AGA08001 Analysis for Economic Development

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Integrated Arts and Sciences Department of Integrated Arts and Sciences

Lecture Code AGA08001 Subject Classification Specialized Education

Subject Name 開発経済分析論

Subject Name
（Katakana）カイハツケイザイブンセキロン

Subject Name in
English Analysis for Economic Development

Instructor KOYAMA TATSUYA

Instructor
(Katakana) コヤマ　タツヤ

Campus Higashi-Hiroshima Semester/Term 2nd-Year,  Second Semester,  4Term

Days, Periods, and Classrooms (4T) Thur5-8：IAS J305

Lesson Style Lecture Lesson Style
(More Details) Face-to-face

Credits 2.0 Class Hours/Week 4 Language of Instruction J : Japanese

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 24 : Social Sciences

Course Area（Discipline） 03 : Economics

Eligible Students

Keywords Quantitative social science; policy evaluation; development policy; statistics.

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Integrated Arts and Sciences
（Knowledge and Understanding）
・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework.
（Abilities and Skills）
・The ability and skills to collect and analyze necessary literature or data among various sources of information on individual academic disciplines.
（Comprehensive Abilities）
・The ability to conduct research proactively by combining knowledge,  understanding, and skills for the tasks, based on flexible creativity and imagination.

Class Objectives
/Class Outline This course introduces the foundations of quantitative methods for evaluating development policies. The effects of policies on complex human societies, in development and beyond, cannot be fully predicted from theory alone. Their effectiveness must therefore be assessed empirically using observed data. The analysis of social data presents various challenges to identifying causal relationships, and evaluating policy effects requires strategies for addressing these challenges. Although this course focuses on quantitative and statistical methods, understanding these challenges and how to address them can also provide insights useful for qualitative analysis.

The course covers introductory statistical analysis, with causal inference as a central theme from the outset. Two class sessions are held on each teaching day. In general, the first session focuses on theoretical concepts, while the second consists of programming exercises using R, a language for statistical computing.

Class Schedule lesson1: Introduction: How can we evaluate policy effects?
lesson2: Installing R and getting started
lesson3: Causality and potential outcomes
lesson4: Numerical calculations and descriptive statistics in R
lesson5: Population and sample
lesson6: Summarizing and visualizing a single variable in R
lesson7: Correlation, causation, and prediction
lesson8: Summarizing and visualizing relationships between two variables in R
lesson9: Fundamentals of regression analysis
lesson10: Regression analysis in R
lesson11: Probability
lesson12: Simulation in R
lesson13: Assessing uncertainty
lesson14: Statistical hypothesis testing in R
lesson15: Social experiments in development economics

A written final exam will be held at the end of the term and will account for 70% of the final grade. Short R homework assignments will be given on each teaching day and will account for the remaining 30%.

Text/Reference
Books,etc. Textbook:
Llaudet, Elena, and Kosuke Imai. 2025. 『新・社会科学のためのデータ分析入門導入編』. Translated by Masataka Harada. Iwanami Shoten. (In Japanese.) ISBN: 978-4-00-061677-5.

References:
1. Imai, Kosuke. 2018. 『社会科学のためのデータ分析入門上・下』. Translated by Yuko Kasuya, Masataka Harada, and Hiroki Kubo. Iwanami Shoten. (Japanese translation of Quantitative Social Science: An Introduction, published in two volumes.)
2. 東京大学教養学部統計学教室 (ed.). 1991. 『統計学入門』 [Introduction to Statistics]. 基礎統計学 I. University of Tokyo Press. (In Japanese.)

PC or AV used in
Class,etc. Text, Handouts, moodle

(More Details)

Learning techniques to be incorporated

Suggestions on
Preparation and
Review The relevant textbook sections to read before each class will be specified in advance.

Requirements Students must bring a laptop on each teaching day, as even-numbered sessions (the second half of each teaching day) include practical exercises using R, a programming language for statistical computing.

Prior completion of introductory statistics courses, such as the university’s introductory data science course, is not required. Some course content may be adjusted if a substantial proportion of students have already taken introductory statistics.

Grading Method Written final examination: 70%
Homework assignments: 30%

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	School of Integrated Arts and Sciences Department of Integrated Arts and Sciences
Lecture Code	AGA08001	Subject Classification	Specialized Education
Subject Name	開発経済分析論
Subject Name （Katakana）	カイハツケイザイブンセキロン
Subject Name in English	Analysis for Economic Development
Instructor	KOYAMA TATSUYA
Instructor (Katakana)	コヤマ　タツヤ
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, Second Semester, 4Term
Days, Periods, and Classrooms	(4T) Thur5-8：IAS J305
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face

Credits	2.0	Class Hours/Week	4	Language of Instruction	J : Japanese
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	24 : Social Sciences
Course Area（Discipline）	03 : Economics
Eligible Students
Keywords	Quantitative social science; policy evaluation; development policy; statistics.
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Integrated Arts and Sciences （Knowledge and Understanding）・Knowledge and understanding of the importance and characteristics of each discipline and basic theoretical framework. （Abilities and Skills）・The ability and skills to collect and analyze necessary literature or data among various sources of information on individual academic disciplines. （Comprehensive Abilities）・The ability to conduct research proactively by combining knowledge, understanding, and skills for the tasks, based on flexible creativity and imagination.
Class Objectives /Class Outline	This course introduces the foundations of quantitative methods for evaluating development policies. The effects of policies on complex human societies, in development and beyond, cannot be fully predicted from theory alone. Their effectiveness must therefore be assessed empirically using observed data. The analysis of social data presents various challenges to identifying causal relationships, and evaluating policy effects requires strategies for addressing these challenges. Although this course focuses on quantitative and statistical methods, understanding these challenges and how to address them can also provide insights useful for qualitative analysis. The course covers introductory statistical analysis, with causal inference as a central theme from the outset. Two class sessions are held on each teaching day. In general, the first session focuses on theoretical concepts, while the second consists of programming exercises using R, a language for statistical computing.
Class Schedule	lesson1: Introduction: How can we evaluate policy effects? lesson2: Installing R and getting started lesson3: Causality and potential outcomes lesson4: Numerical calculations and descriptive statistics in R lesson5: Population and sample lesson6: Summarizing and visualizing a single variable in R lesson7: Correlation, causation, and prediction lesson8: Summarizing and visualizing relationships between two variables in R lesson9: Fundamentals of regression analysis lesson10: Regression analysis in R lesson11: Probability lesson12: Simulation in R lesson13: Assessing uncertainty lesson14: Statistical hypothesis testing in R lesson15: Social experiments in development economics A written final exam will be held at the end of the term and will account for 70% of the final grade. Short R homework assignments will be given on each teaching day and will account for the remaining 30%.
Text/Reference Books,etc.	Textbook: Llaudet, Elena, and Kosuke Imai. 2025. 『新・社会科学のためのデータ分析入門導入編』. Translated by Masataka Harada. Iwanami Shoten. (In Japanese.) ISBN: 978-4-00-061677-5. References: 1. Imai, Kosuke. 2018. 『社会科学のためのデータ分析入門上・下』. Translated by Yuko Kasuya, Masataka Harada, and Hiroki Kubo. Iwanami Shoten. (Japanese translation of Quantitative Social Science: An Introduction, published in two volumes.) 2. 東京大学教養学部統計学教室 (ed.). 1991. 『統計学入門』 [Introduction to Statistics]. 基礎統計学 I. University of Tokyo Press. (In Japanese.)
PC or AV used in Class,etc.	Text, Handouts, moodle
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	The relevant textbook sections to read before each class will be specified in advance.
Requirements	Students must bring a laptop on each teaching day, as even-numbered sessions (the second half of each teaching day) include practical exercises using R, a programming language for statistical computing. Prior completion of introductory statistics courses, such as the university’s introductory data science course, is not required. Some course content may be adjusted if a substantial proportion of students have already taken introductory statistics.
Grading Method	Written final examination: 70% Homework assignments: 30%
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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