A little knowledge

一知半解

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

微觀經濟學的很多研究結果都站不住腳：經濟學的不足系列其三

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.

經濟學家、 脫口秀演員尤倫·鮑曼（Yoram Bauman） 總愛說， 微觀經濟學家錯在具體的事情上， 宏觀經濟學家是整個都錯了。 過去十年裏， 全球經歷了金融危機和失業率飆升， 也見證了各路明星經濟學家之間激烈的脣槍舌戰。 宏觀經濟學家首當其衝， 遭到公眾最猛烈的批評。 不過， 從事這門「沉悶的科學」的絕大多數都是微觀經濟學家， 他們致力於研究單個市場中個人和企業的經濟行為。 他們的研究頗具影響力， 且觸及社會政策的方方面面。但是， 和研究世界經濟相比， 微觀經濟學的麻煩並不會更少些， 應當同樣審慎對待。

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.

數十年來， 奠定了經濟學理論的種種假設不斷遭受外界的攻擊， 比如認為人們充分掌握信息以追求自身利益的最大化。 儘管經濟學家知道市場會失靈， 人們也並非總是理性， 但他們的很多研究仍然依賴新古典經濟學的各種假設作為對世界的「足夠好」的描述。 但今時今日， 這套方法——經濟學家兼記者諾亞·史密斯（Noah Smith） 稱之為「經濟學導論主義」（101ism） ——已不像上世紀五六十年代那樣盛行了。 那時， 加里·貝克爾（Gary Becker） 這樣的研究人員會認為， 從犯罪到婚姻， 一切事情都可以用人們理性的自利動機來解釋。 然而正如羅傑·巴克豪斯（Roger Backhouse） 和貝亞特麗斯?謝裏耶（Béatrice Cherrier） 在新編寫的論文集《應用經濟學家的時代》 *（ The Age of the Applied Economist） 中所述， 自70年代起， 經濟學研究已明確轉向了實證主義。

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.

如今最具影響力的經濟學著作多少都會涉及一些來自現實世界的數據。 很多經濟學家因為發現了包含「自然實驗」的獨特數據集而聲名鵲起。 在自然實驗中， 政策或形勢的變化只會影響到人羣的一部分。 這就使得研究人員可以分辨出變化產生的影響。 約翰·多諾霍（John Donohue） 和史蒂芬·列維特（Steven Levitt） 於2001年發表的一項著名的研究就是一個例子。 他們考察了各州在墮胎法律上的差異， 得出結論稱， 上世紀90年代美國犯罪率的下降有50%是由於墮胎合法化促成的。 其他一些經濟學家採用隨機對照試驗（RCT） 來生成關於社會及發展政策影響的實驗數據。 在這類試驗中， 隨機選擇的實驗對象會被給予一項「待遇」， 比如小額貸款或教育補助金券， 而對照組的實驗對象則沒有。 隨後研究人員會對比兩組實驗對象的行為。

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.

這樣的發展變化已催生出更優質、 更重大的研究。 然而經濟學也因此遇到了長期困擾大多數社會科學乃至某些「硬科學」的問題。 研究人員可能會對統計測試加以微調， 或挖掘已有數據， 直到撞見一個有趣的結果。 或者， 他們會從隨機匹配中強行看出顯著性來。 和其他社會科學一樣， 經濟學也存在可複製性危機。 近期， 一項發表在《經濟學雜誌》（Economic Journal） 上的調查考察了將近7000個實證經濟學研究， 發現在半數研究領域， 有接近90%的研究都存在統計效力過低的問題， 即樣本量太小， 無法判斷某個特定影響是否確實存在。 而在那些不存在這一問題的研究中， 有80%都誇大了研究結果。 另一項發表在《科學》 上的研究嘗試複製18個經濟學實驗， 其中有7個無法複製。

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.)

就算一項研究的設計和執行都無可挑剔， 得出的結果也可以有不同的解釋。 類似制度變化或社會規範演變這樣的環境因素必然會起到一些作用， 但研究者並不能充分解釋這類因素的影響。 在一個國家開展一項實驗得出的結果換到另一個國家可能就不成立了， 即使仍在同一個國家， 過一段時期可能也不再站得住腳。 一項研究可能同時存在上述問題中的幾種。 例如， 多諾霍和列維特的論文的批評者認為： 兩位作者的計算機代碼中存在一個錯誤； 他們採用的衡量犯罪的標準美化了研究結果； 他們忽視了這樣一種可能性， 那就是各州犯罪率的變化之所以存在差異， 是因為它們在墮胎法以外的因素上存在差異。 （兩位研究者承認研究確實存在一個錯誤， 但回應說， 把幹擾因素更仔細地考慮進去後， 並不會削弱結論的合理性。 ）

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).

難怪經濟學家連看似簡單的問題都難以作答， 比如最低工資法如何影響就業率。 2017年，兩組研究人員發布了對西雅圖調整最低工資法的評估， 前後只相隔幾天， 但結論迥乎不同（這類研究歷來都有這樣的問題） 。

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.

新技術也許能幫上忙。 在經濟學的所有領域裡， 運用計算機程序在龐大數據集中搜尋規律的機器學習技術正愈發流行。 在將來， 零售商幾乎可以瞭解每筆交易的方方面面， 比如顧客在購買之前考慮過哪些同類產品， 他們在付款時的心率水平等等。 這也許意味著一點經濟學分析都不用做， 就能做出更好的預測、 給出更好的政策建議。 但是其中的陷阱也已經顯而易見。 運用的演算法不夠透明。 而且， 研究者如果要使用最豐富的數據資源， 就得與科技巨頭公司合作， 或者為它們工作， 而這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.

經濟學對政策的影響力比其他社會科學更大。 一旦有了驚人的新發現， 研究人員及其所屬機構會廣而告之， 志趣相投的利益集團和政客會大力宣傳， 社交媒體則會擴大這些新發現的影響力。 然而與新發現相矛盾的研究結果以及對研究做出的糾正卻往往被忽略。 對已發表研究的缺陷保持警覺未必就會導致虛無主義。 但是， 對任何單個研究結果都抱持懷疑是明智的。 本專欄作者決心今後要更嚴格地踐行這個準則。

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

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

（來源自《經濟學人》2018年6月刊，本文用於非商業用途，如需轉載，請標明原文出處，禁止用於盈利用途，後果自負。）


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