A little knowledge

一知半解

Many microeconomics results are shaky. The third in our series on the profession』s shortcomings

微观经济学的很多研究结果都站不住脚：经济学的不足系列其三

MICROECONOMISTS are wrong about specific things, Yoram Bauman, an economist and comedian, likes to say, whereas macroeconomists are wrong in general. Macroeconomists have borne the brunt of public criticism over the past decade, a period marked by financial crisis, soaring unemployment and bitter arguments between the profession』s brightest stars. Yet the vast majority of practising dismal scientists are microeconomists, studying the behaviour of people and firms in individual markets. Their work is influential and touches on all aspects of social policy. But it is no less fraught than the study of the world economy, and should be treated with corresponding caution.

经济学家、 脱口秀演员尤伦·鲍曼（Yoram Bauman） 总爱说， 微观经济学家错在具体的事情上， 宏观经济学家是整个都错了。 过去十年里， 全球经历了金融危机和失业率飙升， 也见证了各路明星经济学家之间激烈的唇枪舌战。 宏观经济学家首当其冲， 遭到公众最猛烈的批评。 不过， 从事这门「沉闷的科学」的绝大多数都是微观经济学家， 他们致力于研究单个市场中个人和企业的经济行为。 他们的研究颇具影响力， 且触及社会政策的方方面面。但是， 和研究世界经济相比， 微观经济学的麻烦并不会更少些， 应当同样审慎对待。

For decades non-economists have attacked the assumptions underlying economic theory: that people are perfectly informed maximisers of their own self-interest, for instance. Although economists are aware that markets fail and humans are not always rational, many of their investigations still rely on neoclassical assumptions as 「good enough」 descriptions of the world. But this 「101ism」, as Noah Smith, an economist and journalist, calls it, is less prevalent than it was in the 1950s and 1960s, when researchers like Gary Becker reckoned everything from crime to marriage could be described in terms of rational self-interest. Since the 1970s, as RogerBackhouse and Béatrice Cherrier describe in 「The Age of the Applied Economist」*, a new collection of essays, the field has taken a decidedly empirical turn.

数十年来， 奠定了经济学理论的种种假设不断遭受外界的攻击， 比如认为人们充分掌握信息以追求自身利益的最大化。 尽管经济学家知道市场会失灵， 人们也并非总是理性， 但他们的很多研究仍然依赖新古典经济学的各种假设作为对世界的「足够好」的描述。 但今时今日， 这套方法——经济学家兼记者诺亚·史密斯（Noah Smith） 称之为「经济学导论主义」（101ism） ——已不像上世纪五六十年代那样盛行了。 那时， 加里·贝克尔（Gary Becker） 这样的研究人员会认为， 从犯罪到婚姻， 一切事情都可以用人们理性的自利动机来解释。 然而正如罗杰·巴克豪斯（Roger Backhouse） 和贝亚特丽斯?谢里耶（Béatrice Cherrier） 在新编写的论文集《应用经济学家的时代》 *（ The Age of the Applied Economist） 中所述， 自70年代起， 经济学研究已明确转向了实证主义。

Most influential economic work today involves at least some data from the real world. Many economists made their names by finding unique datasets containing 「natural experiments」, in which a change in policy or conditions affects only parts of a population. This allows researchers to tease out the effect of the change. In a famous example, published in 2001, John Donohue and Steven Levitt used variations in abortion laws across states to conclude that legalising abortion had been responsible for as much as half of the decline in crime in America in the 1990s. Other economists used randomised controlled trials (RCTs) to generate experimental data on the effects of social and development policies. In RCTs randomly chosen subjects are given a 「treatment」, such as a microloan or a school voucher, while those in a control group are not. The behaviour of the two groups is then compared.

如今最具影响力的经济学著作多少都会涉及一些来自现实世界的数据。 很多经济学家因为发现了包含「自然实验」的独特数据集而声名鹊起。 在自然实验中， 政策或形势的变化只会影响到人群的一部分。 这就使得研究人员可以分辨出变化产生的影响。 约翰·多诺霍（John Donohue） 和史蒂芬·列维特（Steven Levitt） 于2001年发表的一项著名的研究就是一个例子。 他们考察了各州在堕胎法律上的差异， 得出结论称， 上世纪90年代美国犯罪率的下降有50%是由于堕胎合法化促成的。 其他一些经济学家采用随机对照试验（RCT） 来生成关于社会及发展政策影响的实验数据。 在这类试验中， 随机选择的实验对象会被给予一项「待遇」， 比如小额贷款或教育补助金券， 而对照组的实验对象则没有。 随后研究人员会对比两组实验对象的行为。

These developments have led to better, more substantial research. Yet they have also exposed economics to the problems bedevilling most social sciences, and some hard sciences, too. Researchers can tweak their statistical tests or mine available data until they stumble on an interesting result. Or they read significance into a random alignment. Economics, like other social sciences, is suffering a replication crisis. A recent examination in the Economic Journal, of almost 7,000 empirical economics studies, found that in half of the areas of research, nearly 90% of those studies were underpowered, ie, that they used samples too small to judge whether a particular effect was really there. Of the studies that avoided this pitfall, 80% were found to have exaggerated the reported results. Another study, published in Science, which attempted to replicate 18 economics experiments, failed for seven of them.

这样的发展变化已催生出更优质、 更重大的研究。 然而经济学也因此遇到了长期困扰大多数社会科学乃至某些「硬科学」的问题。 研究人员可能会对统计测试加以微调， 或挖掘已有数据， 直到撞见一个有趣的结果。 或者， 他们会从随机匹配中强行看出显著性来。 和其他社会科学一样， 经济学也存在可复制性危机。 近期， 一项发表在《经济学杂志》（Economic Journal） 上的调查考察了将近7000个实证经济学研究， 发现在半数研究领域， 有接近90%的研究都存在统计效力过低的问题， 即样本量太小， 无法判断某个特定影响是否确实存在。 而在那些不存在这一问题的研究中， 有80%都夸大了研究结果。 另一项发表在《科学》 上的研究尝试复制18个经济学实验， 其中有7个无法复制。

Even when a study is perfectly designed and executed, the result is open to interpretation.Environmental factors such as changing institutions or social norms inevitably play some role, but researchers cannot fully account for them. The results of an experiment conducted in one country might not be relevant in another, or in the same country at a later date. Research may suffer from more than one of these problems. Critics of the paper by Messrs Donohue and Levitt reckon, for instance, that the authors』 computer code contained an error, that they used a measure of crime that flattered their results, and that they neglected the possibility that differences in the change in crime across states were caused by differences in factors other than abortion laws. (The pair conceded an error, but responded that taking better account of confounding factors did not weaken their conclusion.)

就算一项研究的设计和执行都无可挑剔， 得出的结果也可以有不同的解释。 类似制度变化或社会规范演变这样的环境因素必然会起到一些作用， 但研究者并不能充分解释这类因素的影响。 在一个国家开展一项实验得出的结果换到另一个国家可能就不成立了， 即使仍在同一个国家， 过一段时期可能也不再站得住脚。 一项研究可能同时存在上述问题中的几种。 例如， 多诺霍和列维特的论文的批评者认为： 两位作者的计算机代码中存在一个错误； 他们采用的衡量犯罪的标准美化了研究结果； 他们忽视了这样一种可能性， 那就是各州犯罪率的变化之所以存在差异， 是因为它们在堕胎法以外的因素上存在差异。 （两位研究者承认研究确实存在一个错误， 但回应说， 把干扰因素更仔细地考虑进去后， 并不会削弱结论的合理性。 ）

Small wonder that economists struggle to answer seemingly straightforward questions, such as how minimum-wage laws affect employment. In 2017 two teams of researchers released assessments of a change in Seattle』s minimum-wage laws within days of each other. Each came to wildly different conclusions (continuing an established pattern of such research).

难怪经济学家连看似简单的问题都难以作答， 比如最低工资法如何影响就业率。 2017年，两组研究人员发布了对西雅图调整最低工资法的评估， 前后只相隔几天， 但结论迥乎不同（这类研究历来都有这样的问题） 。

New techniques could help. Machine learning, in which computer programs comb through vast datasets in search of patterns, is becoming more popular in all areas of economics. A future beckons in which retailers know virtually everything about every transaction, from the competing products buyers considered before their purchases to their heart rates at the moment of payment. That could mean better predictions and policy recommendations without a smidgen of economic analysis. But pitfalls are already apparent. The algorithms used are opaque. And getting access to the richest data will require researchers to work with, or for, giant tech firms which have their own interests.

新技术也许能帮上忙。 在经济学的所有领域里， 运用计算机程序在庞大数据集中搜寻规律的机器学习技术正愈发流行。 在将来， 零售商几乎可以了解每笔交易的方方面面， 比如顾客在购买之前考虑过哪些同类产品， 他们在付款时的心率水平等等。 这也许意味著一点经济学分析都不用做， 就能做出更好的预测、 给出更好的政策建议。 但是其中的陷阱也已经显而易见。 运用的演算法不够透明。 而且， 研究者如果要使用最丰富的数据资源， 就得与科技巨头公司合作， 或者为它们工作， 而这些公司有自身的利益考量。

Economics enjoys greater influence over policy than other social sciences. Striking new findings are publicised by researchers and their institutions, promoted by like-minded interest groups and politicians, and amplified by social media. Conflicting results and corrections are often ignored. Being alert to the shortcomings of published research need not lead to nihilism. But it is wise to be sceptical about any single result, a principle this columnist resolves to follow more closely from now on.

经济学对政策的影响力比其他社会科学更大。 一旦有了惊人的新发现， 研究人员及其所属机构会广而告之， 志趣相投的利益集团和政客会大力宣传， 社交媒体则会扩大这些新发现的影响力。 然而与新发现相矛盾的研究结果以及对研究做出的纠正却往往被忽略。 对已发表研究的缺陷保持警觉未必就会导致虚无主义。 但是， 对任何单个研究结果都抱持怀疑是明智的。 本专栏作者决心今后要更严格地践行这个准则。

*A list of cited studies is at economist.com/micro2018

*欲查看所引用研究的列表， 请访问economist.com/micro2018

（来源自《经济学人》2018年6月刊，本文用于非商业用途，如需转载，请标明原文出处，禁止用于盈利用途，后果自负。）


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