、これは、順位が下がった国ほど、出生率が上がっていると読める。
しかし、これは単純に比較できない、社会進出をチェックする以前に、女性の基本的な権利や生命が脅かされたり、
工業化が進んでいないといった、発展途上国も多く含まれている。
そのような国では、過去の時代から改善されなかったまま、子沢山の社会が続いていることも多い。
明らかに、同じ基準で比較できないものを比較するのは不適切であるので、
これでも、まだ日本と単純に比較するには難しい、様々な文化の違いなどを考慮する必要があるので、
ドイツ、フランス、イタリア、オランダ、ベルギー、ルクセンブルク、フィンランド、スウェーデン、オーストリア、デンマーク、スペイン、ポルトガル、ギリシャ、アイルランド、チェコ、ハンガリー、ポーランド、スロヴァキア、エストニア、スロベニア、ラトビア、リトアニア
その他(16か国)
日本、イギリス、アメリカ合衆国、カナダ、メキシコ、オーストラリア、ニュージーランド、スイス、ノルウェー、アイスランド、トルコ、韓国、チリ、イスラエル、コロンビア、コスタリカ
この中で、出生率が2.9のイスラエルも、再び入れて計算した。
相関係数は-0.06になった。
かろうじて、順位と出生率の関係が負という結果が出たが、やはりOECD諸国の中でも、明らかに比較の難しい、
遠い文化圏の国を同時に比較している、無理な計算があることは否定できない。
そこで、日本と、現在の日本と文化的に近い韓国、北米、ヨーロッパ、オセアニアの国々だけを残して、
再計算する。OECD諸国のうち、コスタリカ、コロンビア、チリ、イスラエル、トルコを除いた。
良好な結果ではあるが、やはり、比較の難しい国を含めている可能性がある。
経済的な規模も、少子化問題対策の参考にするために、揃えて考える。
残った国の中から、更に、G20にも含まれている国だけを残して計算する。
G20に関する基礎的なQ&A
問.G20とは何ですか?
G20とは、G7(フランス、米国、英国、ドイツ、日本、イタリア、カナダ、欧州連合(EU)(G7の議長国順))に加え、アルゼンチン、豪州、ブラジル、中国、インド、インドネシア、メキシコ、韓国、ロシア、サウジアラビア、南アフリカ、トルコ(アルファベット順)の首脳が参加する枠組です。G20の会議には、G20メンバー以外の招待国や国際機関などが参加することもあります。
比較して、最終的に、次の国々を残した。
北欧がない問題が残るものの、妥当な組み合わせであり、日本の少子化対策にも役立つと考えられる。
Country Name | GGIRank | GGIScore | Fertility rate, total (births per woman)2020 |
Germany | 10 | 0.787 | 1.53 |
France | 15 | 0.781 | 1.83 |
Canada | 19 | 0.772 | 1.4 |
United Kingdom | 21 | 0.767 | 1.56 |
Mexico | 25 | 0.754 | 1.905 |
Australia | 44 | 0.731 | 1.581 |
United States | 53 | 0.724 | 1.6375 |
Italy | 76 | 0.707 | 1.24 |
Korea, Rep. | 108 | 0.672 | 0.837 |
Japan | 121 | 0.652 | 1.34 |
結果的に、日本と、文化も経済規模も近い国々の間で計算すると、非常に強い相関があると分かった。
ジェンダー・ギャップ・ランキングの順位が、低くなる国ほど、合計特殊出生率も低く、順位が高くなる国ほど、
出生率も高くなることが分かる。
ジェンダー・ギャップ・ランキングの順位が、少子化の指標として卓越していることが、
今回の計算でも示すことができた。
異次元の少子化対策が求められている、岸田令和日本である。しかし、具体的には、どのような対策が有効なのか。
対策の効果を測定するためにも、出生率と結びつきが強く、しかも、分かりやすい指標が求められている。
そこで、今回は、世界経済フォーラムが発表する、ジェンダー・ギャップ・ランキングに注目したい。
ジェンダー開発学の分野では、ジェンダー・ギャップ・ランキングの順位が高い国で、ジェンダー平等が達成され、
女性が子育てと社会進出を両立しやすく、結果的に、少子化も改善されていることが知られている。
日本の少子化対策についての記事の中で、ジェンダー・ギャップ・ランキングの順位の低迷と、
例えば、次の記事では、題名の中にジェンダー・ギャップ・ランキングの順位と出生数が盛り込まれている。
ジェンダーギャップ121位、出生数90万人以下の日本で、女性たちの未来への備えとは
https://woman.nikkei.com/atcltrc/blog/shirakawatouko/post/dddad1acb2e14d2a9ad1acb2e1cd2a4b/
更に、次の記事では、ジェンダー分野の専門家の対談の中で、GDPや労働生産性と共に出生率、そして、
ジェンダー・ギャップ・ランキングの順位の恥ずかしさについての問題が指摘されている。
ジェンダー指数から、いわゆる、ジェンダー・ギャップ・ランキングの順位が計算される。
上野千鶴子×酒井順子「単身世帯は38%、最も多い家族の姿に。1985年から86年は『女・女格差』元年。女性が3分割された結果、中高年女性単身者の貧困が生まれた」
酒井 2022年の日本のジェンダー指数は、世界146ヵ国中116位。それを永田町のおじさんたちは、恥ずかしいとは思っていないんでしょうね。国際会議に出席する日本代表が男性だけでも平気でいられる。
酒井 出生率も落ちる一方。出生率が高いのは、共働きでケアの公共化がされている場合だと海外ではデータがはっきり出ているのに、なぜ変えようとしないのでしょう。
上野 おじさんたちが合理的選択をしないのは、ホモソーシャルな組織文化を守りたいからだとしか私には思えません。ホモソーシャルな集団のなかで、男として認められたい。そのためには自己犠牲もいとわない。
上で紹介したような関連性の指摘のみならず、実際に、ジェンダー・ギャップ・ランキングや、
類似する、ジェンダー・ギャップを示す指標と、少子化の関係性の分析もなされている。
https://news.yahoo.co.jp/byline/shirakawatoko/20211029-00265552
日本のジェンダーギャップと少子化。この二つはリンクしているとずっと言い続けてきた。日本は世界経済フォーラムが算出するジェンダーギャップ指数では156カ国中120位と先進国では最下位。下から数えた方が早い。先進国に限ってはジェンダーギャップ指数と出生率がリンクしていることがOECD(経済協力開発機構)の分析でわかっている。
2020年4月の内閣府政策統括官(経済社会システム担当)の資料には、「ジェンダーギャップ指数が高い(男女格差が少ない)ほど、出生率は高まる傾向」を示すグラフが掲載されている【図1】。女性が社会進出をすると一旦は少子化になるが、その後回復するかどうかは、ジェンダーギャップをいかに埋めるかにかかっている。
ところが、冒頭のツイートの図にあった通り、他の先進国では事情が違います。女性の社会進出と出生率が相関関係にあるのです。なぜかというと、女性の社会進出と子育てが、トレードオフの関係になっていないからです!
子どもが生まれたら、パートナーたる男性も、当事者としてしっかり家事育児にコミットします。これだけでも、女性の負担はケタ違いでしょう。みての通り、男性の家事育児の負担割合が高い国ほど、出生率が高いのがわかります(我が国は定位置の左下)。
しかし、ジェンダー・ギャップ・ランキングの話をすると、クソリプと呼ばれる意見が寄せられたり、
指標のことに異論を挟む声も、少なくない。そこで今回は、改めて白黒はっきりつけ、
ジェンダー・ギャップ・ランキングが少子化を説明できる、卓越した指標であることを示す。
Fertility rate, total (births per woman) - World Bank Data
ジェンダー・ギャップ・ランキングは、次の、同じ2020年のデータを使う。
https://www.weforum.org/reports/gender-gap-2020-report-100-years-pay-equality/
どちらにも掲載されている、153か国のデータを使って、ジェンダー・ギャップ・ランキングと、
Country Name | GGIRank | GGIScore | Fertility rate, total (births per woman)2020 |
Iceland | 1 | 0.877 | 1.72 |
Norway | 2 | 0.842 | 1.48 |
Finland | 3 | 0.832 | 1.37 |
Sweden | 4 | 0.82 | 1.66 |
Nicaragua | 5 | 0.804 | 2.349 |
New Zealand | 6 | 0.799 | 1.61 |
Ireland | 7 | 0.798 | 1.63 |
Spain | 8 | 0.795 | 1.23 |
Rwanda | 9 | 0.791 | 3.873 |
Germany | 10 | 0.787 | 1.53 |
Latvia | 11 | 0.785 | 1.55 |
Namibia | 12 | 0.784 | 3.349 |
Costa Rica | 13 | 0.782 | 1.555 |
Denmark | 14 | 0.782 | 1.67 |
France | 15 | 0.781 | 1.83 |
Philippines | 16 | 0.781 | 2.777 |
South Africa | 17 | 0.78 | 2.401 |
Switzerland | 18 | 0.779 | 1.46 |
Canada | 19 | 0.772 | 1.4 |
Albania | 20 | 0.769 | 1.4 |
United Kingdom | 21 | 0.767 | 1.56 |
Colombia | 22 | 0.758 | 1.737 |
Moldova | 23 | 0.757 | 1.77 |
Trinidad and Tobago | 24 | 0.756 | 1.631 |
Mexico | 25 | 0.754 | 1.905 |
Estonia | 26 | 0.751 | 1.58 |
Belgium | 27 | 0.75 | 1.55 |
Barbados | 28 | 0.749 | 1.628 |
Belarus | 29 | 0.746 | 1.382 |
Argentina | 30 | 0.746 | 1.911 |
Cuba | 31 | 0.746 | 1.5 |
Burundi | 32 | 0.745 | 5.177 |
Lithuania | 33 | 0.745 | 1.48 |
Austria | 34 | 0.744 | 1.44 |
Portugal | 35 | 0.744 | 1.4 |
Slovenia | 36 | 0.743 | 1.6 |
Uruguay | 37 | 0.737 | 1.477 |
Netherlands | 38 | 0.736 | 1.55 |
Serbia | 39 | 0.736 | 1.48 |
Poland | 40 | 0.736 | 1.38 |
Jamaica | 41 | 0.735 | 1.358 |
Bolivia | 42 | 0.734 | 2.651 |
Lao PDR | 43 | 0.731 | 2.541 |
Australia | 44 | 0.731 | 1.581 |
Zambia | 45 | 0.731 | 4.379 |
Panama | 46 | 0.73 | 2.344 |
Zimbabwe | 47 | 0.73 | 3.545 |
Ecuador | 48 | 0.729 | 2.051 |
Bulgaria | 49 | 0.727 | 1.56 |
Bangladesh | 50 | 0.726 | 2.003 |
Luxembourg | 51 | 0.725 | 1.37 |
Cabo Verde | 52 | 0.725 | 1.908 |
United States | 53 | 0.724 | 1.6375 |
Singapore | 54 | 0.724 | 1.1 |
Romania | 55 | 0.724 | 1.6 |
Mozambique | 56 | 0.723 | 4.713 |
Chile | 57 | 0.723 | 1.537 |
Honduras | 58 | 0.722 | 2.394 |
Ukraine | 59 | 0.721 | 1.217 |
Croatia | 60 | 0.72 | 1.48 |
Bahamas, The | 61 | 0.72 | 1.394 |
Madagascar | 62 | 0.719 | 3.918 |
Slovak Republic | 63 | 0.718 | 1.57 |
Israel | 64 | 0.718 | 2.9 |
Uganda | 65 | 0.717 | 4.693 |
Peru | 66 | 0.714 | 2.216 |
Venezuela, RB | 67 | 0.713 | 2.23 |
Tanzania | 68 | 0.713 | 4.795 |
Bosnia and Herzegovina | 69 | 0.712 | 1.359 |
North Macedonia | 70 | 0.711 | 1.3 |
Montenegro | 71 | 0.71 | 1.75 |
Kazakhstan | 72 | 0.71 | 3.13 |
Botswana | 73 | 0.709 | 2.836 |
Georgia | 74 | 0.708 | 1.971 |
Thailand | 75 | 0.708 | 1.341 |
Italy | 76 | 0.707 | 1.24 |
Suriname | 77 | 0.707 | 2.371 |
Czechia | 78 | 0.706 | 1.71 |
Mongolia | 79 | 0.706 | 2.9 |
El Salvador | 80 | 0.706 | 1.819 |
Russian Federation | 81 | 0.706 | 1.505 |
Ethiopia | 82 | 0.705 | 4.243 |
Eswatini | 83 | 0.703 | 2.89 |
Greece | 84 | 0.701 | 1.34 |
Indonesia | 85 | 0.7 | 2.194 |
Dominican Republic | 86 | 0.7 | 2.303 |
Vietnam | 87 | 0.7 | 1.955 |
Lesotho | 88 | 0.695 | 3.049 |
Cambodia | 89 | 0.694 | 2.381 |
Malta | 90 | 0.693 | 1.13 |
Cyprus | 91 | 0.692 | 1.328 |
Brazil | 92 | 0.691 | 1.649 |
Kyrgyz Republic | 93 | 0.689 | 3 |
Azerbaijan | 94 | 0.687 | 1.7 |
Brunei Darussalam | 95 | 0.686 | 1.796 |
Cameroon | 96 | 0.686 | 4.543 |
Liberia | 97 | 0.685 | 4.174 |
Armenia | 98 | 0.684 | 1.575 |
Senegal | 99 | 0.684 | 4.454 |
Paraguay | 100 | 0.683 | 2.497 |
Nepal | 101 | 0.68 | 2.055 |
Sri Lanka | 102 | 0.68 | 2 |
Fiji | 103 | 0.678 | 2.495 |
Malaysia | 104 | 0.677 | 1.818 |
Hungary | 105 | 0.677 | 1.56 |
China | 106 | 0.676 | 1.281 |
Ghana | 107 | 0.673 | 3.623 |
Korea, Rep. | 108 | 0.672 | 0.837 |
Kenya | 109 | 0.671 | 3.397 |
Belize | 110 | 0.671 | 1.999 |
Sierra Leone | 111 | 0.668 | 4.08 |
India | 112 | 0.668 | 2.051 |
Guatemala | 113 | 0.666 | 2.484 |
Myanmar | 114 | 0.665 | 2.174 |
Mauritius | 115 | 0.665 | 1.44 |
Malawi | 116 | 0.664 | 3.995 |
Timor-Leste | 117 | 0.662 | 3.247 |
Angola | 118 | 0.66 | 5.371 |
Benin | 119 | 0.658 | 5.048 |
United Arab Emirates | 120 | 0.655 | 1.46 |
Japan | 121 | 0.652 | 1.34 |
Kuwait | 122 | 0.65 | 2.14 |
Maldives | 123 | 0.646 | 1.712 |
Tunisia | 124 | 0.644 | 2.114 |
Guinea | 125 | 0.642 | 4.489 |
Vanuatu | 126 | 0.638 | 3.778 |
Papua New Guinea | 127 | 0.635 | 3.274 |
Nigeria | 128 | 0.635 | 5.309 |
Burkina Faso | 129 | 0.635 | 4.869 |
Turkiye | 130 | 0.635 | 1.917 |
Bhutan | 131 | 0.635 | 1.433 |
Algeria | 132 | 0.634 | 2.942 |
Bahrain | 133 | 0.629 | 1.832 |
Egypt, Arab Rep. | 134 | 0.629 | 2.96 |
Qatar | 135 | 0.629 | 1.816 |
Gambia, The | 136 | 0.628 | 4.777 |
Tajikistan | 137 | 0.626 | 3.237 |
Jordan | 138 | 0.623 | 2.873 |
Mali | 139 | 0.621 | 6.035 |
Togo | 140 | 0.615 | 4.323 |
Mauritania | 141 | 0.614 | 4.455 |
Cote d'Ivoire | 142 | 0.606 | 4.472 |
Morocco | 143 | 0.605 | 2.353 |
Oman | 144 | 0.602 | 2.687 |
Lebanon | 145 | 0.599 | 2.103 |
Saudi Arabia | 146 | 0.599 | 2.465 |
Chad | 147 | 0.596 | 6.346 |
Iran, Islamic Rep. | 148 | 0.584 | 1.708 |
Congo, Dem. Rep. | 149 | 0.578 | 6.206 |
Syrian Arab Republic | 150 | 0.567 | 2.798 |
Pakistan | 151 | 0.564 | 3.555 |
Iraq | 152 | 0.53 | 3.551 |
Yemen, Rep. | 153 | 0.494 | 3.886 |
OECD Indicators of Employment Protection
https://www.oecd.org/employment/emp/oecdindicatorsofemploymentprotection.htm
https://stats.oecd.org/Index.aspx?DataSetCode=EPL_R#
OECD indicators of employment protection database: summary indicators and items
https://www.oecd.org/els/emp/OECDEmploymentProtectionLegislationDatabase.xlsx
Annex Table 3.A.1. Structure of Version 4 of the OECD EPL indicators for dismissing regular workers
Annex Table 3.A.2. Weighting in the OECD EPL indicators (Version 4) for dismissing regular workers
・EPTT:有期雇⽤契約
『派遣契約の EPL 指標のバージョン 1 〜 3 は、有期契約または派遣派遣契約の労働者の雇⽤制限に限定されていました。バージョン 4 では、これらの指標の範囲が有期契約の解約費⽤にまで拡⼤されました1。これは、派遣契約の全体的な規制レベルと労働市場における制度的⼆元論の程度をより適切に把握するためです。したがって、定期労働者保護の指標と同じモデルに基づいて、有期雇⽤契約(EPTT)の個別の終了に対する保護の 2 つの指標 (i) 満了⽇、および (ii) 満了前の 2 つの指標が構築されました。個別解雇(EPR)に反対します。このノートでは、2 つの新しい EPTT 指標と、⼀時契約規制 (EPT) の総合指標の新しいバージョン 4 を紹介します。』
以前の「解雇の難しさ」には
6. 試⽤期間
7. 報酬
8.復職
だったが、現在は
・「解雇の難しさ」から「不当解雇に関する規制の枠組み」に変更。
・「9.請求の最⼤時間」が「不当解雇に関する規制の枠組み」ではなく「不当解雇規制の施行」の分類に移動。
Item 5 サブアイテム: {
Item 5a: 経済的理由による解雇 理由:審査員の自由度
Item 5b: 経済的理由による解雇 理由:解雇の具体的な代替案と解雇の場合の拘束⼒のある義務
Item 5d: 経済的理由による解雇 理由:解雇の正当な理由
}
Item 6: 試用期間の長さ
Item 9: 不当解雇の訴えを起こすまでの期間
26位
33位
33位
3.4ポイント(6段階中)
4位(1.Portugal 2.Mexico 3.Korea)
2.75ポイント(6段階中)
16位
2ポイント(6段階中)
24位
Item 5: 2ポイント
Item 5a: 4ポイント
Item 5b: 2ポイント
Item 5c: 0ポイント
Item 5d: 2.625ポイント
Item 6: 6ポイント
Item 7: 1ポイント
Item 8: 2ポイント
Item 9: 6ポイント
調べるのも今はここまでが限界。
現在は違うが以前の「解雇の難しさ」が実際の解雇の難しさのための指標になっていたか疑問。
特に、Item5ならまだわかるけどItem6~9は解雇の難しさに入れるべきか疑問。
Item5自体はそこまで高くない。
Item6とItem9が平均を押し上げている。
僕は何にもわからない素人だけど正直これで解雇規制について語ることはできないかなといった印象。
専門家の方々にはもっと公平に具体的にデータを使って話をしてもらいたいと思った。
解雇規制について語っている人は専門家も含めてバイアスが強すぎる人が多いので注意したい。
https://www.dir.co.jp/report/research/economics/europe/20140318_008337.pdf
"OECD の日本の労働市場に対する評価や勧告とはどのようなものなのかを、再度確認してみよう。毎年刊行されている“Employment Outlook”や“Economic Policy Reforms”、随時公表される調査書などの内容を見てみると、OECD は「労働市場の二極化(labour market dualism)」が日本の大きな問題であると一貫して指摘している。日本で頻繁に取り上げられる「正規雇用の解雇がほとんど不可能」ということではなく、それが正規/非正規の大きな格差を生み出していること、そして格差を是正する規制がないことを問題視しているのがわかる。 "
おまえはMEXICOのお客さんから問い合わせを受けたことがあるか?
MEXICOのお客さんの問い合わせに返信出来てこそ真の男だ!
MEXICOのお客さんの問い合わせがなかったら、おまえはだたの腰抜けだ!
今日はオレがその真の男へなるためMEXICOのお客さんの問い合わせの返信の仕方を教えてやる!
あのさー
各国から年に2~3回は問い合わせが来るんだけど
なんだか地球の裏側から問い合わせが来るのは感慨深いものがあるわよね。
大丈夫なんでしょうね?
お客さんには翻訳サービス使ってるから勘弁してちょんまげ!って前置きをしているから
いままで、
まあ通じてるんでしょうね。
便利な世の中でもあるとともに、
いまや世界の裏側にもお荷物が届けられる便利さはゴイスーだわ。
ただ一番面倒くさいというか手間だというのが
送料の算出がいちいち重さを量って全部手作業になるのが手間っちゃー手間なのよね。
国内だと大手運送会社のサービスを使った伝票発行システムがシステマチックに揃ってるから
何件あっても瞬時に出力ができるのよね。
海外発送がそこがネックだわ。
件数が多くなってきたら手間だけど、
その時考えましょう。
きっと問題を解決するソリューションが解決してくれることをイノベーションでありインスパイヤザネクスト日立アンドスーパーヒトシクンでもあるわ。
でも我ながら、
メキシコの人!聞こえてますかー!って心の中で唱えてしまっちゃったわよ。
心の中でよ。
あとさ
海外の住所ってさ、
メキシコのメキシコ州とかなんかメキシコの中にメキシコがある感じで
日本で言う静岡の日本平って日本日本と続くとなんかこれ正解なのかしら?って疑ってしまうわ。
名前もさー
例えで言って書くと
これ同じ字面が2つ続いてるけど間違いじゃないのかしら?
私の知ってるそういう名前は前田前田とザ・たっちしか知らないわ。
でもまあ、
怒られたこともないし、
私が腰抜けでないことは確かね。
うふふ。
朝起きれなかったのでおにぎり握れませんでした。
気持ちは握っていたのよ!
夢の中で!夢の中で!
今日はもう早く寝ることを心に誓っていきたいと思うわ。
いつか買い置きしておきたいところだし
いっそのこと
いいの探してみようかしらね。
すいすいすいようび~
今日も頑張りましょう!
とにかく、テキサス州をセックス州に、メキシコをレキシコにするしかないんだよ
響きがいいですね それだけだよ
意味がいるんだよ
そもそも宇宙のあらゆることに意味なんてないでーす!!その通り!!!!うるせえ!!!!!!死ね!!!!!!!
人生、こんな苦しいのに、意味がないってことにしちゃったら救いがなさすぎるよ
齧り付いてでも意味があるってことにすりゃいいんだ
だからね
テックスメックスってきいたところで頭になんのイメージもわかねーわけ
荒れ狂う自然のパワー、躍動、野生のいとなみ!そういうものを感じるでしょうが
そりゃ痛みを伴う改革だよ
でもさあ先人はそうやって変えてきてんじゃん
俺たちも先に進もうぜ
Allowed countries
AE - United Arab Emirates
AL - Albania
BE - Belgium
BG - Bulgaria
BI - Burundi
BM - Bermuda
BN - Brunei Darussalam
BO - Bolivia (Plurinational State of)
BZ - Belize
CD - Congo (Democratic Republic of the)
CH - Switzerland
CK - Cook Islands
CN - China
CO - Colombia
CR - Costa Rica
CY - Cyprus
CZ - Czech Republic
DE - Germany
DO - Dominican Republic
EE - Estonia
EG - Egypt
FI - Finland
FK - Falkland Islands (Malvinas)
FM - Micronesia (Federated States of)
GB - United Kingdom of Great Britain and Northern Ireland
GG - Guernsey
GH - Ghana
GN - Guinea
GS - South Georgia and the South Sandwich Islands
HK - Hong Kong
HM - Heard Island and McDonald Islands
HT - Haiti
HU - Hungary
IN - India
IO - British Indian Ocean Territory
IR - Iran (Islamic Republic of)
JE - Jersey
JO - Jordan
KE - Kenya
KI - Kiribati
KW - Kuwait
KZ - Kazakhstan
LA - Lao People's Democratic Republic
LB - Lebanon
LI - Liechtenstein
LK - Sri Lanka
LR - Liberia
LU - Luxembourg
LY - Libya
ME - Montenegro
NO - Norway
NU - Niue
OM - Oman
PE - Peru
PL - Poland
PM - Saint Pierre and Miquelon
QA - Qatar
RE - Réunion
RS - Serbia
RU - Russian Federation
RW - Rwanda
SC - Seychelles
SH - Saint Helena, Ascension and Tristan da Cunha
SK - Slovakia
SO - Somalia
SZ - Swaziland
TF - French Southern Territories
TJ - Tajikistan
TL - Timor-Leste
TN - Tunisia
TO - Tonga
TR - Turkey
TZ - Tanzania, United Republic of
UG - Uganda
UM - United States Minor Outlying Islands
US - United States of America
UY - Uruguay
UZ - Uzbekistan
VC - Saint Vincent and the Grenadines
VE - Venezuela (Bolivarian Republic of)
VI - United States Virgin Islands
VU - Vanuatu
YE - Yemen
ZA - South Africa
ZM - Zambia
ZW - Zimbabwe
I would like to write about what I know and understand about the Soka Gakkai because the D.C. Times published an article titled "China's Manipulation of Japan, NPOs and Soka Gakkai Act as Pipeline = U.S. Think Tank Report".
First of all, as a premise, the Soka Gakkai is a cult.
This is because there is a definition of a religious cult, and the reality of the Soka Gakkai falls under that definition in many ways.
You can read more about the definition of a religious cult and mind control in the book "Combating Cult Mind Control: The #1 Best-selling Guide to Protection, Rescue, and Recovery from Destructive Cults ".
The Soka Gakkai is also a collection of criminals, sick people and poor people.
In fact, the Soka Gakkai is similar to the mafia gangs in Italy and Mexico, and it has reigned as the largest criminal and anti-society organization in Japan in the name of a religious organization.
Many of its members have been brainwashed and are unable to recognize and judge themselves as normal human beings.
In the 1950s and 1980s, Soka Gakkai members were forcibly recruited to join the Soka Gakkai, and nowadays, it is estimated that about 10% of the Japanese people are members of the Gakkai (Soka Gakkai members).
In particular, the Soka Gakkai has infiltrated civil servants, specifically the police force, the fire department, and the Self-Defense Forces, and it has been revealed that 20 to 30% of the Metropolitan Police Department's employees are members of the Soka Gakkai.
There is always a certain percentage of Soka Gakkai members in elementary, middle, and high school classes, and in corporate workplaces, and therefore it is taboo to criticize the Soka Gakkai in those communities.
This is because the Gakkai members in each community monitor the words and actions of their community members in the same way as the mainland communists who have infiltrated Hong Kong, and if someone speaks out against the Soka Gakkai, they will target that person and initiate a campaign of sabotage.
The sabotage is similar to the CPC's repressive actions against human rights activists in Hong Kong, including obstructing, harassing, and following them around, an act that has been described as mass stalking.
For example, in Japan, if you make a placating statement in a school class or at work that the Soka Gakkai is a cult religious group because it meets the definition of a cult group, members of the Gakkai in the community get madly angry (depending on the degree of mind control they are receiving) or bite off their anger to deny the statement.
Then they label the person who made such a statement as "anti", and they also share information about the antis with other members of the Soka Gakkai, and begin to perceive them as "beings to be punished by Buddha", to be targets of surveillance and group attacks.
In reality, however, the definition of a religious cult was not defined for the Soka Gakkai but for dangerous religious groups such as Aum Shinrikyo and People's Temple, which were intended to prevent ordinary people from being harmed by them.
The Soka Gakkai falls under the definition of a cult because the Soka Gakkai has cult-like tendencies.
When Soka Gakkai members are pointed out to the Soka Gakkai, instead of thinking "Let's fix what's wrong with my religious group," they think of suppressing their critics (anti) and silencing them, which is a pattern of thinking and behavior of a fanatic of a religious cult, and the sarin gas attack (terrorism). I feel that the followers of Aum Shinrikyo at the time when it was founded must have had a similar pattern of thinking and behavior.
Believers in cult groups are unconsciously mind-controlled and brainwashed, so they don't think that they should change their way of thinking and behavior when criticism is pointed out to them. In this respect, their attitude is similar to that of the Chinese Communist Party towards the demands of human rights activists in Hong Kong, i.e., the fanatics of cult groups such as the Soka Gakkai are not normal human beings.
By the way, there is an organization called JCP in Japan, which is also anti-American and illegal in the United States.
It is well known that some anti-American organizations cooperate with each other in order to undermine this country by signing a pact called "Soko Kyodo Agreement" and facilitating agents of anti-Japanese and anti-American groups.
It is obvious that the JCP is an anti-American terrorist organization in nature and that the JCP is a cult-like organization when it signs an agreement with a religious cult.
From another point of view, the Soka Gakkai, to its followers, appears to be a huge organization that carries out fraudulent and criminal activities such as Ponzi schemes and network businesses. It also has elements of a black business, and believers who join the Soka Gakkai are becoming materially and mentally exhausted.
The following blog, run by Mr. Sinifié, exposes the reality of the Soka Gakkai. It contains the testimonies and experiences of many current and former Soka Gakkai members and ex-members who have left the Gakkai.
It is clear that this reality of the Soka Gakkai is far removed from the original role of religion, which is to provide individuals with peace of mind and spiritual support.
As the saying goes, "like begets friend," it is only natural for the Soka Gakkai to try to maintain a good relationship with the CPC.
However, many Chinese who have worked in Japan seem to dislike the Soka Gakkai and return to their countries.
Although the Soka Gakkai employs a different strategy than Aum Shinrikyo and has infiltrated many organizations such as corporations, police, fire departments, the Self-Defense Forces, and local government officials, the Soka Gakkai members who have infiltrated the Kasumigaseki bureaucracy and the Self-Defense Forces are considered dangerous to the U.S. because they are inherently dangerous.
Because they are essentially anti-American and may act as agents to cooperate with the CPC.
There are some findings that are common knowledge among intellectuals in the U.S. and Europe but have not been made known to the Japanese people in Japan because the media and bureaucrats have stopped them.
One of them is that the Soka Gakkai headquarters has been sending donations from Gakkai members to Noriega (former general, now imprisoned) in Panama for large-scale tax evasion and money laundering.
Noriega received a large amount of money from Daisaku Ikeda of the Soka Gakkai and invested it in his own drug business, spreading drugs on an international level.
Daisaku Ikeda of the Soka Gakkai has been investing and managing the donations collected from Gakkai members in Noriega's drug business as well as tax evasion and money laundering. At the same time, the Soka Gakkai and Daisaku Ikeda invested the donations they received from Gakkai members in Noriega's drug business as a means of tax evasion and money laundering, and returned the profits to the domestic market to help the Soka Gakkai executives line their pockets and build Soka Gakkai facilities and Soka University.
The fact that Daisaku Ikeda raised Noriega's profile in the Seikyo Shimbun during the same period must be undeniable to those Gakkai members who have subscribed to the Seikyo Shimbun.
In particular, there are many Gakkai members at the level of police organizations, the Metropolitan Police Department and prefectures, who have been causing social problems and covering up crimes committed by Gakkai members in Japan.
Well, if they are in a state of unconscious brainwashing and mind control, they may not believe the contents, and may assume a pattern of behavior such as getting angry, grumpy, or attacking the writer.
In other words, one can expect a lot of denial of facts like the followers of Aum Shinrikyo, which is easy to expect, but this (the issue of Soka Gakkai and drug business, tax evasion, and money laundering) is a fact that was revealed because Noriega was arrested and imprisoned for spreading drugs in the US. This is a fact that is well known as common knowledge in the U.S. and Europe.
The fact that the Soka Gakkai is a criminal organization is very difficult to deny.
先進国って一つとでも思ってるんだろうか
Flag of Oregon.svg オレゴン州 - 1994年「尊厳死法 (Death with Dignity Act)」成立
Flag of Oregon.svg ワシントン州 – 2009年
Flag of Montana.svg モンタナ州 - 2009年
Flag of Vermont.svg バーモント州 - 2013年
Flag of New Mexico.svg ニューメキシコ州 - 2014年
Flag of California.svg カリフォルニア州 - 2015年[8]
ルクセンブルクの旗 ルクセンブルク - 2008年「安楽死法」可決。
メチャ好きなバンドがいくつかあるんだけど、誰とも語り合えないし、当該バンドメンが日本に公演しにくることも無い
日本向けに情報提供も無いし、どこで愛情表現をしたら良いのか分からない、はけ口が無い
ですのでここで吐き出したい
1位 Zoé!
2位 DLD!
3位 Kinky!
ZoéとDLDはしっとりした曲調と低めのボーカルの組み合わせが良い
Zoé - Azul
ZoéのボーカルLeón Lrreguiさんはソロでも良い曲を出しているよ
DLDはもともとのバンド名がDildoで、このバンド名は何…?と思いながら聴いてたんだけと、5年くらい前にDLDになって、Dildoだった過去は抹殺されまし
た(されてない)
ウィキペディアには何やら書いてあるけどスペイン語なので分からない
kinkyはね、メキシコ音楽を外向けなポップにして聴かせてくれる良いセンスでした(過去)
Kinky - A Donde Van Muertos
けどもういい加減おじさんなんだよね、カワイイ役割はおしまいにしたいところ
最近の曲はよく分かんない
メキシコ色が強めかな〜
広告っぽい感じも強くて、迷ってる時期なのかな
迷うにしてはベテランすぎる時期なんだけど
がんばえ〜〜〜()
以上です!
読んでくれてありがと〜
毎回「○○(バンド名) banda mexico」とかでググってる
バンド名に特徴が無いと見付からないし…特徴的すぎるとそれはそれでダサいし……
難しいね〜〜〜
先日DLDの新しいアルバム『Trancsender』が出ました!聴いてね〜
いつか誰かと『DLDのバンド名がかつてDildoだった』ことで語り合うのが夢です!
https://www.youtube.com/watch?v=ghZpyHP7B_g
動画を再生できません この動画には Sony Music Entertainment (Japan) Inc. さんのコンテンツが含まれており、お住まいの地域では著作権上の問題で権利所有者によりブロックされています。
Allowed countries
AD - Andorra AE - United Arab Emirates AF - Afghanistan AG - Antigua and Barbuda AI - Anguilla AL - Albania AM - Armenia AO - Angola AQ - Antarctica AR - Argentina AS - American Samoa AT - Austria AU - Australia AW - Aruba AX - Åland Islands AZ - Azerbaijan BA - Bosnia and Herzegovina BB - Barbados BD - Bangladesh BE - Belgium BF - Burkina Faso BG - Bulgaria BH - Bahrain BI - Burundi BJ - Benin BL - Saint Barthélemy BM - Bermuda BN - Brunei Darussalam BO - Bolivia (Plurinational State of) BR - Brazil BS - Bahamas BT - Bhutan BV - Bouvet sland BW - Botswana BY - Belarus BZ - Belize CA - Canada CC - Cocos (Keeling) Islands CD - Congo (Democratic Republic of the) CF - Central African Republic CG - Republic of the Congo CH - Switzerland CI - Côte d'Ivoire CK - Cook Islands CL - Chile CM - Cameroon CN - China CO - Colombia CR - Costa Rica CU - Cuba CV - Cabo Verde CX - Christmas Island CY - Cyprus CZ - Czech Republic DE - Germany DJ - Djibouti DK - Denmark DM - Dominica DO - Dominican Republic DZ - Algeria EC - Ecuador EE - Estonia EG - Egypt EH - Western Sahara ER - Eritrea ES - Spain ET - Ethiopia FI - Finland FJ - Fiji FK - Falkland Islands (Malvinas) FM - Micronesia (Federated States of) FO - Faroe Islands FR - France GA - Gabon GB - United Kingdom of Great Britain and Northern Ireland GD - Grenada GE - Georgia (country) GF - French Guiana GG - Guernsey GH - Ghana GI - Gibraltar GL - Greenland GM - Gambia GN - Guinea GP - Guadeloupe GQ - Equatorial Guinea GR - Greece GS - South Georgia and the South Sandwich Islands GT - Guatemala GU - Guam GW - Guinea-Bissau GY - Guyana HK - Hong Kong HM - Heard Island and McDonald Islands HN - Honduras HR - Croatia HT - Haiti HU - Hungary ID - Indonesia IE - Republic of Ireland IL - Israel IM - Isle of Man IN - India IO - British Indian Ocean Territory IQ - Iraq IR - Iran (Islamic Republic of) IS - Iceland IT - Italy JE - Jersey JM - Jamaica JO - Jordan KE - Kenya KG - Kyrgyzstan KH - Cambodia KI - Kiribati KM - Comoros KN - Saint Kitts and Nevis KP - North Korea KR - Korea (Republic of) KW - Kuwait KY - Cayman Islands KZ - Kazakhstan LA - Lao People's Democratic Republic LB - Lebanon LC - Saint Lucia LI - Liechtenstein LK - Sri Lanka LR - Liberia LS - Lesotho LT - Lithuania LU - Luxembourg LV - Latvia LY - Libya MA - Morocco MC - Monaco MD - Moldova (Republic of) ME - Montenegro MG - Madagascar MH - Marshall Islands MK - Republic of Macedonia ML - Mali MM - Myanmar MN - Mongolia MO - Macao MP - Northern Mariana Islands MQ - Martinique MR - Mauritania MS - Montserrat MT - Malta MU - Mauritius MV - Maldives MW - Malawi MX - Mexico MY - Malaysia MZ - Mozambique NA - Namibia NC - New Caledonia NE - Niger NF - Norfolk Island NG - Nigeria NI - Nicaragua NL - Netherlands NO - Norway NP - Nepal NR - Nauru NU - Niue NZ - New Zealand OM - Oman PA - Panama PE - Peru PF - French Polynesia PG - Papua New Guinea PH - Philippines PK - Pakistan PL - Poland PM - Saint Pierre and Miquelon PN - Pitcairn PR - Puerto Rico PS - State of Palestine PT - Portugal PW - Palau PY - Paraguay QA - Qatar RE - Réunion RO - Romania RS - Serbia RU - Russian Federation RW - Rwanda SA - Saudi Arabia SB - Solomon Islands SC - Seychelles SD - Sudan SE - Sweden SG - Singapore SH - Saint Helena, Ascension and Tristan da Cunha SI - Slovenia SJ - Svalbard and Jan Mayen SK - Slovakia SL - Sierra Leone SM - San Marino SN - Senegal SO - Somalia SR - Suriname ST - Sao Tome and Principe SV - El Salvador SY - Syrian Arab Republic SZ - Swaziland TC - Turks and Caicos Islands TD - Chad TF - French Southern Territories TG - Togo TH - Thailand TJ - Tajikistan TK - Tokelau TL - Timor-Leste TM - Turkmenistan TN - Tunisia TO - Tonga TR - Turkey TT - Trinidad and Tobago TV - Tuvalu TW - Taiwan TZ - Tanzania, United Republic of UA - Ukraine UG - Uganda UM - United States Minor Outlying Islands US - United States of America UY - Uruguay UZ - Uzbekistan VA - Vatican City State VC - Saint Vincent and the Grenadines VE - Venezuela (Bolivarian Republic of) VG - British Virgin Islands VI - United States Virgin Islands VN - Viet Nam VU - Vanuatu WF - Wallis and Futuna WS - Samoa YE - Yemen YT - Mayotte ZA - South Africa ZM - Zambia ZW - Zimbabwe
https://issuu.com/sasf3 http://www.mobypicture.com/user/adfss/view/18962541 https://kinja.com/vmadsfngn https://mubi.com/lists/canada-mexico https://dribbble.com/asdfdgshs
https://gist.github.com/d39655b8a64fd10dbf0a http://omeka.org/forums/profile/sdfsgmm http://jsperf.com/erfgn https://geekli.st/vasdfg/links/174597 https://www.codecademy.com/asdfsgsg https://www.instapaper.com/p/5085962 https://www.wattpad.com/user/df34rfaas https://gitlab.com/u/sddfssfb https://soundation.com/user/bcpr9017 http://delicious.com/canadamex http://preview.tinyurl.com/hl5ufsp http://bit.do/bS8iU- http://bit.ly/1MHMmI4+
https://www.strava.com/activities/526744703 http://codepen.io/allcarhere/post/m-xico-canada-en http://www.plurk.com/p/ljpwh6 http://www.joomlatune.com/forum/index.php/topic,152979.0.html http://www.minecraftforum.net/forums/minecraft-xbox-one-edition/mcxone-show-your-creation/2642902-mexico-vs-canada https://www.zotero.org/groups/mexico_vs_canada http://www.cplusplus.com/forum/jobs/187533/ https://myspace.com/mexicocanada2 http://flagcounter.boardhost.com/viewtopic.php?pid=278552
Relatives and friends of the 150 passengers and crew on Germanwings Flight 4U 9525 are due to go to the crash site high in the French Alps.
Lufthansa will operate two special flights - one from Barcelona and one from Duesseldorf - to Marseille, and both groups will travel on by road.
Reports say one of the two pilots on the doomed flight had left the cockpit and had been unable to get back in just before the crash on Tuesday.
There were no survivors, officials say.
They say the Airbus 320 from Barcelona to Duesseldorf hit a mountain after a rapid eight-minute descent.
Germanwings chief Thomas Winkelmann said 72 passengers were German citizens, including 16 pupils returning from an exchange trip.
Spain's government said 51 of the dead were Spanish.
Other victims were from Australia, Argentina, Britain, Iran, Venezuela, the US, the Netherlands, Colombia, Mexico, Japan, Denmark and Israel.
Germanwings is a low-cost airline owned by Germany's main carrier Lufthansa.
Cockpit mystery
Families and friends of the victims are expected to arrive at the crash site at Meolans-Revels later on Thursday.
Separately, a bus carrying 14 relatives of Spanish victims left Barcelona on Wednesday for the crash area, because they did not want to fly.
In France, special teams have been prepared to assist the families during their visit.
On Wednesday, French officials said usable data had been extracted from the cockpit voice recorder of the Germanwings plane.
Remi Jouty, the director of the French aviation investigative agency, said there were sounds and voices on the cockpit voice recorder but that it was too early to draw any conclusions.
He said he hoped investigators would have the "first rough ideas in a matter of days" but that the full analysis could take weeks or even months.
But the New York Times quoted an unnamed investigator as saying that one of the pilots had left the cockpit and had been unable to get back in.
"You can hear he is trying to smash the door down," the investigator adds, describing audio from the recorder.
A source close to the investigation told a similar story to the AFP news agency.
There had been earlier reports that the second black box - the flight data recorder - had been found. But Mr Jouty said this was not the case.
'Flying to the end'
Mr Jouty said the plane's last communication was a routine one with air traffic control.
The plane confirmed instructions to continue on its planned flight path but then began its descent a minute later.
Mr Jouty said controllers observed the plane beginning to descend and tried to get back in contact with the pilots but without success.
He ruled out an explosion, saying: "The plane was flying right to the end."
Rank Site Computer/Year Vendor Cores Rmax Rpeak Power1 DOE/NNSA/LANL
United States Roadrunner - BladeCenter QS22/LS21 Cluster, PowerXCell 8i 3.2 Ghz / Opteron DC 1.8 GHz, Voltaire Infiniband / 2008
IBM 129600 1105.00 1456.70 2483.47
2 Oak Ridge National Laboratory
United States Jaguar - Cray XT5 QC 2.3 GHz / 2008
Cray Inc. 150152 1059.00 1381.40 6950.60
3 Forschungszentrum Juelich (FZJ)
Germany JUGENE - Blue Gene/P Solution / 2009
IBM 294912 825.50 1002.70 2268.00
4 NASA/Ames Research Center/NAS
United States Pleiades - SGI Altix ICE 8200EX, Xeon QC 3.0/2.66 GHz / 2008
SGI 51200 487.01 608.83 2090.00
5 DOE/NNSA/LLNL
United States BlueGene/L - eServer Blue Gene Solution / 2007
IBM 212992 478.20 596.38 2329.60
6 National Institute for Computational Sciences/University of Tennessee
United States Kraken XT5 - Cray XT5 QC 2.3 GHz / 2008
Cray Inc. 66000 463.30 607.20
United States Blue Gene/P Solution / 2007
IBM 163840 458.61 557.06 1260.00
8 Texas Advanced Computing Center/Univ. of Texas
United States Ranger - SunBlade x6420, Opteron QC 2.3 Ghz, Infiniband / 2008
Sun Microsystems 62976 433.20 579.38 2000.00
9 DOE/NNSA/LLNL
United States Dawn - Blue Gene/P Solution / 2009
IBM 147456 415.70 501.35 1134.00
10 Forschungszentrum Juelich (FZJ)
Germany JUROPA - Sun Constellation, NovaScale R422-E2, Intel Xeon X5570, 2.93 GHz, Sun M9/Mellanox QDR Infiniband/Partec Parastation / 2009
Bull SA 26304 274.80 308.28 1549.00
11 NERSC/LBNL
United States Franklin - Cray XT4 QuadCore 2.3 GHz / 2008
Cray Inc. 38642 266.30 355.51 1150.00
12 Oak Ridge National Laboratory
United States Jaguar - Cray XT4 QuadCore 2.1 GHz / 2008
Cray Inc. 30976 205.00 260.20 1580.71
13 NNSA/Sandia National Laboratories
United States Red Storm - Sandia/ Cray Red Storm, XT3/4, 2.4/2.2 GHz dual/quad core / 2008
Cray Inc. 38208 204.20 284.00 2506.00
14 King Abdullah University of Science and Technology
Saudia Arabia Shaheen - Blue Gene/P Solution / 2009
IBM 65536 185.17 222.82 504.00
15 Shanghai Supercomputer Center
China Magic Cube - Dawning 5000A, QC Opteron 1.9 Ghz, Infiniband, Windows HPC 2008 / 2008
Dawning 30720 180.60 233.47
16 SciNet/University of Toronto
Canada GPC - iDataPlex, Xeon E55xx QC 2.53 GHz, GigE / 2009
IBM 30240 168.60 306.03 869.40
17 New Mexico Computing Applications Center (NMCAC)
United States Encanto - SGI Altix ICE 8200, Xeon quad core 3.0 GHz / 2007
SGI 14336 133.20 172.03 861.63
18 Computational Research Laboratories, TATA SONS
India EKA - Cluster Platform 3000 BL460c, Xeon 53xx 3GHz, Infiniband / 2008
Hewlett-Packard 14384 132.80 172.61 786.00
19 Lawrence Livermore National Laboratory
United States Juno - Appro XtremeServer 1143H, Opteron QC 2.2Ghz, Infiniband / 2008
Appro International 18224 131.60 162.20
20 Grand Equipement National de Calcul Intensif - Centre Informatique National de l'Enseignement Supérieur (GENCI-CINES)
France Jade - SGI Altix ICE 8200EX, Xeon quad core 3.0 GHz / 2008
SGI 12288 128.40 146.74 608.18
21 National Institute for Computational Sciences/University of Tennessee
United States Athena - Cray XT4 QuadCore 2.3 GHz / 2008
Cray Inc. 17956 125.13 165.20 888.82
22 Japan Agency for Marine -Earth Science and Technology
Japan Earth Simulator - Earth Simulator / 2009
NEC 1280 122.40 131.07
23 Swiss Scientific Computing Center (CSCS)
Switzerland Monte Rosa - Cray XT5 QC 2.4 GHz / 2009
Cray Inc. 14740 117.60 141.50
24 IDRIS
France Blue Gene/P Solution / 2008
IBM 40960 116.01 139.26 315.00
25 ECMWF
United Kingdom Power 575, p6 4.7 GHz, Infiniband / 2009
IBM 8320 115.90 156.42 1329.70
26 ECMWF
United Kingdom Power 575, p6 4.7 GHz, Infiniband / 2008
IBM 8320 115.90 156.42 1329.70
27 DKRZ - Deutsches Klimarechenzentrum
Germany Power 575, p6 4.7 GHz, Infiniband / 2008
IBM 8064 115.90 151.60 1288.69
28 JAXA
Japan Fujitsu FX1, Quadcore SPARC64 VII 2.52 GHz, Infiniband DDR / 2009
Fujitsu 12032 110.60 121.28
29 Total Exploration Production
France SGI Altix ICE 8200EX, Xeon quad core 3.0 GHz / 2008
SGI 10240 106.10 122.88 442.00
30 Government Agency
Sweden Cluster Platform 3000 BL460c, Xeon 53xx 2.66GHz, Infiniband / 2007
Hewlett-Packard 13728 102.80 146.43
31 Computer Network Information Center, Chinese Academy of Science
China DeepComp 7000, HS21/x3950 Cluster, Xeon QC HT 3 GHz/2.93 GHz, Infiniband / 2008
Lenovo 12216 102.80 145.97
32 Lawrence Livermore National Laboratory
United States Hera - Appro Xtreme-X3 Server - Quad Opteron Quad Core 2.3 GHz, Infiniband / 2009
Appro International 13552 102.20 127.20
33 Max-Planck-Gesellschaft MPI/IPP
Germany VIP - Power 575, p6 4.7 GHz, Infiniband / 2008
IBM 6720 98.24 126.34 1073.99
34 Pacific Northwest National Laboratory
United States Chinook - Cluster Platform 4000 DL185G5, Opteron QC 2.2 GHz, Infiniband DDR / 2008
Hewlett-Packard 18176 97.07 159.95
35 IT Service Provider
Germany Cluster Platform 3000 BL2x220, E54xx 3.0 Ghz, Infiniband / 2009
Hewlett-Packard 10240 94.74 122.88
France Frontier2 BG/L - Blue Gene/P Solution / 2008
37 IBM Thomas J. Watson Research Center
United States BGW - eServer Blue Gene Solution / 2005
IBM 40960 91.29 114.69 448.00
38 Commissariat a l'Energie Atomique (CEA)/CCRT
France CEA-CCRT-Titane - BULL Novascale R422-E2 / 2009
Bull SA 8576 91.19 100.51
39 Naval Oceanographic Office - NAVO MSRC
United States Cray XT5 QC 2.3 GHz / 2008
Cray Inc. 12733 90.84 117.13 588.90
40 Institute of Physical and Chemical Res. (RIKEN)
Japan PRIMERGY RX200S5 Cluster, Xeon X5570 2.93GHz, Infiniband DDR / 2009
Fujitsu 8256 87.89 96.76
41 GSIC Center, Tokyo Institute of Technology
Japan TSUBAME Grid Cluster with CompView TSUBASA - Sun Fire x4600/x6250, Opteron 2.4/2.6 GHz, Xeon E5440 2.833 GHz, ClearSpeed CSX600, nVidia GT200; Voltaire Infiniband / 2009
NEC/Sun 31024 87.01 163.19 1103.00
42 Information Technology Center, The University of Tokyo
Japan T2K Open Supercomputer (Todai Combined Cluster) - Hitachi Cluster Opteron QC 2.3 GHz, Myrinet 10G / 2008
Hitachi 12288 82.98 113.05 638.60
43 HLRN at Universitaet Hannover / RRZN
Germany SGI Altix ICE 8200EX, Xeon X5570 quad core 2.93 GHz / 2009
SGI 7680 82.57 90.01
44 HLRN at ZIB/Konrad Zuse-Zentrum fuer Informationstechnik
Germany SGI Altix ICE 8200EX, Xeon X5570 quad core 2.93 GHz / 2009
SGI 7680 82.57 90.01
45 Stony Brook/BNL, New York Center for Computational Sciences
United States New York Blue - eServer Blue Gene Solution / 2007
IBM 36864 82.16 103.22 403.20
46 CINECA
Italy Power 575, p6 4.7 GHz, Infiniband / 2009
IBM 5376 78.68 101.07 859.19
47 Center for Computational Sciences, University of Tsukuba
Japan T2K Open Supercomputer - Appro Xtreme-X3 Server - Quad Opteron Quad Core 2.3 GHz, Infiniband / 2009
Appro International 10368 77.28 95.39 671.80
48 US Army Research Laboratory (ARL)
United States Cray XT5 QC 2.3 GHz / 2008
Cray Inc. 10400 76.80 95.68 481.00
49 CSC (Center for Scientific Computing)
Finland Cray XT5/XT4 QC 2.3 GHz / 2009
Cray Inc. 10864 76.51 102.00 520.80
50 DOE/NNSA/LLNL
United States ASC Purple - eServer pSeries p5 575 1.9 GHz / 2006
IBM 12208 75.76 92.78 1992.96
51 National Centers for Environment Prediction
United States Power 575, p6 4.7 GHz, Infiniband / 2008
IBM 4992 73.06 93.85 797.82
52 Rensselaer Polytechnic Institute, Computational Center for Nanotechnology Innovations
United States eServer Blue Gene Solution / 2007
IBM 32768 73.03 91.75 358.40
53 Naval Oceanographic Office - NAVO MSRC
United States Power 575, p6 4.7 GHz, Infiniband / 2008
54 Joint Supercomputer Center
Russia MVS-100K - Cluster Platform 3000 BL460c/BL2x220, Xeon 54xx 3 Ghz, Infiniband / 2008
Hewlett-Packard 7920 71.28 95.04 327.00
55 US Army Research Laboratory (ARL)
United States SGI Altix ICE 8200 Enhanced LX, Xeon X5560 quad core 2.8 GHz / 2009
SGI 6656 70.00 74.55
56 NCSA
United States Abe - PowerEdge 1955, 2.33 GHz, Infiniband, Windows Server 2008/Red Hat Enterprise Linux 4 / 2007
Dell 9600 68.48 89.59
57 Cray Inc.
United States Shark - Cray XT5 QC 2.4 GHz / 2009
Cray Inc. 8576 67.76 82.33
58 NASA/Ames Research Center/NAS
United States Columbia - SGI Altix 1.5/1.6/1.66 GHz, Voltaire Infiniband / 2008
SGI 13824 66.57 82.94
59 University of Minnesota/Supercomputing Institute
United States Cluster Platform 3000 BL280c G6, Xeon X55xx 2.8Ghz, Infiniband / 2009
Hewlett-Packard 8048 64.00 90.14
60 Barcelona Supercomputing Center
Spain MareNostrum - BladeCenter JS21 Cluster, PPC 970, 2.3 GHz, Myrinet / 2006
IBM 10240 63.83 94.21
61 DOE/NNSA/LANL
United States Cerrillos - BladeCenter QS22/LS21 Cluster, PowerXCell 8i 3.2 Ghz / Opteron DC 1.8 GHz, Infiniband / 2008
IBM 7200 63.25 80.93 138.00
62 IBM Poughkeepsie Benchmarking Center
United States BladeCenter QS22/LS21 Cluster, PowerXCell 8i 3.2 Ghz / Opteron DC 1.8 GHz, Infiniband / 2008
IBM 7200 63.25 80.93 138.00
63 National Centers for Environment Prediction
United States Power 575, p6 4.7 GHz, Infiniband / 2009
IBM 4224 61.82 79.41 675.08
64 NCAR (National Center for Atmospheric Research)
United States bluefire - Power 575, p6 4.7 GHz, Infiniband / 2008
IBM 4064 59.68 76.40 649.51
65 National Institute for Fusion Science (NIFS)
Japan Plasma Simulator - Hitachi SR16000 Model L2, Power6 4.7Ghz, Infiniband / 2009
Hitachi 4096 56.65 77.00 645.00
66 Leibniz Rechenzentrum
Germany HLRB-II - Altix 4700 1.6 GHz / 2007
SGI 9728 56.52 62.26 990.24
67 ERDC MSRC
United States Jade - Cray XT4 QuadCore 2.1 GHz / 2008
Cray Inc. 8464 56.25 71.10 418.97
68 University of Edinburgh
United Kingdom HECToR - Cray XT4, 2.8 GHz / 2007
Cray Inc. 11328 54.65 63.44
69 University of Tokyo/Human Genome Center, IMS
Japan SHIROKANE - SunBlade x6250, Xeon E5450 3GHz, Infiniband / 2009
Sun Microsystems 5760 54.21 69.12
70 NNSA/Sandia National Laboratories
United States Thunderbird - PowerEdge 1850, 3.6 GHz, Infiniband / 2006
Dell 9024 53.00 64.97
71 Commissariat a l'Energie Atomique (CEA)
France Tera-10 - NovaScale 5160, Itanium2 1.6 GHz, Quadrics / 2006
Bull SA 9968 52.84 63.80
72 IDRIS
France Power 575, p6 4.7 GHz, Infiniband / 2008
IBM 3584 52.81 67.38 572.79
73 United Kingdom Meteorological Office
United Kingdom UKMO B - Power 575, p6 4.7 GHz, Infiniband / 2009
IBM 3520 51.86 66.18 562.60
74 United Kingdom Meteorological Office
United Kingdom UKMO A - Power 575, p6 4.7 GHz, Infiniband / 2009
IBM 3520 51.86 66.18 562.60
75 Wright-Patterson Air Force Base/DoD ASC
United States Altix 4700 1.6 GHz / 2007
SGI 9216 51.44 58.98
76 University of Southern California
United States HPC - PowerEdge 1950/SunFire X2200 Cluster Intel 53xx 2.33Ghz, Opteron 2.3 Ghz, Myrinet 10G / 2009
77 HWW/Universitaet Stuttgart
Germany Baku - NEC HPC 140Rb-1 Cluster, Xeon X5560 2.8Ghz, Infiniband / 2009
NEC 5376 50.79 60.21 186.00
78 Kyoto University
Japan T2K Open Supercomputer/Kyodai - Fujitsu Cluster HX600, Opteron Quad Core, 2.3 GHz, Infiniband / 2008
Fujitsu 6656 50.51 61.24
79 SARA (Stichting Academisch Rekencentrum)
Netherlands Power 575, p6 4.7 GHz, Infiniband / 2008
IBM 3328 48.93 62.57 531.88
80 SciNet/University of Toronto
Canada Power 575, p6 4.7 GHz, Infiniband / 2008
IBM 3328 48.93 62.57 531.88
81 IT Service Provider (B)
United States Cluster Platform 3000 BL460c, Xeon 54xx 3.0GHz, GigEthernet / 2009
Hewlett-Packard 7600 48.14 91.20
82 Moscow State University - Research Computing Center
Russia SKIF MSU - T-Platforms T60, Intel Quadcore 3Mhz, Infiniband DDR / 2008
SKIF/T-Platforms 5000 47.17 60.00 265.00
83 National Supercomputer Centre (NSC)
Sweden Neolith - Cluster Platform 3000 DL140 Cluster, Xeon 53xx 2.33GHz Infiniband / 2008
Hewlett-Packard 6440 47.03 60.02
84 IBM - Rochester
United States Blue Gene/P Solution / 2007
IBM 16384 46.83 55.71 126.00
85 IBM Thomas J. Watson Research Center
United States Blue Gene/P Solution / 2009
IBM 16384 46.83 55.71 126.00
86 Max-Planck-Gesellschaft MPI/IPP
Germany Genius - Blue Gene/P Solution / 2008
IBM 16384 46.83 55.71 126.00
87 Texas Advanced Computing Center/Univ. of Texas
United States Lonestar - PowerEdge 1955, 2.66 GHz, Infiniband / 2007
Dell 5848 46.73 62.22
88 HPC2N - Umea University
Sweden Akka - BladeCenter HS21 Cluster, Xeon QC HT 2.5 GHz, IB, Windows HPC 2008/CentOS / 2008
IBM 5376 46.04 53.76 173.21
89 Clemson University
United States Palmetto - PowerEdge 1950/SunFire X2200 Cluster Intel 53xx/54xx 2.33Ghz, Opteron 2.3 Ghz, Myrinet 10G / 2008
Dell/Sun 6120 45.61 56.55 285.00
90 Financial Services (H)
United States Cluster Platform 3000 BL460c G1, Xeon L5420 2.5 GHz, GigE / 2009
Hewlett-Packard 8312 43.75 83.12
91 Ohio Supercomputer Center
United States xSeries x3455 Cluster Opteron, DC 2.6 GHz/QC 2.5 GHz, Infiniband / 2009
IBM 8416 43.46 68.38
92 Consulting (C)
United States Cluster Platform 3000 BL460c G1, Xeon E5450 3.0 GHz, GigE / 2009
Hewlett-Packard 6768 43.00 81.22
93 National Institute for Materials Science
Japan SGI Altix ICE 8200EX, Xeon X5560 quad core 2.8 GHz / 2009
SGI 4096 42.69 45.88
94 IT Service Provider (D)
United States Cluster Platform 3000 BL460c, Xeon 54xx 3.0GHz, GigEthernet / 2009
Hewlett-Packard 6672 42.41 80.06
95 Maui High-Performance Computing Center (MHPCC)
United States Jaws - PowerEdge 1955, 3.0 GHz, Infiniband / 2006
Dell 5200 42.39 62.40
96 Commissariat a l'Energie Atomique (CEA)
France CEA-CCRT-Platine - Novascale 3045, Itanium2 1.6 GHz, Infiniband / 2007
Bull SA 7680 42.13 49.15
97 US Army Research Laboratory (ARL)
United States Michael J. Muuss Cluster (MJM) - Evolocity II (LS Supersystem) Xeon 51xx 3.0 GHz IB / 2007
Linux Networx 4416 40.61 52.99
98 University of Bergen
Norway Cray XT4 QuadCore 2.3 GHz / 2008
Cray Inc. 5550 40.59 51.06 274.73
99 Jeraisy Computer and Communication Services
Saudia Arabia Cluster Platform 3000 BL460c, Xeon 54xx 3 GHz, Infiniband / 2009
Hewlett-Packard 4192 39.70 50.30
100 R-Systems
United States R Smarr - Dell DCS CS23-SH, QC HT 2.8 GHz, Infiniband / 2008
Dell 4608 39.58 51.61
http://www.ideaxidea.com/archives/2009/01/comparison_between_us_states_and_countries.html
という記事があったので、日本の都道府県と世界各国を比べてみました。
色づけの仕方がよく分からなかったので見出しスタイルを使ってみました。
人口もつけた方がおもしろかったのかもしれませんが、
1 | United States | $10,400,000,000,000 |
2 | China | $5,700,000,000,000 |
3 | Japan | $3,550,000,000,000 |
4 | India | $2,660,000,000,000 |
5 | Germany | $2,184,000,000,000 |
6 | France | $1,540,000,000,000 |
7 | United Kingdom | $1,520,000,000,000 |
8 | Italy | $1,438,000,000,000 |
9 | Russia | $1,350,000,000,000 |
10 | Brazil | $1,340,000,000,000 |
11 | Korea, South | $931,000,000,000 |
12 | Canada | $923,000,000,000 |
13 | Mexico | $900,000,000,000 |
14 | Spain | $828,000,000,000 |
東京都 | $696,918,808,333 | |
---|---|---|
15 | Indonesia | $663,000,000,000 |
16 | Australia | $528,000,000,000 |
17 | Turkey | $468,000,000,000 |
18 | Iran | $456,000,000,000 |
19 | Netherlands | $434,000,000,000 |
20 | South Africa | $432,000,000,000 |
21 | Thailand | $429,000,000,000 |
22 | Taiwan | $406,000,000,000 |
23 | Argentina | $391,000,000,000 |
24 | Poland | $368,100,000,000 |
25 | Philippines | $356,000,000,000 |
大阪府 | $319,363,116,667 | |
26 | Pakistan | $311,000,000,000 |
27 | Belgium | $297,600,000,000 |
愛知県 | $280,799,008,333 | |
28 | Colombia | $268,000,000,000 |
29 | Egypt | $268,000,000,000 |
神奈川県 | $256,353,583,333 | |
30 | Saudi Arabia | $242,000,000,000 |
31 | Bangladesh | $239,000,000,000 |
32 | Switzerland | $231,000,000,000 |
33 | Sweden | $227,400,000,000 |
34 | Austria | $226,000,000,000 |
35 | Ukraine | $218,000,000,000 |
36 | Malaysia | $210,000,000,000 |
37 | Greece | $201,100,000,000 |
38 | Hong Kong | $186,000,000,000 |
39 | Vietnam | $183,000,000,000 |
40 | Portugal | $182,000,000,000 |
埼玉県 | $167,323,708,333 | |
41 | Algeria | $167,000,000,000 |
42 | Romania | $166,000,000,000 |
北海道 | $162,536,425,000 | |
千葉県 | $159,674,975,000 | |
43 | Czech Republic | $155,900,000,000 |
44 | Denmark | $155,500,000,000 |
兵庫県 | $151,370,075,000 | |
45 | Chile | $151,000,000,000 |
福岡県 | $145,466,316,667 | |
46 | Norway | $143,000,000,000 |
47 | Finland | $136,200,000,000 |
48 | Hungary | $134,700,000,000 |
49 | Venezuela | $132,800,000,000 |
50 | Peru | $132,000,000,000 |
静岡県 | $131,229,850,000 | |
51 | Israel | $122,000,000,000 |
52 | Ireland | $118,500,000,000 |
53 | Morocco | $115,000,000,000 |
54 | Nigeria | $113,500,000,000 |
55 | Kazakhstan | $105,000,000,000 |
56 | Singapore | $105,000,000,000 |
茨城県 | $92,919,900,000 | |
広島県 | $91,338,816,667 | |
57 | Belarus | $85,000,000,000 |
京都府 | $80,443,708,333 | |
58 | New Zealand | $78,800,000,000 |
新潟県 | $75,076,766,667 | |
59 | Sri Lanka | $73,700,000,000 |
宮城県 | $70,222,666,667 | |
60 | Burma | $70,000,000,000 |
長野県 | $66,131,808,333 | |
61 | Slovakia | $66,000,000,000 |
栃木県 | $65,801,008,333 | |
62 | Uzbekistan | $65,000,000,000 |
群馬県 | $63,058,983,333 | |
63 | Tunisia | $63,000,000,000 |
福島県 | $62,425,766,667 | |
岐阜県 | $59,514,150,000 | |
64 | Syria | $59,400,000,000 |
三重県 | $59,350,608,333 | |
岡山県 | $58,909,933,333 | |
65 | Iraq | $58,000,000,000 |
66 | Dominican Republic | $53,000,000,000 |
67 | United Arab Emirates | $53,000,000,000 |
68 | Sudan | $52,900,000,000 |
69 | Bulgaria | $50,600,000,000 |
70 | Ethiopia | $50,600,000,000 |
71 | Guatemala | $48,000,000,000 |
熊本県 | $47,936,775,000 | |
滋賀県 | $47,421,341,667 | |
山口県 | $46,932,816,667 | |
72 | Puerto Rico | $45,700,000,000 |
鹿児島県 | $43,655,658,333 | |
73 | Ghana | $42,500,000,000 |
74 | Ecuador | $41,700,000,000 |
75 | Libya | $41,000,000,000 |
愛媛県 | $38,989,658,333 | |
76 | Croatia | $38,900,000,000 |
富山県 | $38,056,591,667 | |
岩手県 | $37,923,158,333 | |
石川県 | $37,166,083,333 | |
大分県 | $36,858,066,667 | |
77 | Nepal | $36,000,000,000 |
78 | Slovenia | $36,000,000,000 |
長崎県 | $35,402,875,000 | |
青森県 | $35,400,641,667 | |
79 | Kuwait | $34,200,000,000 |
80 | Congo, Democratic Republic of the | $34,000,000,000 |
山形県 | $33,352,833,333 | |
81 | Costa Rica | $32,300,000,000 |
82 | Kenya | $32,000,000,000 |
奈良県 | $31,038,858,333 | |
83 | Uganda | $31,000,000,000 |
秋田県 | $30,841,583,333 | |
香川県 | $30,334,733,333 | |
84 | El Salvador | $30,000,000,000 |
沖縄県 | $29,795,950,000 | |
宮崎県 | $29,546,075,000 | |
85 | Lithuania | $29,200,000,000 |
和歌山県 | $27,963,441,667 | |
福井県 | $27,402,150,000 | |
86 | Azerbaijan | $27,000,000,000 |
87 | Cameroon | $27,000,000,000 |
88 | Zimbabwe | $27,000,000,000 |
89 | Uruguay | $26,500,000,000 |
山梨県 | $26,084,675,000 | |
90 | Turkmenistan | $26,000,000,000 |
91 | Cuba | $25,900,000,000 |
92 | Serbia and Montenegro | $25,300,000,000 |
93 | Paraguay | $25,000,000,000 |
94 | Cote d'Ivoire | $24,500,000,000 |
佐賀県 | $23,519,375,000 | |
95 | Jordan | $22,800,000,000 |
96 | Tanzania | $22,500,000,000 |
徳島県 | $22,479,416,667 | |
97 | Oman | $22,400,000,000 |
98 | Korea, North | $22,000,000,000 |
99 | Bolivia | $21,000,000,000 |
島根県 | $20,237,891,667 | |
100 | Latvia | $20,000,000,000 |
101 | Luxembourg | $20,000,000,000 |
高知県 | $19,802,325,000 | |
102 | Cambodia | $19,700,000,000 |
103 | Lebanon | $19,300,000,000 |
104 | Mozambique | $19,200,000,000 |
105 | Afghanistan | $19,000,000,000 |
106 | Honduras | $17,600,000,000 |
107 | Panama | $17,300,000,000 |
108 | Qatar | $17,200,000,000 |
鳥取県 | $17,062,225,000 | |
109 | Angola | $16,900,000,000 |
110 | Senegal | $16,200,000,000 |
111 | Guinea | $15,900,000,000 |
112 | Yemen | $15,700,000,000 |
113 | Estonia | $15,200,000,000 |
114 | Botswana | $15,100,000,000 |
115 | Georgia | $15,000,000,000 |
116 | Albania | $14,000,000,000 |
117 | Burkina Faso | $13,600,000,000 |
118 | Kyrgyzstan | $13,500,000,000 |
119 | Mauritius | $13,200,000,000 |
120 | Nicaragua | $12,800,000,000 |
121 | Armenia | $12,600,000,000 |
122 | Namibia | $12,600,000,000 |
123 | Madagascar | $12,600,000,000 |
124 | Haiti | $12,000,000,000 |
125 | Trinidad and Tobago | $11,100,000,000 |
126 | Moldova | $11,000,000,000 |
127 | Chad | $10,000,000,000 |
128 | Macedonia, The Former Yugoslav Repub | $10,000,000,000 |
129 | Jamaica | $10,000,000,000 |
130 | Laos | $9,900,000,000 |
131 | Bahrain | $9,800,000,000 |
132 | Mali | $9,800,000,000 |
133 | Cyprus | $9,400,000,000 |
134 | Rwanda | $9,000,000,000 |
135 | Zambia | $8,900,000,000 |
136 | Niger | $8,800,000,000 |
137 | Macau | $8,600,000,000 |
138 | Tajikistan | $8,000,000,000 |
139 | Togo | $8,000,000,000 |
140 | Bosnia and Herzegovina | $7,300,000,000 |
141 | Benin | $7,300,000,000 |
142 | Malawi | $7,200,000,000 |
143 | Gabon | $7,000,000,000 |
144 | Iceland | $7,000,000,000 |
145 | Malta | $7,000,000,000 |
146 | Brunei | $6,500,000,000 |
147 | Lesotho | $5,600,000,000 |
148 | Mauritania | $5,300,000,000 |
149 | Bahamas, The | $5,200,000,000 |
150 | Mongolia | $5,000,000,000 |
151 | Swaziland | $4,800,000,000 |
152 | Central African Republic | $4,700,000,000 |
153 | Fiji | $4,700,000,000 |
154 | Martinique | $4,500,000,000 |
155 | Somalia | $4,100,000,000 |
156 | Barbados | $4,000,000,000 |
157 | Burundi | $3,800,000,000 |
158 | Guadeloupe | $3,700,000,000 |
159 | Reunion | $3,600,000,000 |
160 | Liberia | $3,500,000,000 |
161 | Eritrea | $3,300,000,000 |
162 | Guam | $3,200,000,000 |
163 | New Caledonia | $3,000,000,000 |
164 | Sierra Leone | $2,800,000,000 |
165 | Bhutan | $2,700,000,000 |
166 | Guyana | $2,700,000,000 |
167 | Gambia, The | $2,600,000,000 |
168 | Congo, Republic of the | $2,500,000,000 |
169 | Netherlands Antilles | $2,400,000,000 |
170 | Virgin Islands | $2,400,000,000 |
171 | Bermuda | $2,250,000,000 |
172 | Jersey | $2,200,000,000 |
173 | Aruba | $1,940,000,000 |
174 | West Bank | $1,700,000,000 |
175 | Man, Isle of | $1,600,000,000 |
176 | Suriname | $1,500,000,000 |
177 | Andorra | $1,300,000,000 |
178 | French Polynesia | $1,300,000,000 |
179 | Guernsey | $1,300,000,000 |
180 | Belize | $1,280,000,000 |
181 | Cayman Islands | $1,270,000,000 |
182 | Equatorial Guinea | $1,270,000,000 |
183 | Maldives | $1,250,000,000 |
184 | Papua New Guinea | $1,200,000,000 |
185 | French Guiana | $1,100,000,000 |
186 | Greenland | $1,100,000,000 |
187 | Guinea-Bissau | $1,100,000,000 |
188 | Faroe Islands | $1,000,000,000 |
189 | Samoa | $1,000,000,000 |
190 | San Marino | $940,000,000 |
191 | Northern Mariana Islands | $900,000,000 |
192 | Monaco | $870,000,000 |
193 | Saint Lucia | $866,000,000 |
194 | Liechtenstein | $825,000,000 |
195 | Solomon Islands | $800,000,000 |
196 | Cyprus | $787,000,000 |
197 | Antigua and Barbuda | $750,000,000 |
198 | Gaza Strip | $735,000,000 |
199 | Seychelles | $626,000,000 |
200 | Djibouti | $619,000,000 |
201 | Cape Verde | $600,000,000 |
202 | Vanuatu | $563,000,000 |
203 | American Samoa | $500,000,000 |
204 | Gibraltar | $500,000,000 |
205 | Comoros | $441,000,000 |
206 | Grenada | $440,000,000 |
207 | East Timor | $440,000,000 |
208 | Dominica | $380,000,000 |
209 | Saint Kitts and Nevis | $339,000,000 |
210 | Saint Vincent and the Grenadines | $339,000,000 |
211 | British Virgin Islands | $320,000,000 |
212 | Micronesia, Federated States of | $277,000,000 |
213 | Tonga | $236,000,000 |
214 | Turks and Caicos Islands | $231,000,000 |
215 | Sao Tome and Principe | $200,000,000 |
216 | Palau | $174,000,000 |
217 | Marshall Islands | $115,000,000 |
218 | Cook Islands | $105,000,000 |
219 | Anguilla | $104,000,000 |
220 | Mayotte | $85,000,000 |
221 | Kiribati | $79,000,000 |
222 | Falkland Islands (Islas Malvinas) | $75,000,000 |
223 | Saint Pierre and Miquelon | $74,000,000 |
224 | Nauru | $60,000,000 |
225 | Wallis and Futuna | $30,000,000 |
226 | Montserrat | $29,000,000 |
227 | Saint Helena | $18,000,000 |
228 | Tuvalu | $12,200,000 |
229 | Niue | $7,600,000 |
230 | Tokelau | $1,500,000 |
参考文献:
http://www.esri.cao.go.jp/jp/sna/kenmin/h15/main.html
http://www.theodora.com/wfb2003/rankings/gdp_2003_0.html
より、$1=\120で計算しました。
ここからトラックバックってとばせないのかな?