只轉英文資料而沒翻譯，表示仍不太懂，知識有限
Hanching Chung
How can we use a natural experiment – situations arising in real life that resemble randomised experiments – to examine whether additional years of education affect future income?
2021 economic sciences laureate Joshua Angrist and his colleague Alan Krueger (now deceased) showed how this could be done in a landmark article. In the US, children can leave school when they turn 16 or 17, depending on the state where they go to school. Because all children who are born in a particular calendar year start school on the same date, children who are born early in the year can leave school sooner than children born later in the year. When Angrist and Krueger compared people born in the first and fourth quarters of the year, they saw that the first group had, on average, spent less time in education. People born in the first quarter also had lower incomes than those born in the fourth quarter. As adults they thus had both less education and lower incomes than those born late in the year.
Because chance decides exactly when a person is born, Angrist and Krueger were able to use this natural experiment to establish a causal relationship showing that more education leads to higher earnings: the effect of an additional year of education on income was nine per cent. It was surprising that this effect was stronger than the association between education and income, which amounted to seven per cent. If ambitious and intelligent people have both high levels of education and high incomes (regardless of education) the result should have been the opposite; the correlation should have been stronger than the causal relationship.
The 2021 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel has been awarded with one half to David Card “for his empirical contributions to labour economics” and the other half jointly to Joshua D. Angrist and Guido W. Imbens “for their methodological contributions to the analysis of causal relationships.”
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This year’s economic sciences laureates have shown that it is possible to answer questions in the social sciences using natural experiments. The key is to use situations in which chance events or policy changes result in groups of people being treated differently, in a way that resembles clinical trials in medicine.
We can imagine a natural experiment as if it randomly divides individuals into a treatment group and a control group. The treatment group is entitled to participate in a programme while the control group is not. This year’s economic sciences laureates Joshua Angrist and Guido Imbens showed that it is possible to estimate the effect of the programme by applying a two-step process (known as the instrumental variables method). The first step investigates how the natural experiment affects the probability of programme participa¬tion. The second step then considers this probability when evaluating the effect of the actual pro¬gramme. Given a few assumptions, which Imbens and Angrist formulated and discussed in detail, the researchers can thus estimate the impact of the programme, even when there is no information about who was actually affected by the natural experiment. One important conclusion is that it is only possible to estimate the effect among the people who changed their behaviour as a result of the natural experiment. It is not possible to determine which individuals are included in this group, but we can determine its size. The effect for this group has been named the local average treatment effect, LATE.
Joshua Angrist and Guido Imbens thus showed exactly what conclusions about cause and effect can be drawn from natural experiments. Their analysis is also relevant for randomised experiments where we do not have complete control over who participates in the intervention, which is the case in almost all field experiments. The framework developed by Angrist and Imbens has been widely adopted by researchers who work with observational data. By clarifying the assumptions necessary to establish a causal relationship, their framework has also increased the transparency – and thus credibility – of empirical research.
The 2021 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel has been awarded with one half to David Card “for his empirical contributions to labour economics” and the other half jointly to Joshua D. Angrist and Guido W. Imbens “for their methodological contributions to the analysis of causal relationships.”
Learn more:
Press release: https://bit.ly/3hC6yn4
Popular information: https://bit.ly/3nAq1bH
Advanced information: https://bit.ly/3EnfuXk
Credibility Revolution--談今年諾貝爾經濟獎得主對於經濟學界的巨大影響（包括對臺灣的經濟學界）
某方面來說，今年諾獎得主的影響力應該是近幾年來說最大的。過去幾年的獎，大多數都是頒給某個專門領域的高手，像是Duflo他們之於發展經濟學的田野實驗，Paul Milgrom之於拍賣，Oliver Hart之於契約理論，但這些對於95%的研究生，大概都沒什麼影響。
但今年的得主比較不一樣，因為他們的文章是我們這些經濟學徒耳熟能詳的，他們提出的研究方法，已經變成了經濟學實證研究的「新標準」。
相關不等於因果，人人都知道。在三位得獎者的研究之前，經濟學差不多還是停在「相關性」研究的階段。但因為社會科學的資料，多半是「觀察性」資料，不容易用實驗方法來取得「因果關係」的結果。這是出於早期不容易從事大規模的田野實驗，又或是許多問題無法妥善地進行人為的實驗（e.g.工業革命）。
幸運的是，三位研究者提出的各種「實證策略」，使得經濟學研究者得以在一定的「統計假設」下，對於觀察性資料，提出因果推論，甚至進一步計算出「究竟一個政策造成了多大的影響」。也就是說，三位學者的研究，有助於經濟學者跨出早期卡在「相關不等於因果」的困境當中。
這三位研究者在實證方面的研究，幾乎定調了當代經濟學實證研究的方法論：大者舉凡工具變數（Instrumental Variables Approach)、差異中的差異法(Difference-in-Differences)、斷點迴歸設計(Regression Discontinuity Design)、配對法（Matching)，小者包括了如何詮釋控制變數、該怎樣處理資料等等。
在理論層次上，三位研究者提出了一個計量經濟學的框架，可以去思考（跟計算）平均政策效果（Average Treatment Effects）等等要評估政策時需要的統計量。他們的框架在觀察性資料的計量模型中代入了「反事實（counterfactual)」的思考，這些東西在傳統的統計學上並不太著墨（其實Imbens跟數學家Rubin有很多合作）。這也使得計量經濟學與傳統的統計學有了相當程度的分離。
各位可能想不到的是，在臺灣，這樣的實證方式，早已經為主流。最早一批將這樣的研究方式帶回臺灣的經濟學家，就包含了臺大經濟系的林明仁教授等人，將這些新觀念利用課堂的機會帶給學生。
以我自己來說，我跟我的同儕都是上林明仁老師等人開設的「應用個體經濟學」的課程學到這些觀念。在課堂上不只學到觀念，也親自實作，進而學習這套實證研究的操作方式。這三個諾獎得主的研究，自然也大量出現在我們課堂的閱讀清單上。
臺大「應用個體經濟學」的課程名稱，老師剛回來開課時，早期還叫「勞動經濟學」，也反映在三位得主他們早期的重大貢獻，集中在「勞動經濟學」的議題上，包括了最低工資、參加越戰對終生收入的影響、政府提供的勞訓（training program)對就業的效果等等，多半圍繞在勞動經濟學的議題上。這些方法也被證明有重大的政策意義，因為他們利用實證資料得到的研究結果，時常與傳統的經濟學觀念下用「直覺」推理出的預測，有所差異（比方說最低工資的影響）。
在實際操作上，三位諾獎得主多半利用政府臨時的政策異動，作為一種「自然實驗」，並去思考如何利用這些自然實驗，來製造出工具變數、斷點或是時間差，進而可以得出因果關係的推論（Imbens相對比較理論一點）。
不過隨著這些「因果推論」方法的普及，並不只勞動經濟學，幾乎所有的經濟學門，現在於處理資料上，都必須去思考「內生性」、「遺漏變數」等議題，並且要想辦法利用不同的實證策略，來得到值得信賴的統計結果，這便是所謂的「Credibility Revolution」。
以我自己的研究為例，雖然我是研究臺灣經濟發展的，比較偏經濟史跟發展經濟學，但當我的論文宣稱「日治時期的商業銀行網絡對戰後臺灣經濟奇蹟有正面影響」，我就必須要處理「相關不等於因果」的問題，於是我選擇採用「工具變數法」，以日治時期商業銀行創辦人的住處當作工具變數，來去估算「日治時期的商業銀行網絡」究竟貢獻多少的戰後經濟成長。
這樣的方法論可以深入到臺灣經濟史的議題（美國的勞動經濟學與臺灣經濟史，坦白說離很遠），有賴於國內一批極為優秀的學者，一邊探討臺灣議題之餘，一邊引進世界上最先進的研究方法。
出於在方法論上的改進，我們可以對於國內各種重大政策的討論上，有更好的評估與測量，有了證據，下一步，我們才能討論到底什麼政策是比較好的。所以媒體朋友如果要訪問相關的領域，歡迎聯絡臺大教應用個體的教授們（e.g. 林明仁教授）。
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