Hans Rosling The Best Stats You Ve Ever Seen
About 10 years ago, I took on the task to teach global development to Swedish undergraduate students. That was after having spent about 20 years, together with African institutions, studying hunger in Africa. So I was sort of expected to know a little about the world.
大约在十年前， 我担当起 给瑞典大学生讲授全球发展的任务 之前的20年我一直在非洲研究饥饿问题 所以大家以为我对世界有些了解 在我们的卡罗林斯卡医学院 我开设了一门本科生课程“全球健康” 刚开课的时候我还有些紧张 因为来听课的都是瑞典大学的优等生 他们或许早已了解我准备教的内容 于是在第一堂课里，我作了一个小测试 其中有一道题让我受益匪浅 下列5对国家中，哪一个的儿童死亡率高于另一个？ 我所选择的配对国家都是 一个的儿童死亡率是另一个的两倍,因为数据差距很大 因此数据本身的不确定性可以忽略不计 今天我不会拿这来考大家 土耳其，波兰，俄罗斯，巴基斯坦和南非 这是瑞典学生的测验结果 让我高兴的是 5题中平均答对的只有1.8题 我这个教授还有这门课 因此都有了存在的必要 但后来有天深夜，当我写总结报告的时候 我突然有了新的发现 瑞典大学的优等生们对世界的了解 竟然还不如黑猩猩 （笑声） 因为黑猩猩们至少能蒙对一半 在两个选项旁边各放一根香蕉，就有一半的几率答对。 这些优等生们却做不到。这不是由于知识缺乏 而是他们先入为主的错误理念 我还把这个测试拿去 给卡罗林斯卡学院的教授们做 （笑声） 他们每年负责颁发诺贝尔医学奖 结果教授们和黑猩猩半斤八两 （笑声） 我意识到很有必要交流一下这个问题 因为多数人并不知道 世界各国的儿童健康的改善 我们作了一个软件，每一个小球代表一个国家 这个是中国，这个是印度 小球的尺寸代表该国的人口，X轴是生育率 我曾问过学生们 如果让你们来审视这个世界 你们的真实想法是什么 其实这些教科书上都是丁丁历险记（带有殖民主义思想的漫画）的人物 （笑声） 学生们回答 世界是由“我们和他们”组成的 “我们”指西方世界 “他们”指第三世界 我又问 “什么是西方世界？” “西方世界寿命长且家庭小； 第三世界寿命短而家庭大。” 那么一起来看看 X轴是生育率，每个妇女的育儿数 从每人1，2，3，4胎，到8胎 我们有1962年之后的各国家庭大小的可靠数据 数据误差相当小。Y轴是平均寿命 从30岁到70岁不等 1962年的时候 的确有一群国家在上面 这些是发达国家，他们家庭小，寿命长 而这些则是发展中国家 他们家庭大，寿命也相对短些 从1962年到今天 世界有什么变化吗？ 我们来看看 学生们正确吗？国家还是分为2类吗? 或者发展中国家的家庭变小（这些小球）移动到了左边? 或者发展中国家人们的寿命变长（这些小球）移动到了上面?
And I started, in our medical university, Karolinska Institute, an undergraduate course called Global Health. But when you get that opportunity, you get a little nervous. I thought, these students coming to us actually have the highest grade you can get in the Swedish college system, so I thought, maybe they know everything I'm going to teach them about.
这些数据都来自于联合国 大家看到没有？ 这个是中国，他们在往上移动，健康状况不断改善 这些绿色的拉丁美洲国家 正朝向小家庭的方向移动 这些黄色的小球是阿拉伯国家 寿命在变长但家庭规模不变 非洲国家是下面的绿球，他们一直在下面 这个是印度 印度尼西亚的移动速度非常快 （笑声） 80年代的时候 孟加拉国仍然和非洲国家在一起 但是80年代的奇迹发生在孟加拉国 妈妈们开始宣传和普及计划生育 他们向左上角移动 90年代恐怖的艾滋病流行 导致非洲国家的平均寿命缩短 而其他国家都向左上角移动 大家都有了长寿命和小家庭，而世界也焕然一新了 （掌声） 现在我们对比一下美国和越南 1964年的美国家庭小寿命长 越南的家庭大而寿命短。这是后来的变化 越战时期的数据显示，尽管战争造成伤亡 越南人的平均寿命仍有提高 70年代末期 越南的计划生育减小了家庭规模 美国人的平均寿命也在延长 而家庭规模不变 到了90年代 越南由计划经济转为市场经济 其经济发展的速度超过了社会的发展 今天（2003）越南人的平均寿命和家庭规模 已经和越战结束时（1974）的美国一样 如果没有看到这些数据的话 我们会低估了亚洲的巨大变化 这些超前于经济发展的社会变革 下面我们换个视角 X轴显示了全世界的收入分布 每天收入1美元，10美元和100美元 富与穷之间的鸿沟几乎消失了，简直是个奇迹 这里还有一个很小的峰，但总体上是均数分布的 我们看看收入的分配情况 这代表全世界人民每年的全部收入 最富有的20%那部分人 得到了全部收入的74% 最贫穷的20%那部分人 只得到2% 可见发展中国家的理念 极其的不确切 我们总以为最富的人应该给最穷的人提供援助 其实中间这部分才是世界人口的主体 而他们仅得到全部收入的24% 这是个老问题了，中间这些人是谁？ 他们在哪些国家？先看非洲 非洲占世界人口的十分之一，多数是穷人 这个代表富裕的经合组织成员国，联合国俱乐部的会员 他们在这边，很小一部分与非洲重叠 这是拉丁美洲，他们可以代表全世界 从最贫穷到最富有的人都在那里 再往上是东欧，东亚还有南亚 过去是什么样子的呢？ 如果我们回到1970年，这里有一个明显的峰 这些绝对贫困的人群中 大多数是亚洲人 那时世界的问题就在于亚洲的贫穷 后来随着人口的增长 数以亿计的亚洲人摆脱了贫困 另外一些人却陷入贫穷，这就是今天的世界 而这是世界银行对未来最乐观的预测 世界再也不是贫富悬殊的，大多数人拥有中等的收入 当然这是指数幂分布的图 因为经济的增长是用百分比来衡量的 我们用百分比的变化来评估经济增长 下面把X轴改为人均国内生产总值 个人的数据转为各大洲的数据 球的大小代表人口的多少 这个是经合组织国家，这是撒哈拉以南非洲 我们把阿拉伯国家 从非洲和亚洲单独分出来 然后把X轴延伸一下 再加上一个新的维度 一个有社会价值的参数 儿童生存率 X轴代表经济 Y轴显示儿童存活的比率 一些国家的99.7%的小孩 可以活到5岁以上 另一些国家只有70% 很明显可以看到 经合组织成员国 和拉丁美洲，东欧，东亚 阿拉伯国家，南亚 以及撒哈拉以南非洲地区的差距 儿童生存率和经济之间 联系非常紧密 下面把撒哈拉以南非洲地区 分解成各个国家 分布靠上边的国家 拥有更高的健康水平 撒哈拉以南的非洲各国是如此分布的 小球的尺寸代表该国人口 塞拉里昂在下边 毛里求斯在上边 毛里求斯是第一个消除了贸易壁垒的国家 他们的蔗糖和纺织品的贸易协定 与欧洲和北美一样 但是非洲内部的差异非常巨大 加纳在中部 塞拉里昂需要人道主义援助 乌干达则需要发展援助 在加纳可以进行投资了 毛里求斯则可以去度假 非洲内部的差异之大确实很惊人 而我们却总以为 非洲国家都差不多 下面分解南亚各国 印度是中间的蓝色大球 而斯里兰卡和阿富汗有着巨大差异 把阿拉伯世界分解来看 尽管是相同的气候，相同的文化 相同的宗教 却有巨大的差异 也门在打内战 邻国阿联酋却躺在钱堆里 而且（阿联酋的）儿童健康数据 包含了所有的外籍劳工 大家总说数据不准确 数据其实比我们想象的好很多 数据是有误差 但柬埔寨和新加坡的差距肯定远大于数据的误差 再看东欧 在苏联经济模式下发展了多年 但在过去10年 却经历了巨大的变化 当今的拉丁美洲 古巴再也不是唯一的健康国家了 几年后智利的儿童死亡率将低于古巴 这些是经合组织成员国 这里显示的就是我们的世界 大概就是这样的情形 如果我们回到过去 看看世界是怎样的 从1960年开始 1960年（中国有）毛泽东 他给中国带来了健康 他去世后邓小平给中国带来了金钱 同时把中国带回到世界的主流当中 其他国家的移动方向也不尽相同 很难找出哪个国家 能代表全世界的发展模式 我们回到1960年做个比较 先选中韩国（左边的小黄球）巴西（右边的黄绿色大球） 乌干达（Y轴上面的小红球） 随着时间的推移，我们看到 韩国的发展速度非常非常快 巴西就慢得多 我们再回到过去 给每个球画出运动的轨迹 可以看到，发展速度的差距非常大 虽然各国的经济和健康 发展的轨迹大同小异 但是健康水平起点较高的国家 发展速度远超过经济水平起点高的 为了说明这一点 我们看看阿联酋 他们从这里出发 一个资源型国家 他们靠石油大把赚钱 但健康绝不是超市里的货物 需要卫生方面的投资 需要提高儿童的教育水平 需要培训卫生工作者 还要教育民众 Sheikh Sayed 干的非常漂亮 尽管油价下跌了 他仍改善了阿联酋的健康 这里我们可以看到 世界发展的主流 各国对资金的分配和使用 都比过去合理的多 这里大家看到各国的数据 基本上都是平均数 但是用平均数可能会很危险 因为国家内部也存在很大的差异 我们看这里 今天的乌干达和1960年的韩国差不多 如果把乌干达分解开 可以看到内部的明显差异 乌干达最富有的20%在右边 最贫穷的在左下边 如果把南非分解开 尼日在下边 他们刚遭受一场恐怖的饥荒 最贫穷的20%的尼日人在最左边 而最富有的20%的南非人在最右边 今天我们仍然在讨论 什么方案能解决非洲的问题 世界上所有的问题非洲都有 我们不可能讨论出一套通用方案 既能解决这些地方的艾滋病问题 同时也适用于这些地方 世界的发展一定要因地制宜来分析 仅从各大洲的水平上来分析是不够的 当学生们接触到这个软件的时候 他们都非常兴奋 此外，政策制定者，各企业部门 都会想知道世界的变化 但为什么大家仍然不知道（世界的变化） 为什么我们无法使用已知的数据呢 我们的联合国，国家统计部门 学院还有非政府组织都拥有数据 但数据被隐藏在底层的数据库里 而公众在上面（太阳）互联网在这里（地平线）并未得到有效的使用 之前我们看到的 关于世界变化的信息 并不包括公众资助的统计数据 的确有一些网站依靠数据库的营养而存在着 但这是要收费的 还有愚蠢的密码和讨厌的统计表格 （笑声，掌声） 这个是行不通的 我们需要什么？ 数据库是现成的 不需要新的数据库 我们有很好的视觉软件 还将有更多的问世 于是我们成立了一个非营利机构 我们称之为“数据与图样的联结” - Gapminder 灵感来自伦敦地铁（他们提醒乘客“小心列车与站台间的缝隙”） 而且我们制作了一个软件 把数据和图样联结起来 这个并不难 需要几个人花几年时间 建立数据库后大家就能看到动画 我们正尝试解放联合国的数据库 少数联合国机构和几个国家已经开放了数据库 但我们最需要的是数据搜索引擎 依靠搜索引擎 我们先把原始数据复制成可搜索的格式 再把数据发布到全世界 外界对这个设想的反应如何呢？ 我尝试跟几个大型统计机构交涉 所有人都说这是不可能的 “这行不通，我们的信息很独特， 不可能像其它数据那样检索的出来 我们也不能免费把数据开放 给全世界的学生们和企业部门使用。” 但这正是我们期望看到的，不是吗？ 下边是公众资助采集的数据 我们希望互联网上长出美丽的花朵 关键的一步 是让这些数据可被搜索到 并借助软件实现动画的演示 我有个很好的消息要告诉大家 新上任的联合国统计部门的领导 并没有说这是不可能的 他只说“我们不能这么做。” （笑声） 他很聪明吧 （笑声） 未来几年中 我们将会看到数据库的变化 我们会用全新的视角 来看收入的分配 这是1970年中国的收入分配 这是1970年美国的收入分配 几乎没有重叠 后来呢？ 中国在增长，再也不像以前那样平等了 它出现在右边，俯视着美国 是不是像个鬼一样 （笑声） 很吓人吧 我认为这些信息很重要 大家很有必要看到这些 另外我最后要给大家展示 每千人中的网民数量 这个软件能让我们很容易的看到 全球各国的近500个参数 通过点击坐标轴 你能轻易改变参数的设定 我们的初衷是 数据免费下载且易于查找 然后再点一下鼠标 数据就成为图表的形式 那样大家就可以 立刻看明白这些数据了 统计学家们不喜欢这样子 他们认为这不能准确地反映事实 传统的统计和分析方法是不能取代的 但数据动画可以帮助提出假说 最后我们看一下当今的互联网世界 网民数量不断向上攀升（X轴是）人均国民生产总值 互联网是一项新技术 但令人惊讶的是 它的普及和国家的经济水平极其一致 这也解释了100美元电脑的重要性 但这是很好的趋势 世界各国的差距将会缩小，不是吗 这些国家的互联网普及速度 超过了经济的发展速度 我也希望大家都可以 自由使用公众资助采集的数据 非常感谢！ (掌声）
So I did a pretest when they came. And one of the questions from which I learned a lot was this one: "Which country has the highest child mortality of these five pairs?" And I put them together so that in each pair of countries, one has twice the child mortality of the other. And this means that it's much bigger, the difference, than the uncertainty of the data.
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I won't put you at a test here, but it's Turkey, which is highest there, Poland, Russia, Pakistan and South Africa. And these were the results of the Swedish students. I did it so I got the confidence interval, which is pretty narrow.
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And I got happy, of course -- a 1.8 right answer out of five possible. That means there was a place for a professor of international health and for my course. (Laughter) But one late night, when I was compiling the report, I really realized my discovery.
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I have shown that Swedish top students know, statistically, significantly less about the world than the chimpanzees. (Laughter) Because the chimpanzee would score half right if I gave them two bananas with Sri Lanka and Turkey. They would be right half of the cases.
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But the students are not there. The problem for me was not ignorance; it was preconceived ideas. I did also an unethical study of the professors of the Karolinska Institute, which hands out the Nobel Prize in Medicine, and they are on par with the chimpanzee there.
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(Laughter) This is where I realized that there was really a need to communicate, because the data of what's happening in the world and the child health of every country is very well aware. So we did this software, which displays it like this. Every bubble here is a country.
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This country over here is China. The size of the bubble is the population, and on this axis here, I put fertility rate. Because my students, what they said when they looked upon the world, and I asked them, "What do you really think about the world?" Well, I first discovered that the textbook was Tintin, mainly.
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(Laughter) And they said, "The world is still 'we' and 'them.' And 'we' is the Western world and 'them' is the Third World." "And what do you mean with 'Western world?'" I said. "Well, that's long life and small family. And 'Third World' is short life and large family." So this is what I could display here.
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I put fertility rate here -- number of children per woman: one, two, three, four, up to about eight children per woman. We have very good data since 1962, 1960, about, on the size of families in all countries. The error margin is narrow.
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Here, I put life expectancy at birth, from 30 years in some countries, up to about 70 years. And in 1962, there was really a group of countries here that were industrialized countries, and they had small families and long lives. And these were the developing countries.
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They had large families and they had relatively short lives. Now, what has happened since 1962? We want to see the change.
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Are the students right? It's still two types of countries? Or have these developing countries got smaller families and they live here?
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Or have they got longer lives and live up there? We start the world, eh? This is all UN statistics that have been available.
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Can you see there? It's China there, moving against better health there, improving there. All the green Latin American countries are moving towards smaller families.
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Your yellow ones here are the Arabic countries, and they get longer life, but not larger families. The Africans are the green here. They still remain here.
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This is India; Indonesia is moving on pretty fast. In the '80s here, you have Bangladesh still among the African countries. But now, Bangladesh -- it's a miracle that happens in the '80s -- the imams start to promote family planning, and they move up into that corner.
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And in the '90s, we have the terrible HIV epidemic that takes down the life expectancy of the African countries. And the rest of them all move up into the corner, where we have long lives and small family, and we have a completely new world. (Applause) (Applause ends) Let me make a comparison directly between the United States of America and Vietnam.
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1964: America had small families and long life; Vietnam had large families and short lives. And this is what happens. The data during the war indicate that even with all the death, there was an improvement of life expectancy.
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By the end of the year, family planning started in Vietnam, and they went for smaller families. And the United States up there is getting longer life, keeping family size. And in the '80s now, they give up Communist planning and they go for market economy, and it moves faster even than social life.
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And today, we have in Vietnam the same life expectancy and the same family size here in Vietnam, 2003, as in United States, 1974, by the end of the war. I think we all, if we don't look at the data, we underestimate the tremendous change in Asia, which was in social change before we saw the economic change. So let's move over to another way here in which we could display the distribution in the world of income.
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This is the world distribution of income of people. One dollar, 10 dollars or 100 dollars per day. There's no gap between rich and poor any longer.
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There's a little hump here. But there are people all the way. And if we look where the income ends up, this is 100 percent of the world's annual income.
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And the richest 20 percent, they take out of that about 74 percent. And the poorest 20 percent, they take about two percent. And this shows that the concept of developing countries is extremely doubtful.
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We think about aid, like these people here giving aid to these people here. But in the middle, we have most of the world population, and they have now 24 percent of the income. We heard it in other forms.
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And who are these? Where are the different countries? I can show you Africa.
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Ten percent of the world population, most in poverty. This is OECD -- the rich countries, the country club of the UN. And they are over here on this side.
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Quite an overlap between Africa and OECD. And this is Latin America. It has everything on this earth, from the poorest to the richest in Latin America.
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And on top of that, we can put East Europe, we can put East Asia, and we put South Asia. And what did it look like if we go back in time, to about 1970? Then, there was more of a hump.
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And most who lived in absolute poverty were Asians. The problem in the world was the poverty in Asia. And if I now let the world move forward, you will see that while population increases, there are hundreds of millions in Asia getting out of poverty, and some others getting into poverty, and this is the pattern we have today.
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And the best projection from the World Bank is that this will happen, and we will not have a divided world. We'll have most people in the middle. Of course it's a logarithmic scale here, but our concept of economy is growth with percent.
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We look upon it as a possibility of percentile increase. If I change this and take GDP per capita instead of family income, and I turn these individual data into regional data of gross domestic product, and I take the regions down here, the size of the bubble is still the population. And you have the OECD there, and you have sub-Saharan Africa there, and we take off the Arab states there, coming both from Africa and from Asia, and we put them separately, and we can expand this axis, and I can give it a new dimension here, by adding the social values there, child survival.
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Now I have money on that axis, and I have the possibility of children to survive there. In some countries, 99.7% of children survive to five years of age; others, only 70. And here, it seems, there is a gap between OECD, Latin America, East Europe, East Asia, Arab states, South Asia and sub-Saharan Africa.
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The linearity is very strong between child survival and money. But let me split sub-Saharan Africa. Health is there and better health is up there.
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I can go here, and I can split sub-Saharan Africa into its countries. And when it bursts, the size of each country bubble is the size of the population. Sierra Leone down there, Mauritius is up there.
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Mauritius was the first country to get away with trade barriers, and they could sell their sugar, they could sell their textiles, on equal terms as the people in Europe and North America. There's a huge difference [within] Africa. And Ghana is here in the middle.
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In Sierra Leone, humanitarian aid. Here in Uganda, development aid. Here, time to invest; there, you can go for a holiday.
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There's tremendous variation within Africa, which we very often make that it's equal everything. I can split South Asia here. India's the big bubble in the middle.
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But there's a huge difference between Afghanistan and Sri Lanka. I can split Arab states. Same climate, same culture, same religion -- huge difference.
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Even between neighbors -- Yemen, civil war; United Arab Emirates, money, which was quite equally and well-used. Not as the myth is. And that includes all the children of the foreign workers who are in the country.
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Data is often better than you think. Many people say data is bad. There is an uncertainty margin, but we can see the difference here: Cambodia, Singapore.
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The differences are much bigger than the weakness of the data. East Europe: Soviet economy for a long time, but they come out after 10 years very, very differently. And there is Latin America.
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Today, we don't have to go to Cuba to find a healthy country in Latin America. Chile will have a lower child mortality than Cuba within some few years from now. Here, we have high-income countries in the OECD.
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And we get the whole pattern here of the world, which is more or less like this. And if we look at it, how the world looks, in 1960, it starts to move. This is Mao Zedong.
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He brought health to China. And then he died. And then Deng Xiaoping came and brought money to China, and brought them into the mainstream again.
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And we have seen how countries move in different directions like this, so it's sort of difficult to get an example country which shows the pattern of the world. But I would like to bring you back to about here, at 1960. I would like to compare South Korea, which is this one, with Brazil, which is this one.
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The label went away for me here. And I would like to compare Uganda, which is there. I can run it forward, like this.
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And you can see how South Korea is making a very, very fast advancement, whereas Brazil is much slower. And if we move back again, here, and we put trails on them, like this, you can see again that the speed of development is very, very different, and the countries are moving more or less at the same rate as money and health, but it seems you can move much faster if you are healthy first than if you are wealthy first. And to show that, you can put on the way of United Arab Emirates.
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They came from here, a mineral country. They cached all the oil; they got all the money; but health cannot be bought at the supermarket. You have to invest in health.
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You have to get kids into schooling. You have to train health staff. You have to educate the population.
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And Sheikh Zayed did that in a fairly good way. In spite of falling oil prices, he brought this country up here. So we've got a much more mainstream appearance of the world, where all countries tend to use their money better than they used it in the past.
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Now, this is, more or less, if you look at the average data of the countries -- they are like this. That's dangerous, to use average data, because there is such a lot of difference within countries. So if I go and look here, we can see that Uganda today is where South Korea was in 1960.
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If I split Uganda, there's quite a difference within Uganda. These are the quintiles of Uganda. The richest 20 percent of Ugandans are there.
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The poorest are down there. If I split South Africa, it's like this. And if I go down and look at Niger, where there was such a terrible famine [recently], it's like this.
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The 20 percent poorest of Niger is out here, and the 20 percent richest of South Africa is there, and yet we tend to discuss what solutions there should be in Africa. Everything in this world exists in Africa. And you can't discuss universal access to HIV [treatment] for that quintile up here with the same strategy as down here.
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The improvement of the world must be highly contextualized, and it's not relevant to have it on a regional level. We must be much more detailed. We find that students get very excited when they can use this.
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And even more, policy makers and the corporate sectors would like to see how the world is changing. Now, why doesn't this take place? Why are we not using the data we have?
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We have data in the United Nations, in the national statistical agencies and in universities and other nongovernmental organizations. Because the data is hidden down in the databases. And the public is there, and the internet is there, but we have still not used it effectively.
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All that information we saw changing in the world does not include publicly funded statistics. There are some web pages like this, you know, but they take some nourishment down from the databases, but people put prices on them, stupid passwords and boring statistics. (Laughter) And this won't work.
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(Applause) So what is needed? We have the databases. It's not a new database that you need.
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We have wonderful design tools and more and more are added up here. So we started a nonprofit venture linking data to design, we called "Gapminder," from the London Underground, where they warn you, "Mind the gap." So we thought Gapminder was appropriate. And we started to write software which could link the data like this.
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And it wasn't that difficult. It took some person years, and we have produced animations. You can take a data set and put it there.
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We are liberating UN data, some few UN organization. Some countries accept that their databases can go out on the world. But what we really need is, of course, a search function, a search function where we can copy the data up to a searchable format and get it out in the world.
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And what do we hear when we go around? I've done anthropology on the main statistical units. Everyone says, "It's impossible.
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This can't be done. Our information is so peculiar in detail, so that cannot be searched as others can be searched. We cannot give the data free to the students, free to the entrepreneurs of the world." But this is what we would like to see, isn't it?
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The publicly funded data is down here. And we would like flowers to grow out on the net. One of the crucial points is to make them searchable, and then people can use the different design tools to animate it there.
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And I have pretty good news for you. I have good news that the [current], new head of UN statistics doesn't say it's impossible. He only says, "We can't do it." (Laughter) And that's a quite clever guy, huh?
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(Laughter) So we can see a lot happening in data in the coming years. We will be able to look at income distributions in completely new ways. This is the income distribution of China, 1970.
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This is the income distribution of the United States, 1970. Almost no overlap. Almost no overlap.
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And what has happened? What has happened is this: that China is growing, it's not so equal any longer, and it's appearing here, overlooking the United States, almost like a ghost, isn't it? (Laughter) It's pretty scary.
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(Laughter) But I think it's very important to have all this information. We need really to see it. And instead of looking at this, I would like to end up by showing the internet users per 1,000.
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In this software, we access about 500 variables from all the countries quite easily. It takes some time to change for this, but on the axes, you can quite easily get any variable you would like to have. And the thing would be to get up the databases free, to get them searchable, and with a second click, to get them into the graphic formats, where you can instantly understand them.
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Now, statisticians don't like it, because they say that this will not show the reality; we have to have statistical, analytical methods. But this is hypothesis-generating. I end now with the world.
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There, the internet is coming. The number of internet users are going up like this. This is the GDP per capita.
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And it's a new technology coming in, but then amazingly, how well it fits to the economy of the countries. That's why the $100 computer will be so important. But it's a nice tendency.
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It's as if the world is flattening off, isn't it? These countries are lifting more than the economy, and it will be very interesting to follow this over the year, as I would like you to be able to do with all the publicly funded data. Thank you very much.
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