Crossing the Stream and Reaching the Sky

In the early stages of its economic reform, China chose to "cross a stream by feeling the rocks."

Limited by expertise and conditions at that time when there was no statistical infrastructure in China to provide accurate and reliable measurements, the chosen path was the only option.

In fact, this path was traveled by many nations, including the U.S.
At the beginning of the 20th century when the field of modern statistics had not taken shape, data were not believable or reliable even if they existed.   Well-known American writer and humorist Mark Twain once lamented about “lies, damned lies, and statistics," pointing out the data quality problem of the time.  During the past hundred years, statistics deployed an international common language and reliable data, establishing a long history of success with broad areas of application in the U.S.  This stage of statistics may be generally called Statistics 1.0.

Feeling the rocks may help to across a stream, but it would be difficult to land on the moon, even more difficult to create smart cities and an affluent society.  If one could scientifically measure the depth of the stream and build roads and bridges, it may be unnecessary to make trials and errors.

The long-term development of society must exit this transitional stage and enter a more scientifically-based digital culture where high-quality data and credible, reliable statistics serve to continuously enhance the efficiency, equity and sustainability of national policies. At the same time, specialized knowledge must be converted responsibly to practical useful knowledge, serving the government, enterprises and the people.

Today, technologies associated with Big Data are advancing rapidly.  A new opportunity has arrived to usher in the Statistics 2.0 era.

Simply stated, Statistics 2.0 elevates the role and technical level of descriptive statistics, extends the theories and methods of mathematical statistics to non-randomly collected data, and expands statistical thinking to include facing the future.

One may observe that in a digital society, whether it is from crossing a stream or reaching the sky, or from governance of a nation to the daily life of the common people, what were once "unimaginable" are now "reality."  Driverless cars, drone delivery of packages, and space travel are no longer imaginations in fictions.  Although their data that can be analyzed in a practical setting are still limited, they are within the realistic visions of Statistics 2.0.

In terms of social development, the U.S. and China are actively trying to improve people’s livelihood, enhance governance, and improve the environment. A harmonious and prosperous world cannot be achieved without vibrant and sustainable economies in both China and the U.S., and peaceful, mutually beneficial collaborations between the nations.

Statistics 2.0 can and should play an extremely important role in this evolution.

The WeChat platform Statistics 2.0 will not use low quality or duplicative information to clog already congested channels, but it values new thinking to share common interest in the study of Statistics 2.0, introducing state-of-the-art developments in the U.S. and China in a simple and timely manner, offering thoughts and discussions about classical issues, exploring innovative applications, and sharing the beauty of the science of data in theory and practice.

WeChat Platform: Statistics 2.0

Not All Data are Created Equal

Suppose we have data on 60,000 households.  Are they useful for analysis? If we add that the amount of data is very large, like 3 TB or even 30 TB, does it change your answer?

The U.S. government collects monthly data from 60,000 randomly selected households and reports on the national employment situation.  Based on these data, the U.S. unemployment rate is estimated to within a margin of sampling error of about 0.2%.  Important inferences are drawn and policies are made from these statistics about the U.S. economy comprised of 120 million households and 310 million individuals.

In this case, data for 60,000 households are very useful.

These 60,000 households represent only 0.05% of all the households in the U.S.  If they were not randomly selected, the statistics they generate will contain unknown and potentially large bias.  They are not reliable to describe the national employment situation.

In this case, data for 60,000 households are not useful at all, regardless of what the file size may be.

Suppose further that the 60,000 households are all located in a small city that has only 60,000 households.  In other words, they represent the entire universe of households in the city.  These data are potentially very useful.  Depending on its content and relevance to the question of interest, usefulness of the data may again range widely between two extremes.  If the content is relevant and the quality is good, file size may then become an indicator of the degree of usefulness for the data.

This simple line of reasoning shows that the original question is too incomplete for a direct, satisfactory answer.  We must also consider, for example, the sample selection method, representation of the sample in the population under study, and the relevance and quality of the data relative to a specified hypothesis that is being investigated.

The original question of data usefulness was seldom asked until the Big Data era began around 2000 when electronic data became widely available in massive amounts at relatively low cost.  Prior to this time, data were usually collected when they were driven and needed by a known specific purpose, such as an exploration to conduct, a hypothesis to test, or a problem to resolve.  It was costly to collect data.  When they were collected, they were already considered to be potentially useful for the intended analysis.

For example, when the nation was mired in the Great Depression, the U.S. government began to collect data from randomly selected households in the 1930s so that it could produce more reliable and timely statistics about unemployment. This practice has continued to this date.

Statisticians initially considered data mining to be a bad practice.   It was argued that without a prior hypothesis, false or misleading identification of “significant” relationships and patterns is inevitable by “fishing,” “dredging,” or “snooping” data aimlessly.  An analogy is the over interpretation or analysis of a person winning a lottery, not necessarily because the person possesses any special skill or knowledge about winning a lottery, but because random chance dictates that some person(s) must eventually win a lottery.

Although the argument of false identification remains valid today, it has also been overwhelmed by the abundance of available Big Data that are frequently collected without design or even structure.  Total dismissal of the data-driven approach bypasses the chance of uncovering hidden, meaningful relationships that have not been or cannot be established as a priori hypotheses.  An analogy is the prediction of hereditary disease and the study of potential treatment.  After data on the entire human genome are collected, they may be explored and compared for the systematic identification and treatment of specific hereditary diseases.

Not all data are created equal and have the same usefulness.

Complete and structured data can create dynamic frames that describe an entire population in detail over time, providing valuable information that has never been available in previous statistical systems.  On the other hand, fragmented and unstructured data may not yield any meaningful analysis no matter how large the file size may be.

As problem solving is rapidly expanding from a hypothesis-driven paradigm to include a data-driven approach, the fundamental questions about the usefulness and quality of these data have also increased in importance.  While the question of study interest may not be specified a priori, establishing it a posteriori to data collection is still necessary before conducting any analysis.  We cannot obtain a correct answer to non-existing questions.

How are the samples selected?  How much does the sample represent the universe of inference?  What is the relevance and quality of data relative to the posterior hypothesis of interest?   File size has little to no meaning if the usefulness of data cannot even be established in the first place.  

Ignoring these considerations may lead to the need to update a well-known quote: “Lies, Damned Lies, and Big Data.”

Lying with Big Data

About 45 years ago, I spent a whopping $1.95 on a little book titled "How to Lie with Statistics."

Besides the catchy title, its bright orange cover has a comic character sweeping numbers under a rug.  Darrell Huff, a magazine editor and a freelance writer, wrote the book in 1954.  It went on to become the most popular statistics book in the world for more than half a century.  A translated version was published in China around 2002.

It takes only a few hours to read the entire book of about 140 pages and 80 pictures leisurely, but it was a major reason why I pursued an education and a professional career in statistics.

The corners of the book are now worn; the pages have turned yellow.  One can identify some of the social changes in the last 60 years from the book.  For example, $25,000 is no longer an enviable annual salary; few of today’s younger generation may know what a “telegram” was; “gay” has a very different meaning now; and “African Americans” has replaced “Negroes” in daily usage.  As indicative of the bygone era, the image of a cigar, a cigarette, or a pipe appeared in at least one out of every five pictures in the book – even babies were puffing away in high chairs.  The word “computer” did not show up once among its 26,000 words.

Huff’s words were simple, but sharp and direct.   He provided example after example that the most respected magazines and newspapers of his time lie with statistics, just like the dreadful “advertising man” and politician.

According to Huff, most humans have “a bias to favor, a point to prove, and an axe to grind.”  They tend to over- or under-state the truth in responding to surveys; those who complete surveys are systematically different from those who do not respond; and built-in partiality occurs in the wording of a questionnaire, appearance of an interviewer, or interpretation of the results. 

There were no desktop computers or mobile devices; statistical charts and infographics were drawn by hand; data collection, especially complete counts like a census, was difficult and costly.  Huff conjectured, and the statistics profession has also concurred, that the only reliable small sample is one that is random and representative where all sources of bias have been removed.

Calling anyone a liar was harsh then, and it still is now.  The dictionary definition of a lie is a false statement made with deliberate intent to deceive.  Huff considered lying to include chicanery, distortion, manipulation, omission, and trickery; ignorance and incompetence were only excuses for not recognizing them as lies.  One may also lie by selectively using a mean, a median, or a mode to mislead readers although all of them are correct as an average.

No matter how broadly or narrowly lies may be defined, it cannot be denied that people do lie with statistics every day.  To some media’s credit, there are now fact-checkers who regularly examine stories or statements, most of them based on numbers, and evaluate their degree of truthfulness.

In the era of Big Data, lies occur in higher velocity with bigger volume and greater variety.

Moore’s law is not a legal, physical, or natural law, but a loosely-fitted regression equation in logarithmic scale.  Each of us has probably won the Nigerian lottery or its variations via email at least a few times.  While measures for gross domestic products or pollution are becoming more accurate because of Big Data, nations liberally use their aggregate or per capita average, depending on which favors their point of view.   

Heavy mining of satellite, radar, audio messages, sensor, and other Big Data may one day solve the tragic mystery of Malaysian Flight MH370, but the many pure speculations, conspiracy theories, accusations of wrongdoing, and irresponsible lies quoting these data have mercilessly added anguish and misery to the families of the passengers and the crew.  No one seems to be tracking the velocity, volume and variety of the false positives that have been generated for this event, or other data mining efforts with Big Data.

The responsibility is of course not on the data; it is on the people.  There is the old saying that “figures don’t lie, but liars figure.”  Big Data – in terms of advancing technology and availability of some massive amount of randomly and non-randomly collected electronic data - will undoubtedly expand the study of statistics and bring our understanding and governance to new heights.

Huff observed that “without writers who use the words with honesty and understanding and readers who know what they mean, the result can only be semantic nonsense.”  Today many statisticians are still using terms like “Type I error” and “Type II error” in promoting statistical understanding, while these concepts and underlying pitfalls are seldom mentioned in Big Data discussions.

At the end of his book, Huff suggested that one can try to recognize sound and usable data in the wilderness of fraud by asking five questions: Who says so? How does he know? What’s missing? Did somebody change the subject? Does it make sense?  They are not perfect, but they are worth asking.  On the other hand, healthy skepticism should not become overzealous in discrediting truly sound and innovative findings.

Faced with the self-raised question of why he wrote the book, especially with the title and content that provides ideas to use statistics to deceive and swindle, Huff responded that “[t]he crooks already know these tricks; honest men must learn them in defense.”

How I wish there is a book about how to lie with Big Data now!  In the meantime, Huff’s book remains as enlightening as it was 45 years ago although the price of the book has gone up to $5.98 and is almost matched by its shipping cost.

Jeremy S. Wu, Ph. D.

jeremy.s.wu@gmail.com 

Smart Wuhan, Built on Big Data

The following is an abstract for a presentation given in the Committee of 100 Fourth Tien Changlin (田长霖) Symposium held in Wuhan, China, on June 20, 2013.

The presentation in Chinese is available at http://jeremy-wu.com/uploads/Wuhan_20130620.pdf.

The urban population in China doubled between 1990 and 2012.  It is estimated that an additional 400 million people will move from the countryside to the cities in the next decade.  China has announced plans to become a well-off society, while maintaining harmony, during this time period.  This is an enormous challenge to China and its cities like Wuhan.  

A well-off society necessarily includes a sound infrastructure and sustainable economic development with entrepreneurial spirits and drive for innovation.  It must constantly improve quality of life for its citizens with effective management of the environment and natural resources.  Most of all, it must change governance so that flexibility, high efficiency and responsiveness are the norms that its citizens would expect.  

If data were letters and single words, statistics would be grammar that binds them together in an international language that quantifies what a well-off society is, measures performance, and communicates results.  Modern technology can now collect and deliver electronic information in great variety with massive volume at rapid speed during the Big Data era.  Combined with open policy, talented people, and partnership between the academia, government, and private sector, Wuhan can get smart with Big Data, as it has started with projects like “China Technology and Science City” and “Citizen’s Home.”  Although there are many areas yet to expand and improve, a smart Wuhan will lead the nation up another level toward a well-off society.

Link to presentation in Chinese: http://jeremy-wu.com/uploads/Wuhan_20130620.pdf.

Is Big Data Gold or Sand?

If we melt all the existing gold in the world and put them together, it would amount to about one third of the Washington Monument.  The value of gold is high because it is rare and has many uses.

Recent hype about Big Data compares it to gold as if you can collect them by simply dipping your hands in it.  If so, many people should already be rich.

It may be more appropriate to compare Big Data to sand, within which there may be gold - sometimes a little, sometimes none at all, and in rare situations, a lot.  Whatever the case, it will need investment and hard work to clean and mine them.  There is no real substitute with software or hardware.

There is value in a big pile of sand too.  According to an ancient saying, you can build pagodas with it; it is also the raw material for silicon chips today.

Of higher values is that Big Data is a yet-to-be totally explored branch of new knowledge.

Source of Chart: http://www.jsmineset.com/2010/05/27/how-money-works/

识别码的要义

在21世纪，大数据承诺将为社会有效治理以及大众信息分享做出贡献。尽管任何数据本身都包含一定的信息与作用，但是关联和整合后的数据不仅减少收集数据的重复性，而且极度的增加它的價值和可用性。识别码在这个过程中不仅促进实际记录和数据的整合，而且是解放大数据威力的关键。如果识别码没有得到正确的使用和管理，它亦将会是系统失灵、误用和滥用、甚至欺诈及犯罪的元凶。因此，除了技术以外，合理的统计学设计，提高质量的反馈，适当的教育和培训，相关的法律法规，公众的认知，这些都将成为识别码和大数据有效和负责任应用的必要条件。

识别码的必要性

在学生入学时，会有档案存储学生的各种数据，比如：姓名，性别，年龄，家庭背景，专业等。当学生选修一门课并获得成绩时，这个结果也被记录下来。当这个学生满足了所有毕业要求，另一条记录会显示出她的加权平均分并且获得的学位。

每一条记录都是这学生的一个“快照”，随时间累积成为行政记录。这些纵向“快照”提供每个学生受教育情况的丰富信息。

当学生进入工作单位，更多的关于她工作的数据将被收集，伴随她一生，这些数据包括：她的职业及工作单位，工作表现，工资及晋升情况，保险和税的支付数额，就、失业状态等。

在同样的情形下，大量关于公司的数据也会被收集。这些数据记录了：最初注册成立，收支财政状况报告，上市情况，收购或者与其他公司合并，所缴税费，收入增长和雇员增加，公司的扩增或是公司的倒闭。

这些行政记录过去被封存于满身尘埃的文件柜里，但是在千禧年伴随着大数据时代的到来，它们大部份都已数字化。

对学生数据及时和适当的整合将会提供空前的细节，使我们更详细地了解这所学校运作情况，比如说毕业率随时间的变化。当数据整合扩展到所有学校，我们将更好的了解这个国家的教育状况，例如它对就业和经济增长的潜力和支持。这些就是21世纪大数据承诺将会给我们带来的变化。从分配资源，评估表现，到制定政策，社会的方方面面都可以从大数据的细节和深度中促进社会有效治理及大众信息分享。

尽管任何数据本身都包含一定的信息与作用，但是关联和整合后的数据将更为重要，因为它不仅减少收集重复的数据，而且极度的增加它的價值和可用性。识别码的要义是促进实际记录和数据的整合。在这个过程中，统计学家可以作出卓越的贡献，运用他们的智慧和知识创立新的统计系统。

识别码的种类

当文件例如纸质表格还未被数字化前，人名或者公司名称是被常用的识别码。通常来说，人们会用相同的名称整合记录并给他们排序，比如英文的字母，中文的笔画，或按时间顺序。  

但是，使用名字的一大弊端是他们并不是独特唯一，特别是在电脑大量处理数据时，这一弊端尤其明显。据2006年的统计，李、王、张、刘这四个中国最大姓氏占了3.34亿人口[1]，超过美国人口总数。同样的中文姓名，也有繁体和简体中文的可能分别。英文名罗伯特（Robert）有至少七种不同使用方法，包括：Bert, Bo, Bob, Bobby, Rob, Robbie, 以及Robby，它在2011年美国出生的男性人名中的使用率排第61位[2,3]，而Bert又可以是英文名Albert的缩写。个人又有可能更改名字或者有不止一个名字；女性可能在结婚后改名。人为的错误又可能增加不正确的名字。跨国不同语言的情况下，引用同一名字更是特别困难。

在注册的过程中，公司的名称会被检查以确保不出现重名。公司的名称包括它的商标也会被当地，全国性以及国际性的规则和法律受到保护。但公司仍有可能使用多个名称，包括它的缩写和公司股票代码，而且它也有可能在合并，重组，被收购的时候变更名称，或者只是简单的更改品牌。

非唯一的识别码会造成不正确链接和合并数据的风险，导致不正确的结果或结论。虽然给一个名称增加辅助信息，比如说年龄，性别和地址，可以减少风险，但是并不能完全去除错误配对记录和数据的可能性，而且会增加处理数据的时间。

识别码可以由一系列的数字、字母或特殊字符(字母数字)组成。越来越多使用单纯的数字来组成识别码，应用於现代的机器排序，链接和合并电子记录。因为纯数字识别码不依赖于语言系统，受到比较少的限制。使用字母数字的识别码，可能适合使用拉丁语系的系统，但是那些非拉丁语系的系统就比较难以使用、明白或理解。同时，数字字符比较字母数字字符容易排序。

当美国在1935年通过社会安全法案时，履行法案遇到的第一个挑战就是创造如何”永久识别每个个体”的识别码，同时保有”以后能够有效和无限制的增加识别对应增长工人的功能”[4]。一个八位字母数字系统最先被提出来，但很快遭到了统计机构、劳工及法律部门的反对。这个变换被描述为“机器会如何深远地映响[政府] 操作”的第一个徵兆[4,5] 。这些都是在计算机实际使用以前发生的事情。

如今，信息科技的巨大影响很明显，不但是政府，商业和个人活动的方方面面，而且影响力还在不断增强。一个识别码可以应用于个人，一家公司，一辆车，一张信用卡，一箱货物，一个电子邮箱账户，一个地方，或者是任何一个实际个体。

如果一条电子记录不包含识别码，或不能和其他记录連接，大数据中称为缺乏“结构”或者叫做“无结构”。从21世纪初开始，“无结构”数据比有“结构”数据出现的频率多得多，但是它们比有“结构”数据包含较少信息內容，也更难应用，特别是在社会和经济方面，我们很难得到后续、连贯和可靠的时序信息。

如何有效地使用识别码将是发挥大数据巨大作用的关键。

有效使用识别码

1.  匹配和合并记录。理想的识别码同时互斥，完全穷尽，在代码和实体间建立了明确的一对一对应关系，同时也会延生到未来的记录。识别码促进对电子记录直接有效地排序、匹配及合并，具有无限扩增实体信息内容的潜能。

2.  匿名和保护身份。 因为代码是实体的匿名，所以它为身份保护提供第一道防线。但随着识别码重要性的增强，以及它与其他数据链接相对容易，通过识别码伪造及盗用身份的危险性和可能性也在增加，这就需要加强对识别码的政策和负责任的管理，以使它起到保护的作用。

3.  基本描述和分类。识别码可以对数据内容提供最基本的描述，迅速从中得到简单的信息或者是总结。随着时间的推移，这个概念延伸到识别码的分类和“元数据”的发展[6,7]，这个过程包含了在数据系统中建立有效的结构以及扩展它们跨系统的应用。

4. 初部质量检查。无意的人为输入错误以及不正确的转录识别码都可能对整体数据和最终分析结果的质量造成破坏。欺诈或恶意改变识别码亦可能会造成对数据的完整性和可靠性严重的破坏。“效验码”[8,9]在早期检查中的使用可使识别码中常见错误降低90%。

5. 促进统计学创新。通过对每个学生数据连续不断的收集和整合，可以建立一个含有所有学生和学校丰富信息的动态框架。在严格保护个体隐私和数据安全的同时，新的变数可以定义用做分析研究，描述一所学校的表现或者是一个国家教育状况的统计结论可以是定时或实时产生。在美国及中国，建立这些动态框架和纵向数据系统都已起步[10]。Data Quality Campaign[11]列“唯一州际连接学生数据和主要数据库学生识别码”为建立全美教育纵向数据系统中最关键的部分。

美国和中国的个人识别码

美国没有全国性的个人识别系统。社会安全号创立于1936年，还在商业使用电脑之前，用于追踪劳工的收入。在电脑大规模使用以后，社会安全号作为识别码表现出一些优势和劣势。

九位的社会安全号由三部分组成：

• 地区号码（三位）- 最初是发放社会安全号的地区代码，后来代表申请邮寄地址的邮政代码
• 组号（两位）- 代表着一个社会安全号集合被指定为一个组
• 系列号码（四位）- 从0001到9999

社会安全号的申请过程中[12]收集人口信息，包括名字，出生地，出生日期，国籍，种族，性别，父母的名字和社会安全号，电话号码和邮政地址。美国社会安全部负责社会安全号的发放。有一些社会安全号被保留，没有使用。一旦一个社会安全号被发放，它应是唯一的，因为它不会被第二次发放。但重复的情况仍可能存在。

1938年，一个钱包的生产厂商显示他们的产品是多么适合社会安全号卡来促销其在百货商场出售的钱包，但是他们使用一张自己员工的社会安全号卡[13]。这导致有四万人错误的使用了这个社会安全号，甚至到1977年还有人将这个号码做作为自己的社会安全号。

自从社会安全号的产生，它被政府部门和私有企业的使用显著增加。从1943年开始，总统行政命令要求各联邦政府部门必须使用社会安全号建立拥有永久账户号码的系统[5]。在1960年代初，政府雇员和个体报税者必须使用社会安全号。到1960年代末，社会安全号被作为军人的识别码。在整个七十年代，当电脑被越来越多使用后，金融活动，如开设新银行账户和申请信用卡和贷款，以及联邦福利的运行中，社会安全号成为必不可少的一部分。从1986年开始，如果父母想要有受抚养人的免税，就必须将其抚养人的社会安全号也列在税表里。在法律实施的第一年，这反欺诈行动减少了七百万的受抚养人数[14]。

社会安全号可以将同一个人的很多电子文件链接合并到一起，因此它本质上作为了非官方全国性识别码，但是它也可能直接造成误用或者滥用，例如身份盗用[15]。社会安全号没有效验码，它并不能有效的作为身份的認证。有学者也展示如何用公开的信息“异常精确的重建社会安全号”[16]。这些年在美国，识别码的这些脆弱奌使得人们更加小心谨慎和负责任的使用社会安全号。1943年要求使用社会安全号的行政命令也被废除，取而代之的是在2008年颁布的行政命令使社会安全号成为可以选择而非必须的。

中国相对较晚开始使用个人识别码。在1999年7月1日，身份证号码由15位提升为18位，其中出生年份由两位变为四位，并且增加效验码。18位身份证号由四部分组成[17,18]:

• 地区代码（六位）— 个人住址的行政编号
• 生日代码（八位）— 按生日的年月日顺序组成
• 系列代码（三位）— 其中奇数代表男性，偶数代表女性
• 效验码（一位）— 使用ISO 7064标志算法，基于前面17位数字计算得到[18,19]

居民身份证由居民常住户口所在地的县级人民政府公安机关基于未满16岁居民的申请签发。居民身份证登记的项目包括：姓名，性别，民族，生日以及居住地址。居民身份证有效期长至永久，也可能短至五年，取决于申请人的年龄。根据官方的声明，居民身份证号在中国电子健康档案中也用于记录个人的健康信息[20]。

中国及美国的商业识别码和工业分类码

美国企业的僱主识别码相当於个人的社会安全号[21]。但是，这里的企业包括地方，州和联邦政府，也包括无雇员的公司，亦包括需要为其雇员缴纳税款的个人公司。僱主识别码是由美国税务局负责指派的一个九位数字，它的形式是GG-NNNNNNN，其中GG在2001年前是公司所在地的代码，而后七位数字没有特别的含义。一旦一个僱主识别码被使用，美国税务局就不会再次使用。另外，每个州亦各有自已的僱主识别码用于税务收缴和行政管理。

在联邦僱主识别码的申请过程中有以下信息被收集:正式名称，交易名称，法人姓名，责任人员，邮政地址，商业地址，公司类型，申请原因，成立时间，财政年度，未来12个月员工数目估计，首次工资发放日期以及公司主营业务[22]。

美国统计部门使用北美工业分类系统（以下称NAICS）来对公司营业进行归类，以期能收集，分析以及发布美国经济的统计信息[23]。在1997年，北美工业分类系统（NAICS）继承取代工业标准分类系统(SIC)。

NAICS是一个层级分类代码系统，其中可能包含有2到6位数字。最高层级的2位数字代表主要经济部门，例如建筑和生产。每个2位数字所代表的部门都包含一系列的3位数字子部门，而它又包含有一系列4位数字的工业集团。例如31到33是表示生产部门，而碾米工业在其所属层级之中：

            311       食品加工制造业

     3112     粮食和油菜籽加工业

     31121   面粉和麦芽生产业

     311212 碾米业       

层级系统其中的一个优势就是它可以相当容易地链式聚集产业总值。比如说，所有代码为311x企业的总和就组成了代码为311的食品加工制造业。

持续用NAICS代码凖确地把企业分类是一大挑战，因为在当今快速变化的动态国际经济环境下，一夜之间过时的行业会被淘汰，新的行业也会出现成长，过去的”高科技企业”及最近的“绿色”行业就是例子之一。使用NAICS代码的过程中有着理解和持续性的问题，例如美国统计局和美国劳工统计局就因为数据来源和NAICS代码分类不同，令到各自创立和维护的商业框架有異[10]。不一致使用NAICS代码破坏甚至造成对时间序列和纵向数据分析无效。

中国的新企业必须向当地质量监督局申请9位数的国家组织机构代码，其中由8位数字（或大写拉丁字母）本体代码和1位数字（或大写拉丁字母）效验码组成。中国的组织机构代码，借鉴原ISO6523《数据交换标识法的结构》（现ISO6523《信息技术组织和组织各部分标识用的结构》）国际标准的基础上，根据GB 11714—1997《全国组织机构代码编制规则》国家标准的规定，编制的全国统一的组织机构代码识别标识码[25]。可以通过网上信息核查系统基于国家组织机构代码查询组织机构的信息[26] 。

国内及国外的经济学家和其他学者十分认可中国工业企业数据库的价值。透过相当的投资，这个丰富的综合数据系统从1998年开始纵向描述中国差不多所有的国有和大型企业（2010年前销售额在500万人民币以上及2010年后销售额在2000万人民币以上的企业）。但是，十分严重的质量问题已有所报导，而主要数据错误原因可以追溯到不正确和不连贯地使用识别码[27]。虽然中国从1989年就开始标准化国家组织机构代码，并且现在已经进行到了第三阶段，但是这个问题仍然存在[28]。

就在上个月，广东省宣布他们运用国家组织机构代码这个平台推动反腐败[29]。中国也有一个根据GS-T4754-2002文件而建立的标准工业分类系统[30]。这个层级系统有四类，其中最高层为一个字母，其余分别有2位，3位，4位数字代码表示较低层级。以前述的的碾米业为例，中国的分类系统中表示为以下层级:

     C         制造业

     C13       农副食品加工业

     C131      谷物磨制

     C1312          大米加工企业

总结

随着科技的轉变和发展，收集大规模的数字化数据的成本将更低，速度也将更快。这些是大数据时代的标志。

这些大数据包含了空前规模的信息。如果数据整合和结构化，它们的价值和功能将会暴涨，超过现有数据系统所能提供。识别码促进数据的链接和合并，是提供这些巨大机会的关键。

识别码能解放大数据的巨大能量。如果我们不能正确使用和管理识别码，它同样可以成为系统失灵，误用和滥用，甚至是欺骗和犯罪行为的罪魁祸首。

现实使用识别码的挑战是多样复杂。除了科技技术，统计学设计和质量回馈途径，适当的教育和培训，有效的政策和监控，以及公众的意识參与都是有效负责使用识别码所必须的。未来的文章中将讨论这些话题。


胡善庆博士, Jeremy.s.wu@gmail.com
丁浩, edwarddh101@gmail.com

参考文献

[1] 360doc.com.  Quantitative Ranking of Chinese Family Names (中國姓氏人口數量), November 25, 2012.  Available at http://www.360doc.com/content/12/1125/17/6264479_250155720.shtml on April 29, 2013.

[2] Wikipedia.  Robert.  Available at http://en.wikipedia.org/wiki/Robert on April 29, 2013.

[3] U.S. Social Security Administration.  Change in Name Popularity.  Available at http://www.ssa.gov/OACT/babynames/rankchange.html on April 29, 2013.

[4] U.S. Social Security Administration.  Fifty Years of Operations in the Social Security Administration, by Michael A. Cronin, June 1985.  Social Security Bulletin, Volume 48, Number 6.  Available at http://www.ssa.gov/history///cronin.html on April 29, 2013.

[5] U.S. Social Security Administration.  The Story of the Social Security Number, by Carolyn Puckett, 2009.  Social Security Bulletin, Volume 69, Number 2.  Available at http://www.ssa.gov/policy/docs/ssb/v69n2/v69n2p55.html on April 29, 2013.

[6] Wikipedia.  Metadata.  Available at http://en.wikipedia.org/wiki/Metadata on April 29, 2013.

[7] Wikipedia. 元数据. Available at http://zh.wikipedia.org/wiki/%E5%85%83%E6%95%B0%E6%8D%AE on April 29, 2013.

[8] Wikipedia.  Check Digit.  Available at http://en.wikipedia.org/wiki/Check_digit on April 29, 2013.

[9] Wikipedia. 效验码. Available at http://zh.wikipedia.org/wiki/%E6%A0%A1%E9%AA%8C%E7%A0%81 on April 29, 2013.

[10] Wu, Jeremy S. 21^st Century Statistical Systems, August 1, 2012.  Available at http://jeremyswu.blogspot.com/2012/08/abstract-combination-of-traditional.html on April 29, 2013.

[11] Data Quality Campaign.  10 Essential Elements of a State Longitudinal Data System.  Available at http://www.dataqualitycampaign.org/build/elements/1 on April 29, 2013.

[12] U.S. Social Security Administration.  Application for a Social Security Card, Form SS-5.  Available at http://www.ssa.gov/online/ss-5.pdf on April 29, 2013.

[13] U.S. Social Security Administration.  Social Security Cards Issued by Woolworth.  Available at http://www.socialsecurity.gov/history/ssn/misused.html on April 29, 2013.

 

[14] Wikipedia.  Social Security Number.  Available at http://en.wikipedia.org/wiki/Social_Security_number, on April 29, 2013.

[15] President’s Identity Theft Task Force. 2007. Combating Identity Theft: A Strategic Plan. Available at http://www.idtheft.gov/reports/StrategicPlan.pdf on April 29, 2013.

[16] Timmer, John.  New Algorithm Guesses SSNs Using Data and Place of Birth, July 6, 2009. Available at http://arstechnica.com/science/2009/07/social-insecurity-numbers-open-to-hacking/ on April 29, 2013.

[17] baidu.com.  GB11643-1999 Citizen Identity Number 公民身份号码.  Available at

http://wenku.baidu.com/view/4f19376348d7c1c708a14587.html on April 29, 2013.

[18] Wikipedia.  Resident Identity Card.  Available at http://en.wikipedia.org/wiki/Resident_Identity_Card_%28PRC%29 on April 29, 2013.

[19] Wikipedia.  ISO 7064.  Available at http://en.wikipedia.org/wiki/ISO_7064:1983 on April 29, 2013.

[20] baidu.com.  Electronic Health Record 电子健康档案. Available at

http://wenku.baidu.com/view/348d5a18a300a6c30c229fec.html on April 29, 2013.

[21] Wikipedia.  Employer Identification Number.  Available at http://en.wikipedia.org/wiki/Employer_identification_number on April 29, 2013.

[22] U.S. Internal Revenue Service.  Form SS-4: Application for Employer Identification Number.  Available at http://www.irs.gov/pub/irs-pdf/fss4.pdf on April 29, 2013.

[23] U.S. Census Bureau.  North American Industry Classification System.  Available at http://www.census.gov/eos/www/naics/index.html on April 29, 2013.

[24] National Administration for Code Allocation to Organizations.  Introduction to Organizational Codes, 组织机构代码简介.  Available at http://www.nacao.org.cn/publish/main/65/index.html on April 29, 2013.

[25] Wikipedia.  ISO/IEC 6523.  Available at http://en.wikipedia.org/wiki/ISO_6523 on April 29, 2013.

[26] National Administration for Code Allocation to Organizations.  National Organization Code Information Retrieval System, 全国组织机构信息核查系统. Available at http://www.nacao.org.cn/ on April 29, 2013.

[27] Nie, Huihua; Jiang, Ting; and Yang, Rudai.  A Review and Reflection on the Use and Abuse of Chinese Industrial Enterprises Database.  World Economics, Volume 5, 2012.  Available at http://www.niehuihua.com/UploadFile/ea_201251019517.pdf on April 29, 2013.

[28] National Administration for Code Allocation to Organizations.  Historical Development of National Organization Codes, 全国组织机构代码犮展历程. Available at http://www.nacao.org.cn/publish/main/236/index.html on April 29, 2013.

[29] National Administration for Code Allocation to Organizations.  Guangdong Aggressively Promotes the Use of identification Codes in its Campaign against Corruption, 广东积极发挥代码在反腐倡廉中的促进作用, March 7, 2013. Available at http://www.nacao.org.cn/publish/main/13/2013/20130307150216299954995/20130307150216299954995_.html on April 29, 2013.

 

[30] baidu.com.  National Economic Industry Classification, GB-t4754-2002, 国民经济行业分类(GB-T4754-2002)(总表).  Available at http://wenku.baidu.com/view/69f04af8c8d376eeaeaa31cf.html on April 29, 2013.