科目ナンバー

GM-OM3-SI02

履修条件・関連科目等

Students must have passed Introductory economics (Macroeconomics), Microeconomics, and Seminar I.

授業で使用する言語

英語

授業で使用する言語（その他の言語）

授業の概要

Students will search for topics of interest, do literature review and seek unanswered research questions to study. Students are expected to have a plan to obtain the data to answer their research questions by the end of the semester. Also, students will study the most “powerful” method in causal inference in practice, randomized controlled trial.

From the literature, students will also study contemporary economic issues, such as (but not limited to) firms and growth, firms’ productivity and development, global competitions, supply of labors, investment in human capital and firms, political connections and firms’ performance, firms’ responses to economic policies, place-based policies and firms’ success, industrial policies, socio-economic problems and solutions, such as aging society, inequality, gender inequality, and etc.

科目目的

The course is to help students to shape their topic of interest and research questions properly. The course is for students to display their investigation on the literature within the topic.

到達目標

By the end of the course, students should be able to set up a research topic for the graduation thesis/project with meaningful research questions. Students can demonstrate how to get relevant literature on the topic. Besides, students should apply for the data access/usage if necessary or collect the necessary data as soon as possible, especially when the semester ends.

授業計画と内容

Course content & schedule (subject to change):

1. Introduction. Research questions..
2. Students’ presentation (1). Methods to claim for causal inference. .
3. Students’ presentation (2). Problem statement and literature review. RStudio: Inferring population characteristics via survey research: Tabulating variables.
4. Students’ presentation (3). Impact evaluation: How to conduct. Policy evaluation for your graduation thesis. RStudio: Inferring population characteristics via survey research: Missing data.
5. Students’ presentation (4). The evaluation problem. Internal, external validity, and trade-offs. How to make claims. RStudio: Histogram and descriptive statistics (2).
6. Students’ presentation (5). Overview of impact evaluation methods. Randomized controlled trials. RStudio: Predicting outcomes using linear regression.
7. Students’ presentation (6). Practical advice for implementing randomized controlled trials. Sample size, sample design and statistical power.
8. Students’ presentation (7). Ethical impact evaluations. Designing questionnaires and other data collection instruments. RStudio: estimating causal effects with observational data.
9. How to find data to answer your research questions. How to engage with the sources.
10. Students’ presentation (8): research progress report. How to protect copyright and get permission.
11. Students’ presentation (9): research progress report. How to apply for access to a database.
12. Students’ presentation (10): research progress report. How to know if your hypothesis is naive?
13. Students’ presentation (11): research progress report. How to improve your hypothesis.
14. Students’ presentation (12): research progress report.

授業時間外の学修の内容

指定したテキストやレジュメを事前に読み込むこと／その他

授業時間外の学修の内容（その他の内容等）

RStudio

授業時間外の学修に必要な時間数／週

・毎週１回の授業が半期（前期または後期）または通年で完結するもの。１週間あたり４時間の学修を基本とします。
・毎週２回の授業が半期（前期または後期）で完結するもの。１週間あたり８時間の学修を基本とします。

成績評価の方法・基準

成績評価の方法・基準（備考）

Students must declare whether they use AI in the preparation of each presentation and must specify the exact prompts or sentences submitted to any AI chatbot. Students will not receive credit if they rely primarily on AI to complete their work or if they fail to properly acknowledge AI usage.

Students who do not have a feasible research topic and appropriate data are not qualified to continue to Seminar IV and V.

課題や試験のフィードバック方法

授業時間内で講評・解説の時間を設ける

課題や試験のフィードバック方法（その他の内容等）

アクティブ・ラーニングの実施内容

プレゼンテーション

アクティブ・ラーニングの実施内容（その他の内容等）

授業におけるICTの活用方法

実施しない

授業におけるICTの活用方法（その他の内容等）

実務経験のある教員による授業

いいえ

【実務経験有の場合】実務経験の内容

【実務経験有の場合】実務経験に関連する授業内容

テキスト・参考文献等

Textbooks

1. Booth, Wayne C., et al. (2024). The Craft of Research, Fifth Edition, University of Chicago Press. ProQuest Ebook Central, https://www.proquest.com/legacydocview/EBC/31597372?accountid=26790

2. Glewwe, P. & Todd, P. (2021). Impact Evaluation in International Development : Theory, Methods, and Practice. The World Bank. Free access: https://ebookcentral.proquest.com/lib/chuouniv-ebooks/reader.action?docID=29176653

3. Elena Llaudet and Kosuke Imai. (2023). Data analysis for social science : a friendly and practical introduction. Princeton University Press. https://ufinity.library.chuo-u.ac.jp/iwjs0002opc/BB01676196 .

4. OpenIntro Statistics (4th Ed). (2019). Diez, Barr, Çetinkaya-Rundel
CreateSpace (ISBN: 978-1943450077). Free access at https://www.openintro.org/book/os/
(Japanese translation textbook is also available for free).

Reference books

1. Mesquita and Flower. (2021). Thinking clearly with data. A guide to quantitative reasoning and analysis. https://ufinity.library.chuo-u.ac.jp/iwjs0002opc/BB01704706.


2. Jonathan Schwabish. (2017). Better Presentations: A guide for scholars, researchers, and wonks. Columbia University Press: New York. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/chuouniv-ebooks/detail.action?docID=4723060&query=Better%20Presentations:%20A%20guide%20for%20scholars,%20researchers,%20and%20wonks

4. Joshua D. Angrist and Jörn-Steffen Pischke. (2015). Mastering 'metrics : the path from cause to effect. Princeton University Press. https://ufinity.library.chuo-u.ac.jp/iwjs0002opc/BB01559792.

5. Rosenbaum. (2023). Causal Inference. MIT press. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/chuouniv-ebooks/detail.action?docID=29673076&query=causal%20inference

その他特記事項

Class attendance is mandatory. Absences without valid reasons are strongly discouraged. The seminars are cumulative and build on prior knowledge. Without a solid understanding of fundamental concepts and consistent participation, it will be extremely difficult to follow the course material and complete the graduation thesis.

All materials introduced in class are directly applied to the graduation thesis. Insufficient attention during classes—including other students’ presentations—or poor attendance will prevent students from properly applying the material and may delay graduation. Students who miss a class are fully responsible for covering the missed content on their own. Students who miss more than five classes will not be eligible to receive course credit.

Pasting course materials into AI chatbots or uploading them to the public domain is strictly prohibited. Such actions would violate copyright laws.

Independent learning of statistical software is an essential component of this course. Simply observing RStudio demonstrations in class without self-practice is ineffective. Students are required to practice independently and re-run the provided RStudio code after each class. Without first working with simple datasets, it is nearly impossible to manage real and complex data.

All presentations, lectures, discussions, and the graduation thesis (research project) are conducted entirely in English. The use of quantitative methods and microdata to address research questions is strongly encouraged.

Students are required to regularly check their university email and Manaba for important announcements. Clear and timely communication is essential for resolving issues. When contacting the instructor, emails must include a clear subject line, the student’s full name, student ID, and a statement indicating that the inquiry concerns this course. Emails should be concise, clear, and proofread before sending.

It is assumed that all students will continue for Seminar IV and V. However, if students opt this out (student ID with 23F, 24F, 25F... only), the students must notify the advisor as early as possible.

参考URL

Seminar's page

https://sites.google.com/g.chuo-u.ac.jp/tienmanhvu/

Instructor's site

https://sites.google.com/view/tienmanhvu/