学部・大学院区分
Undergraduate / Graduate
経済学部
時間割コード
Registration Code
0411539
科目区分
Course Category
Specialized Courses
科目名 【日本語】
Course Title
Labor Economics B(E)
科目名 【英語】
Course Title
Specialized Advanced Lecture(Labor Economics B)
コースナンバリングコード
Course Numbering Code
担当教員 【日本語】
Instructor
工藤 教孝 ○
担当教員 【英語】
Instructor
KUDOH Noritaka ○
担当教員所属【日本語】
instructor's belongs
大学院経済学研究科
担当教員所属【英語】
instructor's belongs
Graduate School of Economics
単位数
Credits
2
配当年次
dividend Yearly
3・4年
開講期・開講時間帯
Term / Day / Period
春 木曜日 3時限
Spring Thu 3
授業形態
Course style
講義
Lecture


授業の目的 【日本語】
Goals of the Course(JPN)
This course is designed to build your research ability in the field of macro-labor economics. This course focuses on the long-run labor market issues such as (1) technological progress and unemployment; and (2) wage inequality. The goal is to catch up with the frontier of research on the aggregate labor market.
授業の目的 【英語】
Goals of the Course
This course is designed to build your research ability in the field of macro-labor economics. This course focuses on the long-run labor market issues such as (1) technological progress and unemployment; and (2) wage inequality. The goal is to catch up with the frontier of research on the aggregate labor market.
到達目標 【日本語】
Objectives of the Course(JPN)
After this course, students should be able to (1) understand the frontier of research in the field of growth and inequality; (2) write their own computer codes to replicate existing quantitative results found in professional articles; and (3) design their own research.
授業の内容や構成
Course Content / Plan
1 Overview: Job Search and Optimal Stopping
2 Worker Flows and Matching
3 The DMP Model
4 Nash Bargaining
5 Strategic Bargaining
6 Labor Market Equilibrium
7 Numerical Analysis
8 Calibration
9 Large Firms and Hours of Work
10 Labor Force Participation
11 Growth and Unemployment
12 Heterogeneous Jobs
13 Labor Share
14 Technological Progress and Unemployment
履修条件・関連する科目
Course Prerequisites and Related Courses
No prerequisite.
Lectures of this course will be delivered in English.
I will assume that the students are familiar with dynamic optimization.
成績評価の方法と基準
Course Evaluation Method and Criteria
There will be 5 or more assignments, in which students are asked to replicate some results from leading research papers. Many assignments require Maxima or Python. The course grade will be determined by the average of the grades of all assignments. To pass the course, you must earn C (which is about 60 out of 100) or above for each assignment.
教科書・参考書
Textbook/Reference Book
There is no textbook you must purchase.
All mandatory reading material (professional articles in leading journals) will be distributed at NUCT.
課外学習等(授業時間外学習の指示)
Study Load(Self-directed Learning Outside Course Hours)
Students need to install some (free) computational packages such as Maxima and Python in your computer and learn the languages.
注意事項
Notice for Students
授業開講形態等
Lecture format, etc.
If the educational activity level is 1 or lower, face-to-face lessons will be held in principle. However, for students who do not wish to take face-to-face lessons, "face-to-face / ICT-based remote classes (two-way or simultaneous on-demand)"will be conducted by NUCT etc. When conducting an on-demand lesson, students should use the NUCT feature "Messages" to ask teachers questions and exchange opinions about lessons with other students.
* If the class format changes after registration (after this notification), we will notify you on the NUCT class site.
遠隔授業(オンデマンド型)で行う場合の追加措置
Additional measures for remote class (on-demand class)